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Coupling Mobile Brain Imaging and Virtual Reality Omnidirectional Treadmill to Explore Attenuated Situational Awareness during Distracted Walking

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期16卷 1063-1076页

作者： Chuang, Chun-Hsiang Chiu, Tsai-Feng Hsu, Hao-Che Lin, Shih-Syun National Tsing Hua University Research Center for Education and Mind Sciences College of Education The Institute of Information Systems and Applications College of Electrical Engineering and Computer Science Hsinchu300 Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300193 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Walking while engaging in distracting activities affects our perception of changes happening on the road, leading to exposure to hazardous conditions. This study aimed to evidence that pedestrians who become distracted when absorbed in their smartphones fail to maintain situational awareness. Thirty-six recruited participants performed a psychomotor vigilance task and a wayfinding task designed to simulate a multitasking scenario;meanwhile, an auditory oddball paradigm presented to participants tested their situational awareness of an external stimulus. The experiment was conducted in virtual reality (VR) environment with an omnidirectional treadmill. Participant's behavioral and electroencephalographic (EEG) responses were measured and analyzed using a newly proposed brain network measure, phase locking distance (PLD), to capture multitasking-associated couplings between brain regions. The current study supports the notion that multitasking can lead to diminished behavioral performance and changes in walking patterns. Despite the increased interregional brain connectivity, which may be an adaptive response to heightened cognitive demands, the associated reduction in event-related EEG activity suggests competition for neural resources during multitasking. Moreover, the observed EEG activities could indicate additional information processing capacity limitations and provide evidence of decreased situational awareness during multitasking, thereby increasing susceptibility to potentially hazardous situations. © 2016 IEEE.

关键词： Electroencephalography

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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A Simulation-based Education Platform for Security Investments Through Culture and Knowledge Convergence 18

A Simulation-based Education Platform for Security Investmen...

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18th European Conference on Games Based Learning, ECGBL 2024

作者： Filippidis, Adam P. Hilas, Constantinos S. Politis, Anastasios Rantos, Konstantinos Mouratidis, Haralambos Department of Computer Informatics and Telecommunications Engineering International Hellenic University Serres Campus Serres Greece Department of Computer Science Democritus University of Thrace Kavala Campus Greece Department of Computer and Systems Sciences Stockholm University Sweden

ISBN: (纸本)9781917204194

Cybersecurity is one of the most critical sectors of information systems and is considered a strategic priority for many Organizations. Information security investments are a crucial aspect of cybersecurity. However, it often happens that the economic decisions, which aim to improve information security, have poor efficiency. One reason is the lack of background on economics of the Chief Information Security Officers who are often not familiar with the evaluation of the non-technical aspects of security, e.g. economics. The other reason is the cultural gap between the different persons related to business management and administration. In this paper we present an interactive - simulation software designed to familiarize the aforementioned persons with information security investments. The software is actually a game that aims to boost learning for all the stakeholders who are involved in the investment process. Α better understanding of the processes, the technical, economic and human factors, along with the cultural convergence of the stakeholders will help boost the efficiency of investments. Design science is used as the research method. The creative method, a brainstorming technique, is combined with the five steps of Design science, i.e. explicate the problem, define the requirements, design and develop the artefact, demonstrate the artefact, and evaluate the artefact, to develop a process framework that will be able to cope with this problem. The artefact is an interactive-simulation platform in which a person could take the decision maker role and (a) deal with events that happen and (b) investigate different aspects of information security investment complexity. The evaluation of the artefact is made with a case study in the Industry, where real world data are used to feed the simulation and real-world decision makers play the game in order to better understand the different aspects of the problem and help us trim and enhance the functioning of the platform.

关键词： Efficiency

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Training Large Models on Heterogeneous and Geo-Distributed Resource with Constricted Networks

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Big Data Mining and Analytics 2025年第4期8卷 966-980页

作者： Zong, Zan Guo, Minkun Zhai, Mingshu Tang, Yinan Li, Jianjiang Zhai, Jidong Tsinghua University Department of Computer Science and Technology Beijing100084 China IEIT SYSTEMS Co. Ltd. Jinan250014 China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China

As the computational demands driven by large model technologies continue to grow rapidly, leveraging GPU hardware to expedite parallel training processes has emerged as a commonly-used strategy. When computational resources within a single cluster are insufficient for large-model training, the hybrid utilization of heterogeneous acceleration hardware has emerged as a promising technical solution. The utilization of heterogeneous acceleration hardware and scheduling of diverse cloud resources have become a focal point of considerable interest. However, these computing resources are often geographically distributed. Due to the lack of awareness of heterogeneous devices and network topologies, existing parallel training frameworks struggle to leverage mixed GPU resources across constrained networks effectively. To boost the computing capability of the connected heterogeneous clusters, we propose HGTrainer, an optimizer designed to plan heterogeneous parallel strategies across distributed clusters for large model training. HGTrainer can adaptively saturate heterogeneous clusters because of the expanded tunable parallelism space for heterogeneous accelerators, with the awareness of relatively lower inter-cluster bandwidth. To achieve this goal, we formulate the model partitioning problem among heterogeneous hardware and introduce a hierarchical searching algorithm to solve the optimization problem. Besides, a mixed-precision pipeline method is used to reduce the cost of inter-cluster communications. We evaluate HGTrainer on heterogeneous connected clusters with popular large language models. The experimental result shows that HGTrainer effectively improves 1.49× training throughput on average for the mixed heterogeneous cluster compared with the state-of-the-art Metis. © 2018 Tsinghua University Press.

