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IEEE Conference on Wireless Communications and Networking

作者： Wei Huang Deyu Zhang Wei Luo Yin Tang School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Edge server placement is a hot issue in mobile edge computing. It is a key prerequisite for deploying edge servers that can meet computing needs and improve resource utilization. This paper studies the joint location deployment problem of edge server and UAV, and proposes a dynamic placement solution for heterogeneous servers based on dynamically changing network environments and computing resource requirements, with the goal of minimizing migration costs. Extensive comparative experiments based on real datasets demonstrate that our algorithm outperforms other algorithms in minimizing costs.

关键词： Multi-access edge computing Costs Heuristic algorithms Prevention and mitigation Autonomous aerial vehicles Dynamic scheduling Servers Resource management Vehicle dynamics

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Application of Large Language Models in Intelligent Preprocessing and Forecasting of Electricity Price

Application of Large Language Models in Intelligent Preproce...

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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Yutian Huang Linghan Huang Yachao Zhu Gang Lei Allen Wang Jianguo Zhu School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia School of Electrical and Computer Engineering The University of Sydney Camperdown Australia S&P Global Commodity Insights Singapore

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

With the gradual liberalization of global energy markets, accurate electricity price forecasting is crucial for effective system operation and market-oriented reforms—especially for emerging Virtual Power Plants (VPPs) that rely on precise market insights. Traditional forecasting methods often struggle to account for the influence of complex unstructured data, such as news events, on price dynamics. To address this challenge, we propose a novel data preprocessing method that leverages large language models (LLMs) to automatically extract, classify, and derive insights from unstructured news articles (sourced from platforms such as WattClarity), which are then integrated with high-frequency electricity price data. To ensure data consistency, a weighted mean algorithm aggregates price records from 5-minute to 30-minute intervals. Our case study on a multi-source dataset demonstrates that this LLM-based technique not only effectively captures critical market signals from textual data—providing a robust framework for supporting market-driven decision-making for VPPs-but also overcomes the limitations of traditional forecasting approaches by harnessing the unique capabilities of LLMs. The integration of unstructured data processing with precise data aggregation underscores the practical advantages of our approach in modern electricity market applications.

关键词： Electric potential Accuracy Electricity Large language models Predictive models Chatbots data models Classification algorithms data mining Forecasting

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Enhancing Session-Based Recommendation with Hypergraph Motifs and Contrastive Learning

Enhancing Session-Based Recommendation with Hypergraph Motif...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tingxuan Chen Liu Yang Zidong Wang Guohui Li Jun Long School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Session-based recommendation (SBR) provides personalized recommendations by analyzing the interactions of anonymous session users. Recent approaches based on graph neural networks (GNNs) focus on pairwise relations to infer potential user preferences. However, real-world user interactions are often complex, involving high-order relations that GNNs may not fully capture. Hypergraphs can naturally model high-order relations, while having untapped potential in SBR. In this paper, we propose a hypergraph convolutional network (MoHyNet) based on hypergraph motifs to improve SBR. Specifically, we construct a hypergraph convolution to extract high-order relations among users, thereby reducing the impact of intra-session irrelevant information on recommendations. Additionally, we introduce hypergraph motifs to characterize users’ behavioral patterns, thus enhancing inter-session information mining. Besides, we incorporate contrastive learning to strengthen the representation of the current session. Extensive experiments on multiple real-world datasets demonstrate the superiority of our proposed model over state-of-the-art approaches.

关键词： Convolution Contrastive learning Graph neural networks Acoustics Convolutional neural networks data mining Speech processing

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Co-Learning Bayesian Optimization

arXiv

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arXiv 2025年

作者： Guo, Zhendong Ong, Yew-Soon He, Tiantian Liu, Haitao The Data Science and Artificial Intelligence Research Center School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Energy and Power Engineering Dalian University of Technology Dalian116024 China

Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such suboptimal problem of BO can attribute to the poor surrogate accuracy of the trained Gaussian process (GP), particularly that in the regions where the optimal solutions locate. Hence, we propose to build multiple GP models instead of a single GP surrogate to complement each other and thus resolving the suboptimal problem of BO. Nevertheless, according to the bias-variance tradeoff equation, the individual prediction errors can increase when increasing the diversity of models, which may lead to even worse overall surrogate accuracy. On the other hand, based on the theory of Rademacher complexity, it has been proved that exploiting the agreement of models on unlabeled information can help to reduce the complexity of the hypothesis space, and therefore achieving the required surrogate accuracy with fewer samples. Such value of model agreement has been extensively demonstrated for co-training style algorithms to boost model accuracy with a small portion of samples. Inspired by the above, we propose a novel BO algorithm labeled as co-learning BO (CLBO), which exploits both model diversity and agreement on unlabeled information to improve the overall surrogate accuracy with limited samples, and therefore achieving more efficient global optimization. Through tests on five numerical toy problems and three engineering benchmarks, the effectiveness of proposed CLBO has been well demonstrated. © 2025, CC BY.

