Msstnet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition

The 49th IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP 2024)


DEU-Net: Dual Encoder U-Net for 3D Medical Image Segmentation

The 22nd International Conference on Computer and Information Technology


CenterMatch: A Center Matching Method for Semi-supervised Facial Expression Recognition

The 6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2023)


Use Active Learning to Construct Japanese Emoji Emotion Database

Communications in Computer and Information Science


Gait Recognition for Laboratory Safety Management Based on Human Body Pose Model


Emotion-Sentence-DistilBERT: A Sentence-BERT-Based Distillation Model for Text Emotion Classification


MDSU-Net: A Multi-attention and Depthwise Separable Convolution Network for Stroke Lesion Segmentation

2022 9th International Conference on Biomedical and Bioinformatics Engineering (ICBBE 2022)


Improved Transformer-based Implicit Latent GAN with Multi-headed Self-attention for Unconditional Text Generation

The Fifth International Conference on Intelligence Science (ICIS2022)


Classification Method of Surface Defects of Aluminum Profile Based on Transfer Learning

International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2022)


Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies


Employing Contrastive Strategies for Multi-label Textual Emotion Recognition

12th International Conference on Intelligent Information Processing (IIP2022)


Improving speech emotion recognition by fusing pre-trained and acoustic features using Transformer and BiLSTM


Creating a Japanese dialogue corpus with multi-level topic analysis

2022 4th International Conference on Natural Language Processing


Improvement of Japanese Text Emotion Analysis by Active Learning Using Transformers Language Model

Proceeding of 2022 IEEE 14th International Conference on Computer Research and Development(ICCRD)


Classification of Steel Strip Surface Defects Based on Optimized ResNet18

2021 IEEE International Conference on Agents (ICA)


Thermal Defect Detection and Location for Power Equipment based on Improved VGG16

Proceedings of the 5th IEEE International Conference on Agent


Prediction and Generation of Multiple Complex Drawing Figures From Partial Drawing Sequences

13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management


Temporal Attention Based TCN-BIGRU Model for Energy Time Series Forecasting

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)


Sentiment Analysis of Chinese Product Reviews Based on BERT Word Vector and Hierarchical Bidirectional LSTM


Application of Multiple Neural Networks in Aspect Sentiment Analysis

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics


Proceedings of the 15th NTCIR Conference on Evaluation of Information Access Technologies


Building Label-Balanced Emotion Corpus Based On Active Learning for Text Emotion Classification

In Proceedings of 2019 4th International Symposium on Artificial Intelligence and Robotics (ISAIR2019)


ELMo+Gated Self-attention Network Based on BiDAF for Machine Reading Comprehension

IEEE 11th International Conference on Software Engineering and Service Science (ICSESS)

Machine reading comprehension (MRC) has always been a significant part of artificial intelligence and the focus in the field of natural language processing (NLP). Given context paragraph, to answer its query, we need to encode complex interaction between the question and the context. In the late years, with the rapid progress of neural network model and attention theory, MRC has made great advances. Especially, attention theory has been widely used in MRC. However, the accuracy of the previous classic baseline model has some upside potential and some of them did not take into account the long context dependence and polysemy. In this paper, for resolving the above problems and further improve the model, we introduce ELMo representations and add a gated self-attention layer to the Bi-Directional Attention Flow network (BIDAF). In addition, we employ the feature reuse method and modify the linear function of answer layer to further improve the performance. In the experiment of SQuAD, we prove this model greatly exceeds the baseline BIDAF model and its performance is close to the average level of human test, which proves the validity of this model.


Gait recognition in real environment using gait energy image generated by Mask R-CNN

2020 IEEE International Conference on Mechatronics and Automation (ICMA)


Attention-based Bi-LSTM-CRF Network for Emotion Cause Extraction in Texts


Binary K-means Tree Ensemble Classifiers

The 15th International Conference on Natural Language Processing and Knowledge Engineering


Multi-scale Receptive Field Fusion Residual Network for Image Classification


Real-time Dynamic Facial Expression Recognition System Based On CNN-GRU


Thermal Fault Area Recognition and Location System based on Infrared Image


Construction of emergency medical support system based on understanding dialect intention


A humanoid robot based on neural network architecture for accompanying children with autism


Capsule Network with TC Loss for Intention Detection


Local Label Correlation Learning for Multi-Label Classification


Detecting Early Stage Depressions Based on Compound Neural Language Understanding


Potential Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering


Development issues on communication robots' application in order to use for the older people care


A fusion model for multi-label emotion classification based on BERT and topic clustering

In International Symposium on Artificial Intelligence and Robotics 2020

SPIE The International Society for Optical Engineering


Human operator's "Characteristic of autonomic nerve activity as psychological burden" and " Clarification of thinking process for AI development" while operating the humanoid robot conversation program


Speech emotion recognition based on data enhancement in time-frequency domain

International Symposium on Artificial Intelligence and Robotics 2020


Collaborative filtering recommendation algorithm based on bisecting K-means clustering


Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge


Surface Defects Classification of Steel Strips based on Transfer Learning and Naive Bayes Model

Proceedings of The 15 th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE20)


Generation of Musical Scores from Chord Sequences Using Neurodynamic Model

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems


A Chinese-Japanese Vowel Priority Lip Matching Scheme Based on Humanoid Robot Ren-Xin

In Proceedings of 2020 5th International Symposium on Artificial Intelligence and Robotics (ISAIR2020)


Environment Recognition Using Robot Camera

2020 5th IEEE International Conference on Big Data Analytics (ICBDA)


Facial Expression Recognition Based on Contextual Generative Adversarial Network

the 6th IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2019)


WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep

IEEE Global Communications Conference (GLOBECOM)


Using Inception-Resnet V2 for Face-based Age Recognition in Scenic Spots

Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019)


Research on Information Recognition of VAT Invoice Based on Computer Vision


A Fast Optimization Algorithm for Nearest Neighbor Architecture Based on Quantum Weight


Investigating voice features for Speech emotion recognition based on four kinds of machine learning methods


Constructing a Fundamental DevelopmentalDrawing Learning Model Using an Arm Robot


Character action recognition based on deep convolutional neural network and action sequence


ExploringUncertain Samples through Active Learning To Enhance Text Emotion Classification


Text multi-label sentiment analysis based on Bi-LSTM


Capture Important Information for Reading andReasoning by A Two-Pass Attention Mechanism in Dynamic Memory Network


TUA1 at the TREC 2019: Deep Learning Track

Proceedings of 28th Text Retrieval Conference (TREC 2019)

National Institute of Standards and Technology (NIST)


The 1st World Science and Technology Development Forum


Can artificial intelligence ride the vast wilderness of big education and big health?


Big Intelligence Education and Big Intelligence Health


Facial Sentiment Classification Based on Resnet-18 Model

Proceedings of 2019 International Conference on Electronic Engineering and Informatics (EEI)


TUA1 at ImageCLEF 2019 VQA-Med: a Classification and Generation Model based on Transfer Learning


International Artificial Intelligence Education Forum


Sentiment Classification and Personality Detection Via Galvanic Skin Response Based on Deep Learning Models

The 5th International Conference on Big Data Computing and Communications


Speech Emotion Recognition with Hybrid Neural Network


Face and Gender Recognition System Based on Convolutional Neural networks

Proceedings of 2019 IEEE International Conference on Mechatronics and Automation (ICMA)


Text Detection for Natural Scene based on MobileNet V2 and U-Net


Position-Free Hand Gesture Recognition Using Single Shot MultiBox Detector Based Neural Network


On the evolution of artificial intelligence and its topics


Proceedings of the 14th NTCIR Conference


Feature Fusion Based on Neural Image Captioning with Spatial Attention

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering (WCSE 2019)


Traffic Sign Recognition Based on Up-sampling Convolution


Research on the Effect of Different Speech Segment Lengths on Speech Emotion Recognition Based on LSTM


Advanced Intelligence and Emotional Robot


Your WiFi Knows You Fall: A Channel Data-driven Device-free Fall Sensing System

the 53rd IEEE International Conference on Communications (ICC)


5G's 5WH and 5G + AI usher in a life revolution

The Second International Workshop on 5G Networking Technologies


Artificial Intelligence and Emotional Robot


Artificial Intelligence · Current Situation of Emotional Robot · Future Direction

University of Electronic Science and Technology of China


Direction of artificial intelligence in the next 30 years


EEG Emotion Recognition Basedon Granger Causality and Caps Net Neural Network

Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2018)

Emotion recognition is a very challenging task in the brain-computer interface field, and it is of great significance in medical, education, military, and other fields. The classification problem is the key to the field of emotion recognition research. In this paper, a classification model based on CapsNet neural network is proposed. By extracting the granger causality feature of original EEG signals, sparse group lasso algorithm is used for feature screening, and the obtained high-relevance feature subset is taken as the input of the network to achieve the final emotional classification. The experimental results show that by adjusting the model parameters and network structure, the constructed CapsNet neural network performs emotional classification on EEG signals, and obtains 88.09% and 87.37% average classification accuracy under valence and arousal emotion dimensions, compared with SVM and CNN. The classification system can obtain better results and significantly improve the performance of EEG emotional classification. This is the first time that CapsNet is applied to EEG emotional classification.


