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arXiv 2025年

作者： Shan, Ying Chen, Jing Zhang, Zichong Wang, Liang Zou, Zhiqiang Li, Min College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China Nanjing Institute of Astronomical and Optics & Technology Chinese Academy of Sciences Nanjing210042 China CAS Key Laboratory of Astronomical Optics & Technology Nanjing Institute of Astronomical Optics & Technology Nanjing210042 China University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Jiangsu Nanjing211135 China

Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer’s perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs. © 2025, CC BY-SA.

关键词： Convolutional neural networks

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Image Encryption Methods in Deep Joint Source Channel Coding: A Review and Performance Evaluation

Image Encryption Methods in Deep Joint Source Channel Coding...

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International Conference on Computer and Communications (ICCC)

作者： Jialong Xu Bo Ai Wei Chen Ang Yang Peng Sun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou China Communication Research Institute vivo Mobile Communication Co. Ltd. Beijing China

ISBN: (纸本)9781665409513

With the introduction of the deep learning (DL), joint source and channel coding (JSCC) has achieved great success and has been demonstrated to have better performance especially in the case of low signal-to-noise ratios (SNRs) and limited bandwidth. As a promising wireless transmission technology, DL based JSCC (DJSCC) can serve different kinds of applications. However, some applications (e.g., biomedical, surveillance, financial and military) have strict requirements on data privacy and data security. Traditional image encryption methods are based on the shuffle operation and changing the intensity of each pixel in the plain image, which changes the ambient structure of the picture and degrades the reconstruct performance of the DJSCC. Perceptual image encryption methods for deep neural networks (DNNs) have been demonstrated to have similar classification accuracy in comparison to the DNNs trained on plain images. However, it is unknown how to conduct image encryption methods in the DJSCC framework. In this paper, we investigate different image encryption methods at first and compare their reconstruction performance. This research work provide a guidance for selecting the image encryption method suitable for DJSCC transmission.

关键词： Deep learning Wireless communication Performance evaluation data privacy Surveillance Neural networks Encryption

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Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

arXiv

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Editorial: AI, data analytics, and mechanism design for DER integration toward net zero

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Frontiers in Energy Research 2022年 10卷

作者： Wang, Hao Ye, Yujian Chen, Yue Chen, Yize Yang, Qing Cui, Qiushi You, Pengcheng Department of Data Science and AI Faculty of Information Technology and Monash Energy Institute Monash University MelbourneVIC Australia School of Electrical Engineering Southeast University Nanjing China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China

The energy system is undergoing a fundamental energy transition by integrating low-carbon distributed 5 energy resources (DERs) in distribution networks to accelerate net zero. The increased DER uptake 6 poses significant challenges in operating energy systems to achieve net zero with high reliability and low 7 cost. In particular, the inherent variability of renewable generation, such as solar photovoltaic systems, 8 brings significant uncertainties to the energy system, causing reliability concerns. DERs also cause power 9 quality issues, such as voltage fluctuations. But the distribution system was not designed to support large Our goal in putting together this research topic is to provide a glance at the wide variety of aspects, 51 spanning from technical modeling and assessment to market-based solutions enabling energy transition 52 towards net zero by integrating DERs. We are also aware that the four papers selected are a small fraction 53 of works focusing on DER integration. We hope that these articles will succeed in attracting readers for 54 further research on this significant research topic of smart grids.

关键词： net zero energy transition smart grid distributed energy resources (DER) artificial intelligence data analytics optimization

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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Few-Shot Semantic Segmentation with Democratic Attention Networks 16th

Few-Shot Semantic Segmentation with Democratic Attention Net...

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16th European Conference on Computer Vision, ECCV 2020

作者： Wang, Haochen Zhang, Xudong Hu, Yutao Yang, Yandan Cao, Xianbin Zhen, Xiantong Beihang University Beijing China Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China AIM Lab University of Amsterdam Amsterdam Netherlands Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates YouKu Cognitive and Intelligent Lab Alibaba Group Hangzhou China

ISBN: (纸本)9783030586003

Few-shot segmentation has recently generated great popularity, addressing the challenging yet important problem of segmenting objects from unseen categories with scarce annotated support images. The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images. In this paper, we propose the Democratic Attention Network (DAN) for few-shot semantic segmentation. We introduce the democratized graph attention mechanism, which can activate more pixels on the object to establish a robust correspondence between support and query images. Thus, the network is able to propagate more guiding information of foreground objects from support to query images, enhancing its robustness and generalizability to new objects. Furthermore, we propose multi-scale guidance by designing a refinement fusion unit to fuse features from intermediate layers for the segmentation of the query image. This offers an efficient way of leveraging multi-level semantic information to achieve more accurate segmentation. Extensive experiments on three benchmarks demonstrate that the proposed DAN achieves the new state-of-the-art performance, surpassing the previous methods by large margins. The thorough ablation studies further reveal its great effectiveness for few-shot semantic segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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A Social-Aware Mode Selection Algorithm for D2D Communications

A Social-Aware Mode Selection Algorithm for D2D Communicatio...

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International Conference on Communications, Signal Processing, and systems, CSPS 2018

作者： Lu, Wei-feng Lin, Wei-jun Chen, Si-guang Yang, Li-jun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China College of IoT Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

ISBN: (纸本)9789811362637

Aiming at mode selection in D2D communication, a mode selection strategy based on social-aware was proposed. In addition to physical connection status, users’ social relationships also served as an observation element. Through relationship between users, the closeness of user relationship can be judged, and then through users’ reference, the similarity between users can be obtained. In this way, by combining physical connection with social relationships, users’ aggregate utility was acquired. According to the physical connection quality and the social relationship strength, taking maximizing users’ aggregate utility as the goal, a random algorithm was designed to match users. Simulation results show that mode selection based on social perception can effectively improve users’ aggregate utility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Aggregates

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Improving the Efficiency of Blockchain Applications with Smart Contract based Cyber-insurance

Improving the Efficiency of Blockchain Applications with Sma...

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IEEE International Conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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Multi-Granularity Power Prediction for data Center Operations via Long Short-Term Memory Network

Multi-Granularity Power Prediction for Data Center Operation...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Ziyu Shen Xusheng Zhang Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.

关键词： Power demand data centers Task analysis Predictive models data models Time series analysis Servers

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Community-based Message Forwarding in Mobile Social Networks

Community-based Message Forwarding in Mobile Social Networks

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作者： Zhiming Chen Yang Xiang School of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing

With the popularity of various smart devices and the application of sensor network technology, message transmission using mobile devices is becoming *** paper focuses on the forwarding in mobile social network(MSN).The MSN is a special Delay Tolerant Network(DTN) consisting of mobile *** MSN, nodes move and share information with each other through carried short-range wireless communication *** nodes in the MSN typically access some building areas more frequently, such as schools, companies, or apartments, while visiting other areas, such as the roads between buildings, less *** building areas that nodes frequently visit are called *** increase delivery ratio and reduce transmission time in MSN, this paper proposes a novel zero-knowledge multi-copy routing algorithm, Mixed Message Forwarding(MMF) which exploits and improves the metric, namely *** reflects the importance of a node in the *** improves copy diffusion by using different directions of node movement as *** facilities called boundary boxes are added to the network *** boxes are special throw *** boxes are relays with large storage space and fixed *** is designed and evaluated, which utilizes the aforementioned boundary boxes to reduce transmission *** simulation results show that the MMF can improve the delivery ratio and reduce the transmission delay, compared with other algorithms.

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