label: url

en: Causal Effects of High Stress Assessed via Interviews on Mental and Physical Health: Toward Computer Agent-Driven Stress Assessment

en: Journal of Occupational and Environmental Medicine


en: Tracking Emotions using an Evolutionary Model of Mental State Transitions: Introducing a New Paradigm

en: Intelligent Computing (A Science Partner Journal)


en: TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis

en: Computers and Electrical Engineering

ja: Computers and Electrical Engineering


en: A Multi-attention and Depthwise Separable Convolution Network for Medical Image Segmentation

en: Neurocomputing

ja: Neurocomputing


en: Dual-TBNet: Improving the Robustness of Speech Features via Dual-Transformer-BiLSTM for Speech Emotion Recognition

en: IEEE/ACM Transactions on Audio, Speech, and Language Processing


en: Neuro or Symbolic? Fine-tuned Transformer with Unsupervised LDA Topic Clustering for Text Sentiment Analysis

en: IEEE Transactions on Affective Computing


en: Prompt Consistency for Multi-label Textual Emotion Detection

ja: IEEE Transactions on Affective Computing


en: Active learning with complementary sampling for instructing class-biased multi-label text emotion classification


en: An efficient framework for constructing speech emotion corpus based on integrated active learning strategies


en: An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

en: Metals

ja: Metals


en: The AI Development Through Transforming Tacit Knowledge to Explicit Knowledge of Nurses' Dialogue for Patients with Dementia

en: International Journal of Advanced Intelligence (IJAI)


en: DFF-ResNet: An Insect Pest Recognition Model based on Residual Networks

en: Big Data Mining and Analytics

ja: Big Data Mining and Analytics


en: Surface defect detection of steel strips based on classification priority YOLOv3-dense network

en: The steel strip is an essential raw material in the machinery industry. Besides, the surface defects of the steel strip directly determine its performance. To achieve rapid and effective detection of the defects, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) neural network was proposed in the present study. The model used YOLOv3 as basic network, implemented priority classification on the images, and then replaced the two residual network modules with two dense network modules. Therefore, the network can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects were detected by the improved network. According to the results, the detection precision of the CP-YOLOv3-dense network is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68 ms.

en: Ironmaking and Steelmaking

ja: Ironmaking and Steelmaking


en: Vowel priority lip matching scheme and similarity evaluation model based on humanoid robot Ren-Xin

en: Journal of Ambient Intelligence and Humanized Computing


en: Toward action comprehension for searching: Mining actionable intents in query entities

en: Journal of the Association for Information Science and Technology


en: Exploring latent semantic information for textual emotion recognition in Blog articles

en: IEEE/CAA Journal of Automatica Sinica

ja: IEEE/CAA Journal of Automatica Sinica


en: Constructing A Short Text Conversation system Based on the Relations Between Posts and Comments

ja: International Journal of Advanced Intelligence (IJAI)


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

en: Recognition and expression of emotions are indispensible functions for robots intended to play a role in the human society. Among various modalities, facial expression is said to be one of the most important factors for represent emotions. In this paper, we present our work on creating humanlike facial expressions for the humanoid robot Actroid, and adapting it to actual human-robot interaction scenario. Facial expressions were created manually using the software Wten source code, by adjusting parameters of facial features. The created expressions were implemented in a xed dialogue between a human and robot. Experiments were conducted with human subjects talking with the robot. Evaluations were done using questionaires on naturalness and e ectiveness of facial expressions during the dialogue.


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


en: Selectional Preferences Based on Distributional Semantic Model

en: In this paper, we propose a approach based on distributional semantic model to the selectional preference in the verb & dobj (direct object) relationship. The distributional representations of words are employed as the semantic feature by using theWord2Vec algorithm. The machine learning method is used to build the discrimination model. 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. By comparing the previous method, the proposed method obtain the promising results with obvious improvement. 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.

en: WSEAS Transactions on Computers

ja: WSEAS Transactions on Computers


en: Developing a Japanese Adverb-Emotion Corpus to Investigate the Effect of Adverbs in Japanese Sentence Emotion Classification

en: International Journal of Advanced Intelligence (IJAI)


en: Semi-Supervised Learning of Author-Specific Emotions in Micro-Blogs

en: Learning emotions from texts has been an active research topic in affective computing. However, the lack of reliable connection between emotions and language features has caused severely biased emotion predictions. Moreover, the author-specific patterns in emotion expression could potentially affect emotion predictions, which has never been studied. In this paper, we propose a semisupervised learning algorithm to learn emotional features from large-scaled micro-blog documents with a Bayesian network, and introduce an emotion transition factor to generate the author-specific emotion predictions. We infer the author-specific emotions in micro-blog streams through belief propagation, and learn the emotional features through an expectation maximization estimation procedure. We report results of single-label and multilabel emotion predictions on a micro-blog stream corpus, and analyze the improvements achieved by the semisupervised feature learning strategy and the incorporation of emotion transition patterns. Finally, we perform personality analysis based on the authors' emotion distribution and examine emotion distributions in the learned features.