EEG Emotion Classification Based On Baseline Strategy

Electroencephalograph (EEG) emotional computing, as an important task of pattern recognition, has received more and more attention in recent years and is widely used in human-computer interaction, emotional computing and medical fields. Most researches have focused on finding particularly effective features and classifiers to achieve higher classification accuracy in some cases, while most methods are only effective under specific tasks or data and lack broad applicability. In this paper, we propose a novel baseline strategy that introducing emotional features to acquire a newly generated baseline and then, calibrate the individualized features in the emotional features, so that decrease the experimental errors and improve the versatility and effectiveness of the classification method. We performed a classification comparison experiment with baseline-strategy and no baseline-strategy on the DEAP dataset. The selected methods adopt different power spectral density (PSD) feature extraction methods and are classified by Support Vector Machine (SVM) and convolutional neural network (CNN) respectively. The results showed that the experiments with the baseline-strategy achieved better classification results.


Realization and Improvement of Object Recognition System on Raspberry Pi 3B+

This paper propose a lightweight convolution neural network based on the depthwise separable convolution and the improved Linear Bottlenecks block. Running time and space complexity of the model are reduced by designing the network structure carefully. The neural network model can run fluently on the device of ARM architecture to complete tasks such as object recognition and object detection. 20 categories of common objects in life from ImageNet are randomly selected to carry out recognition experiments on Raspberry Pi 3B+. The experimental results show that the Top1 accuracy of the model in the testing dataset can reach 91%, and the average recognition speed for 224*224 pixel images can reach 176ms.


Dialogue Act Recognition for Open-Domain Based on Word-level Sequence Annotation with CRF

IEEE International Conference on Software Engineering and Service Science

In dialogue system, natural language understanding has a huge impact on human-machine interaction. Moreover, natural language contains a huge amount of noise within its content because the short length and free format. From this aspect, the demand of this paper is to design the useful feature representations and verify these feature sets by searching suitable machine learning approaches. Besides, the dialogue acts as labels associate with utterance can be viewed as sequence labelling problem. Considering this situation, this paper provides a word-level sequence annotation method, which annotates dialogue structural information and semantic information to each word of utterance. Meanwhile, Linear-CRF is employed in this paper because it is able to natively capture constrains of hidden state sequence and obtain optimal probability, which is perfectly suit for sequence labeling task. Compared with proposed method, this paper uses the Bag-of-word model to present utterance as baseline. According to result, all the designed features outperform the feature in baseline, which indicates that the proposed method is useful for DA recognition task.


Text classification with keywords and co-occurred words in two-stream neural network

In the task of text classification, the results are often affected by the sparseness of the corpus in a certain category. This paper proposed a Text classification method based on keywords and co-occurred words in two-stream neural network, in which the keywords are selected from training corpus, and the co-occurred words are chosen from external corpus, and they both are the input of the aid-stream of neural network, which is to enhance the category characteristics of representation vector in main-stream, especially for the category with less data distribution. The experiment results in Fudan University Corpus proved that the proposed method further improved the accuracy of the traditional GRU network. The proposed aid-stream based on keywords and co-occurred words is of great significance to the task of text classification and will provide method support for other natural language processing task.


Your WiFi Knows You Fall: A Channel Data Driven Device-free Fall Sensing System

Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

Falls are the second leading cause of injury deaths worldwide, inducing over $0.6$ million accidental deaths per year. Among various prevention strategies, fall-related research has been prioritized. However, conventional fall detection solutions relying on computer vision or wearable sensors embody several inherent limitations such as scalability, coverage, and privacy issues. To this end, we present FallSense, a transparent and real-time fall sensing system driven by wireless channel data. FallSense is built on a Dynamic Template Matching (DTM) algorithm, which can start with a light training set and keep updating on usage. FallSense has been realized on commodity WiFi devices and evaluated in real environments. Experimental results show that FallSense outperforms another state-of-the-art approach WiFall in terms of detection precision, false alarmrateand complexity.


ADAPTIVE CONVERSATION SYSTEM BASED ON SCRIPT

Our work is about conversationsystembased on script, supposing to design an adaptive agent. Purpose of this paper is to verify our idea of the transforming from text to image, and to implement an approach to find a new way of feature characterization on conversation text for our system. With sent2vec and SIFT, we designed a way of expression of text features and conducted experiments to classify conversation situations as Image Processing. Good results in close tests and bad results in open tests indicate the necessity of further work on our ideaandthe insignificanceofthe new feature characterization.


Dynamic Facial Expression Recognition based on Two-Stream-CNN with LBP-TOP


Transfer Learning Based on Utterance Emotion Corpus for Lyric Emotion Estimation

In prior research, there are various emotion estimation approaches that are corpus-based, dictionary-based, or rule-based. However, it is necessary to prepare language resources suitable to each domain, which will incur substantial cost. In this paper, we propose an approach to estimate emotion of lyric phrase by using transfer learning. We applied the existing utterance emotion corpus to the lyric emotion estimation task. As the result of an evaluation experiment, we obtained higher accuracy than the baseline method using a simple bag of words feature tf-idf weighted vector and logistic regression model.


Emotion recognition for Twitter language based on lingual and expressional information

Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2018)

Institute of Electrical and Electronics Engineers


Improving LEO Robot ConversationalAbility via Deep Learning Algorithms for 416 Children with Autism


Progressively improving supervised emotion classification through active learning


About emotional robots in responding to aging society

Japan-China Science and Technology Forum on the Aging Society by Utilizing ICT Technology


Facing the essence of artificial intelligence

1 The 60th Anniversary Conference of Department of Computer Science and Technology of Tsinghua University


A Dictionary-Based Test Data Compression Method Using Tri-State Coding


Improvement of Developmental Drawing Imitation Using Recurrent Neural Network Through Incorporation of AVITEWRITE Model

Proceedings of IEEE International Conference on Systems, Man, and Cybernetics


Conference and Labs of the Evaluation Forum (CLEF 2018)


Employing Inception-Resnet-v2 and Bi-LSTM for Medical Domain Visual Question Answering


Investigating Lstm with k-Max Pooling for Text Classification

2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA)

For most deep learning practitioners, text classification is synonymous with recurrent networks and is tackled at word level. The presented study is demonstrated that word-level inputs are better than character-level inputs. We describe the proposed model that utilizes Spatial-Dropout1D, LSTM and k-max pool. Because the word of these representations is measured in a word similarity task, so SpatialDropout can reduce information redundancy; LSTM keep track of information over longer periods of time. k-max pooling keep more useful information. The proposed model is better than the strong baseline methods on six benchmark datasets for sentiment classification and topic categorization. The code will be open.


Speech Data Enhancement Based on hybrid Neural Network

Advances in Multimedia Information Processing PCM 2018


Intelligent robot and its industrialization


Progress of emotional robot and its application


Emotional interaction and intelligent robot


Searching Audio-Visual Clips for Dual-mode Chinese Emotional Speech Database

First Asian Conference on Affective Computing and Intelligent Interaction


Emotional Contagion System By Perceiving Human Emotion Based on Physiological Signals


Emotional Human Machine Conversation Generation Based on SeqGAN


Combination of valence-sensitive loss with restrictive center loss for facial expression recognition

10th International Conference on Advanced Computational Intelligence


Intellectual business society and Jobless society: Unclear power of artificial intelligence


Multi-label Emotion Computing Using Dense Neural Network Based on Distance Feature Represented Ren_CECps

The 12th International Conference on Natural Language Processing and Knowledge Engineering


An improved algorithm of human-robot interaction oriented text similarity calculation


Facial Expression Recognition with Convolutional Neural Networks Based on Joint Loss


Selected LBP-TOP based Dynamic Facial Expression Recognition on Ren-VFEdb


Your WiFi Knows How You Feel: Leveraging Commodity WiFi Devices for Emotion Sensing


Mining Actionable Intents to Facilitate Search Engine Users' Actions

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17)

Chinese Association for Artificial Intelligence


Recognizing facial emotions in the video


Human Pose Recognition in Robots Based on Angle of Joint Vector


Building Emotional Corpus for Microblogging Emoticons


Realization and Improvement of Robot Object Recognition


Exploring the Relations Between Posts and Comments for Short Text Conversation


RESEARCH ON CONSTRUCTION AND ANALYSIS OF JAPANESE-CHINESE EMOTIONAL EXPRESSION CORPUS


Learning Efficient Drawing Sequence Through Training of Recurrent Neural Network Model


Review Score Estimation Based on Transfer Learning of Different Media Review Data

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17)

In this paper, we proposed a model to classify reviews based on review data of different media. Recently, researches have been actively made on transfer learning between different domains with various kinds of big data as a target. Evaluation expressions are usually different in different domains, and this becomes a barrier for reputation analysis. Users usually use different linguistic expressions to refer to the creative works of each media. For example, media such as "anime", "comics", "games" and "movies" have different terms or expressions to be described their creative works. These differences can be considered showing features of each medium and such differences should be found in other expressions as well as in evaluation expressions. We analyzed what effect such differences would cause to classication accuracy by conducting transfer learning between review data ofdifferent media. In this paper, we proved compatibility between original (pre-transferring) and target (post-transferring) media for each medium of the creative works by constructing a review classication model. As a result of evaluation experiments, we could more accurately estimate review score without using SO-Score for training of review fragment based on Long Short Term Memory (LSTM) than the SO-Score based method.


