Intimacy Estimation of the Characters in Drama Scenario

演劇台本における登場人物間の親密度推定手法

Recently, a portable digital device equipped with voice guidance has been widely used, increasing the demand for the usabilityconscious dialogue system. One of the problems with the existing dialogue system is its immature application to non-task-orienteddialogue. Non-task-oriented dialogue requires some scheme that enables smooth and flexible conversations with a user. For example,it would be possible to provide topics related to a person who is familiar with a user or avoid providing topics related to a personwho is not a good friend with the user, by considering relationship with others in real life of the user. In this paper, we focus on thedialogue made by the two characters in a drama scenario, and tried to express their relationship with a scale of intimacy degree.There will be such various elements related to the intimacy degree as the frequency of response to the utterance and the attitude ofa speaker during the dialogue. We focus on the emotional state of the speaker during the utterance and try to realize an intimacyestimation with high accuracy. As the evaluation result, we achieved high accuracy in intimacy estimation than the existing methodbased on speech role.

近年，音声アシスタント機能を搭載した携帯型端末が普及し，より使い手に配慮した対話システムが求められている． 従来型の対話システムの問題点として，雑談のような非タスク型対話への対応が未熟な点があげられる．非タスク型対話 においては，ユーザとの会話を円滑かつ柔軟にするための工夫が必要となる．たとえば，ユーザの現実世界での人間関係 を考慮することによって，ユーザと親しい人物に関する話題の提供を積極的に行ったり，ユーザと親しくない(仲が良く ない)人物に関する話題の提供を避けたりすることができると考える．本論文では，演劇台本を題材に，対話中の2 者間 の人間関係を「親密度」という尺度により表現する．親密度に関わると考えられる要素として，発話の応答回数や発話中 の態度などがある．本論文では，その中でも発話中の感情状態に着目することで，高精度な親密度推定の実現を試みる． 評価実験の結果，発話役割に基づく従来手法を上回る高精度な親密度推定を実現することが出来た．

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics


Rough Set Knowledge Discovery Based Open Domain Chinese Question Answering Retrieval

ラフ集合知識発見に基づくオープンドメイン中国語質問回答検索

Journal of Computer Research and Development


Speech emotion recognition based on improved complete local binary pattern of spectrogram images

スペクトログラム画像の改良された完全局所二値パターンに基づく音声感情認識

A novel speech emotion recognition is proposed in the paper in whichthe improved complete local binary pattern and the power exponential class weighted fusion are used Firstly the original speech sample is transformedinto a spectrum diagram then the 5scale 8-directional Log-Gabor filter is used to deal with the language spectrum in order to enlarge the detail information of the image Then the block histogram features of the uniform complete local binary pattern sign feature and improved complete local binary patternmagnitude feature are extracted and cascaded as a new fusion feature called improved complete local binary patternsign magnitude Finally based on support vector machine these three features are weighted by the decision level in order to achieve the speech emotion recognition The experiment results indicate that theimproved complete local binary patternmagnitude feature and the fusion feature can reduce the feature dimension and improve the effect of system identification The exponential weighted fusion method expands the gap between classifiers and gives a larger weight to the classifier with better classification performance which can finally enhance the performance of speech emotion recognition effectively Compared with other algorithms the experiment results also show the effectiveness of the proposed algorithm

本稿では，改良された完全局所二値パターンとべき指数クラス重み付き融合を用いた新しい音声感情認識を提案した．最初に，原音声サンプルをスペクトル図に変換し，次に画像の詳細情報を拡大するために5スケール，8方向Log ‐ Gaborフィルタを用いて言語スペクトルを扱う．次に一様完全局所二値パターン符号特徴と改良完全局所二値パターン強度特徴のブロックヒストグラム特徴を抽出して改良完全局所二値パターンsign magnitudeと呼ぶ新しい融合特徴として縦続接続した．最後に，サポートベクターマシンに基づいて，これら3つの特徴は，音声感情認識を達成するために決定レベルによって重み付けされます．実験結果は，改良された完全局所二値パターン強度特徴と融合特徴が特徴寸法を減少させ，システム同定の効果を改善することができることを示した．指数加重融合法は分類器間のギャップを拡大し，より良い分類性能で分類器により大きな重みを与え，それは最終的に音声感情認識の性能を効果的に強化することができた．他のアルゴリズムと比較して，実験結果は提案したアルゴリズムの有効性も示した．

Journal of Electronic Measurement and Instrument


Emotion computing using Word Mover's Distance features based on Ren_CECps

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

本論文では，多ラベル感情コーパスRen_CECpsの視覚化において，TF・IDFで表現された値よりも視覚的に優れた文章の感情ラベルを付与する感情分離法(SeTF・IDF)を提案する．文間の距離が変化することにより，視覚化マップが大幅に改善されたことに感銘を受け，中国語テキスト感情分類における特徴表現の方法としてWord Mover's Distance(WMD)アルゴリズムを利用した．Ren_CECpsを対象とする実験実験を行ない，極めて良好な結果が得られた．


User Emotion Modeling and Anomaly Detection Based on Social Media

ソーシャルメディアに基づくユーザ感情モデリングと異常検出

For abnormal emotional detection among micro-blog users, this paper proposes ananomaly detection method based on the joint probability density of multivariate Gaussian model and power-law distribution. In the xperiments, the anomaly detection accuracy is 83. 49% in terms of individual user, and 87. 84% in terms of month. Statistics reveals that individual users' neutral, happy and sad emotions fall into the normal distribution, but the amazed and angry emotions are not. Emotions of micro-blogs released by groups confirm to the power law distribution, but not those by the individual.

マイクロブログ利用者の間の異常な感情検出のために，本論文は多変量Gaussモデルとべき法則分布の同時確率密度に基づく異常検出法を提案した． xperimentsにおいて，異常検出精度は個々のユーザに関して83.49%であり，月に関して87.84%である． 統計によると，個々のユーザーの中立的，幸福，悲しい感情は正規分布に分類されますが，驚きや怒りの感情はそうではありません． グループによって発表されたマイクロブログの感情は，べき乗則分布を確認しますが，個人によるものではありません．

Journal oc Chinese Information Processing


Fuzzy clustering validity index facing data set with complexity structure

複雑性構造を持つデータセットに直面するファジィクラスタリング妥当性指数

How to effectively determine the clustering number is one of the historical challenges in the clustering field A typical algorithm for clustering is the fuzzy c-means algorithm Nowadays it is difficult for clustering validity index to make accurate judgment for complex data structure and the huge disparity of cluster size Aiming at the problem a new clustering validity index VGSDC( index for geometry structure and different clusters) is proposed for the geometry structure of data set and clusters with different size The compactness strategy is obtained by square error sum within the classes as well as weights of membership degree and then the separation computing method is gotten by minimum distance between cluster centers together with distance sum from centers to average center And finally the new validity index VGSDC is obtained Furthermore the best clustering number can be automatically obtained by the number of categories corresponding to the extreme value of VGSDC Through the experiment in 6 data structure it is found that the proposed VGSDC gets the better performance comparing with 11 kinds of clustering validity indexes VGSDC can not only handle multiple types of data sets and also provide full consideration to the structure characteristics and complexity of the data sets As a result it can be applied to large data sets with huge differences of clustering center distance

どのようにしてクラスタリング数を効率的に決定するかは，クラスタリング分野における歴史的課題の一つです．クラスタリングのための典型的なアルゴリズムはファジーc平均アルゴリズムです．今日，クラスタリング妥当性指標が複雑なデータ構造とクラスタサイズの大きな不均衡を正確に判断することは困難です．問題を目指して，新しいクラスタリング妥当性指標VGSDC(幾何構造と異なるクラスタのための指標)をデータセットと異なるサイズのクラスタの幾何構造のために提案した．コンパクト性戦略はクラス内の二乗誤差和とメンバーシップ度の重みによって得られ，次に分離計算法は中心から平均中心までの距離和と共にクラスタ中心間の最小距離によって得られた．そして最後に，新しい有効性指数VGSDCを得た．さらに，VGSDCの極値に対応するカテゴリの数によって最良のクラスタリング数を自動的に得ることができる． 6データ構造における実験を通して，提案したVGSDCは11種類のクラスタ化有効性指数と比較してより良い性能を得ることが分かった． VGSDCは複数の種類のデータセットを扱うことができるだけでなく，データセットの構造特性と複雑さについても十分に考慮しています．その結果，クラスタリング中心距離が大きく異なる大規模データセットに適用することができます．


FMT symmetric I* method of fuzzy reasoning

ファジィ推論のFMT対称I *法

Journal of Nanjing University (Natural Science)


Yield Optimization Technique for Three Dimensional Memory Based on Redundancy Sharing Among Adjacent Layers


Corpus Expansion for Neural CWS on Microblog-Oriented Data with λ-Active Learning Approach

Microblog data contains rich information of real-world events with great commercial values, so microblog-oriented natural language processing (NLP) tasks have grabbed considerable attention of researchers. However, the performance of microblog-oriented Chinese Word Segmentation (CWS) based on deep neural networks (DNNs) is still not satisfying. One critical reason is that the existing microblog-oriented training corpus is inadequate to train e ective weight matrices for DNNs. In this paper, we propose a novel active learning method to extend the scale of the training corpus for DNNs. However, due to a large amount of partiallyoverlappedsentencesinthemicroblogs,itisdi culttoselectsamples with high annotation values from raw microblogs during the active learning procedure. To select samples with higher annotation values, parameter λ is introduced to control the number of repeatedly selected samples. Meanwhile, various strategies are adopted to measure the overall annotation values of a sample during the active learning procedure. Experiments on the benchmark datasets of NLPCC 2015 show that our λ-active learning method outperforms the baseline system and the state-of-the-art method. Besides, the results also demonstrate that the performances of the DNNs trained on the extended corpus are signi cantly improved.

マイクロブログデータには，商業的価値の高い実世界イベントの豊富な情報が含まれているため，マイクロブログ指向の自然言語処理タスクは研究者の注目を集めている．しかし，ディープニューラルネットワークに基づくマイクロブログ指向の中国語ワードセグメンテーションのパフォーマンスは依然として満足できるものではない．既存のマイクロブログ指向の訓練コーパスがDNNのための有効な重み行列を訓練するには不十分である．本稿では，DNNの訓練コーパスの規模を拡張するための新しいアクティブ学習法を提案する．マイクロブログでは重複した部分が大量に存在するため，アクティブな学習手順では，生のマイクロブログからの注釈値が高いサンプルを選別する．より高い注釈値を有するサンプルを選択するために，パラメータλを導入して，繰り返し選択されたサンプルの数を制御する．拡張コーパスで訓練されたDNNのパフォーマンスが大幅に改善されていることを確かめることができた．

IEICE Transactions on Information and Systems


Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences

ビデオシーケンスにおける時空間特徴の融合に基づく表情認識

For facial expression recognition based on video sequences, the changing information of facial regions along the time axis can be described by dynamic descriptors more effectively than static descriptors. This paper proposes an expression recognition method based on the dynamic texture and motion information, learning from the principle of Local Binary Pattern on Three Orthogonal Planes (LBP-TOP), Spatio-Temporal Weber Local Descriptor (STWLD) is proposed to describe the dynamic texture feature information of the facial expression sequence. Moreover, using Block-based Histogram of Optical Flow features (BHOF), the motion information can be described. Through the combination of the dynamic texture and motion information, and finally SVM is applied to complete the expression classification. The results of the cross experiments on the CK + and MMI expression database show that the method achieves better performance than methods using the single descriptors. The comparison experiments with other related methods also prove the superiority of the method.