关键词： Cluster computing

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Online Information-Based Product Evolution Course Mining and Prediction

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International Journal of Information Technology and Decision Making 2024年第2期23卷 599-627页

作者： Chen, Yuh-Jen Chen, Yuh-Min Department of Accounting and Information Systems College of Business Intelligence National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Institute of Manufacturing Information and Systems College of Electrical Engineering and Computer Science National Cheng Kung University Tainan City Taiwan

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success;therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM;it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to

关键词： Forecasting

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Classification of Alzheimer's Disease using Random Oversampling and Albumentations on Convolutional Neural Network 8

Classification of Alzheimer's Disease using Random Oversampl...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

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Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 2689-2719页

作者： Fatma Harby Mansor Alohali Adel Thaljaoui Amira Samy Talaat Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesCairo12622Egypt Department of Computer Science and Information College of Science at Zulfi Majmaah UniversityP.O.Box 66Al-Majmaah11952Saudi Arabia Preparatory Institute for Engineering Studies of Gafsa ZarrougGafsa2112Tunisia Computers and Systems Department Electronics Research InstituteCairo12622Egypt

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional *** examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior *** identifying emotions in the SER process relies on extracting relevant information from audio *** studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals *** these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some *** this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human *** utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over *** next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster *** the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram *** attitude of feature selection is to retain only dominant features by excluding the irrelevant *** this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features ***,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short

关键词： Artificial intelligence application multi features sequential selection speech emotion recognition deep Bi-LSTM

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Optimized Resource Allocation and Queue Management for Traffic Control in MANET

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computer systems science & engineering 2023年第5期45卷 1323-1342页

作者： I.Ambika Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information College of Computer Science and Information TechnologyKing Faisal University36362Saudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks(MANETs).Offering better communication services among the users in a centralized organization is the primary objective of the *** to the features of MANET,this can directly End-to-End Delay(EED)the Quality of Service(QoS).Hence,the implementation of resource management becomes an essential issue in *** paper focuses on the efficient Resource Allocation(RA)for many types of Traffic Flows(TF)in *** Mobile Ad hoc Networks environments,the main objective of Resource Allocation(RA)is to process consistently available resources among terminals required to address the service requirements of the *** three categories improve performance metrics by varying transmission rates and simulation *** solving that problem,the proposed work is divided into Queue Management(QM),Admission Control(AC)and *** effective QM,this paper develops a QM model for elastic(EL)and inelastic(IEL)Traffic *** research paper presents an AC mechanism for multiple TF for effective *** work presents a Resource Allocation Using Tokens(RAUT)for various priority TF for effective ***,nodes have three cycles which are:Non-Critical Section(NCS),Entry Section(ES)and Critical Section(CS).When a node requires any resources,it sends Resource Request Message(RRM)to the *** and inelastic TF priority is determined using Fuzzy Logic(FL).The token holder selects the node from the inelastic queue with high priority for allocating the *** Network Simulator-2(NS-2),simulations demonstrate that the proposed design increases Packet Delivery Ratio(PDR),decrease Packet Loss Ratio(PLR),minimise the Fairness and reduce the EED.

关键词： MANET resource allocation end-to-end delay fuzzy logic QoS

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A Prototype Preceding Vehicle Identification System Development and Field Evaluation 10

A Prototype Preceding Vehicle Identification System Developm...

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10th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Mu, Zeyu Tu, Guancheng Shi, Austin Yang, Kun Sun, Yixin Shen, Cong Park, B. Brian Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Computer Science and Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Civil & Environmental Engineering and Systems & Information Engineering University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9789897587030

Preceding vehicle identification is crucial for establishing cooperative platooning. This paper presents the development of a prototype preceding vehicle identification system (PVIS) and its field evaluation for the assessment of commercial viability. We designed and assembled a prototype consisting of a processing unit (Jetson Nano board), a communication device (Wi-Fi dongle), a GPS unit, and a distance measurement sensor (Terabee sensor). The Jetson Nano integrates the SparkFun GPS-RTK-SMA unit, the Terabee time-of-flight sensor, and the Wi-Fi dongle. The PVIS prototype in the ego vehicle measures the distance to its preceding vehicle and receives the GPS data from potential preceding vehicles with the PVIS prototypes. With these, the PVIS in the ego vehicle determines the connectivity of the preceding vehicle. The field evaluation results showed that the prototype PVIS works as designed, and each successful identification takes about 5.3 seconds. However, it was found that the Terabee (time of flight) sensor, at times, did not properly measure distances, likely due to an angle issue caused by the roadway surface and vibration of the vehicle. We discussed how to overcome the challenges identified and enhance the prototype for successful commercialization. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Vehicle to vehicle communications

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Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

Usefulness of Honeypots Towards Data Security: A Systematic ...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Silaen, Kalpin Erlangga Meyliana, Meyliana Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Hidayanto, Achmad Nizar Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Department of Information System Depok Indonesia

ISBN: (纸本)9798350382914

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity. © 2023 IEEE.

关键词： Machine learning

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