TUA1 at NTCIR-13 Short Text Conversation 2 Task

Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies


Medweb Task: Identify Multi-Symptoms from Tweets Based on Active Learning and Semantic Information


TUA1 at the NTCIR-13 Actionable Knowledge Graph Task: Sampling Related Actions from Online Searching


Facial Expression Recognition Based on Dynamic Fuzzy Density

The 3rd International Conference on Fuzzy Systems and Data Mining


Mental Health Computing and Emotional Robots


Improved Facial Expression Recognition Method Based on ROI Deep Convolutional Neutral Network

7th International Conference on Affective Computing and Intelligent Interaction (ACII2017)


Facial Expression Recognition under Partial Occlusion Based on Fusion of Global and Local Features

The 9th International Conference on Graphic and Image Processing


A Fusion Decision Method Based on the Dynamic Fuzzy Density Assignment

2017 International Conference on Advances in Image Processing


Beijing University of Posts and Telecommunications


Human-Machine Conversation Based on Hybrid Neural Network

Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on

With the rise and popular of artificial intelligence,the technology of conversation between human and machine get more and more attention. Using neural network model on the Encoder-Decoder framework has been wildly used in translation and human-machine conversation. This paper we propose a new hybrid neural network model (HNN) which consists of some essential neural network models (that is RNN, LSTM, and CNN). At the same time, according to the number of words that each sentence contains, we will get three sub-datasets from the original dataset. Then training and testing our models on different sub-datasets. Experimental results show that the best accuracy belongs to different hybrid models on different sub-datasets, which indicate the proposed approach can make use of each models advantages aim at different sub-dataset.


Discernibility Matrix and Rules Acquisition Based Chinese Question Answering System

Lecture Notes in Computer Science (Conference: International Joint Conference on Rough Sets)

Different from English processing, Chinese text processing starts from word segmentation, and the results of word segmentation will influence the outcomes of subsequent processing especially in short text processing. In this paper, we introduce a novel method for Short Text Information Retrieval based Chinese Question Answering. It is developed from the Discernibility Matrix based Rules Acquisition method. Based on the acquired rules, the matching patterns of the training QA pairs can be represented by the reduced attribute words, and the words can also be represented by the QA patterns. Then the attribute words in the test QA pairs can be used to calculate the matching scores. The experimental results show that the proposed representation method of QA patterns has good flexibility to deal with the uncertainty caused by the Chinese word segmentation, and the proposed method has good performance at both MAP and MRR on the test data.


Affective Computing and Intelligent Robots


Numerical simulation of VAWT on the effects of rotation cylinder

ACMME 2017 5th Asia Conference on Mechanical and Materials Engineering


Activity Recognition via Channel Response: From Theoretical Analysis to Real-world Experiments

IEEE 85th Vehicular Technology Conference: VTC2017-Spring


``Silence is Golden'': Exploring Ambient Signals for Detecting Motions in a Real-time Manner

IEEE VTC-fall 2017 (IEEE 86th Vehicular Technology Conference)


Xi'an Fourth Military Medical University


Mental Health Computing and Emotional Robotic


Improve question type classification by integrating rich representation through neural networks

The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16)


An improved clustering algorithm for EEG-based Emotion feature selection


Speech emotion recognition based on MFCC and D-MFCC


Human-robot emotion interaction via human skeleton tracking


Salient Object Detection from Global to Local


Integration of Homography Constraints and Feature Correspondences in the Image Sequences for Traffic Sign Localization


Voice conversion based on RBF neural network


Text classification based on word co-occurrence with background knowledge


Towards a Developmental Human-Robot Interaction System Using Robot Facial Expressions From Human Feedback


Generation of Humanlike Facial Expression for Natural Human-Robot Interaction System


Facial Expression Recognition Based on Hybrid Deep-learning and Shallow Features


Classification of Biological Contacts and Crystal Contacts Using Deep Learning Model


Disambiguating Users' Temporal Intent in Search Queries with Deep Neural Networks


Advanced Intelligence and Multisource Imaging Model


Ego-state Estimation from Short Texts Based on Sentence Distributed Representation


On Autonomous Service Migrations in the Cloud for Mobile Accesses

Parallel and Distributed Systems (ICPADS), 2016 IEEE 22nd International Conference on


Affective Computing and Human Robot Interaction

Peking University Shenzhen Graduate School


A Smart Cloud Robotic System Based on Cloud Computing Services

Cloud Computing and Big Data (CCBD), 2016 7th International Conference on

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.


On measures against aged society based on advanced intelligence


Robot Evolution and Humanitarian Intelligence

Institute of Automation, Chinese Academy of Sciences


Human-like facial expression imitation for humanoid robot based on recurrent neural network

International Conference on Advanced Robotics and Mechatronics (ICARM),


Facial Expression Recognition based on DT-CWT and Weighted Inverse Cloud Model

Modelling, Simulation and Identification / 841: Intelligent Systems and Control


Facial Expression Recognition Adaptive to Face Pose Using RGB-D Camera

Springer International Publishing Switzerland, IEA/AIE 2016


Study on CNN in the recognition of emotion in audio and images

Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on

In this paper, the performance of Convolution Neural Network (CNN) in image recognition and emotion recognition in speech will be compared and presented. Feature extraction and selection in pattern recognition is an important issue and have been frequently discussed. Moreover, two-dimensional signals such as image and voice are hard to be modelled well by traditional models like SVM. The ability of CNN to characterize two-dimensional signals is prominent. And CNN can adaptively extract feature to eliminate the dependence on human subjectivity or experience. It mimics the effect of local filtering in visual cortex cells to dig local correlation in natural dimensional space. In this work, for the problems of the image recognition and emotion recognition in speech, CNN and SVM which is used as baseline for comparison of the recognition effect. Different kernel functions in SVM have been experimented for image recognition with, the best accuracy is 94.17%. However, the accuracy of using CNN is 95.5% (7291 pictures for train and 2007 pictures for test) with less time consuming. In the emotion recognition of speech, the accuracy of CNN is 97.6% corresponds to 55.5% by baseline model (4000 utterances for training, 1500 for validation, 500 for test). The experimental results showed that CNN can effectively extract features and its modeling capability for two-dimensional signals is prominent.


Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies


KGO at the NTCIR-12 Temporalia Task: Exploring Temporal Information in Search Queries


Understanding Blog author's emotions with hierarchical Bayesian models

2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)

The research of inferring emotions in natural language is confronted with two major challenges: the lack of basic knowledge in emotion expressions and the co-occurrence of separate emotions through all language unites. In this paper, we propose a Bayesian inference method to explore the basic knowledge with respect to emotion expression in different semantic dimensions, and to infer the co-occurrence of multiple emotion labels through words to the document. Specifically, we incorporate emotions and semantic dimensions as the latent factors in determining the distribution of observed words in a corpus of Blog articles. For each Blog article, we further generalize emotions from words to the document by incorporating a document specific hierarchy in the emotion distributions. The basic knowledge and co-occurred emotion labels in words and documents are obtained through a Gibbs sampling inference. Our experiment is performed on the well-developed Chinese emotion corpus, i.e. Ren-CECps, which indicates both higher accuracy and better robustness in our word and document emotion predictions compared with those generated by the state-of-the-art emotion prediction algorithms, and demonstrates a distribution of emotions in different word semantic dimensions.


Optimal parameter setting for indoor localization via big data analysis

Computer Communications Workshops (INFOCOM WKSHPS), 2016 IEEE Conference on

Indoor localization remains a hot topic and receives tremendous research efforts during the last few decades. While most previous efforts focus on the designing issue, little effort has been paid to the impact of different environmental parameters on the system performance. To this end, we present an extensive empirical study with real-world experiments to provide sufficient data for analysis. By seizing the fundamental relationship between a given scenario and environmental parameters, we show that the optimal parameter settings can be achieved for the scenario regardless of the localization algorithms.


Handling Environmental Dynamics in Indoor WiFi Fingerprint Localization

2016 IEEE Wireless Communications and Networking Conference (WCNC)


Emotional Robots with human-computer symbiosis


Verb-object Selectional Preferences in Chinese Based on Distributional Semantic Model

18th International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems (MAMECTIS '16)

World Scientific and Engineering Academy and Society

In this paper, we propose a approach based on distributional semantic model on selectional preference task in the verb & dobj (direct object) relationship. The distributional representations of words are employed as the semantic feature by using the Word2Vec algorithm. Experimental results show that the proposed approach is effective to discriminate the compatibility of the object words and the performance could be improved by increasing the number of training data. Moreover, the results demonstrate that the semantics is an universal, effective and stable feature in this task, which is consistent with our awareness of using words.


Automatic Facial Feature Points Extraction and Expression Recognition Based on Video Database

Proceedings of 4th National Conference on Electrical, Electronics and Computer Engineering

In this paper, we propose a facial expression recognition method using Support Vector Machine on mapped coordinate sequence features. The proposed system is almost fully automatic, in which landmark initialization is based on general knowledge with edge information, and missing information compensation is done by ASM. The geometric features of facial expressions were extracted from sequences of facial landmarks. Validation experiments were conducted using facial expression sequences extracted from a video based facial expression database.


