Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without an...Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.展开更多
With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online learning.Most of the current research uses ...With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online learning.Most of the current research uses the comments of the learning platform or the learner’s expression for emotion recognition.The research data on other modalities are scarce.Most of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on data.Because of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning scenarios.The data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they watched.For the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is proposed.This method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological signals.This method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow features.The model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep features.Then,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based features.Knowledge-based features are used to supplement and enhance the deep features of physiological signals.Finally,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,respectively.On the four public data sets,our proposed method also achieves better results compared with the two recent researches.The experiment results show that the proposed multimodal emotion recognition method based on knowledge enhancement has good performance and robustness.展开更多
BACKGROUND With an estimated 121 million abortions following unwanted pregnancies occurring worldwide each year,many countries are now committed to protecting women’s reproductive rights.AIM To analyze the impact of ...BACKGROUND With an estimated 121 million abortions following unwanted pregnancies occurring worldwide each year,many countries are now committed to protecting women’s reproductive rights.AIM To analyze the impact of emotional management and care on anxiety and contraceptive knowledge mastery in painless induced abortion(IA)patients.METHODS This study was retrospective analysis of 84 patients with IA at our hospital.According to different nursing methods,the patients were divided into a control group and an observation group,with 42 cases in each group.Degree of pain,rate of postoperative uterine relaxation,surgical bleeding volume,and postoperative bleeding volume at 1 h between the two groups of patients;nursing satisfaction;and mastery of contraceptive knowledge were analyzed.RESULTS After nursing,Self-Assessment Scale,Depression Self-Assessment Scale,and Hamilton Anxiety Scale scores were 39.18±2.18,30.27±2.64,6.69±2.15,respectively,vs 45.63±2.66,38.61±2.17,13.45±2.12,respectively,with the observation group being lower than the control group(P<0.05).Comparing visual analog scales,the observation group was lower than the control group(4.55±0.22 vs 3.23±0.41;P<0.05).The relaxation rate of the cervix after nursing,surgical bleeding volume,and 1-h postoperative bleeding volumes were 25(59.5),31.72±2.23,and 22.41±1.23,respectively,vs 36(85.7),42.39±3.53,28.51±3.34,respec tively,for the observation group compared to the control group.The observation group had a better nursing situation(P<0.05),and higher nursing satisfaction and contraceptive knowledge mastery scores compared to the control group(P<0.05).CONCLUSION The application of emotional management in postoperative care of IA has an ideal effect.展开更多
The evolution of the society and economy has stimulated the development of Knowledge Service(KS), making it an indispensable solution to address future challenges facing libraries and information institutions. However...The evolution of the society and economy has stimulated the development of Knowledge Service(KS), making it an indispensable solution to address future challenges facing libraries and information institutions. However at present, academic research on knowledge service is falling short and its definition is far from clear and complete. As such,this article proposes the Three-dimensional Framework Knowledge Service(TdFKS) for libraries and information institutions based on the knowledge value chain model. By making reliability analysis and mean value analysis of a questionnaire survey result, the article clarifies the structure of the three-dimensional framework and verifies the rationality of the TdFKS.展开更多
People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language....People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.展开更多
The excessive use of groundwater resources has created numerous environmental consequences in Iran. Many water experts believe that this crisis can be overcome by fostering sustainable environmental behavior in the ut...The excessive use of groundwater resources has created numerous environmental consequences in Iran. Many water experts believe that this crisis can be overcome by fostering sustainable environmental behavior in the utilization of groundwater resources and increasing the farmers' environmental knowledge, attitude and emotions. The objective of this study was to investigate transformation of en-vironmental knowledge to sustainable use of groundwater resources through the analysis of the med-iating role of environmental emotions in Iran's agriculture. This research was carried out via a survey technique within the category of descriptive-correlation and causal-relational research. All the wheat producing farmers of Sistan and Baluchestan Province, which is a clear example of critical conditions for groundwater resources in Iran (N=168,873), constituted the statistical population of the study of whom 384 participants were selected using a stratified random sampling method. The research instrument was a questionnaire whose validity was confirmed by a panel of professionals in agricultural extension, education and water management. The reliability of the items of the questionnaire was also evaluated via a pilot study and Cronbach's alpha (0.70≤α≤0.84). The results of the causal analysis indicated that environmental knowledge (β=0.309) and environmental emotions (β=0.565) have the significant in-fluence on sustainable environmental behavior in the utilization of groundwater among wheat farmers. Therefore, it can be said environmental emotions is an important mediating factor for potentially im-proving water stakeholders' sustainable environmental behavior.展开更多
