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Stance detection on social media, case study: Persian sentences using deep learning architecture

Scientia iranica 2024年第10期31卷 764-773页

作者： Mohammadi, Seyed Mahmoud Farzi, Saeed Alavi, Seyed Majid Joonaghany, Gholamreza Heydary Department of Mathematics and Computer Science Arak Branch Islamic Azad University Arak Iran Department of Computer K. N. Toosi University of Technology Tehran Iran

In the paper, we need to identify the stance of persian language in social networks. While the data set for detecting the stance with persian content have applications. Therefore, with the aim of accurately identifying the stance in the post and extracting the stances of persian language, the user expressed a post that relation to one or more target entities a new method for the first time. Hybrid LSTM-CNN architecture was used and, unlike previous researches, rotational learning rate was used, and a new the method for processing data before entering the network is presented to improve the results, which can stance persian in the network. Identifying the social in addition, to solve the problems related to the lack of data, the BERT model was investigated to detect the persian stance. On the based on the results obtained, tagged data was collected, and after many surveys and numerous meetings. Taged data analyzed from the telegram social network in the field of business sport for a limited time frame, and show how the presented model has achieved higher accuracy than Competitors. At the end of the training course, the proposed model improves results by 11.3% in terms of accuracy. © 2024 Sharif university of technology. All rights reserved.

关键词： Long short-term memory

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Kernel-based Actor-Critic Learning Framework for Autonomous Brain Control on Trajectory

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 554-563页

作者： Song, Zhiwei Zhang, Xiang Chen, Shuhang Tan, Jieyuan Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

Reinforcement learning (RL)-based Brain-Machine Interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multi-step BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this paper, we propose a kernel-based actor-critic learning framework for multi-step BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial Prefrontal Cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multi-step brain control tasks. © 2016 IEEE.

关键词： Adversarial machine learning

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Stochastic Optimization on Parking Lots for Smart Parking Using Reinforcement Learning Methods 14

Stochastic Optimization on Parking Lots for Smart Parking Us...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Khatermohammadi, Davoud Ghalari, Seyed Amir Ebadollahi, Saeed Gil, Bob Iran University of Science and Technology Department of Electrical Engineering Iran British Columbia Institute of Technology Department of Electrical and Computer Engineering Technology Canada

ISBN: (纸本)9798350360134

Various sciences have always considered the maximum use of limited resources and planning to optimize their use. Parking in urban spaces is regarded as a finite resource. This paper investigates the idea of introducing learning algorithms for parking guidance and information systems that employ a central server. Each driver engages in a systematic search during a cycle to identify the parking space with the highest perceived reward among all available options, the optimal parking is determined based on predefined tunings signifying the pursuit of identifying the best parking places to select. To provide estimated optimal parking searching strategies to travelers there are two options linear programming and dynamic programming. Several scenarios will be argued from basic linear programming to Reinforcement Learning methods. The main contribution of this paper lies in the identification of optimal parking spaces for drivers by considering various factors associated with the parking spaces, employing the MAB approach. The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters, as one of the reinforcement learning implementations used in this investigation. Each driver who played this MAB gained its reward with their probabilities. The reinforcement learning methodology is utilized for which both Greedy and ϵ-Greedy simulations are run considering stationary and nonstationary scenarios, meanwhile, each parking selection can change the environment, and regarding this change, scenario assumptions change which is a new scenario in the area of parking problems finally results of these conditions are analyzed while aspects of each scenario are discussed and compared to their efficiency. © 2024 IEEE.

