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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Belanje, Vishnu Shetty Raina, Ankur Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Science Education Research Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9781665454995

Graph states are multipartite quantum states that exhibit rich entanglement because of their topology. We consider graph states for studying a family of quantum error correcting codes such as [[n,\ 1,\ d]] encoding one logical qubit into n physical qubits of the graph state. We investigate the impact of edge connectivity on the minimum d of the resulting graph state codes (GSCs). © 2022 IEEE.

关键词： Decision trees

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Pyneapple-L: Scalable Expressive Learning-based Spatial Analysis 24

Pyneapple-L: Scalable Expressive Learning-based Spatial Anal...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Liu, Yongyi Lee, Nicolas Kang, Yunfan Shahneh, Mohammad Reza Mahmood, Ahmed Chinnam, Vishal Rohith Sarawadekar, Aparna Vivek Oymak, Samet Sabek, Ibrahim Magdy, Amr Department of Computer Science and Engineering University of California Riverside United States Center for Geospatial Sciences University of California Riverside United States Google LLC United States Thomas Lord Department of Computer Science University of Southern California United States Department of Geography & Geographic Information Science University of Illinois Urbana-Champaign United States Electrical Engineering and Computer Science Department University of Michigan-Ann Arbor United States

ISBN: (纸本)9798400711077

This paper demonstrates Pyneapple-L, an open-source library designed to enhance scalable spatial analysis through learning-based techniques. Through collaboration with social scientists and domain experts, we identify scalability challenges inherent in conventional spatial analysis methods, particularly as the data size increases. Pyneapple-L addresses these challenges by leveraging learning-based models to offer scalable solutions. We demonstrate two modules: scalable learning of spatial hotspots along spatial networks and augmented geographically weighted regression. To showcase Pyneapple-L, we have developed a user-friendly frontend web application to interact with different datasets, algorithms, model configurations, and visualize outcomes on interactive maps that support both broad and analytical views. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Study of AI-Driven Fashion Recommender Systems

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SN computer science 2023年第5期4卷 1-19页

作者： Shirkhani, Shaghayegh Mokayed, Hamam Saini, Rajkumar Chai, Hum Yan Department of Computer Science Electrical and Space Engineering Luleå Tekniska Universitet Luleå Sweden Department of Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Selangor Malaysia

The rising diversity, volume, and pace of fashion manufacturing pose a considerable challenge in the fashion industry, making it difficult for customers to pick which product to purchase. In addition, fashion is an inherently subjective, cultural notion and an ensemble of clothing items that maintains a coherent style. In most of the domains in which Recommender Systems are developed (e.g., movies, e-commerce, etc.), the similarity evaluation is considered for recommendation. Instead, in the Fashion domain, compatibility is a critical factor. In addition, raw visual features belonging to product representations that contribute to most of the algorithm’s performances in the Fashion domain are distinguishable from the metadata of the products in other domains. This literature review summarizes various Artificial Intelligence (AI) techniques that have lately been used in recommender systems for the fashion industry. AI enables higher-quality recommendations than earlier approaches. This has ushered in a new age for recommender systems, allowing for deeper insights into user-item relationships and representations and the discovery patterns in demographical, textual, virtual, and contextual data. This work seeks to give a deeper understanding of the fashion recommender system domain by performing a comprehensive literature study of research on this topic in the past 10 years, focusing on image-based fashion recommender systems taking AI improvements into account. The nuanced conceptions of this domain and their relevance have been developed to justify fashion domain-specific characteristics. © 2023, The Author(s).

关键词： Artificial Intelligence (AI) computer vision Content-based image retrieval (CBIR) Deep learning Fashion recommender system

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Sign-Based Distributed Learning with Byzantine Resilience based on Audit Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5774-5788页

作者： Li, Chengxi Xiao, Ming Skoglund, Mikael KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm 10044 Sweden

