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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Cholitha Hettiarachchi, Thiwanka Sathyajith Semasinghe, Lakisuru De Silva, Rajitha Lokuliyana, Shashika Gamage, Narmada Ratnasuriya, Manisha Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka University of Lincoln United Kingdom

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Exploration and manipulation of physical objects are essential for early childhood learning. TangiGuru is an e-Learning platform that allows children to engage with real-world physical objects to provide that essential experience within an e-Learning application. TangiGuru consists of 12 tangible, manipulative objects known as TangiCubes, which are used as a tangible user interface between children and the e-Learning application. It can carry out cognitive learning activities related to colors, languages, shapes and basic math by dynamically varying assigned values by changing the external appearance of TangiCubes. This dynamic nature of the TangiCubes makes it possible to use the same tangibles with endless possibilities compared to traditional tangible learning solutions with static value for each tangible. With the initial observations of how children interact with computers, laptops, and mobile devices, the goal is to provide a playful interface for children by taking the approach to embedding and integrating technology into the children's daily activities. After the prototyping phase, children were evaluated with the traditional tangible learning solutions compared to TangiGuru. They concluded that the more interactive tangible interfaces could make the children perform activities more engagingly. © 2022 IEEE.

关键词： E-learning

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Synergizing CXL with Unified Memory for Scalable GPU Memory Expansion

Synergizing CXL with Unified Memory for Scalable GPU Memory ...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Junseung Lee Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Republic of Korea

Heterogeneous computing, which combines the power of CPUs and GPUs, is increasingly important in today's data-centric era. However, it also presents significant challenges, particularly in terms of GPU memory management. Compute eXpress Link (CXL) technology, which is supported by industry leaders, offers enhanced memory expansion capabilities. Alongside this, NVIDIA's Unified Memory (UM) system simplifies the process of sharing data between CPUs and GPUs. We explore the role of CXL memory in various scenarios within the UM framework while simultaneously highlighting issues such as the handling of page faults at the software level.

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Deep Word Embedding and Language Model based Sentimental Analysis of COVID-19 Tweets

Deep Word Embedding and Language Model based Sentimental Ana...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Sobia Qaiser M. Usman Akram Dept. of Computer and Software Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The great Covid-19 pandemic affected billions of people's lives personally and socially. The research involves the analysis of public's views and opinions shared on Twitter social media platform related to Covid-19 pandemic and its detrimental or non-detrimental effects on public's mental health by using machine learning algorithm. The main purpose of this research includes analyzing public views related to Covid-19 pandemic by classifying the Tweets collected from the Twitter social platform. The proposed approach combines deep word embedding with MiniLM as an encoder to produce word vectors of high-dimensionality to preserve the words' semantic information. The resultant word vectors were used to train the model for the classification of the tweet in five sentiments i.e. Positive, Extremely Positive, Negative, Extremely Negative, and Neutral. The methodology is tested using publicly available Kaggle dataset as well as privately collected tweets. The comparative evaluation of the models revealed that MiniLM outperformed existing BERT based counterparts and attained highest accuracy of 93% with the Kaggle dataset. This analysis can assist the medical health authorities to monitor health information, conduct, and plan interventions to lower the pandemic effect and can help government to take precautionary measures.

关键词： COVID-19 Machine learning algorithms Pandemics Social networking (online) Blogs Semantics Government Mental health Vectors Monitoring

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Aarhus University Dept. Electrical and Computer Engineering Aarhus8000 Denmark Mirpur Univ. Science and Technology Dept. Computer Systems Engineering AJK Mirpur10250 Pakistan North Carolina State University Dept. Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350368369

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025 IEEE.

关键词： Markov chains

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A Robust Scheduling Algorithm for Overload-Tolerant Real-Time systems

A Robust Scheduling Algorithm for Overload-Tolerant Real-Tim...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Amin Avan Akramul Azim Qusay H. Mahmoud Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

A real-time system is overloaded when all the tasks in a workload cannot meet their deadlines, and hence a robust algorithm is essential to maximize the number of tasks that meet their deadlines with the minimum number of miss rates and context switching. Although the Rate Monotonic (RM), Earliest Deadline First (EDF), and Least Laxity First (LLF) algorithms optimally perform and schedule tasks on a non-overloaded system, they have deficient performance when the system is overloaded. Therefore, we propose a new scheduling algorithm for uniprocessor and partitioned multiprocessor systems to address the overload situation. Since the proposed scheduling algorithm operates like EDF non-overloaded conditions, the proposed algorithm is optimal for non-overloaded systems. In addition, the proposed algorithm is robust against overloading situations as it executes the maximum possible tasks in the overload situation instead of missing deadlines of many tasks or burdening context switching to the system. The proposed algorithm allocates a processor to tasks based on the possibility of executing the task. The experimental results demonstrate that the proposed scheduling algorithm maximizes the number of tasks that meet their deadlines in overload conditions without a domino effect and context switching. In addition, the proposed algorithm achieves the lowest miss rate without context switching and the highest efficiency and processor utilization in the overloaded system compared with RM, EDF, and LLF.

