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Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

computer Systems science & engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an Information technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

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Hierarchical Loss for Bi-Level Classification of Speech into Language and Dialects

Hierarchical Loss for Bi-Level Classification of Speech into...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Angra, Ananya Muralikrishna, H. Dileep, A.D. Thenkanidiyoor, Veena MANAS Lab SCEE Indian Institute of Technology Mandi Mandi India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science Engineering Indian Institute of Technology Dharwad Dharwad India Department of Computer Science Engineering National Institute of Technology Goa Goa India

ISBN: (纸本)9798350368741

Spoken dialect identification (DID) is a challenging task due to high similarities between the classes. This becomes further complicated when DID needs to be performed in a multilingual environment, especially when languages are from same language-family, as the scope for confusion is significantly high. In this paper, we propose multiple techniques to address this issue. These techniques motivate the DID system to respect the parent-child relationship between the languages and their dialects by performing bi-level classification of speech. Specifically, in our first approach, we train the language identification (LID) and DID models independently and then combine them together such that the output of LID model selects the DID model, which in turn decides the dialect. However, such independent training does not allow the model to learn the relations/similarities between dialects of different languages. To overcome this limitation, we propose to train an end-to-end DID model which is trained using dialects of all languages in the dataset. While such end-to-end training allows the model to learn inter-dialect similarities in a better way, it does not explicitly prevent a dialect from being misclassified into a dialect of a different language. To address this limitation, we propose a novel hierarchical loss, which motivates the model to maintain the parent-child relationship between language and dialects. Specifically, with the help of an auxiliary language classifier and a primary dialect classifier, the hierarchical loss penalizes the model heavily whenever parent-child relationship is not maintained, i.e., when predicted dialect does not belong to the predicted language. Experiments conducted on a set of closely related Indian languages shows that hierarchical loss based training leads to improvement in the performance. © 2025 IEEE.

关键词： Hierarchical loss Multi-lingual Spoken dialect identification

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Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

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computer Systems science & engineering 2023年第8期46卷 1981-1997页

作者： Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son Graduate University of Science and Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam VNU Information Technology Institute Vietnam National UniversityHanoi100000Vietnam Department of Mathematics University of New MexicoGallup87301New MexicoUSA University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen250000Vietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoi100000Vietnam

Clustering is a crucial method for deciphering data structure and producing new *** to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive *** with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering *** data can lead to incorrect object recognition and *** research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled *** effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI *** experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.

关键词： Safe semi-supervised fuzzy clustering picture fuzzy set neutrosophic set data partition with noises fuzzy clustering

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XOR spin logic operated by unipolar current based on field-free spin-orbit torque switching induced by a lateral interface

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Rare Metals 2024年第8期43卷 3868-3875页

作者： Yan-Ru Li Mei-Yin Yang Guo-Qiang Yu Bao-Shan Cui Jin-Biao Liu Yong-Liang Li Qi-Ming Shao Jun Luo Key Laboratory of Microelectronic Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of Sciences(IMECAS)Beijing 100029China University of Chinese Academy of Sciences(UCAS) Beijing 100049China Songshan Lake Materials Laboratory Dongguan 523808China Institute of Physics Chinese Academy of SciencesBeijing 100190China Key Laboratory for Magnetism and Magnetic Materials of Ministry of Education Lanzhou UniversityLanzhou 730000China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

Spin logics have emerged as a promising avenue for the development of logic-in-memory *** particular,the realization of XOR spin logic gates using a single spin-orbit torque device shows great potential for low-power stateful logic circuits in the next *** this study,we successfully obtained the XOR logic gate by utilizing a spin-orbit torque device with a lateral interface,which was created by local ion implantation in the Ta/Pt/Co/Ta Hall device exhibiting perpendicular magnetic *** angle of the lateral interface is set at 45°relative to the current direction,leading to the competition between symmetry breaking and current-driven Néel-type domain wall ***,the field-free magnetic switching reversed is realized by the same sign of current amplitude at this *** on this field-free magnetic switching behavior,we successfully proposed an XOR logic gate that could be implemented using only a single spin-orbit torque Hall *** study provides a potentially viable approach toward efficient spin logics and in-memory computing architectures.

