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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiao Bai Jin Zheng Xin Ning School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

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VF-PS: how to select important participants in vertical federated learning, efficiently and securely? 22

VF-PS: how to select important participants in vertical fede...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Jiang Lukas Burkhalter Fangcheng Fu Bolin Ding Bo Du Anwar Hithnawi Bo Li Ce Zhang School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Wuhan University and ETH Zürich and OceanBase Ant Group ETH Zürich Peking University Alibaba Group School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering National Engineering Research Center for Multimedia Software Wuhan University University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713871088

Vertical Federated Learning (VFL), that trains federated models over vertically partitioned data, has emerged as an important learning paradigm. However, existing VFL methods are facing two challenges: (1) scalability when # participants grows to even modest scale and (2) diminishing return w.r.t. # participants: not all participants are equally important and many will not introduce quality improvement in a large consortium. Inspired by these two challenges, in this paper, we ask: How can we select l out of m participants, where l C m, that are most important? We call this problem Vertically Federated Participant Selection, and model it with a principled mutual information-based view. Our first technical contribution is VF-MINE—a Vertically Federated Mutual INformation Estimator—that uses one of the most celebrated algorithms in database theory—Fagin's algorithm as a building block. Our second contribution is to further optimize VF-MINE to enable VF-PS, a group testing-based participant selection framework. We empirically show that vertically federated participation selection can be orders of magnitude faster than training a full-fledged VFL model, while being able to identify the most important subset of participants that often lead to a VFL model of similar quality.

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Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI

arXiv

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arXiv 2024年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The Microwave and Fiber Optics Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece The Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

Stacked intelligent metasurface (SIM) is an emerging design that consists of multiple layers of metasurfaces. A SIM enables holographic multiple-input multiple-output (HMIMO) precoding in the wave domain, which results in the reduction of energy consumption and hardware cost. On the ground of multiuser beamforming, this letter focuses on the downlink achievable rate and its maximization. Contrary to previous works on multiuser SIM, we consider statistical channel state information (CSI) as opposed to instantaneous CSI to overcome challenges such as large overhead. Also, we examine the performance of large surfaces. We apply an alternating optimization (AO) algorithm regarding the phases of the SIM and the allocated transmit power. Simulations illustrate the performance of the considered large SIM-assisted design as well as the comparison between different CSI considerations. © 2024, CC BY.

关键词： Energy utilization

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Framework of software design patterns for energy-aware embedded systems 15

Framework of software design patterns for energy-aware embed...

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15th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2020

作者： Schaarschmidt, Marco Uelschen, Michael Pulvermuller, Elke Westerkamp, Clemens Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany Software Engineering Research Group University of Osnabrück Germany

ISBN: (纸本)9789897584213

With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Embedded systems

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History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

arXiv

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arXiv 2023年

作者： Zhang, Tong Liu, Yong Li, Boyang Zeng, Zhiwei Wang, Pengwei You, Yuan Miao, Chunyan Cui, Lizhen School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore Joint NTU-UBC LILY Research Centre Nanyang Technological University Singapore Alibaba Group China School of Software Shandong University China

With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated. In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses. Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory. Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms. Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a history-aware vocabulary. Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models. Human evaluation results support that HAHT generates more human-like, context-relevant and history-relevant responses than baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Packet Aggregation in Mobile networks for IoT Traffic 6

Packet Aggregation in Mobile Networks for IoT Traffic

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6th IEEE International Conference on engineering, Technologies and Applied Sciences, ICETAS 2019

作者： Khan, Ahmad Marwat, Safdar Nawaz Khan Ahmed, Salman Mehmood, Yasir University of Engineering and Technology Peshawar Dept. of Computer Systems Engineering Peshawar Pakistan Communication Networks Research Group University of Bremen Bremen Germany

ISBN: (纸本)9781728140827

IoT data traffic in future mobile networks is expected to grow significantly and, thus, pose challenges for service providers. IoT traffic is anticipated to be in the form of huge number of small data packets. These packets would consume radio resources inadequately and result in reduced spectral efficiency. This paper proposes a unique application of packet aggregation in mobile networks for enhanced spectral efficiency. Additionally, the timer mechanism for avoiding long delays is also incorporated to further improve the system performance. Results indicate that improvement in spectral efficiency would help in increased system capacity. © 2019 IEEE.

关键词： Spectrum efficiency

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DPEM: Dual-Perspective Enhanced Mamba for multivariate time series forecasting

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Information Fusion 2025年 123卷

作者： Hou, Rui Liu, Qiao He, Peng Liu, Yao Huang, Yanwen Xie, Jun Xie, Yang Dai, Tingting School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Guangdong Shenzhen518000 China 54th Research Institute China Electronics Technology Group Corporation Hebei Shijiazhuang050000 China

Multivariate time series forecasting (MTSF) aims to uncover hidden data patterns from historical sequences to predict future states. In practical scenarios, multivariate time series often exhibit different period patterns along the temporal perspective and complex inter-variable spatial dependencies across the variable perspective. Despite significant advancements in existing methods, explicitly capturing spatiotemporal dependence patterns with heterogeneity remains a fundamental challenge. To address these limitations, we propose a novel framework, Dual-Perspective Enhanced Mamba (DPEM), for multivariate time series forecasting. Specifically, we design the Multi-Scale Convolution Mamba (MSCM) module to capture complex periodic patterns from the Channel-Dependent (CD) perspective and the Adaptive Graph Convolution Mamba (AGCM) module to model intricate spatial dependencies among variables from the Channel-Independent (CI) perspective. These two modules explicitly extract heterogeneous temporal and spatial dependencies while mitigating potential interference during feature extraction. Furthermore, to facilitate bidirectional interactions between temporal and spatial features, we introduce a multi-head cross-attention mechanism, seamlessly integrating spatiotemporal dependencies captured from different perspectives. Finally, extensive experiments on 8 widely used benchmark datasets demonstrate that our proposed method achieves state-of-the-art (SOTA) performance and further validate that using Mamba as the backbone network significantly reduces computational costs. The implementation code is available at: https://***/HRFarsighted/DPEM. © 2025

关键词： Feature Selection

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Resource Optimization for LEO Constellation networks: A Multi-satellite Cooperative Coverage Design

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IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on computer Supported Cooperative Work in Design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media Information retrieval Encoding

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