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Sign Language Recognition System – A Review 8th

Sign Language Recognition System – A Review

引用

8th ifip TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Sahayaraj, Jovitha Kaviyapriya, K. Vasuki, P. Sri Sivasubramaniya Nadar College of Engineering Kalavakkam603110 India

ISBN: (纸本)9783031736162

Speech is the easiest and the most basic mode of communication in our day-to-day life. Hence, speech impaired people are at a major disadvantage in this concern. the staple mode of communication for the speech impaired is Sign Language which in turn isolates them from the rest of the world in more ways than one. the Sign Language Recognition (SLR) System is an attempt to compress this isolation by recognizing the gestures of the Sign Language. the application may be used to map the sign into their corresponding text or speech in the given spoken language. In general, the system has been realized either using image processing techniques or with sensor signals employed in smart gloves. the system based on image processing captures the hand gestures made by an individual and maps it to their corresponding alphabets, words or phrases pre-defined in the existing dataset. Although this system proves to be useful, the dwindling accuracy of the image capturing process is a major drawback. the sensor-based system, on the other hand, works using a number of sensors attached to a glove which detects the hand gestures and computes it to its corresponding meaning. the performance of these systems is relatively higher but they also have a few setbacks, one of which is its high price due to the involvement of sensors making it unattainable to people of all economic strata. In addition to this, the extensive usage of wires and other electronic devices increases the weight of the glove making it inconvenient to be worn for long hours. there are a number of Sign languages existing from various parts of the world, each of which have their own recognition systems individually. this comes out as another drawback of the Sign Language Recognition Systems as they aren’t adaptable to all Sign Languages and are restricted to a specific Sign Language as per its dataset. this paper illustrates the state of art of SLR with the detailed descriptions on various Sign Languages, dataset and recogni

关键词： Gesture recognition

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Study of Perception and Cognition in Immersive Digital Twins for Robotic Assembly Processes 16th

Study of Perception and Cognition in Immersive Digital Twins...

引用

16th international conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Cecil, J. Gannina, Vasavi Tentu, Sriram Kumar Oklahoma State Univ Ctr Cyber Phys Syst Dept Comp Sci Stillwater OK 74078 USA

ISBN: (纸本)9783031610431;9783031610448

Today, various types of extended reality (XR) environments are becoming increasingly used in industrial engineering contexts for design, analysis as well as for training activities. User's perception plays a key role especially in such XR based learning and training contexts. Perception refers to the process by which individuals become aware and process the stimuli around them. Interdisciplinary researchers have studied the vital relationship between perception and cognition. In this groundbreaking study, the role of perception and its impact on cognition and learning in fully immersive Virtual Reality (VR) based digital twins is discussed. Schemas have been proposed to help lay the foundation to study the role of perception on cognition and learning. the primary outcomes of this research study underscored the relationships between perception and cognitionwhile also emphasizing the impact of a participant's background in influencing their perception of the target robotics assembly environments.

关键词： Virtual Reality (VR) perception cognition human-computer interaction (HCI) schemas learning environments

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LigNet: Lightweight Hand Tracking for Edge Intelligence 15

LigNet: Lightweight Hand Tracking for Edge Intelligence

引用

2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of things, ithings 2022, 18th IEEE international conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE international conference on Smart Data, SmartData 2022

作者： Huang, Yijing Luo, Chengwen Feng, Xingyu Yang, Zhongru Zhang, Jin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In recent years, as the rise of edge intelligence hand tracking applications have emerged in various IoT systems and applications, such as human-computer interaction, sign language translation, and motion rehabilitation, etc. Due to the hardware and energy limitation of embedded IoT devices, hand tracking is difficult to achieve real-time continuous operation. To achieve this goal, in this paper we propose a set of optimization methods such as space-based error loss and Gaussian process constraints to design a lightweight hand tracking model with low storage overhead and high recognition efficiency. We design and implement the system, and the effectiveness of our proposed methods is verified in the evaluation comparing with the state-of-the-art. © 2022 IEEE.

