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arXiv 2024年

作者： Pianini, Danilo Tomforde, Sven Department of Computer Science and Engineering Alma Mater Studiorum Università di Bologna Cesena Italy Intelligent Systems Group Kiel University Kiel Germany

We are witnessing the rise of autonomous cars, which will likely revolutionize the way we travel. Arguably, the maritime domain lags behind, as ships operate on many more degrees of freedom (thus, a much larger search space): there is less physical infrastructure, and rules are less consistent and constraining than what is found on roads. The problem is further complicated by the inevitable co-existence of autonomous and human-operated ships: the latter may take unpredictable decisions, which require adjustments on the autonomous ones. Finally, the problem is inherently decentralised, there is no central authority, and communication means can be very diverse in terms of communication distance and performance, mandating special care on which information is shared and how. In this work, we elaborate on the challenges of trajectory prediction and adaptation for mixed autonomous and human-operated ships, and we propose initial ideas on potential approaches to address them. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized control

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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RJoules: An Energy Measurement Tool for R

RJoules: An Energy Measurement Tool for R

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Rajrupa Chattaraj Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Systems and Human Analytics Lab Indian Institute of Technology Tirupati India

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

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Modeling and Analysis of Emotion-Oriented Goal Models: Virtual Clinics Case Study

Modeling and Analysis of Emotion-Oriented Goal Models: Virtu...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Alkhomsan, Mashail N. Baslyman, Malak Alshayeb, Mohammad Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Computer and Information Sciences Department Jouf University Sakaka72388 Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Finance and Digital Economy Dhahran31261 Saudi Arabia

[Context & Motivation] Understanding and capturing user emotional requirements are important to increase user acceptance and provide an enhanced user experience. This is essential to ensure continuity of using software systems and have successful adoption of technology. [Question/problem] Incorporating emotional requirements into Requirements Engineering (RE) activities is relatively new. In addition, there is a lack of analysis methods that enable reasoning on design alternatives while considering user emotions. [Principal ideas/results] We propose an Emotion-Oriented Requirements Engineering (EmORE) approach that supports the elicitation, modeling, and analysis of user emotions in relation to other requirements. The effectiveness of EmORE was illustrated by conducting a case study on a Virtual Clinic application, which showed an increase in overall positive emotions and a decrease in negative emotions. [Contribution] The main contributions of the EmORE are providing means for more accurate user emotions elicitation, enabling the analysis of user emotions in relation to the design elements, and quantifying user emotions. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Requirements engineering

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Towards Adaptive Trajectories for Mixed Autonomous and Human-Operated Ships

Towards Adaptive Trajectories for Mixed Autonomous and Human...

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Autonomic Computing and Self-Organizing systems Companion (ACSOS-C), IEEE International Conference on

作者： Danilo Pianini Sven Tomforde Department of Computer Science and Engineering Alma Mater Studiorum—Università di Bologna Cesena Italy Intelligent Systems Group Kiel University Kiel Germany

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

关键词： Roads Anisotropic Trajectory Space exploration Automobiles Marine vehicles Distributed computing

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

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International Research Conference on Smart Computing and systems Engineering (SCSE)

作者： Niruthikka Sritharan Nishaanthini Gnanavel Prathushan Inparaj Dulani Meedeniya Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their "Black-Box" nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped.

关键词： Visualization Decision making Closed box Predictive models Cams Reliability Medical diagnosis

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HYPERBOLIC DISTANCE-BASED SPEECH SEPARATION

arXiv

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arXiv 2024年

作者： Petermann, Darius Kim, Minje Indiana University Department of Intelligent Systems Engineering BloomingtonIN47408 United States University of Illinois at Urbana-Champaign Department of Computer Science IL61801 United States

In this work, we explore the task of hierarchical distance-based speech separation defined on a hyperbolic manifold. Based on the recent advent of audio-related tasks performed in non-Euclidean spaces, we propose to make use of the Poincaré ball to effectively unveil the inherent hierarchical structure found in complex speaker mixtures. We design two sets of experiments in which the distance-based parent sound classes, namely "near" and "far", can contain up to two or three speakers (i.e., children) each. We show that our hyperbolic approach is suitable for unveiling hierarchical structure from the problem definition, resulting in improved child-level separation. We further show that a clear correlation emerges between the notion of hyperbolic certainty (i.e., the distance to the ball's origin) and acoustic semantics such as speaker density, inter-source location, and microphone-to-speaker distance. © 2024, CC BY.

关键词： Source separation

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DBJoules: An Energy Measurement Tool for Database Management systems

arXiv

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arXiv 2023年

作者： Lella, Hemasri Sai Chattaraj, Rajrupa Manasa, Kurra Chimalakonda, Sridhar Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

In the rapidly evolving landscape of modern data-driven technologies, software relies on large datasets and constant data center operations using various database systems to support computation-intensive tasks. As energy consumption in software systems becomes a growing concern, selecting the right database from energy-efficiency perspective is also critical. To address this, we introduce DBJoules, a tool that measures the energy consumption of activities in database systems. DBJoules supports energy measurement of CRUD operations for four popular databases. Through evaluations on two widely-used datasets, we identify disparities of 7% to 38% in the energy consumption of these databases. Hence, the goal is to raise developer awareness about the effect of running queries in different databases from an energy consumption perspective, enabling them to select appropriate database for sustainable usage. The tool’s demonstration is available at https://***/D1MTZum0jok and related artifacts at https://***/DBJoules/. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Interpretable Cervical Cell Classification: A Comparative Analysis

Interpretable Cervical Cell Classification: A Comparative An...

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Advanced Research in Computing (ICARC), International Conference on

作者： Nishaanthini Gnanavel Prathushan Inparaj Niruthikka Sritharan Dulani Meedeniya Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification.

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