With the arrival of the information age, research activities focused on the practice and approaches of knowledge services are on a marked increase as evidenced in the publications of social sciences. According to a so...With the arrival of the information age, research activities focused on the practice and approaches of knowledge services are on a marked increase as evidenced in the publications of social sciences. According to a social network analysis on knowledge service related literature, it reveals that information and knowledge workers often fail to take such an important element as the functional role of an emotive engagement into consideration in their study of knowledge services. It has increasingly become an issue of high profile with the rapid development of digital libraries and their web-based knowledge services in China and anywhere else in the world. In order to have a clearer understanding about issues involved in knowledge servicing so as to maximize the effectiveness and efficiency of digital libraries in their knowledge service performance, the author has conducted surveys for seven times on the online information seeking behavior of graduate students at the Chinese Academy of Sciences with such research methods as questionnaires, interviews and natural observations during September 2006-June 2009. The research result has showed the emotive element has an important role in the user's information seeking behavior and in knowledge services practice. Therefore, knowledge services rendered may be more effective by adding the emotiveness-oriented communication element into such practice. This paper recommends that such an emotiveness-oriented communication approach should be carefully studied and factored into libraries' knowledge services practice.展开更多
Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.展开更多
Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion syste...Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.展开更多
In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunc...In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.展开更多
When the classical constant false-alarm rate (CFAR) combined with fuzzy C-means (FCM) algorithm is applied to target detection in synthetic aperture radar (SAR) images with complex background, CFAR requires bloc...When the classical constant false-alarm rate (CFAR) combined with fuzzy C-means (FCM) algorithm is applied to target detection in synthetic aperture radar (SAR) images with complex background, CFAR requires block-by-block estimation of clutter models and FCM clustering converges to local optimum. To address these problems, this paper pro-poses a new detection algorithm: knowledge-based combined with improved genetic algorithm-fuzzy C-means (GA-FCM) algorithm. Firstly, the algorithm takes target region's maximum and average intensity, area, length of long axis and long-to-short axis ratio of the external ellipse as factors which influence the target appearing probabil- ity. The knowledge-based detection algorithm can produce preprocess results without the need of estimation of clutter models as CFAR does. Afterward the GA-FCM algorithm is improved to cluster pre-process results. It has advantages of incorporating global optimizing ability of GA and local optimizing ability of FCM, which will further eliminate false alarms and get better results. The effectiveness of the proposed technique is experimentally validated with real SAR images.展开更多
In the new era a new more effective family education model can ease unbalanced and inadequate education development.In the new era,parents look forward to a more personalized,active and interactive form of family educ...In the new era a new more effective family education model can ease unbalanced and inadequate education development.In the new era,parents look forward to a more personalized,active and interactive form of family education guidance.This new guidance model will start with improving behavior by focusing on emotional behavior and cognition.The Roast can ease parents'anxiety,lectures and reading groups can supplement parents'knowledge,and mutual discussion,vicarious practice and teaching others can solidify a scientific and effective family education behavior.The family education guidance model of"guiding first,then supplementing and further solidifying"focuses on the participation of parents in the whole process,which can improve the effectiveness of family education guidance.展开更多
Media discourse in the context of intercultural communications is an important channel that countries and cultures use to communicate. It is also a process of meaning interpretation and knowledge production, which exe...Media discourse in the context of intercultural communications is an important channel that countries and cultures use to communicate. It is also a process of meaning interpretation and knowledge production, which exerts a great impact on the establishment of the world's cultural order. This paper discusses media discourse in intercultural communications theoretically from the perspective of knowledge production, media dialogue and meaning construction. It is suggested that an effective ideographic mechanism be developed and improved, and the essential meaning of Chinese culture be initiatively exported and integrated into a knowledge system of cognition and understanding about the world to promote the understanding and exchange between China and other countries and to help create an equal and reasonable world cultural order.展开更多
