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5th International Conference on Innovative technologies and Learning, ICITL 2022

作者： Chien, Yu-Cheng Cheng, Pei-Yu Csui, Lin-Tao Yang, Yeongwook Hooshyar, Danial Huang, Yueh-Min Department of Engineering Science National Cheng Kung University Tainan City Taiwan Department of Information Management National Chin-Yi University of Technology Taichung City Taiwan Department of Information Management Tamkang University New Taipei City Taiwan Division of Computer Engineering Hanshin University Osan-si Korea Republic of School of Digital Technologies Tallinn University Tallinn Estonia

ISBN: (纸本)9783031152726

Maker’s popularity worldwide has led to numerous studies on maker education to enhance learner competitiveness. One important research topic in educational research is how to effectively measure learning performance. Given the rapid development of science and technology, it is now possible to use learner discussion data to measure learning outcomes. We propose using natural language processing (NLP) technology to analyze learner discussion records by using speech-to-text technology to convert audio files into text files and using NLP technology exploring the resultant discussion texts. Experimental results reveal significant relationships between learner discussion and learning effectiveness, participation, and teamwork. This analysis also shows that high-achieving learners often discuss programming-related keywords. The proposed method can be used to analyze learner discussions and thus to measure their learning achievements. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

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Measurement: Sensors 2023年 27卷

作者： Michael Onyema, Edeh Balasubaramanian, Sundaravadivazhagn Suguna S, Kanimozhi Iwendi, Celestine Prasad, B.V.V. Siva Edeh, Chinecherem Deborah Department of Vocational and Technical Education Faculty of Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Information Technology University of Technology and Applied Sciences AlMussanah Oman Department of Computer Applications ArulmiguArthanareeswarar Arts and Science College Tamil Nadu Tiruchengode India School of Creative Technologies University of Bolton United Kingdom Department of Computer Science and Engineering Anurag University Hyderabad India Faculty of Law Enugu State University of Science and Technology Nigeria

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques. © 2023 The Authors

关键词： Object detection

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iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence 2nd

iConDet2: An Improved Conjunctivitis Detection Portable Heal...

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2nd International Conference on Applied Intelligence and Informatics, AII 2022

作者： Adak, Mainak Chatterjee, Aayushman Roy, Nilanjana Dutta Mahmud, Mufti Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031248009

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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3D-CNN Architecture to Improve the Classification Accuracy of the Real-Time Images from IOT Devices

3D-CNN Architecture to Improve the Classification Accuracy o...

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Disruptive technologies (ICDT), International Conference on

作者： Kiran Kumar C B. Aruna Devi Lakshmana Phaneendra Maguluri Mahaveer Singh Naruka Data Science Codecraft Technologies Bangalore Karnataka India Department of Electronics and Communication Engineering Dr.NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP India GL Bajaj Institute of Technology and Management Greater Noida India

The classification of real time images from the fast data capturing devices in Internet of Things (IoT) environment is a critical task. It requires suitable processing and development of a model for increased accuracy in classifying the objects in real-time. Therefore, the necessity in improving the accuracy of classifying the instances is needed post performing the modelling, building and development of a model. In this paper, a three-dimensional (3D) Convolutional Neural Network (CNN) is developed to increase the process of classification for the objects in the real-time environment. The objects needed to train the classifier is supplied and the model is built in python environment. The results show an increased classification accuracy in detecting multi-objectives than the state-of-art models.

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DEEP LEARNING APPLICATION for IMAGE ENHANCEMENT 9

DEEP LEARNING APPLICATION for IMAGE ENHANCEMENT

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9th International Conference "Distributed Computing and Grid technologies in science and education", GRID 2021

作者： Elaraby, Ahmed Elansary, Ismail Nechaevskiy, Andrey Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt Modern Academy for Computer Science and Management Technology Cairo Egypt Meshcheryakov Laboratory of Information Technologies Joint Institute for Nuclear Research Joliot-Curie 6 Dubna 141980 Russia

Recently, deep learning has obtained a central position toward our daily life automation and delivered considerable improvements as compared to traditional algorithms of machine learning. Enhancing of image quality is a fundamental image processing task and. A high-quality image is always expected in several tasks of vision, and degradations like noise, blur, and low-resolution, are required to be removed. The deep techniques approaches can significantly and substantially boost performance compared with classical ones. One of the main research areas where deep learning can make a major impact is imaging. This work presents a survey of deep learning on image enhancement and describes its potential for future research. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Image enhancement

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Building Device Models Using LLMs for Requirements Simulation in Smart Homes

Building Device Models Using LLMs for Requirements Simulatio...

