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arXiv 2022年

作者： Nguyen, Pha Quach, Kha Gia Duong, Chi Nhan Phung, Son Lam Le, Ngan Luu, Khoa Computer Science and Computer Engineering Department University of Arkansas Fayetteville United States Computer Science and Software Engineering Department Concordia University MontrealQC Canada Faculty of Engineering and Information Sciences University of Wollongong NSW Australia

The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car. It is essential for object detection and tracking to address these new challenges in multi-camera settings. In order to address these challenges, this work introduces novel Single-Stage Global Association Tracking approaches to associate one or more detection from multi-cameras with tracked objects. These approaches aim to solve fragment-tracking issues caused by inconsistent 3D object detection. Moreover, our models also improve the detection accuracy of the standard vision-based 3D object detectors in the nuScenes detection challenge. The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method by outperforming prior vision-based tracking methods in multi-camera settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Consumer and Farmer Centric Subscription Based Organic Vegetable/Fruit Delivery System 3

Consumer and Farmer Centric Subscription Based Organic Veget...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Karunarathna, T.M.N.P. Kumari, M.M.A. Kulasinghe, W.D.K.A. Parana Liyanage, T.L. Kodagoda, Nuwan Suriyanwsa, Kushnara Kumari, Disna Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Kings Hospital Colombo Sri Lanka

ISBN: (纸本)9781665408622

For centuries, the economy of Sri Lanka has been backed by the agricultural sector. Even though it has contributed to the development of the nation on a large scale, the economies and living standards of the farmers have not been improved much. Most of the farmers remain poor mainly due to not knowing the market demand and the low buying prices of the middleman. Meanwhile, the consumers in Sri Lanka are buying non-organic vegetables and fruits which are highly contaminated with dangerous chemicals which in turn has contributed to the rapidly increasing non-communicable disease rates in both consumers and farmers. The proposed system is an attempt to solve these problems faced by both farmers and consumers by reducing the farmers-consumer knowledge gap and providing consumers a trustworthy and personalized subscription for organic vegetables/fruits with a donation platform. © 2021 IEEE.

关键词： Crops

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Effect of Enhanced Neurofeedback Stimulus Content on Frontal Alpha Asymmetry 5

Effect of Enhanced Neurofeedback Stimulus Content on Frontal...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Ali, Syed Saad Azhar Hafeez, Yasir Ariffin, Tajol Ashraf Bin Tajol Adil, Syed Hasan Amin, Hafeez Ullah Boon, Tang Tong Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Teknologi Petronas Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Neurofeedback training has various therapeutic effects to mitigate the mental illness such as stress, anxiety, and depression. Neurofeedback is a non-invasive self-training of brain with the help of auditory or visual input stimulus. The enhancement to neurofeedback stimulus content can help to improve the efficacy of neurofeedback training. Therefore, in this study, the game stimulus content is enhanced to find the effect of enhanced parameters towards the efficacy of neurofeedback. The enhanced parameters include the changing of background, colour contrast, and background music. The neurofeedback training sessions are performed to mitigate the stress among university students. The electroencephalogram (EEG) data is recorded and feedback from the participants are also taken. The graphical and statistical analysis of the results of the neurofeedback training using alpha asymmetry show the effectiveness of neurofeedback towards mitigation of stress. © 2022 IEEE.

关键词： Diseases

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Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel CNNs and BiLSTM Neural Networks 23

Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel...

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23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Bahroun, Sahbi Ghayoula, Ridha Manai, Elyes Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Institut Supérieur d'Informatique Université de Tunis El-Manar Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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User centric explanations: A breakthrough for explainable models

User centric explanations: A breakthrough for explainable mo...

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2021 International Conference on Information Technology, ICIT 2021

作者： Hassan, Ali Abdulhak, Mansoor Abdullateef Abdulgabber Sulaiman, Riza Bin Kahtan, Hasan Bangi Malaysia Consultant Researcher Madinah Saudi Arabia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665428705

Thanks to recent developments in explainable Deep Learning models, researchers have shown that these models can be incredibly successful and provide encouraging results. However, a lack of model interpretability can hinder the efficient implementation of Deep Learning models in real-world applications. This has encouraged researchers to develop and design a large number of algorithms to support transparency. Although studies have raised awareness of the importance of explainable artificial intelligence, the question of how to solve the needs of real users to understand artificial intelligence remains unanswered. In this paper, we provide an overview of the current state of the research field at Human-Centered Machine Learning and new methods for user-centric explanations for deep learning models. Furthermore, we outline future directions for interpretable machine learning and discuss the challenges facing this research field, as well as the importance and motivation behind developing user-centric explanations for Deep Learning models. © 2021 IEEE.

