Electric vehicles (EVs) are a major load on the demand side. Flexible aggregation control of EVs can fully adjust clean energy consumption and promote the interaction between EVs and power grid. Due to hierarchical characteristic and differentiated quality of service (QoS) demands in EV aggregation control, a hierarchical edge network is needed to perform real-time, accurate and bi-directional data interaction. Therefore, this paper proposes a hierarchical edge communication network which featured “regional cloud-unmanned aerial vehicle (UAV) aggregator-EVs”. For the “EVs-UAV aggregator” layer, the UAV dynamic mobility deployment algorithm by Dueling deep Q-network (DQN) and the UAV static optimal position calculation method are combined to reduce transmission time delay and energy consumption. For the “UAV aggregator-regional cloud” layer, considering the differential requirements of time and accuracy while uploading load data and downloading control instructions, this paper established a transmission cost optimization problem with adjustable weights. Besides, the sub-channel resource allocation based on stable matching and the uploading decision based on game theory are designed to solve the problem iteratively. Ultimately, comparative simulations with different transmission strategies are provided to verify the effectiveness of the hierarchical transmission model in scenario of EVs aggregation control.

Recently, deep unfolding networks (DUNs) have emerged as a promising technique for image Compressive Sensing (CS) reconstruction by unfolding optimization algorithms, where each stage of the DUNs corresponds to an iteration of the optimization algorithm. DUNs can be divided into convex optimization based methods and non-convex optimization based methods. On the one hand, DUNs based on convex optimization algorithms cannot handle non-convex optimization problems, thereby limiting their use when the prior term is a non-convex function. On the other hand, although DUNs based on non-convex optimization algorithms can handle more complex prior terms to make global optimal solutions closer to the ground truth, there is a high probability that they converge only to a local optimum. Therefore, in practical applications, it is necessary to consider the various characteristics of the problem comprehensively, then design appropriate prior terms and choose convex or non-convex optimization in DUN. This paper proposes ViP-DUN method to learn suitable prior terms and adaptively use convex or non-convex optimization. ViP-DUN learns deep prior terms and variable metrics in a data-driven manner to achieve adaptive use of convex or non-convex optimization. Moreover, we designed a lightweight multi-scale information fusion module in ViP-DUN at the network structure level to further enhance the network's processing capability. Experiments demonstrate that our proposed method can improve image reconstruction quality at multiple compression rates through the adaptive capabilities of the network. Our complete code will be made publicly available upon acceptance.

Speaker verification performance significantly degrades when there exists a language mismatch between training and evaluation. Domain Adversarial Training (DAT) has shown to be effective in mitigating this gap by incorporating adversarial training with domain information (language id). Inspired by recent research of DAT, we propose to decompose and locate features sensitive to speaker identity, so that domain adaptation can better serve the speaker classification task. Additionally, we further promote DAT by replacing alignment based on language identification with alignment of fine-grained phonetic information, where pre-trained speech recognition models are utilized to provide frame-level phonetic labels. The speaker verification model is trained on VoxCeleb, while CnCeleb is used for adversarial training and evaluation. Results show the methods effectively mitigate the performance degradation caused by language mismatch.

Empathy detection (ED) and empathy intent classification (EIC) aim to identify the empathy direction expressed in user utterances and the underlying empathy intent behind them. Previous studies show that facilitating information transfer between tasks can enhance model performance. However, the interaction between ED and EIC in few-shot learning remains underexplored. To this end, we identify the challenges in jointly training ED and EIC in a few-shot setting: establishing effective information transfer between them and improving the model’s generalization capability. We propose a novel model called USB. For information transfer, the interactive module maps empathy and empathy intent labels through utterances to model task correlations. For generalization capability, after capturing empathy and empathy intent representations with an adaptive fusion module, we introduce a multi-level contrastive learning strategy to optimize representations at task and label levels, enhancing generalization. Experimental results on two public datasets show that our model outperforms all baselines.

Empathy detection (ED) aims to understand the user’s empathy direction, while empathy intent classification (EIC) focuses on identifying the empathy intent behind the user’s utterance. Both tasks have garnered significant attention. Recent studies have shown that jointly training these tasks can improve model performance, as their correlation enhances the diversity of information. However, previous studies have relied solely on shallow information transfer between two task representations, failing to fully leverage the inter-task correlation, thus limiting performance. To this end, we propose a novel Label Co-occurrence Transformation Network (LCoT-Net), which models the correlation between the two tasks using the co-occurrence matrix of empathy and empathy intent labels as a medium. By performing category feature transformation at both the label and utterance levels, we achieve two-level mutual task guidance. Experimental results demonstrate that our model achieves competitive performance across various settings on two public datasets.