Reminiscing by older adults can facilitate beneficial outcomes through the preparation for the end of life,the cohesiveness of life narratives,and creation of life meanings.Given this,and the historical challenges of ...Reminiscing by older adults can facilitate beneficial outcomes through the preparation for the end of life,the cohesiveness of life narratives,and creation of life meanings.Given this,and the historical challenges of communication between generations,the objective of this study was two-fold:(1)to harness the beneficial role reminiscence can play in the mental health of older adults;(2)to facilitate generational learning by documenting and thematically analyzing the experiences and knowledge of older adults.We hypothesized that our interviews,which had the stated goal of helping younger people navigate life challenges,would not only act as catalyst for the participants to reminisce but also create a corpus of knowledge which could be later distilled into accessible“pearls of wisdom”.The interviews were conducted in Israel with 102 participants who were between 60 and 93 years of age with six questions constructed to promote further commentary.Through the interviews we were successful in producing a large representation of the older adults’experiences and what they believed would be beneficial for the younger generation.Due to the potential benefits for participants and larger communities we recommend this approach be adopted for future studies.展开更多
In the paper, original formal-logical conception of syntactic and semantic: intensional and extensional senses of expressions of any language L is outlined. Syntax and bi-level intensional and extensional semantics o...In the paper, original formal-logical conception of syntactic and semantic: intensional and extensional senses of expressions of any language L is outlined. Syntax and bi-level intensional and extensional semantics of language L are characterized categorically: in the spirit of some Husserl's ideas of pure grammar, Le^niewski-Ajukiewicz's theory syntactic/semantic categories and in accordance with Frege's ontological canons, Bochefiski's famous motto--syntax mirrors ontology and some ideas of Suszko: language should be a linguistic scheme of ontological reality and simultaneously a tool of its cognition. In the logical conception of language L, its expressions should satisfy some general conditions of language adequacy. The adequacy ensures their unambiguous syntactic and semantic senses and mutual, syntactic, and semantic compatibility, correspondence guaranteed by the acceptance of a postulate of categorial compatibility syntactic and semantic (extensional and intensional) categories of expressions of L. From this postulate, three principles of compositionality follow: one syntactic and two semantic already known to Frege. They are treated as conditions of homomorphism partial algebra of L into algebraic models of L: syntactic, intensional, and extensional. In the paper, they are applied to some expressions with quantifiers. Language adequacy connected with the logical senses described in the logical conception of language L is, of course, an idealization, but only expressions with high degrees of precision of their senses, after due justification, may become theorems of science.展开更多
A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorith...A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorithm is adopted to divide the emotion space. Gaussian mixture model (GMM) is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A (Velance-Arousal) emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.展开更多
基金National Natural Science Foundation of China,Grant/Award Numbers:61671064,61732005National Key Research&Development Program,Grant/Award Number:2018YFC0831700。
文摘Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.
基金supported by the National Science Foundation of China (Grant Nos.62267001,61906051)。
文摘With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online learning.Most of the current research uses the comments of the learning platform or the learner’s expression for emotion recognition.The research data on other modalities are scarce.Most of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on data.Because of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning scenarios.The data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they watched.For the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is proposed.This method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological signals.This method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow features.The model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep features.Then,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based features.Knowledge-based features are used to supplement and enhance the deep features of physiological signals.Finally,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,respectively.On the four public data sets,our proposed method also achieves better results compared with the two recent researches.The experiment results show that the proposed multimodal emotion recognition method based on knowledge enhancement has good performance and robustness.
基金The study was reviewed and approved by Wuhan Maternal and Child Healthcare Hospital(Approval No.2024-013).
文摘BACKGROUND With an estimated 121 million abortions following unwanted pregnancies occurring worldwide each year,many countries are now committed to protecting women’s reproductive rights.AIM To analyze the impact of emotional management and care on anxiety and contraceptive knowledge mastery in painless induced abortion(IA)patients.METHODS This study was retrospective analysis of 84 patients with IA at our hospital.According to different nursing methods,the patients were divided into a control group and an observation group,with 42 cases in each group.Degree of pain,rate of postoperative uterine relaxation,surgical bleeding volume,and postoperative bleeding volume at 1 h between the two groups of patients;nursing satisfaction;and mastery of contraceptive knowledge were analyzed.RESULTS After nursing,Self-Assessment Scale,Depression Self-Assessment Scale,and Hamilton Anxiety Scale scores were 39.18±2.18,30.27±2.64,6.69±2.15,respectively,vs 45.63±2.66,38.61±2.17,13.45±2.12,respectively,with the observation group being lower than the control group(P<0.05).Comparing visual analog scales,the observation group was lower than the control group(4.55±0.22 vs 3.23±0.41;P<0.05).The relaxation rate of the cervix after nursing,surgical bleeding volume,and 1-h postoperative bleeding volumes were 25(59.5),31.72±2.23,and 22.41±1.23,respectively,vs 36(85.7),42.39±3.53,28.51±3.34,respec tively,for the observation group compared to the control group.The observation group had a better nursing situation(P<0.05),and higher nursing satisfaction and contraceptive knowledge mastery scores compared to the control group(P<0.05).CONCLUSION The application of emotional management in postoperative care of IA has an ideal effect.