关键词： Deep learning

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CoATR: Integrating Model and Data-Driven for Learning Sparse Spatio-Temporal Traffic Data

SSRN

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SSRN 2023年

作者： Liao, Tianchi Zhang, Lei Yang, Jinghua Chen, Chuan Zheng, Zibin School of Software Engineering Sun Yat-sen University Zhuhai519000 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Faculty of Innovation Engineering Macau University of Science and Technology Taipa999078 China

Spatio-temporal traffic data collected by various sensing systems have chronic issues of missing and corruption, thus accurate data imputation and prediction have been extensively researched. Previous methods can be divided into two categories: model-driven methods with physical explanations and data-driven deep learning methods. These methods have some limitations: model-driven methods are often linear and may not accurately model the spatio-temporal complexity, while deep learning methods often lack the physical reality making them probe to over-fitting. Inspired by both, we propose a new Convolutional-based generalized Autoregressive Tensor-Ring decomposition method (CoATR) for the completion of spatiotemporal data. CoATR not only retains the advantages of the tensor-ring (TR) decomposition model for global modeling of spatio-temporal data but also exploits the ability of deep networks to model nonlinear features. To be specific, we introduce TR decomposition to capture the global low-rankness and employ multilayer convolutional neural networks to model the global complex interactions among the TR factors. Moreover, we design a new autoregressive network to further explore the local temporal variation in the data. Extensive experiments on a variety of common traffic datasets have validated the effectiveness and superiority of the CoATR over classical model-driven methods and other state-of-the-art data-driven deep learning methods. © 2023, The Authors. All rights reserved.

关键词： Tensors

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Temporal Authorization Graphs: Pros, Cons and Limits 7th

Temporal Authorization Graphs: Pros, Cons and Limits

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7th EAI International Conference on Smart Objects and Technologies for social Good, GOODTECHS 2021

作者： Stojanov, Riste Popovski, Ognen Jovanovik, Milos Zdravevski, Eftim Lameski, Petre Trajanov, Dimitar Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje Macedonia Netcetera Skopje Macedonia OpenLink Software Burlington United Kingdom

ISBN: (纸本)9783030914202

As more private data is entering the web, defining authorization about its access is crucial for privacy protection. This paper proposes a policy language that leverages SPARQL expressiveness and popularity for flexible access control management and enforces the protection using temporal graphs. The temporal graphs are created during the authentication phase and are cached for further usage. They enable design-time policy testing and debugging, which is necessary for correctness guarantee. The security never comes with convenience, and this paper examines the environments in which the temporal graphs are suitable. Based on the evaluation results, an approximated function is defined for suitability determination based on the expected temporal graph size. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Authorization

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A Reliable and Stable V2V Routing Scheme for Warning Message Dissemination in VANETs using Bi-Directional Clustering 22

A Reliable and Stable V2V Routing Scheme for Warning Message...

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Proceedings of the 2022 12th International Conference on Communication and Network Security

作者： Mansoor Khan Shanshan Tu Ghulam Abbas Ziaul Haq Abbas Hisham Alasmary Muhammad Waqas Faculty of Computer Science and Engineering GIK Institute of Engineering and Technology Pakistan Information Technology Beijing University of Technology Beijing 100124 China China Telecommunications and Networking Research Center GIK Institute of Engineering and Technology Pakistan College of Computer Science King Khalid University Kingdom of Saudi Arabia and Information Security and Cybersecurity Unit King Khalid University Kingdom of Saudi Arabia Computer Engineering Department University of Bahrain Computer Engineering 32038 Bahrain. Bahrain and School of Engineering Edith Cowan University Australia

ISBN: (纸本)9781450397520

Road traffic injuries cause considerable life and economic losses to individuals, their families, and nations as a whole. To avoid road accidents, Vehicular Ad-hoc Networks (VANETs) play a vital role by enhancing transportation systems to make them intelligent. To enhance road safety, emergency message dissemination schemes are indispensable in VANET-based applications. This paper proposes a routing protocol for emergency message dissemination, which uses stable clustering for the bi-directional highway scenario. To assure the long-term sustainability of a cluster, a median technique for selecting a CH is devised. Further, an extended form of the K-medoids algorithm for a new node joining a cluster is employed, which ensures that both node and cluster have the same direction for bi-directional road traffic. Simulation results reveal that the proposed protocol proffers enhanced throughput and lowers the packet loss rates and end-to-end delay compared with state-of-the-art routing schemes.

关键词： bi-directional traffic warning message dissemination vehicular ad hoc network clustering

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Constrained Intelligent Frequency Control in an AC Microgrid: An Online Reinforcement Learning Based PID Tuning Approach

Constrained Intelligent Frequency Control in an AC Microgrid...

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IEEE General Meeting Power& Energy Society

作者： K. Nosrati A. Tepljakov E. Petlenkov V. Skiparev J. Belikov Y. Levron Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation control systems with excellent results. Nevertheless, a potential problem is that the application of such advanced techniques with a large search space is not enough to deal with highly dynamic environment and real-time operations of MGs. In this light, the present study introduces a flexible artificial neural network (ANN)-based frequency deviation control solution in a constrained structure that operates as follows. First, the stable controller parameter space of the PID-based AC microgrid is derived by using the stability boundary locus method. Then, the controller parameters are tuned and updated online by searching for an optimal combination of the coefficients with consideration of output variations sensed by a constrained ANN in the derived reduced parameter space. To accomplish this step, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed technique has been verified under a given scenario to demonstrate how the reduced parameter space should facilitate the optimization procedure.

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An Efficient Distributed Framework for Secured Multimedia Content Using Cloud Storage

An Efficient Distributed Framework for Secured Multimedia Co...

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computer Technologies (ICCTech), International Conference on

作者： Yunus Parvej Faniband Iskandar Ishak Fatimah Sidi Marzanah A. Jabar Deloria Shaniska Razin C. H. Yap Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.

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