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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zeng, Xianzhi Jiang, Wenchao Zhang, Shuhao National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Nanyang Technological University Singapore Singapore University of Technology and Design Singapore

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM's effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM's performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM's effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM -a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions. © 2024 Neural information processing systems foundation. All rights reserved.

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Light Fidelity and Internet of Things for Medical Applications 4

Light Fidelity and Internet of Things for Medical Applicatio...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Adi, Puput Dani Prasetyo Sariningrum, Ros Rahmatullah, Rizky Novita, Hessy Situngkir, Tiar Lina Adji, Priyo Hartono Lusiastuti, Angela Mariana Wibowo, Antonia Rahayu Rosaria Prastiyono, Yudi Adiprabowo, Tjahjo Yassir, Muhammad Maulana, Fairuz Iqbal Department of Telecommunication Bandung Indonesia Veterinary Research Center Bandung Indonesia Singaperbangsa University Faculty of Economics Department of Management Karawang Indonesia Bandung Indonesia Langlangbuana University Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Institut Teknologi Dan Bisnis Nobel Department of System and Information System Makassar Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR);using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ Programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things. © 2024 IEEE.

关键词： C++ (programming language)

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A Novel Approach for Achieving FOMs in Space Constrained Optimized Arrays for AESA Applications

A Novel Approach for Achieving FOMs in Space Constrained Opt...

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2024 International Conference on Microelectronics, ICM 2024

作者： Naveed, Athar Tahir, Ghiayas Hassan, Arshad Ali, Shawkat Khan, Arshad Bermak, Amine Nastp Electronics System Design Center Pakistan National University of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9798350379396

This paper defines an approach applied for implementation of application specific beamforming modalities in finite aperture, space-constrained, optimized arrays. To achieve desired response in array factor, appropriate tapering schemes are selected while focusing on array Figures of Merits (FOMs). In this paper, space-constrained optimized arrays of sizes 1 × 32 (linear) and 8 × 8 (planar) are designed using Opti-metrics in High Frequency Structure Simulator (HFSS). The calculated weights and steering scheme are then applied to HFSS models and Matlab based calculations are validated for proof of concept. Array metrics like low SLL, limited beam-broadening, increased directivity and limited scan loss in planned Field of View (FOV) are targeted metrics. This paper also briefly elaborates comparison of various tapering techniques, while focusing on defined FOMs. The working procedure is developed which extends itself from excitation control of linear to planar dimensions. Keeping in view limited computational resources for full wave simulation, an approach using Pattern Multiplication (PM) is suggested to converge to desired results for large sized arrays. The scheme of this paper has also been used in a design of Hybrid beamforming network (BFN). © 2024 IEEE.

关键词： MATLAB

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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Fine-Tuning Large Language Models for Sentiment Classification of AI-Related Tweets

Fine-Tuning Large Language Models for Sentiment Classificati...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Riad, Md Jahid Alam Debnath, Rahul Shuvo, Mahfuzur Rahman Ayrin, Fateha Jannat Hasan, Nobanul Tamanna, Ayesha Akhter Roy, Prosenjit United States Prairie View A & M University Department of Computer Science TX United States International American University Department of Management Information System Los AngelesCA United States Department of Computer Science and Engineering Bangladesh University of Aiub Department of Csse Bangladesh