Aspect-Based Sentiment Analysis (ABSA) seeks to determine the sentiment polarity of specific aspects within a text. Despite the strong performance of Graph Neural Networks (GNNs) based on dependency syntax trees in ABSA, existing methods often fail to differentiate the importance of dependency relations and inadequately capture semantic interactions, limiting their ability to detect implicit sentiments. To address these limitations, we propose Dual-stage Dependency Enhanced Graph Transformer (DSDGT), a serial architecture that combines Transformer and Graph Convolutional Network (GCN) modules with an enhanced dependency mechanism. The Transformer module captures global semantic information, while the GCN models local dependencies. A dual-stage feature processing strategy is employed to integrate both original and enhanced dependency features effectively. Experiments on multiple benchmark datasets demonstrate that DSDGT achieves superior performance in terms of accuracy and F1 scores compared to state-of-the-art methods.

With the intelligent development of vehicles, the number of vehicles equipped with millimeter‐wave (mmWave) radars is increasing, and the possibility of interference between radars is rising dramatically. In automatic driving, it will be common for target detection to be affected by multiple interfering radars. Addressing the mutual interference challenges, an adaptive interference detection method based on support vector machines (SVMs) is proposed. First, a window selection is performed on the received signal and features describing the difference between the normal signal and the interference are extracted. Then, we use a nonlinear SVM to distinguish between the interference and the normal signal. After completing the localization of the interference, we use an autoregressive (AR) prediction model to reconstruct the target echo signal. Results from both multiple interference simulation scenarios and real experimental scenarios show that the accuracy of interference localization and the effect of interference mitigation of the proposed method outperforms the mainstream methods.

Empathy plays an important role in the human dialogue.Detecting the empathetic direction expressed by the user is necessary for empathetic dialogue systems because it is highly relevant to understanding the user's needs.Several studies have shown that empathy intent information improves the ability to response capacity of empathetic dialogue.However, the interaction between empathy detection and empathy intent recognition has not been explored.To this end, we invite 3 experts to manually annotate the healthy empathy detection datasets IEMPATHIZE and TwittEmp with 8 empathy intent labels, and perform joint training for the two tasks.Empirical study has shown that the introduction of empathy intent recognition task can improve the accuracy of empathy detection task, and we analyze possible reasons for this improvement.To make joint training of the two tasks more challenging, we propose a novel framework, Cascaded Label Signal Network, which uses the cascaded interactive attention module and the label signal enhancement module to capture feature exchange information between empathy and empathy intent representations.Experimental results show that our framework outperforms all baselines under both settings on the two datasets.1

The goal of an empathetic dialogue generative sys-tem is to generate coherently and relate emotional responses after finite turns of dialogue. Most of the previous work guided the dialogue system to generate empathetic responses from the dialogue history or emotional reasons. However, in people's daily communication, the empathetic response is often generated by deep reasoning through dialogue history and existing commonsense knowledge. To fill this gap, we propose an empathetic dialogue model of unidirectional synchronous interactive reasoning, Emp- Usir.Specifically, according to the existing commonsense knowledge base, we extract commonsense knowledge with emotional representation from it, and use it as the connection feature of dialogue history and remaining common-sense knowledge to guide the interactive reasoning between multi-turns dialogue history and commonsense knowledge, to imitate human reasoning based on commonsense to generate responses. Then, we propose a cross-token level attention mechanism to learn emotional dependence from reasoning features, to produce more smooth and diverse empathy responses. The experimental results and manual evaluation prove the effectiveness of the Emp-USIR model proposed in this paper on the widely-used benchmark dataset.

