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arXiv 2022年

作者： Dong, Xue Song, Xuemeng Zheng, Na Wei, Yinwei Zhao, Zhongzhou School of Software Shandong University Jinan250101 China School of Computer Science and Technology Shandong University Qingdao266237 China Institution of Data Science National University of Singapore Singapore School of Computing National University of Singapore Singapore DAMO Academy Alibaba Group Zhejiang 311121 China

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user's preferences and item's features with vectorized embeddings, and modeled the user's general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user's different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE), which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user's general preference to items and the latter refers to the user's specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user's preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user's preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE *** Codes 68T07 (Primary) 68T30 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Ε-weakened Robustness of deep neural networks

arXiv

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arXiv 2021年

作者： Huang, Pei Jia, Fuqi Yang, Yuting Ma, Feifei Liu, Minghao Zhang, Jian State Key Laboratory of Computer Science Institution of Software CAS Beijing China Key Laboratory of Intelligent Information Institute of Computing Technology CAS Beijing China

This paper introduces a notation of Ε-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness, which focuses on the "perfect" safe region in the absence of adversarial examples, Ε-weakened robustness focuses on the region where the proportion of adversarial examples is bounded by user-specified Ε. Smaller Ε means a smaller chance of failure. Under such robustness definition, we can give conclusive results for the regions where conventional robustness ignores. We prove that the Ε-weakened robustness decision problem is PP-complete and give a statistical decision algorithm with user-controllable error bound. Furthermore, we derive an algorithm to find the maximum Ε-weakened robustness radius. The time complexity of our algorithms is polynomial in the dimension and size of the network. So, they are scalable to large real-world networks. Besides, We also show its potential application in analyzing quality issues. © 2021, CC BY.

关键词： Deep neural networks

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Awareness requirement and performance management for adaptive systems: a survey

arXiv

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arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

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Federated Learning for Recognizing Private Handwritten Numbers

Federated Learning for Recognizing Private Handwritten Numbe...

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2021 IEEE International Conference on Data science and Computer Application, ICDSCA 2021

作者： Wang, Di Jiang, Yuchen Zhao, Yibo Chen, Panyang Li, Ang Pan, Kun Institution of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Science and Technology College of Computer Science Chongqing University Chongqing China Zibo Shiyan High School International Division Zibo Shandong China Computer Science Department of Computer Science and Software Engineering Auburn University Auburn United States Shanghai Shangde Experimental School Shanghai China School of Computing University of Leeds Leeds United Kingdom

ISBN: (纸本)9781665440530

Federated learning (FL) is a new technology in the field of machine learning, which trains an algorithm across multiple decentralized edge devices or servers holding data samples, without exchanging their data samples. Horizontal Federated Learning (HFL) is a FL method focusing on using the same feature space across all users, which is suitable for situations where users' data feature dimensions overlap more. Because FL has the advantage of privacy protection in the field of manufacturing, we propose an HFL-based deep learning method named FedCNN, which realizes the digits handwritten recognition without violating the privacy or data sharing. Three different methods (local model, centralized model, and HFL-based FedCNN) are used on a public data set (MINIST) to compare the pros and cons of the solutions. The experimental results demonstrate that FedCNN scheme can simultaneously achieve practical privacy preservation and good model generalization performance with Homomorphic Encryption. © 2021 IEEE.

关键词： Data privacy

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An Innovative IoT-Driven Smart Streetlight Management System Design

An Innovative IoT-Driven Smart Streetlight Management System...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Shermina Shekaina Justin P. Thenmozhi Jissy Ann George T. Ananth Kumar Christo Ananth Department of Computing Muscat College Sultanate of Oman Electrical Engineering Department Princess Nourah Bint Abdulrahman University Kingdom of Saudi Arabia Dept. of Electronics and Communication Engineering St.Joseph's College of Engineering (An Autonomous Institution) Chennai India College of ICT British University of Bahrain Kingdom of Bahrain Computer Science and Engineering IFET College of Engineering Viluppuram India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Uzbekistan Adjunct Faculty Samarkand State University Chennai India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

This research introduces a cutting-edge approach to optimizing the management of streetlight systems through automation by using IoT. Traditional methods often suffer from inefficiencies, leading to unnecessary energy consumption and maintenance costs. In this study, we propose a novel automated streetlight management system that leverages advanced technologies such as IoT sensors, machine learning algorithms, and smart scheduling mechanisms. By dynamically adjusting lighting levels based on real-time factors such as traffic density, weather conditions, and time of day, our system aims to achieve significant energy savings while ensuring optimal illumination for pedestrians and vehicles. We present the design architecture of our system, along with experimental results demonstrating its effectiveness in various urban environments. Our findings highlight the potential of automated streetlight management systems to enhance sustainability, improve safety, and reduce operational expenses for municipalities and urban planners.

