Emotion recognition is critical for various applications, including the early detection of mental health disorders and emotion-based smart home systems. Previous studies utilized various sensing methods for emotion recognition, such as wearable sensors, cameras, and microphones. However, these methods are often intrusive or raise significant privacy concerns, which may reduce user acceptance for continuous, long-term deployment. This paper introduces a non-intrusive and privacy-friendly personalized emotion recognition system, EmotionVibe, which leverages footstep-induced floor vibrations for emotion recognition. The main idea of EmotionVibe is that individuals' emotional states influence their gait patterns, subsequently affecting the floor vibrations induced by their footsteps. However, there are two main research challenges: 1) the complex and indirect relationship between human emotions and footstep-induced floor vibrations and 2) the large between-person variations within the relationship between emotions and gait patterns. To address these challenges, we first empirically characterize this complex relationship and develop an emotion-sensitive feature set including gait-related and vibration-related features from footstep-induced floor vibrations. Furthermore, we personalize the emotion recognition system for each user by calculating gait similarities between the target person (i.e., the person whose emotions we aim to recognize) and those in the training dataset and assigning greater weights to training people with similar gait patterns in the loss function. We evaluated our system in human walking experiments with 20 participants, summing up to 37,001 footstep samples. EmotionVibe achieved the mean absolute error (MAE) of 1.11 and 1.07 for valence (unpleasant to pleasant) and arousal (calm to excited) score estimations, respectively, reflecting 19.0% and 25.7% error reduction compared to the baseline method (using only gait-related features without personalization).

Weight difference estimation is crucial in various applications, particularly for identifying items being picked up and put back when people interact with the shelf while shopping in autonomous stores, ensuring precise cost estimation. However, the conventional approach of estimating weight changes requires specialized weight-sensing shelves, which are densely deployed weight scales, incurring intensive sensor consumption and maintenance costs. Prior works explored the vibration-based weight sensing method, but they are limited to the object that can generate vibration through motion. This work demonstrates a system leveraging active vibration sensing for weight difference estimation on shelves at different locations. The main intuition of the system is that the weight placed on the shelf influences the dynamic vibration response of the shelf, thus altering the shelf vibration patterns. Our system achieves a mean absolute error 9.23 grams and mean absolute percentage error 7.9% on the real-store shelf layout.

Conversational Intelligent Tutoring Systems (CITS) have drawn increasing interest in education because of their capacity to tailor learning experiences, improve user engagement, and contribute to the effective transfer of knowledge. Conversational agents employ advanced natural language techniques to engage in a convincing human-like tutorial conversation. In solving math word problems, a significant challenge arises in enabling the system to understand user utterances and accurately map extracted entities to the essential problem quantities required for problem-solving, despite the inherent ambiguity of human natural language. In this study, we propose two possible approaches to enhance the performance of a particular CITS designed to teach learners to solve arithmetic–algebraic word problems. Firstly, we propose an ensemble approach to intent classification and entity extraction, which combines the predictions made by two distinct individual models that use constraints defined by human experts. This approach leverages the intertwined nature of the intents and entities to yield a comprehensive understanding of the user’s utterance, ultimately aiming to enhance semantic accuracy. Secondly, we introduce an adapted Term Frequency-Inverse Document Frequency technique to associate entities with problem quantity descriptions. The evaluation was conducted on the AWPS and MATH-HINTS datasets, containing conversational data and a collection of arithmetical and algebraic math problems, respectively. The results demonstrate that the proposed ensemble approach outperforms individual models, and the proposed method for entity–quantity matching surpasses the performance of typical text semantic embedding models.

Despite the general consensus on the advantages of collaborative learning, most of the research in Intelligent Tutoring Systems (ITSs) only considers the case of individual learners. This is mainly due to the technical challenges in adapting individual learning systems to support collaborative learning. In this paper, we present a framework aimed at adapting individual-learner tutoring systems to enable collaboration among students, leveraging the advantages of both intelligent tutoring systems and peer collaboration, without significant changes to the original Human–Computer Interaction model. The proposed framework is designed under the assumption that the adapted ITS is built as a web application, ensures horizontal scalability of the ITS, and relies exclusively on Open Source software and tools. Evaluation results of the framework demonstrate that while the system’s complexity increases, there are no perceivable rises in response times or cpu usage. • We present a framework for adapting Intelligent Tutoring Systems to collaborative. • Reduced technical challenge for implementing Collaboration. • We provide a case study of the adaptation of the system. • The modified system is not perceivably slower.

