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Leveraging Unlabeled Corpus for Arabic Dialect Identification

computers, Materials & Continua 2025年第5期83卷 3471-3491页

作者： Mohammed Abdelmajeed Jiangbin Zheng Ahmed Murtadha Youcef Nafa Mohammed Abaker Muhammad Pervez Akhter School of Computer Science Northwestern Polytechnical UniversityXi'an710072China Department of Computer Science Applied CollegeKing Khalid UniversityMuhayil6311Saudi Arabia Computer Science Department National University of Modern LanguagesFaisalabad38000Pakistan

Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in *** state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given *** the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world *** alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID ***,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal *** key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the ***,we evaluate the proposed solution on benchmark datasets for *** extensive experiments show that it performs signifcantly better,especially,with sparse labeled *** comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID *** code will be available on GitHub upon the paper's acceptance.

关键词： Arabic dialect identification natural language processing bidirectional encoder representations from transformers pre-trained language models gradient reversal layer

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Enhancing financial time series forecasting through topological data analysis

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Neural Computing and Applications 2025年第9期37卷 6527-6545页

作者： de Jesus, Luiz Carlos Fernández-Navarro, Francisco Carbonero-Ruz, Mariano Department of Quantitative Methods Universidad Loyola Andalucía Córdoba41704 Spain Department of Computer Languages and Computer Science Universidad de Málaga Málaga29010 Spain

Topological data analysis (TDA) is increasingly acknowledged within financial markets for its capacity to manage complexity and discern nuanced patterns and structures. It has been applied effectively to uncover intricate relationships and capture non-linear dependencies inherent in market data. This manuscript presents a groundbreaking study that delves into integrating features derived from TDA to improve the performance of forecasting models for univariate time series prediction. The research specifically examines whether incorporating features extracted from TDA-such as entropy, amplitude, and the number of points obtained from persistent diagrams can provide valuable supplementary information to the baseline forecasting model. Thus, the aim is to determine if these TDA-derived features can boost forecasting accuracy by offering additional insights that existing models might overlook. The N-BEATS model serves as the baseline forecasting model due to its robust generalization capabilities and flexibility in incorporating additional features into the model. The proposed methodology is compared against a univariate N-BEATS model without additional features and other strategies incorporating supplementary features such as temporal decomposition and time delay embeddings. The evaluation includes forecasting for six cryptocurrencies across four distinct time scenarios and four traditional financial instruments across two scenarios each, resulting in 32 datasets. The results obtained were promising, as the proposed method, N-BEATS+TDA, achieved the best results in mean performance and mean ranking for the three metrics considered (MAPE, MAE, and RMSE). Significant differences were observed with the rest of the proposed methods using a significance level of α=0.10, highlighting the effectiveness of integrating TDA features to enhance forecasting models. © The Author(s) 2024.

关键词： Prediction models

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Magnification Independent Breast Cancer Analysis Using Vision Transformer

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Multimedia Tools and Applications 2025年第5期84卷 2029-2057页

作者： Tariq, Shehroz Raza, Rehan Sargano, Allah Bux Habib, Zulfiqar Department of Computer Science COMSATS University Islamabad Lahore Campus Pakistan Department of Computer Science National University of Modern Languages Islamabad Faisalabad Campus Pakistan School of Information Technology Murdoch University Perth Australia

Breast Cancer (BC) remains a critical global health challenge, accounting for millions of lives annually. Most breast cancer detection techniques are designed for binary classification. However, further classifying these types into their subtypes remains challenging due to cell coherence, variations in colour distribution, and intraclass similarity. Additionally, these techniques are limited to specific magnification levels of input images. As a result, the need to develop multiclass classification techniques independent of magnification levels has become increasingly demanding. This research introduces an innovative approach based on an enhanced vision transformer (ViT) for BC analysis that seamlessly handles binary and multiclass classification tasks in a magnification-independent manner. The proposed framework incorporates convolutional layers into the ViT model to simultaneously enhance its capability to capture local and global features. This innovative adjustment also empowers the model to capture complex patterns and subtle features by overcoming the issue of different magnification levels. Extensive experiments are conducted on the benchmark BreaKHis dataset to validate the effectiveness of the proposed framework. It achieves an accuracy of 89.43% for binary classification (benign vs. malignant) and 57.94% and 74.71% for multiclass benign and multiclass malignant subclass classification, respectively. The results confirm that the proposed framework surpasses the performance of the original ViT model and other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Generative adversarial networks based motion learning towards robotic calligraphy synthesis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 452-466页

作者： Xiaoming Wang Yilong Yang Weiru Wang Yuanhua Zhou Yongfeng Yin Zhiguo Gong Department of Computer and Information Science University of MacaoMacaoChina School of Software Beihang UniversityBeijingChina Department of Computer Science and Technology Faculty of Information TechnologyBeijing University of TechnologyBeijingChina School of Foreign Languages Guangzhou Huashang CollegeGuangzhouChina

Robot calligraphy visually reflects the motion capability of robotic *** traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy *** key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data *** this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot *** robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with *** the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is *** calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

关键词： calligraphy synthesis generative adversarial networks Motion learning robot writing

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Large Scale Narrative Messaging around Climate Change: A Cross-Cultural Comparison 1

Large Scale Narrative Messaging around Climate Change: A Cro...

