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IEEE International Symposium on Multimedia

作者： Ihab Al Kabary Ivan Giangreco Heiko Schuldt Fabrice Matulic Moira Norrie Department of Mathematics and Computer Science University of Basel Switzerland Institute of Information Systems ETH Zürich Switzerland

The enormous increase of digital image collections urgently necessitates effective, efficient, and in particular highly flexible approaches to image retrieval. Different search paradigms such as text search, query-by-example, or query-by-sketch need to be seamlessly combined and integrated to support different information needs and to allow users to start (and subsequently refine) queries with any type of object. In this paper, we present QUEST (Query by Example, Sketch and Text), a novel flexible multi-modal content-based image retrieval (CBIR) framework. QUEST seamlessly integrates and blends multiple modes of image retrieval, thereby accumulating the strengths of each individual mode. Moreover, it provides several implementations of the different query modes and allows users to select, combine and even superimpose the mode(s) most appropriate for each search task. The combination of search paradigms is by itself done in a very flexible way: either sequentially, where one query mode starts with the result set of the previous one (i.e., for incrementally refining and/or extending a query) or by supporting different paradigms at the same time (e.g., creating an artificial query image by superimposing a query image with a sketch, thereby directly integrating query-by-example and query-by-sketch). We present the overall architecture of QUEST and the dynamic combination and integration of the query modes it supports. Furthermore, we provide first evaluation results that show the effectiveness and the gain in efficiency that can be achieved with the combination of different search modes in QUEST.

关键词： Image color analysis Image edge detection Search problems Feature extraction Image retrieval Layout Visualization

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A model-based discriminative framework for sets of positive vectors classification: Application to object categorization

A model-based discriminative framework for sets of positive ...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Nizar Bouguila Concordia Institute for Information Systems Engineering Faculty of Engineering and Computer Science Concordia University Montreal QC Canada

Classic support vector machines (SVM) kernels (e.g. polynomial, Gaussian) are unable to take advantage of existing problem-specific knowledge. This is especially true when the problem at hand is the classification of sets (or bags) of vectors that may represent textual or visual (e.g. images, videos) data. This article tackles the problem of the classification of sets of positive vectors via SVM. It describes approaches to generate SVM kernels, appropriate for this problem, from generalized inverted Dirichlet (GID) mixtures. We develop several kernels using the fact that the GID belongs to the exponential family of distributions. The promise of such an approach and its advantages are demonstrated via a challenging application that concerns object categorization.

关键词： Kernel Support vector machines Visualization Computational modeling Vectors Feature extraction Databases

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Performance Characteristics of NTRU and ECC Cryptosystem in context of IoT Environment

Performance Characteristics of NTRU and ECC Cryptosystem in ...

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2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020

作者： Upasana, Ipsita Nandanavanam, Nanditha Nandanavanam, Ankitha Naaz, Nadiya Manipal Institute of Technology Manipal Academy of Higher Education Department of Information and Communication Technology Manipal India School of Engineering and Applied Sciences SUNY University of Buffalo BuffaloIA United States NMAM Institute of Technology Department of Computer Science and Engineering NitteKarnataka India Gaya India

ISBN: (纸本)9781728198859

With the increased use of Internet-of- Things (IoT) devices for daily activities, the necessity of improving the speed and performance of such devices also arise. The devices produce sensitive data at a massive and rapid rate. Since the infrastructure available with such small-scale devices are constrained, an algorithm which provides better security performance and is faster within the constraints is necessitated. This paper discusses two asymmetric algorithms NTRU (Nth Degree Truncated Polynomial) and Elliptic Curve Cryptography (ECC), which are widely known to be secure. ECC is a popular algorithm and widely used because of its low-key size. However, it can be easily broken by quantum computers. NTRU is one of the most recognized algorithms that can resist even quantum attacks. It has less computational complexity due to simple polynomial operations. The need is for an algorithm to provide ample security while taking the time of the computational complexities into consideration. The performance of ECC and NTRU have been evaluated based on key generation time, encryption time, and decryption time. Text data provided by the small-scale IoT devices has been considered for analysis. The analysis gives a deeper view of both algorithms. © 2020 IEEE.

关键词： Internet of things

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HSYS module: A software package for analysis and synthesis of sampled-data control systems 4

HSYS module: A software package for analysis and synthesis o...

