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Fake News Classification: Past, Current, and Future

computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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QAAS:quick accurate auto-scaling for streaming processing

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Frontiers of computer Science 2024年第1期18卷 5-15页

作者： Shiyuan LIU Yunchun LI Hailong YANG Ming DUN Chen CHEN Huaitao ZHANG Wei LI State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

In recent years,the demand for real-time data processing has been increasing,and various stream processing systems have *** the amount of data input to the stream processing system fluctuates,the computing resources required by the stream processing job will also *** resources used by stream processing jobs need to be adjusted according to load changes,avoiding the waste of computing *** present,existing works adjust stream processing jobs based on the assumption that there is a linear relationship between the operator parallelism and operator resource consumption(e.g.,throughput),which makes a significant deviation when the operator parallelism *** paper proposes a nonlinear model to represent operator *** divide the operator performance into three stages,the Non-competition stage,the Non-full competition stage,and the Full competition *** our proposed performance model,given the parallelism of the operator,we can accurately predict the CPU utilization and operator *** with actual experiments,the prediction error of our model is below 5%.We also propose a quick accurate auto-scaling(QAAS)method that uses the operator performance model to implement the auto-scaling of the operator parallelism of the Flink *** to previous work,QAAS is able to maintain stable job performance under load changes,minimizing the number of job adjustments and reducing data backlogs by 50%.

关键词： stream processing performance model auto-scaling

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(Information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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An aspect-based sentiment analysis model for Arabic game reviews based on hybrid transformers models

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Neural Computing and Applications 2025年 1-23页

作者： Hammad, Mahmoud AbuEnnab, Noor Al-Refai, Mohammed IT Department Ajman University Ajman United Arab Emirates Software Engineering Department Jordan University of Science and Technology Irbid Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data content generated by interested customers and reviewers motivated researchers and data scientists to conduct ABSA. ABSA has become increasingly popular in recent years due to its versatility in e-commerce, social media, and customer feedback analysis. However, ABSA faces several significant challenges, including determining the aspects and their sentiment polarities (positive, negative, or neutral) in a given text. Moreover, ABSA faces particular challenges in non-English languages such as Arabic due to the lack of resources and mature models. Typically, ABSA tackles one or more of the ABSA research tasks: (T1) aspect term extraction, (T2) aspect term polarity, (T3) aspect category identification, and (T4) aspect category polarity. To identify the aspects and their corresponding sentiment polarities in a given text, accurate and efficient NLP techniques are required. Despite growing interest in Arabic ABSA, the lack of annotated datasets and pre-trained models has hindered its development. In this research, we have collected a dataset of Arabic game reviews and annotated them using three annotators, and then we trained an ABSA deep learning model based on the BERT pre-trained model combined with zero-shot learning (ZSL) to tackle all the four aforementioned tasks. Our best performing model achieved a high accuracy on all four tasks with an accuracy of 91.61% on T1, 90.99% on T2, 79.08% on T3, and 88.17% on T4. Finally, we compared our model’s accuracy with the state-of-the-art Arabic-based ABSA models on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

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Unveiling the Power of Deep Learning: A Comparative Study of LSTM, BERT, and GRU for Disaster Tweet Classification

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 526-534页

作者： Ullah, Ihsan Jamil, Anum Ul Hassan, Imtiaz Kim, Byung-Seo Department of Software and Communication Engineering Hongik University Korea Republic of Department of Physics NED University of Engineering & Technology Karachi Pakistan Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan

Disasters have serious effects on people's lives and buildings. Therefore, social media platforms, such as Twitter, have become more critical. They are crucial tools for responding to and managing disasters effectively. This study examined the effectiveness of various deep learning models, such as bidirectional encoder representations from transformers (BERT), gated recurrent units (GRU), and long short-term memory (LSTM) for classifying disaster-related tweets. Twitter data related to different disasters were collected using hashtags. The data were then cleaned, preprocessed, and manually annotated by a team. The annotated data were divided into training, validation, and testing sets. The data were used to train three models based on BERT, GRU, and LSTM for the categorical classification of disaster tweets. Finally, the three models were evaluated and compared using the test data. BERT achieved an accuracy of 96.2%, making it the most effective model. In contrast, the LSTM and GRU models achieved an accuracy of 93.2% and 88.4%, respectively. These findings underscore the potential effectiveness of deep learning models in classifying disaster-related tweets, offering insights that could enhance disaster management strategies, refine social media monitoring processes, bolster public safety, and provide directions for future research. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Disasters

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of Information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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An Efficient Heterogeneous Ring Signcryption Scheme for Wireless Body Area Networks

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computer Systems Science & engineering 2023年第11期47卷 2061-2078页

作者： Qingqing Ning Chunhua Jin Zhiwei Chen Yongliang Xu Huaqi Lu Faculty of Computer&Software Engineering Huaiyin Institute of TechnologyHuai’an233003China

Wireless body area networks(WBANs)are an emerging technology for the real-time monitoring of physiological *** provide a mechanism for collecting,storing,and transmitting physiological data to healthcare ***,the open wireless channel and limited resources of sensors bring security *** ensure physiological data security,this paper provides an efficient Certificateless Public Key Infrastructure Heterogeneous Ring Signcryption(CP-HRSC)scheme,in which sensors are in a certificateless cryptosystem(CLC)environment,and the server is in a public key infrastructure(PKI)*** could solve the limitations of key escrow in identity-based cryptography(IBC)and certificate management for public keys in *** PKI is suited for the server because it is widely used on the ***,this paper designs a ring signcryption method that allows the controller to anonymously encrypt physiological data on behalf of a set of sensors,but the server does not exactly know who the sensor *** construction of this paper can achieve anonymity,confidentiality,authentication,non-repudiation,and integrity in a logically single *** the computational Diffie-Hellman(CDH)problem,the formal security proof is provided in the random oracle model(ROM).This paper demonstrates that this scheme has indistinguishability against adaptive chosen ciphertext attacks(IND-CCA2)and existential unforgeability against adaptive chosen message attacks(EUF-CMA).In terms of computational cost and energy usage,a comprehensive performance analysis demonstrates that the proposed scheme is the most *** to the three existing schemes,the computational cost of this paper’s scheme is reduced by about 49.5%,4.1%,and 8.4%,and the energy usage of our scheme is reduced by about 49.4%,3.7%,and 14.2%,respectively.

关键词： Wireless body area networks certificateless cryptosystem public key infrastructure security ring singncryption

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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Image Representations of Numerical Simulations for Training Neural Networks

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computer Modeling in engineering & Sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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