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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering Singapore College of Design and Engineering Singapore Institute of Data Science Singapore N.1 Institute for Health Singapore National University of Singapore Singapore

ISBN: (纸本)9798331527235

Symbolic Regression (SR) is the task of finding a concise white-box mathematical expression that fulfills a given machine learning (ML) objective. In this work, we introduce the first-of-its-kind SR-based ensemble combiner (SyREC) for ML which utilizes the quasi-arithmetic mean (parameterized by a function f) as an ensemble combiner and discovers f via SR. Our Sy REC demonstrates advantages over existing ensemble combiners, i.e., averaging methods and meta-learners. Compared to averaging methods, Sy REC allows for increased combiner complexity via exploring a large function class and provides a learning process to tune the ensemble combiner to the specific dataset. Compared to meta-learners, SyREC preserves important ensemble properties, like idempotency, monotonicity and boundedness, which produce predictions more consistent with the base models. We note SyREC produces a white-box ensemble combiner that lies in the intersection of both categories of existing combiners, addressing the weaknesses while capturing the strengths of each category. To evaluate Sy REC, we present experiments on 16 open-source PMLB datasets and commonly used base models, including a large variety of experiments and analyses across different settings: (i) regression and classification tasks, (ii) equally weighted combiners and custom-weighted combiners, (iii) homogeneous and heterogeneous ensembles, (iv) sequential and parallel ensembles. We find that in all these settings, SyREC shows consistent improvement over existing ensembling approaches. This indicates that SyREC is a robust ensemble combiner, which produces explainable and consistent ensemble predictions. © 2024 IEEE.

关键词： Contrastive Learning

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Heterogeneous Memory Allocation Scheme for Batch Jobs with Intel Optane DC Persistent Memory and DRAM 9

Heterogeneous Memory Allocation Scheme for Batch Jobs with I...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Chang, Che-Wei Zhang, Kai-Jie Chang Gung University School of Electrical and Computer Engineering College of Engineering Department of Computer Science and Information Engineering No.259 Wenhua 1st Rd. Guishan Dist. Taoyuan333 Taiwan

ISBN: (纸本)9798350398380

It is a promising approach to include Non-Volatile Memory (NVM) and DRAM together as the main memory to provide huge memory space for large objects and guarantee short access latency for frequent writes and reads. To provide feasible and suitable memory allocation for all applications on a sever, our solution analyzes applications to derive the read-write ratio, memory footprint, and access pattern of each application. Heterogenous memory allocation scheme is then developed to reduce the makespan of batch jobs. © 2023 IEEE.

关键词： Dynamic random access storage

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 379-391页

作者： Goudarzi, Hamed Hesamzadeh, Mohammad Reza Bunn, Derek Fotuhi-Firuzabad, Mahmud Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Management and Control Tehran11365-11155 Iran Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden London Business School Department of Management Science and Operations LondonNW1 4SA United Kingdom Illinois Institute of Technology Electrical and Computer Engineering Department ChicagoIL60616 United States

This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm. © 2023 IEEE.

关键词： Power markets

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Hands and Palms Recognition by Transfer Learning for Forensics: A Comparative Study 23

Hands and Palms Recognition by Transfer Learning for Forensi...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Fkiri, Obeb Mejri, Mohamed Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuebecQCG1V 0A6 Canada Department of Computer Science National School of Engineers of Carthage University of Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

In the realm of forensic science, precise identification of individuals holds paramount importance in both investigative procedures and legal proceedings. Hands and palms recognition has emerged as a valuable biometric modality within forensic applications, owing to the distinct and intricate features inherent to these anatomical regions. The elaborate patterns of veins, creases, and ridges present on palms and fingers serve as rich sources of biometric data, crucial for accurate identification purposes. Furthermore, given the frequent involvement of hands and palms in criminal activities such as theft and assault, their recognition becomes imperative for establishing links between suspects and crime scenes. However, developing robust recognition systems tailored for forensic applications poses notable challenges, including variations in hand poses, lighting conditions, and image quality. To address these hurdles, sophisticated deep learning techniques, notably transfer learning, have been employed. By harnessing pre-trained deep learning models namely NasNetLarge, NasNetMobile, and EfficientNet, initially trained on expansive datasets for general image recognition tasks, we can adapt these models to the specific task of hands and palms recognition in forensic contexts. Our findings reveal that all three models consistently achieved over 92% accuracy across all metrics evaluated, demonstrating their efficacy as strong contenders for the hands-and-palms recognition task. Notably, the EfficientNet model exhibited superior performance compared to its counterparts, boasting more than 95.8% accuracy, precision, F1-score and recall, along with more than 98.6% specificity and 99.4% AUC. © 2024 IOS Press. All rights reserved.

