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arXiv 2023年

作者： Mousavand, Kaveh Paquette, Charles Unit of Representation Theory and Algebraic Combinatorics Okinawa Institute of Science and Technology Okinawa Japan Department of Mathematics and Computer Science Royal Military College of Canada KingstonON Canada

For finite dimensional algebras over algebraically closed fields, we prove some new results on the interactions between bricks and τ-rigid modules, and also on their geometric counterparts– brick components and generically τ-reduced components. First, we give a new characterization of the locally representation-directed algebras and show that these are exactly the algebras for which every brick is τ-rigid (hence, all indecomposable modules are bricks). We then consider analogous phenomena using some irreducible components of module varieties: the indecomposable components, brick components, and the indecomposable generically τ-reduced components. For an algebra A, we prove that if two of these three sets of components coincide, then every indecomposable τ-rigid A-module is a brick. For brick-infinite algebras, we also provide a new construction that yields, in some cases, a rational ray outside of the τ-tilting fan (also called g-vector fan). For tame and E-tame algebras, we obtain new results on some open conjectures on τ-tilting fans and *** Codes 16P10, 16G20, 16D80, 16G60 © 2023, CC BY-NC-ND.

关键词： Brick

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5G Spectrum Research

5G Spectrum Research

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Peiyuan Zhu Lijun Xiao Shu Tan Jiahong Cai Yingzi Huo Ronglin Zhang UUM COB Postgraduate Studies Unit Universiti Utara Malaysia Kuala Lumpur Malaysia School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

With the growing demand for communication and the rapid development of global mobile communication technology, the fourth generation of mobile communication can no longer meet the needs of people in their working lives for mobile network communication, and the fifth generation of mobile communication is born. This paper first introduces three strategies for the use of the 5G spectrum, namely spectrum dedication, spectrum re-farming, and spectrum sharing. Secondly, it introduces the three main application scenarios for the 5G spectrum, namely enhanced mobile broadband (eMBB), ultrahigh reliability and ultra-low latency services (URLLC), and massive IoT communications (MTC). The presentation concludes with a summary of the impact of the 5G spectrum in today’s society and an outlook on its future development.

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Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model

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SN computer science 2021年第6期2卷 452页

作者： Das, Monidipa Ghosh, Akash Ghosh, Soumya K. Machine Intelligence Unit (MIU) Indian Statistical Institute (ISI) Kolkata India Department of Computer Science and Engineering Jadavpur University (JU) Kolkata India Department of Computer Science and Engineering Indian Institute of Technology (IIT) Kharagpur India

COVID-19, a life-threatening infection by novel coronavirus, has broken out as a pandemic since December 2019. Eventually, with the aim of helping the World Health Organization and other health regulators to combat COVID-19, significant research effort has been exerted during last several months to analyze how the various factors, especially the climatic aspects, impact on the spread of this infection. However, due to insufficient test and lack of data transparency, these research findings, at times, are found to be inconsistent as well as conflicting. In our work, we aim to employ a semantics-driven probabilistic framework for analyzing the causal influence as well as the impact of climate variability on the COVID-19 outbreak. The idea here is to tackle the data inadequacy and uncertainty issues using probabilistic graphical analysis along with embedded technology of incorporating semantics from climatological domain. Furthermore, the theoretical guidance from epidemiological model additionally helps the framework to better capture the pandemic characteristics. More significantly, we further enhance the impact analysis framework with an auxiliary module of measuring semantic relatedness on regional basis, so as to realistically account for the existence of multiple climate types within a single spatial region. This added notion of regional semantic relatedness further helps us to attain improved probabilistic analysis for modeling the climatological impact on this disease outbreak. Experimentation with COVID-19 datasets over 15 states (or provinces) belonging to varying climate regions in India, demonstrates the effectiveness of our semantically-enhanced theory-guided data-driven approach. It is worth noting that our proposed framework and the relevant semantic analyses are generic enough for intelligent as well as explainable impact analysis in many other application domains, by introducing minimal augmentation. © 2021, The Author(s), under exclusive licence to Sp

关键词： Climate variability COVID-19 Semantic Bayesian analysis Theory-guided approach

