Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite ...Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.展开更多
Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So a...Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So affective computing is getting more attention from researchers. In this paper, we propose an amygdala-inspired affective computing framework to realize the recognition of all kinds of human personalized emotions. Similar to the amygdala, the instantaneous emergency emotion is first computed more quickly in a low-redundancy convolutional neural network compressed by pruning and weight sharing with hashing trick. Then, the real-time process emotion is identified more accurately by the memory level neural networks, which is good at handling time-related signals. Finally, the intracranial emotion is recognized in personalized hidden Markov models. We demonstrate on Facial Expression of Emotion Dataset and the recognition accuracy of external emotions(including the emergency emotion and the process emotion) reached 85.72%. And the experimental results proved that the personalized affective model can generate desired intracranial emotions as expected.展开更多
This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of conte...This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of context-aware service during preparation,organization and delivery,as well as the resulting changes in service acceptance of consumers.Because of these changes,the context-aware service modes in the cloud computing environment change are intelligent,immersive,highly interactive,and real-time.According to active and responded service,and authorization and non-authorized service,the paper drew a case diagram of context-aware service in Unified Modeling Language(UML) and established four categories of context-aware service modes.展开更多
Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower ...Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.展开更多
The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solutio...The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.展开更多
Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This pape...Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This paper presents a comparative analysis of different machine learning methods for emotion recognition using EEG data.The objective of this study was to identify the most effective algorithm for accurately classifying emotional states using EEG signals.The EEG brainwave dataset:Feeling emotions dataset was used to evaluate the performance of various machine-learning techniques.Multiple machine learning techniques,namely logistic regression(LR),support vector machine(SVM),Gaussian Naive Bayes(GNB),and decision tree(DT),and ensemble models,namely random forest(RF),AdaBoost,LightGBM,XGBoost,and CatBoost,were trained and evaluated.Five-fold cross-validation and dimension reduction techniques,such as principal component analysis,tdistributed stochastic neighbor embedding,and linear discriminant analysis,were performed for all models.The least-performing model,GNB,showed substantially increased performance after dimension reduction.Performance metrics such as accuracy,precision,recall,F1-score,and receiver operating characteristic curves are employed to assess the effectiveness of each approach.This study focuses on the implications of using various machine learning algorithms for EEG-based emotion recognition.This pursuit can improve our understanding of emotions and their underlying neural mechanisms.展开更多
Smart cars are promising application domain for ubiquitous computing. Context-awareness is the key feature of a smart car for safer and easier driving. Despite many industrial innovations and academic progresses have ...Smart cars are promising application domain for ubiquitous computing. Context-awareness is the key feature of a smart car for safer and easier driving. Despite many industrial innovations and academic progresses have been made, we find a lack of fully context-aware smart cars. This study presents a general architecture of smart cars from the viewpoint of context- awareness. A hierarchical context model is proposed for description of the complex driving environment. A smart car prototype including software platform and hardware infrastructures is built to provide the running environment for the context model and applications. Two performance metrics were evaluated: accuracy of the context situation recognition and efficiency of the smart car. The whole response time of context situation recognition is nearly 1.4 s for one person, which is acceptable for non-time critical applications in a smart car.展开更多
Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Re...Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.展开更多
In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workf...In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workflows in order to get mare large-scale or complicated workflow. They only provide a simple workflow model, not a composite workflow model. In this paper, the autorhs propose a context-aware workflow model to support composite workflows by expanding the patterns of the existing context-aware wrY：flows, which support the basic woddlow patterns. The suggested workflow model of. fers composite workflow patterns for a context-aware workflow, which consists of various flow patterns, such as simple, split, parallel flows, and subflow. With the suggested model, the model can easily reuse few of existing workflows to make a new workflow. As a result, it can save the development efforts and time of context-aware workflows and increase the workflow reusability. Therefore, the suggested model is expected to make it easy to develop applications related to context-aware workflow services on ubiquitous computing environments.展开更多
