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Human-Centered Cognitive Systems (HCCS)

作者： Maeeda Khalid Adnan Ashraf Waqas Haider Bangyal Muddesar Iqbal School of Computer Science & Technology Beijing Institute of Technology (BIT) Beijing China Department of Computer Science Facuty of Computing Kohsar University Murree Murree Pakistan Department of Communication & Network Engineering Prince Sultan University Riyadh Saudi Arabia

The significance of vehicle identification and monitoring is increasing in the field of traffic management. The Intelligent Transportation System (ITS) is a highly efficient way to address the issue of traffic congestion in metropolitan areas and is a prominent focus in the development of smart cities. One specific application is the monitoring and forecasting of fluid flow. The suggested system aims to implement automated vehicle identification and recognition processing utilizing static image datasets. Significant progress in vehicle detection technology has been made due to the emergence of unmanned driving and intelligent transportation research. The suggested system utilizes the deep learning technique to investigate the vehicle detection algorithm, specifically employing the fundamental phase target detection algorithm known as the YOLO algorithm. Hence, the initial approach involves the manipulation of visual data from a publicly available collection of road vehicles for the purpose of training. A vehicle detection model is developed using the YOLO algorithm to demonstrate the detection performance separately. The suggested system’s contribution is in the enhancement of the conventional YOLO network’s architecture, enabling precise identification of vehicle targets.

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Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays 10

Augmented Reality Shopping and IoT-Enabled Virtual Try-On wi...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sridevi, V. Azath, H. Vijayakumar, R. Anbuselvan, N. Amirthalingam, V. Arunkumar, S. Academy of Maritime Education and Training Deemed To Be University Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research focuses on how augmented reality (AR) and the Internet of Things (IoT) might work together to create a novel retail environment. This integrated system revolutionizes how customers engage with products by fusing the digital and real worlds. Smartphones and other devices with AR capabilities allow customers to virtually try on products before purchasing. Smart mirrors and sensor-enabled shelving are just two examples of the IoT-powered interactive displays available today. These fusions of AR and the IoT improve the retail experience and facilitate choice. Capturing data from the physical environment, sensors allow for customized experiences. The extensive product data stored by cloud services paves the way for rapid data processing. However, there are obstacles, such as ensuring data privacy and security and coordinating AR-IoT smoothly. Finding a happy medium between technological complexity and ease of use is essential for a satisfying buying experience. The combination of AR and the IoT broadens the retail experience and has the potential to revolutionize the way people purchase. © 2024 IEEE.

关键词： Internet of things

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Comparing the Effect of Code Optimizations on Simulation Runtime Across Synchronous Cellular Automata Models of HIV

Comparing the Effect of Code Optimizations on Simulation Run...

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2021 Winter Simulation Conference, WSC 2021

作者： Li, Junjiang Giabbanelli, Philippe J. Koster, Till Miami University Department of Computer Science Software Engineering OH45056 United States Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Models developed by domain experts occasionally struggle to achieve a sufficient execution speed. Improving performances requires expertise in parallel and distributed simulations, hardware, or time to profile performances to identify bottlenecks. However, end-users in biological simulations of the Human Immunodeficiency Virus (HIV) have repeatedly demonstrated that these resources are either not available or not sought, resulting in models that are developed through user-friendly languages and platforms, then used on workstations. This situation becomes problematic when performances cannot cope with the salient characteristics of the phenomenon that is modeled, as is the case with cellular automata (CA) models of HIV. In this paper, we optimize the Python code of CA models of HIV to scale the number of cells handled by a simulation on a workstation commonly available to end-users. We demonstrate this scalability via five HIV CA models and compare these results to assess how modeling choices can impact runtime. © 2021 IEEE.