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4th European Control Conference, ECC 1997

作者： Hara, Shinji Yamamoto, Yutaka Fujioka, Hisaya Department of Computational Intelligence and Systems Science Tokyo Institute of Technology 4259 Nagatsuta-cho Midoriku Yokohama226 Japan Division of Applied Systems Science Faculty of Engineering Kyoto University Kyoto606-01 Japan Department of Mechanical Engineering for Computer Controlled Machinery Faculty of Eng. Osaka University Yamadaoka 2-1 Suita Osaka565 Japan

ISBN: (纸本)9783952426906

Hsys Module is a suite of Xmath functions for the analysis and synthesis of linear hybrid systems, which include purely continuous-time and discrete-time systems and sampled-data control systems. It provides all the functionality necessary to carry out the analysis and synthesis of such systems and a wide variety of utilities to facilitate design. This paper shows the data structure, the list of functions and the function overloading. It also gives an example of H∞ synthesis.

关键词： Sampled data control systems

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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Data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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On the robustness of EEG tensor completion methods

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science China(Technological sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Deep Learning-based Spider Wasp Optimization Approach for Enhancing Brain Tumor Detection and Physical Therapy Prediction

Computer Methods and Programs in Biomedicine Update

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computer Methods and Programs in Biomedicine Update 2025年

作者： Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah Neurosurgery Department Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O. Box 84901 Riyadh 11681 Saudi Arabia Department of Rehabilitation science Faculty of applied medical science Jordan University of Science and Technology King Abdullah University Hospital Irbid Jordan Department of Pathology King Abdullah University Hospital Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Department of Medical Laboratory Sciences Al Balqa Applied University Salt Jordan Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 70800 Poruba-Ostrava Czech Republic Computer Science Department Al al-Bayt University Mafraq 25113 Jordan

A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms

关键词： Brain Tumor Pediatric Machine Learning Rehabilitation physical therapy Deep Learning Metaheuristic Optimization Feature Selection

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Complementary organizational mechanisms - A case study on information technology business value

Complementary organizational mechanisms - A case study on in...

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18th European Conference on information systems, ECIS 2010

作者： Poon, Simon K. Poon, Josiah Peters, Georg Gupta, Naresh Land, Lesley School of Information Technologies University of Sydney 1 Cleveland Street NSW 2006 Australia Department of Computer Science and Mathematics Munich University of Applied Sciences Lothstrasse 64 80335 München Germany School of Electrical and Information Engineering University of Sydney 1 Cleveland Street NSW 2006 Australia School of Information Systems Technology and Management University of New South Wales NSW 2052 Australia

ISBN: (纸本)9780620471725

The complex relationships between organizational practices have been the focus of information technology business value (ITBV) research in recent years. There appears to be a discernible trend toward a more nuanced view in which the effects of information technology (IT), various organizational practices and their relationships are systematically investigated. There is also emerging evidence of recent focus in organizational factors and an increasing shift towards "complementarities" in which organizational performance is linked to combining organizational practices in synergistic ways. The objective of this paper is to investigate important issues in ITBV research by examining complementarities of various resources within an organization. The goal of this paper is to establish a framework for analyzing different configurations of complementarities. The Configuration and Interestingness framework (CIF) proposed in this paper provides evidence on how important the understanding of those complex relationship structures amongst organizational practices is to maximizing business value. This framework serves as a preliminary attempt to reveal different possible classes of complementarities structures.

关键词： information systems

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Component-driven engineering of database applications 06

Component-driven engineering of database applications

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Asia-Pacific Conference on Conceptual Modelling

作者： Klaus-Dieter Schewe Bernhard Thalheim Massey University Department of Information Systems & Information Science Research Centre Palmerston North New Zealand Christian Albrechts University Kiel Department of Computer Science and Applied Mathematics Kiel Germany

ISBN: (纸本)9781920682354

Though it is commonly agreed that the design of large database schemata requires group effort, database design from component subschemata has not been investigated thoroughly. In this paper we investigate snowflake-like subschemata of database schemata expressed in the Higher-order Entity-Relationship Model (HERM). These subschemata are almost hierarchical in the sense that they may contain cycles in the schema, but not in the instances. We show that each HERM schema can be decomposed into such subschemata using a small set of composition constructors. We then describe how the composition of components can be seen as a database design primitive leading to component-driven database design and re-design pragmatics.

关键词： Database applications database design database schema schema conceptual space Primitive Hierarchical entity relationship model Snowflakes

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