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IntelliChain: An Intelligent and Scalable Framework for Decentralized Applications on Public Blockchain Technologies: An NFT Marketplace Case Study

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TechRxiv

TechRxiv 2023年

作者： Rasolroveicy, Mohammadreza Fokaefs, Marios The Department of Computer and Software engineering Polytechnique Montreal MontrealQCH3T 1J4 Canada

Non-fungible tokens (NFTs) have been attracting the interest of both technical and non-technical parties, including collectors and traders, among others. The number of transactions in NFTs surpassed $50 billion in 2022. Blockchain technology’s advantages as a distributed, immutable, and transparent database make it ideal for verifying the ownership of digital goods created by their producers. On the other hand, high computation and transaction costs are known disadvantages of public blockchain networks like Ethereum V1, used in NFT marketplaces. To address these inefficiencies, other public blockchain systems have emerged as replacements for NFT marketplaces, each with its own unique properties. When planning such an NFT exchange, it is vital, but not trivial, to select the most appropriate public blockchain platform. In this work, we make two contributions to support this decision. In this paper, we present IntelliChain, a self-adaptive framework that can predict the best optimal transaction fees (also known as a gas fee) for blockchain to reduce the errors and also an ability to switch the public blockchain-based dynamic needs such as transaction fees and stability of the network. © 2023, CC BY.

关键词： Blockchain

BlockCompass: A benchmarking platform for blockchain performance

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TechRxiv

TechRxiv 2023年

作者： Rasolroveicy, Mohammadreza Haouari, Wejdene Fokaefs, Marios The Department of Computer and Software Engineering Polytechnique Montreal MontrealQCH3T 1J4 Canada

Blockchain technology has gained momentum among researchers and other stakeholders due to its immutability and transparency. Several blockchain platforms with different kinds of consensus protocols have been proposed. However, this makes choosing and configuring such a platform, a non-trivial task. Several benchmarking tools have been presented to test the performance of blockchain solutions. However, these solutions are either limited to specific blockchain platforms or require complex configurations. Moreover, they tend to focus on transactional evaluation models, which may be counter-intuitive for longer-running instances under continuous workloads. In this work, we present BlockCompass, an all-inclusive blockchain benchmarking tool that can be easily configured and extended. We demonstrate how BlockCompass can evaluate the performance of a variety of blockchain platforms and configurations, including Ethereum Proof-of-Authority, Ethereum Proof-of-Work, Hyperledger Fabric Raft, Hyperledger Sawtooth with Proof-of-Elapsed-Time, Practical Byzantine Fault Tolerance and Raft consensus algorithms against workloads that continuously fluctuate over time. We also present the results of a usability study about the convenience and facility offered by BlockCompass in blockchain benchmarking. © 2023, CC BY.

关键词： Blockchain

Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

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arXiv 2023年

作者： Dakhel, Arghavan Moradi Nikanjam, Amin Majdinasab, Vahid Khomh, Foutse Desmarais, Michel C. Department of Computer and Software Engineering Polytechnique Montreal MontrealQCH3T 1J4 Canada

Context: One of the critical phases in the software development life cycle is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. Objective: To improve over this limitation, in this paper, we introduce MuTAP (Mutation Test case generation using Augmented Prompt) for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Method: Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Results: Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully-automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Conclusion: Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases in PUTs

关键词： software testing

Hsm–Based Architecture to Secure Server-Side Data

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SSRN

SSRN 2023年

作者： Dib, Marc El Khoury, Franjieh Pierre, Samuel Department of Computer and Software Engineering Polytechnique Montreal MontrealQCH3T 1J4 Canada

With the rapid development of the Internet and computer technology, mobile applications are becoming more popular to automatically perform everyday tasks at any time and place. Moreover, data is stored on a distant server and accessed remotely. This leads to stealing or damaging information. In this paper, we propose a security architecture based on the Hardware Security Module (HSM) to protect server-side data against outsider attacks. This architecture relies on three cryptographic modules to ensure the protection of the private key and the sensitive data against theft and corruption. The implementation of this architecture showed that the gain in data security is very noticeable in the three modules. The first module increased the safety of the private key storage to prevent an attacker from using it. The second module allows performing regular queries on encrypted fields where all attack vectors related to data sniffing were eliminated. Finally, the third module works in the background to achieve a secure hybrid backup and a complete restoration of the entire database in case of destruction of data. Moreover, the results prove the effectiveness and the performance of our proposed architecture in terms of response times at the level of security of the private key (6% to 92% faster) and the backup of the data (0.7 to 32µs per entry). © 2023, The Authors. All rights reserved.