关键词： human computer interaction

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Secure Single-Server Fuzzy Deduplication without Interactive Proof-of-Ownership in Cloud 24

Secure Single-Server Fuzzy Deduplication without Interactive...

引用

24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Cheng, Shuai Zeng, Shengke Feng, Yawen Xiao, Jixiang Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu China Xihua College Xihua University Chengdu China

ISBN: (纸本)9798350319934

the redundant of multimedia data made an unnecessary waste in encrypted cloud storage, unlike text with completely consistent content, multimedia data allows a certain degree of similarity in deduplication, In this work, we focus on the multimedia data which takes a seriously proportion of storage in scenarios such as data outsourcing to propose secure fuzzy deduplication without the additional servers based on Convergent Encryption(CE), say the Single-server Fuzzy Deduplication (SSFD). Compared to the related fuzzy deduplication, SSFD is strong at resisting brute-force attacks caused by server-server collusion, moreover, we also put server-client collusion attacks into security solutions. Additionally, to enhance the security of data, the proposed scheme provides both protection against replay attacks and verification of label consistency and adds no extra communication such as Proof of Ownership(PoW)in interaction. We separately presented a formal security analysis and performed performance at last to prove security solutions and evaluate the experimental results, it shows SSFD provides both a reliable fuzzy images secure deduplication protocol and a computationally feasible solution. © 2022 IEEE.

关键词： Cryptography

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Design and implementation of a control system based on gesture recognition using the YOLO algorithm 4

Design and implementation of a control system based on gestu...

引用

4th international conference on Advanced Algorithms and Signal Image Processing, AASIP 2024

作者： Yang, Jiajun Wang, Dan College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

Gesture recognition, as a non-contact and intuitive human-computer interaction, has a unique advantage in the field of home control. this paper proposes a gesture recognition for home control based on YOLOV8, and the YOLOV8 model is one of the modern advanced object detection models. YOLOV8 model has great potential in the field of gesture recognition due to its advantages of high precision, real-time performance and adaptability. Specific gestures are collected using OPENMV for data acquisition, and pre-processed by color smoothing, canny edge detection algorithm, adaptive threshold algorithm, morphology closing operation, sharpening, etc. the processed images were imported into the YOLOv8 model for training, the trained YOLOv8 model was used for gesture recognition, the recognition results were converted into string format, and the recognition results were sent to the STM32F103 development board through serial communication to further control the home appliance. © 2024 SPIE.

关键词： Gesture recognition

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An Integrating Semantic and Structural Features Method for Knowledge Graph Embedding 8

An Integrating Semantic and Structural Features Method for K...

引用

8th international conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Xiao, Lizhong Yin, Chenxu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350369564

Knowledge graph embedding is a method of projecting entities and relations into continuous low-dimensional embeddings, which is commonly used for inferring and predicting missing entities or relations in the knowledge graph. However, most existing embedding methods do not fully consider the rich semantic and structural features contained in the knowledge graph, leading to poor feature expression and affecting the accuracy of downstream tasks. To address this problem, we propose a knowledge graph embedding method ISSKE that integrates semantic and structural features. First, we use the ALBERT pre-training model to embed the textual description information of entities and relations. then, we propose a relation-weighted graph convolutional neural network model R-WGCN to learn the embedding representation of entities and relations simultaneously. Finally, we put forward Conv-Internal as the decoder, which uses internal embedding to construct 2D convolutional filters. Conv-Internal can not only promote the interaction between entities and relations but also significantly reduce the number of parameters and improve the training and inference efficiency. the experimental results show that the performance of this model on tasks such as triple classification and link prediction is better than that of baseline methods. In the link prediction task on FB15k-237 and WN18RR datasets, compared with KG-BERT, the Hits@10 indicator increased by 23.8% and 17.0%, respectively. © 2023 IEEE.

关键词： Knowledge graph

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Integrating Industry 4.0 Technologies for Enhanced Safety engineering: A Comprehensive Review and Analysis 25th

Integrating Industry 4.0 Technologies for Enhanced Safety En...

