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9th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2020

作者： Das, Abhishek Snv, Bhargav Sanketh Reddy, N. Phalachandra, H.L. Pes University Dept. of Computer Science and Engineering Bangalore India Center for Cloud Computing and Big Data Pes University Bangalore India

ISBN: (纸本)9781728195766

The rapidly increasing growth of big data is leading to increased usage of distributed computing and clusters as this can enable tasks to be split and processed in parallel resulting in higher computational efficiency. Environments like Apache Hadoop use Distributed file systems like the HDFS to support large clusters of typically commodity hardware to achieve the same. Commodity hardware present in HDFS consumes significant energy even when not in an active state, and considering our fragile ecosystem, reduction of the carbon footprint and conservation of energy in these environments is the focus of our work. We have evolved on a dynamic approach to Hadoop Distributed File Systems (HDFS) that provides an energy efficient paradigm for Hadoop clusters by segregating the servers in the cluster to two 'Zones', a 'Hot-Zone', and a 'Cold-Zone'. We use energy efficient storage devices (SSDs), to store data that is frequently used for the hot zone. The Cold-Zone would have lesser energy efficient devices (HDDs) and would be used to store data that is not frequently used. The Hot-Zone would always be in an active state to compute while the cold zone would have the HDDs to be in a low power consumption state. We have also enhanced the block placement policy for HDFS to support dynamic block transfer among the two zones. © 2020 IEEE.

关键词： Energy efficiency

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A motion-based compression algorithm for resource-constrained video camera traps

arXiv

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arXiv 2024年

作者： Ratnayake, Malika Nisal Gallon, Lex Toosi, Adel N. Dorin, Alan Agents and Decision-Making Group Dept. of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia Monash e-Research Centre Monash University ClaytonVIC3800 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3052 Australia Dept. of Software Systems and Cybersecurity Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm’s capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Integration of Classification and Segmentation for Computer-Aided Diagnosis System of Drowning

Integration of Classification and Segmentation for Computer-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuwen Zeng Xiaoyong Zhang Kei Ichiji Noriyasu Homma Tohoku University Graduate School of Medicine Tohoku University Sendai Japan Dept. of General Engineering National Institute of Technology Sendai College Sendai Japan Tohoku University Graduate School of Medicine Center for Data-Driven Science and Artificial Intelligence Tohoku University Sendai Japan Tohoku University Graduate School of Medicine Graduate School of Biomedical Engineering Tohoku University Sendai Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The decline in traditional autopsy practices has led to the rise of autopsy imaging as a non-invasive alternative. However, the shortage of forensic pathologists skilled in postmortem image interpretation presents a significant challenge. Our study addresses this gap by advancing the capabilities of computer-aided diagnosis (CAD) systems in forensic diagnosis. This study builds on a previous work which developed a CAD system for drowning diagnosis and identified a critical limitation: the inconsistency between human expertise and the decision basis of the CAD system. To alleviate this issue, we present an end-to-end CAD system based on Y-Net that not only classifies post-mortem images into drowning or non-drowning but also segments regions of interest in alignment with human expertise. Experiment results showed that the model achieved an accuracy of 0.92 for classification and a mean squared error of 0.04 for segmentation, offering a promising performance and medically consistent results in drowning diagnosis.

关键词： Image segmentation Visualization Solid modeling Accuracy Forensics Autopsy Neural networks

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Mechanical Analog for Cities

arXiv

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arXiv 2022年

作者： Makris, Nicos Moghimi, Gholamreza Godat, Eric Vu, Tue Dept. of Civil and Environmental Engineering Southern Methodist Univ DallasTX75275 United States Data Science and Research Services OIT Southern Methodist Univ DallasTX75275 United States

Motivated from the increasing need to develop a quantitative, science-based, predictive understanding of the dynamics and response of cities when subjected to hazards, in this paper we apply concepts from statistical mechanics and microrheology to develop mechanical analogs for cities with predictive capabilities. We envision a city to be a matrix where people (cell-phone users) are driven by the city's economy and other associated incentives while using the collection of its infrastructure networks in a similar way that thermally driven Brownian probe particles are moving within a complex viscoelastic material. Mean-square displacements (ensemble averages) of thousands of cell-phone users are computed from GPS location data to establish the creep compliance and the resulting impulse response function of a city. The derivation of these time-response functions allows the synthesis of simple mechanical analogs that model satisfactorily the city's behavior under normal conditions. Our study concentrates on predicting the response of cities to acute shocks (natural hazards that stress the entire urban area) that are approximated with a rectangular pulse with finite duration;and we show that the solid-like mechanical analogs for cities that we derived predict that cities revert immediately to their pre-event response suggesting that they are inherently resilient. Our findings are in remarkable good agreement with the recorded response of the Dallas metroplex following the February 2021 North American winter storm which happened at a time for which we have dependable GPS location data. © 2022, CC BY.

