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IEEE International Conference on Intelligent computing and Information Systems (ICICIS)

作者： Ahmed Nouh Khaled Ahmed Ahmed Tarek Nada Magdy Hala M. Ebied Ahmed Khaled Radwa Reda Scientific Computing Department Faculty Of Computer & Information Sciences Ain Shams University Cairo Egypt

Recently, the challenges of security systems in protecting citizens from crime and disasters have become very difficult. While some organizations have developed fire detection security systems, incidents of intrusions, thefts, and hostage-taking in government and private agencies indicate the inadequacy of current security systems. In this paper, the security system combines the detection of people, weapons, and fires through the analysis of surveillance cameras and videos is presented. The system is built by using deep learning techniques. The system deals with the video frames and processes them using a set of digital filters. The system analyzes the tires and goes through the processes of detecting faces, detecting weapons, and detecting fires respectively. After that, the classification stage begins to identify people’s faces if they are outlaws and to identify the type of weapon. The deep learning models were applied in the classification process: Convolutional Neural Network (CNN), VGG-19 network, and GoogleNet with Inception module. The Inception network archives the highest accuracy for face recognition. The VGG network archived 95.5%, and 99.7% for weapon and fire detection respectively.

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Content-based Image Retrieval based on Convolutional Neural Networks 10

Content-based Image Retrieval based on Convolutional Neural ...

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10th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Ghaleb, Moshira S. Ebied, Hala M. Shedeed, Howida A. Tolba, Mohamed F. Ain Shams University Scientific Computing Faculty of Computer and Inofrmation Sciences Cairo Egypt

ISBN: (纸本)9781665440769

Images can communicate a service, brand or product. Moreover, images provide depth and context to a description or story and give a much more intense experience than writing alone. Image retrieval is the highest searching performance process, especially in large databases. Search by content is a powerful tool for many industries. Images are more complicated than texts. Each image has important features like color, texture, and edges. Searching by part of image is termed content-based image retrieval. Convolution neural network achieved high accuracies in images feature extraction and classification. For high-performance image retrieval, we proposed an intelligent model using CNN. The model was applied on Cifar10 and Mnist datasets. The proposed model achieved 92.9% and 99.8% accuracies for Cifar10 and Mnist datasets respectively. The model achieved the highest accuracies and best processing performance compared by State-of-arts-models. © 2021 IEEE.

关键词： Convolutional neural networks

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Attention Detection using Electro-oculography Signals in E-learning Environment 10

Attention Detection using Electro-oculography Signals in E-l...

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10th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Abdo, Yassmina Yahya, Ehab Ismail, Hussein Saleh, Mostafa Reda, Radwa Shedeed, Howida A. Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9781665440769

Recently, on account of the Covid-19 pandemic online learning has become a main strategy of learning in any educational environment. The main problem is the lack of focus or attention during a student watching an online lecture or video due to ways of distraction that can occur and that's what interrupts the students and make them lose their focus. In this research paper, we propose the attention detection system using an Electro-oculogram (EOG) signal generated by eye movement. The first goal of this study was detecting the un-focusing periods in the video. The proposed process of recognizing the un-focusing period depends on recognizing the eye movement direction. 6 classes of eye movements were classified in this study: left, right, up, down, blinking, and no movement. The process started by collecting our benchmark dataset from 50 subjects, which was considered the largest dataset at all for eye movements, using a special hardware device. The pre-processing step was down to filter the signals from any noise that may be caused by the surrounding environment or involuntary movements using the band-pass filter. Finally, the classification step was down. Three models of deep learning were examined for the best classifier: Convolution Neural Network (CNN), Inception Network, and VGG Network. Our experiments achieved the best results with the Inception model, reaching an average accuracy of 93.63%. The second goal of this study was to recognize the Attention-deficit hyperactivity disorder (ADHD) pattern in the student's recording. The model succeeded to recognize this disordered pattern. © 2021 IEEE.

关键词： Students

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in Engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Algorithms for the Prize-Collecting k-Steiner Tree Problem

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Tsinghua Science and Technology 2022年第5期27卷 785-792页

作者： Lu Han Changjun Wang Dachuan Xu Dongmei Zhang School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China

In this paper,we study the prize-collecting k-Steiner tree(PCkST) *** are given a graph G=(V,E) and an integer *** graph is connected and undirected.A vertex r ∈ V called root and a subset R?V called terminals are also given.A feasible solution for the PCkST is a tree F rooted at r and connecting at least k vertices in *** a vertex from the tree incurs a penalty cost,and including an edge in the tree incurs an edge *** wish to find a feasible solution with minimum total *** total cost of a tree F is the sum of the edge costs of the edges in F and the penalty costs of the vertices not in *** present a simple approximation algorithm with the ratio of 5.9672 for the *** algorithm uses the approximation algorithms for the prize-collecting Steiner tree(PCST) problem and the k-Steiner tree(kST) problem as *** we propose a primal-dual based approximation algorithm and improve the approximation ratio to 5.