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Precision Prevention through Social Media: Report of Four Cases

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Yearbook of Medical Informatics 2025年第1期33卷 52-57页

作者： Gabarron, Elia Lopez-Campos, Guillermo Davies, Shauna Chomutare, Taridzo Tan, IrisThiele Isip Petersen, Carolyn Department of Education ICT and Learning Østfold University College Halden Norway Norwegian Centre for E-health Research University Hospital of North Norway Tromsø Norway Queen's University Belfast Belfast United Kingdom Faculty of Nursing University of Regina Canada Department of Computer Science Faculty of Science and Technology UiT The Arctic University of Norway Tromsø Norway Medical Informatics Unit College of Medicine University of the Philippines Manila Philippines Department of Artificial Intelligence and Informatics Mayo Clinic Rochester MN United States

BACKGROUND: Precision prevention involves using biological, behavioral, socioeconomic, and epidemiological data to improve health for a particular individual or group. With almost 63% of the global population using social media, these platforms show promise to deliver tailored messaging and personalized interventions to individuals. OBJECTIVES: To describe the personalization elements and behavior components used in a sample of precision prevention interventions delivered through social media. METHODS: To identify examples of cases, a search was done on ***, searching for 'other terms: prevention' + 'Intervention/Treatment: social media intervention' + 'study results: With results. The selected cases were described, personalization elements reported, and their adopted intervention components were coded according to the Behavior Change Wheel (BCW) framework. RESULTS: A total of four cases employing personalization in their interventions were identified. Three of these cases targeted women's health. The intervention period varied from two to eight months, with participant commitment ranging from active involvement on five out of seven days to monthly participation. The BCW interventions of persuasion and incentivization, were most frequently utilized, while education and coercion were used sparingly in the selected cases. Notably, none of the four cases reported the use of training, restrictions, or modeling. CONCLUSIONS: Social media has the potential to serve as a tool for digital phenotyping and contribute to the advancement of precision prevention. Challenges include the social media platform set-up and ensuring all ethical considerations are met. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Internet-based intervention Precision medicine Prevention Social media

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A New U-Net Based System for Multi-Cultural Wedding Image Classification

SSRN

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SSRN 2023年

作者： Shivakumara, Palaiahnakote Kumar, C. Pavan Nemade, Jagrut J. Michael, Kshitiz Kumar, Akash Anami, Basavaraj S. Pal, Umapada Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Indian Institute of Information Technology Dharwad India KLE Institute of Technology India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Use of social media for communication, sharing expressing views, broadcasting news, threatening and blackmailing has become an integral part of society. One such activity is understanding multi-cultural wedding images uploaded on social media. This paper presents a novel method based on the combination of U-Net, Convolutional Neural Network and Random Forest for classification of multicultural wedding images. In the case of wedding images, bride and bridegroom draw the attention of the viewers. This observation led to propose U-Net for segmenting the region of bride and bridegroom in a novel way. Similarly, it is noted that the costumes of bride and bridegroom are vital information for differentiating different cultures. This cue motivated us to extract features using CNN for classification. Since the extracted features using CNN are capable of discriminating images of different classes, we propose a simple and effective Random-Forest for Multicultural Wedding Image Classification. The efficiency of the proposed model is demonstrated by testing it on our own dataset of six multi-cultural wedding classes and standard dataset of wedding and non-wedding images classes. Experimental results on both the datasets show that the proposed model outperforms the state-of-the-art models in terms of average classification rate © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Multi-Modal Deep Learning Based Approach for House Price Prediction

arXiv

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arXiv 2024年

作者： Hasan, Md. Hasebul Jahan, Md. Abid Ali, Mohammed Eunus Li, Yuan-Fang Sellis, Timos Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Monash University Clayton Australia Archimedes Research Unit Athena Research Center Marousi Greece

Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house;and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://***/4P0N/mhpp Copyright © 2024, The Authors. All rights reserved.

关键词： Houses

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SimIoT: A Simulator for Verification and Profiling of Complex IoT Deployments

SimIoT: A Simulator for Verification and Profiling of Comple...