Invited Speech The New Era of Robots Brought by Artificial Intelligence, China National Convention Center


Hybrid Model Based Influenza Detection with Sentiment Analysis from Social Network

Procedings of 4th Natioal Conference on Social Media Processing


An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language

Proceedings of the 7th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. Wepropose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As theresult of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation increased up to 66.7% in Naive Bayes.


Keynote Speech Natural Language Understanding and Affective Computing

the 10th International Conference on Natural Language Processing and Knowledge Engineering


Towards An Optimal Parameter Setting for Indoor Localization: An Empirical Study


Slang Analysis Based on Variant Information Extraction Focusing on the Time Series Topics


Distinguish human translation and machine translation based on hybrid model


A hybrid kernel based method for relation extraction and gene-disease interaction network construction


Improvements in Convolutional Neural Network for Recognizing Emotion in Speech


Dual-modality emotion recognition of facial expressions and gestures


Face Recognition Based on ULBP and BP Neural Network


Facial expression recognition based on MBP and sparse representation


Real Time Influenza Detection based on Discriminative Model with Emotional Factors from Social Networks


Chinese Microblog Sentiment Classification Based on Convolution Neural Network with Content Extension Method

Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015)

Related research for sentiment analysis on Chinese microblog is aiming at analyzing the emotion of posters. This paper presents a content extension method that combines post with its' comments into a microblog conversation for sentiment analysis. A new convolutional auto encoder which can extract contextual sentiment information from microblog conversation of the post is proposed. Furthermore, a DBN model, which is composed by several layers of RBM(Restricted Boltzmann Machine) stacked together, is implemented to extract some higher level feature for short text of a post. These RBM layers can encoder observed short text to learn hidden structures or semantics information for better feature representation. A ClassRBM (Classification RBM) layer, which is stacked on top of RBM layers, is adapted to achieve the final sentiment classification. The experiment results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than state-of-the-art surface learning models such as SVM or NB, which also proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.


Estimate the Intimacy of the Characters Based on Their Emotional States for Application to Non-Task Dialogue

Non-task-oriented dialogue requires someschemes that enable smooth and flexible conversations with auser. For example, it would be possible to go beyond the closedrelationship between the system and the user by consideringthe users relationship with others in real life. In this paper,we focused on the dialogue made by the two characters in adrama scenario, and tried to express their relationship with ascale of intimacy degree. There will be such various elementsrelated to the intimacy degree as the frequency of response to theutterance and the attitude of a speaker during the dialogue. Wefocused on the emotional state of the speaker during the utteranceand tried to realize intimacy estimation with higher accuracy. Asthe evaluation result, we achieved higher accuracy in intimacyestimation than the existing method based on speech role.


Facial Expression Recognition based on Gabor Wavelet Transform and Histogram of Oriented Gradients

Proceedings of 2015 IEEE International Conference on Mechatronics and Automation

In order to get more effective expression features,this paper proposes an approach based on Gabor feature andHistogram of Oriented Gradients (HOG). Gabor Wavelet filter isfirst used as preprocessing stage for feature extraction. Handingthe characteristics with a large number of dimensions, binaryencoding (BC) is applied for dimensionality reduction.Dimensionality of the feature vector is reduced by using HOGalgorithm. Experiments were performed on Cohn-Kanade facialexpression database and the support vector machine classifier isused for expression classification. We obtained experimentalresults with an average recognition rate of 92.5%, which revealsthat the proposed method is superior to other Gabor Wavelettransform based approaches under the same experimentalenvironment.


A SYNTHETIC AND COMPUTIONAL LANGUAGE MODEL FOR INTERACTIVE DIALOGUE SYSTEM

Proceedings of International Conferences Intelligent Systems and Agents 2015


Low Power Multistage Test Data Compression Scheme


Professional Nurses Attitudes Towards the Introduction of Humanoid Nursing Robots (HNRs) to the Hospital

International Association for Human Caring 36th International Conference


Gene-disease Relation Extraction and Gene Interaction Network Construction

Proceedings of 7th International Conference on Bioinformatics and Computational Biology


Fast Indoor Localization of Smart Hand-held Devices Using Bluetooth

Mobile Ad-hoc and Sensor Networks (MSN), 2014 10th International Conference on

Indoor localization remains a hot topic during the last few decades. Previous research mainly relies on wireless fingerprints and thus demands a large number of APs or labor-intensive site survey. To this end, by exploring Bluetooth features and leveraging user motions, we introduce a novel localization scheme that needs neither APs nor site survey. More specifically, through a systematic experimental study, we gain in-depth understandings of Bluetooth characteristics e.g., The impact of various factors such as distance, orientation, and obstacles on the Bluetooth RSSI (Received Signal Strength Indicator). With the empirical experiences, a novel motion-assisted localization model is built to describe the relationship between RSSI and device location. Based on the model, we design a localization scheme that iteratively adjusts the search directions according to RSSI changes to approach the target device. We prototype and evaluate our system in several real-world scenarios. Extensive experiments show that the proposed scheme is efficient in terms of localization accuracy, searching time and energy consumption.


Proceedings of the 11th NTCIR Conference


This paper details our participation in the NTCIR-11 Temporalia task including Temporal Query Intent Classification(TQIC) and Temporal Information Retrieval (TIR). In the TQIC subtask, we explore the rich temporal information in the labeled and unlabeled search queries. Semi-supervised and supervised linear classiers are learned to predict the temporal classes for each search query. In the TIR subtask, we perform temporal ranking based on the technique of learning-to-rank. Two classes of features are investigated for estimating the document relevance.


In this paper, we detail our participation in two subtasks: subtopic mining and document ranking of the NTCIR-11 IMine task. In the subtopic mining subtask, to discover the latent hierarchy among query-like strings, our key idea is to structurally parse query-like strings by characterizing pairwise dependency in the bag-of-units perspective. Then the clustering algorithm (i.e., affinity propagation) and the Sainte-Laguë algorithm are used to obtain the target list that represents a two-level hierarchy of subtopics. In the document ranking subtask, we deploy the newly proposed 0-1 MSKP model for diversied document ranking against unclear topics. A subset of documents are optimally chosen like lling up multiple subtopic knapsacks.


Joined cepstral distance features two-stage multi-class classification for emotional speech

Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on

This letter presents a joined cepstral distance and voice quality feature two-stage multi-class classification with DAG-SVM for emotional speech. The Harmonic to Noise Ratio (HNR) is applied to detect the throat diseases because it can reflect characteristics of the throat. Meanwhile, these characteristics are also strong emotional basis to distinguish emotion in speech. The cepstrum and cepstral distance is able to measure differences as well, which are well used for endpoint detecting in speech signals. In this work, cepstral distance is used for measuring the similarity between frames in emotional statement and in neutral signals. The experiment shows that cepstral distance can increase the recognition rate of emotion sad, and can balance the rate of other classes of emotion except angry. Finally, aiming at the characteristics that the different emotional expression ability of these feature set is different, a two-state classification is applied to solve confusion in multi-emotion recognition. In the recognition, Chinese mandarin emotion database is used and a large training set (1134+378 utterances) ensures a powerful modeling capability for predicting emotion.


WeWatch: Bi-Screen Video Watching Experience on Smart Devices

The high resolution and widescreen mobile devices have tremendous applications in our daily lives. In this paper, we demonstrate a specific bi-screen video sharing application called WeWatch that a high resolution video is played across screens of two mobile devices. It helps users to enhance their watching experience on the Android system. In order to efficiently use idle mobile devices, we develop a novel algorithm to share videos with devices with different screen sizes and resolutions. Our research provides a method which automatically controls videos based on different distances between devices. The distance is determined by the empirical WiFi RSSI values obtained through the experimental studies. We have prototyped the system and conducted a real-world energy efficiency and user experience evaluations. Results show that WeWatch has negligible impact on battery lifetime while over 80% users like our application.


Fruit recognition based on multi-feature and multi-decision

the 9th International Conference on Natural Language Processing and Knowledge Engineering


Search Result Diversification via Filling Up Multiple Knapsacks

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Result diversification is a topic of great value for enhancing user experience in many fields, such as web search and recommender systems. Many existing methods generate a diversified result in a sequential manner, but they work well only if the preceding choices are optimal or close to the optimal solution. Moreover, a manually tuned parameter (say,) is often required to trade off relevance and diversity. This makes it difficult to know whether the failures are caused by the optimization criterion or the setting of . In context of web search, we formulate the result diversification task as a 0-1 multiple subtopic knapsack problem (MSKP), where a subset of documents are optimally chosen like filling up multiple subtopic knapsacks. This formulation yields no trade-off parameters to be specified beforehand. Solving the 0-1 MSKP is NP-hard, we treat the optimization of 0-1 MSKP using a graphical model over latent binary variables as a maximum posterior inference problem, and tackle it with the max-sum belief propagation algorithm. To validate the effectiveness and efficiency of the proposed 0-1 MSKP model, we conduct a series of experiments on two TREC diversity collections. The experimental results show that the proposed model outperforms several state-of-the-art methods significantly, not only in terms of standard diversity metrics (-nDCG, nERRIA and subtopic recall), but also in terms of efficiency.


