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Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: 1) robot; 2) algorithm; and 3) consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated. [ABSTRACT FROM AUTHOR]

This study examined the use of avatars to provide feedback to influence action identities, anxiety, mood, and performance during job interview training. We recruited 36 university students for the experiment and divided them into two groups. The first group received avatar-based feedback whereas the other group received self-feedback after the first interview session. Results showed that the avatar-based feedback group experienced significantly higher levels of action identities, reduced anxiety, and happier mood in the second interview after the feedback session. Additionally, compared to the self-feedback group, the avatar-based feedback group rated their performance better in the second interview. Furthermore, the effect of avatar feedback on mood and performance varied depending on the participants’ initial anxiety levels. For those with low initial anxiety, avatar feedback resulted in higher levels of action identification, a marginally significant increase in positive mood, higher pitch, better word usage, and better self-assessment. In contrast, for those with high initial anxiety, avatar feedback did not cause any significant changes in the action identities or moods, however, it reduced anxiety and the use of weak words. We believe that our findings have implications not only for improving job interview performance but also for designing future communication coaching systems. [ABSTRACT FROM AUTHOR]

Emergent states are temporal group phenomena that arise from collective affective, behavioral, and cognitive processes shared among the group's members during their interactions. Cohesion is one such state, mainly conceptualized by scholars as affective in nature, and frequently distinguished into the two dimensions social and task cohesion. Whereas social cohesion is related to the need of belonging to a group, task cohesion is related to the group's goals and tasks. In this article, we emphasize the importance of behavioral interaction dynamics to predict cohesion's dynamics. Drawing from Social Science insights, we investigate the interplay between social and task cohesion to predict their dynamics across group tasks from nonverbal behavioral features. Three computational architectures exploiting transfer learning are presented. Transfer learning capitalizes on information learnt by a model for a specific dimension to predict the dynamics of the other dimension. Results show that integrating the influence of social cohesion to predict dynamics of task cohesion outperforms state-of-the-art. To predict dynamics of social cohesion, a model integrating the reciprocal impact of social and task cohesion significantly improves performance with respect to the state-of-the-art and a model only integrating the impact of task cohesion on dynamics of social cohesion. [ABSTRACT FROM AUTHOR]

Computer mouse tracking is a simple and cost-efficient way to gather continuous behavioral data. As theory suggests a relationship between affect and sensorimotor processes, the computer mouse might be usable for affect sensing. However, the processes underlying a connection between mouse usage and affect are complex, hitherto empirical evidence is ambiguous, and the research area lacks longitudinal studies. The present work brings forward a longitudinal field study in which 179 participants hourly self-reported their affect while their mouse usage was tracked both during their self-directed, contextless as well as task-bound computer use over the course of 14 days, resulting in a dataset comprising 10,760 instances of data collection. Extensive statistical analysis using null hypothesis significance testing and machine learning reveal weak and sporadic relationships between mouse usage and longitudinal self-reported affect at best. The results of this study challenge the use of computer mouse tracking for longitudinal affect sensing and point to a necessity for more research. [ABSTRACT FROM AUTHOR]

Deep learning based video depression analysis has been recently an interesting and challenging topic. Most of existing works focus on learning single-scale facial dynamics of participants for depression detection. Besides, they usually adopt expensive deep learning models with high computational complexity, resulting in difficulty in real-time clinical applications. To address these two issues, this work proposes a lightweight Multi-scale Temporal Difference Attention Networks (MTDAN) integrating the temporal difference and attention mechanism to model both short-term and long-term temporal facial behaviors for automated video depression detection. Initially, two simple yet effective sub-branches, i.e., a Short-term Temporal Difference Attention Network (ST-TDAN), and a Long-term Temporal Difference Attention Network (LT-TDAN), are designed to perform individually short-term and long-term depressive behavior modeling. Then, a simple Interactive Multi-head Attention Fusion (IMHAF) strategy is employed for integrating short-term and long-term spatiotemporal features, followed by a linear fully-collected layer for depression score prediction. Experiments on two public AVEC2013 and AVEC2014 datasets show that our proposed method not only achieves highly competitive performance to state-of-the-art methods, but also has much smaller computational complexity than them on video depression detection tasks. [ABSTRACT FROM AUTHOR]

