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Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

IEEE Transactions on Services computing 2025年

作者： Wu, Xiaohu Yu, Han Casale, Giuliano Gao, Guanyu Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing China Nanyang Technological University WeBank-NTU Joint Research Centre on Fintech Singapore Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Imperial College London Department of Computing London United Kingdom Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Premier cloud service providers (CSPs) offer on-demand and spot instances (i.e., virtual machines) with time-varying features in availability and price. While interacting with a CSP, what concerns users is the process of cost-effectively utilizing these instances, possibly in addition to self-owned instances. A job in data-intensive applications is represented by a directed acyclic graph (DAG) whose nodes represent tasks and the DAG can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we make some mild assumptions on the usage of various instances and give an analytical understanding of the expected behaviors while utilizing instances. Based on this, we propose informed heuristic policies to determine the allocation of deadlines and instances. The policies are parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, intuitive greedy policies are used as baselines to validate the effectiveness of the proposed analytical solutions. The cost improvement is up to 23.58% when spot and on-demand instances are considered and up to 50.08% when self-owned instances are also considered. © 2008-2012 IEEE.

关键词： Cloud platforms

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Towards harmless rawlsian fairness regardless of demographic prior 24

Towards harmless rawlsian fairness regardless of demographic...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xuanqian Wang Jing Li Ivor W. Tsang Yew-Soon Ong School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore and College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair.

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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AI-Enable Heart Sound Analysis: PASCAL Approach for Precision-Driven Cardiopulmonary Assessment 8th

AI-Enable Heart Sound Analysis: PASCAL Approach for Precisio...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Choudhury, Tanupriya Singh, Teekam Kotecha, Ketan Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune411045 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819994885

A large number of medical professionals depended on manual methods to assess the features of heart sounds. Because of the hands-on nature of this method, it was necessary for them to generate waveforms representing heart sounds and then carefully analyses the various components of these sounds. In contrast, the findings of our most recent research provide an improved methodology that automates the process of separating heart sound data and extracting the relevant factors from those signals. This update is very helpful for applications of machine learning in thoracentesis, and it aligns well with the developments of the most recent clinical imaging technologies. A novel "accordion" approach is presented in our methodology, which we have dubbed PASCAL. This method not only makes use of reference data that is determined by sudden shifts, but it also incorporates an improved classification model that has been customised for the highest possible level of precision. This is particularly clear when looking at the Dataset that We Have for Characterising Heart Rate. It is of the utmost importance to emphasise the fact that the performance as well as the false positive (Fp) rates of our classification phase were thoroughly evaluated. While this was going on, we looked at how the features of the ECG signal changed over time. The findings of our investigation are really positive. A fantastic F1-score of 94.38% and a respectable accuracy rate of 93.52% were reached by our suggested technique for identifying and classifying the capabilities of a healthy cardiopulmonary system. The primary sound (S1 duration), the subsequent sound (S2 duration), the overall cardiac cycle, the duration of ventricular systole, the time taken for ventricular contraction, and the ratio between the systolic and diastolic phases were some of the aspects of heart sounds that we examined in great detail. This ground-breaking method makes it possible to conduct an accurate study of the two most important p

关键词： Electrocardiograms

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Sentiment Analysis in Social Media Marketing: Leveraging Natural Language Processing for Customer Insights 8th

Sentiment Analysis in Social Media Marketing: Leveraging Nat...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Choudhury, Tanupriya Singh, Teekam Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune411045 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819994885

In this day and age of online marketing, social media platforms have evolved into essential tools for companies to use in order to communicate with their consumers, as well as to advertise the goods and services they provide. Understanding client emotion, tastes, and views may be significantly aided by perusing the vast amounts of user-generated information that can be found on these many platforms. This study investigates the use of sentiment analysis, a branch of natural language processing (NLP), as a potent instrument for gleaning useful consumer insights from data collected from social media platforms like as Facebook and Twitter. The application of computer methods in the process of automatically determining and categorising the sentiment that is represented in textual data, such as tweets, Facebook posts, and online reviews, is what is known as sentiment analysis. In this article, the primary methodology and techniques used in sentiment analysis, such as lexicon-based, machine learning, and deep learning approaches, are dissected and discussed. In addition to this, it sheds light on the difficulties and factors to take into account that are unique to sentiment analysis when applied to the context of data from social media, such as the management of noisy and unstructured language, the management of sarcasm, and the management of context-dependent sentiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Mentored learning: improving generalization and convergence of student learner via teaching feedback

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 15891-15935页

作者： Xiaofeng Cao Yaming Guo Heng Tao Shen Ivor W. Tsang James T. Kwok School of Artificial Intelligence Jilin University Changchun China and Australian Artifcial Intelligence Institute (AAII) University of Technology Sydney (UTS) NSW Australia School of Artificial Intelligence Jilin University Changchun China School of Computer Science and Technology Tongji University Shanghai China and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Centre for Frontier AI Research and Institute of High Performance Computing Agency for Science Technology and Research(A*STAR) Singapore Singapore and College of Computing and Data Science Nanyang Technological University Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