关键词： Hazards

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Gradual Further Pre-training Architecture for Economics/Finance Domain Adaptation of Language Model

Gradual Further Pre-training Architecture for Economics/Fina...

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IEEE International Conference on Big data

作者： Hiroki Sakaji Masahiro Suzuki Kiyoshi Izumi Hiroyuki Mitsugi Department of Systems Innovation School of Engineering The University of Tokyo Tokyo Japan Data-driven Science Dept. Frontier Technologies Research Center. Daiwa Institute of Research Ltd. Tokyo Japan

ISBN: (纸本)9781665480468

We propose a new pre-trained architecture for economics/finance domain adaptation of language models in this paper. Pre-trained language models have become commonplace in a wide range of language processing applications. Because they learn from generic documents such as Wikipedia, many pre-trained language models are not fully adapted to the domain. As a result, there are two approaches: one is to develop a domain-specific pre-trained language model, and the other is to adapt the model learned on general documents to the domain through further pre-training with domain texts. However, no definitive better method has been discovered, and each project is working on it in different ways. As a result, this study focuses on the Japanese language’s economics/financial domains and investigates how pre-trained language models can be better adapted to domain-specific tasks.

关键词： Adaptation models Biological system modeling Encyclopedias Big data data models Internet Online services

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Empirical Analysis of Bitcoin network (2016-2020)

Empirical Analysis of Bitcoin network (2016-2020)

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2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020

作者： Kumar, Ajay Kumar, Abhishek Nerurkar, Pranav Ghalib, Muhammad Rukunuddin Shankar, Achyut Wen, Zheng Qi, Xin NIT Patna Dept. of CSE Bihar India NMIMS University Dept. of CEIT VJTI Mumbai Dept. of Data Science Mumbai India VIT School of Computer Science and Engineering Vellore India Amity University Department of CSE India Waseda University Japan

ISBN: (纸本)9781728187556

Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 01 Jan 2016 00:00:00 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph. © 2020 IEEE.

关键词： Open source software

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Urban Vertical Farming with IoT and Precision Monitoring for a Sustainable Future

Urban Vertical Farming with IoT and Precision Monitoring for...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Unni Krishnn, A. Dutt, Amit Singh, Jay Bhavana, Munugapati Baswaraju, Swathi Mohammed, Baydaa Fathil Venkatraman, Akila Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science-Data Science Bangalore India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

The fast expansion of population worldwide and urbanisation had been estimated to have 80 % of the population in urban areas in the upcoming 50 years leads to an exponential rise in demand of food. These needs provide an urgent call for developing novel approaches to achieve global food demands amid sustainability problems. Urban Vertical Farming (UVF) offers an attractive solution for growing crops in the vertical dimension of structures, the urban skyscrapers, where land use is limited. However, it is fairly limited research which applies the current state of art in cutting edge technologies such as the Internet of Things, to try to automate and monitor farm processing. Thus, in this review paper, gaps in existing research, the potential for IoT enabled vertical farming, as well as challenges such as energy use, economic feasibility and urban policy integration are examined. Vertical farming can be enhanced through resource efficiency, scalability and level of sustainability through the use of IoT systems and AI driven data analytics which will also help promote urban food security. The research gives a summary of the current state, technological capabilities, and potential way forward in the process of successful vertical farming implementation in urban sets. © 2025 IEEE.