Social signal recognition techniques based on nonverbal behavioral sensing allow conversational robots to understand the user’s social signals, thereby enabling them to adopt interaction strategies based on internal states inferred from the social signals. This research investigates how the online social signal recognition and adaptive dialog strategy influences the dynamic change in a user’s inner state. For this purpose, we develop a semiautonomous interview robot system with an online speaker’s willingness recognition module and an adaptive question selection module based on the willingness level. The online recognition model of speaker willingness is trained from multimodal nonverbal features extracted using a novel interview corpus, which allows appropriate interview questions to be chosen based on the estimated willingness level of the user. We conduct the experiment using the system to evaluate the effectiveness of adaptive question selection based on the willingness recognition model. First, the multimodal willingness recognition model is evaluated using the interview corpus. The best recognition accuracy of willingness level (high or low) was $72. 8\%$ 72. 8 p e r c n t ; with the random forest classifier. Second, 27 interviewees were interviewed with the two interview robot systems: (I) with the adaptive question selection module based on willingness recognition and (II) with a random question selection strategy. The proposed adaptive question strategy significantly increased the number of utterances with high willingness compared with the baseline system (II); thus, adaptive question selection with online willingness recognition elicited the speaker’s willingness even though the model cannot be estimated with near-perfect accuracy. [ABSTRACT FROM AUTHOR]

We report on a naturalistic study investigating the effects of routine driving on cardiovascular activation. We recruited 21 healthy young adults from a broad geographic area in the Southwestern United States. Using the participants’ own smartphones and smartwatches, we monitored for a week both their driving and non-driving activities. Monitoring included the continuous recording of 1) heart rate throughout the day, 2) hand motion during driving as a proxy of persistent texting, and 3) contextualized driving data, complete with traffic and weather information. These high temporal resolution variables were complemented with the drivers’ biographic and psychometric profiles. Our analysis suggests that anxiety predisposition and high speeds are associated with significant cardiovascular activation on drivers, likely linked to sympathetic arousal. Surprisingly, these associations hold true under good weather, normal traffic, and with experienced drivers behind the wheel. The said findings call for attention to insidious effects of apparently benign drives even for people in their prime. Accordingly, our research contributes to intriguing new discourses on driving affect and personal health informatics. [ABSTRACT FROM AUTHOR]

This letter presents a hybrid control of robot vehicle using the behavior cloning algorithm with PID control for enhanced driving performance. The behavior cloning algorithm based on the convolutional neural network (CNN) has been implemented to the autonomous driving algorithm of a robot vehicle by implementing a supervised learning approach. The algorithm mimics human driving behaviors for autonomous driving by obtaining and learning manual(human) control data. In order to implement the algorithm on the real system, a low-cost camera sensor and controllers with actuators are used so that it achieves robust driving performance. With a successfully developed and validated CNN-based behavior cloning model, a classic PID control algorithm has been combined as a lateral controller to propose an AI-powered hybrid control architecture which improves the performance of trajectory tracking in various environments using the proposed behavior cloning algorithm. The performance of the proposed algorithm has been evaluated via robot vehicle tests. It has been shown from the simulation studies and vehicle tests that the proposed algorithm enhances driving performance. [ABSTRACT FROM AUTHOR]

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network. [ABSTRACT FROM AUTHOR]

Predicting pedestrian crossing trajectories has become a primary task in aiding autonomous vehicles to assess risks in pedestrian–vehicle interactions. As agile participants with changeable behavior, pedestrians are often capable of choosing from multiple possible crossing trajectories. Current research lacks the ability to predict multimodal trajectories with interpretability, and it also struggles to capture low-probability trajectories effectively. Addressing this gap, this article proposes a multimodal trajectory prediction model that operates by first estimating potential motion trends to prompt the generation of corresponding trajectories. It encompasses three sequential stages. First, pedestrian motion characteristics are analyzed, and prior knowledge of pedestrian motion states is obtained using the Gaussian mixture clustering method. Second, a long short-term memory model is employed to predict future pedestrian motion states, utilizing the acquired prior knowledge as input. Finally, the predicted motion states are discretized into various potential motion patterns, which are then introduced as prompts to the Spatio-Temporal Graph Transformer model for trajectory prediction. Experimental results on the Euro-PVI and BPI datasets demonstrate that the proposed model achieves cutting-edge performance in predicting pedestrian crossing trajectories. Notably, it significantly enhances the diversity, accuracy, and interpretability of pedestrian crossing trajectory predictions. [ABSTRACT FROM AUTHOR]

With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) will become a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it’s also crucial to consider collaboration between AVs and HVs. Human-like interaction is a required capability for AVs, especially at common unsignalized intersections, as human drivers of HVs expect to maintain their driving habits for intervehicle interactions. This article uses the social value orientation (SVO) in the decision making of vehicles to describe the social interaction among multiple vehicles. Specifically, we define the quantitative calculation of the conflict-involved SVO at unsignalized intersections to enhance decision making based on the reinforcement learning method. We use naturalistic driving scenarios with highly interactive motions for the performance evaluation of the proposed method. The experimental results show that SVO is more effective in characterizing intervehicle interactions than conventional motion-state parameters like velocity, and the proposed method can accurately reproduce naturalistic driving trajectories compared to behavior cloning. [ABSTRACT FROM AUTHOR]

Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K-nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow. [ABSTRACT FROM AUTHOR]

