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arXiv 2022年

作者： Soud, Majd Liebel, Grischa Hamdaqa, Mohammad School of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Context: Smart contracts are computer programs that are automatically executed on the blockchain. Among other issues, vulnerabilities in their implementation have led to severe loss and theft of cryptocurrency. In contrast to traditional software, smart contracts become immutable when deployed to the Ethereum blockchain. Therefore, it is essential to understand the nature of vulnerabilities in Ethereum smart contracts to prevent these vulnerabilities in the future. Existing classifications exist, but are limited in several ways, e.g., focusing on single data sources, mixing dimensions, or providing categories that are not orthogonal. Objective: This study aims to characterize vulnerabilities and code weaknesses in Ethereum smart contracts written in Solidity, and to unify existing classifications schemes on Ethereum smart contract vulnerabilities by mapping them to our classification. Method: We extracted 2143 vulnerabilities from public coding platforms (i.e., GitHub and Stack Overflow) and popular vulnerability databases (i.e., National Vulnerability Database and Smart Contract Weakness Registry) and categorized them using a card sorting approach. We targeted the Ethereum blockchain in this paper, as it is the first and most popular blockchain to support the deployment of smart contracts, and Solidity as the most widely used language to implement smart contracts. We devised a classification scheme of smart contract vulnerabilities according to their error source and impact. Afterwards, we mapped existing classification schemes to our classification. Results: The resulting classification consists of 11 categories describing the error source of a vulnerability and 13 categories describing potential impacts. Our findings show that the language specific coding and the structural data flow categories are the dominant categories, but that the frequency of occurrence differs substantially between the data sources. Conclusions: Our findings enable researchers to better un

关键词： Smart contract

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Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

arXiv

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arXiv 2023年

作者： Salari, Soorena Rasoulian, Amirhossein Rivaz, Hassan Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Department of Electrical and Computer Engineering Concordia University MontrealQC Canada

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time. © 2023, CC BY-NC-ND.

关键词： Ultrasonics

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Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

Dense Error Map Estimation for MRI-Ultrasound Registration i...

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IEEE Symposium (IUS) Ultrasonics

作者： Soorena Salari Amirhossein Rasoulian Hassan Rivaz Yiming Xiao Department of Computer Science and Software Engineering Concordia University Montreal QC Canada Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

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The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment

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Journal of Hardware and Systems Security 2021年第3期5卷 208-222页

作者： Abbasi, Hossein Ezzati-Jivan, Naser Bellaiche, Martine Talhi, Chamseddine Dagenais, Michel R. Department of Computer and Software Engineering Ecole Polytechnique Montreal Canada

In most scenarios, distributed denial of service (DDoS) attacks can be categorized into three distinct groups: (1) attacks targeting and consuming bandwidth, (2) attacks targeting selected applications and (3) attacks targeting connection-layer exhaustion. This study discusses in depth our proposal of a unique, inclusive model that has the ability to precisely detect and categorize DDoS attacks with the help of comparing normal traffic and resource usage against the traffic and resource utilization reported during potential attack situations. Since the features from all three attack categories are dependent upon each other, we based the metrics of our detection model on data collected from all three types during each attack. Additionally, we utilized the cumulative sum algorithm for the sake of change detection in traffic and resource usage patterns.

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Advanced Simulation and Design Methodology of Josephson Traveling-Wave Parametric Amplifiers (JTWPA) for Quantum Electronics Applications

Advanced Simulation and Design Methodology of Josephson Trav...

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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Aziz Oukaira Yvon Savaria Mounir Boukadoum Ahmed Lakhssassi Department of Engineering and Computer Science University of Québec in Outaouais (UQO) Gatineau QC Canada Department of Electrical Engineering Polytechnique Montreal Montreal QC Canada Department of Computer Science University of Quebec in Montreal (UQAM) Montreal QC Canada Department of Engineering and Computer Science (UQO) University of Québec in Outaouais Gatineau QC Canada

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

This paper examines the use of a quantum electronics Josephson Traveling-Wave Parametric Amplifier (JTWPA). The JTWPA presents distinctive features for amplifying quantum signals through the nonlinear Josephson junction, influenced by quantum effects at microwave energy scales. The study focuses on characterizing this quantum effects-based parametric amplification, offering perspectives in communication and information processing technologies. The results, obtained with the Advanced Design System (ADS) pathWave tool suggest promising applications of JTWPA, paving the way for significant advances in quantum electronics. Notably, the findings indicate that at a fixed frequency of 4 GHz and a constant pump current of 1.85 µA, the transmission efficiency of JTWPA decreases by 32% when exporting the design as layouts using the High-Frequency Structural Simulator (HFSS). Consequently, optimization with ADS emerges as a crucial step to assess transmission and information processing before the physical implementation of the JTWPA design.