关键词： prize-collecting Steiner tree approximation algorithm

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Correction: A chemotaxis reaction–diffusion model for Multiple Sclerosis with Allee effect

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Ricerche di Matematica 2023年第1期73卷 47-49页

作者： Bisi, Marzia Groppi, Maria Martalò, Giorgio Soresina, Cinzia Department of Mathematical Physical and Computer Sciences University of Parma Parma Italy Department of Mathematics and Scientific Computing University of Graz Graz Austria

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Nawrotzki’s algorithm for the countable splitting lemma, constructively 9

Nawrotzki’s algorithm for the countable splitting lemma, co...

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9th Conference on Algebra and Coalgebra in computer Science, CALCO 2021

作者： Sokolova, Ana Woracek, Harald Department of Computer Sciences University of Salzburg Austria Institute of Analysis and Scientific Computing TU Wien Austria

ISBN: (纸本)9783959772129

We reprove the countable splitting lemma by adapting Nawrotzki’s algorithm which produces a sequence that converges to a solution. Our algorithm combines Nawrotzki’s approach with taking finite cuts. It is constructive in the sense that each term of the iteratively built approximating sequence as well as the error between the approximants and the solution is computable with finitely many algebraic operations. © Ana Sokolova and Harald Woracek.

关键词： Iterative methods

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Mobile Money Phishing Cybercrimes: Vulnerabilities, Taxonomies, Characterization from an Investigation in Cameroon 14th

Mobile Money Phishing Cybercrimes: Vulnerabilities, Taxonomi...

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14th EAI International Conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Nzeket Njoya, Alima Tchakounté, Franklin Atemkeng, Marcellin Udagepola, Kalum Priyanath Bassolé, Didier Department of Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere Cameroon Ngaoundere Cameroon Department of Mathematics Rhodes University Grahamstown6140 South Africa Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia Laboratory of Mathematics and Computer Science University of Joseph Ki-Zerbo Ouagadougou Burkina Faso

ISBN: (纸本)9783031348952

Mobile Money (MM) technologies are popular in developing countries where people are unbanked, and they are exploited as means of financial transactions in the economy. Sophisticated cyber-phishing techniques successfully target MM accounts. Related countermeasures are rare and the existing ones are so technical that people without minimal knowledge cannot be helped. Making the knowledge around cybercrime facts is therefore relevant since it provides a good basis for further technical research. In this vein, this paper dissects phishing cybercrime strategies observed within the Cameroonian cyberspace. We provide identified vulnerabilities, process design of generic MM attack, taxonomies of attacks, and classification of the latter based on criteria. Findings about commonalities and dissimilarities reveal two aspects to really consider when designing solutions: emotion and interactions. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Taxonomies

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A chemotaxis reaction-diffusion model for Multiple Sclerosis with Allee effect

arXiv

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arXiv 2023年

作者： Bisi, Marzia Groppi, Maria Martalò, Giorgio Soresina, Cinzia Department of Mathematical Physical and Computer Sciences University of Parma Italy Institute for Mathematics and Scientific Computing University of Graz Graz8010 Austria

In this paper, we study a modification of the mathematical model describing inflammation and demyelination patterns in the brain caused by Multiple Sclerosis proposed in [Lombardo et al. (2017), Journal of Mathematical Biology, 75, 373-417]. In particular, we hypothesize a minimal amount of macrophages to be able to start and sustain the inflammatory response. Thus, the model function for macrophage activation includes an Allee effect. We investigate the emergence of Turing patterns by combining linearised and weakly nonlinear analysis, bifurcation diagrams and numerical simulations, focusing on the comparison with the previous *** Codes 92C15, 92C17, 35K57, 70K50 Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear analysis

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Regularized two-stage submodular maximization under streaming

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Science China(Information sciences) 2022年第4期65卷 102-111页

作者： Ruiqi YANG Dachuan XU Longkun GUO Dongmei ZHANG Beijing Institute for Scientific and Engineering Computing Beijing University of Technology School of Mathematical Sciences University of Chinese Academy Sciences School of Computer Science and Technology Qilu University of Technology School of Computer Science and Technology Shandong Jianzhu University

In the problem of maximizing regularized two-stage submodular functions in streams, we assemble a family ■ of m functions each of which is submodular and is visited in a streaming style that an element is visited for only once. The aim is to choose a subset S of size at most ■ from the element stream ■, so as to maximize the average maximum value of these functions restricted on S with a regularized modular term. The problem can be formally cast as ■, where c:■ is a non-negative modular function and ■ is a non-negative monotone non-decreasing submodular function. The well-studied regularized problem of ■ is exactly a special case of the above regularized two-stage submodular maximization by setting m=1 and ■=k. Although f(·)-c(·) is submodular, it is potentially negative and non-monotone and admits no constant multiplicative factor approximation. Therefore, we adopt a slightly weaker notion of approximation which constructs S such that f(S)-c(S)≥ρ·f(O)-c(O) holds against optimum solution O for some ρ∈(0, 1). Eventually, we devise a streaming algorithm by employing the distorted threshold technique, achieving a weaker approximation ratio with ρ=0.2996 for the discussed regularized two-stage model.

关键词： submodular maximization streaming model two-stage threshold technique approximation algorithms

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