How to drive safely in complex real-world traffic settings has long been a question and challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of AVs, and their safety and rationality are crucial. Several decision-making techniques and algorithms have been applied to AVs; however, they are still subject to limitations and deficiencies, making it impossible to fully guarantee safety. Thus, it is necessary to conduct a safety assessment of the state performance of the DecSs of AVs to reduce driving risks and implement immediate safety measures. This article reviews relevant research on safety assessment methods of AVs’ DecSs. The insufficiency of the current DecSs in terms of safety is analyzed, and the importance and definition of decision-making safety assessment (DMSA) are established. On this basis, assessment indexes related to DMSA are analyzed. From the perspectives of methodology and industrialization development, the existing safety assessment methods are summarized categorically, their applicability and limitations are discussed, and corresponding constructive strategies for improvement are proposed. Additionally, the deficiencies of the existing DMSA methods and the areas that need improvement are analyzed. In this regard, the future challenges and development opportunities of DMSA are proposed. This article provides a useful reference for the perfect DMSA method. [ABSTRACT FROM AUTHOR]

In real-time systems, it is essential to verify the end-to-end constraints that regulate the external input/output (I/O) semantics of the head and tail tasks in each effect chain during the design phase and preserve them during implementation. The logical execution time (LET) model has been adopted by the industry due to the predictability and composability of its timed behavior. However, during the execution of LET-based effect chains, there are ineffective jobs whose outputs are redundant or unused and do not contribute to the external I/O behavior. This letter proposes an offline optimization method for deriving multiframe tasks that achieve the external timed I/O semantics of the LET-based effect chains with reduced utilization. The method first removes ineffective jobs from each effect chain and further explores the benefits of removing jobs for single and crossing effect chains by loosening the LET interval. The method is evaluated using synthetic benchmarks that mimic real-world automotive applications. [ABSTRACT FROM AUTHOR]

Typical communication and detection issues in a nonideal information environment, such as sensing failure, communication and sensing delay, and packet loss, further aggravate the adverse impacts of human-driven vehicle (HV) uncertainty on a mixed vehicle platoon. To guarantee the performance of the mixed vehicle platoon featuring HVs and connected and automated vehicles under the nonideal information environment, this article proposes a platoon control strategy integrating a combined longitudinal and lateral control and message recovery. Specifically, by building the dataset associated with HV behaviors, a long short-term memory (LSTM) predictor is established to recover the problematic HV messages (i.e., position, velocity, and heading) caused by the nonideal information environment. Furthermore, based on the boundary of the HV states, an evaluation and correction (EC) method is presented to suppress the adverse impacts of prediction failures. Then, a combined longitudinal and lateral controller cooperating with the LSTM predictor and EC method is developed to enhance the stability and safety of the mixed vehicle platoon under the nonideal information environment. In a theoretical analysis, the relatedness between the asymptotic stability and string stability of the platoon and predictor accuracy is strictly proved. Finally, comparative experiments verify the effectiveness of the proposed control strategy by employing driver-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

Behavioral observation plays an essential role in the diagnosis of Autism Spectrum Disorder (ASD) by analyzing children's atypical patterns in social activities (e.g., impaired social interaction, restricted interests, and repetitive behavior). To date, this process still heavily relies on the questionnaire survey, clinical observation, or retrospective video analysis, leading to high demand for professionals with massive labor costs. This article proposes a standardized platform for stimulating, gathering, analyzing, modeling, and interpreting human behavioral data in the application of computer-aided ASD diagnosis. By a structured assessment process, the proposed system can automatically evaluate children's multiple social interaction skills using the captured audio-visual data and provide the final diagnostic suggestions. We collect a multimodal behavioral database of 95 participants (71 children with ASD and 24 age-matched typical controls) in a real clinic environment, the Third Affiliated Hospital of Sun Yat-sen University, China. On the clinical database, our proposed computer-aided ASD diagnosis system obtains an accuracy of 88.42% for identifying ASD children with an average age of 24 months, representing a performance comparable to top-level human experts. As a unified and replicable solution, it has good potential to be promoted to less developed areas with limited high-quality medical resources. [ABSTRACT FROM AUTHOR]

The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters’ gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ. [ABSTRACT FROM AUTHOR]

We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experiment where brain activity collected from 31 participants produced in response to viewing images is associated with their self-reported preferences. First, we show that brain responses present a graded response to preferences, and that brain responses alone can be used to train classifiers that reliably estimate preferences. Second, we show that brain responses reveal additional preference information that correlates with group preference, even when participants self-reported having no such preference. Our analysis of brain responses carries significant implications for researchers in general, as it suggests an individual's explicit preferences are not always aligned with the preferences inferred from their brain responses. These findings call into question the reliability of explicit and behavioral signals. They also imply that additional, multimodal sources of information may be necessary to infer reliable preference information. [ABSTRACT FROM AUTHOR]

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this article examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features. [ABSTRACT FROM AUTHOR]