Objective: Mood Assessment via Animated Characters (MAAC) is a novel, computer-based instrument to improve assessment and communication about feelings in young children with internalizing distress. Well-validated asse...Objective: Mood Assessment via Animated Characters (MAAC) is a novel, computer-based instrument to improve assessment and communication about feelings in young children with internalizing distress. Well-validated assessment instruments are lacking for those under age eight years. Method: Children ages 4 - 10 years with primary diagnosis of anxiety disorder (n = 74;33 boys, 41 girls) or no diagnosis (n = 83;40 boys, 43 girls) completed MAAC for 16 feelings. Those 8 - 10 years also completed standardized measures of internalizing symptoms. Results: MAAC’s emotions clustered into positive, negative, fearful, and calm/neutral factors. Clinical children rated themselves less positive (difference score -3.18;p = 0.002) and less calm/neutral (difference score -2.06;p = 0.04), and explored fewer emotions spontaneously (difference score = -2.37;p = 0.02) than nonanxious controls. Older children’s responses correlated with scores on several standardized measures. Conclusions: MAAC appears to be highly engaging, with clinical utility in the assessment of young anxious children. Applications in other populations are considered for future study.展开更多
Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scala...Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scalability enables access to unlimited resources for mobile devices, so more studies have focused on cloud computingbased mobile services. Due to the stability of wireless networks, changes of Quality of Service （QoS） level and user＇ real-time preferences, it is becoming challenging to determine how to adaptively choose the ＂appropriate＂ service in mobile cloud computing environments. In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service＇s evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive imps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experiment and simulation demonstrate that our approach is effective.展开更多
The development of ubiquitous computing systems benefits tremendously from the service-oriented computing concept in seamless interoperation of heterogeneous devices. However, architectures, services interfaces and ne...The development of ubiquitous computing systems benefits tremendously from the service-oriented computing concept in seamless interoperation of heterogeneous devices. However, architectures, services interfaces and network implementation of the existing service-oriented systems differ case by case. Furthermore, many systems lack the capability of being applied to resource constrained devices, for example, sensors. Therefore, we propose a standardized approach to present a service to the network and to access a networked service, which can be adopted by arbitrary types of devices. In this approach, services are specified and exposed through a set of standardized interfaces. Moreover, a virtual community concept is introduced to determine a secure boundary within which services can be freely discovered, accessed and composed into applications;a hierarchical management scheme is presented which enables the third party management of services and their underlying resources. In this way, application control logic goes into the network and environment context is dealt with intelligently by the system. A prototype system is developed to validate our ideas. Results show the feasibility of this open distributed system software architecture.展开更多
Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluatio...Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluation,which is applicable in many real-world applications such as healthcare,call centers,robotics,safety,and virtual reality.This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state.The authors designed a Temporal Convolutional Network(TCN)core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification.The proposed network extracts valid sequential cues automatically from speech signals,which performed better than state-of-the-art(SOTA)and traditional machine learning algorithms.Results of the proposed method show a high recognition rate compared with SOTAmethods.The final unweighted accuracy of 80.84%,and 92.31%,for interactive emotional dyadic motion captures(IEMOCAP)and berlin emotional dataset(EMO-DB),indicate the robustness and efficiency of the designed model.展开更多
In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The mi...In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The middleware,which offers services in such environments,should support the automation services suited for the user using various types of situational information around the user.In this paper,based on context-aware workflow language(CAWL),we propose a CAWL based composite workflow handler for supporting composite workflow services,which can integrate more than two service flows and handle them.The test results shows that the proposed CAWL handler can provide the user with the composite workflow services to cope with various demands on a basis of a scenario document founded on CAWL.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
With the rapid development of Ubiquitous computing,context-aware technology as one of the core contents of the former has made a series of research progress and achievements.Context-aware technology can automatically ...With the rapid development of Ubiquitous computing,context-aware technology as one of the core contents of the former has made a series of research progress and achievements.Context-aware technology can automatically provide the corresponding services according to the strategy given by the inference engine through sensing the related context of the environment and tasks.It is because context-aware technology can significantly improve the intelligence of computer interaction,the application of this technology to“Intelligent community”which is a new concept built on the highly developed Internet and sensor networks has become more promising and meaningful.As an important part of the intelligent community,the technology is also of great use for the disabled health service.Based on the research of the theory of context-aware technology and the examples of international development applications,this paper designs an ontology based context-aware system framework named CADHS for disabled health service.The hierarchy based framework uses ontology to standardize and formalize context information and complete context modeling.This framework includes the collection and encapsulation of the original context information and constructs a formal reasoning model.Moreover,the framework provides query and subscription services as the external interface of the system.On the basis of this framework,the DHS-Protosystem(Disabled Health Service Protosystem)is designed and implemented.This paper introduces implementation of query and subscription service module.展开更多
