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Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements

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Journal of Social computing 2025年第1期6卷 43-50页

作者： Amila Chethana Nanayakkara B. T. G. Samantha Kumara R. M. Kapila Tharanga Rathnayaka Center for Computer Studies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). By utilizing Twitter data, the study developed a Protest Social Media Archetype to capture the evolution of tweet activity during key protest periods, identifying common patterns and notable variations in public engagement. Techniques such as LOESS regression and correlation analysis were used to model fluctuations in online activity and assess the impact of social media on public mobilization during socio-political unrest. The findings revealed distinct temporal signatures for each movement, showing similarities in initial engagement and differences in sustained activity across various contexts. These insights underscore the role of digital platforms in protest organization and global solidarity, offering a framework for future studies on digital activism and protest dynamics.

关键词： Analytical models Social computing Fluctuations Social networking (online) Blogs Refining Organizations Predictive models Real-time systems Mathematics

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ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems

ML-based Modeling to Predict I/O Performance on Different St...

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International Conference on High Performance computing

作者： Yiheng Xu Pranav Sivaraman Hariharan Devarajan Kathryn Mohror Abhinav Bhatele Palantir Technologies Inc. Denver USA Department of Computer Science University of Maryland College Park USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application's I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify performance bottlenecks when using burst buffers and parallel file systems, and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and file system selections. We use IOR, an I/O benchmark to gather performance data for training the machine learning model. Our model can predict the better performing storage system for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%.

关键词： Training Accuracy File systems Computational modeling High performance computing Machine learning Predictive models Benchmark testing Data models Supercomputers

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Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Using Data Analytics to Derive Business Intelligence: A Case Study

arXiv

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arXiv 2023年

作者： Orji, Ugochukwu Obianuju, Ezugwu Ezema, Modesta Ugwuishiwu, Chikodili Ukwandu, Elochukwu Agomuo, Uchechukwu Department of Computer Science Faculty of Physical Science University of Nigeria Enugu State Nsukka Nigeria Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Wales United Kingdom

The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics (BIA) has become a huge trend in today’s IT world as companies of all sizes are looking to improve their business processes and scale up using data-driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike-share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as "Chicago Divvy Bicycle Sharing Data" on Kaggle. The authors used the R-Tidyverse library in RStudio to analyze the data and followed the six data analysis steps of;ask, prepare, process, analyze, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real-world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users. © 2023, CC BY-NC-SA.

关键词： Data Analytics

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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House Price Prediction using Random Forest Machine Learning Technique

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Procedia computer science 2022年 199卷 806-813页

作者： Abigail Bola Adetunji Oluwatobi Noah Akande Funmilola Alaba Ajala Ololade Oyewo Yetunde Faith Akande Gbenle Oluwadara Department of Computer Science Faculty of Computing and Informatics Ladoke Akintola University of Technology Nigeria Computer Science Department College of Pure and Applied Sciences Landmark University Nigeria Accounting Department College of Business Sciences Landmark University Nigeria

Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat-sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. UCI Machine learning repository Boston housing dataset with 506 entries and 14 features were used to evaluate the performance of the proposed prediction model. A comparison of the predicted and actual prices predicted revealed that the model had an acceptable predicted value when compared to the actual values with an error margin of ±5.

关键词： Sales forecasting House Price Prediction Machine Learning Random Forest Algorithm

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Extending the ElGamal Cryptosystem to the Third Group of Units of n

arXiv

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arXiv 2025年

作者： Hamza, Jana EL Hindi, Mohammad Kadri, Seifeddine Kadri, Therrar Awad, Yahya Department of Mathematics and Physics Faculty of Arts and Sciences Lebanese International University Bekaa Lebanon Department of Mathematics and Computer Science Faculty of Science Beirut Arab University Beirut Lebanon Faculty of Applied Computing and Technology Norof University College Kristiansand Norway Department of Science Nourthwestern Polytechnic Grande Prairie Canada

In this paper, we extend the ElGamal cryptosystem to the third group of units of the ring n, which we prove to be more secure than the previous extensions. We describe the arithmetic needed in the new setting. We also provide some numerical simulations that shows the security and efficiency of our proposed *** Codes 20D45, 68W30 © 2025, CC0.

关键词： Digital arithmetic

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Enhancing Sign Language Recognition: A CNN-BiLSTM Approach for Accurate Gesture Interpretation

Enhancing Sign Language Recognition: A CNN-BiLSTM Approach f...

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Next Generation Electronics (NEleX), International Conference on

作者： Baghavathi Priya S P V R Subba Rao TamilSelvi Madeswaran Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences Nizwa Oman

A crucial area of study and technology called Sign Language Recognition (SLR) seeks to reduce communication barriers for people whose first language is sign language. ways of getting in touch. It involves developing mechanisms that able to interpret and decode sign language motions, making it easier more inclusive and reachable interactions for the people of the hard of hearing and the deaf. Making use of a hybrid's power Bidirectional Long Short and Convolutional Neural Network, A new SLR called CNN-BiLSTM (CNN-Term Memory) model is shown in this research work. A remarkable accuracy of 98.6% is attained using the use of a dataset for Indian Sign Language, facilitating the effective recognition of gestures in sign language. In the framework of Indian Sign Language: This innovative method has a lot of promise. to enhance communication and accessibility for the hearing impaired and unable to hear others. It serves as a vital connection to further welcoming relationships and improved living standards.

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Enhancement of Energy Optimization in Semi Joint Multipath Routing Protocol using QoS Based on Mobile Ad-Hoc Networks

Enhancement of Energy Optimization in Semi Joint Multipath R...

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Delhi Section Conference (DELCON), IEEE

作者： Anil Kumar R. K. Shukla R. S. Shukla Department of Computer Applications Invertis University Bareilly India Department of Applied Science & Humanities Invertis University Bareilly India College of Computing & Informatics Electronic University Saudi Arabia

Ensuring quality of service (QoS) is a main challenge for mobile ad hoc networks due to the transmission of packet and frequency range giving and ability constraints of Wi-Fi growth. Since the study of protocol of MANET is dynamic, conquering is forever a weighty problem. Therefore, battery performance is a key factor in MANET, which enables reliable communication without interruption of power supply. To avoid packet loss when transmitting data packets, many studies have been conducted to improve battery performance There is currently no information about battery performance optimization in MANET. This paper describes a techniques developed to recover MANET battery life and improve packet transmission quality. Network performance will degrade due to battery dependent nodes in MANET. Therefore, to ensure better performance, we used the protocol Multipath Routing Ad-hoc On Demand Distance Vector (MAODV) and. Neural Network Based Hopfield Disjoint Path Set Selection (NNHDPS). This improves network longevity compared to existing protocols. The results were obtained using a grid simulator, with significantly improved performance compared to existing energy optimization strategies.

关键词： Protocols Quality of service Throughput Routing Routing protocols Batteries Reliability

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