Emotion recognition based on body movements is vital in human-computer interaction. However, existing emotion recognition methods predominantly focus on enhancing classification accuracy, often neglecting the provision of textual explanations to justify their classifications. In this paper, we propose an Emotion-Action Interpreter powered by LargeLanguage Model (EAI-LLM), which not only recognizes emotions but also generates textual explanations by treating 3D body movement data as unique input tokens within large language models (LLMs). Specifically, we propose a multi-granularity skeleton tokenizer designed for LLMs, which separately extracts spatio-temporal tokens and semantic tokens from the skeleton data. This approach allows LLMs to generate more nuanced classification descriptions while maintaining robust classification performance. Furthermore, we treat the skeleton sequence as a specific language and propose a unified skeleton token module. This module leverages the extensive background knowledge and language processing capabilities of LLMs to address the challenges of joint training on heterogeneous datasets, thereby significantly enhancing recognition accuracy on individual datasets. Experimental results demonstrate that our model achieves recognition accuracy comparable to existing methods. More importantly, with the support of background knowledge from LLMs, our model can generate detailed emotion descriptions based on classification results, even when trained on a limited amount of labeled skeleton data.

Recently, depression recognition through EEG has gained significant attention. However, two challenges have not been properly addressed in prior automated depression recognition and classification studies: (1) EEG data lacks an explicit topological structure. (2) Capturing spatio-temporal features of EEG signals is difficult. In this paper, we propose Multi-scale Adaptive Spatial-Temporal Graph Convolutional Network (MAST-GCN) for mining latent topological structure among EEG channels and capturing discriminative spatio-temporal features. First, we integrate Adaptive Graph Convolution (AGC) that merges the inherent graph construction method with a data-driven graph reconstruction method. The model uses attention mechanism to learn an adaptive topological structure and semantic information from different layers and classes. Second, we propose Multi-Scale Time Convolutional Layer (MS-TCL), which captures long-term dependence from EEG data. Since Graph Convolution is weak for aggregating the spatio-temporal information, we have implemented a 3D Graph Convolution (G3D) to directly capture the spatio-temporal dependencies by reconstructing the spatio-temporal graph. The experimental results demonstrate that MAST-GCN consistently outperforms state-of-the-art methods on two datasets. Furthermore, we use the gradient-based saliency maps for interpretability analysis, discovering the active brain regions and important electrode pairs related to depression.

Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.

This paper focuses on contrastive learning for gait-based emotion recognition. The existing contrastive learning approaches are rarely suitable for learning skeleton-based gait representations, which suffer from limited gait diversity and inconsistent semantics. In this paper, we propose a Cross-coordinate contrastive learning framework utilizing Ambiguity samples for self-supervised Gait-based Emotion representation (CAGE). First, we propose ambiguity transform to push positive samples into ambiguous semantic space. By learning similarities between ambiguity samples and positive samples, our model can learn higher-level semantics of the gait sequences and maintain semantic diversity. Second, to encourage learning the semantic invariance, we uniquely propose cross-coordinate contrastive learning between the Cartesian coordinate and the Spherical coordinate, which brings rich supervisory signals to learn the intrinsic semantic consistency information. Exhaustive experiments show that CAGE improves existing self-supervised methods by 5%–10% accuracy, and it achieves comparable or even superior performance to supervised methods.

As the challenge of depression problems increases today, it is important to effective and timely detect for patients' treatments and depression prevention. Current methods of automated depression diagnosis depend almost entirely on audio, video, and Electroencephalogram (EEG) etc. In this paper, we propose a novel method to detect depression using gait data that is collected by Kinect camera. Its key components include a camera rectification method and a rigid-body representation of the human body. The camera rectification method achieves the purpose of improving data processing accuracy by changing the camera angle. The rigid-body representation can not only improve the robustness of detecting depression patients with noisy input, but also can reduce the classification time. We evaluate our method on the depression gait dataset in postgraduate students. The proposed method has an outstanding performance in classic machine learning algorithms, and the best accuracy can achieve 88.89%. Our solution provides a new method for automatic depression detection (ADD) that has exciting implications in clinical theory and practice, and has the advantages of high accuracy, inexpensive, low time cost, and no-contact.

Nowadays, e-commerce has become prevalent world-wide. With the big success of e-commerce, many malicious promotion services also rise: with the goal of increasing sales, malicious merchants attempt to promote their target items by illegally optimizing the search results using fake visits, purchases, etc. In this paper, we study the fraud detection problem on large-scale e-commerce platforms. First, we develop an efficient and scalable AnTi-Fraud system (ATF) to detect e-commerce frauds for large-scale e-commerce platforms, and implement it in parallel on a large-scale computing platform, called Open Data Processing Service (ODPS). Then, we evaluate ATF using two real large-scale e-commerce datasets (with tens of millions users and items). The results demonstrate that both the precision and the recall of ATF can achieve 0.97+, which suggests that ATF is very effective. More importantly, we deploy ATF on the Taobao platform of Alibaba, which is one of the world's largest e-commerce platforms. The evaluation results show that ATF can also achieve an accuracy of 98.16% on Taobao, which again suggests that ATF is very effective and deployable in practice. Our study in this paper is expected to shed light on defending against online frauds for practical e-commerce platforms.