关键词： Sensitive data

BittyBuzz: A Swarm Robotics Runtime for Tiny Systems

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arXiv 2023年

作者： Dah-Achinanon, Ulrich Belhaddad, Emir Khaled Ricard, Guillaume Beltrame, Giovanni Department of Computer and Software Engineering of Polytechnique Montreal MontrealQCH3T 1J4 Canada

Swarm robotics is an emerging field of research which is increasingly attracting attention thanks to the advances in robotics and its potential applications. However, despite the enthusiasm surrounding this area of research, software development for swarm robotics is still a tedious task. That fact is partly due to the lack of dedicated solutions, in particular for low-cost systems to be produced in large numbers and that can have important resource constraints. To address this issue, we introduce BittyBuzz, a novel runtime platform: it allows Buzz, a domain-specific language, to run on microcontrollers while maintaining dynamic memory management. BittyBuzz is designed to fit a flash memory as small as 32 kB (with usable space for scripts) and work with as little as 2 kB of RAM. In this work, we introduce the BittyBuzz implementation, its differences from the original Buzz virtual machine, and its advantages for swarm robotics systems. We show that BittyBuzz is successfully integrated with three robotic platforms with minimal memory footprint and conduct experiments to show computation performance of BittyBuzz. Results show that BittyBuzz can be effectively used to implement common swarm behaviors on microcontroller-based systems. © 2023, CC BY.

关键词： software design

Frequency-based View Selection in Gaussian Splatting Reconstruction

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arXiv 2024年

作者： Li, Monica M.Q. Lajoie, Pierre-Yves Beltrame, Giovanni The Department of Computer and Software Engineering Polytechnique Montréal MontrealQCH3T 1J4 Canada

Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform 3D Gaussian Splatting reconstructions with as few input images as possible. Although 3D Gaussian Splatting has made significant progress in image rendering and 3D reconstruction, the quality of the reconstruction is strongly impacted by the selection of 2D images and the estimation of camera poses through Structure-from-Motion (SfM) algorithms. Current methods to select views that rely on uncertainties from occlusions, depth ambiguities, or neural network predictions directly are insufficient to handle the issue and struggle to generalize to new scenes. By ranking the potential views in the frequency domain, we are able to effectively estimate the potential information gain of new viewpoints without ground truth data. By overcoming current constraints on model architecture and efficacy, our method achieves state-of-the-art results in view selection, demonstrating its potential for efficient image-based 3D reconstruction. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D reconstruction

FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles

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arXiv 2024年

作者： Quéméneur, Cyprien Cherkaoui, Soumaya The Department of Computer and Software Engineering Polytechnique Montréal MontrealQCH3T 1J4 Canada

The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and intelligent transportation systems (ITS), by enabling low-latency big data processing in a dense interconnected network that comprises vehicles, infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily reliant on machine learning (ML) and can strongly benefit from the wealth of sensory data generated at the edge, which calls for measures to reconcile model training with preserving the privacy of sensitive user data. Federated learning (FL) stands out as a promising solution to train sophisticated ML models in vehicular networks while protecting the privacy of road users and mitigating communication overhead. This paper examines the federated optimization of the cutting-edge YOLOv7 model to tackle real-time object detection amid data heterogeneity, encompassing unbalancedness, concept drift, and label distribution skews. To this end, we introduce FedPylot, a lightweight MPI-based prototype to simulate federated object detection experiments on high-performance computing (HPC) systems, where we safeguard server-client communications using hybrid encryption. Our study factors in accuracy, communication cost, and inference speed, thereby presenting a balanced approach to the challenges faced by autonomous vehicles. We demonstrate promising results for the applicability of FL in IoV and hope that FedPylot will provide a basis for future research into federated real-time object detection. The source code is available at https://***/cyprienquemeneur/fedpylot. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

4D-MultispectralNet: Multispectral Stereoscopic Disparity Estimation using Human Masks

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arXiv 2022年

作者： Duplessis-Guindon, Philippe Bilodeau, Guillaume-Alexandre LITIV Lab Department of Computer and Software Engineering Polytechnique Montreal Canada

Multispectral stereoscopy is an emerging field. A lot of work has been done in classical stereoscopy, but multispectral stereoscopy is not studied as frequently. This type of stereoscopy can be used in autonomous vehicles to complete the information given by RGB cameras. It helps to identify objects in the surroundings when the conditions are more difficult, such as in night scenes. This paper focuses on the RGB-LWIR spectrum. RGB-LWIR stereoscopy has the same challenges as classical stereoscopy, that is occlusions, textureless surfaces and repetitive patterns, plus specific ones related to the different modalities. Finding matches between two spectrums adds another layer of complexity. Color, texture and shapes are more likely to vary from a spectrum to another. To address this additional challenge, this paper focuses on estimating the disparity of people present in a scene. Given the fact that people's shape is captured in both RGB and LWIR, we propose a novel method that uses segmentation masks of the human in both spectrum and than concatenate them to the original images before the first layer of a Siamese Network. This method helps to improve the accuracy, particularly within the one pixel error range. Copyright © 2022, The Authors. All rights reserved.

关键词： Stereo image processing

Analyze, Debug, Optimize: Real-Time Tracing for Perception and Mapping Systems in ROS 2

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arXiv 2022年

作者： Lajoie, Pierre-Yves Bédard, Christophe Beltrame, Giovanni Department of Computer and Software Engineering Polytechnique Montréal Montreal Canada

Perception and mapping systems are among the most computationally, memory, and bandwidth intensive software components in robotics. Therefore, analysis, debugging, and optimization are crucial to improve perception systems performance in real-time applications. However, standard approaches often depict a partial picture of the actual performance. Fortunately, instrumentation and tracing offer a great opportunity for detailed performance analysis of real-time systems. In this paper, we show how our novel open-source tracing tools and techniques for ROS 2 enable us to identify delays, bottlenecks and critical paths inside centralized, or distributed, perception and mapping systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Real time systems