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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Shi Chen Xiaohong Chen Zhi Jin Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High-Confidence Software Technologies Peking University Ministry of Education China Department of Computer Science and Technology Peking University Beijing China

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

The implementation of model-based requirements simulations in smart homes is an effective strategy that allows users to comprehend the execution impact of their personalized needs before deployment. This necessitates the use of device models in advance. In this paper, we propose an automatic modeling method for smart home devices that utilizes a Large Language Model (LLM) to assist end users in modeling their devices. The method involves extracting information directly from device manuals using the extraction capabilities of the LLM, deducing the automata models using the reasoning capabilities of the LLM, and verifying the model quality through the properties of valid device models. Our evaluation has demonstrated that our method can generate high-quality device models. The resultant model can be utilized for smart home scenario simulations, assisting users in validating their requirements. Thus, this provides a cost-effective and scalable solution for offering device models to simulate user requirements in smart homes.

关键词： Large language models Conferences Automata Smart homes Manuals Information retrieval Cognition

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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

arXiv

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arXiv 2024年

作者： Liang, Weixin Izzo, Zachary Zhang, Yaohui Lepp, Haley Cao, Hancheng Zhao, Xuandong Chen, Lingjiao Ye, Haotian Liu, Sheng Huang, Zhi McFarland, Daniel A. Zou, James Y. Department of Computer Science Stanford University United States Machine Learning Department NEC Labs America United States Department of Electrical Engineering Stanford University United States Graduate School of Education Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Computer Science UC Santa Barbara United States Department of Biomedical Data Science Stanford University United States Department of Sociology Stanford University United States Graduate School of Business Stanford University United States

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood

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EtherClue:Digital investigation of attacks on Ethereum smart contracts

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Blockchain(Research and Applications) 2021年第4期2卷 12-29页

作者： Simon Joseph Aquilina Fran Casino Mark Vella Joshua Ellul Constantinos Patsakis Department of Computer Science University of MaltaMsidaMSD 2080Malta Department of Informatics University of PiraeusPiraeus18534Greece Information Management Systems Institute Athena Research CenterMarousi15125Greece Centre for Distributed Ledger Technologies University of MaltaMsidaMSD 2080Malta

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen *** vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such contracts will not be *** a vulnerable contract is instantiated on the blockchain and becomes the target of attacks,the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were *** this work,we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise(IoC)specially crafted for use in the *** definitions need to capture the side-effects of successful exploitation in the context of the Ethereum ***,we define a model for smart contract execution,comprising multiple abstraction levels that mirror the multiple views of code execution on a ***,we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue,a prototype tool for investigating Ethereum security *** results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less ***,they are contract-specific and suffer from false *** the other hand,fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation,which is nevertheless applicable for practical use.

关键词： Blockchain forensics Ethereum attacks Indicators of compromise Ethereum operational semantics

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A Discrete-Time Image Hiding Algorithm Transform Using Wavelet and SHA-512

A Discrete-Time Image Hiding Algorithm Transform Using Wavel...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Santosh Kumar Savya Sachi Neetu Avnish Kumar Abhishek Jain M.S.R. Prasad CSE-IOT Noida Institute of Engineering and Technology Greater Noida Department of Information Technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar India College of Smart Computing COER University Roorkee U.K. India Department of Computer Science and Engineering Glocal University Saharanpur Department of Computer Science and Engineering Uttranchal University Dehradun U.K. India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur AP India

Steganography is an ancient technique used to conceal information within another medium, enhancing security alongside cryptographic methods. This paper's steganography approach revolves around concealing data within the skin region of a cover image converted from RGB to YCbCr colour space. The YCbCr colour model facilitates skin tone detection, employing its components Yellow, Chromatic Blue, and Chromatic Red. The secret information is first encrypted using the SHA-512 algorithm for robust data hiding. Next, a frequency domain approach, specifically the Discrete Wavelet Transform (DWT), is employed for embedding, proving more effective than the Discrete Cosine Transform (DCT). The key idea is to use an object-oriented steganography mechanism that focuses on tracking skin tone objects within the image, thereby enhancing security. The secret encrypted data is embedded in one of the high-frequency sub-bands of DWT, specifically within skin pixels in that sub-band. This approach demonstrates higher security levels through experimentation while maintaining satisfactory PSNR (Peak Signal-to-Noise Ratio) values. By adopting this technique, the paper highlights an efficient and secure way to achieve hidden communication without compromising the cover image's visual quality.”

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