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10th International Conference on computer, Control, Informatics and its Applications, IC3INA 2023

作者： Ramdhani, Taufik Iqbal Basuki, Akbari Indra Susanto, Heru Umbara, Raditya Panji Arifin, Samsul Faiza, Ninon Nurul Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science Nad Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia

ISBN: (纸本)9798350394870

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies. © 2023 IEEE.

关键词： Smart contract

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Rational Jacobi Kernel Functions: A novel massively parallelizable orthogonal kernel for support vector machines

Rational Jacobi Kernel Functions: A novel massively parallel...

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Distributed Computing and High Performance Computing (DCHPC), International Conference on

作者： Mahdi Movahedian Moghaddam Alireza Afzal Aghaei Kourosh Parand Department of Computer and Data Science Shahid Beheshti University Tehran Iran Institute for Cognitive and Brain Sciences Shahid Beheshti University Tehran Iran

Machine learning has recently seen a significant upsurge in its influence across diverse scientific domains. Among the array of machine learning techniques, the support vector machine (SVM) has emerged as a powerful supervised method. To enhance the SVM’s precision, the adoption of kernel methods has become commonplace. Notably, the integration of orthogonal functions as the SVM’s kernel has led to substantial improvements in accuracy. However, it is essential to note that the use of these functions also introduces heightened time complexity to the SVM along with imposing some limitations on data. In response to this challenge, a general formulation of orthogonal Jacobi kernel functions has been proposed. This formulation allows the user to choose classical Jacobi polynomial kernels, fractional or rational ones. Next, we develop a data-parallel technique implemented on heterogeneous computing systems to accelerate the computation of kernel matrix. The ensuing experimental results, conducted on the benchmark datasets, were founded on three distinctive rational mappings: algebraic, exponential, and logarithmic. Spanning data sizes from 700 to 3800, these mappings exhibited remarkable speedup factors of 3.967, 4.599, and 2.807, respectively.

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Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models

arXiv

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arXiv 2024年

作者： Feng, Chengzhe Sun, Yanan Li, Ke Zhou, Pan Lv, Jiancheng Lu, Aojun The College of Computer Science Sichuan University Chengdu610065 China The Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive. Prompt learning, a recent development in the field of natural language processing, emerges as a potential solution to address this challenge. In this paper, we investigate the effectiveness of prompt learning in code intelligence tasks. We unveil its reliance on manually designed prompts, which often require significant human effort and expertise. Moreover, we discover existing automatic prompt design methods are very limited to code intelligence tasks due to factors including gradient dependence, high computational demands, and limited applicability. To effectively address both issues, we propose Genetic Auto Prompt (GenAP), which utilizes an elaborate genetic algorithm to automatically design prompts. With GenAP, non-experts can effortlessly generate superior prompts compared to meticulously manual-designed ones. GenAP operates without the need for gradients or additional computational costs, rendering it gradient-free and cost-effective. Moreover, GenAP supports both understanding and generation types of code intelligence tasks, exhibiting great applicability. We conduct GenAP on three popular code intelligence PLMs with three canonical code intelligence tasks including defect prediction, code summarization, and code translation. The results suggest that GenAP can effectively automate the process of designing prompts. Specifically, GenAP outperforms all other methods across all three tasks (e.g. improving accuracy by an average of 2.13% for defect prediction). To the best of our knowledge, GenAP is the first work to automatically design prompts for code intelligence PLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Design

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Two-stage predict+optimize for mixed integer linear programs with unknown parameters in constraints 23

Two-stage predict+optimize for mixed integer linear programs...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xinyi Hu Jasper C.H. Lee Jimmy H.M. Lee Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Sciences & Institute for Foundations of Data Science University of Wisconsin-Madison WI USA

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al. proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new simpler and more powerful framework called Two-Stage Predict+Optimize, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods.