Nowadays, many works are interested in adapting to the context without taking into account neither the responsiveness to adapt their solution, nor the ability of designers to model all the relevant concerns. Our paper...Nowadays, many works are interested in adapting to the context without taking into account neither the responsiveness to adapt their solution, nor the ability of designers to model all the relevant concerns. Our paper provides a new architecture for context management that tries to solve both problems. This approach is also based on the analysis and synthesis of context-aware frameworks proposed in literature. Our solution is focus on a separation of contextual concerns at the design phase and preserves it as much as possible at runtime. For this, we introduce the notion of independent views that allow designers to focus on their domain of expertise. At runtime, the architecture is splitted in 2 independent levels of adaptation. The highest is in charge of current context identification and manages each view independently. The lowest handles the adaptation of the application according to the rules granted by the previous level.展开更多
Humans,as intricate beings driven by a multitude of emotions,possess a remarkable ability to decipher and respond to socio-affective cues.However,many individuals and machines struggle to interpret such nuanced signal...Humans,as intricate beings driven by a multitude of emotions,possess a remarkable ability to decipher and respond to socio-affective cues.However,many individuals and machines struggle to interpret such nuanced signals,including variations in tone of voice.This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations.In particular,the authors propose a real-time processing method that captures and evaluates emotions in speech,utilizing a terminal device like the Raspberry Pi computer.Furthermore,the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology,which involves analyzing audio files from renowned emotional speech databases.To aid incomprehension,the authors present visualizations of these audio files in situ,employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib.The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions.Notably,the classification accuracies achieved are 70% and 77%,respectively,demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server.The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker’s emotional state in less than one second on a Raspberry Pi.These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains.展开更多
基金supported by the Major Research Instrument Development Project of the National Natural Science Foundation of China(82327810)the Foundation of the President of Hebei University(XZJJ202202)the Hebei Province“333 talent project”(A202101058).
文摘Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
基金supported by National Key R&D Program of China, No. 2018YFB1003905Natural Science Foundation of China, No.61873026the Fundamental Research Funds for the Central Universities, No.FRFTP-18-008A3
文摘Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So affective computing is getting more attention from researchers. In this paper, we propose an amygdala-inspired affective computing framework to realize the recognition of all kinds of human personalized emotions. Similar to the amygdala, the instantaneous emergency emotion is first computed more quickly in a low-redundancy convolutional neural network compressed by pruning and weight sharing with hashing trick. Then, the real-time process emotion is identified more accurately by the memory level neural networks, which is good at handling time-related signals. Finally, the intracranial emotion is recognized in personalized hidden Markov models. We demonstrate on Facial Expression of Emotion Dataset and the recognition accuracy of external emotions(including the emergency emotion and the process emotion) reached 85.72%. And the experimental results proved that the personalized affective model can generate desired intracranial emotions as expected.
基金the National Key Basic Research Program of China，the National Natural Science Foundation of China，the Ministry of Education of the People's Republic of China，the Fundamental Research Funds for the Central Universities of China
文摘This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of context-aware service during preparation,organization and delivery,as well as the resulting changes in service acceptance of consumers.Because of these changes,the context-aware service modes in the cloud computing environment change are intelligent,immersive,highly interactive,and real-time.According to active and responded service,and authorization and non-authorized service,the paper drew a case diagram of context-aware service in Unified Modeling Language(UML) and established four categories of context-aware service modes.
文摘Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.
文摘The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
文摘Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This paper presents a comparative analysis of different machine learning methods for emotion recognition using EEG data.The objective of this study was to identify the most effective algorithm for accurately classifying emotional states using EEG signals.The EEG brainwave dataset:Feeling emotions dataset was used to evaluate the performance of various machine-learning techniques.Multiple machine learning techniques,namely logistic regression(LR),support vector machine(SVM),Gaussian Naive Bayes(GNB),and decision tree(DT),and ensemble models,namely random forest(RF),AdaBoost,LightGBM,XGBoost,and CatBoost,were trained and evaluated.Five-fold cross-validation and dimension reduction techniques,such as principal component analysis,tdistributed stochastic neighbor embedding,and linear discriminant analysis,were performed for all models.The least-performing model,GNB,showed substantially increased performance after dimension reduction.Performance metrics such as accuracy,precision,recall,F1-score,and receiver operating characteristic curves are employed to assess the effectiveness of each approach.This study focuses on the implications of using various machine learning algorithms for EEG-based emotion recognition.This pursuit can improve our understanding of emotions and their underlying neural mechanisms.
