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International Conference on Computer Supported Cooperative Work in Design

作者： Yunfei Song Yongqiang Gao Yipei He Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Vehicle Edge computing (VEC) is a critical technology that can achieve low latency and energy consumption for Telematics. However, with the high-speed mobility of new energy-electric vehicles and their cross-regional nature, performing high-quality service of vehicle tasks on the VEC model is still challenging. Considering the electric vehicle range problem, this paper plans to jointly optimize task offloading, task result forwarding and computational resource allocation (OOFR) within the maximum tolerable delay of vehicle tasks to minimize vehicle tasks' delay and energy consumption. The non-orthogonal multiple access (NOMA) technology is used with roadside units (RSUs) to achieve multiplexing of limited spectrum resources. This allows multiple vehicle users to perform task transmission simultaneously, thus reducing vehicle task transmission delay. In addition, we propose a cooperative game approach based on NOMA for task grouping to reduce the signal interference of vehicle task transmission. Finally, a deep reinforcement learning method is proposed for task offloading decision selection. A simulation platform is built to compare with MEC, COMO and MADDPG methods, combined with simulation results, showing that the superiority of our proposed scheme is verified.

关键词： NOMA Energy consumption Simulation Games Electric vehicles Deep reinforcement learning Telematics

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scIAMC:Single-Cell Imputation via adaptive matrix completion

scIAMC:Single-Cell Imputation via adaptive matrix completion

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Shuai Zhang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts.

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Aligning Academic with Industrial Needs: Investigating DevOps 24

Aligning Academic with Industrial Needs: Investigating DevOp...

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Proceedings of the 28th International Conference on Evaluation and Assessment in software engineering

作者： Saima Rafi Muhammad Azeem Akbar Sajjad Mahmood School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Software Engineering LUT University Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9798400717017

Over recent years, DevOps has gained more attention within the software industry, owing to its pivotal role in facilitating continuous software delivery. The significant demand for DevOps practitioners necessitates substantial adjustments in conventional software engineering courses in higher educational institutions. Recent studies have highlighted DevOps principles, challenges, best practices, and tools to facilitate continuous delivery and deployment pipelines from an industrial perspective. As numerous universities are now introducing courses related to DevOps for students majoring in software engineering and computer science, this high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies, including practices for teaching DevOps, training in DevOps, the level to which DevOps is currently taught and applied to measure the gap between teaching practices compared to industrial requirements. Our vision paper aims to highlight the crucial need for updated policies and tools to improve DevOps training in higher education, providing guidelines. By aligning academic curricula with industry standards, we can ensure students are better equipped for the demands of the workforce, fostering a seamless transition from academia to industry and promoting innovation in software engineering practices.

关键词： Challenges DevOps Guidelines Industry Practices Teaching

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Exploration of Rose Curves with NetPad 17th

Exploration of Rose Curves with NetPad

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Li, Jing Zhang, Jing Rao, Ailing Chen, Ruxian Wang, Ying Guan, Hao Institute of Computing Science and Technology Guangzhou University Guangzhou China Taibo Primary School Zixi China Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9789819924455

Dynamic geometry system has been promoted actively in mathematical education because of its potential to comprehensively improve the core mathematical literacy required in an increasingly technology-based society. The ability to dynamically drag geometric elements to compare and discover relationships, and the ability to control movements and observe trajectory, are what make dynamic geometry system a powerful and efficient mathematical learning tool. This paper focuses on the application of dynamic geometry systems in inquiry-based maths education in middle school. Specifically, an inquiry-based learning activity named "mathematically drawing flowers with NetPad" is designed for students to study a characteristic feature of rose curves, i.e., the relationship between the number of petals and coefficient of rose curve equation. By sequentially graphing equations ρ= A· cos(nθ) and ρ= A· cos(nθ) + B with NetPad in a constructive way, and then observing, reasoning, verifying, and expressing, students can closely experience a complete generating process of maths knowledge, and further improve their innovation awareness and practical ability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach

IEEE Networking Letters

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IEEE Networking Letters 2023年第3期5卷 154-158页