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Multi-Model Deep Learning Fusion for Improved Alzheimer’s Disease Classification with MRI and Cognitive data

Multi-Model Deep Learning Fusion for Improved Alzheimer’s D...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Minu Inba Shanthini Watson Benjamin K Maithili E Chandralekha D. Gowtham Salla Anand Kumar Reddy Kota Dharma Teja Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Given its increasing impact and enormous global societal impact, Alzheimer’s disease (AD) presents a serious issue in the healthcare industry. Effective therapies and management techniques for AD depend on early detection and a precise diagnosis. On the other hand, traditional diagnostic approaches frequently depend on expensive imaging methods and arbitrary clinical judgments, which causes delays in diagnosis and less than ideal results. By utilizing MRI scans and handwriting samples, the suggested strategy embraces the ability of multi-modal data integration to offer a novel perspective to AD prediction. A non-invasive and economical method of identifying minor motor and cognitive problems suggestive of early-stage $A D$ is handwriting analysis, whereas MRI offers important insights into the structural brain abnormalities linked to AD. To rigorously pre-process and extract significant features from MRI and handwriting data, state-of-the-art deep learning techniques are used. A multimodal neural network architecture is then fed these features for combined analysis and prediction. Accuracy rates of $\mathbf{8 8 \%}$ for handwriting data and $\mathbf{9 0} \%$ for MRI images are attained by each independently trained model. A unified classifier is then used to aggregate the outputs of different models and refine the categorization through the addition of denser layers A remarkable $\mathbf{9 7 \%}$ accuracy on the test set is achieved by this combined model, which considerably improves diagnostic accuracy. The technique provides a comprehensive approach to AD prediction by combining structural and functional information from MRI with handwriting data, enabling early detection and individualized treatment plans. In order to create robust and trustworthy diagnostic tools, the results emphasize how crucial it is to integrate many data modalities and use deep learning approaches.

关键词： Deep learning Accuracy Magnetic resonance imaging data integration Feature extraction Motors data models Alzheimer's disease Biological neural networks Resilience

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LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights

arXiv

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arXiv 2023年

作者： Held, Lena Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of Human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 hours of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://***/datasets/TrustHLT/LaCour. © 2023, CC BY-SA.

关键词： Laws and legislation

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Based on Transfer Learning a Performance Analysis and Comparative Study of CNN for Accurate Plant Disease Detection

Based on Transfer Learning a Performance Analysis and Compar...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Nithin S Nishanth M S Ashwini Kodipalli Trupthi Rao Kumaraswamy S Dept of Computer Science and Engineering Global Academy of Technology Banglore India Dept of Artificial Intelligence and Data Science Global Academy of Technology Banglore India Department of Computer Science and Engineering Global Academy of Technology Banglore India

This paper investigates the use of Convolutional Neural Networks (CNNs) for plant disease diagnosis, utilizing the notion of transfer learning to develop a deep CNN network at a low cost. The dataset utilized includes 30,000 pictures from 18 distinct categories. Several architectural models, including ResNet50, ResNet50V2, VGG16, VGG19, EfficientNetB0, MobileNet, MobileNetV2, NASNetMobile, and DenseNet, were created using transfer learning methodologies. The models are trained on the target plant disease dataset while keeping the features learnt from large-scale image datasets like ImageNet. Performance criteria such as accuracy, precision, and recall are used to assess the models' accuracy in detecting various plant diseasesx. Furthermore, the research investigates the effect of dropout on accuracy and other model parameters in the context of transfer learning. Finally, the outcomes of a tailored model with and without batch normalization are compared.

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Evolutionary Transfer Neural Architecture Search Across Spaces via Representation Learning

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hou, Boyu Feng, Liang Chen, Xuefeng Tang, Jing Tan, Kay Chen Liao, Xiaofeng Chongqing University College of Computer Science China Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong999077 Hong Kong Data Science and Analytics Thrust Guangzhou511453 China