ビデオシーケンスに基づく表情認識については，時間軸に沿った顔領域の変化する情報は，静的記述子よりも動的記述子によって記述することができる．本論文では，3つの直交平面上の局所二値パターン(LBP-TOP)の原理から学んだ動的テクスチャと動き情報に基づく表現認識法を提案し，動的テクスチャを記述するために時空間局所記述子(STWLD)を提案した．表情シーケンスの特徴情報．さらに，オプティカルフロー特徴のブロックベースのヒストグラム(BHOF)を使用して，動き情報を記述することができる．動的テクスチャと動き情報の組み合わせにより，そして最後にSVMが表現分類を完成するために適用されます． CK +およびMMI発現データベースに関するクロス実験の結果は，この方法が単一の記述子を使用する方法よりも優れた性能を達成することを示している．他の関連方法との比較実験もこの方法の優位性を証明している．

Journal of Electronics and Information Technology


Detecting Users' Anomalous Emotion Using Social Media for Business Intelligence

Anomaly detection in sentiment analysis refers to detecting users' abnormal opinions, sentiment patterns or special temporal aspects of such patterns. Users' emotional state extracted from social media contains business information and business value for decision making. Social media platforms, such as Sina Weibo or Twitter, provide a vast source of information, which include user feedbacks, opinions and information on most issues. Many organizations also leverage social media platforms to publish information about events, products, services, policies and other topics frequently, analyzing social media data to identify abnormal events and make decisions in a timely manner is a beneficial topic. This paper adopts the multivariate Gauss distribution with the power-law distribution to model and analyze the users' emotion of micro-blogs and detect abnormal emotion state. With the measure of joint probability density value and the validation of the corpus, anomaly detection accuracy of individual user is 83.49% and of different month is 87.84% by this method. Through the distribution test, the results show that individual users' neutral, happy and sad emotions obey the normal distribution, but the surprised and angry emotions do not. Besides, emotions of micro-blogs released by groups obey power-law distribution, but the individual emotions do not. This paper proposes a quantitative method for abnormal emotion detection on social media, which automatically captures the correlation between different features of the emotions, and saves a certain amount of time by batch calculation of the joint probability density of data sets. The method can help the businesses and government organizations to make decisions according to the user's affective disposition, intervene early or adopt proper strategies if needed.

感情分析における異常検出は，ユーザの異常な意見，感情のパターンまたはそのようなパターンの特別な時間的側面を検出することを指す．ソーシャルメディアから抽出されたユーザーの感情状態には，意思決定のためのビジネス情報とビジネス価値が含まれている．本稿では，べき乗則分布を用いた多変量ガウス分布を用いて，ユーザのマイクロブログの感情をモデル化して分析し，異常な感情状態を検出する．ジョイント確率密度値の測定とコーパスの検証では，個々のユーザの異常検出精度は83.49%であり，月ごとの異常検出率は87.84%である．


About unclear power of artificial intelligence

この招待論文において，ビッグデータ・クラウド・深層学習による駆使した人工知能の現状・問題・将来展望を述べた．特に，人工知能の具体的なパワーについて論じた．さらに，人工知能の三つレベルにおける計算知能・感知知能・認知知能について，著者の観点を述べて，このからの人工知能は，理解をメインの挑戦として，推進しなければならない．

Communication of Chinese Association for Artificial Inteigence


Exploring latent semantic information for textual emotion recognition in Blog articles


An Improved K Means Algorithm Based on Initial Clustering Center Optimization

The K means algorithm is widely known for its simplicity and fastness in text clustering. However, the selection of the initial clus tering center with the traditional K means algorithm is some random, and therefore, the fluctuations and instability of the cluster ing results are strongly affected by the initial clustering center. This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection. The experiment results show that the improved K means clustering algorithm is superior to the traditional algorithm.

K平均アルゴリズムは，テキストクラスタリングの簡潔さと堅牢性で広く知られているが，従来のK平均アルゴリズムを用いた初期クラスター中心の選択はランダムであるため，クラスタリング結果の変動および不安定性は，初期クラスター化の中心に強く影響される． 本論文では，中心点選択の不確実性を解消するために初期クラスタリング中心を選択するアルゴリズムを提案した． 実験結果は，改善されたK平均クラスタリングアルゴリズムが従来のアルゴリズムより優れていることを示している．


In this paper, we describe a method of emotion analysis on social big data. Social big data means text data that is emerging on In ternet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis. Because these data are constructed by manual annotation, their quality is high but their quantity is low. If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data, we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications, which we could not know before. In this paper, we create an emotion analysis model that integrate the high quality emotion corpus and the automatic constructed corpus that we created in our past studies, and then analyze a large scale corpus consisting of Twitter tweets based on the model. As the result of time series analysis on the large scale corpus and the result of model evaluation, we show the effective ness of our proposed method.

本稿では，社会的ビッグデータに対する感情分析手法について述べる．ソーシャルビッグデータとは，インターネットソーシャルネットワーキングサービスに出現しているテキストデータを意味します．私たちは，感情分析のために，これらのコーパスに多言語Webコーパスと注釈付き感情タグを収集します．これらのデータは手作業による注釈で構成されているため，品質は高いが量は少ない．このコーパスに基づいて高品質の感情分析モデルを作成し，社会的ビッグデータの分析に用いると，以前には知り得なかったインターネット通信における感情的感情や人の行動を統計的に分析することができます．本稿では，過去の研究で作成した高性能感情コーパスと自動構築体を統合し，そのモデルに基づいたTwitterのツイートからなる大規模なコーパスを解析するための感情分析モデルを作成する．大規模尺度コーパスの時間軸解析とモデル評価の結果，提案手法の有効性を示す．


Emotion and Cognitive Reappraisal Based on GSR Wearable Sensor

Various wearable equipment enables us to measure people behavior by physiological signals. In our research, we present one gal vanic skin reaction (GSR) wearable sensor which can analyze human emotions based on cognition reappraisal. First, We research the factors of emotional state transition in Arousal Valence Stance(AVS) emotional space. Second, the influence of the cognition on emotional state transition is considered, and the reappraisal factor based on Gross regulation theory is established to correct the effectiveness from cognitive reappraisal ability to emotional state transition. Third, based on the previous work, we establish a GSR emotion sensing system for predicting emotional state transition and considering the correlation between GSR signals and emotions. Finally, an overall wearable sensor layout is built. In the experiment part, we invited 30 college students to wear our GSR sensors and watch 14 kinds of affective videos. We recorded their GSR signals while asking them to record their emotional states synchronously. The experiment results show different reappraisal factors can predict subjects'emotional state transition well and indirectly confirm the feasibility of the Gross regulation theory.

様々なウェアラブル機器により，私たちは生理的信号によって人の行動を測定することができる．我々の研究では，認知再評価に基づいて人間の感情を分析することができるガルバニック皮膚反応(GSR)ウェアラブルセンサを提示する．まず，AVS(Arousal Valence Stance)感情空間における感情状態遷移の要因を調べる．第2に，感情状態遷移への認知の影響を考慮し，認知再評価能力から感情状態遷移への有効性を矯正するために総調節理論に基づく再評価因子を確立する．第3に，感情状態遷移を予測し，GSR信号と感情との相関関係を考慮したGSR感情感知システムを構築した．最後に，ウェアラブルなセンサレイアウト全体が構築される．実験の部では，30名の大学生にGSRセンサーを装着させ，14種類の感情的なビデオを観覧した．彼らの感情状態を同期して記録するように求めながら，GSR信号を記録した．実験結果は異なる再評価因子が被験者の感情状態遷移を予測することができることを示しており，間接的に総調節理論の実行可能性を確認した．


Multi-classifier ensemble based on dynamic weights

In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the realtime importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified.

本研究では，信頼性の低い判定情報の干渉を低減し，融合判定の精度を向上させるために，動的重みに基づく新しいマルチ分類器アンサンブル法を提案する．アルゴリズムは，現在のターゲットに対する分類器のリアルタイム重要度を記述するための決定信頼性を定義し，この信頼性をトレーニングデータセットの分類器によって計算された信頼度と組み合わせ，分類器に融合重みを動的に割り当てる．他の方法と比較して，異なるサンプル領域を識別するだけでなく，特定のターゲットを特定する際に決定情報を出力する能力を考慮して，異なる分類器の重み付けの融合判定への寄与が完全に評価される．パブリック顔データベースの実験結果は，提案手法が単一分類器や一般的な融合アルゴリズムよりも高い分類精度を得ることができることを示している．提案手法の実現可能性と有効性を検証した．


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

We propose a model to classify reviews based on review data from different media sources. Recently, research has been actively conducted on transfer learning between different domains with various kinds of big data as the target. The fact that evaluation expressions often vary in different domains presents a barrier to reputation analysis. Users commonly use various linguistic expressions to refer to creative works, depending on the specific media form.For example, the terms or expressions used in anime to describe creative works within that medium are different from the expressions used in comics, or games or movies. These differences can be considered as features of each individual medium. We should expect, then, that there would be differences in evaluation expressions among the various media, as well. We analyze the effects of such differences on classification accuracy by conducting transfer learning between review data from different media and demonstrate compatibility between the original (pre-transfer) and target (post-transfer) media by constructing a review classification model. As a result of our evaluation experiments, we are able to more accurately estimate review scores without using SO-Scores for training review fragments based on Long Short-Term Memory (LSTM) rather than using a method based on SO-Scores.

