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Versioning temporal characteristics of JSON-based big data via the τJSchema framework

International Journal of Cloud Computing 2021年第5-6期10卷 406-441页

作者： Brahmia, Safa Brahmia, Zouhaier Grandi, Fabio Bouaziz, Rafik Department of Computer Science Faculty of Economics and Management University of Sfax Road of the Aerodrome Km 4.5 P.O. Box 1088 Sfax3018 Tunisia DISI - Department of Computer Science and Engineering University of Bologna Viale Risorgimento 2 BO BolognaI-40136 Italy

In previous work, we have proposed the use of a framework, named τJSchema (temporal JSON schema), for the definition and validation of temporal JSON documents that conform to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema, annotated with a set of temporal logical and temporal physical characteristics. Subsequently, we have extended τJSchema to support versioning of conventional JSON schemas. In this work, we complete the picture by extending our framework to also support versioning of temporal characteristics. In fact, we propose a suitable versioning technique and provide a complete set of low-level change operations for the maintenance of these characteristics;for each operation, we define its arguments and its operational semantics. With this extension, τJSchema provides a full support of temporal versioning of JSON-based big data at both instance and schema levels. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Big data

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Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy

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Journal of Electrical Systems 2024年第1期20卷 66-75页

作者： Mall, Pawan Kumar Srivastava, Swapnita Patel, Mitul M. Kumar, Aniruddh Narayan, Vipul Kumar, Sanjay Singh, P.K. Singh, D.S. GL Bajaj Institute of Technology and Management India Department of Electronics and Communication Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Knowledge Park-2 Greater Noida India Galgotias University Uttar Pradesh Gautam Buddha Nagar India Rajkiya Engineering College Azamgarh India Computer Science and Engineering Department Madan Mohan Malaviya University Of Technology Uttar Pradesh Gorakhpur273010 India Computer Science and Engineering Department Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning *** study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%. © JES 2024 on-line: ***

关键词： Support vector machines

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Cost-Effective Activity Control of Asymptomatic Carriers in Layered Temporal Social Networks

arXiv

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arXiv 2024年

作者： Moradian, Masoumeh Dadlani, Aresh Kairgeldin, Rasul Khonsari, Ahmad The School of Computer Engineering K. N. Toosi University of Technology Tehran Iran Tehran Iran The School of Electrical and Computer Engineering University of Tehran Iran The Department of Computing Science University of Alberta EdmontonT6G 2E8 Canada The Department of Electrical and Computer Engineering Nazarbayev University Astana010000 Kazakhstan The Department of Electrical Engineering and Computer Science University of California MercedCA95343 United States

The robustness of human social networks against epidemic propagation relies on the propensity for physical contact adaptation. During the early phase of infection, asymptomatic carriers exhibit the same activity level as susceptible individuals, which presents challenges for incorporating control measures in epidemic projection models. This paper focuses on modeling and cost-efficient activity control of susceptible and carrier individuals in the context of the susceptible-carrier-infected-removed (SCIR) epidemic model over a two-layer contact network. In this model, individuals switch from a static contact layer to create new links in a temporal layer based on state-dependent activation rates. We derive conditions for the infection to die out or persist in a homogeneous network. Considering the significant costs associated with reducing the activity of susceptible and carrier individuals, we formulate an optimization problem to minimize the disease decay rate while constrained by a limited budget. We propose the use of successive geometric programming (SGP) approximation for this optimization task. Through simulation experiments on Poisson random graphs, we assess the impact of different parameters on disease prevalence. The results demonstrate that our SGP framework achieves a cost reduction of nearly 33% compared to conventional methods based on degree and closeness centrality. © 2024, CC BY.