The progress of the times has brought about a leap forward in people’s thinking. Under the rapid economic development environment, the physical field of young people has been unable to withstand the current teaching system, and many problems of poor physical fitness have emerged. In order to solve similar problems, to improve the physical quality of young people, it is bound to find a new teaching method different from the traditional physical education teaching in a special environment, The research on the integration of virtual reality technology and teaching has been rolling forward and never stopped in recent years. With the continuous upgrading of virtual reality technology, virtual reality devices that can be used have already joined the ranks of families. Let these virtual reality devices connect to the Internet through the WEB application settings, and then design according to different situations has become a reality. This research is an application development based on Web virtual reality technology, including network virtual reality technology, application modeling design and the use of Internet connection. With reference to this application, physical education teaching and classroom practice data, this research mainly introduces the current research situation of virtual reality technology and teaching, and establishes the model based on the integration of Web application design and virtual reality technology, Extract, including but not limited to the number of projects, activities and experiences, use the Internet of Things data upload and data processing technology to complete the data screening, obtain valuable data, and then use these data for the practical application value of virtual reality technology in physical education teaching. Complete the improvement. It is in line with the integration of modern virtual reality technology and physical education teaching to obtain data that can reflect the real situation, then conduct practical teaching, obtain reliable practical data, and evaluate teaching. It can be clearly seen from the research results that the combination of virtual reality technology and physical education teaching is of great significance to improve students’ interest in learning and enthusiasm for sports, but there are also some shortcomings, such as different teaching steps and goals will affect students’ enthusiasm for learning. Therefore, further improvement is needed to find a stable way to improve students’ enthusiasm for learning. The integration of virtual reality technology and physical education teaching is proposed.

Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator–PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.

A lot of Pull Requests (PRs) appear in Github everyday, and thus it is a very important work to review these PRs quickly in Github. Labeling PRs according to the PRs classification can improve the success rate and the review efficiency. However, recent research works have shown that most of the PRs are not labeled, and if the PR is labeled, it is done manually. To solve this problem, we propose a supervised classification model combined with supervised topics model and Naive Bayes classifier, which can make the PR be classified automatically. The method creates a one-one relationship between labels and PRs, and the approach classifies most PRs automatically with the only label which record the closest topic of PRs. The experimental results show that the proposed model can reach a precision of 60% in majority situation. The proposed model may support a better result via adjusting the parameters in case.

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.

A novel method is proposed for moving wheeled vehicle and tracked vehicle classification using micro-Doppler features from returned radar signals within short dwell time. In this method, an adaptive analysis technique called Empirical Mode Decomposition (EMD) is utilized to decompose the motion components of moving vehicles, and a hierarchical classification structure using the decomposition results of returned signals is proposed to discriminate the two kinds of vehicles. The first stage of the structure elementarily identifies the tracked vehicle data by checking the existence of its unique feature and a further classification via our proposed features based on EMD is implemented in the second stage by using Support Vector Machine (SVM) classifier. Experimental results based on the simulated data and measured data are presented, including the performance analysis for low signal-to-noise ratio (SNR) case, generalization evaluation for different target circumstances and comparison with some related methods.

We present Avalanche, a prototyping framework that addresses the issues of power estimation and optimization for mixed hardware and software embedded systems. Avalanche is based on a generic embedded system architecture consisting of embedded CPU, custom hardware, and a memory hierarchy. For system-level power estimation, given various system parameters like cache sizes, cache policies, and bus width, etc., Avalanche is able to rapidly evaluate/estimate power and performance and thus facilitate comprehensive design space explorations. For system-level power optimization, Avalanche offers different modes reflecting various design scenarios: if no hardware/software partitioning or only partial partitioning has been conducted, Avalanche guides the designer in finding power-aware hardware/software partitioning; when a system has already been partitioned, Avalanche can optimize system parameters such as cache and memory size; if system parameters and partitioning are given, Avalanche applies additional optimizations for power including source-to-source compiler transformations. Avalanche has been deployed during the design phase of real-world applications including an MPEG II encoder in a set-top box design. Extensive design space explorations in terms of power and performance could be conducted within several hours and various optimization techniques led to power reductions of up to 94% without performance losses and only a slight increases in total chip size (i.e., transistor count).

In this paper we describe a new hardware/software partitioning approach for embedded reconfigurable architectures consisting of a general-purpose processor (CPU), a dynamically reconfigurable datapath (e.g. an FPGA), and a memory hierarchy. We have developed a framework called Nimble that automatically compiles system-level applications specified in C to executables on the target platform. A key component of this framework is a hardware/software partitioning algorithm that performs fine-grained partitioning (at loop and basic-block levels) of an application to execute on the combined CPU and datapath. The partitioning algorithm optimizes the global application execution time, including the software and hardware execution times, communication time and datapath reconfiguration time. Experimental results on real applications show that our algorithm is effective in rapidly finding close to optimal solutions.

