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Methodological Issues with Empirical Studies in Software Engineering (WSESE), IEEE/ACM International Workshop on

作者： Bianca Minetto Napoleão Ritika Sarkar Sylvain Hallé Fabio Petrillo Marcos Kalinowski Université du Québec à Chicoutimi Chicoutimi Canada École de Technologie Supérieure Montreal Canada Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798400705670

ISBN: (纸本)9798350388688

Context: The constant growth of primary evidence and Systematic Literature Reviews (SLRs) publications in the Software Engineering (SE) field leads to the need for SLR Updates. However, searching and selecting evidence for SLR updates demands significant effort from SE researchers. Objective: We present emerging results on an automated approach to support searching and selecting studies for SLR updates in SE. Method: We developed an automated tool prototype to perform the snowballing search technique and to support the selection of relevant studies for SLR updates using Machine Learning (ML) algorithms. We evaluated our automation proposition through a small-scale evaluation with a reliable dataset from an SLR replication and its update. Results: Effectively automating snowballing-based search strategies showed feasibility with minor losses, specifically related to papers without Digital Object Identifier (DOI). The ML algorithm giving the highest performance to select studies for SLR updates was Linear Support Vector Machine with approximately 74% recall and 15% precision. The use of such algorithms with conservative thresholds to minimize the risk of missing papers can already significantly reduce evidence selection efforts. Conclusion: The preliminary results of our evaluation point in promising directions, indicating the potential of automating snowballing search efforts and of reducing the number of papers to be manually analyzed by about 2.5 times when selecting evidence for updating SLRs in SE.

关键词： Support vector machines Analytical models Automation Machine learning algorithms Prototypes Search problems approximation algorithms Reliability Proposals Software engineering

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Flow Sharing Reliability in Energy Harvesting Wireless Sensing Networks

Flow Sharing Reliability in Energy Harvesting Wireless Sensi...

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IEEE International Conference on Communications (ICC)

作者： Salwa Abougamila Mohammed Elmorsy Ehab S. Elmallah Department of Computing Science University of Alberta Edmonton Canada Department of Computer Science MacEwan University Edmonton Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper introduces a new resource sharing problem in wireless sensor networks (WSNs) that employ energy harvesting for prolonged network uptime. The problem is on managing a given infrastructure of EH-WSNs by supporting concurrent applications. Each application is characterized by a set of traffic generating nodes, a sink node, and a minimum required traffic rate that should be periodically delivered to its sink node. The overall EH-WSN is modelled by a probabilistic graph where energy fluctuation over time in each node is described by a probability distribution and handled by adjusting the flow relaying capacity of a node. Performance of the obtained network management scheme is assessed by a reliability metric on the formulated probabilistic graph. We call the formulated problem the flow sharing reliability (FS-REL) problem in EH-WSNs. We present a heuristic algorithm to cope with the problem using ideas from minimum cost multi-commodity flows in networks and approximation of flow reliability using a factoring algorithm. We also present numerical results that give more insights into the problem and the proposed solution.

关键词： Wireless communication Measurement Wireless sensor networks Heuristic algorithms Probabilistic logic approximation algorithms Probability distribution

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Communicate or Sense? AP Mode Selection in mmWave Cell-Free Massive MIMO-ISAC

Communicate or Sense? AP Mode Selection in mmWave Cell-Free ...

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Asilomar Conference on Signals, Systems & Computers

作者： Weixian Yan Ozan Alp Topal Zinat Behdad Özlem Tuğfe Demir Cicek Cavdar Department of Computer Science KTH Royal Institute of Technology Kista Sweden Department of Electrical-Electronics Engineering TOBB University of Economics and Technology Ankara Turkiye

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Integrated sensing and communication (ISAC) is a promising technology for future mobile networks, enabling sensing applications to be performed by existing communication networks, consequently improving the system efficiency. Millimeter wave (mmWave) signals provide high sensing resolution and high data rate but suffer from sensitivity to blockage. Cell-free massive multiple-input multiple-output (MIMO), with a large number of distributed access points (APs), can overcome this challenge by providing macro diversity against changing blockages and can save energy consumption by deactivating unfavorable APs. Thus, in this work, we propose a joint dynamic AP mode selection and power allocation scheme for mmWave cell-free massive MIMO-ISAC, where APs are assigned either as ISAC transmitters, sensing receivers, or shut down. Due to the large size of the original problem, we propose three different sub-optimal algorithms that minimize the number of active APs while guaranteeing the sensing and communication constraints. Numerical results demonstrate that assigning ISAC transmitters only satisfying communication constraints, followed up by sensing receiver assignment only for sensing constraint achieves the best performance-complexity balance.