ja: テキストから感情を学習することは，情動計算における積極的な研究課題であった．しかし，感情と言語機能との間の信頼できる接続の欠如は，ひどく偏った感情予測を引き起こした．さらに，感情表現における著者固有のパターンは，決して研究されていない感情予測に潜在的に影響を与える可能性がある．本論文では，ベイジアンネットワークを用いた大規模マイクロブログ文書から感情的特徴を学習し，著者固有の感情予測を生成する感情遷移要因を導入するための半教師付き学習アルゴリズムを提案する．私たちは，信念の伝播を通じてマイクロブログストリームの著者固有の感情を推測し，期待最大化推定手順を通じて感情的な特徴を学習する．我々は，マイクロブログストリームコーパス上の単一ラベルとマルチラベルの感情予測の結果を報告し，半教師付きの特徴学習戦略と感情遷移パターンの組み込みによって達成された改善を分析する．最後に，著者らの感情分布に基づいたパーソナリティ分析を行い，学習した特徴の感情分布を調べる．

en: IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


en: Customer emotion detection by emotion expression analysis on adverbs

en: The growing interests of affective computing(AC) and its implementation in e-business demandresearchers to explore the applications of AC techniques indetecting customer emotions. Recent studies have shownthat language is powerful in conveying emotions, and agood understanding of major language features bears greatimplications for customer relationship management.However, the effect of adverbs in text emotion predictionhas only been briefly mentioned in a few related works, butnever thoroughly studied. This paper addresses the gap byinvestigating how to detect customer emotions throughanalyzing the adverbs in emotion expressions. In particular,we develop a Japanese adverb emotion corpus, analyzeemotion usage of adverbs, and further derive an adverbemotionlexicon and its rule base. Utilizing these resources,we design and perform experiments to classify emotions in sentences so as to evaluate the effectiveness of differentadverb features including adverbs, adverb emotions, andadverb emotion rules. Our experiments and analysis showthe great impact of adverbs on emotion expressions, whichcan be applied to assist e-businesses in improving customerrelated processes.

ja: 本研究では，顧客の感情のずれを検出するために，副詞の感情表現について検討する．我々が開発した日本語副詞感情コーパスに基づき，異なる副詞を含んだ副詞素性の有効性を評価する．

en: Information Technology and Management

ja: Information Technology and Management


en: Examining Accumulated Emotional Traits in Suicide Blogs with an Emotion Topic Model

en: Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people's daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors.

ja: 最近の研究は，感情的な形質と自殺との間の信頼できる関係を証明している．しかし，自殺の検出と予防は，主に臨床センターで実施されており，効果的な治療を限られた人々の集団に限定している．一般人の自殺リスクの検出を助けるために，私たちは，人々の毎日の文章(ブログ)から蓄積された感情情報を探索し，自殺行動を予測するこれらの感情的特徴を調べることによって，新しい方法を提案する．複雑な感情トピック(CET)モデルを使用して，8つの基本感情カテゴリと5つの感情強度レベルに基づいて，ブログストリーム内の基礎感情および感情関連トピックを検出する．我々は3つの累積感情特性，すなわち連続ブログ感情の蓄積，共分散および推移を提案し，感情特性と自殺リスクとの関係を調べるために一般化線形回帰アルゴリズムを用いる．我々の実験結果は，感情遷移特性が自殺リスクをより区別する傾向にあり，線形回帰における3つの特性の組み合わせがより差別的な予測を生成することを示唆している．

en: IEEE Journal of Biomedical and Health Informatics


en: Employing Hierarchical Bayesian Networks in Simple and Complex Emotion Topic Analysis

en: Traditional emotion models, when tagging single emotions in documents, often ignore the fact that most documents convey complex human emotions. In this paper, we join emotion analysis with topic models to find complex emotions in documents, as well as the intensity of the emotions, and study how the document emotions vary with topics. Hierarchical Bayesian networks are employed to generate the latent topic variables and emotion variables. On average, our model on single emotion classification outperforms the traditional supervised machine learning models such as SVM and Naive Bayes. The other model on the complex emotion classification also achieves promising results. We thoroughly analyze the impact of vocabulary quality and topic quantity to emotion and intensity prediction in our experiments. The distribution of topics such as Friend and Job are found to be sensitive to the documents' emotions, which we call emotion topic variation in this paper. This reveals the deeper relationship between topics and emotions.

ja: 従来の感情モデルは，ドキュメント全体に対して単一の感情を識別していた．これは，ほとんどのドキュメントでは複数の感情が混ざって表現されている事実を無視していることになる．そこで本論文では，ドキュメントに複雑な感情を見つけるために感情の強度とトピック・モデルを感情分析に導入し，ドキュメントが表わす感情がトピックに応じてどのように変化していくかについて分析した．潜在的なトピックと感情の関係を表現するために，階層的ベイズネットワークを用いた．実験を行ったところ，提案した手法は単一の感情分類に対して従来の手法より優れた結果を得た．更に複雑な感情分類に対しても有効であることが確認された．得られた結果を詳細に分析したところ，ドキュメント中のトピックの分布が表現している感情と密接な関係があることがわかった．

en: Computer Speech & Language

ja: Computer Speech & Language


en: Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

en: In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word e-motion information from text, and discover the dis-tribution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, surprise, anxiety, sorrow, anger and hate. We use a hierarchical Bayesian network to model the emo-tions and topics in the text. Both the complex emo-tions and topics are drawn from raw texts, without considering any complicated language features. Our experiment shows promising results of word emo-tion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by ex-amining the emotion topic variation in an emotion topic diagram.

en: China Communications

ja: China Communications


en: Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition

en: In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words,negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-of speech,three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition.

ja: 本研究では感情キーワードの付与と，感情タイプ認識における，修飾関係の重要度を推定する手法を提案した．具体的には，中国語感情コーパスRen-CECpsを用いて，程度を表す語，否定語，接続語の3つの修飾素性を抽出する．実験の結果，文に対する感情キーワードの付与と，感情タイプの認識の精度の改善を達成できた．

en: International Journal of Intelligent Engineering and Systems