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Building Trust in Deep Learning Models via a Self- Interpretable Visual Architecture

Building Trust in Deep Learning Models via a Self- Interpret...

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Annual Conference on Privacy, Security and Trust, PST

作者： Weimin Zhao Qusay H. Mahmoud Sanaa Alwidian Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

Deep learning models are being utilized and further developed in many application domains, but challenges still exist regarding their interpretability and consistency. Interpretability is important to provide users with transparent information that enhances the trust between the user and the learning model. It also gives developers feedback to improve the consistency of their deep learning models. In this paper, we present a novel architectural design to embed interpretation into the architecture of the deep learning model. We apply dynamic pixel-wised weights to input images and produce a highly correlated feature map for classification. This feature map is useful for providing interpretation and transparent information about the decision-making of the deep learning model while keeping full context about the relevant feature information compared to previous interpretation algorithms. The proposed model achieved 92% accuracy for CIFAR 10 classifications without finetuning the hyperparameters. Furthermore, it achieved a 20% accuracy under 8/255 PGD adversarial attack for 100 iterations without any defense method, indicating extra natural robustness compared to other Convolutional Neural Network (CNN) models. The results demonstrate the feasibility of the proposed architecture.

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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning

arXiv

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arXiv 2023年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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GPU Accelerated Modelling and Real-time Rendering of Fluid Motion

GPU Accelerated Modelling and Real-time Rendering of Fluid M...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： William Valentine Ramakrishnan Mukundan Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes a fluid rendering pipeline that uses OpenGL-4 shaders to employ the parallel processing capabilities of the GPU. The fluid’s surface mesh is produced using tessellation shader stages where the input patches are assigned tessellation levels based on the fluid heightmap’s curvature. The curvature is stored using a texture buffer object which allows access by shaders, thus allowing the tessellation calculations to be carried out in parallel. Use of this adaptive tessellation method increases both the simulation’s framerate as well as its capacity to handle a greater number of primitives. Furthermore, it more optimally distributes the mesh’s vertices to effectively increase the level of detail without using more primitives. Polygon culling using the geometry shader further optimises the number of primitives used to define the fluid surface. The Phong-Blinn model is used for surface lighting. We propose two GPU-based fluid surface flow visualisation methods. Texture buffer objects can be used to store and update a surface texture. Alternatively, particle positions are updated each frame using the geometry shader and stored in a buffer object using transform feedback. These flow visualisation techniques are particularly effective for communicating the swirling motion of vortices.

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Unveiling The Arsenal of User Data Protection Tools and Practices

Unveiling The Arsenal of User Data Protection Tools and Prac...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syed Moiz Abdul Ahad Abro Mansoor Ebrahim Abdul Basit Abro Dept. of Computer Science Iqra University Main Campus Karachi Pakistan Dept. of Software Engineering Iqra University Main Campus Karachi Pakistan Dept. of Computer Science Indus University Karachi Pakistan

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world.

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Efficient Design of Multi-group Multicast Beamforming via Reconfigurable Intelligent Surface

Efficient Design of Multi-group Multicast Beamforming via Re...

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Asilomar Conference on Signals, systems & computers

作者： Mohammad Ebrahimi Min Dong Dept. of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada

This paper considers a multi-group multicasting scenario facilitated by a reconfigurable intelligent surface (RIS). We propose a fast and scalable algorithm for the joint design of the base station (BS) multicast beamforming and the RIS passive beamforming to minimize the transmit power subject to the quality-of-service (QoS) constraints. By exploring the structure of the joint optimization problem, we show that this QoS problem can be broken into a BS multicast QoS subproblem and an RIS max-min-fair (MMF) multicast subproblem, which are solved alternatingly. In our proposed algorithm, we utilize the optimal multicast beamforming structure to obtain the BS beamformers efficiently. Furthermore, we reformulate the challenging RIS multicast subproblem and employ a first-order projected sub gradient algorithm (PSA) to solve it, which yields closed-form updates. Simulation results show the efficacy of our proposed algorithm in performance and computational cost compared to other alternative methods.

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