Neural Architecture Search (NAS) has emerged as a crucial method for automating the design of deep learning models. Despite its potential, NAS frequently requires substantial computational and hardware resources. To mitigate these challenges, transferable NAS (TNAS) has been introduced, leveraging prior NAS results to enhance performance on new tasks. However, existing methods largely focus on knowledge transfer within identical neural search spaces, overlooking the potential for cross-domain transferability. Motivated by this gap, we explore evolutionary TNAS across heterogeneous search spaces by learning common neural representations. In particular, we introduce a novel approach that encodes both operational and topological information of neural architectures into a unified sequence using a simple tokenizer. This sequence is then processed by a variational auto-encoder, with a Transformer-based encoder to capture rich neural representations and a decoder that reconstructs the original sequence. By utilizing these latent representations, we further establish an inter-domain mapping that acts as a bridge, enabling effective explicit solution transfer among diverse search spaces to enhance the evolutionary NAS process. To harness this capability, we develop an evolutionary sequential transfer optimization approach that transfers knowledge during population initialization, providing both flexibility and adaptability. To the best of our knowledge, this work serves as the first attempt in the literature exploring evolutionary TNAS across diverse spaces. Moreover, we demonstrate the utility of our method through comprehensive empirical studies using different architecture spaces, including NAS-Bench-101, NAS-Bench-201, and the DARTS search space. Our results show that the proposed method significantly enhances the adaptability and performance of NAS across varied domains. © 1997-2012 IEEE.

关键词： Transfer learning

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Transforming Skin Disease Diagnosis: Harnessing Compound-Scaled AI Network for Enhanced Image Analysis and Precision Medicine

Transforming Skin Disease Diagnosis: Harnessing Compound-Sca...

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Disruptive Technologies (ICDT), International Conference on

作者： S. P. Santhoshkumar Torthi Ravichandra Nandipati Mounika R Ranjani S Gajalakshmi Jim Mathew Philip Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Computer Science and Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Artificial intelligence and data science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Millions worldwide suffer from skin conditions, making prompt and precise diagnosis essential to successful treatment. However, conventional diagnostic techniques are time-consuming, dependent on physician knowledge, and frequently unavailable in low-resource locations. The compound-scaled AI network, an advanced deep-learning model, addresses these challenges by providing precise and scalable image analysis. Utilizing high-resolution imaging and compound scaling, it can accurately detect, classify, and segment various skin disorders, including benign and malignant lesions. While questions remain about whether AI matches human specialists in diagnosing all pigmented skin lesions, studies have compared AI performance against experts in identifying clinically significant cases. This research highlights the compound-scaled AI network's potential in dermatology, particularly for telemedicine, early cancer detection, and clinical workflow integration. By enhancing diagnostic accessibility and accuracy, this AI model could transform dermatology, enabling more personalized and equitable treatment worldwide. Impressively, it achieved 99.6% training accuracy and 98.4% testing accuracy, demonstrating deep learning's effectiveness in automating skin disorder detection and classification.

关键词： Training Accuracy Image analysis Precision medicine Computational modeling Dermatology Transforms Skin Lesions Artificial intelligence

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Impact of Internet of Things (IoT) in Healthcare: Challenges and Application 3

Impact of Internet of Things (IoT) in Healthcare: Challenges...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Saini, Aradhna Dhuriya, Gaurav Pillai, Sofia K Shukla, Garima Agrawal, Urvashi Kasana, Hemant Noida Institute of Engineering & Technology Department of Data Science Greater Noida India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Greater Noida India Jhulelal Institute of Technology Department of Electronics and Telecommunication Engineering Nagpur India Galgotias University Department of Computer Science Greater Noida India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Software engineering and electronic designing have been converted into quite possibly the most eminent innovative outcome, the Internet of Things (IoT). The term Internet of Things refers to the organisation of physical items that are outfitted with sensors, equipment, programming, and other advances to interface and exchange data with other gadgets and frameworks over the internet. The effect of IoT in medical care has been altogether expanded over the most recent couple of years. IoT is assuming a significant part in the medical care area to expand dependability, precision and usefulness. With the populace increment, medical problems are additionally rising and the need for mechanical arrangement is requested. This is the place where IoT assumes a major part and IOT overhauling present-day medical services with promising innovative, efficient and friendly perspectives. Web of Things execution into the medical services area, specifically, information asset the executives, security, loss of protection, access control, and partner coordinated effort. IoT will be a vital piece of the patient's finding, treatment, recuperation, and observing interaction sooner rather than later. It is apparent that an enormous number of articles will be associated with the IoT network. This paper audits the most recent IoT applications in the health care space and most recent advancements. This paper likewise features the exploration holes in the IoT. The goals of this audit paper to help analysts in growing further developed and productive IoT based applications to help medical care. © 2022 IEEE.

关键词： Internet of things

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