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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Choudhry, Rashmi Sivadharshini, B. Dua, Madhav Nirmala, V. Husain, Saif O. Arunkumar, R.S. Ies University Department of Information Technology M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Karpagam Academy of Higher Education Coimbatore Faculty of Engineering Department of Science and Humanities Tamilnadu India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Saveetha School of Engineering Department of Cse Chennai India

ISBN: (纸本)9798350390759

This work focuses on breast cancer classification in mammograms using SVMs classifier and histogram of oriented gradients features. The envisioned system is designed in such a way to enhance the diagnostic precision through integration of the virtues of both SVMs and HOG in identifying discriminative characteristics of breast tissue. Special emphasis was given on comparing and testing the performance of the intended model with other models on a public mammogram dataset containing both malignant and benign cases;the optimized SVM model provided an accuracy of 92%. 5%, sensitivity of 91. 0%, specificity of 93. 74 (fig 5) and 8%, and an AUC-ROC of 0. 95. This study shows that the method is efficient in discriminating between the malignant and benign first axillary lymph node tissue. The developed CAD system was found to possess the prospect of becoming a valuable support system for radiologists in the early detection of breast cancer. © 2024 IEEE.

关键词： Lung cancer

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Automation of data flow class testing using hybrid evolutionary algorithms

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第1期14卷 317-330页

作者： Jain, Neetu Porwal, Rabins Department of Computer Application Faculty of System Sciences Mewar University Chittorgarh India Department of Information Technology Lal Bahadur Shastri Institute of Management New Delhi India

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations. © 2021 Bentham Science Publishers.

关键词： Genetic algorithms

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Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

arXiv

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arXiv 2022年

作者： Wei, Shoulin Li, Yadi Lu, Wei Li, Nan Liang, Bo Dai, Wei Zhang, Zhijian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Faculty of Science Kunming University of Science and Technology Kunming650500 China

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveysa) © 2022, CC BY.

关键词： Surveys

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Machine learning in materials genome initiative:A review

引用

Journal of Materials Science & technology 2020年第22期54卷 113-122页

作者： Yingli Liu Chen Niu Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and TechnologyKunming650500China Light Alloy Research Institute Central South UniversityChangsha410083China Chinese Academy of Engineering Beijing100088China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China Chengdu MatAi Technology Co. LtdChengdu610041China

Discovering new materials with excellent performance is a hot issue in the materials genome *** experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

关键词： Materials genome initiative(MGI) Materials database Machine learning Materials properties prediction Materials design and discovery

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A Comprehensive Survey of Few-shot Learning: Evolution, applications, Challenges, and Opportunities

arXiv

引用

arXiv 2022年

作者： Song, Yisheng Wang, Ting Mondal, Subrota K. Sahoo, Jyoti Prakash The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The Faculty of Information Technology Macau University of Science and Technology Macao China The Department of Computer Science & Information Technology Institute of Technical Education and Research Siksha 'o' Anusandhan University India

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. To enrich this survey, in each subsection we provide in-depth analysis and insightful discussion about recent advances on these topics. Moreover, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research. © 2022, CC BY.

关键词： Surveys

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Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

引用

Precision Clinical Medicine 2024年第2期7卷 132-145页

作者： Zehua Wang Ruichong Lin Yanchun Li Jin Zeng Yongjian Chen Wenhao Ouyang Han Li Xueyan Jia Zijia Lai Yunfang Yu Herui Yao Weifeng Su Guangdong Key Laboratory of Cross-Application of Data Science and Technology Beijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Faculty of Innovation Engineering Macao University of Science and TechnologyTaipaMacao 999078China Department of Computer and Information Engineering Guangzhou Huali CollegeGuangzhou 511325China Department of Pathology Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Guangzhou National Laboratory Guangzhou 510005China Dermatology and Venereology Division Department of Medicine SolnaCenter for Molecular MedicineKarolinska InstitutetStockholm 17177Sweden Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical OncologyBreast Tumor CentrePhase I Clinical Trial CentreSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China The Second Clinical Medical College Southern Medical UniversityGuangzhou 510515China Faculty of Medicine Macao University of Science and TechnologyTaipaMacao 999078China

Background:The prognosis of breast cancer is often unfavorable,emphasizing the need for early metastasis risk detection and accurate treatment *** study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival(DFS).Methods:We retrospectively collected pathology imaging,molecular and clinical data from The Cancer Genome Atlas and one independent institution in *** developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal(DeepClinMed-PGM)model for DFS prediction,integrating clinicopathological data with molecular *** patients included the training cohort(n=741),internal validation cohort(n=184),and external testing cohort(n=95).Result:Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve(AUC)*** the training cohort,AUC values for 1-,3-,and 5-year DFS predictions increased to 0.979,0.957,and 0.871,while in the external testing cohort,the values reached 0.851,0.878,and 0.938 for 1-,2-,and 3-year DFS predictions,*** DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts,including the training cohort[hazard ratio(HR)0.027,95%confidence interval(CI)0.0016-0.046,P<0.0001],the internal validation cohort(HR 0.117,95%CI 0.041-0.334,P<0.0001),and the external cohort(HR 0.061,95%CI 0.017-0.218,P<0.0001).Additionally,the DeepClinMed-PGM model demonstrated C-index values of 0.925,0.823,and 0.864 within the three cohorts,***:This study introduces an approach to breast cancer prognosis,integrating imaging and molecular and clinical data for enhanced predictive accuracy,offering promise for personalized treatment strategies.

关键词： breast cancer multi-modality deep learning pathological disease-free survival

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Model Checking Quantum Continuous-Time Markov Chains 32

Model Checking Quantum Continuous-Time Markov Chains

引用

32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

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PoW-Based Sybil Attack Resistant Model for P2P Reputation Systems 2nd

PoW-Based Sybil Attack Resistant Model for P2P Reputation Sy...

引用

2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Li, Biaoqi Fu, Xiaodong Yue, Kun Liu, Li Liu, Lijun Feng, Yong Yunnan Provincial Key Laboratory of Computer Technology Application Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Information Science and Engineering Yunnan University Kunming650091 China

ISBN: (纸本)9789811592126

Peer-to-peer and other distributed systems are known particularly vulnerable to Sybil attack, so is P2P reputation systems. Reputation helps users to make better decisions in P2P systems, but Sybil attacker can obtain multiple identities and pretends to be multiple. It can control more than "one vote" to let others’ reputation change just as the attacker wants. This paper presents a Sybil attacker resistant model for P2P reputation systems by introducing multiple rounds of PoW (Proof-of-Work) verification and dynamic difficulty adjustment. A savvy attacker usually doesn’t carry out attacks without positive returns, so we minimize the expected profit of attackers through the using of puzzles. The costs and benefits are quantified as effectiveness in our model. We explore the effectiveness in two types of comparison, one is in dynamic or static difficulty adjustment, the other is executing once or multiple rounds of verification, and show the effectiveness of our model both analytically and experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Peer to peer networks

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

引用

TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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