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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Telli Khaled Kraa Okba Yassine Himeur Mohamed Boumehraz Abdelmalik Ouamane Kamel Benbraika Shadi Atalla Wathiq Mansoor Energy-Systems-Modelling Laboratory Mohamed Khider University Biskra Algeria College of Engineering and Information Technology University of Dubai Dubai UAE LI3C Laboratory University of Biskra Biskra Algeria Department of Computer Science University of El-Oued El-Oued Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network.

关键词： 6G mobile communication Base stations Privacy 5G mobile communication Satellite broadcasting Quality of service Path planning

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GraphThought: Graph Combinatorial Optimization with Thought Generation

arXiv

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arXiv 2025年

作者： Huang, Zixiao Guo, Lifeng Sheng, Junjie Chen, Haosheng Li, Wenhao Jin, Bo Lu, Changhong Wang, Xiangfeng School of Computer Science and Technology East China Normal University China School of Mathematical Sciences East China Normal University China School of Computer Science and Technology Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China Key Laboratory of Mathematics and Engineering Applications MoE East China Normal University China

Large language models (LLMs) have demonstrated remarkable capabilities across various domains, especially in text processing and generative tasks. Recent advancements in the reasoning capabilities of state-of-the-art LLMs, such as OpenAI-o1, have significantly broadened their applicability, particularly in complex problem-solving and logical inference. However, most existing LLMs struggle with notable limitations in handling graph combinatorial optimization (GCO) problems. To bridge this gap, we formally define the Optimal Thoughts Design (OTD) problem, including its state and action thought space. We then introduce a novel framework, GraphThought, designed to generate high-quality thought datasets for GCO problems. Leveraging these datasets, we fine-tune the Llama-3-8B-Instruct model to develop Llama-GT. Notably, despite its compact 8B-parameter architecture, Llama-GT matches the performance of state-of-the-art LLMs on the GraphArena benchmark. Experimental results show that our approach outperforms both proprietary and open-source models, even rivaling specialized models like o1-mini. This work sets a new state-of-the-art benchmark while challenging the prevailing notion that model scale is the primary driver of reasoning capability. © 2025, CC BY.

关键词： Combinatorial optimization

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A design method for high fabrication tolerance integrated optical mode multiplexer

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science China(Information sciences) 2020年第6期63卷 184-194页

作者： Bitao SHEN Haowen SHU Linjie ZHOU Xingjun WANG State Key Laboratory on Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Nano-optoelectronics Frontier Center of Ministry of Education Peking University

The tapered asymmetric directional coupler has the potential to realize high fabrication tolerance and high transmission efficiency on-chip mode multiplexer. However, the geometry parameter selection of tapered structure remains empirical. In this paper, we propose a design method for the tapered structure based on genetic algorithm. Combined with the adjusted coupling equations and interpolation method, lowtime-cost optimization can be realized. Three mode multiplexers(TE0&TE1, TE0&TE2 and TE0&TE5)are designed by our method. According to simulation results, the insertion loss of the designed devices is lower than 1.8 dB and the crosstalk is lower than-15 dB when the fabrication error is within required range(±10 nm for TE0&TE2 and TE0&TE5, and ±20 nm for TE0&TE1) in the bandwidth of 1.5–1.6 μm. In addition, the entire optimization process takes only 2 h for each device, which is around the time cost of a single 3 D simulation.

关键词： mode multiplexer directional coupler taper structure coupled-mode theory genetic algorithm

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Towards a Longitudinal Comparison Between Different Strategies for Android Malware Detection

Towards a Longitudinal Comparison Between Different Strategi...

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International Conference on Theoretical and Applicative Aspects of computer science (ICTAACS)

作者： Abdelhak Mesbah Ibtihel Baddari Mohamed Amine Riahla Department of Computer Science Faculty of Sciences LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria Department of Electrical Systems Engineering Faculty of Technology LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions.

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Decision-Feedback Single-Layer Read Reconstruction and Separation for Three-dimensional Magnetic Recording

Decision-Feedback Single-Layer Read Reconstruction and Separ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Yanzhe Liao Kezheng Zhang Yugen Jian Shaobing Wang Jincai Chen Ping Lu Ke Luo School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Three-dimensional magnetic recording (3DMR) based on energy-assisted recording techniques by either heat or microwave is a leading storage solution. Separation and detection are impeded by inter-symbol interference, inter-track interference, and inter-layer interference, especially when using traditional tunneling magnetoresistive (TMR) readers. To support a multi-layer magnetic recording system at an recording areal density of 4 Tbits/inch 2 on a dual-layer recording medium, we propose a neural network estimator to reconstruct the top-layer read response signal from the decision feedback of a Viterbi detector. Then we separate the bottom-layer signal by subtracting the reconstructed top-layer signal from the mixed readback signal. For the critical bound of channel bit error rate (BER), our method gains over 7 dB than a multiple-output detecting system.