We present hardware ML (HML), an innovative hardware description language (HDL) based on the functional programming language SML. Features of HML not found in other HDL's include polymorphic types and advanced type checking and type inference techniques. We have implemented an HML type checker and a translator for automatically generating VHDL from HML descriptions. We generate a synthesizable subset of VHDL and automatically infer types and interfaces. This paper gives an overview of HML and discusses the translation from HML to VHDL and the type inference process.

This paper introduces the first hardware/software co-synthesis algorithm of distributed real-time systems that optimizes the memory hierarchy along with the rest of the architecture. Memory hierarchies (caches) are essential for modern embedded cores to obtain high performance. They also represents a significant portion of the cost, size and power consumption of many embedded systems. Our algorithm synthesizes a set of real-time tasks with data dependencies onto a heterogeneous multiprocessor architecture that meets the performance constraints with minimized cost. Unlike previous work in co-synthesis, our algorithm not only synthesizes the hardware and software portions of the applications, but also the memory hierarchies. It chooses cache sizes and allocates tasks to caches as part of cosynthesis. The algorithm is built upon a task-level performance model for memory hierarchies. Experimental results, including examples from the literature and results on real-life examples such as an MPEG-2 encoder, show that our algorithm is efficient, and compared with existing algorithms, it can reduce the overall cost of the synthesized system.

Embedded system design is one of the most challenging tasks in VLSI CAD because of the vast amount of system parameters to fix and the great variety of constraints to meet. In this paper we focus on the constraint of low energy dissipation, an indispensable peculiarity of embedded mobile computing systems. We present the first comprehensive framework that simultaneously evaluates the tradeoffs of energy dissipations of software and hardware such as caches and main memory. Unlike previous work in low power research which focused only on software or hardware, our framework optimizes system parameters to minimize energy dissipation of the overall system. The trade-off between system performance and energy dissipation is also explored. Experimental results show that our Avalanche framework can drastically reduce system energy dissipation.

This paper introduces the first hardware/software co-synthesis algorithm of distributed real-time systems that op-timizes memory hierarchy along with the rest of the archi-tecture. Our algorithm synthesize a set of real-time tasks with data dependencies onto a heterogeneous multiproces-sor architecture that meets the performance constraints with minimized cost. Our algorithm chooses cache sizes and al-locates tasks to caches as part of co-synthesis. Experimental results, including examples from the literature and results on an MPEG-2 encoder, show that our algorithm is efficient and compared with existing algorithms, it can reduce the overall cost of the synthesized system. 1

This paper introduces the first high-level (task-level)model of hierarchical memories and describes a scheduling andallocation algorithm for system-level synthesis of heterogeneousmultiprocessors. Caches are essential for modern RISC embeddedcores to obtain sustained high performance. However, caches havereceived limited use in priority-driven preemptive real-time systemsdue to the unpredictability of caches-average-case improvementsare of no use in systems with hard deadlines. Program-levelcache models do not take into account preemptions between multipletasks running at multiple rates on embedded cores. Our task-levelmodel of performance in the presence of memory hierarchiesprovides an efficient means to bound the guaranteed memory performanceof tasks running in a multi-rate, multi-tasking environment.Our system synthesis algorithm uses software-based cachepartitioning and reservation techniques to guarantee cache hitsfor some tasks and therefore improve task schedulability. Experimentalresults show that our model significantly improves schedulabilityof real-time tasks and can be evaluated efficiently duringsystem-level synthesis.

The authors survey the state-of-the-art in real-time operating systems (RTOSs) from the system synthesis point of view. RTOSs have a very long research history which provides important theoretical results and useful industrial implementations. Convergence of applications, technology, and market trends of embedded systems implies a strong need for new generation of RTOS. Therefore, new system synthesis problem areas, notably hardware/software co-design and synthesis for systems-on-silicon (SOS), are opening up new avenues for RTOS research and development. The paper starts with a survey of classical academic and industrial RTOS work and continues with a survey of recent results related to co-design and design systems-on-silicon. They conclude by outlining future directions for the SOS RTOS.