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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bharti, Vijay Goel, Rohini Department of Computer Science and Engineering Ambala133203 India

ISBN: (纸本)9798331519056

Therapid advancements in imaging technologies have led to an increased demand for high-quality image resolution, particularly in fields requiring precise visual detail such as medical imaging, satellite imagery, and digital photography. This study introduces the Recalibrated SRGAN, an advanced super-resolution Generative Adversarial Network that incorporates Squeeze-and-Excitation blocks for feature recalibration and Pixel Shuffle with ICNR initialization to minimize up-sampling artifacts. The main objective was to enhance the quality of super-resolved images while maintaining computational efficiency. We evaluated the Recalibrated SRGAN against traditional methods like Nearest-Neighbour, Bilinear, and Bicubic interpolations, as well as a standard GAN, across multiple scales and metrics including PSNR, SSIM, and runtime performance. Experiments were conducted on varied image resolutions, demonstrating that the Recalibrated SRGAN not only achieves superior image quality but also reduces computational time compared to a standard GAN. These findings indicate substantial modifications in PSNR and SSIM evaluations across all measures. For example, the technique obtained a PSNR of 33.2 and an SSIM of 0.9546 at a value of x2 that was superior to any other approach. Additionally, this model maintained competitive execution times that further substantiated the model's utility in real-world scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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SpotFlow: Tracking Method Calls and States at Runtime 24

SpotFlow: Tracking Method Calls and States at Runtime

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46th International Conference on Software Engineering: Companion, ICSE-Companion 2024

作者： Hora, Andre Department of Computer Science UFMG Belo Horizonte Brazil

ISBN: (纸本)9798400705021

Understanding the runtime behavioral aspects of a software system is fundamental for several software engineering tasks, such as testing and code comprehension. For this purpose, typically, one needs to instrument the system and collect data from its execution. Despite the importance of runtime analysis, few tools have been created and made public to support developers extracting information from software execution. In this paper, we propose SpotFlow, a tool to ease the runtime analysis of Python programs. With Spot- Flow, practitioners and researchers can easily extract information about executed methods, run lines, argument values, return values, variable states, and thrown exceptions. Finally, we present tool prototypes built on top of SpotFlow to support software testing and code comprehension and we detail how SpotFlow runtime data can support novel empirical studies and datasets. SpotFlow is publicly available at https://***/andrehora/spotflow. Video: https://***/jhOv3nKz-u4. © 2024 IEEE computer Society. All rights reserved.

关键词： Python

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Near-Optimal Online Resource Allocation in the Random-Order Model 23

Near-Optimal Online Resource Allocation in the Random-Order ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Cohen, Saar Agmon, Noa Department of Computer Science Bar Ilan University Israel

We study the problem of allocating either divisible or indivisible items (goods or chores) among a set of agents, where the items arrive online, one at a time. Each agent's non-negative value for an item is set by an adversary upon the item's arrival. Our focus is on a unifying algorithmic framework for finding online allocations that treats both fairness and economic efficiency. For this sake, we aim to optimize the generalized means of agents' received values, covering a spectrum of welfare functions including average utilitarian welfare and egalitarian welfare. In the traditional adversarial model, where items arrive in an arbitrary order, no algorithm can give a decent approximation to welfare in the worst case. To escape from this strong lower bound, we consider the random-order model, where items arrive in a uniformly random order. This model provides us with a major breakthrough: we devise algorithms that guarantee a nearly-optimal competitive ratio for certain welfare functions, if the welfare obtained by the optimal allocation is sufficiently large. We prove that our results are almost tight: if the optimal solution's welfare is strictly below a certain threshold, then no nearly-optimal algorithm exists, even in the random-order model. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Resource allocation

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Learning a Distributed Hierarchical Locomotion Controller for Embodied Cooperation 8

Learning a Distributed Hierarchical Locomotion Controller fo...

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8th Conference on Robot Learning, CoRL 2024

作者： Hong, Chuye Huang, Kangyao Liu, Huaping Department of Computer Science and Technology Tsinghua University China

In this work, we propose a distributed hierarchical locomotion control strategy for whole-body cooperation and demonstrate the potential for scaling to large numbers of agents. Our method utilizes a hierarchical structure to break down complex tasks into smaller, manageable sub-tasks. By incorporating spatiotemporal continuity features, we establish the sequential logic necessary for causal inference and cooperative behaviour in sequential tasks, thereby facilitating efficient and coordinated control strategies. Through training within this framework, we demonstrate enhanced adaptability and cooperation, leading to superior performance in task completion compared to the original methods. Moreover, we construct a set of environments as the benchmark for embodied cooperation. A visual depiction of highlights of the learned cooperative behaviours can be found on this website. © 2024 Proceedings of Machine Learning Research.

关键词： Federated learning

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Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods 3

Enhancing Early Detection of Cardiovascular Disease through ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Didwania, Suramya Amrutha, K. Resmi, K.R. Vineetha, K.R. Christ University Department of Computer Science Bengaluru India

ISBN: (纸本)9798350384598

cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Naïve Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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Abstracted Power and Responsibility in computer science Ethics Education

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2023年第1期4卷 96-102页

作者： Peterson, Tina L. Ferreira, Rodrigo Vardi, Moshe Y. The University of Texas at Austin Department of Computer Science AustinTX78712 United States Rice University Department of Computer Science HoustonTX77005 United States

As computing becomes more powerful and extends the reach of those who wield it, the imperative grows for computing professionals to make ethical decisions regarding the use of that power. We propose the concept of abstracted power to help computer science students understand how technology may distance them perceptually from consequences of their actions. Specifically, we identify technological intermediation and computational thinking as two factors in computer science that contribute to this distancing. To counter the abstraction of power, we argue for increased emotional engagement in computer science ethics education, to encourage students to feel as well as think regarding the potential impacts of their power on others. We suggest four concrete pedagogical approaches to enable this emotional engagement in computer science ethics curriculum, and we share highlights of student reactions to the material. © 2020 IEEE.