Age estimation and gender classification of facial images based on Local Directional Pattern

Proceedings of the 3rd IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2014)


Adaptive Facial expression recognition method based on MBP and HMOG feature


Face recognition based on adaptive weighting and fuzzy fusion with single training sample


Emotion Predicting Method Based on Emotion State Change of Personae according to the Other's Utterance

It is a very difficult task to predict others emotion from dialogue flow for dialogue system or robot, however, that is a natural task for human.As the reason, because of the emotion of human changes at all times when he / she have a conversation with other people, these changes would be considered that oc-curred by responding internal emotional state to stimulus from external world .In the researches about dialogue system, most of them are superficial feature of utterance for question-answering system, thus, internal emotional state and stimulus from external world.In this paper, we focused on the scenario dialogue sentences. We annotated the various information to the corpora for helping analyze dialogue flow or human relationships and constructed the scenario emotion corpus. We discussed that the others utterances or emotions how effect to emotion changes by analyzing this cor-pora.As the method to predict the others emotion changes from self-emotion, we proposed the predicting method based on the emotion predicting weight. As the result of evaluation experiment, the effectiveness of the proposed method could be confirmed.


WiFi-Assisted Human Activity Recognition

IEEE Asia Pacific Conference on Wireless and Mobile

This paper investigates the indoor activity recognition issue and proposes a novel recognition framework by exploring WiFi ambient signals. The key idea is to use data mining techniques to abstract footprints of different activities on the radio signal strength (RSS) data. Our experiments show that even using a single feature and the common k-NN classifier activities such as walking, sitting and standing can be recognized with a high accuracy, i.e. 75%. To further improve the performance, a new feature has been abstracted to represent the fluctuation of sampled data and a novel algorithm named fusion algorithm has been specifically designed based on the classification tree. Experiments show that the proposed fusion algorithm significantly outperforms the k-NN classifier in terms of both the average recognition ratio (from 75% to 92.58%) and the computational complexity. Compared to previous solutions relying on either special hardware or the cooperation of tested subjects, the proposed recognition framework is a passive and device-free solution that could be integrated into any WLAN network with low overheads.


Real Time Early-stage Influenza Detection with Emotion Factors from Sina Microblog

25th International Conference on Computational Linguistics

Influenza is an acute respiratory illness that occurs every year. Detection of Influenza in its earliest stage would reduce the spread of the illness. Sina microblog is a popular microbloggingservice, provides perfect sources for flu detection due to its real-time nature and largenumber of users. In this paper we investigate the real-time flu detection problem and describea Flu model with emotion factors and sematic information (em-flu model). Experimental resultsshow the robustness and effectiveness of our method and we are hopeful that it wouldhelp health organizations in identifying flu outbreak and take timely actions to control.


1.Emotional interaction and Advanced Intelligent Robot, Keynote Speaker, CRSSC-CWI-CGrC 2014,


Word Frequency Statistics Model for Chinese Base Noun Phrase Identification

The Chinese base phrase identification plays an important role in the field of natural language processing. It needs to be improved in the recognition scope and methods currently. This paper presents a method based on word frequency statistics model for Chinese base noun phrase identification: Building the noun phrase dictionary by training corpus, calculating the co-occurrence frequency and threshold of the noun phrase, and constructing word table according to the different roles of the words in the noun phrase. Unknown word processing and rule templates are added. Improve the results with error correction processing at last. Experiments on the test corpus show that the average precision and average recall rate of the base noun phrases identification in different areas are 91.28% and 93.22%.


Construction of a Chinese Emotion Lexicon from Ren-CECps

This paper presents an automatic method to build a Chinese emotion lexicon based on the emotion corpus Ren-CECps. The method includes word extraction and emotion classification. Firstly, sentences are parsed to extract candidate emotional words. By making use of the words co-occurrence in the corpus, we get the similarity between words. And then Support Vector Machine (SVM) is adopted to classify the candidate emotional words. Experiment on the manual labeled words has shown that our classification method achieved high precision. Finally we apply our method on unlabeled corpus to get emotional words.


The Role of Pre-processing in Twitter Sentiment Analysis

Recently, increasing attention has been attracted to Social Networking Sentiment Analysis. Twitter as one of the most fashional social networking platforms has been researched as a hot topic in this domain. Normally, sentiment analysis is regarded as a classification problem. Training a classifier with tweets data, there is a large amount of noise due to tweets shortness, marks, irregular words etc. In this work we explore the impact pre-processing methods make on twitter sentiment classification. We evaluate the effects of URLs, negation, repeated letters, stemming and lemmatization. Experimental results on the Stanford Twitter Sentiment Dataset show that sentiment classification accuracy rises when URLs features reservation, negation transformation and repeated letters normalization are employed while descends when stemming and lemmatization are applied. Moreover, we get a better result by augmenting the original feature space with bigram and emotions features. Comprehensive application of these measures makes us achieve classification accuracy of 85.5%.


Multi-strategy based Sina Microblog Data Acquisition for Opinion Mining

As an important media for social interactions and information dissemination through the internet, Sina microblog contains emotional state and important opinion of participants. Dealing with microblog data belongs to big data areas, the premise of which is to obtain a large amount of microblog data for further analysis and data mining. For commercial interests as well as security considerations, the access to the data is becoming increasingly difficult and the API Sina microblog officially provided doesnt support large amount of data mining. In this paper, we try to design a platform that is mainly based on the access mechanism of multi-strategy and existing resources to collect data stably from Sina microblog. The results demonstrate that a combination of API and web crawler allows efficient data mining. In such way, sentiment analysis and opinion mining are performed on the data obtained by the multi-strategy method, which proved that the proposed solutions will be allowed to build straightforward application of hot words searching, opinion mining and sentiment analysis.


Affective Computing and Emotional Robot, Florida State University

Florida State University,Tallahassee, USA


Mental Health Sensing and Computing Based on Affective Interaction,

Harvard University(Medical School), Boston, USA


A test data compression scheme based on position information coding

Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on

Under the condition of Built-off Self-test (BOST), a novel test data compression scheme based on position information coding is presented, which is targeting test data and test power consumption. The proposed scheme gets the next test stimuli by reversing some bits of the responses corresponding to the previous test vectors. During the process of bit flip, test power problem should be taken into account; both the shift power and capture power during the test should be limited in the scope of security. For shift power, need to modify the scan chain by adding blocking logic; and the capture power reduction through the test cube filling based on the response. Experimental results justify the efficacy of the proposed method in attaining test-data compression; the average compression ratio is 73.63% on the premise of capture power consumption reduced by 20%.


Design and realization of 3D NOC multicast router base on multicast rotational routing arithmetic

IEEE The Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

Traditional 3D NoC router may encounter the situations of congestion and hot spot. And it also doesn't have the function of multicast. In this paper, we present a new routing arithmetic called Multicast Rotational Routing Arithmetic (MRRA). It can handle several destination addresses at the same time, recombine packets, sent the same data to the different destination nodes, and avoid the congestion. The proposed algorithm is implemented by the new multicast 3D NoC router which is also proposed in this paper. The 3D NoC router is designed in Verilog HDL, simulated by Modelsim, synthesized and prototyped by Synplify and FPGA respectively. The experiment results show that the multicast router of MRRA can achieve a very good performance in terms of throughput and delay. The increased area utilization also can be tolerated.


Test wrapper optimization technique using BDF and GA for 3D IP cores

To reduce pre-bond and post-bond test cost for 3D IP (Three Dimensional Intellectual Property) cores, this paper proposed a test wrapper optimization technique using BFD(Best Fit Decreasing) and GA (Genetic Algorithm) algorithm under the constraints of TSVs(Through Silicon Vias) number, the proposed technique firstly used BFD to balance the length of pre-bond wrapper chains to reduce pre-bond test time. Then, on the basis of optimization results of pre-bond wrapper chains, the GA was used to stitch pre-bond wrapper chains to form balanced post-bond wrapper chains under the constrained TSVs number to reduce hardware overhead and post-bond test time. Experimental results demonstrated the presented methodology can effectively reduce hardware overhead at the cost of little increased test time.


A low power BIST scheme based on block encoding

With the development of integrated circuit manufacturing technology, low power test has become a focus of concern during testing fields. This paper proposes a new low power BIST (built-in self test) scheme based on block encoding which first exploit a block re-encoding method to optimize the test cube, and then a low power test based on LFSR (linear feedback shift register) reseeding is applied. According to the compatibility of flag, the scheme proposes a grouping algorithm based on flag to divide and reorder the test cubes in the test cube set. Experimental results show that the scheme not only obtain better test compression ratio and test data storage, but also reduce the test power consumption effectively.


A connected component-based distributed method for overlapping community detection

In order to find the overlapping community structure more quickly and accurately in complex network, this paper proposes a Connected Component-Based Distributed method (CCBD) for overlapping community detection. CCBD first divides edges set in the network into smaller one. Next, it seeks out connected component using distributed platforms and gives a serials number to them according to certain rules. Then, it classifies these connected components according to the serials number. Task nodes determine whether any two connected components and non-propagating edges can be connected to become a larger community. Finally, CCBD negotiates a new serials name for the new community. Through repeated iterations, we get the community structure in the network. Nodes belong to multiple communities are overlapping nodes. Experiment results show that CCBD has higher time efficiency benefiting from distributed computing. Moreover, the quality of communities detected by CCBD surpasses those found by other algorithms.