关键词： Viterbi algorithm Magnetic separation Bit error rate Neural networks Interference Microwave theory and techniques Tunneling magnetoresistance

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Multi-Objective Archiving

arXiv

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arXiv 2023年

作者： Li, Miqing López-Ibáñez, Manuel Yao, Xin The School of Computer Science University of Birmingham United Kingdom Alliance Manchester Business School University of Manchester United Kingdom The Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multi-objective optimisation (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators;there is lack of systematic study of archiving methods from a general theoretical perspective. In this paper, we attempt to conduct a systematic overview of multiobjective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto compliant indicators (e.g., ϵ-indicator), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Quaternary Neural Network Equalization for Three-Dimentional Magnetic Recording

Quaternary Neural Network Equalization for Three-Dimentional...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Ke Luo Yufei Wu Yanzhe Liao Shaobing Wang Yugen Jian Jincai Chen Ping Lu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

One approach considered promising for advancing the development of magnetic data storage systems is three-dimensional magnetic recording. But when the recording density rises, media noise becomes a bigger problem for magnetic recording technology. Machine learning algorithms have been proven to outperform traditional numerical techniques in solving the magnetic recording signal quality issue. We compared the detection bit error rate (BER) performance of a per-layer shallow neural network equalizer and a quaternary neural network equalizer for two recording layers. For a two-layer recording density of 4 Tb/in 2 , the results show that the combined quaternary neural network equalization and detection scheme outperforms the per-layer shallow neural network equalization and one-dimensional Viterbi detection and outperforms the conventional two-dimensional generalized partial response equalizer combined with a quaternary Viterbi detector, especially for the bottom layer.

关键词： Machine learning algorithms Equalizers Viterbi algorithm Buffer storage Neural networks Bit error rate Noise

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CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Retrieval

CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Ret...

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International Conference on computer Supported Cooperative Work in Design

作者： Mengying Xu Hanjiang Lai Jian Yin School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P. R. China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods.

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SEE-MCAM: Scalable Multi-Bit FeFET Content Addressable Memories for Energy Efficient Associative Search

SEE-MCAM: Scalable Multi-Bit FeFET Content Addressable Memor...

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IEEE International Conference on computer-Aided Design

作者： Shengxi Shou Che-Kai Liu Sanggeon Yun Zishen Wan Kai Ni Mohsen Imani X. Sharon Hu Jianyi Yang Cheng Zhuo Xunzhao Yin College of Information Science and Electronic Engineering Zhejiang University China Department of Information and Computer Science University of California Irvine USA School of Electrical and Computer Engineering Georgia Institute of Technology USA Department of Electrical and Microelectronic Engineering Rochester Institute of Technology USA Department of Computer Science and Engineering University of Notre Dame USA School of Micro-Nano Electronics. Zhejiang University China Key Laboratory of Collaborative Sensing and Autonomous Unmaned Systems of Zhejiang Province China

Artificial intelligence has made remarkable advancements in recent years, leading to the development of algorithms and models capable of handling ever-increasing amounts of data. The computational demands of these algorithms necessitate circuit and architecture designs that go beyond the von-Neumann paradigm. Content addressable memories (CAMs), which implement parallel associative search functionality within memory blocks to overcome the memory wall bottleneck, have proven to be effective for data-intensive tasks. While current CAM designs have achieved higher storage density and energy efficiency than their CMOS-based counterparts by leveraging emerging non-volatile memories (NVM), most of these implementations are limited to binary storage cells. In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existing FeFET-based CAM designs. We validated the functionality of our proposed designs by achieving 3 bits per cell CAM functionality, resulting in 3x improvement in storage density. The area per bit of the proposed SEE-MCAM cell is 8% of the conventional CMOS CAM. We thoroughly investigated the scalability and robustness of the proposed design. Evaluation results suggest that the proposed 2FeFET-1 T SEE-MCAM achieves 9.8× more energy efficiency and 1.6× less search latency compared to the CMOS CAM, respectively. When compared to existing MCAM designs, the proposed SEE-MCAM can achieve 8.7× and 4.9× more energy efficiency than ReRAM-based and FeFET-based MCAMs, respectively. Benchmarking results show that our approach provides up to 3 orders of magnitude improvement in speedup and energy efficiency over a GPU implementation in accelerating a novel quantized hyperdimensiona

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