关键词： Students

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A Comprehensive Review of Learning-based Fuzz Testing Techniques 10

A Comprehensive Review of Learning-based Fuzz Testing Techni...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Cheng, Hao Li, Dongcheng Zhao, Man Li, Hui Wong, W. Eric China University of Geosciences School of Computer Science Wuhan China California State Polytechnic University-Humboldt Department of Computer Science Arcata United States University of Texas at Dallas Department of Computer Science Richardson United States

ISBN: (纸本)9798350362930

Fuzz testing has emerged as a dominant approach for identifying vulnerabilities, significantly improving software development and testing. Yet, traditional fuzz testing often grapples with inefficiencies and poor code coverage, relying heavily on the practitioner's expertise. With the rapid advancements in machine learning and deep learning within artificial intelligence, these technologies promise to revolutionize fuzz testing. This article critically examines learning-based fuzz testing methodologies. It starts by outlining fuzz testing's concept, core procedures, and established strategies. The discussion then shifts to the integration of machine learning and deep learning in fuzz testing, encompassing seed generation, scheduling, test case mutation, selection, target program analysis, and result evaluation. The paper concludes by addressing the current research gaps in this domain and speculating on future trends and opportunities for growth. © 2024 IEEE.

关键词： Deep learning

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Enhancing Intrusion Detection Systems through Advanced Feature Engineering and Machine Learning Techniques 2

Enhancing Intrusion Detection Systems through Advanced Featu...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Patel, Aditya Department of Computer Science Adelphi University United States

ISBN: (纸本)9798331541217

This study investigates enhancing intrusion detection systems (IDS) through machine learning and advanced feature engineering. I evaluate classification algorithms, including k-nearest neighbors (KNN) and support vector machines (SVM), on the ADFA-LD 12 dataset, finding that SVMs, especially with two-sequence feature spaces, outperform other methods. A detailed analysis of linear and sigmoid kernels shows that the two-sequence space improves detection accuracy, with recursive feature elimination identifying optimal performance with fewer than 240 features. One-class SVMs, used for outlier detection, perform poorly. The study underscores the potential of SVMs and suggests further research into scalable algorithms and improved kernel methods for unsupervised detection. © 2024 IEEE.

关键词： Adversarial machine learning

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RYAN: A tool for explaining and visually analyzing the evolution of Relaxed Functional Dependencies

RYAN: A tool for explaining and visually analyzing the evolu...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Caruccio, Loredana Cirillo, Stefano Iuliano, Gianpaolo Polese, Giuseppe Stanzione, Roberto University of Salerno Department of Computer Science Fisciano Italy

ISBN: (纸本)9798350362480

The importance of exploiting profiling metadata, such as Relaxed Functional Dependencies (RFDs), to support advanced data processing tasks, continues to grow also due to the availability of algorithms capable of automatically extracting them from data. Nevertheless, in order to use this type of metadata in real-life contexts, it is also necessary to ensure their correct interpretation of their meaningfulness and their possible evolution over time. To this end, in this paper, we present a new tool that allows visual analysis and explainability of how discovery results evolve according to changes in the data. More specifically, it provides a comprehensive overview of the impact that data changes, by possibly analyzing in-depth affected dependencies and understanding motivations underlying their evolution through a textual explanation. The effectiveness of the proposed tool has been evaluated by conducting a user study, which highlighted RYAN's capability to yield an intuitive visualization of RFD discovery results and to provide a clear explanation of the reasons that led to the evolution of RFDs. © 2024 IEEE.

关键词： Data assimilation

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Deadlock-Free Verification of the SACI-1 OBC Using Circus Time Action and FDR 4

Deadlock-Free Verification of the SACI-1 OBC Using Circus Ti...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Agina, Duaa Adel Masaud Elsherif, Adnan University of Tripoli Computer Science Department Tripoli Libya

ISBN: (纸本)9798350372632

Formal Specification and Validation of computer Systems is an important development methodology in software engineering, especially in life-critical systems. Formal software development involves the use of formal specification languages to specify the system behavior and the implementation of the specification. Model checking tools and theorem proving tools can be used to validate if the system implementation satisfies the specification or, to explore specific characteristics such as liveness and deadlock free. Circus Time Action (CTA) is a formal specification language developed to add time related observations to the semantic model of Circus family of formal specification languages. A mapping between Circus semantic model, based on Unified Theories of programming (UTP), and CTA semantic model exists. Such mapping permits a sound syntax mapping from CTA to the original Circus syntax. Additionally, a framework for validating the untimed properties of CTA has been created, enabling the validation of specifications properties written in CTA using a variety of model checking techniques and, therefore, including Failures Divergence Refinement (FDR). In the present work, we explored the use of such a framework by applying it to a case study. We used CTA to specify the implementation of First Brazilian Scientific Application Satellite (SACI-1) On-Board computer (OBC). Further, we applied the mapping between Circus Time Action and Circus to obtain an untimed specification of the SACI1-OBC. FDR is used to check for deadlock-free property of the SACI1-OBC specification. The obtained results were fully consistent with the previous similar work of SACI-1-OBC. © 2024 IEEE.

关键词： Formal specification

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