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

For affective computing to have an impact outside the laboratory, facial expressions must be studied in rich naturalistic situations. We argue negotiations are one such situation as they are ubiquitous in daily life, often evoke strong emotions, and perceived emotion shapes decisions and outcomes. Negotiations are a growing focus in AI research and applications, including agents that negotiate directly with people and attempt to use affective information. We introduce the DyNego-WOZ Corpus, which includes dyadic negotiation between participants and wizard-controlled virtual humans. We demonstrate the value of this corpus to the affective computing community by examining participants’ facial expressions in response to a virtual human negotiation partner. We show that people's facial expressions typically co-occur with the end of their partner's speech (suggesting they reflect a reaction to the content of this speech), that these reactions do not correspond to prototypical emotional expressions, and that these reactions can help predict the expresser's subsequent action. We highlight challenges in working with such naturalistic data, including difficulties of expression recognition during speech, and the extreme variability of expressions, both across participants and within a negotiation. Our findings reinforce arguments that facial expressions convey more than emotional state but serve important communicative functions. [ABSTRACT FROM AUTHOR]

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants, respectively, to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots’ emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g., allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants’ (in the role rescue workers) situational awareness. Our results and proposed methodologies (a) provide insights on how emotions could help conveying messages in the context of SAR, and (b) show evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context. [ABSTRACT FROM AUTHOR]

Depression is a common psychiatric disorder worldwide. However, in China, a considerable number of patients with depression are not diagnosed, and most of them are not aware of their depression. Despite increasing efforts, the goal of automatic depression screening from behavioral indicators has not been achieved. A major limitation is the lack of available multimodal depression corpus in Chinese since linguistic knowledge is crucial in clinical practice. Therefore, we first carried out a comprehensive survey with psychiatrists from a renowned psychiatric hospital to identify key interview topics which are highly related to the diagnosis of depression. Then, a semi-structural interview study was conducted over a year with subjects who have undergone clinical diagnosis and professional assessment. After that, Visual, acoustic, and textual features were extracted and analyzed between the two groups, statistically significant differences were observed in all three modalities. Benchmark evaluations of both single modal and multimodal fusion methods of depression assessment were also performed. A multimodal transformer-based fusion approach achieved the best performance. Finally, the proposed Chinese Multimodal Depression Corpus (CMDC) was made publicly available after de-identification and annotation. Hopefully, the release of this corpus would promote the research progress and practical applications of automatic depression screening. [ABSTRACT FROM AUTHOR]

High-quality parent-child conversational interactions are crucial for children's social, emotional, and cognitive development. However, many children have limited exposure to these interactions at home. As increasingly accessible and scalable interventions in child development, interactive technologies, such as social robots, have great potential for facilitating parent-child interactions. However, such technology-based interventions are still underexplored, as the technologies’ limited ability to understand the social-emotional dynamics of human dyadic interactions impedes their effective delivery of timely, adaptive interventions. To advance research on resolving this roadblock, we present a “dyadic affect in multimodal interaction - parent to child” (DAMI-P2C) dataset collected during a study of 34 parent-child pairs, where parents and children (3-7 years old) engaged in reading storybooks together. In contrast to existing public datasets for social-emotional behaviors in dyadic interactions, each instance for both participants in our dataset was annotated for affect by three labelers. Additionally, the dataset contains audiovisual recordings as well as each dyad's sociodemographic profiles, co-reading behaviors, affect labels, and body joints. We describe the dataset's main characteristics and provide a preliminary analysis of the interrelations between sociodemographic profiles, co-reading behaviors, and affect labels. The dataset provides us with useful insights into the computing and social science fields. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the global model’s availability. Currently, most existing works rely on an Independently Identical Distribution (IID) situation and identify malicious gradients using plaintext. However, we demonstrates that current works cannot handle the data heterogeneity scenario challenges and that publishing unencrypted gradients imposes significant privacy leakage problems. Therefore, we develop APFed, a layered privacy-preserving defense mechanism that significantly mitigates the effects of poisoning attacks in data heterogeneity scenarios. Specifically, we exploit HE as the underlying technique and employ the median coordinate as the benchmark. Subsequently, we propose a secure cosine similarity scheme to identify poisonous gradients, and we innovatively use clustering as part of the defense mechanism and develop a hierarchical aggregation that enhances our scheme’s robustness in IID and non-IID scenarios. Extensive evaluations on two benchmark datasets demonstrate that APFed outperforms existing defense strategies while reducing the communication overhead by replacing the expensive remote communication method with inexpensive intra-cluster communication. [ABSTRACT FROM AUTHOR]

Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, therefore, do not exploit the full potential of the hardware telemetry. In this paper, we propose XMD, an HMD that uses an expansive set of telemetry channels extracted from the different subsystems of SoC. XMD exploits the thread-level profiling power of the CPU-core telemetry, and the global profiling power of non-core telemetry channels, to achieve significantly better detection performance than currently used Hardware Performance Counter (HPC) based detectors. We leverage the concept of manifold hypothesis to analytically prove that adding non-core telemetry channels improves the separability of the benign and malware classes, resulting in performance gains. We train and evaluate XMD using hardware telemetries collected from 723 benign applications and 1033 malware samples on a commodity Android Operating System (OS)-based mobile device. XMD improves over currently used HPC-based detectors by 32.91% for the in-distribution test data. XMD achieves the best detection performance of 86.54% with a false positive rate of 2.9%, compared to the detection rate of 80%, offered by the best performing signature-based Anti-Virus(AV) on VirusTotal, on the same set of malware samples. [ABSTRACT FROM AUTHOR]

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial strategies; 2) network state is time-varying because each host decides whether to mutate IP address; and 3) most methods mainly focus on enhancing security, but ignore the survivability of existing connections. In this paper, an Intelligence-Driven Host Address Mutation (ID-HAM) scheme is proposed to address aforementioned challenges. We firstly model a Markov decision process (MDP) to describe the mutation process, and design a seamless mutation mechanism. Secondly, to remove infeasible actions from the action space of MDP, we formulate address-to-host assignments as a constrained satisfaction problem. Thirdly, we design an advantage actor-critic algorithm for HAM, which aims to learn from scanning behaviors. Finally, security analysis and extensive simulations highlight the effectiveness of ID-HAM. Compared with state-of-the-art solutions, ID-HAM can decrease maximum 25% times of scanning hits while only influencing communication slightly. We also implemented a proof-of-concept prototype system to conduct experiments with multiple scanning tools. [ABSTRACT FROM AUTHOR]

Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One issue is the rise in the number of abnormal behaviors that affect system performance due to possible sensor faults and attacks. The combination of safety risks, which are usually caused by random sensor faults and security risks that can happen during any random system state, makes the full coverage testing of the cyber-physical system challenging. Existing techniques are inadequate to deal with complex safety and security co-risks against cyber-physical systems. In this paper, we propose AST-SafeSec, an analysis methodology for both safety and security aspects that utilizes reinforcement learning to identify the most likely adversarial paths at various normal or failure states of a cyber-physical system that can influence system behavior through its sensor data. The methodology is evaluated using an autonomous vehicle scenario by incorporating a security attack into the stochastic sensor elements of a vehicle. Evaluation results show that the methodology analyzes the interaction of malicious attacks with random faults and identifies the incident caused by the interactions and the most likely path that leads to the incident. [ABSTRACT FROM AUTHOR]

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant’s traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spintronic technology promises to significantly increase the efficiency and scalability of neural networks by employing optimized task-oriented device components that exhibit intrinsic nonlinearity, temporal nonlocality, scalability, and electrical tunability. In particular, the functional response of spin-transfer torque oscillators can be designed to naturally emulate the building blocks of neural networks, such as short-term memory, hierarchy, and nonlinearity. We propose spin-transfer nano-oscillators as a dynamic neuron that can be used in a neural network coupled with a fully connected layer to perform classification tasks. In this concept, successive nodes of the neural network correspond to successive time steps, so that the nonlinearity and memory of the system can be naturally exploited. The tunability of the device allows to project initial configurations in well-defined regions of the phase space where classification is easily performed. Furthermore, training is performed using optimal control theory. We emphasize that the devices benefit from more realistic models compared to simpler analytical models and is robust against device-to-device variations. We tested the performance of the network on two types of datasets and obtained 99% accuracy. Although these systems are computationally expensive, their hardware implementation is simple and inexpensive. [ABSTRACT FROM AUTHOR]

Different from the traditional deterministic user equilibrium (DUE) model based on fixed travel time, the stochastic user equilibrium (SUE) model is established by assuming the perceived route travel time as a random variable. It plays an important role in traffic assignment, wherein the setting of route choice parameters decides system performance. This article studies parameter calibration of widely used SUE models including the multinomial logit (MNL), length-based C-logit (LCL), and congestion-based C-logit (CCL) models. We establish a bilevel model in which the upper level is designed to minimize the error between the real and model-based traffic flow patterns, whereas the lower level copes with the SUE traffic assignment. A series of innovative methods are built to solve this bilevel model. Considering the nonlinear calibration problem in the upper level, a Bayesian optimization method is used. For the SUE traffic assignment in the lower level, dedicated algorithms are proposed, which combine a partial linearization algorithm and a feasible direction method. Furthermore, two revised solution methods are provided: a Barzilai–Borwein (BB) step-size method and a parallel framework. Finally, the proposed model and corresponding solution approach are validated based on a case study of Yuyao, China. Solutions show that the CCL SUE model has the highest precision while requiring the longest computation time. LCL takes only 11.7% of the computation time of CCL when reaching an acceptable precision. [ABSTRACT FROM AUTHOR]