Subtopic Mining via Modifier Graph Clustering

Advances in Knowledge Discovery and Data Mining

Understanding the information need encoded in a user query has long been regarded as a crucial step of effective information retrieval. In this paper, we focus on subtopic mining that aims at generating a ranked list of subtopic strings for a given topic. We propose the modifier graph based approach, under which the problem of subtopic mining reduces to that of graph clustering over the modifier graph. Compared with the existing methods, the experimental results show that our modifier-graph based approaches are robust to the sparseness problem. In particular, our approaches that perform subtopic mining at a fine-grained term-level outperform the baseline methods that perform subtopic mining at a whole query-level in terms of I-rec, D-nDCG and D#-nDCG.


Gene disease association extraction by text mining and network analysis

Proceedings of the Fifth International Workshop on Health Text Mining and Information Analysis (Loudi)

Association for Computational Linguistics

Biomedical relations play an important role in biological processes. In this work, we combine information filtering, grammar parsing and network analysis for gene-disease association extraction. The proposed method first extracts sentences potentially containing information about gene-diseases interactions based on maximum entropy classifier with topic features. And then Probabilistic ContextFree Grammars is applied for gene-disease association extraction. The network of genes and the disease is constituted by the extracted interactions, network centrality metrics are used for calculating the importance of each gene. We used breast cancer as testing diseasefor system evaluation. The 31 top ranked genes and diseases by the weighted degree, betweenness, and closeness centralities have been checked relevance with breast cancer through NCBI database. The evaluation showed 83.9% accuracy for the testing genes and diseases, 74.2% accuracy for the testing genes.


A Novel Method for Recognizing Emotions of Weblog Sentences

proceedings of IEEE/SICE SII2013 The Sixth Symposium on System Integration

With plenty of online resources constantly increasing (like weblog, product reviews, news reviews, etc.), it is difficult to read them and obtain the useful information, especially emotion information. The emotion analysis on internet online information has received much attention from natural language processing field in recent years. In most existing works, single-label emotion analysis have been studied by many scientists, it often ignores the complexity of human feelings. This paper is dedicated to construct the multi-label emotion topic model for recognizing the complicated emotions of weblog sentences based on Chinese emotion corpus Ren-CECps. We employ latent topic variables and emotion variables to find complex emotions of the sentence. The results of experiments indicate that the model is reasonable and effective in recognizing the mixed emotions of weblog sentences.


A New Preprocessing Algorithm and Local Binary Pattern Based Facial Expression Recognition

This paper presents a new preprocessing algorithm with local binary pattern for facial expression recognition. Firstly, we establish the skin color model to extract the face region, and then we use the cumulative projection to obtain the completely face region. Secondly, we rotate the inclined faces, normalize all the images, and remove the effect of light to get the required experimental samples. Finally, we use rotation invariance uniform local binary pattern operator to get the facial expression features, and then the support vector machine classifier is used for expression classification. The algorithm is implemented with Matlab and experimented on Indian male facial expression database. The proposed method obtained an accuracy of 72.75% which shows the effectiveness of the proposed algorithm.


Microblogging Hot Events Emotion Analysis Based on Ren-CECps

Since Sina microblogging was first launched in August 2009, that seems to have become the major social media communication platform in China, microblogging is increasingly deep into life. In this study, we analyze the sentiment of microbloging hot events based on Ren-CECps. According to hot events in chronological, choosing the microblogging hot events of Beijing Heavy Fog and Xi Jinping, Peng Liyuan Visit for instance, crawling through sina microblogging we can get the corpus that contains the specified events. The method uses word frequency statistics for access microblogging corpus, analyzing trends in specific keywords concerns. At the same time, according to specific keywords related to micro-Bo, we can calculate the emotions of related microblogging via Ren-CECps. Finally, according to changes from time to time, we make a corresponding data charts, visual display of such trends. In this study, we illustrate that we can get a good result of emotion analysis using Ren-CECps in such a microblogging Chinese text with fragmentation and discrete feature. As a further indication of such excellent Chinese emotion corpus, the practical and research value in it are enormous.


Feature Fusion of HOG and WLD for Facial Expression Recognition

A considerable amount of research work has beendone for facial expression recognition using local or globalfeature extraction methods. Weber Local Descriptor (WLD), asimple and robust local image descriptor, is recently developedfor local feature extraction. In facial expression recognition, theinformation contained in the local is important for therecognition result. The Histograms of Oriented Gradients(HOG) can well describe the local area information usinggradient and orientation density distribution of the edge. Inorder to solve the lack of contour and shape information only byWLD features and to extract facial local features moreefficiently, we propose a hybrid approach that combines theWLD with HOG features. We divide the images into blocks andweight each of them, then extract the two features and fuse them.At last, the weighted fused histograms are used to classify facialexpressions by chi-square distance and the nearest neighbormethod. The proposed method is applied on popular JAFFEand Cohn-Kanade facial expression databases and recognitionrate is up to 93.97% and 95.86%. Compared with the GaborWavelet, LBP, and AAM and experimental results show that theproposed method achieves better performance for facialexpression recognition.


Reduce the Dimensions of Emotional Features by Principal Component Analysis for Speech Emotion Recognition

In this paper, the principal component analysis (PCA) is applied to speech emotion recognition for improving the accuracy of the system. The traditional prosodic features like pitch-related features and formant-related features are extracted from the Berlin speech database [7] and a Chinese database. These collected feature data is processed by PCA to remove the irrelevant information. After that, three kinds of features including the processed features by PCA, unprocessed features and other speech-related features are used to train a SVM classifier. And six emotions are tested in the experiment. The classification accuracy of the processed features by PCA is about 3.1% higher than the unprocessed features and about 17.6% higher than the MFCC features when using 240 utterances. The recognition accuracies among different emotions in both databases are also presented in the study.


Combine Sentiment Lexicon and Dependency Parsing for Sentiment Classification

With the rapid development of internet technology and e-commerce sites, there are more and more products review in the network. People are willing to make a survey on the internet before purchasing the products. The automatic identification of the sentiment of comments is necessary. We propose a method, which combines sentiment lexicon and dependency parsing to determine the sentiment orientation and the positive or negative attitudes of the topic. The dependency parsing is used to get the objects and sentiment words. Then the allocation of weights is done, and finally the positive and negative results of the products evaluation are concluded. Experiments show that the validity and efficiency of the proposed method.


Facial Expression Recognition Based on Active Appearance Model & Scale-Invariant Feature Transform

The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in facial expression recognition. However, the results of this model with non-frontal face are not ideal. Thus, we propose a new method for facial expression recognition based on AAM and scale-invariant feature transform (SIFT). The proposed method uses AAM to locate the feature points of a facial expression image. SIFT descriptors are then utilized to describe these feature points, and the gradient direction histogram of the pixels surrounding these points are used to form point feature vectors. The chi-square distance and the nearest neighbor classifier is applied to accomplish the facial expression recognition task. The experimental results from standard expression databases and multi-posture expressions show that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non-frontal facial expressions under some deflection angles.


Facial Expression Recognition Algorithm Based on Feature Fusion Adaptive Weighted HLAC

In this paper, a novel method of facial expression recognition based on adaptive weighted higher-order local autocorrelation coefficient (WHLAC) is presented. Firstly, The method gets whole face region and sub-regions of eyebrows, eyes, nose and mouth. Secondly, the global features of the face regions and local features of the sub-regions are extracted by HLAC, the weights of sub-regions are calculated by fisher linear discriminant (FLD), and then these two parts features are fused together by the weights. Finally, the fused features are classified by FLD. The experimental results show that the proposed method has higher recognition rate and lower computational cost than Gabor, WLBP, HLAC for facial expression recognition.


Facial Expression Recognition Based on Multi-Scale Vector Triangle

proceedings of IEEE/SICE SII2013 The Sixth Symposium on System Integration

Image description is not sufficient in traditional facial expression recognition (FER) methods, therefore this paper proposes a FER method based on multi-scale vector triangle. It combines vector triangle pattern with image pyramid to extract facial expression features. Firstly, construct a facial image pyramid to produce images in different scales. Secondly, divide each image into blocks, and extract vector triangle features of each sub-image. Then, use histogram to statistical characteristics, and calculate Euclidean distance between the histograms. Finally, fusion weighted eigenvalues and come to the recognition results. Multi-scale vector triangle pattern can not only avoid the loss of information in image asymmetric regions, but also reflect image features in different scales. It can describe images more adequately. In order to verify the effectiveness of the algorithm, this paper uses the Japanese Female Facial Expression (JAFFE) database to do the experiments and compare the results with Complete Local Binary Patterns (CLBP), Gabor wavelet, Active Appearance Models (AAM) and so on. Experimental results indicate that this method has higher recognition rate and better real-time effect.


