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Information and Network Technologies (ICINT), International Conference on

作者： Yotam Ashkenazi Shlomi Dolev Sergey Frenkel Manish Kumar Sweta Kumari ram Prasadh Narayanan Archit Somani Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel The Institute of Informatics Problems Russian Academy of Science Russia Department of Computer Science New Jersey Institute of Technology New Jersey United States Department of Computer Science and Engineering Shiv Nadar University Delhi India Institute for Information and Communication Systems Science Walton Institute Ireland

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Broadcasting stands as a vital function within wireless sensor networks (WSNs), where nodes disseminate messages throughout the entire network, radiating in all possible directions. Our focus lies on the issue of three-dimensional broadcasting, involving n sensors/robots positioned uniformly across a three-dimensional space. We propose an exponentially fast hybrid collaborative broadcasting algorithm in three-dimensional space "growing ball" through successive propagation steps. We study the effect of various parameters such as the density of the growing ball (ρ), distance (d), the energy emitted by node (E emit ), and ϵ used by the collaborative broadcast. Our approach is based on the idea of dividing the 3D space into two halves that reduce mathematical intricacy. The number of nodes N k in the k th step of the growing ball is the factor of (1+ϵ) k implies the exponential fast broadcast.

关键词： Wireless sensor networks Three-dimensional displays Collaboration Broadcasting

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An Early Warning System to Predict Earthquakes based on WT-ARIMA-ANN Model 3

An Early Warning System to Predict Earthquakes based on WT-A...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Babu, Uppara raghu raha, Shrinwantu Khan, Mujtaba Ali Yamini, C. Jayadurga, R. Kalra, Gourav Srinivasa Ramanujan Institute of Technology Department of Civil Engineering Andhra Pradesh India Bhairab Ganguly College Department of Geography Belgharia Kolkata India University of North Carolina Department of Computer Science College of Computing and Informatics Charlotte United States PSNA College of Engineering and Technology Department of Mathematics Tamil Nadu Dindigul India Prince Shri Venkateshwara Padmavathy Engineering College Department of MBA Tamil Nadu Chennai India Department of Mechanical Engineering Mullana India

ISBN: (纸本)9798350343632

A devastating earthquake is one of the Earth's most destructive natural disasters. Real-time seismic monitoring facilities can evolve into Earthquake Early Warning (EEW) systems with the help of modern technology, property damage, reducing the number of casualties, and economic losses. The proposed EEW system's goal is to supply a precise damage assessment just before an earthquake hits a certain area. The proposed method involves preprocessing, feature extraction, and model training. It is recommended to preprocess the data by first filtering it and then reformatting it. Filtering is essential for getting rid of irrelevant or noisy information. In feature extraction, the Principal Component Analysis (PCA) provides a useful perspective on the data by establishing a new coordinate system, and it also offers a method for reducing the data's dimensionality. Once the features have been obtained, the models are trained with WT-ARIMA-ANN. The proposed method outperforms two widely used alternatives, ARIMA and ANN. © 2023 IEEE.

关键词： Autoregressive Integrated Moving Average (ARIMA) Earthquake Early Warning (EEW) Principal Component Analysis (PCA)

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Improved RC6 Block Cipher Based on Data Dependent Rotations

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computers, Materials & Continua 2022年第1期70卷 1921-1934页

作者： Osama S.Faragallah Ibrahim F.Elashry Ahmed AlGhamdi Walid El-Shafai S.El-rabaie Fathi E.Abd El-Samie Hala S.El-sayed Mohamed A.Elaskily Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Informatics Electronic Research Institute(ERI)CairoEgypt

This paper introduces an Improved RC6(IRC6)cipher for data encryption based on data-dependent *** proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard(AES).Four parameters are used to characterize the proposed *** parameters are the size of the word(w)in bits,the number of rounds(r),the length of the secret key(b)in bytes,and the size of the block(L)in *** main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6,resulting in a variable block size for plaintext and *** IRC6 cipher is designed to improve the robustness against attacks by increasing the diffusion for each round and providing greater security with fewer *** effectiveness of the proposed IRC6 scheme is verified against theoretical *** proposed IRC6 scheme depends on full diffusion and confusion mechanisms regardless of the utilized block *** proposed IRC6 scheme saves 70%of the encryption time and 64%of the decryption time of *** simulation results prove that the IRC6 achieves a better encryption/decryption time compared to the ***,the proposed IRC6 is anticipated to fulfill the market needs and system security requirements.