基金supported by the National Planning Office of Philosophy and Social Science(Grant No.06BTQ027)
文摘The evolution of the society and economy has stimulated the development of Knowledge Service(KS), making it an indispensable solution to address future challenges facing libraries and information institutions. However at present, academic research on knowledge service is falling short and its definition is far from clear and complete. As such,this article proposes the Three-dimensional Framework Knowledge Service(TdFKS) for libraries and information institutions based on the knowledge value chain model. By making reliability analysis and mean value analysis of a questionnaire survey result, the article clarifies the structure of the three-dimensional framework and verifies the rationality of the TdFKS.
基金supported by Basic Science Research Program through the NRF(National Research Foundation of Korea)the MSIT(Ministry of Science and ICT),Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)and the Gachon University research fund of 2019(Nos.NRF2019R1A2C1008412,2015-0-00932,GCU-2019-0773).
文摘People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.
文摘The excessive use of groundwater resources has created numerous environmental consequences in Iran. Many water experts believe that this crisis can be overcome by fostering sustainable environmental behavior in the utilization of groundwater resources and increasing the farmers' environmental knowledge, attitude and emotions. The objective of this study was to investigate transformation of en-vironmental knowledge to sustainable use of groundwater resources through the analysis of the med-iating role of environmental emotions in Iran's agriculture. This research was carried out via a survey technique within the category of descriptive-correlation and causal-relational research. All the wheat producing farmers of Sistan and Baluchestan Province, which is a clear example of critical conditions for groundwater resources in Iran (N=168,873), constituted the statistical population of the study of whom 384 participants were selected using a stratified random sampling method. The research instrument was a questionnaire whose validity was confirmed by a panel of professionals in agricultural extension, education and water management. The reliability of the items of the questionnaire was also evaluated via a pilot study and Cronbach's alpha (0.70≤α≤0.84). The results of the causal analysis indicated that environmental knowledge (β=0.309) and environmental emotions (β=0.565) have the significant in-fluence on sustainable environmental behavior in the utilization of groundwater among wheat farmers. Therefore, it can be said environmental emotions is an important mediating factor for potentially im-proving water stakeholders' sustainable environmental behavior.
文摘With the arrival of the information age, research activities focused on the practice and approaches of knowledge services are on a marked increase as evidenced in the publications of social sciences. According to a social network analysis on knowledge service related literature, it reveals that information and knowledge workers often fail to take such an important element as the functional role of an emotive engagement into consideration in their study of knowledge services. It has increasingly become an issue of high profile with the rapid development of digital libraries and their web-based knowledge services in China and anywhere else in the world. In order to have a clearer understanding about issues involved in knowledge servicing so as to maximize the effectiveness and efficiency of digital libraries in their knowledge service performance, the author has conducted surveys for seven times on the online information seeking behavior of graduate students at the Chinese Academy of Sciences with such research methods as questionnaires, interviews and natural observations during September 2006-June 2009. The research result has showed the emotive element has an important role in the user's information seeking behavior and in knowledge services practice. Therefore, knowledge services rendered may be more effective by adding the emotiveness-oriented communication element into such practice. This paper recommends that such an emotiveness-oriented communication approach should be carefully studied and factored into libraries' knowledge services practice.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
文摘Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.
文摘In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.
