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OptiShard: An Optimized and Secured Hierarchical Blockchain Architecture

OptiShard: An Optimized and Secured Hierarchical Blockchain ...

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Future Technologies Conference, FTC 2020

作者： Kantesariya, Shyam Goswami, Dhrubajyoti Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9783030630881

Blockchain has become an emerging decentralized computing technology for transaction-based systems due to its peer-to-peer consensus protocol over an open network consisting of untrusted parties. Bitcoin and other major alt-coins based on monolithic blockchain architecture exhibit significant performance overhead which in turn make them highly non-scalable. Imposing hierarchy in blockchain can improve performance, however it adds on additional security and fault-tolerance measures necessary for correctness of transaction validation. This paper presents a hierarchical blockchain architecture named OptiShard, which addresses the issues of performance, fault-tolerance, and security in the presence of faulty and malicious nodes. The hierarchy comes as a result of dividing the network nodes into multiple disjoint shards. Majority of transactions are distributed among these shards in non-overlapped fashion (i.e., one-to-one mapping of transactions to shards). The model of OptiShard provides a theoretical measure to determine optimal shard size based on two parameters: performance and correctness of transaction validation in the presence of malicious or faulty nodes. The theoretical measure provides guaranteed majority of good shards by allowing to choose the right shard size and forms the basis of network and workload sharding protocols discussed in this paper. OptiShard also provides a mechanism for identifying faulty shards through the overlapping of a small fraction of transactions across all the shards so that all faulty transactions can be discarded. Experimental results exhibit the impact of sharding the network on performance and conform to the theoretical results. © 2021, Springer Nature Switzerland AG.

关键词： Blockchain

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A multi-dimensional quality analysis of android applications

A multi-dimensional quality analysis of android applications

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Conference of the Centre for Advanced Studies on Collaborative Research - 29th Annual International Conference on computer Science and software engineering, CASCON 2019

作者： Ghari, Soude Rasolroveicy, Mohammadreza Hadian, Mahsa Fokaefs, Marios Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Quality is a multi-faceted aspect of software. As described by international standards, the process of quality assurance is concerned not only with the functionality of the software, but also with performance, security, maintainability, reliability and others. However, during the maintenance phase of software development, developers usually focus on one aspect at a time, for example improving design or fixing bugs, either due to time constraints or because of specific priorities. In this work, we present a study to show that quality issues do not occur in isolation during development. We study the source code from 10 Android applications and we explore problems around reliability, maintainability and security. In addition, we study the impact of maintenance activities around these problems on other quality aspects like performance and energy consumption. Our first objective is to find if quality problems of different types occur together and if there is correlation between specific types. Secondly, we want to see if fixing problems always has monotonically positive effect on the general quality or special attention needs to be taken when fixing s pecific pr oblems. Our long-term goal is to create tool support for the multi-dimensional analysis and assurance of software quality. © 2019 Copyright held by the owner/author(s).

关键词： Application programs

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Efficient Asynchronous GCN Training on a GPU Cluster 27

Efficient Asynchronous GCN Training on a GPU Cluster

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Zhang, Yi Goswami, Dhrubajyoti Concordia University Department of Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9781665408783

Research on Graph Convolutional Networks (GCNs) has increasingly gained popularity in recent years due to the powerful representational capacity of graphs. A common assumption in traditional synchronous parallel training of GCNs using multiple GPUs is that load is perfectly balanced. However, this assumption may not hold in a real-world scenario where there can be imbalances in workloads among GPUs for various reasons. In a synchronous parallel implementation, a straggler in the system can limit the overall speed up of parallel training. To address these performance issues, this research investigates approaches for asynchronous decentralized parallel training of GCNs on a GPU cluster. The techniques investigated are based on graph clustering and the Gossip protocol. The research specifically adapts the approach of Cluster GCN, which uses graph partitioning for SGD based training, and combines with a gossip algorithm specifically designed for a GPU cluster to periodically exchange gradients among randomly chosen partners (GPUs). In addition, it incorporates a work pool mechanism for load balancing among GPUs. The gossip algorithm is proven to be deadlock free. The implementation is performed on a deep learning cluster with 8 Tesla V100 GPUs per compute node, and PyTorch and DGL as the software platforms. Experiments are conducted on different benchmark datasets. The results demonstrate superior performance with similar accuracy scores, as compared to traditional synchronous training which uses 'all reduce' to synchronously accumulate parallel training results. © 2021 IEEE.

