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arXiv 2024年

作者： Katharria, Aashu Rajwar, Kanchan Pant, Millie Velásquez, Juan D. Snášel, Václav Deep, Kusum Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Statistical Quality Control and Operations Research Unit Indian Statistical Institute Hyderabad India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Department of Industrial Engineering University of Chile Santiago Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mathematics Indian Institute of Technology Roorkee Roorkee India

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval

arXiv

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arXiv 2025年

作者： Luo, Junyu Zhao, Yusheng Luo, Xiao Xiao, Zhiping Ju, Wei Shen, Li Tao, Dacheng Zhang, Ming State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computer Science University of California Los Angeles United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States JD Explore Academy China College of Computing & Data Science Nanyang Technological University Singapore

Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high retrieval efficiency. However, existing methods typically fail to address potential noise in the target domain, and directly align high-level features across domains, thus resulting in suboptimal retrieval performance. To address these challenges, we propose a novel Cross-Domain Diffusion with Progressive Alignment method (COUPLE). This approach revisits unsupervised efficient domain adaptive retrieval from a graph diffusion perspective, simulating cross-domain adaptation dynamics to achieve a stable target domain adaptation process. First, we construct a cross-domain relationship graph and leverage noise-robust graph flow diffusion to simulate the transfer dynamics from the source domain to the target domain, identifying lower noise clusters. We then leverage the graph diffusion results for discriminative hash code learning, effectively learning from the target domain while reducing the negative impact of noise. Furthermore, we employ a hierarchical Mixup operation for progressive domain alignment, which is performed along the cross-domain random walk paths. Utilizing target domain discriminative hash learning and progressive domain alignment, COUPLE enables effective domain adaptive hash learning. Extensive experiments demonstrate COUPLE’s effectiveness on competitive benchmarks. © 2025, CC BY.

关键词： Content based retrieval

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Introduction

Information Security and Cryptography

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information Security and Cryptography 2020年 3-13页

作者： Alvim, Mário S. Chatzikokolakis, Konstantinos McIver, Annabelle Morgan, Carroll Palamidessi, Catuscia Smith, Geoffrey Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Informatics and Telecommunications University of Athens Athens Greece Department of Computing Macquarie University SydneyNSW Australia School of Computer Science & Engineering University of New South Wales Trustworthy Systems Data61 CSIRO SydneyNSW Australia Inria Saclay and LIX École Polytechnique Institut Polytechnique de Paris Palaiseau France School of Computing & Information Sciences Florida International University MiamiFL United States

Protecting sensitive information from improper disclosure or corruption is a longstanding, fundamental goal of computer security: but it is one that is not currently being achieved well at all, as is evident from cont... 详细信息

关键词： Sensitive data

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Assistive Technical Communication of Montenegrin eServices: a Case Study

Assistive Technical Communication of Montenegrin eServices: ...

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International Scientific-Professional Conference on information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory information technology

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A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud data Centers

A GA-Based Energy Aware Virtual Machine Placement Algorithm ...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Xiaodong Wu School of Mathmatics and Computer Science Quanzhou Normal University Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (纸本)9781665496407

Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions.

关键词： data centers Cloud computing Energy consumption Heuristic algorithms Simulation Programming Virtual machining

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on information Technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security information technology

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Exponentially Decaying Quantum Simulation Error with Noisy Devices

arXiv

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arXiv 2025年

作者： Xu, Jue Zhao, Chu Fan, Junyu Zhao, Qi QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China International Quantum Academy Guangdong Shenzhen China

Quantum simulation is a promising way toward practical quantum advantage, but noise in current quantum hardware poses a significant obstacle. We theoretically and numerically revealed that not only the physical error but also the algorithmic error in a single Trotter step decreases exponentially with the circuit depth. In particular, according to our results, we derive the optimal number of Trotter steps and the noise requirement to guarantee total simulation precision. At last, we demonstrate that our improved error analysis leads to significant resource-saving for fault-tolerant Trotter simulation. By addressing these aspects, this work systematically characterizes the robustness of Trotter simulation errors in noisy quantum devices and paves the way toward practical quantum advantage. © 2025, CC BY.

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Traffic Flow Prediction via Weighted Combination of ARIMA and WASDNN Models

Traffic Flow Prediction via Weighted Combination of ARIMA an...

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第33届中国控制与决策会议

作者： Xiao Liu Yunong Zhang Min Yang Zhongxian Xue Chengxu Ye School of Electronics and Information Technology Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University School of Computer Science Qinghai Normal University

The modeling and prediction of traffic flow are important in transportation study and traffic network *** from individual models,a hybrid model which is a combination of autoregressive integrated moving average(ARIMA) and weights and structure determination neural network(WASDNN) models,is proposed for modeling and prediction of traffic *** with individual models like ARIMA and WASDNN,the proposed ARIMAWASDNN model has better performance in modeling and prediction of traffic flow of Shenzhen city as an *** addition,prediction results of different activation functions of hidden layer neurons are *** is more,the proposed ARIMA-WASDNN model also performs better than the popular long short-term memory(LSTM) model in predicting traffic flow of Shenzhen *** effectiveness and superiority of ARIMA-WASDNN model are substantiated by results of numerical experiments.

关键词： Time series traffic flow prediction autoregressive integrated moving average(ARIMA) model weights and structure determination(WASD) algorithm

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

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arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Automated Mobility Context Detection with Inertial Signals

arXiv

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arXiv 2022年

作者： Bevilacqua, Antonio Alcock, Lisa Caulfield, Brian Gazit, Eran Hansen, Clint Ireson, Neil Ifrim, Georgiana The Insight Centre for Data Analytics School of Computer Science University College Dublin Ireland The Insight Centre for Data Analytics School of Public Health Physiotherapy and Sports Science University College Dublin Ireland The Clinical Ageing Research Unit Faculty of Medical Sciences Newcastle University United Kingdom The Center for the Study of Movement Congnition and Mobility Department of Neurology Tel Aviv Sourasky Medical Center The Department of Neurology Kiel University Germany The Organisation Information and Knowledge Group Department of Computer Science University of Sheffield United Kingdom

Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further supported by the continuous development of wearable sensor devices, which are getting progressively smaller, cheaper, and more energy efficient. The external environment and mobility context have an impact on walking performance, hence one of the biggest challenges when remotely analysing gait episodes is the ability to detect the context within which those episodes occurred. The primary goal of this paper is the investigation of context detection for remote monitoring of daily motor functions. We aim to understand whether inertial signals sampled with wearable accelerometers, provide reliable information to classify gait-related activities as either indoor or outdoor. We explore two different approaches to this task: (1) using gait descriptors and features extracted from the input inertial signals sampled during walking episodes, together with classic machine learning algorithms, and (2) treating the input inertial signals as time series data and leveraging end-to-end state-of-the-art time series classifiers. We directly compare the two approaches through a set of experiments based on data collected from 9 healthy individuals. Our results indicate that the indoor/outdoor context can be successfully derived from inertial data streams. We also observe that time series classification models achieve better accuracy than any other feature-based models, while preserving efficiency and ease of use. © 2022, CC BY-NC-SA.

关键词： Remote control

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