基金supported by the National Natural Science Foundation of China(6107113961171122)+1 种基金the Fundamental Research Funds for the Central Universities"New Star in Blue Sky" Program Foundation the Foundation of ATR Key Lab
文摘When the classical constant false-alarm rate (CFAR) combined with fuzzy C-means (FCM) algorithm is applied to target detection in synthetic aperture radar (SAR) images with complex background, CFAR requires block-by-block estimation of clutter models and FCM clustering converges to local optimum. To address these problems, this paper pro-poses a new detection algorithm: knowledge-based combined with improved genetic algorithm-fuzzy C-means (GA-FCM) algorithm. Firstly, the algorithm takes target region's maximum and average intensity, area, length of long axis and long-to-short axis ratio of the external ellipse as factors which influence the target appearing probabil- ity. The knowledge-based detection algorithm can produce preprocess results without the need of estimation of clutter models as CFAR does. Afterward the GA-FCM algorithm is improved to cluster pre-process results. It has advantages of incorporating global optimizing ability of GA and local optimizing ability of FCM, which will further eliminate false alarms and get better results. The effectiveness of the proposed technique is experimentally validated with real SAR images.
文摘In the new era a new more effective family education model can ease unbalanced and inadequate education development.In the new era,parents look forward to a more personalized,active and interactive form of family education guidance.This new guidance model will start with improving behavior by focusing on emotional behavior and cognition.The Roast can ease parents'anxiety,lectures and reading groups can supplement parents'knowledge,and mutual discussion,vicarious practice and teaching others can solidify a scientific and effective family education behavior.The family education guidance model of"guiding first,then supplementing and further solidifying"focuses on the participation of parents in the whole process,which can improve the effectiveness of family education guidance.
文摘Media discourse in the context of intercultural communications is an important channel that countries and cultures use to communicate. It is also a process of meaning interpretation and knowledge production, which exerts a great impact on the establishment of the world's cultural order. This paper discusses media discourse in intercultural communications theoretically from the perspective of knowledge production, media dialogue and meaning construction. It is suggested that an effective ideographic mechanism be developed and improved, and the essential meaning of Chinese culture be initiatively exported and integrated into a knowledge system of cognition and understanding about the world to promote the understanding and exchange between China and other countries and to help create an equal and reasonable world cultural order.
文摘Reminiscing by older adults can facilitate beneficial outcomes through the preparation for the end of life,the cohesiveness of life narratives,and creation of life meanings.Given this,and the historical challenges of communication between generations,the objective of this study was two-fold:(1)to harness the beneficial role reminiscence can play in the mental health of older adults;(2)to facilitate generational learning by documenting and thematically analyzing the experiences and knowledge of older adults.We hypothesized that our interviews,which had the stated goal of helping younger people navigate life challenges,would not only act as catalyst for the participants to reminisce but also create a corpus of knowledge which could be later distilled into accessible“pearls of wisdom”.The interviews were conducted in Israel with 102 participants who were between 60 and 93 years of age with six questions constructed to promote further commentary.Through the interviews we were successful in producing a large representation of the older adults’experiences and what they believed would be beneficial for the younger generation.Due to the potential benefits for participants and larger communities we recommend this approach be adopted for future studies.
文摘In the paper, original formal-logical conception of syntactic and semantic: intensional and extensional senses of expressions of any language L is outlined. Syntax and bi-level intensional and extensional semantics of language L are characterized categorically: in the spirit of some Husserl's ideas of pure grammar, Le^niewski-Ajukiewicz's theory syntactic/semantic categories and in accordance with Frege's ontological canons, Bochefiski's famous motto--syntax mirrors ontology and some ideas of Suszko: language should be a linguistic scheme of ontological reality and simultaneously a tool of its cognition. In the logical conception of language L, its expressions should satisfy some general conditions of language adequacy. The adequacy ensures their unambiguous syntactic and semantic senses and mutual, syntactic, and semantic compatibility, correspondence guaranteed by the acceptance of a postulate of categorial compatibility syntactic and semantic (extensional and intensional) categories of expressions of L. From this postulate, three principles of compositionality follow: one syntactic and two semantic already known to Frege. They are treated as conditions of homomorphism partial algebra of L into algebraic models of L: syntactic, intensional, and extensional. In the paper, they are applied to some expressions with quantifiers. Language adequacy connected with the logical senses described in the logical conception of language L is, of course, an idealization, but only expressions with high degrees of precision of their senses, after due justification, may become theorems of science.
基金Supported by the National Natural Science Foundation of China (60703049)the "Chenguang" Foundation for Young Scientists (200850731353)the National Postdoctoral Foundation of China (20060400847)
文摘A personalized emotion space is proposed to bridge the "affective gap" in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean (FCM) clustering algorithm is adopted to divide the emotion space. Gaussian mixture model (GMM) is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A (Velance-Arousal) emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.