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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Luo, Yutian Li, Jinze Chen, Jie Liu, Jian University at Buffalo The State University of New York School of Engineering and Applied Sciences Department of Computer Science and Engineering United States University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350357950

Steel is one of the important basic industrial raw materials, and the development of steel production technology directly affects the infrastructure construction, the development of manufacturing industry and the prosperity of the national economy. The identification of surface defects on steel plates has garnered increasing attention. Such defects not only impede the ability of medium and heavy plates to meet user production specifications but also pose the risk of significant production accidents. These challenges can have an immeasurable adverse impact on the production enterprise. Due to the swift advancements in computer vision, vision-based steel plate surface inspection technology has reached a high level of maturity. We collected images of steel plate surface defects, this encompasses the detection of various defects such as overburning, pits, compressed rust skin, and other irregularities originating from the production line of a domestic medium and heavy steel plate factory, and used five models (yolov8, yolov5, Faster-RCNN, RCNN, and DETR) for training and detection after pre-processing techniques such as labelling and data enhancement. Upon evaluating the detection outcomes, it becomes evident that the Yolov8 model exhibits robust accuracy and rapid computational speed when applied to the task of detecting defects on steel surfaces. This proficiency lends dependable technical support for various applications, including industrial quality inspection and the analysis of surface defects. Furthermore, the performance of Yolov8 under different sets of loss functions are evaluated, and the optimal setting is selected for surface defect detection. © 2023 IEEE.

关键词： data pre-processin defect detection Yolov8

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Impact of a Mobile Serious Game on Learning Outcomes for Complex Problem Solving Skills 14

Impact of a Mobile Serious Game on Learning Outcomes for Com...

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14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Miladinovic, Igor Schefer-Wenzl, Sigrid Bajic-Kerndl, Katharina VISSE University of Applied Sciences Computer Science Campus Vienna Vienna Austria Research Group for Industrial Software University of Technology Vienna Vienna Austria

ISBN: (纸本)9798350399431

Serious games are games that aim for a learning effect. They should increase motivation and commitment of students and thus lead to better learning outcomes. This approach is used at the UAS Campus Vienna, where students of the master degree program 'Software Design and Engineering' have to complete the course 'Complex Problem Solving'. This course was introduced to meet the need of computer science graduates to be able to solve complex problems in their professional life. The course is based on the Theory of Constraints by Goldratt, which can be taught playing a dice game that simulates a production chain consisting of production steps that build on one another. In this paper we present design and development of this serious game as a digital mobile variant and evaluate its impact on learning outcomes. The survey results show that the serious game improves the understanding of the principles of the Theory of Constraints among students. Furthermore, the students partly found the digital version easier to understand than the analogue dice game. © 2023 IEEE.

关键词： Serious games

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & Engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

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Intelligent Automation & Soft Computing 2023年第5期36卷 2111-2128页

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research CenterFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingﬁrm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalﬁndings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identiﬁcation efﬁciency of the proposed method.

关键词： Complex human activity recognition wearable inertial sensors deep learning construction workers automatic recognition

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Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining 21

Toward Anomaly Detection in the Blockchain Using Numerical A...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Kramberger, Renata Fister, Iztok Kamisalic, Aida Zagreb University of Applied Sciences Department of Information Technology and Computing Zagreb Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350343366

With the global increase in the popularity of cryptocurrencies, the need for anomaly detection and fraudulent behavior is reaching an all-Time high. In our paper, we propose a novel method of anomaly detection with the use of Numerical Association Rule Mining with Differential Evolution. The experiment was conducted by using the Dogecoin blockhain, and the dataset contained all of the transactions from one month. Our results contained 303 rules, with the best fitness function value of 0.8. © 2023 IEEE.