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Euromicro Symposium on Digital System Design

作者： José Antonio de la Torre Fernando Rincón Marco Zennaro Julian Caba Jesús Barba Juan Carlos López Technology and Information Systems Department School of Computer Science University of Castilla-La Mancha Ciudad Real Spain Science Technology and Innovation Unit Abdus Salam International Centre for Theoretical Physics Trieste Italy

The proliferation of Internet of Things (IoT) technologies in various industrial sectors has brought forth new challenges that demand attention for achieving technological maturity. One such challenge is the lack of tools for emulating the diverse components present in IoT architectures, leading to the continuous verification of each component in the chain, which proves to be a complex task. This paper addresses the verification problem in Edge/Fog IoT platforms through comprehensive end-to-end testing. To tackle this challenge, we have developed a modular IoT simulator (SimIoT) capable of efficiently emulating thousands of IoT devices in realistic scenarios, including hospitals, airports, and smart cities. The simulator allows testing of Edge platforms without the need for programming expertise. Furthermore, we demonstrate the feasibility of our simulator by presenting a use case involving the profiling of an open-source IoT platform.

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN 2

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rastogi, Ritesh Shanthi, T. Kumar Naik, Pramod Sheela, M. Sahaya Mishra, A.K. Nandhini, T.J. Delhi India Paavai Engineering College Department of Ece Chennai India Dayananda Sagar University Departmeny of Computer Science Engineering Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Dhofar University Math Unit Foundation Program Salalah Oman Chennai India

ISBN: (纸本)9798350347579

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the 'channel capacity' in a 5G ultradense network, when the phrase 'channel capacity' is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes;both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process. © 2023 IEEE.

关键词： Energy efficiency

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Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification

arXiv

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arXiv 2024年

作者： Maitra, Arkadip Mitra, Shree Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Department of Computer Science Ramakrishna Mission Vivekananda Educational and Research Institute Belur Belur India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India CVPR Unit Indian Statistical Institute Kolkata Kolkata India Dept. of EECE Indian Institute of Technology Kharagpur Kharagpur India

Self-supervised learning has developed rapidly over the last decade and has been applied in many areas of computer vision. Decorrelation-based self-supervised pretraining has shown great promise among non-contrastive algorithms, yielding performance at par with supervised and contrastive self-supervised baselines. In this work, we explore the decorrelation-based paradigm of self-supervised learning and apply the same to learning disentangled stroke features for writer identification. Here we propose a modified formulation of the decorrelation-based framework named SWIS which was proposed for signature verification by standardizing the features along each dimension on top of the existing framework. We show that the proposed framework outperforms the contemporary self-supervised learning framework on the writer identification benchmark and also outperforms several supervised methods as well. To the best of our knowledge, this work is the first of its kind to apply self-supervised learning for learning representations for writer verification tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Enhancing Quantum Key Distribution with Entanglement Distillation and Classical Advantage Distillation

arXiv

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arXiv 2024年

作者： Sun, Shin Goodenough, Kenneth Bhatti, Daniel Elkouss, David Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan College of Information and Computer Science University of Massachusetts Amherst 140 Governors Dr AmherstMA01002 United States

Realizing secure communication between distant parties is one of quantum technology’s main goals. Although quantum key distribution promises information-theoretic security for sharing a secret key, the key rate heavily depends on the level of noise in the quantum channel. To overcome the noise, both quantum and classical techniques exist, i.e., entanglement distillation and classical advantage distillation. So far, these techniques have only been used separately from each other. Herein, we present a two-stage distillation scheme concatenating entanglement distillation with classical advantage distillation. For advantage distillation, we utilize a fixed protocol, specifically, the repetition code;in the case of entanglement distillation, we employ an enumeration algorithm to find the optimal protocol. We test our scheme for different noisy entangled states and demonstrate its quantitative advantage: our two-stage distillation scheme achieves finite key rates even in the high-noise regime where entanglement distillation or advantage distillation alone cannot afford key sharing. We also calculate the security bounds for relevant QKD protocols with our key distillation scheme and show that they exceed the previous security bounds with only advantage distillation. Since the advantage distillation part does not introduce further requirements on quantum resources, the proposed scheme is well-suited for near-term quantum key distribution tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum channel

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