本研究では，異なるメディアのレビューデータに基づいたレビュー分類モデルの構築を提案します．現在，多種多様なビッグデータを対象とした異なるドメイン間での転移学習に関する研究が盛んである．異なるドメイン間では，評価表現が異なることが多く，評判分析の障壁となる．異なるメディアにおける著作物に触れているユーザが異なる表現を使用することは多い．たとえば，著作物に関して，「アニメ」や「漫画」，「ゲーム」，「映画」など，異なるメディアにおいては異なる用語や表現が存在する．評価表現以外にもメディアの違いの特徴が表れると考えられる．我々はメディア間でのレビューデータの転移学習をおこなうことで，こうした違いが分類精度にどんな影響を及ぼすかについて分析した．本研究では，著作物の媒体ごとに，転移先と転移元のメディアの相性について，レビューの評価分類モデルを構築することで明らかにした．また，評価実験の結果，Long Short Term Memoryを用いて，レビュー片の学習においてSo-Scoreを用いずに，SO-Scoreに基づく手法よりも正確に評価スコアの推定をおこなえた．

International Journal of Advanced Intelligence (IJAI)


Fine-Grained Emotion Elements Extraction and Tendency Judgment Based on Mixed Model

Nowadays, with the development of internet technology and electronic commerce, the Web storages huge number of product reviews comment by customers. Product reviews tend to be more objective in reflecting the real situation of the product, more and more customers post product reviews at merchant websites in order to make an informed choice. However, a large number of reviews made it difficult to track the comments and suggestions that customers made. In this paper, a fine-grained emotional element detection and emotional tendency judgment method based on conditional random fields (CRFs) and support vector machine (SVM) was proposed. This model introduces semantics and word meaning in CRF model to improve the robustness. In SVM model, deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.


MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices

Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.

動きは人間の存在と活動の重要な指標である．室内モーション検知の分野における最近の進展により，侵入検知や睡眠モニタリングなどのアプリケーションを通じて生活経験を強化する可能性がある．しかし，既存のソリューションは，可用性，信頼性，プライバシー問題などのいくつかの重要な短所にまだ直面している．そのような不足を克服するために，無線周波数(RF)に基づくデバイスフリーの動き検出システム(MoSense)は，リアルタイムで信頼性の高い透過的な検出サービスを提供するために動きによって誘発される遍在するWiFi信号の減衰を利用して設計される． MoSenseの設計と実装には，1)定常状態の特徴付けと2)ノイズの多いサブキャリアの2つの課題がある．最初のチャレンジでは，動きを区別するために静止状態を特徴付けるための無音解析モデルが提案されている．第2の課題では，雑音チャネルからの動きの影響をよりよく捕捉する特定のサブキャリアを選択する距離ベースのメカニズムを設計し，サブキャリア間の類似性を測定する． MoSenseのプロトタイプは，実際の環境で実現され評価される． MoSenseは既存のWiFiインフラストラクチャと互換性があることを考慮すると，低コストでありながら動きの検出に有望なソリューションとなる．


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


Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis

This paper presents a Deep Belief Nets (DBN) model and a multi-modality feature extraction method to extend features , dimensionalities of short text for Chinese microblogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the microblogging features according to the relationship between commenters and posters through constructing microblogging social network as input information. Multi-modality features are combined and adopted as the input vector for DBN. A DBN model, which is stacked with several layers of Restricted Boltzmann Machine (RBM), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of input data to learn hidden syntactic structures for better feature representation. A Classification RBM (ClassRBM) layer, which is stacked on top of the former RBM layers, is adapted to achieve the final sentiment classification. The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than the state of the art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.

Journal of Electronics and Information Technology


LED Phototherapy With Gelatin Sponge Promotes Wound Healing in Mice

Tiny but highly efficient, a light emitting diode (LED) can power a therapy device, such as a phototherapy device, and, at the same time, decrease the device's size requirements. In this study, a LED phototherapy device was designed to investigate the possible impact on wound healing using a mouse model and a cell line exposed to red and blue light. To enhance wound phototherapy, a gelatin sponge was fabricated. Results showed that the red and blue lights promoted cell growth and wound healing, while the blue light with a gelatin sponge protected the wound from infection in the early stages of wound healing. The LED phototherapy device combined with the gelatin sponge, therefore, has potential significance in clinical application for wound healing.


A Stacking Scheme Using Adjacent Redundancy Across Dies for 3D-stacked Memory


A combined cepstral distance method for emotional speech recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%.

感情は人間と機械の最大の違いであるが，マシンが感情で動作する場合，マシンはより多くの人に受け入れられる．声は最も自然であり，毎日のコミュニケーションにおいて容易に理解され，受け入れられる方法である．感情声の認識は，人工知能の重要な分野である．しかし，感情の認識では，2つの感情が特に混乱の影響を受けやすいという現象がしばしば存在する．本論文では，感情音声認識のための2群マルチクラス感情分類におけるケプストラム距離法を提案する．ケプストラム距離と音声エネルギーとの組み合わせは，音声認識における音声信号終点検出としてよく使用される．この研究では，ケプストラム距離の使用は，感情的な信号とニュートラルな信号のフレーム間の類似性を測定することを目的としている．これらの特徴は，有向非循環グラフサポートベクトルマシン分類のために入力される．最後に，複数感情認識における混乱を解決するために，2群分類戦略が採用される．実験結果は，ケプストラム距離が悲しい感情の認識率を増加させ，過大なフィッティングを排除して認識結果のバランスをとることを示す．

International Journal of Advanced Robotic Systems


Sentiment analysis of text based on three-way decisions

In recent years, affective computing has received much attention in the area of natural language processing and arti cial intelligence. Sentiment orientation recognition of text is one of important parts in affective computing. A methodisproposedtorecognizethemulti-labelsentimentorientationsofChinesetextbasedonthree-waydecisions.Firstly, sentiment orientation and intensity of sentiment words from texts are identi ed by sentiment lexicons, Tongyi Cilin and HowNet.Subsequently,sentimentorientationoftextaredividedintothreedomains,includingpositive,negativeandboundary domain,accordingtotheirsentimentintensityandtheappropriatedecision-makingthresholds.Lastly,sentimentorientations of texts in the boundary domain are distinguished pursuant to sentimental characteristics of sentences in texts. The results of experiments show that the method of multi-label sentiment analysis of Chinese text, based on three-way decisions, is effective for identifying sentiment orientations of texts.

近年，情動計算は自然言語処理と人工知能の分野で大きな注目を集めている．テキストのセンチメントオリエンテーション認識は，情動計算において重要な部分の1つである．テキストからのセンチメントオリエンテーションとセンチメントワードの強さは，感情レキシコン，Tongyi Cilin，HowNetによって識別される．境界領域は文章中の文の感情的特徴に従って区別される．実験の結果は，テキストの感情の向きを特定するために，三方向決定に基づく中国語テキストのマルチラベルセンチメント分析の方法が有効であることを示している．

Journal of Intelligent and Fuzzy Systems


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

Creation of a developmental structure for a human-robot interaction model is essential for practical human robot interaction. We speci cally focus on creating a developmental system of the robot's facial expression based on spoken dialogue of the robot. A humanoid robot Actroid is used to create smooth facial expressions. In the proposed method, we rst create a xed dialogue system with a prede ned facial expression related to each of the robot's dialogue. During communication with a human subject, a feedback (judging if the robot's facial expression was natural or not) is given by the human after the robot's utterance. The facial expression related with the dialogue is changed randomly to a di erent facial expression if the human's feedback denote the robot's expression as unnatural. We constructed the feedback using two types of methods: button pressing and auditory. The actual experiments show that the robot is capable of acquiring natural facial expressions after several communication trials with the human subject.

International Journal of Advanced Intelligence (IJAI)


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

Human personality multilaterally consists of complex elements. Egogram is a method to classify personalities into the patterns according to the combinations of the five levels of ego-states. With recent development of Social Networking Service (SNS), more researches have been trying to judge personality from the statements on SNS. However, there are several problems in the personality judgment based on the superficial information of the statements. Concretely, personality is not always reflected on every statement and the tendency of the statements with influence of personality changes with the times. It is also important to collect sufficient amount of statement data including the results of personality judgment. In this paper, to realize automatic egogram judgement, we focused on the short texts on the SNS, especially microblogs, and represented the comments on Twitter by distributed representation (sentence vector) in the pre-training. Then, we tried to create a model to estimate ego-state level of each user by using a deep neural network. The experimental result showed that our proposed method estimated ego-state with higher accuracy than the baseline method based on bag of words. To investigate the change of personality according to time, we also analyzed how the match rates of the estimation results changed before/after the egogram judgment. Moreover, we confirmed that the personality pattern classification was improved by adding a feature expressing the formal degree of the sentence.

人間の性格は，複雑な要素が組み合わさって，多面的に構成される．性格診断手法としてのエゴグラムは，5つの自我状態の高低の組み合わせに基づき，性格をパターン分類するものである．近年のSNSの発達にともなって，性格をSNS上の発言から診断しようという試みが増えてきた．しかし，発言の表層情報を手掛かりとした性格診断は，自我状態を推定するうえでいくつかの問題を抱える．具体的には，性格がすべての発言に反映されているわけではないこと，また，個人の性格に起因する発言の傾向は時間とともに変化していくものであるということなどがあげられる．また，十分な量の性格診断結果を伴う発言の事例を収集することも重要となる．本研究では，SNS，とくにMicroblog 上の発言(ショートテキスト)をもとに，エゴグラム診断を自動化するために，Twitter 上の発言を事前学習において分散表現(文ベクトル)により表し，Deep Neural Network による機械学習を用いて，ユーザごとに自我状態のレベルを推定するモデルの構築を試みる．評価実験の結果，提案手法による自我状態推定モデルは，Bag of Words を素性としたベースライン手法よりも高精度%であることを示した．また，時間経過による性格変化を調べるため，エゴグラム診断の前後において，推定結果の一致率にどのような変化が起きているかを分析した．さらに本論文では，文のフォーマル度合いを特徴づける素性を追加することで性格パターン分類の精度向上を確認した．


Voice conversion based on RBF neural network

Recently, voice conversion has becoming the research hotspot, because of its widely application areas. However, the voice conversion technology is still immature. By the researching of existing voice conversion models, the voice conversion system based on the RBF neutral network was designed, and the system simulation was implemented. During conversion, the unvoiced speech was excluded and the voiced speech was reserved. The LPC was the extracted from the source and target speech, then convert the LPC to LSP. The LPS was trained by RBF neural network after time-aligned. Obtained mapping function was used to convert the source LSP to target LSP, and synthesis the speech. Finally, the converted speech evaluated by ABX and MOS to test the tendency and quality of the speech.


In the eld of facial expression recognition, in order to make up for insuf cient information of monogenic magnitude and orientation which is extracted by monogenic binary pattern, and enhance robustnessofsparseclassi erforfacialexpressionclassi cation,afacialexpressionrecognitionmethodbased on monogenic multi-feature and fused sparse representation is proposed in this paper. First of all, the preprocessed images of facial expression are ltered by the monogenic signal with the purpose of acquiringtheinformationofmonogenicmagnitude,orientation,andphase.Secondly,wefusemagnitude, orientation and phase information with the method of monogenic binary pattern, monogenic oriented gradient, and enhanced monogenic phase respectively, to form different expression features and construct the corresponding classi ers. Finally facial expression recognition is completed by using the regularized least-square theory to optimize the weight of double classi er. Experiments are performed on JAFFE and Cohn-Kanade facial expression databases and the average rates reach up to 97.30% and 99.33% respectively. The results of experiments show that the proposed method effectively improves the recognition ef ciency of facial expression.