关键词： Dynamic programming

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Analyzing the effects of congestion on planning time index–Grey models *** forest regression

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International Journal of Transportation science and technology 2023年第2期12卷 578-593页

作者： Shahriar Afandizadeh Zargari Navid Amoei Khorshidi Hamid Mirzahossein Hanif Heidari School of Civil Engineering Iran University of Science and Technology(IUST)TehranIran Department of Civil Engineering-Transportation Planning Faculty of Engineering and TechnologyImam Khomeini International University(IKIU)QazvinIran School of Mathematics and Computer Science Damghan UniversityDamghanIran

Travel time and its predictability level are important factors influencing travel behavior and the role of traffic congestion as an important factor in the unreliability of travel time is *** congestion is divided into two categories:Recurring and *** this paper,the effect of recurring congestion,defined as the ratio of the traffic speed over one hour to the free flow speed,will be investigated on the reliability of travel time in terms of the planning time index(PTI)on a 1.467-mile segment along the IS-64 freeway in Chesapeake,*** do so,two methods have been analyzed in this study:the grey models(GM)and the random forest regression(RFR).By using mean absolute percentage error(MAPE)as a criterion to judge,RFR could show a better and more satisfying performance in predicting PTI values when congestion *** the following,to make prediction results of RFR more understanding and easier to use,bagging and bootstrapping are used to improve the model results and more accurately predict the ***,the outputs were drawn using scatter plots for both peaks *** graphs has shown that for congestion values in the range of 1 to 0.9,PTI is reliable in both *** congestion starts to decrease from 0.9 and reaches 0.7 or 0.75,depending on peak type,PTI is moving in the unreliable area,but it isn’t ***,when the congestion value becomes smaller,the rate of change in PTI in both peaks increases.

关键词： Congestion Travel time Reliability Grey models Random forest

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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Submerged rigid vegetation effects on flow hydrodynamics within the pool morphology

River

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River 2024年第3期3卷 260-271页

作者： Kourosh Nosrati Hossein Afzalimehr Hamidreza Raeisifar Mohammad Nazari-Sharabian Moses Karakouzian Faculty of Civil Engineering Iran University of Science and TechnologyTehranIran Department of Mathematics Engineeringand Computer ScienceWest Virginia State UniversityInstituteWest VirginiaUSA Department of Civil and Environmental Engineering University of Nevada Las VegasLas VegasNevadaUSA

This research is centered on a comprehensive investigation into the impact of turbulence on the movement and dispersion of materials within a three-dimensional(3D)bedform,speciﬁcally when there is a continuous presence of rigid vegetation submerged in theﬂ*** achieve our research objectives,we conducted extensive velocity measurements within a channel featuring this submerged *** measurements were carried out using an Acoustic Doppler Velocimeter(ADV).Additionally,our study delved into the intricate structures and turbulent characteristics of theﬂow,considering the coexistence of submerged vegetation and a 3D gravel *** pool featured entrance and exit slopes measuring 3 and 2.5°,*** experimental setup took place in a straightﬂume,measuring 14 m in length,0.9 m in width,and 0.6 m in ***ﬂume was equipped with transparent side walls to facilitate ***,our investigation extended to the spatial variations in velocity and turbulence *** analyzed various parameters including turbulence kinetic energy,integral turbulence lengths,dispersion coefﬁcients,and advective *** results revealed that integral length scales offer key insights into turbulent eddy *** the presence of vegetation and a 3D bedform,turbulent eddies undergo notable changes,ﬂattening in the longitudinal direction and expanding in the transverse and vertical ***,longitudinal advection is notably higher compared toﬂows without vegetation in a uniformﬂow or bare channel,especially for z/H>*** indicates that the presence of vegetation and a 3D bedform leads to an increase in turbulent kinetic energy(k values)that surpasses the reduction in the time-averaged velocity component(“U”)in the U×k term,thereby enhancing longitudinal advection.

关键词： 3D pool advection dispersion integral turbulent length submerged rigid vegetation

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An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期8卷 635-644页

作者： Shabestari, Fatemeh Jafari Navimipour, Nima Islamic Azad University Sofian Branch Department of Computer Engineering Sofian Iran Kadir Has University Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines. © 2017 IEEE.

关键词： Energy utilization

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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