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arXiv 2021年

作者： Navidan, Hojjat Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shah-Mansouri, Vahid Windridge, David School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395-515 Iran Cognitive Telecommunication Research Group Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering Faculty of Technology and Engineering Standard Research Institute Alborz31745-139 Iran School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom Department of Computer Science School of Science and Technology Middlesex University LondonNW4 4BT United Kingdom

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

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Identifying Users with Wearable Sensors based on Activity Patterns

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Procedia computer Science 2020年 177卷 8-15页

作者： Muhammad Ehatisham-ul-Haq Maryam Naseer Malik Muhammad Awais Azam Usman Naeem Asra Khalid Mustansar Ali Ghazanfar Department of Computer Engineering University of Engineering and Technology Taxila Punjab 47050 Pakistan Whitecliffe Technologies Wellington New Zealand School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering & Computer Science Victoria University of Wellington New Zealand School of Architecture Computing and Engineering University of East London United Kingdom

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns.

关键词： Activity recognition machine learning user identification wearable sensors

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When creators meet the metaverse: A survey on computational arts

arXiv

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arXiv 2021年

作者： Lee, Lik-Hang Lin, Zijun Hu, Rui Gong, Zhengya Kumar, Abhishek Li, Tangyao Li, Sijia Hui, Pan Augmented Reality and Media Lab KAIST Korea Republic of Department of Economics University College London United Kingdom Computational Media & Arts The Hong Kong University of Science and Technology Hong Kong Center for Ubiquitous Computing University of Oulu Finland Department of Computer Science University of Helsinki Finland School of Mathematical Sciences Queen Mary University of London United Kingdom Department of Architecture Inha University Korea Republic of

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace. © 2021, CC BY-NC-SA.

关键词： Surveys

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Rolling stock shunt operation planning in urban rail transit depots with maintenance consideration

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Transportation Research Part B: Methodological 2025年 199卷

作者： Dian Wang Ling Yao Andrea D’Ariano Shuguang Zhan Lisha Wang School of Automotive and Transportation Engineering Hefei University of Technology Hefei Anhui 230009 China Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome 00146 Italy School of Traffic and Transportation Beijing Jiaotong University Beijing 100044 China School of Architecture and Urban Planning Chongqing University Chongqing 400030 China

This study investigates the daily rolling stock shunt operation planning in a rail depot. Given the layout of a depot and the rolling stocks (that arrive at and depart from this depot within an operation day) with given maintenance schedule, the studied problem lies in determining: 1) the position where each rolling stock is parked, outside washed, and/or maintained, and 2) the conflict-free shunting plan of rolling stocks to move within a depot. We transform the track-circuits in the depot into different multi-layer directed graphs to illustrate the shunting processes of rolling stocks. By means of these graphs, we formulate the studied problem as a mixed integer linear programming model by considering a more general variant of four requirements that make sense in practice but are not or rarely considered in previous works and by presenting a flexible technique to model the track capacity, to reduce the additional shunting movements of rolling stocks. Besides, we design a two-stage decomposition manner to efficiently solve real-life problem instances, wherein the problem in each stage is addressed by a presented logic-based Benders decomposition algorithm enhanced by customized acceleration mechanisms. Finally, a set of realistic and real-life instances with different scales (derived from the largest depot of the Chongqing Rail Transit Line 3 in China) are investigated. Computational results demonstrate that our best algorithm solves a real-life instance to optimality in approximately 8 s that is considerably shorter than the time of rail staffs to solve this instance manually, thus our approach can provide strong automatical computer-aided decision supports. Our approach is also very efficient in optimizing another objective that is also widely used, and can provide management insights to rail staffs.