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (Nos. 2006AA01Z198, and2008AA01Z132)the National Natural Science Foundation of China(No. 60533040)the National Science Fund for Distinguished Young Scholars of China (No. 60525202)
文摘Smart cars are promising application domain for ubiquitous computing. Context-awareness is the key feature of a smart car for safer and easier driving. Despite many industrial innovations and academic progresses have been made, we find a lack of fully context-aware smart cars. This study presents a general architecture of smart cars from the viewpoint of context- awareness. A hierarchical context model is proposed for description of the complex driving environment. A smart car prototype including software platform and hardware infrastructures is built to provide the running environment for the context model and applications. Two performance metrics were evaluated: accuracy of the context situation recognition and efficiency of the smart car. The whole response time of context situation recognition is nearly 1.4 s for one person, which is acceptable for non-time critical applications in a smart car.
基金supported by the National Research Foundation of Korea funded by the Korean Government through the Ministry of Science and ICT under Grant NRF-2020R1F1A1060659 and in part by the 2020 Faculty Research Fund of Sejong University。
文摘Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.
基金supported by the The Ministry of Knowledge Economy,Korea,the ITRC(Information Technology Research Center)support program(ⅡTA-2009-(C1090-0902-0007))
文摘In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workflows in order to get mare large-scale or complicated workflow. They only provide a simple workflow model, not a composite workflow model. In this paper, the autorhs propose a context-aware workflow model to support composite workflows by expanding the patterns of the existing context-aware wrY：flows, which support the basic woddlow patterns. The suggested workflow model of. fers composite workflow patterns for a context-aware workflow, which consists of various flow patterns, such as simple, split, parallel flows, and subflow. With the suggested model, the model can easily reuse few of existing workflows to make a new workflow. As a result, it can save the development efforts and time of context-aware workflows and increase the workflow reusability. Therefore, the suggested model is expected to make it easy to develop applications related to context-aware workflow services on ubiquitous computing environments.
文摘Objective: Mood Assessment via Animated Characters (MAAC) is a novel, computer-based instrument to improve assessment and communication about feelings in young children with internalizing distress. Well-validated assessment instruments are lacking for those under age eight years. Method: Children ages 4 - 10 years with primary diagnosis of anxiety disorder (n = 74;33 boys, 41 girls) or no diagnosis (n = 83;40 boys, 43 girls) completed MAAC for 16 feelings. Those 8 - 10 years also completed standardized measures of internalizing symptoms. Results: MAAC’s emotions clustered into positive, negative, fearful, and calm/neutral factors. Clinical children rated themselves less positive (difference score -3.18;p = 0.002) and less calm/neutral (difference score -2.06;p = 0.04), and explored fewer emotions spontaneously (difference score = -2.37;p = 0.02) than nonanxious controls. Older children’s responses correlated with scores on several standardized measures. Conclusions: MAAC appears to be highly engaging, with clinical utility in the assessment of young anxious children. Applications in other populations are considered for future study.
基金the third level of 2011 Zhejiang Province 151 Talent Project and National Natural Science Foundation of China under Grant No.61100043
文摘Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scalability enables access to unlimited resources for mobile devices, so more studies have focused on cloud computingbased mobile services. Due to the stability of wireless networks, changes of Quality of Service （QoS） level and user＇ real-time preferences, it is becoming challenging to determine how to adaptively choose the ＂appropriate＂ service in mobile cloud computing environments. In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service＇s evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive imps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experiment and simulation demonstrate that our approach is effective.
文摘The development of ubiquitous computing systems benefits tremendously from the service-oriented computing concept in seamless interoperation of heterogeneous devices. However, architectures, services interfaces and network implementation of the existing service-oriented systems differ case by case. Furthermore, many systems lack the capability of being applied to resource constrained devices, for example, sensors. Therefore, we propose a standardized approach to present a service to the network and to access a networked service, which can be adopted by arbitrary types of devices. In this approach, services are specified and exposed through a set of standardized interfaces. Moreover, a virtual community concept is introduced to determine a secure boundary within which services can be freely discovered, accessed and composed into applications;a hierarchical management scheme is presented which enables the third party management of services and their underlying resources. In this way, application control logic goes into the network and environment context is dealt with intelligently by the system. A prototype system is developed to validate our ideas. Results show the feasibility of this open distributed system software architecture.