关键词： Machine learning algorithms Urban areas Lighting Energy efficiency Sensor systems Scheduling Safety Internet of Things Sustainable development System analysis and design

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Power-Efficient Secured Hardware Design of AES Algorithm on High Performance FPGA

Power-Efficient Secured Hardware Design of AES Algorithm on ...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Keshav Kumar Vijay Singh Gaurav Mishra B Ravindra Babu. Nandita Tripathi Pramod Kumar University Institute of Computing Chandigarh University Punjab India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering & Tchnology Amity University Uttar Pradesh Graeter Noida Uttar Pradesh India CSE Department Adama Science and Technology University Adama Ethiopia the Institution AIPS Amity University Greater Noida Uttar Pradesh India Department of ECE Lloyd Institute of Engineering and Technology Greater Noida India

With the expansion and growth of industries, the two major issues exist that affect both civilization and the environment. technology development has made it more difficult to communicate and transmit data over secure channels. The use of Green Communication (GC) technology and energy-efficient parts can lessen the lack of electricity and energy. The usage of various technologies under one framework is the main topic of this study. In this article, a hardware implementation of the Advanced Encryption Standard (AES) algorithm is shown. Field Programmable Gate Array (FPGA) devices are taken into consideration for hardware implementations. The power calculation of the AES algorithm is computed for several clock speeds of the Spartan-7 FPGA, and the results are examined using VIVADO tool.

关键词： Performance evaluation Industries Green products Logic gates Hardware Encryption Informatics

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Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

arXiv

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arXiv 2022年

作者： Maaroof, Bestan B. Rashid, Tarik A. Abdulla, Jaza M. Hassan, Bryar A. Alsadoon, Abeer Mohammadi, Mokhtar Khishe, Mohammad Mirjalili, Seyedali Information Technology Department College of Commerce University of Sulaimani KRI Iraq Computer Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology KRI Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani KRI Sulaymaniyah Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Sydney Australia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application. © 2022, CC BY.

关键词： Particle swarm optimization (PSO)

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Harmony Search: Current Studies and Uses on Healthcare Systems

arXiv

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arXiv 2022年

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Alsadoon, Abeer Hassan, Bryar A. Mohammadi, Mokhtar Abdullah, Jaza M. Chhabra, Amit Ali, Sazan L. Othman, Rawshan N. Hasan, Hadil A. Azad, Sara Mahmood, Naz A. Abdalrahman, Sivan S. Rasul, Hezha O. Bacanin, Nebojsa Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Sydney Australia Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Information Technology Lebanese French University KRI Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology KRI Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani KRI Sulaymaniyah Iraq Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Pharmaceutical Chemistry College of Medicals and Applied Sciences Charmo University Peshawa Street Chamchamal Sulaimani46023 Iraq Singidunum University Danijelova 32 Belgrade11000 Serbia

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method. © 2022, CC BY.

关键词： Optimization

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MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer

arXiv

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arXiv 2024年

作者： Wu, Chengyu Wang, Chengkai Wang, Yaqi Zhou, Huiyu Zhang, Yatao Wang, Qifeng Wang, Shuai Department of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Management Hangzhou Dianzi University Hangzhou China College of Media Engineering Communication University of Zhejiang Hangzhou China School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Radiation Oncology Sichuan Cancer Hospital and Institution Sichuan Cancer Center School of Medicine Radiation Oncology Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China School of Cyberspace Hangzhou Dianzi University Hangzhou China Suzhou Research Institute Shandong University Suzhou China

Esophageal cancer is one of the most common types of cancer worldwide and ranks sixth in cancer-related mortality. Accurate computerassisted diagnosis of cancer progression can help physicians effectively customize personalized treatment plans. Currently, CT-based cancer diagnosis methods have received much attention for their comprehensive ability to examine patients' conditions. However, multi-modal based methods may likely introduce information redundancy, leading to underperformance. In addition, efficient and effective interactions between multimodal representations need to be further explored, lacking insightful exploration of prognostic correlation in multi-modality features. In this work, we introduce a multi-modal heterogeneous graph-based conditional feature-guided diffusion model for lymph node metastasis diagnosis based on CT images as well as clinical measurements and radiomics data. To explore the intricate relationships between multi-modal features, we construct a heterogeneous graph. Following this, a conditional feature-guided diffusion approach is applied to eliminate information redundancy. Moreover, we propose a masked relational representation learning strategy, aiming to uncover the latent prognostic correlations and priorities of primary tumor and lymph node image representations. Various experimental results validate the effectiveness of our proposed method. The code is available at https://***/wuchengyu123/MMFusion. © 2024, CC BY.

关键词： Diagnosis

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Fitness dependent optimizer with neural networks for COVID-19 patients

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Computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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