关键词： Palmprint recognition

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6G service-oriented space-air-ground integrated network: A survey

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Chinese Journal of Aeronautics 2022年第9期35卷 1-18页

作者： Nan CHENG Jingchao HE Zhisheng YIN Conghao ZHOU Huaqing WU Feng LYU Haibo ZHOU Xuemin SHEN School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Cyber Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada School of Computer Science and Engineering Central South UniversityChangsha 410083China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial networks,and terrestrial *** 6G SAGINs,a wide variety of network services with the features of diverse requirements,complex mobility,and multi-dimensional resources will pose great challenges to service provisioning,which urges the develop-ment of service-oriented *** this paper,we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented ***,we present the requirements of service-oriented networks,and then propose a service-oriented SAGINs management *** categories of critical technologies are presented and discussed,i.e.,heterogeneous resource orchestration technologies and the cloud-edge synergy technologies,which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains,according to the service features and *** addition,the potential future research directions are also presented and discussed.

关键词： Mobile Edge Computing(MEC) Network Function Virtual-ization(NFV) Network slicing Service-oriented network Software Defined Network-ing(SDN) Space-Air-Ground Inte-grated Networks(SAGINs)

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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Outage Performance Analysis in Cognitive RIS-NOMA System under Imperfect CSI 3

Outage Performance Analysis in Cognitive RIS-NOMA System und...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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Advanced dairy cow monitoring: enhanced detection with precision 3D tracking

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Multimedia Tools and Applications 2025年第18期84卷 19117-19146页

作者： Wang, Ranran Li, Yingxiu Yue, Peng Yuan, Chunhong Tian, Fuyang Lu, Xin College of Mechanical and Electronic Engineering Shandong Agricultural University Shandong Tai’an271018 China Department of Computer Science Faculty of Engineering University of Sheffield SheffieldS1 4DP United Kingdom Faculty of Agriculture Iwate University Iwate Morioka020-8550 Japan Faculty of Science and Engineering Iwate University Iwate Morioka020-8550 Japan

Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. © The Aut

关键词： K-means clustering

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Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning

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Iran Journal of computer science 2025年第2期8卷 365-378页

作者： Kundu, Shreyan Mukhopadhyay, Souradeep Talukdar, Rahul Das, Semanti Adhikari, Subhajit Computer Science and Engineering (Artificial Intelligence) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Computer Science and Automation Indian Institute of Science CV Raman Road Karnataka Bangalore 560012 India Computer Science and Engineering (core) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Department of Lifescience Christ University Hosur Main Road Bhavani Nagar Karnataka Bangalore 560029 India Department of Information Technology RCCIIT KOLKATA West Bengal Kolkata 700015 India

Jute is a vital agricultural commodity contributing significantly to the GDP of countries like Bangladesh, India, Myanmar, and China. However, because of its inaccuracy and slowness, its vulnerability to pest infestations-which are often handled by manual inspections-poses serious cost concerns. This study suggests a unique method for early and accurate pest identification that combines contrastive and supervised learning. Contrastive learning enhances feature representation by distinguishing between positive and negative samples, ensuring that instances within the same class are closely grouped while maintaining separation between different classes. It reduces false negatives by classifying some samples as negative and others with the same label as positive. Supervised learning enables precise pest identification by aligning features with distinctive characteristics of each class. Metrics including precision, recall, F1 score, ROC curve, and confusion matrix are used to assess the hybrid model’s performance;the findings show notable accuracy gains over conventional techniques. This scalable and dependable solution lowers losses caused by pests and provides a sustainable method of growing jute using cutting-edge advanced machine-learning techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Augmentation Contrastive learning Supervised learning Triplet loss

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