The performance of Conversational Agents (CAs) relies heavily on intent classification and entity extraction. However, the effectiveness of these tasks is often hindered by the absence of an explicit model that accounts for their interdependence. To address this limitation, we introduce a novel approach that leverages the inter-dependency between intents and entities to improve the Exact Match Accuracy (EMA) of CAs. The approach evaluates the consistency of the output generated by the Natural Language Understanding (NLU) component based on a specification that outlines all valid combinations of intents and entities. If an inconsistent output is detected, the ranking of predicted intents is leveraged to determine the most probable intent that aligns with the identified entities. The technique guarantees the preservation or improvement of EMA and can be applied as a post-processing step in combination with any existing NLU method that returns a ranking of intents. The proposed approach was evaluated through an ablation study conducted using three different NLU models (DIET, DCA-Net and Bi-model) on four distinct conversational datasets: ATIS, SNIPS, NLU-Benchmark, and AWPS. The results demonstrated that our method led to a consistent improvement in the EMA across all NLU methods and datasets. The magnitude of the improvement varies depending on the method, dataset, and training data size, ranging from below 1% when using DCA-Net or Bi-model on the SNIPS dataset with full training data, to over 9% when using Bi-model or DIET on AWPS with 10% of the available training data. • A novel approach to increase the exact match accuracy in NLU systems is presented. • The method is non-deteriorating and seamlessly integrates with existing NLU systems. • The technique leverages the inherent inter-dependency between intents and entities. • It amends incompatible outputs by using a human-provided constraint specification. • The system’s performance consistently improved in 4 commonly used NLU datasets.

This article investigates the asynchronous estimator design for the interval type-2interval type-2 (IT2) fuzzy Markov jump systems subject to dynamic quantization and deception attacks. From the perspective of the attacker, a novel protocol-based deception attackdeception attack (DA) strategy is proposed, which utilizes the information of quantized output to assess the importance degree of transmission signals. Furthermore, in order to conserve the limited energy of the adversary, the independent attack strategies are designed for different sensors. Besides, the hidden Markov modelhidden Markov model (HMM) is applied to observe the system mode. Employing the Lyapunov stability theory and linear matrix inequality method, the sufficient conditions are acquired to guarantee the strictly-dissipative performance of the estimation error. Finally, two examples are illustrated to confirm the efficacy of the designed estimator and the advantage of the proposed attack tactics.

Abstract Natural Language Understanding (NLU) components are used in Dialog Systems (DS) to perform intent detection and entity extraction. In this work, we introduce a technique that exploits the inherent relationships between intents and entities to enhance the performance of NLU systems. The proposed method involves the utilization of a carefully crafted set of rules that formally express these relationships. By utilizing these rules, we effectively address inconsistencies within the NLU output, leading to improved accuracy and reliability. We implemented the proposed method using the Rasa framework as an NLU component and used our own conversational dataset AWPS to evaluate the improvement. Then, we validated the results in other three commonly used datasets: ATIS, SNIPS, and NLU-Benchmark. The experimental results show that the proposed method has a positive impact on the semantic accuracy metric, reaching an improvement of 12.6% in AWPS when training with a small amount of data. Furthermore, the practical application of the proposed method can easily be extended to other Task-Oriented Dialog Systems (T-ODS) to boost their performance and enhance user satisfaction.

In this article, a novel dynamic event-triggered protocol is constructed to deal with the filtering problem for affine systems presented by Takagi–Sugeno (T–S) fuzzy model. Unlike the traditional fuzzy systems, a unified framework of T–S fuzzy affine systems is formulated, which is more capable of approximation over different operating regions. For achieving desirable performance while saving communication resources, the memory-based dynamic event-triggered protocol is constructed by flexibly exploiting a series of historically triggered packets. The fading channel is time-varying and described as a novel nonhomogeneous Markov process, in which a higher level deterministic switching signal regulates the variation of transition probabilities. By adopting the region-dependent Lyapunov theory, the mean-square exponentially stable, and expected$H_{\infty }$performance for resulting systems is guaranteed. In the end, an inverted pendulum model is applied to verify the results of the theoretical analysis.

The availability of low-cost wireless physiological sensors has allowed the use of emotion recognition technologies in various applications. In this work, we describe a technique to predict emotional states in Russell’s two-dimensional emotion space (valence and arousal), using electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals. For each of the two dimensions, the proposed method uses a classification scheme based on two Hidden Markov Models (HMMs), with the first one trained using positive samples, and the second one using negative samples. The class of new unseen samples is then decided based on which model returns the highest score. The proposed approach was validated on a recently published dataset that contained physiological signals recordings (EEG, ECG, EMG) acquired during a human-horse interaction experiment. The experimental results demonstrate that this approach achieves a better performance than the published baseline methods, achieving an F1-score of 0.940 for valence and 0.783 for arousal, an improvement of more than + 0.12 in both cases.