关键词： Cryptography block cipher RC6,IRC6,AES

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Retraction Note: Studying efficacy of traditional software quality parameters in quantum software engineering

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Faryal, Memoona Rubab, Saddaf Khan, Malik M. Khan, Muhammad Attique Shehab, Abdulaziz Tariq, Usman Chelloug, Samia Allaoua Osman, Lobna National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah UAE Department of Computer Science HITEC University Taxila Taxila Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Information Systems College of Computer Science Mansoura University Mansoura Egypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering & Technology Talkha Egypt

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Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators 16

Measurement of Synthetic-Dimension Band Structures in Silico...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Zhang, Ruming Li, Liucun Kamata, Mikiya Baba, Toshihiko Ozawa, Tomoki Ota, Yasutomo Iwamoto, Satoshi Research Center for Advanced Science and Technology The University of Tokyo Tokyo153-8904 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8533 Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (纸本)9798350372076

关键词： Silicon photonics

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes

Comparative Analysis of Movie Recommendation Systems Using F...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Santosh Kumar Majhi Preetipadma Sahani Rosy Pradhan Biswaranjan Acharya Foteini Grivokostopoulou Andreas Kanavos Vassilis C. Gerogiannis Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya (Central University) Bilaspur India Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla Odisha India Department of Electrical Engineering Veer Surendra Sai University of Technology Burla Odisha India Department of Computer Engineering AI Marwadi University Rajkot India Computer Technology Institute and Press “Diophantus” Patras Greece Department of Informatics Ionian University Corfu Greece Department of Digital Systems University of Thessaly Larissa Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features; collaborative, which processes user behavior patterns; and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Sentiment analysis Filtering Reviews Soft sensors Data integrity Entertainment industry Motion pictures Reliability Root mean square Recommender systems

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Parameterized Saga of First-Fit and Last-Fit Coloring

arXiv

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arXiv 2024年

作者： Agrawal, Akanksha Lokshtanov, Daniel Panolan, Fahad Saurabh, Saket Verma, Shaily Department of Computer Science and Engineering Indian Institute of Technology Madras India Department of Computer Science University of California Santa Barbara United States School of Computing University of Leeds United Kingdom Theoretical Computer Science The Institute of Mathematical Sciences HBNI India Department of Informatics University of Bergen Norway Algorithm Engineering Group Hasso Plattner Institute Germany

The classic greedy coloring (first-fit) algorithm considers the vertices of an input graph G in a given order and assigns the first available color to each vertex v in G. In the Grundy Coloring problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the Partial Grundy Coloring, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex v is a color previously used for another vertex if such a color is available. Partial Grundy coloring of a graph corresponds to the vertex coloring produced by the greedy coloring heuristics called last-fit coloring, which assigns the last available color to each vertex in the given order. Whether Grundy Coloring and Partial Grundy Coloring admit fixed-parameter tractable (FPT) algorithms, algorithms with running time f(k)nO(1), where k is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for Grundy Coloring in the negative by showing that the problem is W[1]-hard. For Partial Grundy Coloring, they obtain an FPT algorithm on graphs that do not contain Ki,j as a subgraph (a.k.a. Ki,j-free graphs). Aboulker et al. re-iterate the question of whether there exists an FPT algorithm for Partial Grundy Coloring on general graphs and also asks whether Grundy Coloring admits an FPT algorithm on Ki,j-free graphs. We give FPT algorithms for Partial Grundy Coloring on general graphs and for Grundy Coloring on Ki,j-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and "representative-family" like sets for Ki,j-free graphs that we use in obtaining our results may have wi

关键词： Graph algorithms

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Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10530-10543页

作者： Luyang Luo Yanwen Li Zhizhong Chai Huangjing Lin Pheng-Ann Heng Hao Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Sai Kung Hong Kong Department of Biomedical Informatics Harvard University Cambridge MA USA Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Computer Science and Engineering Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://***/poiuohke/SASR_Net .

关键词： Lesions Image segmentation Feature extraction Medical diagnostic imaging Superresolution Diseases Decoding Computed tomography Technological innovation Skin

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning computer architecture Generators Data models Standards Interoperability Load flow

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