作者： Elsayem, Mariam Abou-Zeid, Hatem Afana, Ali Givigi, Sidney Queen's University of Kingston School of Computing KingstonK7L 2N8 Canada University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada R&d Ericsson Inc. Baseband Systems OttawaK2K 2V6 Canada

Future wireless networks will support applications demanding high data-rates, ultra-low latency, and high reliabilities. One technology for such ultra-reliable low latency communication (URLLC) is grant-free access for uplink resources, which enables user equipment (UE) to transmit data over pre-allocated resources, reducing signaling overhead and communication latency. This letter proposes a novel ensemble Deep Reinforcement Learning grant-allocator architecture combining offline and online learning providing robust performance with a wide range of dynamic network and UE scenarios. Results show enhancement of the overall latency of UEs for URLLC applications achieving less than 20 Transmission Time Interval latency for 95% of the transmissions. © 2019 IEEE.

关键词： Reinforcement learning

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories

arXiv

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arXiv 2024年

作者： Wang, Tianlong Jiao, Xianfeng Zhu, Yinghao Chen, Zhongzhi He, Yifan Chu, Xu Gao, Junyi Wang, Yasha Ma, Liantao School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Scotland Edinburgh United Kingdom Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education National Engineering Research Center for Software Engineering Peking University Beijing China

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the "truthful" direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑ 142%), LLaMA2 (↑ 24%), Alpaca (↑ 36%), Vicuna (↑ 28%), LLaMA2-Chat (↑ Copyright © 2024, The Authors. All rights reserved.

关键词： Context free languages

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Energy-Efficient Dynamic Asynchronous Federated Learning in Mobile Edge computing Networks

Energy-Efficient Dynamic Asynchronous Federated Learning in ...

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IEEE International Conference on Communications (ICC)

作者： Guozeng Xu Xiuhua Li Hui Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.

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Histopathology of Malignant Tissues Using High-Resolution Microscopy @ 630nm

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Lasers in engineering 2025年第4-6期59卷 295-308页

作者： Amjad, M. Ullah, H. Ullah, S. Batool, Z. Nazir, A. Gilanie, G. Habib, S. Abdullah-Al-Wadud, M. Dad, M. Umar Biophotonics Imaging Techniques Laboratory Department of Physics The Islamia University of Bahawalpur Baghdad-ul Jadeed Campus Bahawalpur Pakistan Department of Histopathology Nishtar Medical University Multan Pakistan Department of Artificial Intelligence Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Institute of Chemistry The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Physics and Electronic Science East China Normal University Shanghai200062 China

Tissue samples (10 samples of basal cell carcinoma (BCC) of nose, 10 specimens of adenocarcinoma prostate, 12 of ovary, 12 of renal cell carcinoma (RCC) of kidney, and 14 samples of breast cancer) were received in paraffin-embedded blocks, stained with H&E and investigated using a high-resolution microscope. The BCC of the skin showed tumor nests, necrosis, cytological atypia, and abnormal cell to nuclei ratio, and the nodular type and Breslow thickness is 3.0 cm. For prostate cancer, Gleason’s grade is 4+4 and the obtained score is 8/10. Its grade group is 4. In ovary cancer, the squamous discrepancy is very usual. Some alternates are present like mucin rich and secretory, small cells with scanty cytoplasm settled in fragments, violent tumors, and cystic and solid areas in addition to hemorrhage and necrosis. In RCC, tumor cells have sharply outlined cell boundaries and the cytoplasm ranges from optically clear to deeply granular. Tubular, papillary, and cystic formation and abundant glycogen, abnormal mitotic activity with debris all over the tissue sample is seen. The breast tumor showed a grey-white solid area, with 10 lymph nodes identified in the axilla. The specimen is of the type right mastectomy with axillary clearance and the tumor types are invasive ductal cell carcinoma having grade 2 and lobular carcinoma. Resected margins are free of tumor. Axillary lymph node 3/10 shows metastatic carcinoma. ©2025 Old City Publishing, Inc.

关键词： Tumors

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN Computer Science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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