Amygdala-Inspired Affective Computing: to Realize Personalized Intracranial Emotions with Accurately Observed External Emotions

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.

Context-Aware Service IVlodes in the Cloud Computing Environment

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.

Context-aware computing-based reducing cost of service method in resource discovery and interaction

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.

Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum/Soft Computing Approach

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.

1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

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.

Context-aware Workflow Model for Supporting Composite Workflows

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.

Mood assessment via animated characters: An instrument to access and evaluate emotions in young children

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.

Adaptive Service Selection Method in Mobile Cloud Computing

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.

A Generic Service Architecture for Secure Ubiquitous Computing Systems

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.

TC-Net:A Modest&Lightweight Emotion Recognition System Using Temporal Convolution Network

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.

A CAWL handler for context-aware composite workflow services

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.

Context-Aware Technology of Disabled Health Service for Intelligent Community

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.

Intermediate Common Model—The Solution to Separate Concerns and Responsiveness in Dynamic Context-Aware System

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.

Enhancing Human-Machine Interaction:Real-Time Emotion Recognition through Speech Analysis

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.

Image acquisition system for agricultural context-aware computing

Context-aware computing is a new mode originated from ubiquitous computing.Its emergence brings a substantial change to traditional computing and related service.Image is a pervasive tool for context awareness.A large number of applications are developed based on images analysis.In this paper,an image acquisition system is presented for agricultural context-aware computing.The potential use of the system includes production evaluation,precise management and assistant control.The system includes four modules:the camera system,the control system,mechanism,and communication.The system can be easily installed in target crop fields.The camera system is composed of a binocular stereo camera and a color camera.Two cubic images and a corresponding texture image are collected for each plant in the process of data acquisition.An accessorial software system is developed to control and manage the capture system.Experiments show that the presented system is effective for image acquisition of agricultural context-aware computing.

情感对话技术综述

情感对话技术着眼于对话机器人的“情商”,旨在让对话机器人具有像人类一样观察、理解和表达情感的能力.这项技术可以看作是情感计算和对话技术的交叉,兼顾对话机器人的“智商”和“情商”,从而实现对用户的精神陪伴、情感慰藉和心理疏导.结合对话中情感的特点,对情感对话技术进行全面解析:1)规划对话场景下的情感识别、情感管理、情感表达等3个重要的技术点,并拓展多模态场景下的情感对话技术.2)介绍情感对话4项关键技术的最新研究进展,并总结这些技术面临的主要挑战和可能解决方案.3)介绍情感对话技术的数据资源.4)分析情感对话技术的难点,展望其未来发展方向与前景.

情感智媒影响下传媒产业生态的系统变革、演化机制与规制路径

情感智媒是集情感计算、生物监测、机器视觉等数字技术于一体的新型智能媒介,是计算媒体在技术、形态和价值网络上的又一次升维。随着情感智媒以未来媒介的形态强势涌入传媒生态体系,厘清情感智媒的演进逻辑和治理机制成为传媒产业研究的重要命题。研究以传媒产业的创新与演化增长理论为分析视角,以“技术—制度—偏好”的协同演化分析框架指出情感智媒总体遵循着“增长”与“择优”的演化逻辑向着扩展媒介的感知维度方向进化。情感智媒产业经济的发展受社会建构作用会经历“技术创新—偏好转变—子群扩散—竞争择优—规则与结构重置”的协同演进过程。未来情感智媒产业的科学治理应在“情感能动者”的认知基础上,遵循“以人为本”的价值理念,与时俱进地构造和迭代数字治理体系。

大学计算机基础课程思政教学方法探索

课程思政是贯彻落实党的二十大精神,推进党的二十大精神入脑、入心、入行的有效渠道。把党的二十大精神全面融入课程思政教学,依据大学计算机基础课程特色、课程目标、学情分析,结合应用型本科院校服务地方的人才培养目标和学为中心的理念,充分挖掘课程的思政元素,并从家国情怀、价值观、为人处事方法三个维度凝练12个典型的课程思政教学案例,通过采用线上线下混合式教学方法的授课实现知识传授和价值塑造相统一的教书育人效果。

基于依存句法分析的智能人机交互产品用户体验研究

智能人机交互产品因其交互自然化而备受关注,其用户体验研究尤为重要。现有对该类产品的用户体验研究聚焦于用户行为信息分析,而从在线评论中挖掘用户体验特征则是较新方向。基于依存句法分析和产品特征词库,通过人工设定提取规则从评论中提取产品特征和与之搭配的用户情感观点,并采用情感计算量化观点以获得用户体验特征。以智能屏产品为例展开研究,结果表明所提方法能解决产品特征和情感观点无法同步提取的问题,同时发现用户对产品的智能语音交互具有较高偏好程度,而对智能功能则满意度不足。根据结果针对产品的智能系统、外观及硬件配置等提出优化设计建议。

智情技术赋能的教考分离研究

随着教育现代化的推进,传统的教考制度面临教考内容不对应、评价标准不统一、学习方式应试化等弊端,极大地约束了学生创新能力和综合素质的培养。通过构建智情技术赋能的教考分离模型,分析其在题库管理、混合考试、智能阅卷、成绩分析和试卷归档等多场景中的应用,进而探析智情技术赋能的教考分离价值,从而推动教学改革和课程建设,实现师生智能化考核评价,并增强学生自主学习和个性化学习,以期为高校教考分离改革提供理论指导和实践参考,最终实现全面提高人才培养质量的目标。