关键词： Semi-supervised learning

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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A Lightweight Aerial Image Object Detector Based on Mask Information Enhancement

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Liming Zhao, Shuai Liu, Zhehao Zhang, Wanjun Qiao, Baojun Liu, Yang Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng China Henan University School of Computer and Information Engineering Henan Kaifeng China

Object detection in UAV imagery is highly valuable in both military and civilian domains. However, deploying conventional detectors is challenging due to limited computing resources on UAV platforms. Additionally, UAV images often contain densely arranged objects, demanding higher accuracy from detectors. In response to these challenges, we proposed the lightweight aerial object detection method based on mask feature enhancement, named BFDet. First, to enhance the feature extraction capability of the model under limited parameters, we designed the Balanced Channel Attention Layer (BCA Layer). The BCA Layer improves the extraction of high-quality channel information during feature processing. Second, to enhance model performance while minimizing parameters, we designed a feature extraction network utilizing the Efficient Feature Extraction Module (EFEM) and the Downsampling Module (DM) as its backbone. This design reduces the number of parameters while improving the extraction of discriminative features. Third, to enrich the semantic information of features, we proposed a Progressive Space Pyramid Pooling (PSPP) module, obtaining features with rich semantic information to enhance model performance. Finally, to tackle the challenge of detecting densely packed objects, we proposed a Mask Information Enhancement Module (MIEM) to obtain the fine-grained features, enhancing the model's discriminative ability in dense scenes. The proposed BFDet underwent extensive experimentation on both the Vis-Drone and UAVDT datasets. The mAP0.5 and FPS values of BFDet reach 51.4% and 33 respectively on the VisDrone dataset, and the parameter number is only 5.6M. © 1963-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints

arXiv

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arXiv 2025年

作者： Gao, Yansong Peng, Huaibing Ma, Hua Dai, Zhiyang Wang, Shuo Hu, Hongsheng Fu, Anmin Xue, Minhui The School of Computer Science and Software Engineering The University of Western Australia Australia The School of Computer Science and Engineering Nanjing University of Science and Technology China Data61 CSIRO Australia The School of Cyber Science and Engineering Shanghai Jiao Tong University China The School of Information and Physical Sciences The University of Newcastle Australia

For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprints within the targeted underlying victim models, we present a fresh temporal paradigm for understanding these attacks. Of paramount discovery is that these imprints are encapsulated within a single loss metric, spanning universally across diverse tasks such as classification and regression, and modalities including image, text, and audio. Recognizing the distinct nature of loss between adversarial and clean examples, we exploit this temporal imprint for AE detection by proposing TRAIT (Traceable Adversarial Temporal Trajectory Imprints). TRAIT operates under minimal assumptions without prior knowledge of attacks, thereby framing the detection challenge as a one-class classification problem. However, detecting AEs is still challenged by significant overlaps between the constructed synthetic losses of adversarial and clean examples due to the absence of ground truth for incoming inputs. TRAIT addresses this challenge by converting the synthetic loss into a spectrum signature, using the technique of Fast Fourier Transform to highlight the discrepancies, drawing inspiration from the temporal nature of the imprints, analogous to time-series signals. Across 12 AE attacks including SMACK (USENIX Sec’2023), TRAIT demonstrates consistent outstanding performance across comprehensively evaluated modalities (image, text, audio), tasks (classification and regression), datasets (nine datasets), and model architectures (e.g., ResNeXt50, BERT, RoBERTa, AudioNet). In all scenarios, TRAIT achieves an AE detection accuracy exceeding 97%, often around 99%, while maintaining a false rejection rate of 1%. TRAIT remains effective under the formulated strong adaptive attacks. Copyright © 2025, The Authors. All rights reserved.

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Channel Gain Map Estimation for Wireless Networks Based on Scatterer Model

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IEEE Transactions on Wireless Communications 2025年

作者： Sun, He Zhu, Lipeng Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a sign

关键词： Least squares approximations

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Performance Analysis of Differential Amplifiers Using Cadence with GPDK45, GPDK90, and GPDK180

Performance Analysis of Differential Amplifiers Using Cadenc...

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Devices for Integrated Circuit (DevIC)

作者： Tomal Suvro Sannyashi Sk Babul Akhtar Tanmay Sinha Roy Biswajit Jana Soumen Ghosh Electronics and Communication Engineering Swami Vivekananda University Kolkata Electrical and Electronics Engineering Institute of Engineering and Management Kolkata School of Computer Science Engineering and Technology Bennett University UP Computer Science and Engineering (Data Science) Haldia Institute of Technology West Bengal

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a comprehensive analysis of differential amplifier circuits designed using three process technologies: GPDK45, GPDK90, and GPDK180. Utilizing Cadence design and simulation tools, we perform detailed simulations to evaluate and compare key performance parameters of these architectures. The analysis focuses on critical metrics such as propagation delay, gain, power consumption, and bandwidth, which play a pivotal role in determining the overall efficiency and suitability of differential amplifiers for various applications. The simulation results highlight the impact of technology scaling on the performance characteristics, offering insights into trade-offs and optimization strategies. By systematically comparing the outputs across these process nodes, the study provides valuable guidelines for the selection of process technology based on specific design requirements and application domains.

关键词： Performance evaluation Power demand Simulation Bandwidth Very large scale integration Differential amplifiers Performance analysis Optimization Propagation delay Guidelines

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