Conducting fraud transactions has become popular among e-commerce sellers to make their products favorable to the platform and buyers, which decreases the utilization efficiency of buyer impressions and jeopardizes the business environment. Fraud detection techniques are necessary but not enough for the platform since it is impossible to recognize all the fraud transactions. In this paper, we focus on improving the platform's impression allocation mechanism to maximize its profit and reduce the sellers' fraudulent behaviors simultaneously. First, we learn a seller behavior model to predict the sellers' fraudulent behaviors from the real-world data provided by one of the largest e-commerce company in the world. Then, we formulate the platform's impression allocation problem as a continuous Markov Decision Process (MDP) with unbounded action space. In order to make the action executable in practice and facilitate learning, we propose a novel deep reinforcement learning algorithm DDPG-ANP that introduces an action norm penalty to the reward function. Experimental results show that our algorithm significantly outperforms existing baselines in terms of scalability and solution quality.

Firstly, the paper gives a brief introduction to worldwide applications of specified traffic signal controllers which output direct-current signal. Secondly, related technologies to develop such traffic signal controllers as well as the problems that need attention are addressed in detail. Thirdly, several real benefits of using DC traffic signal controllers are given. Finally, the prospects of direct-current traffic signal controllers are analyzed and concluded.

FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment.

Fountain-code based cloud storage system provides reliable online storage solution through placing unlabeled content blocks into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement could introduce additional delay. In this paper, we show that multiple stage retrieval of fragments is effective to reduce the file-retrieval delay. We first develop a delay model for various multiple stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given requirements on success decodability. Our numerical results suggest a fundamental tradeoff between the file-retrieval delay and the target probability of successful file decoding, and that the file-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

Indirect field oriented control of induction machine has been widely applied in industry applications. This control scheme is relatively simple but strongly relies on the parameters of the machine, such as the rotor time constant and mutual inductance. The parameter deviation could result in the decrease of control performance, and this is vital especially for the torque control mode. In this paper, the effects of the deviation of rotor time constant and mutual inductance on the rotor flux and the electromagnetic torque are discussed in details. A new algorithm of on-line parameter estimation using the steady state voltage model based rotor flux observation is proposed, in which the rotor time constant and mutual inductance are corrected by the error of phase angle and magnitude of the observed rotor flux. The simulation and experimental results validates the effectiveness in improving torque control of the new method.

Luby transform (LT) codes provide an efficient way to transfer information over erasure channels. Past research has shown that LT codes can perform well for a large number of input symbols. However, mathematical analysis and simulation results have revealed that the packet overhead for LT decoders can be as large as 100% when the number of input symbols is small. Designing an efficient decoder to handle a small number of symbols becomes an imminent research issue. In this paper, we make an observation that LT decoders often fail to recover all the input symbols, while LT encoders have a high probability of producing a full-rank coefficient matrix. Motivated by this observation, we propose a novel decoding algorithm called LT-W, in which we incorporate the use of the Wiedemann solver into LT decoding to extend the decodability of LT codes. Extensive experiments show that our proposed method reduces the packet overhead significantly and yet preserves the efficiency of the original LT decoding process.

Among different setups of cloud storage systems, fountain-codes based distributed cloud storage system provides reliable online storage solution through placing coded content fragments into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement introduces additional delay, which is critical for content retrieval or downloading applications. In this paper, we show that multiple-stage retrieval of fragments is effective to reduce the content-retrieval delay. We first develop a delay model for various multiple-stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given the success decodability requirement. Our numerical results demonstrate that the content-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

Rateless codes such as LT codes have become more and more popular due to their abilities to handle varying channel conditions without much feedback. However, rateless codes have the drawback of unable to provide intermediate outputs when delivering layer-coded media content. Although some methods have been proposed to produce intermediate outputs based on adjusting the distribution of LT codes, they are typically content-dependent and unable to guarantee that a more important layer can always be decoded before the decoding of a less important layer. In this paper, we propose a simple joint unequal loss protection (ULP) and LT coding (ULP-LT) scheme for layered media delivery, where different numbers of FEC are allocated to different layers to guarantee the priority and LT codes are used to deal with varying channel conditions. Simulation results show that with a small amount of overhead allocated to ULP, the ULP-LT scheme can produce good intermediate performance while still enjoying the nice features provided by LT codes.