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8th Conference on information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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Semantic association rules for data interestingness using domain ontology

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International Journal of Metadata, Semantics and Ontologies 2022年第1期16卷 47-67页

作者： Abhilash, C.B. Mahesh, Kavi Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka Dharwad India

The COVID-19 pandemic is a major public health crisis threatening people’s health, well-being, freedom to travel and the global economy. Understanding COVID-19 symptoms for determining the severity of cases is critical. This study aimed to discover interesting facts from the COVID-19 data set considering symptoms, medicines and comorbidity. For data mining research, the semantic web raises new possibilities. Resource Description Framework (RDF) triple format is commonly used to express semantic web data. Association Rule Mining (ARM) is one of the most effective methods of detecting frequent patterns. However, finding potential rules is a difficult task. We propose an improved method that uses ontology with ARM for finding semantic-rich rules from COVID-19 data sets. The outcomes are semantic association rules that are potentially beneficial for decision-makers. We compare our results with one of the most recent approaches in this field to demonstrate the importance of ontology-based methods. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： COVID-19

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Submersible Position Prediction and Identification Based on Hybrid Neural Network Algorithm and Monte Carlo Method 2

Submersible Position Prediction and Identification Based on ...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Xie, Lingyi Li, Xinqi Zhang, Mufan Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen China Southern University of Science and Technology Department of Information Systems and Management Engineering Shenzhen China Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China

ISBN: (纸本)9798350386639

This paper proposes a method for predicting and identifying the position of a submersible using a hybrid neural network algorithm and the Monte Carlo method. Initially, we employ a hybrid neural network algorithm to establish a predictive model for ocean current parameters based on data from the Ionian Sea detection station. Additionally, we analyze the forces acting on the submersible and its motion to construct a physical model for the submersible's vertical movement. By integrating the effects of these two models, we can predict the three-dimensional trajectory of the submersible. Furthermore, to accurately analyze the potential location of the submersible after an accident, we utilize the Monte Carlo method and infinitesimal method, employing the K-means clustering algorithm to analyze the initial deployment points of the device. Based on the probability distribution, we select appropriate search methods. As indicated by the established probability function model, the probability of the submersible being covered decreases over time. The search results serve as adjustment parameters for the probability function model;the more search results obtained, the higher the probability of locating the submersible is. © 2024 IEEE.

关键词： Intelligent systems

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study 2

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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2nd IEEE International Conference on data, Decision and systems, ICDDS 2023

作者： Hegadi, Rajendra Akella, Syam Sankar Karthik Reddy, Koushik Kumar Pavan, C. Indian Institute of Information Technology Dharwad Department of Data Science and Intelligent Systems Karnataka India Indian Institute of Information Technology Dharwad Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350314168

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology. © 2023 IEEE.

关键词： Smart contract

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ProfileOTMM: Automated Glycan Motif Analysis Through Pattern Recognition of Tree Structures

ProfileOTMM: Automated Glycan Motif Analysis Through Pattern...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Totsuka, Kento Hosoda, Masae Aoki-Kinoshita, Kiyoko F. Shinomiya, Norihiko Graduate School of Science and Engineering Soka University Tokyo Japan Joint Support-Center for Data Science Research Research Organization of Information and Systems Database Center for Life Science Chiba Japan Tokyo Japan

ISBN: (纸本)9798350350821

In recent times, tree-structured data has been rapidly spreading with the advent of the World Wide Web and Bioinformatics. It is employed as a common format for the storage and depiction of data. Glycans, essential substances in all organisms, are biomolecules that play diverse functional roles in the body. They are commonly modeled as tree-structured data and analyzed using machine learning techniques. Nevertheless, more detailed information is imperative to comprehensively understand the biological attributes and functionalities of gly-cans. In this research, we aim to propose a new model called profileOTMM (profile Ordered Tree Markov Model) for the complete automation of glycan analysis. As an initial step, feature engineering was conducted using the OTMM model, which serves as the foundational model for profileOTMM. By improving the accuracy of glycan data input through OTMM, we integrated monosaccharides, the elemental constituents of glycans, along with their linkage types into tree-structured glycan data. This enhanced approach enabled the extraction of biologically meaningful structures with a high degree of precision and accuracy, surpassing the results of previous research efforts. This research will encourage the understanding of glycan structures and their functions. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain data security Storage Management Utilization

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Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence 27

Towards the New XAI: A Hypothesis-Driven Approach to Decisio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Le, Thao Miller, Tim Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO's Data61 Australia

ISBN: (纸本)9781643685489

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines. © 2024 The Authors.