关键词： Graphics processing unit

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Centimeter-Wave Electronically Scanned Array for Future 6G Applications

Centimeter-Wave Electronically Scanned Array for Future 6G A...

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European Conference on Antennas and Propagation, EuCAP

作者： Mohamed Mamdouh M. Ali Mostafa O. Shady Syed M. Sifat Khelifa Hettak Larbi Talbi Ke Wu Department of Electrical Engineering Faculty of Engineering Assiut University Assiut Egypt Department of Electrical and Computer Engineering University of Quebec in Outaouais Gatineau QC Canada R&D Department Scientific Microwave Corporation Montreal QC Canada Dept. of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Department of Electrical Engineering Polytechnique Montreal Montreal QC Canada

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work presents an electronically controlled reconfigurable scanned array utilizing reflective phase shifters, designed for future 6G applications. Operating within the centimeter-wave (cmWave) frequency band (13–17 GHz), the electronically scanned array (ESA) demonstrates excellent performance. The system comprises a 2 × 2 horn antenna array interconnected with four digitally controlled variable reflect-type phase shifters, each constructed using a 90° hybrid coupler with a movable short-circuit. Additionally, the assembly includes E-H feeding networks, facilitating uniform signal distribution to the antenna array. Simulation results of the complete ESA exhibit outstanding performance, showcasing continuous mainlobe scanning in elevation and azimuth planes. The proposed ESA achieves a matching level below -15 dB with a stable gain of 14 ± 2 dBi over the operating bandwidth.

关键词： Phased arrays 6G mobile communication Azimuth Simulation Horn antennas Spaceborne radar Phase shifters Couplers Reflector antennas Radar antennas

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Online Domination: The Value of Getting to Know All Your Neighbors 46

Online Domination: The Value of Getting to Know All Your Nei...

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46th International Symposium on Mathematical Foundations of computer Science, MFCS 2021

作者： Harutyunyan, Hovhannes A. Pankratov, Denis Racicot, Jesse Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9783959772013

We study the dominating set problem in an online setting. An algorithm is required to guarantee competitiveness against an adversary that reveals the input graph one node at a time. When a node is revealed, the algorithm learns about the entire neighborhood of the node (including those nodes that have not yet been revealed). Furthermore, the adversary is required to keep the revealed portion of the graph connected at all times. We present an algorithm that achieves 2-competitiveness on trees. We also present algorithms that achieve 2.5-competitiveness on cactus graphs, (t1)- competitiveness on K1,t-free graphs, and Θ( √ Δ) for maximum degree Δ graphs. We show that all of those competitive ratios are tight. Then, we study several more general classes of graphs, such as threshold, bipartite planar, and series-parallel graphs, and show that they do not admit competitive algorithms (i.e., when competitive ratio is independent of the input size). Previously, the dominating set problem was considered in a different input model (often together with the restriction of the input graph being always connected), where a vertex is revealed alongside its restricted neighborhood: those neighbors that are among already revealed vertices. Thus, conceptually, our results quantify the value of knowing the entire neighborhood at the time a vertex is revealed as compared to the restricted neighborhood. For instance, it was known in the restricted neighborhood model that 3-competitiveness is optimal for trees, whereas knowing the neighbors allows us to improve it to 2-competitiveness. © Hovhannes A. Harutyunyan, Denis Pankratov, and Jesse Racicot;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of computer Science (MFCS 2021).