关键词： Filed-free magnetic switching Spin-orbit torque XOR logic gate Lateral interface

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Real-time programmable metasurface for terahertz multifunctional wave front engineering

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Light(science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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A Deep Learning Approach for YouTube Music Recommendation Based on Facial Emotion 2

A Deep Learning Approach for YouTube Music Recommendation Ba...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Renugadevi, R. Selvamuthukumar, M. Manikanta, K. Jeyaprakash, K. Kalaiarasi, G. Arul Edwin Raj, A. Vignan 's Foundation for Science Technology and Research Computer Science and Engineering Andhrapradesh Guntur India Vignan 's Foundation for Science Technology and Research Mechanical Engineering Andhrapradesh Guntur India Kalasalingam Academy of Research and Education Electronics and Communication Engineering Tamilnadu Virudhunagar India Vignan 's Foundation for Science Technology and Research Advanced Computer Science and Engineering Andhrapradesh Guntur India Universiti Tun Hussein Onn Malaysia Persiaran Tun Dr. Ismail Johor86400 Malaysia

ISBN: (纸本)9798350366570

In the contemporary digital era, the intersection of emotion recognition technology and music recommendation systems has paved the way for a transformative user experience. This research introduces an innovative 'Emotion-Based Music Recommendation System,' designed to enhance the personalization of music consumption. Leveraging real-time facial expression analysis and a curated music dataset, the system dynamically adapts to users' emotional states, providing a sealess and engaging music discovery process. The integration of Facial Recognition (FR) technology enables the system to capture and interpret users' moods instantaneously, eliminating the need for manual classification of songs. This novel approach not only streaDlines the user experience but also holds significant potential for mitigating revenue loss associated with prolonged music search times. The research utilizes Python and PyCharm, employing Haar cascades for face detection and collaborative filtering and content-based filtering methods for music recommendation. The datasets utilized include FER, containing grayscale images of faces classified into seven emotions, and YouTube Music data, incorporating diverse attributes for mood-based classification. Through this synthesis of facial recognition and music datasets, the proposed system stands poised to redefine the landscape of music recommendation, emphasizing real-time adaptability and user-centricity. © 2024 IEEE.

关键词： Collaborative filtering

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An Insight Into Security Issues Prevailing In The Cloudified Era 1

An Insight Into Security Issues Prevailing In The Cloudified...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Nemirajaiah, Naveen Kumar Adalagere Raju, Channa Krishna Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Kunigal Road Maralur Karnataka Tumakuru India Sri Siddhartha Institute of Technology Department of Computer Science and Engineering B. H. Road Agalakote Karnataka Tumakuru India

ISBN: (纸本)9798350356816

The term 'cloudified era' refers to concept of computing at remote servers to perform everyday operations which involves data storage, processing and software applications been hosted on remote servers accessed over via internet. This shift to cloud-based infrastructure extends so much profit with respect to scalability, cost-efficiency, enhanced collaboration, and remote accessibility. The cloud services providers implement typical security measures and adhere to stringent compliance standards and certifications, which improve data protection for organizations. However, businesses must still manage respective cloud work setups to address potential risks like data breaches. One major concern is the surge in ransomware and malware attacks aimed at cloud environments. Cybercriminals exploit weaknesses in VM configurations, allowing them to deploy virus that encrypts vital data and demand ransom for decryption. Furthermore, vulnerabilities to hypervisors, presents a critical security challenges as attacks are particularly damaging the core layer of virtualization technology. The rising incidence of hypervisor vulnerabilities demands ongoing vigilance and the adoption of strong security protocols to maintain the integrity and security of cloud environments. This paper examines the associated security challenges and proposes a system that enhances security measures in the current cloud-centric era. © 2024 IEEE.

关键词： Cloud security

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Scale Bilateral Filter Based Medical Image Fusion Using CNN

Multi-Scale Bilateral Filter Based Medical Image Fusion Usin...

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2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023

作者： Shibu, D.Sunderlin Juliebersiyal, D. Bhuvaneswari, M. Suriya, A. Harishna, S. Sethu Institute of Technology Department of Biomedical Engineering India Karpagam Academy of Higher Education Department of Biomedical Engineering India Kgisl Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350318784

Biomedical image fusion technique has widely utilized in medical diagnosis. In this work, a successful biomedical image fusion technique by Multi-scale Bilateral Filter Using CNN is developed to attain a combined image with preserved edge details and all other traits of the input images. Since borders are the vital part in medical image understanding, proposed work has been implemented with multiscale Bilateral Filter decomposition combined with CNN in NSCT domain. In this technique, initially multiscale Bilateral decomposition filter is used in two dissimilar modals of the clinical case input images. low frequency layer is reconstructed using NSCT - CNN fusion scheme and High frequency layer is reconstructed by CNN fusion scheme. Finally combining LFL and HFL, the fused image is obtained. The experimental outcomes prove that this technique attains high-tech presentation in together subjective and objective analysis. © 2023 IEEE.

关键词： Image fusion

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Helping Language Models Learn More: Multi-Dimensional Task Prompt for Few-shot Tuning

Helping Language Models Learn More: Multi-Dimensional Task P...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Weng, Jinta Zhang, Jiarui Hu, Yue Fa, Daidong Xu, Xiaofeng Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer and Information Qiannan Normal University for Nationalities Duyun China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models. © 2023 IEEE.

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