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

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.


Text Classification Based on Word Co-occurrence with Background knowledge

This article proposes a data treatment strategy to generate a new text representation model with discriminative feature for the sake of text classification. This feature is determined by word co-occurrence information, and is extracted by measuring the cosine similarity between word co-occurrence vectors of testing documents and background knowledge in Chinese language. The background knowledge is composed by the word co-occurrence matrix of all words in background corpus based on top n keywords in each category which selected by TFIDF value. This strategy is to get discriminative features to reduce the ambiguity and noise inherent of the traditional representation model. In the experiments, using a linear SVM classifier, we investigate the effects of the proposed method with two Chinese classification corpora, and two background corpora are used as background knowledge. Results show that, compared with conventional method, the proposed strategy performs better in the values of precision, recall and F1 score.

本論文では，テキスト分類のために差別的な特徴を持つ新しいテキスト表現モデルを生成するためのデータ処理戦略を提案する．この特徴は，単語共起情報によって決定され，テスト文書の単語共起ベクトルと中国語の背景知識とのコサイン類似度を測定することによって抽出される．背景知識は，TFIDF値によって選択された各カテゴリの上位n個のキーワードに基づいて，背景コーパス内のすべての単語の単語共起行列によって構成される．この戦略は，伝統的な表現モデルに内在するあいまい性とノイズを減らすために差別的な特徴を得ることができる．実験では，線形SVM分類器を用いて，2つの中国分類コーパスを用いて提案された方法の効果を調べたが，従来の方法と比較して，提案された手法が，精度，リコールおよびF1スコアの値においてより良好な結果が得られたことを示した．


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


Trends Detection of Flu based on Essemble Models with Emotional Factors from Social Networks

In uenza is an acute respiratory illness and widespread activity that occurs every year. Detection and prevention of in uenza in its earliest stage would reduce the spread range of the illness. Sina microblog is a popular microblogging service in China, which can be treated as perfect reference sources for u detection because of its real-time character. A large number of active users post about their daily life continually. In this paper, we investigate the real-time u detection problem and propose a u detection model with emotion factors and semantic information. First, we extract u-related microblog posts automatically in real time by adopting support vector machine (SVM) lter and semantic features. We use association rule mining to extract strongly associated features as additional features for posts to overcome the limitation of 140 words, including sentiment information, which can help us to classify the posts without u-related features. Then, the conditional random eld model is revised and applied to detect the transition time of u so that we can nd out which place is more likely to have in uenza outbreak and when it is more likely to have in uenza outbreak in a city or province in China. Experimental results on detecting u situation during certain times in some locations show the robustness and effectiveness of the proposed model, which might help health authorities in predicting u outbreak ahead and take timely control action and response.

インフルエンザは毎年発生する急性呼吸器疾患である．インフルエンザの初期段階における検出と予防は，病気の広がりの範囲を減少させるだろう．シーナマイクロブログは人気のマイクロブロギングサービスであり，リアルタイム性のために，フル検出のための完全なリファレンスソースとして扱うことができると考える．多数のアクティブユーザーが日常生活を継続的に投稿している．本論文では，リアルタイムの流れ検出問題を検討し，感情因子と意味情報を用いた流れ検出モデルを提案する．まず，支援ベクトルマシン(SVM)フィルタと意味論的特徴を採用することにより，マイクロブログ記事をリアルタイムで自動的に抽出する．関連ルールのマイニングを使用して，関連する機能を含まない投稿を分類するのに役立つ感情情報を含む140語の制限を克服し，投稿の追加機能として強く関連する機能を抽出する．次に，条件付きランダムフィールドモデルを改訂して適用することで，その遷移時間を検出して，どの地域がインフルエンザ発生の可能性が高いか，また，中国の都市または地域でのインフルエンザ発生の可能性が高い場所を見つけることができる．いくつかの場所で特定の時間帯の流れの状況を検出するための実験結果は，提案されたモデルの有効性を示した．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Fuzzy Systems Based on Universal Triple I Method and Their Response Functions

The fuzzy systems based on the universal triple I method are investigated, and then theirresponse functions are analyzed. First, the conclusions show that 100 fuzzy systems via theuniversal triple I method are approximately interpolation functions, which can be used inpractical systems, and that 90 ones are approximately ¯tted functions, which may be usable.Second, as its special cases, the Compositional Rule of Inference (CRI) method and the triple Imethod are discussed, with the results that 19 fuzzy systems via the CRI method and 2 ones viathe triple I method are practicable. Therefore, the universal triple I method has larger e®ectivechoosing space, which can obtain more usable fuzzy systems than the others. Lastly, it is foundthat the ¯rst implication and second implication, respectively, embody the function of rule baseand reasoning mechanism, further demonstrating the reasonability of the universal triple Imethod.

普遍的な3つのI手法に基づくファジィシステムは調査され，それらの応答関数は分析された．まず，100の3つのI手法を介したファジィシステムは，実際のシステムにおいて用いることができる挿入関数，そして，90は，使うことができると結論付ける．つぎに，特別な場合として，合成された推論ルールと3つのI手法が議論され，CRI手法を介した19のファジィシステムと2つの3つのI手法が実践可能である．

International Journal of Information Technology & Decision Making


Dual-modality emotion recognition based on composite spatio-temporal features

複合時空間特徴に基づくデュアルモダリティ感情認識

Objective In view of existing algorithms, volume local binary pattern is applied to the feature extraction of video frames. However, problems such as large feature dimension, weak robustness to illumination, and noise exist. This study proposes a new feature description algorithm, which is temporal-spatial local ternary pattern moment. This algorithm introduces three value patterns, and it is extended to the temporal-spatial series to describe the variety of pixel values among adjacent frames. The value of texture feature is represented by the energy values of the three value model matrixes, which are calculated according to the gray-level co-occurrence matrix. Considering that the temporal-spatial local ternary pattern moment only describes the texture feature, it lacks the expression of image edge and direction information. Therefore, it cannot fully describe the characteristics of emotional videos. The feature of 3D histograms of oriented gradients is further fused to enhance the description of the emotion feature. Composite spatio-temporal features are obtained by combining two different features. Method First, the emotional videos are preprocessed, and five frame images are obtained by K mean clustering, which are used as the expression and body posture emotion sequences. Second, TSLTPM and 3DHOG features are extracted from the expression and gesture emotion sequences, and the minimum Euclidean distance of the feature between the test sequence and labeled emotion training set is calculated. The calculated value is used as independent evidence to construct the basic probability assignment function. Finally, according to the rules of D-S evidence theory, the expression recognition result is obtained by fused BPA. Result Experimental results on the bimodal expression and body posture emotion database show that complex spatio-temporal features exhibit good recognition performance. The average recognition rates of 83.06% and 94.78% are obtained in the single model identification of facial expressions and gestures, respectively, compared with other algorithms. The average recognition rate of the single-expression model is 9.27%, 12.89%, 1.87%, and 1.13% higher than those of VLBP, LBP-TOP, TSLTPM, and 3DHOG, respectively. The average recognition rate of the single-gesture model is 24.61%, 27.55%, 1.18%, and 0.98% higher than those of VLBP, LBP-TOP, TSLTPM, and 3DHOG, respectively. The average recognition rate after the fusion of these two models is 96.86%, which is higher than the rate obtained by a single model. This result confirms the effectiveness of emotion recognition under the fusion of expression and gesture. Conclusion The TSLTPM feature proposed in our paper extends the VLBP, which is effective in describing the local features of video images, into the temporal-spatial local ternary pattern. The proposed feature has low dimensionality, and it can enhance the robustness to illumination and noise. The composite spatio-temporal features fused with 3DHOG and TSLTPM can fully describe the effective information of emotional videos, and it enhances the classification performance of such videos. The effectiveness of the proposed algorithm in comparison with other typical feature extraction algorithms is also demonstrated. The proposed algorithm is proven suitable for identifying the emotion of static background videos, and the superiority of the fusion method in this study is verified.

本論文では，時空間局所3値パターンモーメントである新しい特徴記述アルゴリズムを提案する．このアルゴリズムは，3つの値パターンを導入し，隣接するフレーム間のピクセル値の多様性を記述するために時間 - 空間シリーズに拡張されている．テクスチャ特徴の値は，3値モデル行列のエネルギー値によって表され，それらは，グレイレベル共起行列に従って計算される．時間 - 空間的局所3値パターンモーメントがテクスチャ特徴を記述するだけであることを考慮すると，それは画像のエッジ及び方向情報の表現を欠いている．複合時空間特徴は，2つの異なる特徴を組み合わせることによって得られる．まず，感情映像を前処理し，K平均クラスタリングによって5つのフレーム画像を取得し，それらを表情および身体姿勢の感情シーケンスとして使用する．第2に，表情とジェスチャー感情シーケンスからTSLTPMと3DHOG特徴を抽出し，テストシーケンスとラベル付き感情訓練セット間の特徴の最小ユークリッド距離を計算する．計算された値は，基本的な確率割り当て関数を構築するための独立した証拠として使用されます．最後に，D-S証拠理論の規則に従って，発現認識結果は溶融BPAによって得られる．結果バイモーダル表現と身体姿勢感情データベースの実験結果は，複雑な時空間特徴が良好な認識性能を示すことを示している．


G-LBP and Variance Cross Projection Function for Face Recognition

顔認識のためのG-LBPと分散クロスプロジェクション機能

In order to enhance robustness of traditional Gabor features towards illumination, expression and pose variance and overcome its high dimension problem, the paper proposes a face recognition method based on Gabor, local binary patter and variance projection entropy improved algorithm. First, the multi direction multi-scale fusion Gabor image is coded with LBP, and the coded image fused and the histograms of image block calculated. Second, a local projection entropy feature extraction is adopted for face images with anti-geometric distortion variance projection entropy and cross variance projection entropy operator. Finally, the face recognition is completed by using BP neutral network to fuse and make decision weightily. The G-LBP feature extraction reduces the redundancy of data greatly, and maintains the integrity of the effective information. Variance projection of entropy and cross entropy improves the richness of the feature. The weighted fusion in decision-making layer plays an important role of integration between the classifiers and improves the recognition rate of face recognition. Compared with other literature algorithms, experiment results verify the effectiveness and superiority of the proposed algorithm.

従来のGabor特徴の照度，表現，ポーズ分散に対する頑健性を高め，その高次元問題を克服するために，Gaborの局所バイナリパターンと分散投影エントロピー改良アルゴリズムに基づく顔認識手法を提案する．まず，多方向マルチスケール融合ガボール画像をLBPでコード化し，コード化画像を融合し，画像ブロックのヒストグラムを計算する．第2に，局所的投影エントロピー特徴抽出は，反幾何歪み分散投影エントロピー及び交差分散投影エントロピー演算子を有する顔画像に対して採用される．最後に，BPニュートラルネットワークを使用して顔認識を行う． G-LBP特徴抽出は，データの冗長性を大幅に低減し，有効情報の完全性を維持する．エントロピーとクロスエントロピーの分散投影は，特徴の豊かさを改善する．意思決定層における重み付けされた融合は，分類器間の統合の重要な役割を果たし，顔認識の認識率を改善する．他の文献アルゴリズムと比較する実験結果は，提案されたアルゴリズムの有効性と優位性を検証した．


Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.