关键词： Energy consumption Transmitters Heuristic algorithms Receivers Integrated sensing and communication approximation algorithms Resource management Millimeter wave communication Optimization Signal resolution

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Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates

arXiv

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arXiv 2024年

作者： Dereich, Steffen Graeber, Robin Jentzen, Arnulf Institute for Mathematical Stochastics Faculty of Mathematics and Computer Science University of Münster Germany School of Data Science The Chinese University of Hong Kong CUHK-Shenzhen Shenzhen China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong CUHK-Shenzhen Shenzhen China Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany

MSC Codes 60J22 (Primary), 65K10, 60J20, 65C40 (Secondary)Deep learning (DL) approximation algorithms – typically consisting of a class of deep artificial neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method – are nowadays the key ingredients in many artificial intelligence (AI) systems and have revolutionized our ways of working and living in modern societies. For example, SGD methods are used to train powerful large language models (LLMs) such as versions of ChatGPT and Gemini, SGD methods are employed to create successful generative AI based text-to-image creation models such as Midjourney, DALL-E, and Stable Diffusion, but SGD methods are also used to train DNNs to approximately solve scientific models such as partial differential equation (PDE) models from physics and biology and optimal control and stopping problems from engineering. It is known that the plain vanilla standard SGD method fails to converge even in the situation of several convex optimization problems if the learning rates are bounded away from zero. However, in many practical relevant training scenarios, often not the plain vanilla standard SGD method but instead adaptive SGD methods such as the RMSprop and the Adam optimizers, in which the learning rates are modified adaptively during the training process, are employed. This naturally rises the question whether such adaptive optimizers, in which the learning rates are modified adaptively during the training process, do converge in the situation of non-vanishing learning rates. In this work we answer this question negatively by proving that adaptive SGD methods such as the popular Adam optimizer fail to converge to any possible random limit point if the learning rates are asymptotically bounded away from zero. In our proof of this non-convergence result we establish suitable pathwise a priori bounds for a class of accelerated and adaptive SGD methods, which are also of independent interest. Copyright © 2024,

关键词： approximation algorithms

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Efficient Calculation of Electromagnetic Scattering Problem with Multiple Excitations by Sherman-Morrison-Woodbury Formula

Efficient Calculation of Electromagnetic Scattering Problem ...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuting Shen Runze Liu Jianchang Cai Yang Bao College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology)) Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, the Sherman-Morrison-Woodbury (SMW) formula is applied to accelerate the direct solution of electromagnetic scattering problem with multiple excitations by electric field integral equation (EFIE). To reduce the calculation burden in method of moments (MoM), the dense discretized impedance matrix is transformed into several diagonal matrix blocks via the SMW formula. The low rank matrices are compressed by Lagrange interpolations. The adaptive cross approximation algorithm (ACA) is applied to reduce the storage costs for multiple right-hand sides (RHS) matrix, which can solve the monostatic radar cross section (RCS) over a wide angular band efficiently. The numerical results demonstrate both the effectiveness and accuracy of the proposed method.

关键词： Interpolation Radar cross-sections Accuracy Electromagnetic scattering Integral equations approximation algorithms Radar antennas Impedance Electric fields Method of moments

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Privacy-Preserving Behind-the-Meter PV-Load Disaggregation via Prior Knowledge Constrained Blind Source Separation

Privacy-Preserving Behind-the-Meter PV-Load Disaggregation v...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yingrui Zhuang Lin Cheng Kaidi Huang Zijin Li Bo Wang Yongliang Zhao Department of Electrical Engineering Tsinghua University Beijing China Electric Power Research Institute State Grid Beijing Electric Power Company Beijing China Tongzhou Electric Power Supply Company State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Accurate disaggregation of behind-the-meter (BTM) photovoltaic (PV) generation and load demand (PV-load) is essential for utility operators to make informed decisions (e.g., demand response). Existing methods for BTM PV-load disaggregation commonly rely on the availability of detailed information, such as observations of neighboring end users’ PV generation or load demand and geographical location, physical parameters of the PV panel and weather conditions, or labeled historical disaggregated observations. However, such information is often unavailable in practice due to privacy concerns or high economic costs for monitoring devices. Instead, this paper proposes a privacy-preserving BTM PV-load disaggregation algorithm that only requires the observed net load data based on prior knowledge constrained blind source separation. Specifically, ensemble empirical mode decomposition is first used to enhance the signal dimension of the observed net load, and then prior knowledge constrained independent component analysis is applied to disaggregate PV generation and load demand from the enhanced mixed signal. Furthermore, the demand response potential of the BTM PV-load unit is evaluated based on the disaggregation results. Numerical experiments on a real-world dataset demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Privacy Accuracy Empirical mode decomposition Independent component analysis System integration approximation algorithms Demand response Blind source separation Robustness Real-time systems

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Flexible Association and Placement for Open-RAN

Flexible Association and Placement for Open-RAN

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Hiba Hojeij Guilherme Iecker Ricardo Mahdi Sharara Sahar Hoteit Véronique Vèque Stefano Secci CNRS CentraleSupélec Laboratoire des Signaux et Systèmes (L2S) Université Paris-Saclay Gif-sur-Yvette France IRIT/Toulouse INP University of Toulouse France Innovation Division Orange Paris France CEDRIC CNAM France

