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arXiv 2024年

作者： Zafar, Osama González, Rosemarie Santa Wilkins, Gabriel Morales, Alfonso Ayday, Erman Department of Computer and Data Sciences Case Western Reserve University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States College of Letters & Science University of Wisconsin–Madison United States

Digital agriculture—the use of technology in agricultural practices—plays an integral role in agricultural research, with the potential to improve crop yield, disease resilience, and long-term soil health. However, it raises privacy concerns and risks, such as adverse pricing, price discrimination, increased insurance costs, and manipulation of essential agricultural resources. These concerns deter farm owners and operators from sharing their data because it could be used against them. This study addresses the need for privacy-preserving frameworks that enables secure data sharing for digital agriculture practices. We propose a privacy-preserving framework designed to mitigate privacy risks while enabling comprehensive data analysis. The proposed tool allows the agricultural community to benefit from research driven policy making that establishes meaningful connections across public and private digital agriculture datasets. Our privacy-preserving algorithm behind the framework can be used to: (i) identify similar farmers based on specified attributes using multiple private datasets, (ii) provide aggregate information such as time and location, (iii) determine price and availability trends of target products, and (iv) correlate these trends with relevant public policy databases, such as food insecurity data, without compromising privacy. We validate the framework using real-life datasets from Farmer’s Market. Also, we demonstrate the framework’s efficacy and practicality by training various machine learning models on linked privacy-preserved private and public data. The numerical experiments showcase how the framework can support policymakers and researchers in implementing digital agriculture related to food insecurity and pricing. Our work addresses critical privacy concerns, thereby facilitating more effective and secure agricultural research. It represents a significant contribution to digital agriculture by enabling the secure integration and analysis of data, p

关键词： Differential privacy

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Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content

Deep Models for Visual Sentiment Analysis of Disaster-relate...

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MediaEval 2021 Workshop, MediaEval 2021

作者： Ahmad, Khubaib Ayub, Muhammad Asif Ahmad, Kashif Al-Fuqaha, Ala Ahmad, Nasir Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

This paper presents a solutions for the MediaEval 2021 task namely"Visual Sentiment Analysis: A Natural Disaster Use-case". The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification subtask, and, two multi-label multi-class image classification subtasks. Both the multi-label classification tasks cover different sets of labels. In our proposed solutions, we mainly rely on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet. Both the pre-trained models are fine-tuned for each of the three subtasks using different strategies. Overall encouraging results are obtained on all of the three subtasks. On the single-label classification subtask (i.e. subtask 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification tasks i.e., subtask 2 and subtask 3, the weighted F1-scores of our Inceptionv3 based solutions are 0.572 and 0.516, respectively. Similarly, the weighted F1-scores of our VggNet-19 based solution on the subtask 2 and subtask 3 are 0.584 and 0.495, respectively. Copyright 2021 for this paper by its authors.

关键词： Sentiment analysis

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources.

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Stab-forests: Dynamic data structures for efficient temporal query processing 27

Stab-forests: Dynamic data structures for efficient temporal...

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27th International Symposium on Temporal Representation and Reasoning, TIME 2020

作者： Hellings, Jelle Wu, Yuqing Exploratory Systems Lab Department of Computer Science University of California DavisCA United States Computer Science Department Pomona College ClaremontCA United States

ISBN: (纸本)9783959771672

Many sources of data have temporal start and end attributes or are created in a time-ordered manner. Hence, it is only natural to consider joining datasets based on these temporal attributes. To do so efficiently, several internal-memory temporal join algorithms have recently been proposed. Unfortunately, these join algorithms are designed to join entire datasets and cannot efficiently join skewed datasets in which only few events participate in the join result. To support high-performance internal-memory temporal joins of skewed datasets, we propose the skip-join algorithm, which operates on stab-forests. The stab-forest is a novel dynamic data structure for indexing temporal data that allows efficient updates when events are appended in a time-based order. Our stab-forests efficiently support not only traditional temporal stab-queries, but also more general multi-stab-queries. We conducted an experimental evaluation to compare the skip-join algorithm with state-of-the-art techniques using real-world datasets. We observed that the skip-join algorithm outperforms other techniques by an order of magnitude when joining skewed datasets and delivers comparable performance to other techniques on non-skewed datasets. © Jelle Hellings and Yuqing Wu;licensed under Creative Commons License CC-BY 27th International Symposium on Temporal Representation and Reasoning (TIME 2020).