Self-driving cars and other vehicles are being increasingly demonstrated, released, and deployed on roads. Yet only a handful of special-case vehicles have obtained a permit to go fully driverless, and our passenger cars still require drivers at the steering wheel to correct the car if something goes wrong. Safety drivers, which operate autonomous vehicles in testing phases, upon occasions were unable to correct the faulty behavior of their vehicles, inducing accidents and, unfortunately, casualties. This article proposes a method to analyze the reliability of autonomous vehicles applying the classical reliability theory and software reliability growth models (SRGMs). This method is then applied to real-world data to get some predictions on what is really needed to safely remove the driver and go fully driverless. [ABSTRACT FROM AUTHOR]

In recent years, the application of deep learning for water-body detection in synthetic aperture radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the dung beetle optimizer-convolutional neural network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs the DBO algorithm to search for the optimal hyperparameter of the CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from $H/\alpha $ and model-based (MB) polarization decomposition methods with backscatter characteristics. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and intersection over union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provide new insights and in-depth understanding for detecting water-body. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) swarm is widely applied in many fields, such as disaster relief and geological exploration. However, it is highly vulnerable when facing an attack from internal malicious UAVs due to the underlying disconnected, intermittent, and limited (DIL) network environment. Recently, building trusted networks has been considered as a strategy to address security issues. Nevertheless, deploying a trusted network in a swarm faces many challenges imposed by the dynamic topology and limited resources of DIL networks. In order to overcome these challenges, we developed a blockchain-based UAV trusted self-organizing networking mechanism (BC-UTSON) to enhance internal security. Three key technologies are proposed for assisting BC-UTSON in achieving trustworthiness. UAV practical Byzantine fault tolerance (U-PBFT) is proposed to guarantee the lightweight consensus and real-time full-node trustworthiness evaluation under the hierarchical self-organizing networking structure of a UAV swarm. A blockchain-based multiweighted subjective logic (BMWSL) scheme has been designed to identify malicious UAV nodes based on the trustworthiness evaluation, which also assists the trusted path quality-aware dynamic routing (TPDR) mechanism to prevent data from routing through malicious nodes. The experimental results show that the proposed BC-UTSON performs much better than nonblockchain-based mechanisms for the UAV swarm. [ABSTRACT FROM AUTHOR]

Monitoring sheep activity can be crucial for improving productivity and animal welfare. This work presents the design, manufacture, and test of a collar-type device to monitor sheep behavior. The device consists of an MSP-EXP432P401R microcontroller from Texas Instruments, a Bosch Sensortec’s BMI160 3-axis accelerometer, and a narrowband-IoT BG96 modem from Quectel that includes a global positioning system. The device has two operating modes: 1) validation mode (VM) to test and validate algorithms for characterizing sheep activity and 2) research mode (RM) to support multiday animal experiments to study their behavior. In VM, it sends accelerometer data, the animal’s state (run, walk, stand, or head down), and the location to the Central System every 20 s. VM has an autonomy of 51 h. In RM, the device transmits the animal’s state and the location every 2 or more minutes to extend the autonomy to more than ten days. The microcontroller identifies the sheep’s states (every 5 s) using real-time accelerometer data processed with an algorithm based on the linear discriminant analysis method. We trained a classifier on a PC using a public dataset, and then we ported it to the microcontroller. Preliminary tests show that the sheep’s state identification has a prediction success rate of 88%, opening exciting possibilities for developing an applicable device. [ABSTRACT FROM AUTHOR]

Recent developments in vehicle automation require simulations of human-robot interactions in the road traffic context, which can be achieved by computational models of human behavior such as game theory. Game theory provides a good insight into road user behavior by considering agents’ interdependencies. However, it is still unclear whether conventional game theory is suitable for modeling vehicle-pedestrian interactions at unsignalized locations or if more complex models like behavioral game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms, to answer this question. Unlike the most previous studies that employed naturalistic datasets to test and validate such models, this study utilized a distributed simulation dataset to test and compare the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator to replicate the traffic scenarios for 32 pedestrian-driver pairs. The findings demonstrated that there is a high variability between participant pairs’ behaviors. Our proposed behavioral game-theoretic model outperformed other models in predicting the interaction outcome. This translates to a decrease by 70% and 67% in the root mean squared error (RMSE) when compared to the baseline model, for marked and unmarked crossings, respectively. The model can also predict which interaction will take the longest time to resolve. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory that underscores the complexity of human behavior with implications for the testing and development of automated vehicles.