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In modern Open RAN architectures, the classic gNB radio protocol stack is disaggregated and implemented in different virtualized components, the Centralized Unit (CU), the Distributed Unit (DU), and the Radio Unit (RU). Each of these units is deployed throughout the cloud-enabled RAN infrastructure in order to achieve users' required Quality of Service (QoS). Within this framework, our study is dedicated to maximizing the admission of User Equipments (UEs) into the system while ensuring their specific QoS needs. We focus on two primary tasks: (i) establishing an association between UEs and RUs and (ii) placing CUs and DUs across the network's cloud hosts. We initially address these tasks by formulating the joint association-placement optimization problem, subject to the system's available resources and QoS-related constraints. Although it is an NP-Hard problem, we discuss how we can frame it into an Integer Linear Programming (ILP) model. Then, we propose an approximation algorithm based on the decomposition of the original ILP model. We show through exhaustive simulations that our proposed ILP model provides higher admissibility levels than other baseline models. Moreover, it significantly minimizes the deployment costs and increases the overall fairness. Finally, we remark that our decomposition algorithm presents a short optimality gap in practice, with up to 6% less admissions, while reducing the solution time by up to 98%.

关键词： Cloud computing Costs Protocols Optimization models Quality of service Open RAN approximation algorithms

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Background-Free Detection of Light-Activatable Perfluorocarbon Nanodroplets

Background-Free Detection of Light-Activatable Perfluorocarb...

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Ultrasonics Symposium (LAUS), IEEE UFFC Latin America

作者： Alejandro Ulate Arce Meybelle Castro Valverde Diego S. Dumani School of Electrical Engineering University of Costa Rica UCR San Pedro Costa Rica

ISBN: (数字)9798350349085

ISBN: (纸本)9798350349092

Perfluorocarbon nanodroplets (PFCnDs) have been studied as an activatable ultrasound contrast agent. During vaporization, light-activatable PFCnDs generate characteristic ultrasound and photoacoustic (US/PA) signals across a sequence of images. As more PFCnDs vaporize in a region of interest, the US contrast increases exponentially while PA contrast decreases similarly. Previous studies have explored background-free imaging algorithms to show the location of the activated PFCnDs, by detecting the slope of exponential signals. In this study, we generate background-free images by identifying exponential trends and signal amplitudes, thus giving the location of PFCnDs and scaling the images by approximate PFCnD concentration. Synthetic US/PA images were generated by simulating a time-series of US/PA images during PFCnD activation events. Tissue background signals (offset) and random noise were also included. The algorithm analyzes intensity values to detect exponential trends using the coefficient of determination $(\mathbf{R}^{\mathbf{2}})$ , followed by image reconstruction. A pixel-wise pre-filtering was performed to account for offset and noise. Then, the $\mathbf{R}^{\mathbf{2}}$ coefficient was calculated to evaluate how each pixel's data fit an exponential behavior. Image reconstruction used the parameters of the fitting function generated, using the estimated amplitude value to scale the concentration image. A Pearson correlation coefficient was used to assess the accuracy of the algorithm by comparing the reconstructed image with the ground truth image. The algorithm was tested under presence of up to 70% noise and 50% signal offsets to assess the potential applicability in realistic tissue environments. In these conditions, the algorithm showed strong performance with high correlation coefficients. The successful reconstruction of background-free images suggests the feasibility of this method for further testing in vivo.

关键词： Correlation coefficient In vivo Ultrasonic imaging Noise Imaging Market research approximation algorithms

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Joint Trajectory and Resource Optimization for Multi-UAV Cooperative Computation

Joint Trajectory and Resource Optimization for Multi-UAV Coo...

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IEEE International Conference on Communications Workshops, ICC

作者： Wenlong Xu Tiankui Zhang Xidong Mu Yuanwei Liu Yapeng Wang TianYi Shi School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Faculty of Applied Sciences Macao Polytechnic University Macao China School of Electronic Engineering and Computer Science Queen Mary University of London London U.K.

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Simulation results show that the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different task requirements and available resources.

关键词： Heuristic algorithms Simulation Conferences Benchmark testing Autonomous aerial vehicles approximation algorithms Trajectory

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A Layerwise-Multi-Angle Approach to Fine-Tuning the Quantum Approximate Optimization Algorithm

A Layerwise-Multi-Angle Approach to Fine-Tuning the Quantum ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Leonardo Lavagna Andrea Ceschini Antonello Rosato Massimo Panella Department of Information Engineering Electronics and Telecommunications (DIET) University of Rome "La Sapienza" Rome Italy

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This paper introduces a novel variational quantum algorithm built upon the established Quantum Approximate Optimization Algorithm also known as QAOA. Since the known parameter fixing strategy imposes constraints on QAOA to enhance tractability at the cost of some expressive power, the proposed layerwise approach integrates it with the existing Multi-Angle QAOA technique, which is characterized in turn by height-ened expressiveness through an increased number of parameters, albeit with increased optimization challenges. Consequently, the proposed layerwise-Multi-Angle QAOA combines the strengths of one variant with the limitations of the other, striking a balance in algorithmic design. The effectiveness of the proposed algorithm is assessed through experimental evaluations on the Maximum Cut problem. This study reveals promising results in heuristic predictions, with robustness both in terms of approximation ratio and optimization capabilities.

关键词： Costs Sensitivity Quantum algorithm approximation algorithms Prediction algorithms Robustness Quantum circuit

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