ISBN: (纸本)9798331535476

Fine-tuning large language models (LLMs) for sentiment analysis presents a promising avenue for enhancing the understanding of public sentiment surrounding artificial intelligence (AI) topics. This study explores the fine-tuning of Large Language Models (LLMs) for sentiment analysis on AI-related topics using social media data. We collected approximately 20,000 tweets containing hashtags and comments related to AI concerns, including 'AI Danger,' 'AI Ethics,' 'AI Problems,' 'AI Creating Problem on Jobs,' and 'AI Threat.' The dataset captures public sentiment surrounding potential negative impacts of artificial intelligence. Three cutting-edge LLMs, Llama 2, Gemma, Mistral, and Phi 3, served as our foundational models for fine-tuning. To enhance model performance, we implemented advanced prompt engineering techniques, including Chain-of-Thought (COT), Algorithm-of-Thoughts (AOT), Fewshot and EmotionPrompt. Our results demonstrate that Llama 2 achieves consistent performance across all techniques with accuracies ranging from 82.3% to 83.9%. Phi-3 showed the best performance with EmotionPrompt and Fewshot techniques, achieving accuracies of 87.8% and 87.4% respectively. Gemma 7B performed optimally with Fewshot at 84.0% accuracy, while Mistral achieved 85.0% accuracy using the same technique. The research reveals that Fewshot and AOT consistently produce reliable results across models, with Emotion-Prompt showing model-specific effectiveness. Our findings have applications for enhancing the precision and dependability of sentiment analysis instruments that track public opinion and AI-related speech. © 2024 IEEE.

关键词： Tweets

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Body Language Recognition system for Communication Quality Enhancement

Body Language Recognition System for Communication Quality E...

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E-mobility, Power Control and Smart systems (ICEMPS), International Conference on

作者： M Maharasi V Nandhini Trisha. I Computer Science and Business system Rajalakshmi Engineering College Thandalam chennai

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

The development of computer vision technology in recent days, enhanced the human-computer interface (HCI) systems in a broad spectrum. The recent developments of human computer interfacing such as Augmented reality applications, gesture-based control devices, robotic assistants, patient health care monitoring systems considers human body pose estimation as important process involved in it. The growth of neural computing and artificial intelligence (AI) models plays an optimum role in development of HCI and pose estimation. The presented study considers various existing challenges in body language recognition systems and its role in communication quality enhancement. The comprehensive study considers existing state of art approaches and its drawbacks, challenges, technology, tool in terms of achieving better accuracy and formulated a robust human pose estimation model using machine learning technology. The suggested architecture and solutions derived from the proposed method are well explored in the article.

关键词： Human computer interaction Technological innovation computer vision Machine learning algorithms Computational modeling Biological system modeling Pose estimation

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Control and Stability of Large-scale Power system with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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A Proposed Mala-Net CNN Algorithm for Malaria Early Detection Based on Thick Blood Smear 2

A Proposed Mala-Net CNN Algorithm for Malaria Early Detectio...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Indra, Zul Jusman, Yessi Elfizar Mahdiyah, Evfi Salambue, Roni Winarso, Doni Universitas Riau Departement of Computer Science Pekanbaru Indonesia Universitas Muhammadiyah Yogyakarta Departement of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Riau Departement of Information System Pekanbaru Indonesia

ISBN: (纸本)9798350389241

Malaria has spread worldwide since the early 20th century and causes nearly half a million deaths each year. Malaria is actually not a dangerous disease and can be easily cured if the treatment can be carried out effectively. Therefore, efforts to identify the presence of the plasmodium parasite early will greatly help reduce the death rate caused by this disease. However, the fact is that this disease often does not get serious attention. Generally, this disease is only treated in the critical phase because it is considered just a common flu. This research is intended to produce a computer-aided disease diagnosis (CAD) system enriched with CNN algorithms to assist in early malaria diagnosis. This CAD system is expected to provide an effective and reliable malaria diagnosis, and avoid the limitations of manual diagnosis. In order to develop the CAD applications, this study proposed a novel CNN architecture called as Mala-Net by utilizing red thick blood dataset. In conclusion, this study successfully surpassed previous studies used as benchmarks with an accuracy value above 97%. © 2024 IEEE.

关键词： Diseases

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Bat Algorithm: A Review on Theory, Modifications and Applications 3

Bat Algorithm: A Review on Theory, Modifications and Applica...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Singh, Hakam Witarsyah, Deden Misra, Alok Tiwari, Raj Gaurang Chitkara University School of Engineering and Technology Department of Computer Science and Engineering Himachal Paradesh Baddi India Telkom University School of Industrial and System Engineering Information System Departement Bandung Indonesia Lovely Professional University School of Computer Application Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350394559

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development. © 2023 IEEE.

关键词： Cloud computing

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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