Mining Semantic Orientation of Multiword Expression from Chinese Microblogging with Discriminative Latent Model

2013 International Conference on Asian Language Processing

Extracting semantic orientation of Multiword Expression, especially some newly generated Multiword Expression from internet, is an important task for sentiment analysis of web texts or other real word text as some Multiword Expressions can express more integrative sentiments than words units. This paper proposes a method contains a novel latent discriminative algorithm, which attempts to attack this problem by integrating discriminative model and latent value model. Although Chinese Multiword Expressions consist of multiple words, the semantic orientation of the Multiword Expression is not just simple integration of orientations of the component words, as some words can invert the affective orientation so the Multiword Expressions can have totally opposite semantic orientation. In order to capture the property of such Multiword Expressions, hidden semi-CRF which includes a latent valuable layer, which can be used to address dual-sequence labeling tasks synchronously, is adopted. The method is tested experimentally by adopting a manually labeled set of positive and negative Multiword Expressions from microblog or other internet resources, and the experiments have shown very promising results, which is comparable to the best value ever reported.


Speech emotion recognition using combination of features

Fourth International Conference on Intelligent Control and Information Processing

In this paper, we study how speech features' numbers and statistical values impact recognition accuracy of emotions present in speech. With Gaussian Mixture Model (GMM), we identify two effective features, namely Mel Frequency Cepstrum Coefficients (MFCCs) and Auto Correlation Function Coefficients (ACFC) extracted directly from speech signal. Using GMM supervector formed by values of MFCCs, delta MFCCs and ACFC, we conduct experiments with Berlin emotional database considering six previously proposed emotions: anger, disgust, fear, happy, neutral and sad. Our method achieve emotion recognition rate of 74.45%, significantly better than 59.00% achieved previously. To prove the broad applicability of our method, we also conduct experiments considering a different set of emotions: anger, boredom, fear, happy, neutral and sad. Our emotion recognition rate of 75.00% is again better than71.00% of the method of hidden Markov model with MFCC, delta MFCC, cepstral coefficient and speech energy.


Network lifetime optimization in wireless healthcare systems: Understanding the gap between online and offline scenarios

In this paper, we study the network Lifetime Maximization problem in Mobile healthcare sensor systems (LMM). For the healthecare system, we consider a dynamic scenario where users are mobile at their own wills and periodically report their personal health information (PHI) to a static sink, e.g. a powerful server, for further processing and distributing. The objective is to optimize the network lifetime by flow scheduling. The major difficulty lies in the time-dependent network topologies. Therefore, we propose a novel temporal-spatial network modeling method by extending current model with time dimension. Based on this model, we show that if the movement of users are known in advance (i.e. offline case), the problem can be optimally solved in polynomial time by a linear programming. However, the online LMM problem is much more difficult to tackle, since we prove that there exists no online algorithm with a constant performance ratio to the offline optimal algorithm in terms of the network lifetime. We further design simulations to show the performance gap between online and offline LMM. Considering the user mobility within a given scenario follows some certain patterns, we show the potential improvements of using a prediction-based method. This investigation provides certain insights on designing efficient online algorithms for the LMM problem.


Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions

Proceedings of 26th Pacific Asia Conference on Language,Information and Computation

Most of Japanese slang words such as Wakamono Kotoba are analyzed as unknown word or segmented wrongly by the morphological analysis system. These problems are causing negative effect on sentiment analysis in text. These words generally have many varieties of notations and conjugations, and they lack versatility. As a result, many of them are not registered in the dictionaries, making morphological analysis more difficult. In this paper, we aimed to decrease such negative effects of Wakamono Kotoba for the accuracy of emotion estimation from sentence and proposed a method to increase the accuracy by using a classification method based on machine learning. In this method we used emotional expressions which had high relevance with Wakamono Kotoba as feature. As a result, the proposed method obtained 20% higher accuracy than the method only using morpheme Ngram as feature.


CLASS-INDEXING: THE EFFECTIVENESS OF CLASSS-PACE-DENSITY IN HIGH AND LOW-DIMENSIONAL VECTOR SPACE FOR TEXT CLASSIFICATION

IEEE International Conference on Cloud Computing and Intelligence Systems

Most of the previous studies of termweighting schemes emphasize on the documentindexing-based term weighting approach to addressautomatic text classification (ATC). In this study, weintroduce class-indexing-based term-weightingapproaches and judge their effects in high andcomparatively low-dimensional dataset over the TF.IDFapproach. First, we implement a class-indexing-basedTF.IDF.ICF term weighting approach in which theinverse class frequency (ICF) is incorporated; providepositive discrimination on rare terms and biased againstfrequent terms in the TC task. Therefore, we revised theICF function and implemented a new inverse class spacedensity frequency (ICSF) multiplied by TF.IDF, andgenerated the TF.IDF.ICSF that provides a positivediscrimination on infrequent and frequent terms. Wepresent detailed evaluation of each category for theReuters-21578 and 20 Newsgroups datasets. Theexperiment results show that the proposed classindexing-based TF.IDF.ICSF term weighting approachplays a significant role to overcome the problem ofdimensionality reduction in the feature space.


RELATION EXTRACTION FROM WIKIPEDIA ARTICLES BY ENTITIES CLUSTERING

Wikipedia is an encyclopedia based on wikitechnology. It is multilingual high quality knowledgebase. In this work a episode based extraction method areproposed to extract relations from Wikipedia articles.The entities are clustered and labeled. The relationextraction is benefited by the information redundancyprovided by the clusters. A strict Wikipedia entitiesclustering algorithm based on the category system andfirst sentence of the article is approached. This workrequired less manual assist. And the relations areabundant. The results are comparable with other works.


Role-explicit Query Identification and Intent Role Annotation

The 21st ACM International Conference on Information and Knowledge Management

Understanding the information need or intent encoded within a query has long been regarded as an essential factor of effective information retrieval. For better query representation and understanding, two intent roles (kernel-object and modifier) are introduced to structurally parse a class of role-explicit queries, which constitute a majority of common user queries. Furthermore, we focus on two research problems: RP-1: Given a role-explicit query, how to identify the kernel-object and modifier, namely intent role annotation; RP-2: How to determine whether an arbitrary query is role-explicit or not. To solve RP-1, we propose a simplified word n-gram role model (SWNR), which quantifies the generating probability of a role-explicit query and performs intent role annotation effectively. Using a set of discriminative features, we build classifiers to address RP-2 in a supervised manner. The experimental results show that: (1) SWNR can achieve a satisfactory performance, more than 73% in terms of different metrics; (2) The classifiers can achieve more than 90% precision in identifying role-explicit queries; (3) Compared with traditional techniques for query representation and understanding, e.g., name entity recognition in query and class-level query intent inference, intent role annotation provides a more flexible framework and a number of applications can benefit from annotating role-explicit queries, such as intent mining and diversified document ranking.


A Japanese Adverb Emotion Corpus for Japanese Emotion Analysis

8th International Conference on Natural Language Processing and Knowledge Engineering

Computing emotions from text has become a popular direction in natural language processing. The words are extensively studied as features to predict emotions in previous works, but very few studies have distinguished words with particular part of speech features in this task. In Japanese adverbs which are frequently used in both written and spoken language, have been observed to remarkably impact the sentence emotions. In this paper, we focus on such impacts of adverbs among sentences, and develop a Japanese adverb-emotion corpus with totally 3864 Japanese sentences. Adverb features are extracted from sentences, and new adverb categories, an adverb-emotion lexicon, as well as adverb-emotion rules are summarized according to the different functions of these adverbs in text emotion expressions. The developed Japanese adverb-emotion corpus lays a foundation for further Japanese emotion analysis and recognition in text.


POMDP Based Affective Model and It's Application on An Intelligent Music Player

Partially Observable Markov Decision Processes (POMDP) model has been confirmed to be appropriate for spoken dialogue system. Based on this model, we construct a new factored POMDP model to describe the emotional dialogue system. First we divide the users state into users goal (or intention) and user's emotion. In order to respond effectively to the users state, we divide the agents action into emotional coping and goal response accordingly. Then we construct the observation model, the users goal model, and the users emotion model respectively. Finally, the model is proved to be valid by an example of an intelligent music player.


How to and Why Question Answering using Syntactic Semantic and Pragmatic Features

Question answering system assists people to acquire the valuable information in a natural way. "Why" and "how to" questions express two kinds of essential information requirements. The two kinds of complex questions that need more information to find the answers. In this paper, syntactic, semantic and pragmatic features are employed to find answers for "why" and "how to" questions. And ranking learning approaches are applied to re-rank the candidate answers. And wrappers strategy are used to select the optimized features combination. The results show that answers are improved by using the combination of syntactic, semantic and pragmatic features.


Feature Based Review Classification for Fine-grained Sentiment Analysis

The researches on subjectivity, opinions or sentiments, named sentiment analysis, have a great deal of applications in E-business, opinion polls and many other fields. Feature-based sentiment classification can provide more fine grained sentiment analysis. In the problem of feature-level sentiment classification, identifying implicit review features is a big challenge. In this paper, we propose a new measure of PMI-TFIDF by combining PMI (Point wise mutual information) and TF-IDF (term frequencyinverse document frequency) to measure the association of words for extending related features. By adding the importance measure of a word to documents, PMI-TFIDF can provide more effective distinction between words than PMI. Based on PMI-TFIDF, the related words with high relevance for a feature can be obtained. Kernel methods (KMs) are state-of-the-art for solving machine learning problems. Based on the measure of PMI-TFIDF, we applied Polynomial Kernel (PK) method to get the similarities between a review and the related word lists of different features for feature based review classification. The best experimental results on feature based book reviews classification achieved the precision of 82.5%.