文摘Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluation,which is applicable in many real-world applications such as healthcare,call centers,robotics,safety,and virtual reality.This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state.The authors designed a Temporal Convolutional Network(TCN)core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification.The proposed network extracts valid sequential cues automatically from speech signals,which performed better than state-of-the-art(SOTA)and traditional machine learning algorithms.Results of the proposed method show a high recognition rate compared with SOTAmethods.The final unweighted accuracy of 80.84%,and 92.31%,for interactive emotional dyadic motion captures(IEMOCAP)and berlin emotional dataset(EMO-DB),indicate the robustness and efficiency of the designed model.
基金The MSIP(Ministry of Science,ICT&Future Planning),Korea,under the ITRC(Information Technology Research Center)support program(NIPA-2013-H0301-13-2006)supervised by the NIPA(National IT Industry Promotion Agency)
文摘In distributed computing environment,workflow technologies have been continuously developed.Recently,there is an attempt to apply these technologies to context-aware services in ubiquitous computing environment.The middleware,which offers services in such environments,should support the automation services suited for the user using various types of situational information around the user.In this paper,based on context-aware workflow language(CAWL),we propose a CAWL based composite workflow handler for supporting composite workflow services,which can integrate more than two service flows and handle them.The test results shows that the proposed CAWL handler can provide the user with the composite workflow services to cope with various demands on a basis of a scenario document founded on CAWL.
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.
文摘With the rapid development of Ubiquitous computing,context-aware technology as one of the core contents of the former has made a series of research progress and achievements.Context-aware technology can automatically provide the corresponding services according to the strategy given by the inference engine through sensing the related context of the environment and tasks.It is because context-aware technology can significantly improve the intelligence of computer interaction,the application of this technology to“Intelligent community”which is a new concept built on the highly developed Internet and sensor networks has become more promising and meaningful.As an important part of the intelligent community,the technology is also of great use for the disabled health service.Based on the research of the theory of context-aware technology and the examples of international development applications,this paper designs an ontology based context-aware system framework named CADHS for disabled health service.The hierarchy based framework uses ontology to standardize and formalize context information and complete context modeling.This framework includes the collection and encapsulation of the original context information and constructs a formal reasoning model.Moreover,the framework provides query and subscription services as the external interface of the system.On the basis of this framework,the DHS-Protosystem(Disabled Health Service Protosystem)is designed and implemented.This paper introduces implementation of query and subscription service module.
基金the U-Insither Project(collaborative project between the Universite Nice Sophia Antipolis and EDF R&D/STREP).
文摘Nowadays, many works are interested in adapting to the context without taking into account neither the responsiveness to adapt their solution, nor the ability of designers to model all the relevant concerns. Our paper provides a new architecture for context management that tries to solve both problems. This approach is also based on the analysis and synthesis of context-aware frameworks proposed in literature. Our solution is focus on a separation of contextual concerns at the design phase and preserves it as much as possible at runtime. For this, we introduce the notion of independent views that allow designers to focus on their domain of expertise. At runtime, the architecture is splitted in 2 independent levels of adaptation. The highest is in charge of current context identification and manages each view independently. The lowest handles the adaptation of the application according to the rules granted by the previous level.
文摘Humans,as intricate beings driven by a multitude of emotions,possess a remarkable ability to decipher and respond to socio-affective cues.However,many individuals and machines struggle to interpret such nuanced signals,including variations in tone of voice.This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations.In particular,the authors propose a real-time processing method that captures and evaluates emotions in speech,utilizing a terminal device like the Raspberry Pi computer.Furthermore,the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology,which involves analyzing audio files from renowned emotional speech databases.To aid incomprehension,the authors present visualizations of these audio files in situ,employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib.The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions.Notably,the classification accuracies achieved are 70% and 77%,respectively,demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server.The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker’s emotional state in less than one second on a Raspberry Pi.These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains.