In this paper, we study the problem of distributed learning (DL) with devices transmitting sign information of the local gradients to the server under communication constraints, where the devices are susceptible to Byzantine attacks. For this problem, a sign-based gradient descent method with majority vote and stochastic 1-bit quantization (Sign-M-stochastic) has been proposed very recently. However, the Byzantine resilience of Sign-M-stochastic is inherently limited, based on the fact that all Byzantine devices and honest devices participate equally in the training process. To overcome this drawback and enhance the resilience to Byzantine attacks, inspired by the audit-based distributed detection systems, we propose a novel DL method with an audit mechanism (DL-AM). In each iteration, the sign information of the local gradients are obtained by the devices from stochastic 1-bit quantization. All devices, partitioned into groups, send the sign information to the server through multiple paths, both directly and via other devices in the same group. This approach provides the server with additional information about the identities of the devices, which enables the server to form the global model update by aggregating the sign information of different devices with varying weights. We analyze the convergence performance of the proposed method from a theoretical perspective. Finally, numerical results demonstrate the superiority of DL-AM over the baseline methods. © 2005-2012 IEEE.

关键词： Audit mechanism Byzantine resilience convergence analysis distributed learning sign information

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Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Meteorology Predicting Technology Based on Artificial Intell...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Abitova, Gulnara Mukashev, Daniyar Aitmukhanbetova, Elvira Rakhimzhanova, Mira Uskenbayeva, Gulzhan Kulniyazova, Korlan Binghamton University Dept Electrical Engineering and Computer Science Binghamton United States Astana It University Dept. of Computer Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Dept. System Analysis and Management Astana Kazakhstan

ISBN: (纸本)9798350372229

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage. © 2024 IEEE.

关键词： artificial intelligence data analysis machine learning neural networks predictive analytics weather Forecasting

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Multi-level Learning for Semi-supervised Multi-view Classification 11

Multi-level Learning for Semi-supervised Multi-view Classifi...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Song, Na Yang, Shundong Cai, Zhiling Lin, Jian Huang, Sujia Putian University School of Mechanical Electrical and Information Engineering Putian China Hainan University School of Computer Science and Technology Haikou China Fujian Agriculture and Forestry University College of Computer and Information Science Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350345346

Multi-view semi-supervised classification primarily aims to enhance classification accuracy when dealing with limited labeled samples. Although existing methods have shown impressive performance, significant challenges still persist in efficiently propagating label information and capturing global relationship information within multi-view data. To address the aforementioned challenges, a Graph Convolutional Network (GCN)-based framework is presented, which explores the acquisition of multilevel representations, encompassing feature-level representations and the fusion of global structural associations. The state-of-the-art performance of the proposed model is unequivocally supported by a comprehensive set of empirical results. © 2023 IEEE.

关键词： computer vision

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Relative Power Loss Analysis of Poly-Si PV Panels: An Overview in Eastern Indian Climatic Condition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1775-1780页

作者： Pal, Sudipta Basu Das Bhattacharya, Konika Mukherjee, Dipankar Department of Computer Science Technology University of Engineering Management Kolkata India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Department of Electronics and Telecommunication Engineering Indian Institute of Engineering Science and Technology Shibpur India

Renewable energies are generated from natural processes that are continuously renewed. Therefore, it is required to combine the PV modules either in series combinations or parallel combinations for developing the appropriate Photovoltaic (PV) panel/PV arrays. Several PV modules are connected in a series–parallel combination to constitute PV arrays. These PV arrays currently act as a decentralized source of D.C power for onward transmission. When identical wattages PV modules are coupled in a parallel or series connection, there must be a probability to the PV module's manufacturing process that gives rise to power loss. Mainly these types of losses are called Mismatch losses. Mismatch losses (power losses) in PV panels principally occur in terms of Relative Power Loss (RPL). Therefore, the authors carried out I–V characterizations of different technology of PV modules under varying insolation and temperatures. The main aim of the work is to develop the right kind of photovoltaic (PV) panel/PV arrays in a particular Eastern Indian climatic zone. From the authors' characterization of RPL, analysis was conducted to judge which technology is most suitable to build a large size array/BIPV. The experimental results reveal lower mismatch losses of poly-Si PV modules for crystalline silicon and amorphous silicon counterparts. © 2022, The Institution of Engineers (India).