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Sentiment Analysis with LLMs: Evaluating QLoRA Fine-Tuning, Instruction Strategies, and Prompt Sensitivity 34

Sentiment Analysis with LLMs: Evaluating QLoRA Fine-Tuning, ...

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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Topal, Mustafa Burak Bozanta, Aysun Basar, Ayse Bogazici University Computer Engineering Istanbul Turkey Bogazici University Management Information Systems Istanbul Turkey Toronto Metropolitan University Data Science Laboratory TorontoON Canada

ISBN: (纸本)9798331504830

In this study, we explore the performance of large language models (LLMs) and provide insights into the strengths and weaknesses of these models in sentiment analysis. In addition, we explore the impact of various instruction methods and fine-tuning techniques on the models' performance and analyze how sensitive they are to different prompts. Although there are studies comparing LLMs on sentiment analysis tasks, there is a limited number of studies on fine-tuning strategies, instruction variety, and prompt sensitivity of LLMs for sentiment analysis. For this purpose, we used Llama2-7B, Lama3-8B, Mistral-7B, Gemma-7B, and Bloom-7B and compared them with BERT and Roberta. We evaluated fine-tuned and original chat versions with zero and few-shot experiments. We generated various instruction strategies and observed their effects on the performance of the models. We also compared Quantized Low-Rank Adapters (QLoRA) fine-tuning and full fine-tuning with all parameters. The fine-tuned versions performed better compared to the original chat versions on almost all tasks. The fine-tuned and chat versions of Llama3-8B and Mistral-7B outperformed others on these tasks, while the Llama2-7B-chat model often produced invalid responses. Our research indicates that QLoRA is a practical substitute for full fine-tuning, as it lessens computational demands without sacrificing performance. Fine-tuned models were less affected by variations in test prompts compared to chat models. This is likely because their specialized training allows them to better handle queries within their domain. In this exploratory study, we provide insights into the potential of LLMs in sentiment analysis tasks. © 2024 IEEE.

关键词： Sensitivity analysis

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Advances in intelligent computing approaches for solving problems related to photonic crystal fibers: Advances in Intelligent..: D. Yang et al.

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Optical and Quantum Electronics 2025年第1期57卷 1-31页

作者： Yang, Dan Zhao, Yuyu Liu, Hong Li, Yijin Tang, Chang Li, Shijun Lingye, Zhenglin College of Information Science & Engineering Northeastern University Shenyang110819 Thailand National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 Thailand

Photonic crystal fiber (PCF) has shown a promising application in various fields of modern optics due to its excellent transmission characteristics. However, conventional numerical solution methods have limitations such as high computational effort and low efficiency in designing PCF structures and characterizing pulse propagation in PCFs. With the advancement of artificial intelligence technology, various types of intelligent computing approaches, especially intelligent algorithm and machine learning algorithm (IML), provide new perspectives to solve the above difficulties. Therefore, an increasing number of researchers are committed to using IML to break through the limitations of conventional methods and accordingly open up a new interdisciplinary research direction. This paper begins with a brief overview of common optical properties and typical algorithms. Subsequently, it reviews specific areas where PCF and IML are combined, including the prediction and optimization of optical properties, the design of polarization devices, fiber lasers and amplifiers, as well as the control and prediction of Supercontinuum generation. Finally, this paper offers insights into the future developments and challenges in this emerging interdisciplinary field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supercontinuum generation

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STAGGER: Enabling All-in-One Subarray Sensing for Efficient Module-level Processing in Open-Bitline ReRAM 24

STAGGER: Enabling All-in-One Subarray Sensing for Efficient ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Chengning Feng, Dan Hu, Yuchong Tong, Wei Liu, Jingning Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage Systems Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706011

Emerging resistive RAM (ReRAM) devices can in-situ execute vector-matrix-multiplication (VMM) and is able to achieve energy-efficient in-memory scientific computing. However, the peripheral separated S&Hs and ADCs for row buffering and sensing in conventional designs are the system bottleneck. We propose an ADC-less all-in-one processing-in-ReRAM design that enables the precharge once, readout multiple-bits (PORM) functionality for overlapping the tRCD latencies of different-significance result bits sensed out on the same bitline. Specifically, to support PORM sensing, we propose a cascaded-feedback bitline sensing architecture for VMM and a buffering-and-sensing-collocated sense amplifier elemental design with the bitline and the storage node fully decoupled for enabling conflict-free column accesses. We further propose cross-level inter-leaving mechanism for successive column VMM accesses to reduce the overall latency through improving the hardware spatiotemporal utilization. Experimental results show that our proposed design achieves 297% overall performance improvement and 85.8% energy reduction, compared with an aggressive baseline. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Feedback amplifiers

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