Semantic Orientation Extraction of Chinese Phrases by Discriminative Model and Global Features

Extracting semantic orientation of phrases, especially some newly generated phrases frominternet, is an important task for sentiment analysis of web texts or other real word textas some phrases can express more integrative sentiments than words units. This paperproposes a method contains a novel latent discriminative algorithm, which attempts toattack this problem by integrating discriminative model and latent value model. AlthoughChinese phrases consist of multiple words, the semantic orientation of the phrase is notjust simple integration of orientations of the component words, as some words can invertthe aective orientation so the phrases can have totally opposite semantic orientation. Inorder to capture the property of such phrases, hidden semi-CRF which includes a latentvaluable layer, which can be used to address dual-sequence labeling tasks synchronously,is adopted. The method is tested experimentally by adopting a manually labeled setof positive and negative phrases from microblog or other internet resources, and theexperiments have shown very promising results, which is comparable to the best valueever reported.


Judgment of Depressive Tendency from Emotional Fluctuation in Weblog

Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2012

Some people who have tendency toward depression describe their inner thoughts vividly in their weblogs. Such descriptions are sometimes made consciously but at other times unconsciously by the authors. If such unconscious authors could become aware of their own symptom of the depression, that would contribute to early detection and treatment of the sickness. To achieve this, it is essential to find out the tendency of depression from the descriptions in weblog. Because the symptom of depression is not static, we attempted to detect the tendency of depression focusing on the temporal fluctuation of emotion of the authors. As many weblogs describe what happened or what the authors felt in daily life like a personal journal, we thought that weblogs were ideal for obtaining the time-varying emotion of the authors. In this paper, we proposed the method to detect the tendency of depression in weblog. Our method is based on the temporal fluctuation of emotion described by the authors.


Universal triple I method and its application to textual emotion polarity recognition

World Scientific Proceedings Series on Computer Engineering and Information Science 5


Emotion Ontology Construction from Chinese Knowledge

Computational Linguistics and Intelligent Text Processing


Exploring Emotional Words for Chinese Document Chief Emotion Analysis

Proceedings of 25th Pacific Asia Conference on Language, Information and Computation(PACLIC2011)

In this study, we develop a document emotion analysis model by making use of the function of emotional words annotation. Eight basic emotion categories have been selected, including Expect, Joy, Love, Surprise, Anxiety, Sorrow, Anger and Hate, for both the word and the document level emotion analysis. We introduce two parameters, term relevance and term frequency, to evaluate the relations between the word emotions and the document emotions. Promising experimental results reveal the effectiveness of our document emotion analysis model under different emotion situations.


The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25)


Research on the possibility of humanoid robots to assist in medical activities in nursing homes and convalescent wards

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

In Japan, increasing attention has been given to using humanoid robots to solve problems concerning personnel shortage and to reduce duty workload in clinical and nursing situations. Currently, the development of robots is not yet at the level where they can function like human medical and health care professionals. For this reason, it is imperative to discover ways in which humanoid robots can be used to solve the issues of personnel shortage and duty workload. The aim of this research is to clarify the impressions nurse and care staff has toward humanoid robots. The research focuses on 1) whether there are any duties humanoids can undertake and 2) what is required of humanoids to perform clinical functions. This research was approved by the ethical review board of University Hospital A, and a mail survey was conducted between August and September, 2010. The respondents were 939 members of nurse and care staff working in nursing homes and convalescent wards. Among the reasons given by respondents who think that humanoids cannot do any clinical duty, the most common was Not clear what and how much ability humanoids have, chosen by 70.41 percent of respondents. Regarding the qualities required of humanoid robots on the aspect of ethical considerations, 63.79 percent chose Not causing harm to humans, making it the most common response. From the above results, it appears that personnel working in nursing homes and convalescent wards do not have enough accurate information on humanoid robots. It is therefore necessary that for future engagement of humanoid robots in clinical duty, advanced and detailed information on the ability and performance of humanoid robots be disseminated, so that not only patients but also medical staff can accept them without difficulty.


Complex Emotion Categorization and Tagging for Chinese

To understand emotion and make machine emotion is one of the goals of affective computing. In order to understand language from interface of machine, both the meaning and the emotion are necessary to be interpreted correctly. But until now the study of fine-grained theory of emotion is still full of challenges. In this paper, a novel method to analyze emotion category is proposed according to the statistics of affective word hierarchy in Dictionary of contemporary Chinese. The emotion hierarchy is called complex emotion. Firstly, over 7,000 common affective words have be annotated and their detailed explanations had been collected for an affective lexicon, then we analyze the consistent relationship in the affective lexicon and consequently 52 salient complex emotion state hierarchies are categorized and tagged by a lexical affective clustering algorithm. The complex emotions are compared to the traditional definitions of basic emotions in psychology and have been evaluated to be valid by kappa value in the experiment. Moreover we also have tagged the semantic orientation for the collected words.


Emotion Estimation of Wakamono Kotoba Based on Distance of Word Emotional Vector

Recently, there are many research that aim at estimating emotion from text. The meaning of language expression that we use in daily life varies depending on the context used. In that sense, language expression conveys ambiguous information. Wakamono Kotoba are words used by young people, and they also have semantic ambiguity. Most of the Wakamono Kotoba are not registered in the existing dictionaries, making it difficult for a system to recognize the meaning of Wakamono Kotoba. In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applied Earth Movers Distance to vector of words. As a result of the evaluation experiment, higher estimation accuracy was obtained by considering emotional distance between words -- an approach that had not been used in the conventional research -- than by using only word importance value.


Differences of Japanese Kanji and Kana during the Memory Processing


Selecting clause emotion for sentence emotion recognition

Sentence emotion recognition allows for deeper analysis of textual emotion. Based on the finding that sentence emotional focus can be expressed by some clauses in this sentence, this work proposes to select clause emotion for sentence emotion recognition. In the first step, a Maximum entropy (MaxEnt) classification model has been built for word emotion recognition. In the second step, a homogeneous Markov model (HMM) classification method is used for clause emotion classification. In the third step, nine text features are selected, and genetic algorithm (GA) is used to specify the weight of each text feature. The sentence emotion is an addition of all selected clause states in this sentence. The experimental results showed that there are 9.1% and 3.6% improvement for the two tasks respectively when comparing with the baseline. It is demonstrated that clause selection is able to improve the performance of sentence emotion recognition significantly.


Sampling Latent Emotions and Topics in a Hierarchical Bayesian Network

Text emotion analysis suffers from the lack of faithful emotion features, and the difficulty of mining multiple emotions that are mixed together. In this paper, we provide a Gibbs sampling method to solve these two problems. We explicitly characterize the emotion combination phenomenons, and predict the complex emotions of words together with the emotion intensities for each singular emotion through raw texts. Both emotions and emotion intensities are embedded as latent random variables in a hierarchical Bayesian network, while only the words and some preliminary expectations are represented as observed variables. The model which we call word emotion topic (WET) model, also depicts the distribution of word emotions among different topics, which helps to study the variation of word topics and word emotions. Experiment shows promising results of word emotion prediction, which outperforms traditional parsing methods such as Hidden Markov Model and Conditional Random Fields on raw text. The result also presents interesting emotion-topic variations through blog articles.


Reverse universal triple I method of (1,1,2) type for the Lukasiewicz implication

A new fuzzy reasoning method called the reverse universal triple I method of (1,1,2) type (reverse universal triple I method for short) is proposed and investigated by means of the famous Lukasiewicz implication, which contains the reverse triple I method as its special case. The reverse triple I principles are improved, and the optimal solutions of reverse universal triple I method are obtained for Fuzzy Modus Ponens (FMP) and Fuzzy Modus Tollens (FMT). Furthermore, it is found that the reverse universal triple I method has the reversibility property. Lastly, the reverse universal triple I method is further generalized to the alpha-reverse universal triple I method, and the related principles and optimal solutions are achieved.


In the near future, robots with advanced intelligence will be used for the service of society, families, and individuals. In this paper, we propose the concept of an advanced intelligence machine, which is a device that uses both natural and artificial intelligence and is capable of affective recognition and generation of affective speech and behavior. We describe the concept of a robotics cloud for use by advanced intelligence machines in human-robot interactions and provide a theoretical framework and technology blueprint of the construction of a robotics cloud. In addition, we propose a robotics school for building robotics clouds and describe a framework for the school, its certification program, and a method for authentication of the school. The influence of robots on the development of society, scientific and technological progress, and human-robot relationships are also discussed.


Modification Relations based Emotional Keywords Annotation using Conditional Random Fields

The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011)


Modification Relations Based Emotional Keywords Annotation Using Conditional Random Fields

Proceedings of 2011 Fourth International Conference on Intelligent Networks and Intelligent Systems

In this study, we demonstrate the effectiveness of modification features for emotional keywords annotation on Chinese sentences extracted from a Chinese emotion corpus. Eight basic word emotion categories have been selected and Conditional Random Fields is employed in eight experiments with different sets of features. Our result shows that modification features make the contribution to emotional keywords annotation.