关键词： Acceleration mechanisms Benders decomposition Mixed integer linear programming Passenger rail transportation Rolling stock shunting

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ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

arXiv

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arXiv 2020年

作者： Naveros, Francisco Carrillo, Richard R. Ros, Eduardo Luque, Niceto R. University of Granada Granada Spain Aging in Vision and Action Institut de la Vision Inserm–UPMC–CNRS Paris France Department of Computer Architecture and Technology University of Granada Granada Spain

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network’s own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each

关键词： Brain

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ExploreBP: A Simulation Tool for Mobile Browser Energy Optimization 11th

ExploreBP: A Simulation Tool for Mobile Browser Energy Optim...

引用

11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Zhang, Jin Wei, Xin Liu, Zhen Liu, Fangxin Li, Tao Lu, Tingjuan Gong, Xiaoli Nankai University Tianjin China IT Department Chinese PLA 117 Hospital Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030322151

The browser is one of the most commonly used applications. Users tend to pursue a good user experience and care more about the performance of the browser, while ignoring the power consumption of the browser. This paper proposes a method to reduce the energy consumption of web browsing. In order to better quantify the user experience, this paper uses the first screen load time as the evaluation metric of user experience. First, according to the relationship between the network speed and the first screen load time, find the most suitable primary frequency at a specific network speed, and define the point as the balance point. When the primary frequency is greater than the primary frequency corresponding to the balance point, the first screen load time will almost never change. The balance points of different web pages are also different. Then adjust the CPU frequency according to the balance point of the webpage and the network speed, which can reduce the browser energy consumption and reduce the impact on the user experience. At the same time, this paper proposes a simulation tool ExploreBP, which is used to simulate the working state of the network speed and different web pages to find the optimal energy consumption configuration. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Frequency modulation

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On the comparison of classic and deep keypoint detector and descriptor methods 11

On the comparison of classic and deep keypoint detector and ...

引用

11th International Symposium on Image and Signal Processing and Analysis, ISPA 2019

作者： Bojanic, David Bartol, Kristijan Pribanic, Tomislav Petkovic, Tomislav Donoso, Yago Diez Mas, Joaquim Salvi Faculty of Electrical Engineering and Computing University of Zagreb Zagreb EU Croatia Faculty of Science Yamagata University Yamagata Japan University of Girona Department of Computer Architecture and Technology Girona EU Spain

ISBN: (纸本)9781728131405

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. © 2019 IEEE.

关键词： Deep learning

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A Neuronal Network Model of the Primate Visual System: Color Mechanisms in the Retina, LGN and V1

A Neuronal Network Model of the Primate Visual System: Color...

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作者： Martínez-Cañada, Pablo Morillas, Christian Pelayo, Francisco Department of Computer Architecture and Technology University of Granada Granada Spain Centro de Investigacion en Tecnologias de la Informacion y de Las Comunicaciones University of Granada Granada Spain

Color plays a key role in human vision but the neural machinery that underlies the transformation from stimulus to perception is not well understood. Here, we implemented a two-dimensional network model of the first stages in the primate parvocellular pathway (retina, lateral geniculate nucleus and layer 4Cβ in V1) consisting of conductance-based point neurons. Model parameters were tuned based on physiological and anatomical data from the primate foveal and parafoveal vision, the most relevant visual field areas for color vision. We exhaustively benchmarked the model against well-established chromatic and achromatic visual stimuli, showing spatial and temporal responses of the model to disk- and ring-shaped light flashes, spatially uniform squares and sine-wave gratings of varying spatial frequency. The spatiotemporal patterns of parvocellular cells and cortical cells are consistent with their classification into chromatically single-opponent and double-opponent groups, and nonopponent cells selective for luminance stimuli. The model was implemented in the widely used neural simulation tool NEST and released as open source software. The aim of our modeling is to provide a biologically realistic framework within which a broad range of neuronal interactions can be examined at several different levels, with a focus on understanding how color information is processed. © 2019 World Scientific Publishing Company.

关键词： Color

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Cacao-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Muselli, Marco Caviglia, Daniele D. Ferrari, Enrico Verda, Damiano Chible, Hussein Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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