关键词： Cost effectiveness

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Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model

arXiv

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arXiv 2024年

作者： Zhan, Runzhe Yang, Xinyi Wong, Derek F. Chao, Lidia S. Zhang, Yue NLP2CT Lab Department of Computer and Information Science University of Macau China School of Engineering Westlake University China

While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been raised about the depth of this alignment, with some critiques suggesting it is merely "superficial". We critically examine this hypothesis within the scope of cross-lingual generation tasks, proposing that the effectiveness of SFT may be constrained by its reliance on prior tokens to guide cross-lingual generation. Based on this crucial insight, and in response to the challenges posed by the costly and limited availability of non-English data for SFT, we introduce a novel training-free alignment method named PRETTY, which employs minimal task-related prior tokens to bridge the foundation LLM and the SFT LLM, achieving comparable performance without training. Experiments on machine translation and part-of-speech tagging across eight languages demonstrate the efficacy of PRETTY in cross-lingual settings. Remarkably, by initiating the decoding process with only one or two prior tokens, foundation LLMs can achieve performance comparable to their SFT counterparts. This method presents a cost-effective alternative to SFT and advances the democratization of multilingual LLMs. © 2024, CC BY.

关键词： Foundations

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Using a Riemannian Elastic Metric for Statistical Analysis of Tumor Cell Shape Heterogeneity 6th

Using a Riemannian Elastic Metric for Statistical Analysis...

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The 6th International Conference on Geometric science of Information, GSI 2023

作者： Li, Wanxin Prasad, Ashok Miolane, Nina Dao Duc, Khanh Department of Computer Science University of British Columbia VancouverBCV6T 1Z2 Canada Department of Chemical and Biological Engineering and School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Department of Mathematics University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9783031382987

We examine how a specific instance of the elastic metric, the Square Root Velocity (SRV) metric, can be used to study and compare cellular morphologies from the contours they form on planar surfaces. We process a dataset of images from osteocarcoma (bone cancer) cells that includes different treatments known to affect the cell morphology, and perform a comparative statistical analysis between the linear and SRV metrics. Our study indicates superior performance of the SRV at capturing the cell shape heterogeneity, with a better separation between different cell groups when comparing their distance to their mean shape, as well as a better low dimensional representation when comparing stress statistics. Therefore, our study suggests the use of a Riemannian metric, such as the SRV as a potential tool to enhance morphological discrimination for large datasets of cancer cell images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Aircraft Simulation in Game Development Using Reinforcement Learning

Aircraft Simulation in Game Development Using Reinforcement ...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Md. Navid Redwan Talha Tonmoy Hassan Al Sabab Shaila Sharmin Mithila Syed Hasib Ahmed Department of Electrical and Computer Engineering North south University (NSU) Dhaka Bangladesh Department of Electrical and Electronics Engineering American International University-Bangladesh (AIUB) Bangladesh Dr. Anwarul Abedin Institute of Innovation (D2A2I) Dhaka Bangladesh Department of Computer Science and Engineering Leading University (LU) Sylhet Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj

关键词： Training Atmospheric modeling Neural networks Decision making Reinforcement learning Games Autonomous aerial vehicles Aircraft navigation Trajectory Aircraft

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TongueMobile: automated tongue segmentation and diagnosis on smartphones

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Neural Computing and Applications 2023年第28期35卷 21259-21274页

作者： Huang, Zih-Hao Huang, Wei-Cheng Wu, Hsien-Chang Fang, Wen-Chieh Department of Computer Science and Information Engineering National Chiayi University No. 300 Syuefu Rd. Chiayi City600355 Taiwan School of Post-Baccalaureate Chinese Medicine Tzu Chi University No. 701 Sec. 3 Zhongyang Rd. Hualien City Hualien County97004 Taiwan Department of Chinese Medicine Taipei Tzu Chi Hospital The Buddhist Tzu Chi Medical Foundation No. 289 Jianguo Rd. Xindian Dist New Taipei City23142 Taiwan Department of Computer Science and Information Engineering National Dong Hwa University Shoufeng Township No. 1 Sec. 2 University Rd. Hualien County974301 Taiwan

Tongue diagnosis is a useful process in traditional Chinese medicine to assess diseases non-invasively by visually inspecting the tongue and its various properties. In this study, we developed an automated tongue diagnosis system with a mobile app for the general public. The image-segmentation component extracts the tongue body image from an input photograph taken by a smartphone. The tongue-coating color classification component predicts the category of the coating color. The segmented image and diagnosis results are returned to the app and shown to the user. Experimental results show that Mask R-CNN is the optimal choice for tongue-image segmentation under various input image conditions based on the mean interaction over union value of 91 % and the Dice score of 95 % . ResNeXt outperformed other baseline tongue-coating color classification models. In addition, when the input image is adjusted with our color-correction modules in advance, the classification accuracy of ResNeXt101 is improved by approximately 12 % . © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Image segmentation