关键词： Query processing

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Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

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Procedia computer science 2022年 201卷 659-664页

作者： Ashraf S. Mashaleh Noor Farizah Binti Ibrahim Mohammed Azmi Al-Betar Hossam M.J. Mustafa Qussai M. Yaseen School of Computer Sciences Universiti Sains Malaysia Penang Malaysia Computer Center Al-Balqa Applied university Al-Salt Jordan Artificial Intelligence Research Center (AIRC) Ajman University Ajman UAE Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan IS and CS Department Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris’ Hawks’ cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model’s spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3.

关键词： Machine learning k-NN Harris Hawks optimizer (HHO) email spam detection

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SEE-MCAM: Scalable Multi-bit FeFET Content Addressable Memories for Energy Efficient Associative Search

arXiv

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arXiv 2023年

作者： Shou, Shengxi Liu, Che-Kai Yun, Sanggeon Wan, Zishen Ni, Kai Imani, Mohsen Sharon Hu, X. Yang, Jianyi Zhuo, Cheng Yin, Xunzhao College of Information Science and Electronic Engineering Zhejiang University China School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Information and Computer Science University of California Irvine United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology United States Department of Computer Science and Engineering University of Notre Dame United States School of Micro-Nano Electronics Zhejiang University China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

Artificial intelligence has made remarkable advancements in recent years, leading to the development of algorithms and models capable of handling ever-increasing amounts of data. The computational demands of these algorithms necessitate circuit and architecture designs that go beyond the von-Neumann paradigm. Content addressable memories (CAMs), which implement parallel associative search functionality within memory blocks to overcome the memory wall bottleneck, have proven to be effective for data-intensive tasks. While current CAM designs have achieved higher storage density and energy efficiency than their CMOS-based counterparts by leveraging emerging nonvolatile memories (NVM), most of these implementations are limited to binary storage cells. In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existing FeFET-based CAM designs. We validated the functionality of our proposed designs by achieving 3 bits per cell CAM functionality, resulting in 3× improvement in storage density. The area per bit of the proposed SEE-MCAM cell is 8% of the conventional CMOS CAM. We thoroughly investigated the scalability and robustness of the proposed design. Evaluation results suggest that the proposed 2FeFET-1T SEE-MCAM achieves 9.8× more energy efficiency and 1.6× less search latency compared to the CMOS CAM, respectively. When compared to existing MCAM designs, the proposed SEE-MCAM can achieve 8.7× and 4.9× more energy efficiency than ReRAM-based and FeFET-based MCAMs, respectively. Benchmarking results show that our approach provides up to 3 orders of magnitude improvement in speedup and energy efficiency over a GPU implementation in accelerating a novel quantized hyperdimensional

关键词： Associative storage

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Geometric theory on large-scale and local determination of density dependence of a recovering large carnivore population

arXiv

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arXiv 2023年

作者： Shen, Yunyi Olson, Erik R. Van Deelen, Timothy R. Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Environmental Sciences Northland College United States Department of Forest and Wildlife Ecology University of Wisconsin–Madison United States

Density-dependent population growth is a feature of large carnivores like wolves (Canis lupus), with mechanisms typically attributed to resource (e.g. prey) limitation. Such mechanisms are local phenomena and rely on individuals having access to information, such as prey availability at their location. Using over four decades of wolf population and range expansion data from Wisconsin (USA) wolves, we found that the population not only exhibited density dependence locally but also at landscape scale. Superficially, one may consider space as yet another limiting resource to explain landscape-scale density dependence. However, this view poses an information puzzle: most individuals do not have access to global information such as range-wide habitat availability as they would for local prey availability. How would the population "know" when to slow their range expansion? To understand observed large-scale spatial density dependence, we propose a reaction-diffusion model, first introduced by Fisher and Kolmogorov, with a "travelling wave" solution, wherein the population expands from a core range that quickly achieves local carrying capacity. Early-stage acceleration and later-stage deceleration of population growth can be explained by early elongation of an expanding frontier and a later collision of the expanding frontier with a habitat boundary. Such a process does not require individuals to have global density information. We illustrate our proposal with simulations and spatial visualizations of wolf recolonization in the western Great Lakes region over time relative to habitat suitability. We further synthesize previous studies on wolf habitat selection in the western Great Lakes region and argue that the habitat boundary appeared to be driven by spatial variation in mortality, likely associated with human use of the landscape. © 2023, CC BY-SA.

关键词： Lakes

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Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

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Mobile Information systems 2022年第1期2022卷

作者： Durga, S. Daniel, Esther Onesimu, J. Andrew Sei, Yuichi Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore641008 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics The University of Electro-Communications Chofu1828585 Japan

Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC's available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements. © 2022 S. Durga et al.

关键词： Quality of service

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