B2C電子商取引の急速な発展とオンラインショッピングの普及に伴い，Webは顧客による膨大な数の製品レビューコメントを保存している．多数のレビューでは，製造者または潜在的な顧客が作成したコメントおよび提案を追跡することが困難になった．本論文では，混合モデルに基づく感情オブジェクトと感情語を含む感情要素と商品レビューの傾向を抽出する方法を提案する．まず，特徴テンプレートの頑健性を向上させるための特徴として，感情的要素，リードインセマンティックおよび単語の意味を抽出する条件付きランダムフィールドを構築し，階層的フィルタリングエラーのルールを使用した．次に，サポートベクトルマシンを構築し，ファイングレイン要素の感情的な傾向を分類し，製品のレビューから重要な情報を得た．

International Journal of Networked and Distributed Computing


Facial expression recognition method based on multi-regional D-S evidences theory fusion

To achieve decision-level fusion of multi-regional features and highlight the credibility of different regional evidences, a facial expression recognition method based on multi-regional evidence fusion is proposed. A block histogram of gradient Gabor features in three regions, namely eyebrows, eyes, and mouth, is extracted from a facial image and regarded as evidence in expression classification. Then, category membership and regional contribution are solved with the region-weighted semisupervised fuzzy c-means clustering algorithm to construct initial basic probability assignment (BPA) and emphasize the importance of different evidences, respectively. The initial BPA of evidence is further reassigned by combining region contribution and evidence supportability to reduce evidential conflict. Finally, the final decision-level fusion of multi-regional evidences is obtained based on the Dempster Shafer (D S) combination rule. The experimental results for the Cohn Kanade expression database show that the BPA construction method based on category-membership degree and the reassignment strategy based on region contribution and evidence supportability improves the recognition rate and maintains good robustness for all types of expressions. Compared with existing decision-level fusion strategies and classification methods, the proposed recognition framework based on D S evidences theory has the advantages in recognition performance and reliability, particularly in increasing the recognition rate for expressions that are difficult to distinguish, such as fear, sadness, and disgust.

多地域特徴の決定レベル融合を達成し，異なる地域証拠の信頼性を強調するために，多地域証拠融合に基づく表情認識方法を提案する．顔画像から眉毛，目，口の3つの領域における勾配ガボール特徴のブロックヒストグラムを抽出し，発現分類の証拠とする．次に，初期加重確率割り当て(BPA)を構築し，異なる証拠の重要度を強調するために，領域加重半教師付きファジーC平均クラスタリングアルゴリズムを用いて，カテゴリメンバーシップと地域貢献を解決する．最後に，マルチ地域証拠の最終的な決定レベルの融合はDempster Shafer(D S)の組み合わせルールに基づいて得られる． Cohn Kanadeの表現データベースの実験結果は，カテゴリー - メンバーシップ度に基づくBPA構築方法と，地域貢献と証拠サポート性に基づく再割り当て戦略が認識率を改善し，すべてのタイプの表現に対して良好な堅牢性を維持することを示している．


GlycCompSoft: Software for Automated Comparison of Low Molecular Weight Heparins Using Top-Down LC/MS Data

Low molecular weight heparins are complex polycomponent drugs that have recently become amenable to top-down analysis using liquid chromatography-mass spectrometry. Even using open source deconvolution software, DeconTools, and automatic structural assignment software, GlycReSoft, the comparison of two or more low molecular weight heparins is extremely time-consuming, taking about a week for an expert analyst and provides no guarantee of accuracy. Efficient data processing tools are required to improve analysis. This study uses the programming language of Microsoft Excel™ Visual Basic for Applications to extend its standard functionality for macro functions and specific mathematical modules for mass spectrometric data processing. The program developed enables the comparison of top-down analytical glycomics data on two or more low molecular weight heparins. The current study describes a new program, GlycCompSoft, which has a low error rate with good time efficiency in the automatic processing of large data sets. The experimental results based on three lots of Lovenox®, Clexane® and three generic enoxaparin samples show that the run time of GlycCompSoft decreases from 11 to 2 seconds when the data processed decreases from 18000 to 1500 rows.

低分子量ヘパリンは，最近，液体クロマトグラフィー - 質量分析を用いたトップダウン分析に従順になった複雑なポリ成分薬物である．オープンソースのデコンボリューションソフトウェア，DeconTools，自動構造割り当てソフトウェア，GlycReSoftを使用しても，2つ以上の低分子量ヘパリンの比較には非常に時間がかかり，熟練したアナリストにとっては約1週間かかる．この研究では，マクロ機能用の標準機能と質量分析データ処理用の特定の数学モジュールを拡張している．開発されたプログラムは，2つ以上の低分子量ヘパリンについてのトップダウン分析グリコックスデータの比較を可能にする．


Selectional Preferences Based on Distributional Semantic Model

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.


Dual-modality Emotion Recognition Model Based on Temporal-spatial LBP Moment and Dempster-Shafer Evidence Fusion


Current Situation and Development of Intelligence Robots

Industrial intelligent robots are treated as a measure of na tionalscientificlevelandtechnologyinnovation,andalsothe important symbol of high level manufacturing, while service intelligent robots can directly affect people's daily lives. The development of artificial robots in different areas is at tracting much attention around the world. This article re views the current situation and development of Chinese and internationalintelligentrobotmarketsincludingindustrialro botsandservicerobots.Theintelligentrobottechnologyand theclassificationofrobotsarealsodiscussed.Finally,appli cations of intelligent robots in various fields are concluded andthedevelopmenttrendsandoutlookofintelligentrobots areexplored.

産業用インテリジェントロボットは，高レベル製造の重要な象徴であり，サービスインテリジェントロボットは人々の日常生活に直接影響を及ぼすことができる．これは技術革新と技術革新の尺度としても扱われている． 異なる分野の人工ロボットの開発は，世界中で注目を集めている． 本稿では，産業ロボットと産業ロボットを含むインテリジェントロボット市場の現状と発展状況を調査し研究する．インテリジェントロボット技術とロボットの分類を議論する．最後に，様々な分野のインテリジェントロボットの応用が結論づけられ，発展と知的ロボットの将来を展望する．


Automatic Facial Expression Learning Method Based on Humanoid Robot XIN-REN

The ability of a humanoid robot to display human-like facial expressions is crucial to the natural human computer interaction. To fulfill this requirement for an imitative humanoid robot, XIN-REN, an automatic facial expression learning method is proposed. In this method, first, a forward kinematics model, which is designed to reflect nonlinear mapping relationships between servo displacement vectors and corresponding expression shape vectors, is converted into a linear relationships between the mechanical energy of servo displacements and the potential energy of feature points, based on the energy conservation principle. Second, an improved inverse kinematics model is established under the constraints of instantaneous similarity and movement smoothness. Finally, online expression learning is employed to determine the optimal servo displacements for transferring the facial expressions of a human performer to the robot. To illustrate the performance of the proposed method, we conduct evaluation experiments on the forward kinematics model and the inverse kinematics model, based on the data collected from the robot's random states as well as fixed procedures by animators. Further, we evaluate the facial imitation ability with different values of the weighting factor, according to three sequential indicators (space-similarity, time-similarity, and movement smoothness). Experimental results indicate that the deviations in mean shape and position do not exceed 6 pixels and 3 pixels, respectively, and the average servo displacement deviation does not exceed 0.8%. Compared with other related studies, the proposed method maintains better space time similarity with the performer, besides ensuring smoother trajectory for multiframe sequential imitation.

著者は，すでに感情表現ができる第1作者と外見がそっくりなヒューマノイドロボットXIN-RENを開発した．しかしながら，知能機械として，ヒューマノイドロボットは，人間と同様の外観を持つ必要があるだけでなく，人間と同程度の感情表現能力を有する必要がある．とりわけ表情は，感情を表現する最も直接的な方法である．本論文では，ヒューマノイドロボットの実用化の視点から，ロボット表情自立学習の手法を提案した．本手法は第1に，マルチポーズAAM (active appearance model) 定位の48の特徴点を用いた表情ベクトル組成を行い，またエネルギー保存の法則により運動制御ベクトルとその表情ベクトルの順運動学モデルが確立され，表情学習の瞬間的類似性を保持させる．次に，時間多項式関数に基づいてAAMを構築することにより，表情学習の円滑さの維持を実現させる．最後に，順運動学モデルとAAMに基づいて逆運動学モデルを構築し，さらに重み付き逆運動学モデルを経て，ロボットの動的表情の自立学習およびモーターの連続運動の実時間的平滑化を実現させる．実験結果から，提案した表情自立学習手法が，ロボットの表情運動に対する人為的処理の複雑度を軽減させ，ロボットの表情学習の忠実度合いおよび平滑さを改善させたことが示された．

IEEE Transactions on Human-Machine Systems


Mining the impact of social news on the emotions of users based on Deep Model

This work investigates the deep features in social news which can influence the emotions of people.Three kinds of feature compression methods are used to extract shallow features from the granularities of unigram word,bigram word and theme.The work used Support Vector Machine to select the optimal shallow features of three granularities,and the optimal F1_macro are 60.5% 62.1% 63.3% separately.The work introduced Deep Belief Network (DBN) model to train and abstract the optimal shallow features,so we can get the deep features.The optimal F1_macro of DBN3 are 61.4% 63.5% 66.1% respectively.The experimental results show that the deep features abstracted by Deep Belief Network have more semantic information and better performance than shallow features in determining the influence on people's emotions by social news.

Journal of Chinese Information Processing


Biomedical Named Entity Recognition Based on Deep Conditional Random Fields

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence


Ironically linguistic judgement based on hybrid neural network model via multi-feature fusion


Occluded facial expression recognition based on the fusion of local features

To reduce the effect of partial occlusion in facial expression recognition, this paper proposes a new method of facial expression recognition based on local feature fusion. Method First, the normalized images are processed by the Gaussian filter to reduce the effect of noise. According to their different contributions in facial expression recognition, all the images are then divided into two main parts: near the eye and near the mouth. To analyze considerable structure detail, these two parts are further divided into several non-overlapping blocks. The following two patterns are used to extract the features of each sub block: the difference center-symmetric local binary pattern, which is the change of center-symmetric local binary pattern; and the gradient center-symmetric local directional pattern, which is the change of difference local directional pattern. The features are marked as two binary sequences, which are then cascaded to obtain the characteristic histogram of the sub block. The final histogram of the image is obtained by cascading the histogram of each sub block. Finally, the nearest neighbor method is used for classification. Chi-square distance is used to calculate the distance among the characteristic histograms of the testing and training images. Considering the difference of the amount of information contained in each sub block and to reduce the effect of occlusion further, information entropy is used to weigh chi-square distance adaptively. Result Three cross experiments are conducted on JAFFE and CK databases. The average recognition accuracies in random occlusion, mouth occlusion, and eye occlusion cases are 92.86%, 94.76%, and 86.19% on JAFFE database, and are 99%, 98.67%, and 99% on CK database. Conclusion In the aspect of feature extraction, our method describes the image from two aspects: one is the difference of the pixel values in the gradient direction, and the other is the difference of the edge response values between gradient directions. Accordingly, the image can be fully described. In the aspect of occlusion, image segmentation and information entropy are used to weigh chi-square distance adaptively. Thus, our method can effectively reduce the effect of occlusion. Under the same experimental conditions, experimental results show the effectiveness and superiority of the proposed method to other classical local feature extraction and occlusion handling methods.