关键词： Food security

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Efficient two-stream network for violence detection using separable convolutional LSTM

arXiv

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arXiv 2021年

作者： Islam, Zahidul Rukonuzzaman, Mohammad Ahmed, Raiyan Kabir, Md. Hasanul Farazi, Moshiur Dept. of Computer Science and Engineering Islamic University of Technology Dhaka Bangladesh Data61 Csiro Canberra Australia

Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned security monitoring systems, internet video filtration, etc. In this work, we propose an efficient two-stream deep learning architecture leveraging Separable Convolutional LSTM (SepConvLSTM) and pre-trained MobileNet where one stream takes in background suppressed frames as inputs and other stream processes difference of adjacent frames. We employed simple and fast input pre-processing techniques that highlight the moving objects in the frames by suppressing non-moving backgrounds and capture the motion in-between frames. As violent actions are mostly characterized by body movements these inputs help produce discriminative features. SepConvLSTM is constructed by replacing convolution operation at each gate of ConvLSTM with a dept.wise separable convolution that enables producing robust long-range Spatio-temporal features while using substantially fewer parameters. We experimented with three fusion methods to combine the output feature maps of the two streams. Evaluation of the proposed methods was done on three standard public datasets. Our model outperforms the accuracy on the larger and more challenging RWF-2000 dataset by more than a 2% margin while matching state-of-the-art results on the smaller datasets. Our experiments lead us to conclude, the proposed models are superior in terms of both computational efficiency and detection accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Impact of AI-Driven Automation on Job Displacement and Skill Development: A Societal Perspective

Impact of AI-Driven Automation on Job Displacement and Skill...

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Silchar Subsection Conference (SILCON), IEEE

作者： A. Balakumar Pravin D Sawant Divya Nimma Shamim Ahmad Khan Ayasha Siddiqua Kalaiarasan C Department of ECE K.Ramakrishnan College of Engineering Trichy India Dept of Commerce Narayan Zantye College of Commerce Bicholim Goa India PhD in computational science University of Southern Mississippi Data Analyst in UMMC USA Glocal School of Science & Technology Glocal University U.P India Dept. of Information Technology and Security Jazan University Jazan KSA Department of Commerce IDE University of Madras Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

As artificial intelligence (AI) continues to reshape industries, understanding its impact on job displacement and skill development is crucial. This research investigates these dynamics utilization Gradient Boosting Machines (GBM) for predictive analysis and Explainable AI (XAI) techniques to interpret model outcomes. in order to higher recognize those dynamics, this work employs Explainable AI (XAI) tools to examine model findings and Gradient Boosting Machines (GBM) for predictive evaluation. The study intends to expect developments in task displacement and pinpoint new ability calls for via analysing how AI-driven automation impacts employment features throughout several sectors. the prevailing examine offers whole insights into AI's impact on workforce dynamics considering, in comparison to prior research, it combines GBM for precise forecasting with XAI methodologies like SHAP values and characteristic importance rankings for transparent interpretation of models. The research makes use of a dataset that consists of process titles, AI effects, venture distributions, and AI Workload Ratios throughout industrial domains, and it uses Python as the implementation tool. employing XAI approaches, activity displacement chance is anticipated by means of GBM fashions and the motives using these projections are explained. Performance indicators including recall, accuracy, precision, and F1 score attest to the GBM algorithm's effectiveness in forecasting displacement from work. The examination of AI Workload Ratios, that assists in efficient staffing and adaptability, reveals that the effects of automation differ among disciplines. In order to minimise any adverse effects and maximize the potential that artificial intelligence (AI) technologies offer, this research underscores that artificial intelligence is transforming the structure of occupations and emphasizes the necessity for innovative methods in employment growth and policymaking.

关键词： Industries Analytical models Automation Accuracy Explainable AI Employment Predictive models Boosting Forecasting Predictive analytics

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DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing

DeepCare: Deep Learning-Based Smart Healthcare Framework usi...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Deborsi Basu Vikram Krishnakumar Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Communication Engineering SASTRA Deemed Univ. Thanjavur Tamil Nadu India Dept. of Data Science and Computer Science School of Applied Computational Sciences MMC USA Dept. of Electronics and Electrical Comm. Engg. Indian Institute of Technology Kharagpur India

ISBN: (数字)9781665473408

ISBN: (纸本)9781665473415

5G and beyond communication networks require satisfying very low latency standards, high reliability, high-speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software-Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability.

关键词： 5G mobile communication Network slicing Stability criteria Resource management Telecommunication network reliability Artificial intelligence Sustainable development

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