关键词： Viruses

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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Retraction Note: Exploring subcellular location anomalies: a novel quantum bioimaging paradigm

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Kumar, Kailash Abbas, Syed Hauider Gupta, Manish Waris, Saiyed Faiayaz Bordoloi, Dibyahash Kirubakaran, M. K. College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Computer Science and Engineering Integral University Lucknow India Department of Electronics and Communication Engineering GLA University Mathura India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India

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An Explainable Deep Learning Approach for EEG- Induced Motor Imagery Classification

An Explainable Deep Learning Approach for EEG- Induced Motor...

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IEEE India Conference (INDICON)

作者： Biswarup Ganguly Ahona Ghosh Sharmistha Dey Indranil Sarkar Sriparna Saha Electrical Engineering Department Meghnad Saha Institute of Technology Kolkata India Department of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal India University Institute of Computing Chandigarh University Gharuan Punjab India Department of Computer Application Guru Nanak Institute of Technology Kolkata India

Movement interpretation from the motor imagery (MI) signal of electroencephalogram (EEG) is an emerging field that can potentially upgrade the quality of life of individuals with motor impairments. In this manuscript, an explainable deep learning (x-DL) approach has been proposed to identify four class MI form EEG signals. The acquired EEG signals are preprocessed and then converted to time-frequency (TF) spectra for extracting the frequency content of the MI signals. The obtained TF images are then fed to a deep learning (DL) framework employing residual convolutional neural network for proper classification. The classification score, obtained from the network, has been propagated through the layers of the DL framework up to the input layer for obtaining layer wise relevance maps. These relevance maps signify the explainability of the proposed network, in terms of visual results. Comparative analysis reveals the effectiveness of the proposed x-DL module using a benchmark dataset. The obtained results and the utilization of signals from only two EEG channels demonstrate the possibility of employing the suggested strategy for developing a cost-effective brain-computer interfacing architecture applicable in real-time healthcare applications.

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems

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computers, Materials & Continua 2021年第2期66卷 1577-1594页

作者： Srinath Doss Jothi Paranthaman Suseendran Gopalakrishnan Akila Duraisamy Souvik Pal Balaganesh Duraisamy Chung Le Van Dac-Nhuong Le Faculty of Computing Botho UniversityGaboroneBotswana Department of Computer Science Gaborone University College of Law and Professional StudiesGaboroneBotswana Vels Institute of Science Technology&Advanced StudiesChennai600117India Global Institute of Management and Technology West Bengal741102India Faculty of Computer Science and Multimedia Lincoln University CollegeKelantan15050Malaysia Center for Visualization&Simulation Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical *** datum is sensitive,and hence security is a must in transforming the sensational *** this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text *** encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 *** reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded *** show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed *** results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable *** the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.

关键词： Internet of Things medical images cryptography memetic algorithm steganography

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Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds

arXiv

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arXiv 2023年

作者： Li, Tianqi Pang, Guansong Bai, Xiao Zheng, Jin Zhou, Lei Ning, Xin School of Computer Science and Engineering Beihang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Artificial Intelligence Wuhan University of Technology China Institute of Semiconductors Chinese Academy of Sciences China

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of "Zero-Shot Open-Set Recognition" (ZS-OSR), where a model is trained under the ZSL setting but it is required to accurately classify samples from the unseen classes while being able to reject samples from the unknown classes during inference. We perform large experiments on combining existing state-of-the-art ZSL and OSR models for the ZS-OSR task on four widely used datasets adapted from the ZSL task, and reveal that ZS-OSR is a non-trivial task as the simply combined solutions perform badly in distinguishing the unseen-class and unknown-class samples. We further introduce a novel approach specifically designed for ZS-OSR, in which our model learns to generate adversarial semantic embeddings of the unknown classes to train an unknowns-informed ZS-OSR classifier. Extensive empirical results show that our method 1) substantially outperforms the combined solutions in detecting the unknown classes while retaining the classification accuracy on the unseen classes and 2) achieves similar superiority under generalized ZS-OSR settings. Our code is available at https://***/lhrst/ASE. © 2023, CC BY.

关键词： Zero-shot learning

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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