PAWS: Passive Human Activity Recognition Based on WiFi Ambient Signals

Indoor human activity recognition remains a hot topic and receives tremendous research efforts during the last few decades. However, previous solutions either rely on special hardware, or demand the cooperation of subjects. Therefore, the scalability issue remains a great challenge. To this end, we present an online activity recognition system, which explores WiFi ambient signals for RSSI (Received Signal Strength Indicator) fingerprint of different activities. It can be integrated into any existing WLAN networks without additional hardware support. Also it does not need the subjects to be cooperative during the recognition process. More specifically, we first conduct an empirical study to gain in-depth understanding of WiFi characteristics, e.g., the impact of activities on the WiFi RSSI. Then, we present an online activity recognition architecture that is flexible and can adapt to different settings/conditions/scenarios. Lastly, a prototype system is built and evaluated via extensive real-world experiments. A novel fusion algorithm is specifically designed based on the classification tree to better classify activities with similar signatures. Experimental results show that the fusion algorithm outperforms three other well-known classifiers (i.e., NaiveBayes, Bagging and k-NN) in terms of accuracy and complexity. Important sights and hands-on experiences have been obtained to guide the system implementation and outline future research directions.

屋内の人間活動認識は依然として注目されており，ここ数十年の間に莫大な研究努力を受けている．しかし，これまでのソリューションは，特別なハードウェアに依存するか，または主体の協力を必要とする．したがって，スケーラビリティの問題は依然として大きな課題です．この目的のために，我々は様々な活動のRSSI指紋のためにWiFi環境信号を探索するオンライン活動認識システムを提案する．最初に，WiFiのRSSIに対する活動の影響など，WiFi特性の詳細な理解を得るための実証研究を実施する．次に，さまざまな設定/条件/シナリオに柔軟に対応できるオンラインアクティビティ認識アーキテクチャを紹介する．最後に，プロトタイプのシステムが構築され，広範な実世界実験によって評価される．実験結果は，融合アルゴリズムが，正確さおよび複雑さの点で，3つの他の周知の分類器より優れていることを示した．


Optimal design of 3D chip scan chains based on cores-hierarchical-placement

Journal of Electronic Measurement and Instrumentation


Bad-Die Recycling Technique for Yield Enhancement of Three-Dimensional Memories


Sentiment Analysis for Chinese Microblog based on Deep Neural Networks with Convolutional Extension Features

Related research for sentiment analysis on Chinese microblog is aiming at the analysis procedure of posts. The length of short microblog text limits feature extraction of microblog. Tweeting is the process of communication with friends, so that microblog comments are important reference information for related post. A contents extension framework is proposed in this paper combining posts and related comments into a microblog conversation for features extraction. A novel convolutional auto encoder is adopted which can extract contextual information from microblog conversation as features for the post. A customized DNN(Deep Neural Network) model, which is stacked with several layers of RBM (Restricted Boltzmann Machine), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of input data to learn hidden structures for better high level features representation. A ClassRBM (Classification RBM) layer, which is stacked on top of RBM layers, is adopted to achieve the final sentiment classification label for the post. Experimental results show that, with proper structure and parameters, the performance of proposed DNN on sentiment classification is better than state of the art surface learning models such as SVM or NB, which proves that the proposed DNN model is suitable for short-length document classification with proposed feature dimensionality extension method.

マイクロブログに対する感情分析のための関連研究は，投稿の分析手順を目指している．短いマイクロブログテキストは，マイクロブログの特徴抽出を制限する．ツイートは友人とのコミュニケーションの過程であるため，マイクロブログのコメントは関連する投稿の重要な参考情報である．本稿では，コンテンツ抽出のためのポストと関連するコメントをマイクロブログの会話に統合するコンテンツ拡張フレームワークを提案する．ポストの特徴としてマイクロブログの会話から文脈情報を抽出することができる斬新な畳み込み自動エンコーダが採用されている．ニューラルネットワークの構造を初期化するために，RBM(Restricted Boltmann Machine)のいくつかの層でスタックされたカスタマイズされたDNN(ディープニューラルネットワーク)モデルが実装されている． RBM層は，入力データの確率分布サンプルを取って隠れた構造を学習し，より高いレベルの特徴表現を得ることができる．ポストの最終的なセンチメント分類ラベルを達成するために，RBM層の上に積み重ねられたClassRBM(分類RBM)層が採用される．実験結果は，SVMやNBなどの最先端の表面学習モデルよりも，適切な構造とパラメータを用いてセンチメント分類に対する提案されたDNNの性能が優れていることを示した．


AAH: accurate activity recognition of human beings using WiFi signals

The flourishing social networks nowadays have greatly enriched our ways of communications and thus brought people in the world much closer than ever. However, critical contexts of the traditional face-to-face communications, for example, body gestures, could be missing during the online communication, hampering the user experiences. To fill in the blank, this paper presents a passive and devices-free activity recognition system, by harvesting fingerprints of different activities from ubiquitous WiFi signals. It can be integrated into any existing WLAN networks without additional hardware supports. Also, it does not need the subjects to be cooperative during the recognition process. A prototype system is built and evaluated via extensive real-world experiments. By comparing with three state-of-the art solutions, that is, K-nearest neighbor, naive Bayes, and bagging, we show the superiority of the proposed method in terms of accuracy and complexity.

現在，繁栄しているソーシャルネットワークは，コミュニケーションの方法を大幅に強化し，世界中の人々をこれまで以上に近づけた．しかし，オンラインコミュニケーション中には，身体のジェスチャなどの従来の対面型通信の重要なコンテキストが失われ，ユーザーエクスペリエンスが損なわれる可能性がある．本稿では，WiFiのさまざまな信号からさまざまな活動の指紋を採取することにより，受動的かつデバイスフリーの活動認識システムを紹介する．追加のハードウェアをサポートすることなく，既存のWLANネットワークに統合することができる．プロトタイプシステムは，広範な現実世界の実験によって構築され評価される．K-nearest neighbor，naive Bayes，baggingの3つの最先端の解と比較することにより，提案手法の精度と複雑さの優位性を示した．

Concurrency and Computation : Practice & Experience


Face Recognition Method Based on Multi Features Description and Local Fusion Classification Decision


Variable Differently Implicational Inference for R- and S-Implications

As a generalization of the compositional rule of inference (CRI) algorithm and the fully implicational algorithm, the differently implicational algorithm of fuzzy inference not only inherit the advantages of the fully implicational algorithm, but also has stronger practicability. Then, the variable differently implicational algorithm was proposed to make the current differently implicational algorithms compose a united whole. In this paper, the variable differently implicational algorithm is further researched focusing on the fuzzy modus tollens (FMT) problem. The differently implicational principle for FMT is improved. Moreover, the unified solutions of the variable differently implicational algorithm for FMT are accomplished for R- and S-implications. Following that, as an important index of fuzzy inference, the continuity of this algorithm is analyzed for main R- and S-implications, in which excellent performance is obtained. Finally, its optimal solutions as well as inference examples are provided for several specific R- and S-implications.

構成的推論(CRI)アルゴリズムと完全暗黙アルゴリズムの一般化として，ファジィ推論の異なる暗示アルゴリズムは，完全暗示アルゴリズムの利点を継承するだけでなく，より実用性が高い．次に，現在の異なった暗黙のアルゴリズムを統一された全体にするために，異なって暗示的なアルゴリズムが提案された．本稿では，ファジィモードストレンズ(FMT)問題を中心に，様々な意味的アルゴリズムをさらに研究する． FMTの異なる意味合いの原理が改善されている．さらに，FMTのための様々な示唆的アルゴリズムの統一された解は，RおよびS-含意のために達成される．その後，ファジィ推論の重要な指標として，優れた性能が得られる主なRおよびSの影響について，このアルゴリズムの連続性が分析される．最後に，その最適解および推論の例が，いくつかの特定のRおよびSの含意について提供される．

International Journal of Information Technology & Decision Making


Chinese micro-blog sentiment analysis based on semantic features and PAD model

With the increasing impact of social networks, microblog becomes important carrier of information and social interaction for human beings, which contains emotional states that have important research significance. We try to analysis the microblog text with the methods of emotional vocabulary, combining domain knowledge of psychology and affective computing, continuous dimension of emotion psychology PAD model which is adopted as basis of sentiment analysis. Emotional state inherent in the text is analyzed to obtain a more accurate result and achieve purposes of emotional analysis. At the same time, to achieve emotional microblog text computability from the aspect of personal characteristics. Experimental results show that the method can improve the microblog text sentiment analysis accuracy and precision. The method is able to get a good application in the different themes and different emotional features.

15th IEEE/ACIS International Conference on Computer and Information Science


Detect the Emotions of Whe Public Based on Cascade Neural Network Model


Emotional Element Detection and Tendency Judgment Based on Mixed Model with Deep Features


A novel factored POMDP model for affective dialogue management

Partially observable Markov decision process (POMDP) model has been demonstrated many times to be suited for robust spoken dialogue management. Recently, some factored representations of POMDP model are designed for specific dialogue tasks. This paper proposes a novel factored POMDP model to describe a new application of affective dialogue management. Different from existing models, the user's state space and the system's observation space are both divided into two distinct components: goal and emotion. Moreover, the system's action space is for the first time factored into two parts, i.e., goal response and emotion response, and the reward function is accordingly updated by weighted sum of the two-part rewards. An example of intelligent music player is given to explain how to apply the new model to build an affective dialogue system. Four experiments are designed to reveal the influence of key parameters on the system performance. The simulation results demonstrate the rationality and feasibility of the proposed model.