Student learners typically engage in an iterative process of actively updating its hypotheses, like active learning. While this behavior can be advantageous, there is an inherent risk of introducing mistakes through incremental updates including weak initialization, inaccurate or insignificant history states, resulting in expensive convergence cost. In this work, rather than solely monitoring the update of the learner's status, we propose monitoring the disagreement w.r.t. $\mathcal{F}^\mathcal{T}(\cdot)$ between the learner and teacher, and call this new paradigm "Mentored Learning", which consists of 'how to teach' and 'how to learn'. By actively incorporating feedback that deviates from the learner's current hypotheses, convergence will be much easier to analyze without strict assumptions on learner's historical status, then deriving tighter generalization bounds on error and label complexity. Formally, we introduce an approximately optimal teaching hypothesis, hT, incorporating a tighter slack term $\left(1+\mathcal{F}^{\mathcal{T}}(\widehat{h}_t)\right)\Delta_t$ to replace the typical 2Δt used in hypothesis pruning. Theoretically, we demonstrate that, guided by this teaching hypothesis, the learner can converge to tighter generalization bounds on error and label complexity compared to non-educated learners who lack guidance from a teacher: 1) the generalization error upper bound can be reduced from R(h*)+4ΔT-1 to approximately R(hT )+2ΔT-1, and 2) the label complexity upper bound can be decreased from 4θ(TR(h*) + 2O(√T)) to approximately 2θ(2TR(hT) + 3O(√T)). To adhere strictly to our assumption, self-improvement of teaching is proposed when hT loosely approximates h*. In the context of learning, we further consider two teaching scenarios: instructing a white-box and black-box learner. Experiments validate this teaching concept and demonstrate superior generalization performance compared to fundamental active learning strategies, such as IWAL (Beygelzimer et al

关键词： machine teaching hypothesis pruning active learning error disagreement convergence generalization error label complexity

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Enhanced Ship Classification in SAR Imagery using Fine-tuned DenseNet and Self-attention

Enhanced Ship Classification in SAR Imagery using Fine-tuned...

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International Conference on computing and Networking Technology (ICCNT)

作者： Kosaraju Sai Chethan Vadlamudi Lokesh Kunta Vignaj Reddy Rimjhim Padam Singh Smita Srivastava Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Centre for Artificial Intelligence & Robotics Defense Research & Development Organization Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The categorization of ships in synthetic aperture radar (SAR) imagery is vital for civilian and military applications, including maritime management and reconnaissance. Deep learning techniques, such as Inception, DenseNet, Fusion Transformers, and EfficientNet, are used to extract multiscale information from SAR images, improving classification capabilities. Advanced solutions are necessary to address challenges in SAR imagery, such as speckle noise, varying illumination conditions, and complex sea clutter. The FUSAR-Ship dataset, which utilizes GF-3 SAR technology, offers a comprehensive collection of diverse scenarios including sea, land, coast, river, and islands. With over 5000 ship chips containing AIS messages and examples of various clutter types, FUSAR-Ship is a benchmark dataset for ship classification tasks. Our research involves thorough testing of advanced classification models using the FUSAR dataset. Through meticulous evaluation of a wide range of ship instances, scales, and maritime scenes, we aim to demonstrate the robustness and adaptability of these models. By refining their architectures and optimizing their parameters, we strive to enhance the capabilities of classification models and advance the performance of ship classification in SAR imagery. Our efforts contribute to enhancing maritime surveillance, management, and reconnaissance in both civilian and military domains.

关键词： Adaptation models Attention mechanisms Computational modeling Surveillance Noise Reconnaissance Radar polarimetry Marine vehicles Clutter Synthetic aperture radar

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AI Security and Cyber Risk in IoT Systems

arXiv

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arXiv 2024年

作者： Radanliev, Petar De Roure, David Maple, Carsten Nurse, Jason R.C. Nicolescu, Razvan Ani, Uchenna Department of Computer Sciences University of Oxford United Kingdom Department of Computer Science School of Computing and Engineering Huddersfield University Huddersfield United Kingdom Oxford e-Research Centre Department of Engineering Sciences University of Oxford United Kingdom WMG Cyber Security Centre University of Warwick United Kingdom School of Computing University of Kent United Kingdom University College London United Kingdom School of Computer Science and Mathematics Keele University United Kingdom

Internet-of-Things (IoT) refers to low-memory connected devices used in various new technologies, including drones, autonomous machines, and robotics. The article aims to understand better cyber risks in low-memory devices and the challenges in IoT risk management. The article includes a critical reflection on current risk methods and their level of appropriateness for IoT. We present a dependency model tailored in context towards current challenges in data strategies and make recommendations for the cybersecurity community. The model can be used for cyber risk estimation and assessment and generic risk impact assessment. The model is developed for cyber risk insurance for new technologies (e.g., drones, robots). Still, practitioners can apply it to estimate and assess cyber risks in organisations and enterprises. Furthermore, this paper critically discusses why risk assessment and management are crucial in this domain and what open questions on IoT risk assessment and risk management remain areas for further research. The paper then presents a more holistic understanding of cyber risks in the IoT. We explain how the industry can use new risk assessment, and management approaches to deal with the challenges posed by emerging IoT cyber risks. We explain how these approaches influence policy on cyber risk and data strategy. We also present a new approach for cyber risk assessment that incorporates IoT risks through dependency modelling. The paper describes why this approach is well suited to estimate IoT risks. © 2024, CC BY.

关键词： Cyber attacks

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