引用

25th international conference on human-computer interaction (HCI international)

作者： Hutchins, Savannah Jhaveri, Niral Duffy, Vincent G. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9783031480461;9783031480478

Advancements in technology over the past few decades have led to rapid changes in manufacturing and process industries. this industry-wide shift towards automation and digitization is better known as Industry 4.0, or the fourth industrial revolution. As new Industry 4.0 technologies are adopted and "smart factories" become commonplace, the impact on the health and safety of manufacturing workers needs to be understood. the aim of this systematic review is to understand how Industry 4.0 technologies can be integrated without introducing new risks to worker health and safety through a literature review and database, content, and co-citation analyses. the literature analysis revealed publications analyzing connections between Industry 4.0 and worker health and safety, however, as an emerging field, relevant studies and publications are limited. More funding and research into the relationship between Industry 4.0 and worker OHS will be critical to the advancement, implementation, and acceptance of new technologies. Future research should likely concentrate on establishing concepts that incorporate human factors considerations into the design of integrated safety systems.

关键词： Industry 4.0 Smart Factory Health Safety engineering Automation

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Optimal strategy selection for attack graph games using deep reinforcement learning 24

Optimal strategy selection for attack graph games using deep...

引用

作者： Zhang, Yuantian Liu, Feng Chen, Huashan Institute of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350319934

While various defense mechanisms have been proposed in cybersecurity, it is still unclear how these defense mechanisms should be deployed in practice to mitigate the damage of cyber attacks. In this work, we propose a Stackelberg game model to simulate the interaction between cyber attackers and defenders. We develop a reinforcement learning (RL) based approach to seek the optimal defense strategy. We further design a policy iteration method to accelerate the convergence speed of training. We conduct experiments with real network data and various game settings to evaluate the performance of our approach. Experiment results show that our RL-based approach outperform baselines, and the approach is robust to the uncertainty security environment. © 2022 IEEE.

关键词： Game theory

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Semi-mind controlled robots based on Reinforcement Learning for Indoor Application 8

Semi-mind controlled robots based on Reinforcement Learning ...

引用

8th international conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Naidji, Ilyes Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Computer Control and Management Engineering Sapienza University of Rome Italy

this paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement Learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes. © 2023 Copyright for this paper by its authors.

关键词： Feedback

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Design of a Hybrid AI- based Phishing Website Detection using LSTM, CNN, and Random Forest based Ensemble Learning Analysis 8

Design of a Hybrid AI- based Phishing Website Detection usin...

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8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kavya, S. Sumathi, D. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350367904

Phishing attacks are always surfacing as key threats against internet users, necessitating advanced detection methods. Blacklist-based systems and rule-based models of phishing detection generally have had critical limitations in dealing with evolving tactics and new phishing schemes. Some of these approaches fail to cope with the temporal and visual patterns of phishing sites, which are crucial for timely and accurate detection. To overcome these difficulties, this work introduces a hybrid AI-based phishing website detection model that utilizes several machine learning and deep learning techniques to improve the accuracy of the detection and remove false positives. the proposed model uses LSTM networks, Genetic Algorithms, Random Forest, and CNN through the stacking ensemble framework. Since LSTM is adopted to capture the temporal dependencies in the website traffic and user interaction patterns, this model can effectively model their phishing behavior over time. GA is used for bioinspired feature selection to reduce the dimensionality of features while optimizing model performance. Random Forest is used as a base layer addressing structured features like URL characteristics and WHOIS information. CNNs are incorporated to extract feature content from a webpage and images that carry various visual indicators often used in phishing attacks including counterfeit logos or banners. A meta-classifier is then used to combine the outputs of LSTMs, CNN, and RF and generate the final classification to boost the detection rate. the proposed hybrid model surpasses the existing techniques and facilitates the analysis of temporal, visual, and structured data, making the detection considerably more accurate. Achieving accuracy of as much as 96-97% and having an AUC of 0.97 with a false positive rate below 3%, the model then impacts the more powerful and more flexible phishing detection system, which is then capable of being more protective against higher sophisticated phishing te

关键词： Phishing

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