部分的に観測可能なマルコフ決定プロセス(POMDP)モデルは，頑強な音声対話管理に適していることが何度も証明されている．最近，特定の対話タスクのために，POMDPモデルのいくつかの因数表現が設計されている．本稿では，情緒的対話管理の新しい応用を記述するための新しい因子分解POMDPモデルを提案する．既存のモデルとは異なり，ユーザの状態空間とシステムの観測空間は，目的と感情という2つの異なるコンポーネントに分かれている．さらに，システムの行動空間は，2つの部分，すなわち目標応答および感情応答に最初に因数分解され，したがって，報酬関数は，2部報酬の加重和によって更新される．インテリジェント音楽プレーヤーの例は，感情的な対話システムを構築するために新しいモデルを適用する方法を説明するために与えられる． 4つの実験は，システム性能に対する主要パラメータの影響を明らかにするために設計されている．シミュレーション結果は，提案されたモデルの合理性と実行可能性を示した．

Journal of Intelligent and Fuzzy Systems


Facial Expression Recognition Using ROI-KNN Deep Convolutional Neural Networks

Deep neural networks have been proved to be able to mine distributed representation of data including image, speech and text. By building two models of deep convolutional neural networks and deep sparse recti¯er neural networks on facial expression dataset, we make contrastive evaluations in facial expression recognition system with deep neural networks. Additionally, combining region of interest (ROI) and K-nearest neighbors (KNN), we propose a fast and simple improved method called OI-KNN" for facial expression classi¯cation, which relieves the poor generalization of deep neural networks due to lacking of data and decreases the testing error rate apparently and generally. The proposed method also improves the robustness of deep learning in facial expression classi¯cation.


Hybrid Chinese Text Classification Approach Using General Knowledge from Baidu Baike

Most of the previous studies focused on enriching text representation to address text classi cation (TC) task. However, conventional classi cation approaches with VSM (vector space model) on Chinese text study intensively only the words and their relationship in some speci c corpus/dataset but ignore the basic concept of categories and the general knowledge behind the words learned and used to recognize entities by people. This paper focuses on enriching text representation and proposes a novel approach, which complements information from the online Chinese encyclopedia Baidu Baike for Chinese TC. The similarities between every text and each concept of categories and the most related words from Baidu Baike are added to the feature space. The performance of the proposed approach is measured on the Fudan University TC corpus, which is an imbalanced Chinese dataset. In the experiments, the proposed Baidu Baike-based concept similarity approach obtains promising results when compared with a previous research and the conventional method, with macro-precision of 90.31%, recall of 75.45%, and F1 score 80.32%, which are about 0.02%, 0.15%, 0.12%, respectively, higher than the conventional method, which obviously improves the recall for some small categories while keeping precision at high level and improving the macro F1 score. Moreover, the proposed approach has good expandability, so that many other knowledge bases could be integrated and many other concepts could be referred to improve the effectiveness.

本稿では，テキスト表現を充実させることに焦点を当て，オンライン中国語百科事典Baidu Baikeの中国語TCの情報を補完する新しいアプローチを提案する． Baidu Baikeのすべてのテキストとカテゴリの各概念と最も関連性の高い単語との類似点が特徴空間に追加される．提案されたアプローチのパフォーマンスは，不均衡な中国のデータセットである復旦大学TCコーパスで測定される．実験では，提案されたBaidu Baikeベースのコンセプト類似アプローチは，マクロ精度90.31%，リコール75.45%，F1スコア80.32%で，従来の方法と比較して約0.02 %，0.15%，0.12%と向上している．これは明らかに高精度を保ちつつマクロF1スコアを向上させながら，いくつかの小さなカテゴリのリコールを改善した．さらに，提案された手法は良好な拡張性を有しているため，他の多くの知識ベースを統合することができ，他の多くの概念を参照して効率を改善することができる．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Detecting Influenza States based on Hybrid Model with Personal Emotional Factors from Social Networks

In this paper, we exhibit how social media data can be used to detect and analyze real-word phenomena with several data mining techniques. We investigate the real-time flu detection problem and propose a flu state detection model with personal emotional factors and semantic information (Em-Flu model). First, we extract flu-related microblog posts automatically in real-time using a hybrid model composed by Support Vector Machine with features extracted from Restricted Boltzmann Machine. In order to overcome the limitation of 140 words for posts, expect for sentiment related features, association semantic rules are also adopted as additional features, such as bag of words, negative words, degree adverbs and sentiment words dictionary. For flu state detection at specific location, we propose an unsupervised model based on personal emotional factors to figure out what state of flu in specific place. For comparison, a supervised model is also built by adopting Conditional Random Fields to decide whether a poster has ``really'' catch flu and what influenza stage the poster is in. Some statistic methods and prior rules are adopted in supervised model to get the flu state of specific locations by counting the number of microblog posts in different flu states. By considering personal emotional factors, spatial features and temporal patterns of influenza, the performance of unsupervised and supervised models are both improved. The system could tell when and where influenza epidemic is more likely to occur at certain time in specific locations. In different experiments results, the hybrid models show robustness and effectiveness than state-of-the-art unsupervised and supervised model only considering the number of posts.

本稿では，ソーシャルメディアデータを用いて，いくつかのデータマイニング手法を用いて，実際の現象を検出して分析する方法を提案する．我々は，リアルタイムのインフルエンザ検出問題を調査し，個人的な感情的要因および意味情報(Em-Fluモデル)を有するインフルエンザ状態検出モデルを構築した．まず，制限付きボルツマンマシンから抽出した特徴を持つサポートベクターマシンで構成されるハイブリッドモデルを使用して，インフルエンザ関連のマイクロブログ記事をリアルタイムで自動的に抽出する．異なるインフルエンザ状態のマイクロブログ投稿の数によって，特定の場所のインフルエンザ状態を検出する．インフルエンザの個人的な感情的要因，空間的特徴および一時的パターンを考慮することにより，監督されていないモデルおよび監視されたモデルのパフォーマンスが改善される．このシステムは，インフルエンザの流行がいつ，どこで，特定の場所で特定の時期に発生する可能性が高いかを特定することができる．実験結果では，ハイブリッドモデルは，ポストの数のみを考慮して，最先端の教師なしモデルおよび監視モデルよりも堅牢性および有効性を示した．


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

International Journal of Advanced Intelligence (IJAI)


Face recognition method based on local mean pattern description and double weighted decision fusion for classification


Low Power Multistage Test Data Compression Scheme


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

Recently, with increase of the number of users of Social Networking Sites, online communications have been more and more actively made, raising the possibility to use the big data on SNS for analyzing the diversity of language. Japanese language uses varieties of character types and such character types are combined and used for creating words and phrases. Therefore, it is difficult to morphologically analyze such words and phrases even though morphological analysis is a basic processing in natural language processing. Such words and phrases that are not registered in morphological analysis dictionaries are usually not defined strictly and semantic interpret for them seems to vary depending on deindividual. In this study, we chronologically analyze the topics related to slang on Twitter. In this paper, as a validation experiment, we conducted a topic analysis experiment chronogically by using the sequential tweet data, and discussed the difference of topic change according to the slang types.

近年，SNS の利用者増加に伴い，Web上でのコミュニケーションがよりいっそう活発になった． これにより，言葉の多様性について，SNS上のビッグデータを用いて解析できる可能性が出てきた．日本語では扱われる文字種が多いことからも，様々な表現が存在し，自然言語処理において基本的な処理である形態素解析が，比較的難しいことが問題となっている．こうした形態素解析の辞書に登録されていないような未知表現に対しては，厳密な定義が存在していないこともしばしばであり，意味解釈における個人差が大きいことが考えられる．本研究では，Twitter上の俗語に関連したトピックを時系列で分析する．本論文では，検証実験として，連続するTweetデータを用いたトピックの時系列分析実験をおこない，俗語の種類によるトピック変化の違いを考察する．変化しない情報(不変情報)が，一定期間においてどのような変化を示すかを実験により示し，標準語との差異を調べる．また，俗語の変化の仕方を変動値とし，特徴ベクトルとすることで，似たような傾向を示す俗語をクラスタリングする手法を提案し，実験結果を分析する．


Textual Emotion Recognition for Enhancing Enterprise Computing

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of emotion state in text is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

感性コンピューティングにおける関心の高まりは，企業システムにおける異なるアプリケーションの需要という価値ある研究トピックをもたらした．本研究は，企業コンピューティングの強化のためのテキストの感情認識という，感性コンピューティング技術の一部の領域について調査する．複数ラベルのテキストからの感情認識は，1つのラベルの感情認識よりも，総合的な感情の理解を可能とする．テキストにおける感情状態の表現は，テキストにおける多次元の感情を引き起こす．我々の手法は，間接的な感情，あいまいな感情，複数の感情を生起する単語の感情認識を考慮する．実験結果は，従来のbag-of-wordsモデルよりも良い結果を示した．


Multi-Label Emotion Recognition of Weblog Sentence Based on Bayesian Networks

An increasing number of common users, in the Internet age, tend to express their emotions on the Web about everything they like or dislike. As a consequence, the number of all kinds of reviews, such as weblogs, production reviews, and news reviews, grows rapidly. This makes it difficult for people to understand the opinions of the reviews and obtain useful emotion information from such a huge number of reviews. Many scientists and researchers have attached more attention to emotion analysis of online information in the natural language processing field. Different from previous works, which just focused on the single-label emotion analysis, this paper takes into account rich and delicate emotions and gives special regard to multi-label emotion recognition for weblog sentences based on the Chinese emotion corpus (Ren-CECps). Using the theory of Bayesian networks and probabilistic graphical model, the latent emotion variable and topic variable are employed to find out the complex emotions of weblog sentences. Our experimental results on the multi-label emotion topic model demonstrate the effectiveness of the model in recognizing the polarity of sentence emotions.

本論文では，単一ラベル感情分析に焦点を当てたこれまでの研究とは異なり，豊かで繊細な感情を考慮に入れ，中国感情コーパス(Ren-CECps)に基づくウェブログ文に対するマルチラベル感情認識を特に考慮する．ベイジアンネットワークと確率的なグラフィカルモデルを用いて，潜在感情変数とトピック変数を用いて，Weblog文の複雑な感情を調べる．多ラベル感情トピックモデルに関する我々の実験結果は，センテンス感情の極性を認識する際のモデルの有効性を示している．


Facial Expression Recognition Based on the Optimal Matching of Multi-feature and Multi-classifier


Overview of Speech Synthesis in Development and Methods

In human computer interaction the most natural and the best way to exchange is the communication by human voice.Which is mainly related to speech synthesis that is the technology of converting text to speech.This paper provides a concise but deep introduction to the development of speech synthesis and propose the problems and solutions in the development of speech synthesis.The researchers who are just entering the field of speech synthesis can stand on the shoulders of giants and have a clear and deep understanding of voice synthesis and start working with a correct judgment.In this paper first the effective methods which are already exist and mainstream in speech synthesis are overall introduced and the main idea of these methods as well as their advantages and disadvantages are described.On this basis we inspire new ideas.After that this paper illustrates the efforts respectively at home and abroad in recent years that researchers have done in the field of speech synthesis.Then objective evaluate and analyze gains and losses in synthesis technology improvements and draw the trend of synthesis technology in recent years.Finally get the prospect in speech synthesis pointing the bottleneck in development and trying to give direction to solve them.