关键词： Solar panels

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Exceptional-point degeneracy as a desirable operation point for an oscillator array with discrete nonlinear gain and radiative elements

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Physical Review Applied 2024年第2期21卷 024037-024037页

作者： Alireza Nikzamir Filippo Capolino Department of Electrical Engineering and Computer Science University of California Irvine 92697 CA United States

We show that an oscillator array prefers to operate at an exceptional point of degeneracy (EPD) occurring in a waveguide periodically loaded with discrete nonlinear gain and radiating elements. The concept of the EPD is employed to conceptualize an exceptional synchronization regime, which leads to enhanced radiating power efficiency. The system maintains a steady-state degenerate mode of oscillation at a frequency of 3 GHz, even when the small-signal nonlinear gain values are nonuniform along the array. We designed the system using small-signal gain to work at the EPD of zero phase shift in consecutive unit cells. Contrarily to the original expectation of zero phase shift, after reaching saturation, the time-domain signal in consecutive unit cells displays a π phase shift. Hence, we demonstrate that the saturated system tends to oscillate at a distinct EPD, associated to a π phase shift between consecutive cells, than the one at which the system was originally designed using small-signal gain. This alternative EPD at which the nonlinear system is landing is associated to higher radiating power efficiency with respect to power provided by nonlinear gains. Finally, we demonstrate that the oscillation frequency is independent of the length of the array, contrarily to what happens ordinary oscillating systems based on one-dimensional cavity resonances. These findings may have a high impact on high-power radiating arrays with distributed active elements.

关键词： Electromagnetic waves & oscillations Optical & microwave phenomena Optoelectronics Photonics Coupled oscillators Non-Hermitian systems Waveguide arrays Cavity resonators

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Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC Converter

Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wanyeki, Babuabel M. Boles, Jessica D. Lang, Jeffery H. Perreault, David J. Meta Platforms Inc New York CityNY United States University of California Berkeley Electrical Engineering and Computer Sciences BerkeleyCA United States Massachusetts Institute of Technology Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798350316445

Magnetics pose a significant challenge for power converter miniaturization. Approaches to address this include power converters based on piezoelectric resonators (PRs) rather than magnetics, and switched-capacitor (SC) converters which utilize only capacitors. However, PR-based converter efficiencies tend to decrease at high voltage conversion ratios, and purely SC-based converters are not capable of efficiently regulating voltage. Through the hybridization of PR and SC-based converters, there is an opportunity for each to address the drawbacks of the other to create a high power density, high efficiency power converter for applications requiring high voltage conversion ratios and wide, continuous voltage conversion ranges. In this paper, we propose a two-stage dc-dc converter with a PR-based regulation stage and a SC-based transformation stage. We detail a new simplified eight-stage switching sequence for the PR stage and experimentally validate this concept in a prototype converter. © 2023 IEEE.

关键词： dc-dc power converters piezoelectric resonators switched capacitor converters switching sequences

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Circumventing Google Play vetting policies: a stealthy cyberattack that uses incremental updates to breach privacy

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4785-4794页

作者： Muhammad, Zia Amjad, Faisal Iqbal, Zafar Javed, Abdul Rehman Gadekallu, Thippa Reddy Department of Computer Science North Dakota State University Fargo United States Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan Islamabad Pakistan School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Today digital technologies are evolving to accommodate small businesses and young entrepreneurs by reducing their time-to-market while encouraging rapid innovation in mobile, Extended Reality (XR), Internet of Things (IoT), cloud, and edge devices. The leading operating system Android typically takes one to a few days to perform application vetting and go to production by leveraging code analysis technologies in their Play Protect anti-malware program. However, developers with malicious intent are looking to circumvent this detection mechanism by exploiting Google’s relatively lenient trust policies that allow for package distribution and feature updates. This paper develops a proof-of-concept malware that exploits customers’ trust and Google’s policies to circumvent popular voice search applications. Our results show that attackers can initially circumvent Play Protect by uploading benign applications to build trust and then add malicious feature updates incrementally to distribute highly intrusive malware into user systems. This malware can scan and collect private user data from the device and exfiltrate it to the command-and-control server. The contributions are three-fold. (1) A proof-of-concept stealthy malware and publishing mechanism has developed that highlights the relative ease with which Google Play Protect policies may be subverted. (2) a comprehensive evaluation has been performed using major publicly available anti-malware solutions. (3) Recommendations and policies have been suggested to prevent this attack and ensure users’ privacy concerns (IMUTA is a novel attack in which malicious functionality is slowly added to a benign application through updates. This attack evades malware detection tools and exploits user trust. The attack can be launched against any application distribution platform like the Play Store). © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile security

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