Autism is a prevalent neurodevelopmental disorder characterized by impairments in social and communicative behaviors. Possible connections between autism and facial expression recognition have recently been studied in the literature. However, most works are based on facial images or short videos. Few works aim at Autism Diagnostic Observation Schedule (ADOS) videos due to their complexity (e.g., interaction between interviewer and interviewee) and length (e.g., usually last for hours). In this paper, we attempt to fill this gap by developing a novel discriminative few shot learning method to analyze hour-long video data and exploring the fusion of facial dynamics for the trait classification of ASD. Leveraging well-established computer vision tools from spatio-temporal feature extraction and marginal fisher analysis to few-shot learning and scene-level fusion, we have constructed a three-category system to classify an individual into Autism, Autism Spectrum, and Non-Spectrum. For the first time, we have shown that certain interview scenes carry more discriminative information for ASD trait classification than others. Experimental results are reported to demonstrate the potential of the proposed automatic ASD trait classification system (achieving 91.72% accuracy on the Caltech ADOS video dataset) and the benefits of few-shot learning and scene-level fusion strategy by extensive ablation studies. [ABSTRACT FROM AUTHOR]

Many shortcomings of chaos-based applications stem from the weak dynamic properties of the chaotic maps they use. To alleviate this problem, inspired by the buffeting effect in aeroelasticity, this article proposes the buffeting chaotification model (BCM). Using the especially designed buffeting and modulo operators, the BCM can generate numerous new chaotic maps with strong dynamic properties from existing one-dimensional chaotic maps. The effectiveness of the BCM is mathematically proven according to the Lyapunov exponent, and further numerical experiments confirm the superiority of the chaotic maps generated by the BCM in terms of the dynamic properties. The field-programmable gate array implementation also shows that the BCM owns simplicity in hardware devices. To investigate the practical application, a scheme for constructing the pseudorandom number generator is designed. Performance analyses indicate that our generators have a strong ability to produce high-quality pseudorandom sequences rapidly. [ABSTRACT FROM AUTHOR]

The correct application of efficient and safe driving techniques plays an important role for professional drivers. Monitoring and analyzing driving data can promote changes in the sector in terms of the better use of vehicles, reduction in energy consumption, and improved on-road safety. However, the results in driving performance can vary considerably among different fleets that have received the same training in efficient and safe driving. The aim of this article is to perform an in-depth analysis of the driving performance of professional drivers during their working day, taking into account the influence of fleet management decisions. For this, we have selected four urban public transport companies with clear differences in terms of the employees scheduled and rostered drivers to bus lines. The driving behavior of 745 drivers has been evaluated over a period of 10 months, considering performance in terms of efficient and safe driving through the use of driving patterns. A total of 6,517,983.995 km of real-time driving data retrieved from vehicles every 1.5 s has been analyzed. The results show significant differences in the evolution and acquisition of new driving habits. In addition, significant observations from this article provide valuable information for fleet managers and allow them take advantage of data provided by the adoption of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

As there are many factors affecting vehicle lane changes in a tunnel, which leads to the unstable state of vehicles during lane changes and an increase of collision events, a new vehicle lane-changing model is proposed by considering the influence of typical factors such as noise and brightness in a tunnel under a vehicle-to-everything (V2X) environment. First, V2X-based technology enables real-time access to a target vehicle surrounding information characteristics in a tunnel, establishing a lane-changing decision model to quantify the willingness of vehicles to change lanes. Second, considering safety as a prerequisite for a lane change, establishing a vehicle safety lane-change-distance model and a minimum safe-distance model was introduced for comparison to evaluate the safety of lane changing. On this basis, considering the noise and brightness effects of a tunnel, the relationship between brightness, noise, and response time of human-driven vehicles, hybrid driving vehicles, and autonomous vehicles is quantitatively analyzed, and then a new vehicle lane-changing model in a tunnel is established. The results of the research show that brightness in a tunnel has a more significant effect on the driver than noise. At the same time, autonomous driving, as well as hybrid driving, has better stability and comfort with less change in velocity, acceleration, and other states during lane changing in a tunnel compared to manned driving, which proves the reasonableness of the model and helps to provide a model basis for research of real vehicle lane changing under a V2X environment. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) inevitably confront dilemmas in which collisions cannot be avoided, and collision avoidance involves ethical issues due to harm distribution among traffic participants. Regarding how AVs should respond to dilemmas, ethical divergence is the main barrier that exists universally in human society. To overcome this barrier, the rational ethical propensity, i.e., the inclination of evasive steering (IES), of Chinese respondents in two typical dilemmas was collected via an online survey to construct AVs’ ethical model. Respondents were partitioned into two subgroups with relative ethical preferences to address the main ethical divergence based on their IES. The analytical IES ethical model, describing relationships among the respondents’ ethical intention and sensitive factors, was proposed. Three IES models for the two partitioned subgroups and the total respondents were fitted. Personal ethics setting (PES) was implemented with the subgroups’ models as the boundaries of operation space. PES-based trajectory planning experiments were conducted, and simulation results showed that AVs make evasive decisions in line with their ethical preference. [ABSTRACT FROM AUTHOR]

Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations. [ABSTRACT FROM AUTHOR]

Our biology affects how we interact with the world, including how we learn new knowledge and respond to challenges. This article explores the impact of neurochemicals in our brain on learning and explains how to leverage our biology to improve education and problem solving, focusing on computing education. Within this context, the article particularly examines the role of failure while learning. Learning, especially in technical fields, includes making errors on the path to success. Although these errors trigger the necessary neurochemical conditions for rapid learning, these failures can also be demotivating. To gain the benefits of failure while mitigating its negative consequences, this article recommends evidence-based behavioral strategies for making the best out of failing while learning and designing for failure in learning environments. [ABSTRACT FROM AUTHOR]

To maximize national investments in science gateways (SGs) and cyberinfrastructure (CI), understanding and harnessing the factors that enable user adoption are critically valuable. In this article, we discuss how the social science concepts of influencers and opinion leaders apply in SG/CI domains and help drive user adoption. We identify three key aspects of influencers based on a systematic analysis of 132 interviews with SG/CI users and experts from 2016–2019: who they are, what qualities they possess, and how they communicate. We find that SG/CI influencers will be most effective if they possess mentoring roles, are active and visible members of an academic or professional community, and facilitate growth within SG/CI at the individual and community levels. We also outline 12 techniques organized under four methodological approaches that SG/CI staff can employ to recruit influencers to help promote adoption. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) control is a promising solution for the grid-forming converters to enhance the inertia of the converter-based power grid. The virtual inertia and output impedance of the VSG are the important behaviors to be focused. However, the virtual inertia of grid-forming converters may cause active power oscillation due to the interaction with the power inertia of grid through the low-frequency band grid impedance. Besides, the resonance can be also triggered by the interaction between middle-frequency impedance of power grid and output impedance of converter. In order to test the stability of grid-forming converter, accurate emulation of grid behaviors from more aspects, including inertia and impedance under wider frequency ranges, is becoming critical. This article proposes a grid emulation method to mimic the inertia and impedance characteristics of power grid, targeting for the stability test of grid-forming converter under multifrequency bands. A high-switching-frequency converter and a low-switching-frequency converter are adopted in the proposed emulator structure to achieve both high capability in respect to control bandwidth and power level. Moreover, the virtual impedance control is integrated with the VSG control to emulate the line impedance and inertia within relatively wider frequency ranges. The realization of virtual impedance is achieved without derivation terms nor low pass filter. Finally, the performance of the proposed emulator is verified by various simulations and experimental measurements. [ABSTRACT FROM AUTHOR]

Many efforts have been devoted to describe the multi-timescale thermal dynamics of power modules and frequency-domain modeling is a relatively new approach. Unfortunately, only the frequency-domain response of thermal impedance has been studied in recent works, so the existing models can only describe the temperature behaviors of semiconductors. In the reality, it is not only the temperature of semiconductors which is important but also of other parts of the package. In this article, a novel perspective to study the thermal dynamics by analyzing heat flow behaviors is proposed. Frequency spectrum analysis in finite-element method simulation has been first used in this article, and it reveals that heat flow of the power semiconductor device behaves as a multi-layer low-pass filter (LPF). As a result, a novel modelling method of heat flow with a 7order-3frequencies LPF has been developed in this article and it can provide a comprehensive description of heat flow behaviors for power modules at full bandwidth. Meanwhile, the effects on heat flow behaviors brought by boundary conditions are also considered to ensure that the proposed model can be easily adapted to different boundary conditions. The effectiveness and accuracy of the proposed model has been verified by both simulations and experiments. [ABSTRACT FROM AUTHOR]

Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. We also intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in https://github.com/sunlex0717/DissectingTensorCores. [ABSTRACT FROM AUTHOR]

Distributed computing supports large scale and data-intensive computations with the cooperation of a multitude of parties, each responsible for a portion of the workload. Such parties are often not fully reliable and may return incorrect results. In this article, we address the problem of assessing the integrity of the computation results. We provide a comprehensive characterization of two techniques, sentinels and twins, evaluating their effectiveness and synergy. Sentinels are pre-computed tasks whose result is known apriori, and enable checking returned results against a ground truth. Twins are replicated tasks assigned to different workers, and enable cross-checking returned results for a same task. The analysis considers many questions that arise in the design of a concrete integrity assessment strategy and identifies the parameters that have a critical impact on the overall protection. Our model enables to tune the integrity controls so to achieve best effectiveness. The model can be applied to a variety of scenarios and offers guidelines that can find extensive application. [ABSTRACT FROM AUTHOR]

Even for humans, it can be challenging to recognize, interpret, and respond to emotions. Can artificial intelligence (AI) do any better? Technologies often referred to as “emotion AI” detect and analyze facial expressions, voice patterns, muscle activity, and other behavioral and physiological signals associated with emotions. [ABSTRACT FROM AUTHOR]