人間のコンピュータとの対話では，最も自然で最も良い方法は人間の声によるコミュニケーションである．主にテキストを音声に変換する技術である音声合成に関連している．このペーパーは，スピーチの発達音声合成の分野に参入している研究者は，巨人の肩に立つことができ，音声合成をはっきりと深く理解し，正しい判断で作業を開始することができる．本稿では，音声合成において既に存在し，主流である有効な方法を全体的に導入し，これらの方法の主なアイデアとその長所と短所を記述する．この基礎から我々は新しいアイデアを鼓舞する．近の合成技術の向上と合成技術のトレンドを評価し，分析する．最後に，音声合成の見通しを得て，開発におけるボトルネックを指摘し，それらを解決する方向づけを試む．


Weighted high-order hidden Markov models for compound emotions recognition in text

Emotion recognition in text has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this paper, we explore an efficient identification of compound emotions in sentences using hidden Markov models (HMMs). In this problem, emotion has temporal structure and can be encoded as a sequence of spectral vectors spanning an article range. The major contributions of the research include the (i) proposal of weighted high-order HMMs to determine the most likely sequence of sentence emotions in an article. The weighted high-order HMMs take into account the impact degree of context emotions with different lengths of history; (ii) introduction of a representation of compound emotions by a sequence of binary digits, namely emotion code; (iii) development of an architecture that uses the emotions of simple sentences as part of known states in the weighted high-order hidden Markov emotion models for further recognizing more unknown sentence emotions. The experimental results show that the proposed weighted high-order HMMs is quite powerful in identifying sentence emotions compared with several state-of-the-art machine learning algorithms and the standard n-order hidden Markov emotion models. And the use of emotion of simple sentences as part of known states is able to improve the performance of the weighted n-order hidden Markov emotion models significantly.

本稿では，隠れマルコフモデル(HMM)を用いた文中の複合感情の効率的な同定について検討する．この問題では，感情は時間的な構造を有し，物品の範囲にまたがる一連のスペクトルベクトルとして符号化することができる．この研究の主な貢献は，(i)論文中の文の感情の最も可能性の高いシーケンスを決定するための加重高次HMMの提案である．加重された高次HMMは，異なる長さの履歴を伴う文脈感情の影響度を考慮に入れる． (ii)複合感情の表現を一連の2進数字，すなわち感情コードで導入する． (iii)加重された高次隠れマルコフ感情モデルの既知の状態の一部として単純文の感情を使用して，より未知の文感情をさらに認識するアーキテクチャの開発．実験結果は，提案された加重高次HMMが，いくつかの最先端の機械学習アルゴリズムおよび標準のn次隠れマルコフ感情モデルと比較して，センテンス感情を識別するのに非常に強力であることを示す．そして，既知の状態の一部としての単純な文の感情の使用は，重み付けされたn次の隠れマルコフ感情モデルの性能を有意に改善することができる．


Role-explicit query extraction and utilization for quantifying user intents

In this study, we focus on how to extract role-explicit queries from a Chinese query log. A framework that performs role-explicit query extraction concurrently with intent role annotation is proposed. Instead of independently processing each query, the human wisdom hidden in mul-sessions is deployed to quantify the certainty of a word being the kernel-object. A sliding-window algorithm is proposed to extract role-explicit queries per mul-session. To filter the unreliable results produced by this algorithm, a pattern-based algorithm that performs a global purification is designed. The entire framework enables us to obtain a repository of sufficient role-explicit queries from a query log without human intervention. We also investigate the usefulness of aggregative role-explicit query extraction. Based a repository of role-explicit queries, we derive the richness value to quantify the richness of kernel-object oriented intents. An experimental application of the proposed framework for role-explicit query extraction to the query log SogouQ shows that: it achieves satisfactory performance. Furthermore, the richness value provides a way to capture underspecified and/or ambiguous queries, which allows selective operations to be performed depending on the nature of the queries.

この研究では，中国語のクエリログからロール明示的なクエリを抽出する方法に焦点を当てる．インテントロール注釈と同時にロール明示的クエリー抽出を行うフレームワークを提案する．マルチスライスウィンドウアルゴリズムは，マルチセッションごとにロール明示的なクエリを抽出するために提案された．このアルゴリズムによって生成された信頼性の低い結果をフィルタリングするために，グローバルな浄化を実行するパターンベースのアルゴリズムが設計される．全体のフレームワークは，人間が介入することなく，クエリログから十分なロール明示的なクエリのリポジトリを取得することを可能にした．ロール・エリート・クエリ抽出のための提案されたフレームワークの実験的な応用は，満足な性能を達成することを示した．


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

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.

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．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Facial expression recognition based on AAM-SIFT and adaptive regional weighting

The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non-frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognition based on AAM-SIFT and adaptive regional weighting. First, multi-pose AAM templates are used for pose estimation and feature point location of the facial expression image. For effective and efficient description of these feature points, a hybrid representation, which integrates gradient direction histograms based on the descriptors of scale-invariant feature transform (SIFT) and AAM, is utilized to form AAM-SIFT features. Meanwhile, according to different expression regions, AAM-SIFT features are divided into different groups and the obtained adaptive weights by means of a regional weighted method based on the fuzzy C-means (FCM) clustering algorithm. Finally, the membership degree computed by FCM, which represents the possibility for each class, is regarded as the input feature vector for support vector machine (SVM) classifier. Extensive experiments on BU-3DFE database with six facial expressions and seven poses demonstrate the effectiveness of different types of weighting strategies and the influence of different features. Comparison with other state-of-art methods illustrates that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non-frontal facial expressions.

本論文では，顔表情認識の新しい手法として，AAM-SIFTと適応領域重みに基づく方法を提案する．大規模な実験で得られた領域重みを使い，実験を行った結果により提案した手法の有効性を確かめることができた．


Low Power Deterministic Test Scheme based on Viterbi

Journal of Compter-Aided Design & Computer Graphics


Customer emotion detection by emotion expression analysis on adverbs

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.

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．


Research on pasive human activity recognition using WiFi ambient signals

Although traditional k-nearest neighbor(K-NN) and bagging can recognize effectively less human activities using WiFi ambient signal, recognizing seven activities, namely, empty, walking, sitting, standing, sleeping, fallen and running, is not ideal. To further improve the performance, a new algorithm named fusion algorithm has been designed. It significantly outperform K-NN and bagging in terms of the recognition ratio in both single-subject and multi-subject experiments.

Journal of University of Scinece and Technology of China


Face Recognition Based on ULBP and BP Neural Network

Face recognition is a research hotspot in computer vision field. Compared with the traditional feature extraction algorithms, Local Binary Pattern has many advantages, such as high accuracy, fine, light illumination invariance, etc., so it is widely used to extract facial texture feature. Firstly, we put the image evenly into blocks and constructed sub-image sets. This would be more efficient to extract feature data. Through combining with BP neural network, we make use of decision fusion and weighted fusion two ways to obtain the classification results of unknown samples. Experimental results on ORL face database show that this method is effective.

International Journal of Advanced Intelligence (IJAI)


Facial Expression Recognition Based on MBP and Sparse Representation

Recently, sparse representation attracts widespread attention in the field of pattern recognition and artificial intelligence. Gabor features based sparse representation classifier had been introduced in face recognition research field and achieved good recognition effect. But its enormous time cost and space cost and computational complexity in the real system cannot be ignored. So this paper proposed a novel facial expression recognition method based on monogenic binary pattern and sparse representation. The proposed method extracts features from the facial expression image by using monogenic binary pattern and utilizes information entropy to weight the feature block, then constructs sparse dictionary and makes use of residual value for classification. Our proposed method was tested on the popular FERET and Cohn-Kanade facial database and obtained the average rates 94.92% and 97.46% respectively. The experimental results show that the sparse dictionary which is constructed by the proposed features has stronger ability for sparse representation and has better performance in time and space efficiency.


Social Network Influenza Epidemic Detection Based on SVM and CRF

Influenza is an acute respiratory illness and widespread activity that occurs every year. Detection and prevention of Influenza in its earliest stage would reduce the spread range of the illness. Sina microblog is a popular micro blogging service in China, which could provide perfect reference sources for flu detection due to its real-time characteristic and large number of active users posting about their daily life continually. In this paper, we investigate the real-time flu detection problem and propose a flu detection model with emotion factors and semantic information (Em-Flu model). First, we extract flu-related microblog posts automatically in real-time using a simple SVM filter. We use association rule mining to extract strongly associated features as additional features of posts to overcome the limitation of 140 words for posts, including sentimental analysis information which can help to classify the posts without features. Then Conditional Random Field model is revised and applied to detect the transition time of flu that we can find out which place is more likely for influenza outbreak and when an influenza outbreak is more likely in a particular city or province in China. The experimental results display when and where influenza epidemic is more likely to occur and show the robustness and effectiveness of the proposed model might help health organizations in predicting a flu outbreak allowing them to take appropriate action in a timely manner.


Dual-modality emotion recognition of facial expressions and gestures

We carry out a research of dual-modality emotion recognition about facial expressions and gestures and then propose a method combined facial expressions and gestures for dual-modality emotion recognition. Firstly, we obtain the expression image and gesture image by pre-processed the hand-over-face image. The pretreatment included reconstruct facial expression image through PCA algorithm. Secondly, we extract the LBP features from expression image and the Hu moment invariant features and Fourier descriptor features from gesture image. We use SVM and KNN to coarse classification recognition the expression features and gesture features respectively. Finally, we mix classification results of facial expressions and gestures together by using the linear weighted voting algorithm proposed in this paper. The experimental results show that this method has the better discrimination in the dual-modality emotional database established by ourselves.

我々は，表情とジェスチャーに関するデュアルモダリティ感情認識の研究を行い，デュアルモダリティ感情認識のための表情とジェスチャーを組み合わせた方法を提案する．まず，手のひら顔画像を前処理して表現画像とジェスチャ画像を得る．前処理は，PCAアルゴリズムを用いて表情画像を再構成することを含む．第2に，表現画像からのLBP特徴，およびジェスチャ画像からのHuモーメント不変特徴およびフーリエ記述子特徴を抽出する．我々は，SVMとKNNを用いて，表現特徴とジェスチャー特徴のそれぞれを分類認識に粗くする．最後に，本論文で提案した線形加重投票アルゴリズムを用いて表情とジェスチャの分類結果を合わせる．実験結果は，この方法が，自分自身によって確立された二重モダリティの感情データベースにおいて優れた差別を有することを示している．


Facial Expression Recognition Based on asymmrtric region local gradient coding

非対称領域局所勾配符号化に基づく表情認識

顔表情認識は近年盛んに研究されてきたが，多くの問題点が残されている．本稿では，非対称領域局所勾配符号化に基づく表情認識手法を提案する．提案した手法に基づき実験システムを構築し，様々な実験を行なった．実験結果から，提案した手法の有効性を確かめることができた．