关键词： Forestry

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Fully Distributed Shape Sensing of a Flexible Surgical Needle Using Optical Frequency Domain Reflectometry for Prostate Interventions

Fully Distributed Shape Sensing of a Flexible Surgical Needl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jacynthe Francoeur Dimitri Lezcano Yernar Zhetpissov Raman Kashyap Iulian Iordachita Samuel Kadoury Department of Electrical Engineering Polytechnique Montréal Montréal QC Canada Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Departments of Electrical Engineering and Physics Engineering Polytechnique Montréal Montréal QC Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal QC Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor. The proposed method uses simple trigonometric and geometric modeling of the fiber using optical frequency domain reflectometry (OFDR), without requiring prior knowledge of tissue properties or needle deflection shape and amplitude. Our optimization process includes a reproducible calibration process and a novel tip curvature compensation method. We validate our approach through experiments in artificial isotropic and inhomogeneous animal tissues, establishing ground truth using 3D stereo vision and cone beam computed tomography (CBCT) acquisitions, respectively. Our results yield an average RMSE ranging from 0.58 ± 0.21 mm to 0.66 ± 0.20 mm depending on the chosen spatial resolution, achieving the submillimeter accuracy required for interventional procedures.

关键词： Optical fiber sensors Accuracy Three-dimensional displays Shape Reflectometry Needles Robot sensing systems

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Deep image clustering using self-learning optimization in a variational auto-encoder 25th

Deep image clustering using self-learning optimization in a ...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Tran, Duc Hoa Meunier, Michel Cheriet, Farida Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Department of Engineering Physics Polytechnique Montréal Montréal Canada

ISBN: (纸本)9783030687892

Deep image clustering approaches typically use autoencoder architectures to learn compressed latent representations suitable for clustering tasks. However, they do not effectively regulate the latent space during training, leading to low performance and diminished applicability to different datasets. In this paper, we propose a deep clustering model combining maximum mean discrepancy (MMD) regularization and self-learning clustering optimization to mitigate this problem. Specifically, we first train the network to improve its image reconstruction ability by minimizing both reconstruction loss and MMD divergence from a target distribution. Then, the model gradually learns from its own high-confidence predictions to further optimize the latent distribution. We validate the network’s performance on different benchmark image sets using standard clustering metrics, without changing network configuration or adjusting hyper-parameters between datasets. The proposed model provides top clustering performance across datasets while being more robust than state-of-the-art methods. © Springer Nature Switzerland AG 2021.

关键词： Image reconstruction

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A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase

A Mutation-Guided Assessment of Acceleration Approaches for ...

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International Mining software Repositories, MSR

作者： Zhili Zeng Tao Xiao Maxime Lamothe Hideaki Hata Shane McIntosh Software REBELs University of Waterloo Canada Division of Information Science Nara Institute of Science and Technology Japan Department of Computer Engineering and Software Engineering Polytechnique Montréal Canada Faculty of Engineering Shinshu University Japan

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Continuous Integration (CI) is a popular software development practice that quickly verifies updates to codebases. To cope with the ever-increasing demand for faster software releases, CI acceleration approaches have been proposed; however, adoption of CI acceleration is not without risks. For example, CI acceleration products may mislabel change sets (e.g., a build labeled as failing that passes in an unaccelerated setting or vice versa) or produce results that are inconsistent with an unaccelerated build (e.g., the underlying reasons for failure differ between (un)accelerated builds). These inconsistencies threaten the trustworthiness of CI acceleration *** this paper, we propose an approach inspired by mutation testing to systematically evaluate the trustworthiness of CI acceleration. We apply our approach to YourBase, a program analysis-based CI acceleration product, and uncover issues that hinder its trustworthiness. First, we study how often the same build in accelerated and unaccelerated CI settings produce different mutation testing outcomes. We call mutants with different outcomes in the two settings "gap mutants". Next, we study the code locations where gap mutants appear. Finally, we inspect gap mutants to understand why acceleration causes them to survive. Our analysis of ten open-source projects uncovers 2,237 gap mutants. We find that: (1) the gap mutants account for 0.11%–23.50% of the studied mutants; (2) 88.95% of gap mutants can be mapped to specific source code functions and classes using the dependency representation of the studied CI acceleration product; and (3) 69% of gap mutants survive CI acceleration due to deterministic reasons that can be classified into six fault patterns. Our results show that even deterministic CI acceleration solutions suffer from trustworthiness limitations, and highlight the ways in which trustworthiness could be pragmatically improved.