This article focuses on the issue of nonstationary filtering for uncertain fuzzy Markov switching affine systems (FMSASs) with quantization effects and deception attacks (DAs). The resulting FMSASs are comprised of Markov switching piecewise-affine systems over a set of operating regions. To characterize the multinetwork-induced constraints, the measurement output is quantized before being transmitted, and a compensation scheme is applied to tackle the quantized measurement output loss intermittently. Meanwhile, the randomly occurring DAs are involved, in which the attack behaviors are identified by the bounded stochastic signals. Differently, to deal with the multinetwork-induced constraints, a novel nonstationary region-dependent affine filter strategy is developed. By resorting to a mode-dependent and region-dependent Lyapunov functional and S-procedure theory, sufficient conditions are elicited such that the filtering error system is mean-square exponentially stable. Finally, the practicability of the derived results is verified by a practical tunnel diode circuit model.

Due to the characteristic of large-radix routers, the Dragonfly topology can achieve low diameter, high performance/cost ratio. However, in the Dragonfly networks deployed with Remote Direct Memory Access (RDMA), existing packet-level routing algorithms which are mostly based on queue length information, are neither good enough to achieve load balancing nor meet the requirement of in order. To tackle the above issues, we first analyze the drawbacks of flow-level source routing in RDMA-enabled Dragonfly networks. Then, a flow-level rerouting scheme that can estimate traffic distribution and link load based on the routers' history information is proposed. Finally, the simulation results show that our scheme can obtain significant performance gains over existing algorithms in both average flow completion time (AFCT) and saturation throughput. In particular, under the adversarial traffic pattern, our scheme can greatly reduce the AFCT of flow-level UGAL by 25% and improve the saturation throughput by 13% while avoiding disorder.

Monitoring the compliance of social distancing is critical for schools and offices to recover in-person operations in indoor spaces from the COVID-19 pandemic. Existing systems focus on vision- and wearable-based sensing approaches, which require direct line-of-sight or device-carrying and may also raise privacy concerns. To overcome these limitations, we introduce a new monitoring system for social distancing compliance based on footstep-induced floor vibration sensing. This system is device-free, non-intrusive, and perceived as more privacy-friendly. Our system leverages the insight that footsteps closer to the sensors generate vibration signals with larger amplitudes. The system first estimates the location of each person relative to the sensors based on signal energy and then infers the distance between two people. We evaluated the system through a real-world experiment with 8 people, and the system achieves an average accuracy of 97.8% for walking scenario classification and 80.4% in social distancing violation detection.

The ability to recognize edges of an object fast and accurately is a fundamental goal in computer vision and image processing. The facility is important in automatic manufacturing environment in industry. This paper defines and investigates an efficient method of edge extraction of image using directional tracing algorithm. Based on the approach of linear feature extraction presented by R. Nevatia and K.R. Babu (1980), our algorithm is developed for the general situation of edge extraction. The technique employs the basic and intuitive principle that an edge pixel should possess local maximal gradient but no more domain information or knowledge is required. An effective and robust tracing strategy is proposed. Our algorithm has been tested on different kinds of images, and the results are satisfactory.

This paper investigates a fundamental problem of determining the position and orientation of a three-dimensional (3-D) object using a single perspective image view. The technique is focused on the interpretation of trihedral angle constraint information. A new closed form solution based on Kanatani's formulation is proposed. The main distinguishing feature of the authors' method over the original Kanatani formulation is that their approach gives an effective closed form solution for a general trihedral angle constraint. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using "angle to angle correspondence information." A detailed implementation of the authors' technique is presented. Different trihedral angle configurations were generated using synthetic data for testing the authors' approach of finding object orientation by angle to angle constraint. The authors performed simulation experiments by adding some noise to the synthetic data for evaluating the effectiveness of their method in a real situation. It has been found that the authors' method worked effectively in a noisy environment which confirms that the method is robust in practical application.>

A fundamental problem of determining the position and orientation of a 3-D object using a single perspective image view is defined and investigated. The technique is based on the interpretation of trihedral angle constraint information. A new closed-form solution to the problem is proposed. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using angle to angle correspondence information. Simulation experiments show that the authors' method is effective and robust for real application.>