关键词： Performance evaluation Degradation Design methodology Layout Information processing Microwave communication Josephson junctions

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RetinaVR: Democratizing Vitreoretinal Surgery Training with a Portable and Affordable Virtual Reality Simulator in the Metaverse

arXiv

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arXiv 2024年

作者： Antaki, Fares Doucet, Cédryk Milad, Daniel Giguère, Charles-Édouard Ozell, Benoit Hammamji, Karim The CHUM School of Artificial Intelligence in Healthcare MontrealQC Canada Department of Ophthalmology Université de Montréal MontrealQC Canada Department of Ophthalmology Centre Hospitalier de l’Université de Montréal MontrealQC Canada Department of Computer Engineering and Software Engineering Polytechnique Montréal Chemin de Polytechnique Montréal Canada MontrealQC Canada

We developed and validated RetinaVR, an affordable and immersive virtual reality simulator for vitreoretinal surgery training, using the Meta Quest 2 VR headset. We focused on four core fundamental skills: core vitrectomy, peripheral shaving, membrane peeling, and endolaser application. The validation study involved 10 novice ophthalmology residents and 10 expert vitreoretinal surgeons. We demonstrated construct validity, as shown by the varying user performance in a way that correlates with experimental runs, age, sex, and expertise. RetinaVR shows promise as a portable and affordable simulator, with potential to democratize surgical simulation access, especially in developing countries. © 2024, CC BY.

关键词： Developing countries

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MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair

arXiv

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arXiv 2024年

作者： Dehghan, Meghdad Wu, Jie J.W. Fard, Fatemeh H. Ouni, Ali Department of Computer Science University of British Columbia Kelowna Canada Department of Software and IT Engineering ETS Montreal University of Quebec Montreal Canada

[Context] Large Language Models (LLMs) have shown good performance in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. Adapters are specialized, small modules designed for parameter efficient fine-tuning of LLMs for specific tasks, domains, or applications without requiring extensive retraining of the entire model. These adapters offer a more efficient way to customize LLMs for particular needs, leveraging the pre-existing capabilities of the large model. Merging LLMs and adapters has shown promising results for various natural language domains and tasks, enabling the use of the learned models and adapters without additional training for a new task. [Objective] This research proposes continual merging and empirically studies the capabilities of merged adapters in Code LLMs, specially for the Automated Program Repair (APR) task. The goal is to gain insights into whether and how merging task-specific adapters can affect the performance of APR. [Method] In our framework, MergeRepair, we plan to merge multiple task-specific adapters using three different merging methods and evaluate the performance of the merged adapter for the APR task. Particularly, we will employ two main merging scenarios for all three techniques, (i) merging using equal-weight averaging applied on parameters of different adapters, where all adapters are of equal importance;and (ii) our proposed approach, continual merging, in which we sequentially merge the task-specific adapters and the order and weight of merged adapters matter. By exploratory study of merging techniques, we will investigate the improvement and generalizability of merged adapters for APR. Through continual merging, we will explore the capability of merged adapters and the effect of task order, as it occurs in real-world software projects. © 2024, CC BY.

关键词： Program debugging

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Fragmentation-Aware VNF Placement: A Deep Reinforcement Learning Approach

Fragmentation-Aware VNF Placement: A Deep Reinforcement Lear...

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IEEE International Conference on Communications (ICC)

作者： Ramy Mohamed Marios Avgeris Aris Leivadeas Ioannis Lambadaris Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering École de technologie sup érieure Montreal Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper we address the challenge of efficiently deploying Virtual Network Functions (VNFs) in network infrastructures. This is particularly crucial when facing resource fragmentation, where available resources are not fully utilized due to the fluctuating allocation and deallocation of virtual network requests. Traditional optimization techniques often fall short in managing the dynamic complexities of VNF placement. To overcome this, we introduce a novel online VNF placement strategy using Deep Reinforcement Learning (DRL) combined with a Reward Constrained Policy Optimization (RCPO). This method leverages the flexibility of DRL and the constraint integration capacity of RCPO, ensuring compliance with performance and resource limitations while minimizing resource fragmentation. The results demonstrate that our DRL-based method surpasses existing methods, resulting in more effective resource management and less resource fragmentation.

关键词： Metalearning Adaptive learning Costs Transfer learning Complex networks Deep reinforcement learning Complexity theory

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Parkinson’s Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations

arXiv

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arXiv 2024年

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Deep Learning-Based Anomaly Detection in 5G Cellular Networks

Deep Learning-Based Anomaly Detection in 5G Cellular Network...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Umar Farooq Aroosa Hameed Aris Leivadeas Ioannis Lambadaris Department of Software and IT Engineering École de technologie supérieure Montreal Canada Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The telecommunication industry saw a dramatic shift with the introduction of 5G technologies. This new cellular generation brought lightening speed, massive capacity, and better connectivity. Despite the many introduced opportunities, several challenges emerge at the same time, especially in the field of performance assurance. An example of such challenges is the rapid identification of network anomalies, which is critical for maintaining network performance and ensuring user satisfaction. Traditional techniques of detecting anomalies have fallen short given the unique operating requirements of 5G networks. To address this issue, this paper presents a novel technique applying deep learning for the detection of cellular network anomalies. Specifically, we leverage and exploit the capabilities of several Long-Short-Term-Memory (LSTM) and Artificial Neural Networks (ANN) flavors, such as LSTM with ANN and Bidirection-LSTM (BiLSTM) with ANN, to excel at identifying network anomalies for the preservation and efficiency of 5G networks. Our work includes extensive experimentation and the attained results show that our proposed models are overwhelmingly adapting to preserving the integrity of the network in the fast-paced, ever-changing realm of cellular networks.

关键词： Cellular networks Industries Deep learning Adaptation models Accuracy 5G mobile communication Quality of service Communications technology Sustainable development Anomaly detection

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