关键词： Accelerometers Codes Source coding Life estimation software Complexity theory Reliability

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Efficient Detection of Selfish Mining Attacks on Large-Scale Blockchain Networks

Efficient Detection of Selfish Mining Attacks on Large-Scale...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Fatemeh Erfan Martine Bellaiche Talal Halabi Department of Computer and Software Engineering Polytechnique Montréal Montréal QC Canada Department of Computer Science Université Laval Québec QC Canada

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Selfish mining attacks pose a significant and ongoing security threat to blockchain networks, including major platforms like Bitcoin and Ethereum. Understanding and effectively countering these attacks is crucial for maintaining the stability and integrity of these networks. This attack strategy involves a miner or a group of miners seeking to gain an advantage by delaying the immediate broadcasting of their mined blocks to the network. The selfish miners potentially mine more blocks in secret, increasing their rewards at the expense of other miners and the stability of the blockchain. Research has mainly focused on detecting this attack by analyzing data related to blocks and forks. This paper presents a new approach to efficiently detect selfish mining attacks in large-scale networks by analyzing various network indicators. To achieve this, we simulate a Bitcoin network and generate a dataset consisting of several features of individual miners and the overall network. We select the most reliable indicators by analyzing network features and reducing feature dimensions. Then, we employ random forest classification (RFC) to classify benign and selfish network behaviors. This approach not only achieves enhanced accuracy (99.96%), surpassing state-of-the-art methods utilizing deep learning, but also significantly reduces computational, storage, and temporal complexities. In doing so, it fortifies blockchain security more efficiently and accurately.

关键词： Deep learning Accuracy Computational modeling Bitcoin software quality Stability analysis software reliability Security Data mining Principal component analysis

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Advanced Smart Contract Vulnerability Detection Using Large Language Models

Advanced Smart Contract Vulnerability Detection Using Large ...

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Cyber Security in Networking Conference (CSNet)

作者： Fatemeh Erfan Mohammad Yahyatabar Martine Bellaiche Talal Halabi Department of Computer and Software Engineering Polytechnique Montréal Montréal QC Canada Department of Computer Science Université Laval Québec QC Canada

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

With the rapid expansion of using smart contracts, protecting the security of these contracts has become crucial. Existing analysis tools for detecting vulnerabilities in smart contracts are unreliable as they often fall short in accuracy, primarily due to their low recall rates-a significant challenge in this field. In this work, we utilize the open-source SolidiFi benchmark dataset to detect vulnerabilities related to Integer overflow/underflow (IoU), reentrancy (RE), and timestamp dependency (TD). These contracts, verified and available on Etherscan, proved unsuitable for direct application of LLMs due to comments, functions, and variables that might reveal the nature of the vulnerabilities. To address this, we performed several preprocessing steps to prepare the dataset for further research. We utilize a large language model to identify vulnerable code, provide reasoning for the vulnerabilities, explain how an attacker might exploit them, and propose fixed code. We design our prompts using chain-of-thought and expert patterns. Finally, we evaluate the results using various metrics and expert reviewers to assess the correctness of the reasoning, potential security risks, and code fixes. Our experiments demonstrate that our approach outperforms existing tools and methods. Notably, our recall rates are significantly high-93.5%, 95.4%, and 93.8%-addressing the challenge of low recall in detecting IoU, RE, and TD vulnerabilities, respectively.

关键词： Measurement Analytical models Codes Accuracy Large language models Smart contracts Benchmark testing Cognition Security computer crime

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