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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Dimitris Asimopoulos Ilias Siniosoglou Vasileios Argyriou Sotirios K. Goudos Konstantinos E. Psannis Nikoleta Karditsioti Theocharis Saoulidis Panagiotis Sarigiannidis R&D Department MetaMind Innovations P.C Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

In the digital era, with escalating privacy concerns, it’s imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among word sequences, ELMo delivers contextual word representations using deep bidirectional language models and Transformers introduce self-attention mechanisms that provide enhanced scalability. Our study is positioned as a comparative analysis of these models, emphasising their synergistic potential in addressing text anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo individually outperform traditional methods. The inclusion of Transformers, when compared alongside with the other models, offers a broader perspective on achieving optimal text anonymisation in contemporary settings.

关键词： Data privacy Analytical models Scalability Transformers Conditional random fields Data models Information filtering

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Exfiltration-resistant Proxy Re-Encryption for IoT Data Sharing in Unreliable Clouds

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Yuyang Zhao, Liang Li, Fagen Takagi, Tsuyoshi School of Cyberspace Security Hainan University Haikou570000 China Department of Mathematical Informatics Graduate School of Information Science and Technology The university of Tokyo Tokyo113-8656 Japan School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management [Sichuan University Ministry of Education China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which we implement using the JPBC library. The security analysis reveals that the PRE-RF scheme offers effective resistance against ASA, while also providing strong collision security, chosen plaintext attack security, and key-private security. Compared to similar PRE schemes with more security features, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system;also, our scheme’s computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, as security levels rise, the portion of time dedicated to reverse firewall operations within our scheme diminishes. These advantages collectively render it a secure and efficient choice for sharing IoT data in unreliable cloud environments. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

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Model of a Centralized System for Quality Assurance in Higher Education 10

Model of a Centralized System for Quality Assurance in Highe...

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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Hadzhikoleva, Stanka Orozova, Daniela Hadzhikolev, Emil Andonov, Nedelcho University of Plovdiv "paisii Hilendarski" Department of Mathematics and Informatics Plovdiv Bulgaria Burgas Free University Department of Computer Science and Engineering Burgas Bulgaria

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154565

Quality assurance in higher education is a task that is invariably the focus of the authorities. Quality standards place increasingly higher demands on educational institutions. Quality assurance procedures are cumbersome, they require a large amount of information to be collected by the higher education staff, information which is subject to verification by experts from the evaluating institution. The use of a centralized software system for external quality evaluation will unify the accreditation procedures and will attribute to their optimization. This article proposes a formal generalized net model of a centralized system for quality assurance in higher education. It aims to outline principles, guidelines, models and processes that guide the creation of specific software applications. The model described is based on our experience in creating and using the COMPASS stand-alone application for (self)assessment and accreditation in higher education. © 2020 IEEE.

关键词： Accreditation

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Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization

arXiv

引用

arXiv 2022年

作者： Peng, Chong Liu, Yang Chen, Yongyong Wu, Xinxin Cheng, Andrew Kang, Zhao Chen, Chenglizhao Cheng, Qiang College of Computer Science and Technology Qingdao University China The College of Computer Science Harbin Institute of Technology The School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Kentucky Department of Statistics and Applied Probability University of California Santa Barbara United States The Institute of Biomedical Informatics University of Kentucky

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel 2,log norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the 2,log-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named 2,log-shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectroscopy

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COMPUTATION-EFFICIENT ERA: A COMPREHENSIVE SURVEY OF STATE SPACE MODELS IN MEDICAL IMAGE ANALYSIS

arXiv

引用

arXiv 2024年

作者： Heidari, Moein Kolahi, Sina Ghorbani Karimijafarbigloo, Sanaz Azad, Bobby Bozorgpour, Afshin Hatami, Soheila Azad, Reza Diba, Ali Bagci, Ulas Merhof, Dorit Hacihaliloglu, Ilker School of Biomedical Engineering University of British Columbia BC Canada Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University Brookings United States Faculty of Mechanical Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Lunit Inc. Amsterdam Netherlands Department of Radiology Northwestern University Chicago United States Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Department of Medicine University of British Columbia BC Canada

Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emergence of transformers has altered this paradigm due to their superior performance. Built upon these advances, Vision Transformers (ViTs) have conjoined Convolutional Neural Networks (CNNs) as two leading foundational models for learning visual representations. However, transformers are hindered by the O(N2) complexity of their attention mechanisms, while CNNs lack global receptive fields and dynamic weight allocation. State Space Models (SSMs), specifically the Mamba model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence. Capitalizing on the advances in computer vision, the field of medical imaging has heralded a new epoch with Mamba models. Intending to help researchers navigate the surge, this survey seeks to offer an encyclopedic review of Mamba models in medical imaging. Specifically, we start with a comprehensive theoretical review forming the basis of SSMs, including Mamba architecture and its alternatives for sequence modeling paradigms in this context. Next, we offer a structured classification of Mamba models in the medical field and introduce a diverse categorization scheme based on their application, imaging modalities, and targeted organs. Consequently, we explore various applications and their practical use cases in medicine, such as image segmentation, reconstruction, registration, classification, language processing, multi-modal understanding, and other medically related challenges. Finally, we summarize key challenges, discuss different future research directions of the SSMs in the medical domain, and prop

关键词： Medical applications

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MultiNet with Transformers: A Model for Cancer Diagnosis Using Images

arXiv

引用

arXiv 2023年

作者： Barzekar, Hosein Patel, Yash Tong, Ling Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Health Informatics and Administration University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. The cost makes large-scale biological image classification impractical. In this study, we provide unique deep neural network designs for multiclass classification of medical images, in particular cancer images. We incorporated transformers into a multiclass framework to take advantage of data-gathering capability and perform more accurate classifications. We evaluated models on publicly accessible datasets using various measures to ensure the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks. © 2023, CC BY.

关键词： computer aided diagnosis

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Using Natural Language Processing to Predict Costume Core Vocabulary of Historical Artifacts

Using Natural Language Processing to Predict Costume Core Vo...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Madhuvanti Muralikrishnan Amr Hilal Chreston Miller Dina Smith-Glaviana Computer Science Dept Virginia Tech Blacksburg VA Informatics Lab Virginia Tech Blacksburg VA Department of Computer and Systems Engineering Alexandria University Egypt Apparel Housing and Resource Management Dept Virginia Tech Blacksburg VA

Historic dress artifacts are a valuable source for human studies. In particular, they can provide important insights into the social aspects of their corresponding era. These insights are commonly drawn from garment pictures as well as the accompanying descriptions and are usually stored in a standardized and controlled vocabulary that accurately describes garments and costume items, called the Costume Core Vocabulary. Building an accurate Costume Core from garment descriptions can be challenging because the historic garment items are often donated, and the accompanying descriptions can be based on untrained individuals and use a language common to the period of the items. In this paper, we present an approach to use Natural Language Processing (NLP) to map the free-form text descriptions of the historic items to that of the controlled vocabulary provided by the Costume Core. Despite the limited dataset, we were able to train an NLP model based on the Universal Sentence Encoder to perform this mapping with more than 90% test accuracy for a subset of the Costume Core vocabulary. We describe our methodology, design choices, and development of our approach, and show the feasibility of predicting the Costume Core for unseen descriptions. With more garment descriptions still being curated to be used for training, we expect to have higher accuracy for better generalizability.

关键词： Training Vocabulary Design methodology Clothing Buildings Machine learning Natural language processing

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Efficient power supplies for future micro-datacentres: A review 12

Efficient power supplies for future micro-datacentres: A rev...

引用

12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Chrysostomou, Michael Christofides, Nicholas Ioannou, Stelios Polycarpou, Alexis Marouchos, Christos Frederick University Department of Electrical Engineering Computer Engineering and Informatics Y. Frederickou Str. Nicosia Cyprus University of Central Lancashire El. and Computer Eng Pyla Cyprus

ISBN: (纸本)9781839535635

The data processing growth of the last decade led to a vast need of datacenter deployment globally and the energy saving schemes play a vital role nowadays. The convergence of Information Technology (IT) and Telecommunication - named Information and Communication Technologies (ICT) - and the 5G technology high latency requirements demand the decentralization of datacenters, i.e. the deployment of micro-datacenters. The huge growth in micro-datacenter deployment and their considerable share in worldwide energy demand, mandate the need for energy saving techniques. The Uninterruptible Power Supplies (UPS) of micro-datacenters are one of the key areas that can be improved, and which can lead to an increase in the overall energy efficiency of the micro-datacenters. This is a review paper on the state-of-the-art power supply systems and topologies used primarily in micro-datacenters and aims to identify ways to increase the overall energy efficiency of micro-datacenters power supply systems. Furthermore, this work presents an elaborate investigation of the power supply losses and proposes systems that can improve conversion efficiency of UPS systems in different loading conditions. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Uninterruptible power systems

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

引用

Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

引用

Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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