关键词： Association rules

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A DES Homomorphic Scheme for Blackbox Data Analytics

A DES Homomorphic Scheme for Blackbox Data Analytics

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International Multidisciplinary Information technology and Engineering Conference (IMITEC)

作者： Luthando Mletshe Nthabiseng Modiba Lucky Makhoere Phumezo Ntlatywa Mnoneleli Nogwina Colin Chibaya Faculty of Natural and Applied Science Computer Science and Information Technology Kimberely South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Data confidentiality is a critical concern in corporate settings, particularly as data analysts seek to uncover trends and insights from sensitive datasets. Traditional approaches often require plaintext data, posing a significant risk to data privacy during analytics processes. In response to this challenge, homomorphic encryption schemes have emerged as a promising solution to enable secure data analytics while preserving confidentiality. In this work-in-progress paper, we present an ongoing implementation of a homomorphic scheme based on the Data Encryption Standard (DES) as a proof of concept. Our goal is to showcase how DES homomorphic encryption can facilitate secure data analytics by allowing computations on encrypted data without compromising confidentiality. While our work is still in the early stages, we aim to empower data custodians by enabling them to share complete cipher datasets with confidence, knowing that data analytics operations remain blackbox and preserve data availability. Overall, this work represents an ongoing effort to advance data security practices and contribute to the development of practical solutions for secure data analytics.

关键词： Data privacy Data analysis Random access memory Closed box Market research Environmental factors Homomorphic encryption Information technology Standards Glass box

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An Enhanced Feature Selection Approach Using Capuchin Search Algorithm for high-dimensional Biological Data Classification 17

An Enhanced Feature Selection Approach Using Capuchin Search...

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Jaber, Iyad Hassouneh, Yousef Khemaja, Maha Birzeit University Department of Computer Science Birzeit Palestine ISITCom Hammam Sousse University of Sousse Higher Institute of Applied Science and Technology of Sousse Prince Research Lab Tunisia

ISBN: (纸本)9798350338904

Meta-heuristic algorithms were effectively utilized in solving a wide array of problems in business, science, data mining, and finance. They are population-based approaches that develop novel optimal solutions based on the natural behaviors of creatures. A meta-heuristic method implied in reducing the number of present features and improving classification accuracy is the wrapper feature selection approach which was specifically proposed in this paper. This approach was based on the Capuchin Search Algorithm (CapSA) [1], which is mainly inspired by the functional behavior of Capuchin monkeys in food-searching actions through leaping, swinging, and climbing, all implemented in solving constrained and global optimization problems. This paper aims to enhance the CapSA algorithm to search in a binary search space rather than a continuous one. The proposed improvements on the algorithm can efficiently outperform other present algorithms with fewer selected features, less computational time, and high solution accuracy. The enhanced algorithm, named (EbCapSA) was tested afterwards on several biological benchmark datasets and then followed by a broad comparative study with four other algorithms, including the Gray Wolf Optimizer (GWO) [2], Particle Swarm Optimization (PSO) [3], the Firefly Algorithm (FA) [4], the Whale Optimization Algorithm (WOA) [5] and the original CapSA Algorithm. Overall results rendered a superiority of the proposed approach and clear outperformance based on the quality of the final solution in searching for the most favorable feature subsets. © 2023 IEEE.

关键词： Feature Selection

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A Survey on Multi-Objective Optimization in Microgrid Systems 13

A Survey on Multi-Objective Optimization in Microgrid System...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Islam, Saiful Mostaghim, Sanaz Hartmann, Michael Otto von Guericke University Magdeburg Srh University of Applied Sciences Germany Otto von Guericke University Magdeburg Computational Intelligence Faculty of Computer Science Germany Berlin School of Technology Srh University of Applied Sciences Germany

ISBN: (纸本)9798350308365

Renewable energy resources (RES) are closely linked to grid stability problems. The main challenge of RES is the uncertainty caused by parameters such as irradiation, wind velocity, or temperature. In this connection, Multi-Objective Optimization (MOO) Algorithms can be used as an intelligent control system for a microgrid by ensuring continuity of power supply in case of grid failures. Based on grid reliability and local demand, MOO can make real-time decisions about disconnecting from the primary grid (islanding) and when to reconnect. It can help in MG control systems by optimizing energy storage systems (ESS) operation by determining when to charge and discharge them based on electricity prices, grid demand, and renewable energy availability. This survey discusses an MG and its different challenges with RES with Photovoltaic (PV) as a primary component and prominent energy sources such as a utility grid, Diesel Genset (DG), Wind Turbine (WT) and ESS. In this research, a unique classification approach of different problems in Microgrid (MG) is identified and discussed. This paper focuses on the MOO algorithms used in MG for their energy management systems (EMS) and technical and economic problems and investigates how MOO can play a significant role in solving such problems. © 2024 IEEE.

关键词： Windmill

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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