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Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2024年

作者： Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. Kato, Nei Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States Western University Department of Computer Science LondonONN6G 2V4 Canada Augusta University School of Computer and Cyber Sciences AugustaGA30912 United States Tohoku University Graduate School of Information Sciences Sendai980-8579 Japan

With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous networks, mobile broadband users are generating massive volumes of data that undergo fast processing and computing to obtain actionable insights. While analyzing this huge amount of data typically involves machine and deep learning-based data-driven Artificial Intelligence (AI) models, a key challenge arises in terms of providing privacy assurances for user-generated data. Even though data-driven techniques have been widely utilized for network traffic analysis and other network management tasks, researchers have also identified that applying AI techniques may often lead to severe privacy concerns. Therefore, the concept of privacy-preserving data-driven learning models has recently emerged as a hot area of research to facilitate model training on large-scale datasets while guaranteeing privacy along with the security of the data. In this paper, we first demonstrate the research gap in this domain, followed by a tutorial-oriented review of data-driven models, which can be potentially mapped to privacy-preserving techniques. Then, we provide preliminaries of a number of privacy-preserving techniques (e.g., differential privacy, functional encryption, Homomorphic encryption, secure multi-party computation, and federated learning) that can be potentially adopted for emerging communication networks. The provided preliminaries enable us to showcase the subset of data-driven privacy-preserving models, which are gaining traction in emerging communication network systems. We provide a number of relevant networking use cases, ranging from the B5G core and Radio Access Networks (RANs) to semantic communications, adopting privacy-preserving data-driven models. Based on the lessons learned from the pertinent use cases, we also identify several open research challenges and hint toward possible solutions. © 1998-2012 IEEE.

关键词： communication networks data-driven models deep learning federated learning machine learning Privacy preservation

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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Stock Price Manipulation Detection using Variational Autoencoder and Recurrence Plots

Stock Price Manipulation Detection using Variational Autoenc...

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International Joint Conference on Neural Networks (IJCNN)

作者： Khaled Safa Ammar Belatreche Salima Ouadfel Department of Computer Science NTIC Faculty University of Constantine2 Abdelhamid Mehri Constantine Algeria Department of Computer and Information Sciences Fac. of Engineering and Environment Northumbria University Newcastle Upon Tyne UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Stock price manipulation refers to deceptive traders' practices which aim to influence the normal market behaviour in order to make illicit profit at the expense of other genuine market participants. Such manipulation undermines investors’ confidence in the financial market and damages its efficiency and integrity. There remains a growing need for developing robust anomaly detection methods capable of reliably identifying increasingly sophisticated manipulation attempts. Inspired by the recent success of deep learning in computer vision, we propose a novel approach for stock market manipulation detection that leverages the combined power of Recurrence Plots (RP) and beta Variational Autoencoders (beta-VAEs). The proposed approach first splits stock price time series data into overlapping temporal windows, each of which is transformed into a colour image using Recurrence Plots (RP). Then, a beta-VAE network composed of two Convolutional Neural Networks (CNNs) is trained on these images derived from normal market activity. The resulting model effectively learns the inherent characteristics of normal (i.e legitimate) trading behaviour. Finally, the mean square error between the original images and the reconstructed ones is used as the manipulation (i.e anomaly) detection score measure for flagging significant deviations indicative of potential manipulation. The efficacy of the proposed approach is rigorously validated on 1-level tick data obtained from the LOBSTER project. Evaluation results demonstrate superior manipulation detection performance which is evidenced by a highly promising area under the ROC curve (AUC). The robustness of this performance can be attributed to the beta-VAE's ability to extract pertinent features of normal market behavior from the proposed RP-generated 2D representations of stock price data. This novel framework paves the way for the development of more effective market abuse detection systems which contribute to a safer, fairer and more tr

关键词： Representation learning Regulators Image color analysis Time series analysis Transforms Feature extraction Robustness

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