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1st Workshop on Natural Language Processing Meets Climate Change, ClimateNLP 2024

作者： Zhou, Haiqi Hobson, David G. Ruths, Derek Piper, Andrew Department of Languages Literatures and Cultures School of Computer Science McGill University Canada

ISBN: (纸本)9798891761599

In this study, we explore the use of Large Language Models (LLMs) such as GPT-4 to extract and analyze the latent narrative messaging in climate change-related news articles from North American and Chinese media. By defining "narrative messaging" as the intrinsic moral or lesson of a story, we apply our model to a dataset of approximately 15,000 news articles in English and Mandarin, categorized by climate-related topics and ideological groupings. Our findings reveal distinct differences in the narrative values emphasized by different cultural and ideological contexts, with North American sources often focusing on individualistic and crisis-driven themes, while Chinese sources emphasize developmental and cooperative narratives. This work demonstrates the potential of LLMs in understanding and influencing climate communication, offering new insights into the collective belief systems that shape public discourse on climate change across different cultures. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Towards a General Metric for Energy Efficiency in Cloud Computing Data Centres: A Proposal for Extending of the ISO/IEC 30134-4 14

Towards a General Metric for Energy Efficiency in Cloud Comp...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Juiz, Carlos Bermejo, Belen Fernández-Montes, Alejandro Fernández-Cerero, Damián Department of Computer Science Universitat de les Illes Balears Palma Spain Department of Computer Languages and Systems University of Seville Spain

ISBN: (纸本)9789897587016

For some years now, energy efficiency has been one of the concerns of cloud system administrators. To improve energy efficiency, in recent years standards such as ISO/IEC 30134-4 and ISO/IEC 21836 have emerged. Both standards are focused on the evaluation of physical servers, taking into account the power consumed and the maximum peak of performance, under running a SPEC benchmark. In this way, the server consolidation through virtualization is not considered in these standards, being the consolidation of servers as one of the most applied techniques to improve energy efficiency in cloud data centres. This work proposes an extension of the methodology proposed in these standards to measure energy efficiency in consolidated servers. As a result, it has been demonstrated through real experimentation that the proposed generical methodology considers the consolidation of servers in any type of virtualization environment. This methodology helps system administrators to manage cloud data centres and servers more efficiently. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Energy efficiency

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Alpha Fusion Adversarial Attack Analysis Using Deep Learning

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computer Systems science & Engineering 2023年第7期46卷 461-473页

作者： Mohibullah Khan Ata Ullah Isra Naz Sajjad Haider Nz Jhanji Mohammad Shorfuzzaman Mehedi Masud Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan School of Computer Science(SCS) Taylor’s UniversitySelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The deep learning model encompasses a powerful learning ability that integrates the feature extraction,and classification method to improve *** Neural Networks(CNN)perform well in machine learning and image processing tasks like segmentation,classification,detection,identification,*** CNN models are still sensitive to noise and *** smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN *** paper presents an alpha fusion attack analysis and generates defense against adversarial *** proposed work is divided into three phases:firstly,an MLSTM-based CNN classification model is developed for classifying COVID-CT ***,an alpha fusion attack is generated to fool the classification *** alpha fusion attack is tested in the last phase on a modified LSTM-based CNN(CNN-MLSTM)model and other pre-trained *** results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion *** highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%.Results elucidate the performance in terms of accuracy,precision,F1 score and Recall.

关键词： Adversarial attack classification deep learning preturbation images

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Queue Assisted Random Early Detection for Congestion in Software Defined Networks 29

Queue Assisted Random Early Detection for Congestion in Soft...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Ullah, Ata Bibi, Sobia Tariq, Moeenuddin Jhanjhi, N.Z. Ray, Sayan Kumar National University of Modern Languages Department of Computer Science Islamabad Pakistan Taylor's University School of Computer Science Subang Jaya Malaysia

ISBN: (纸本)9798331517816

Software Defined Networks (SDN) utilize the network devices as virtual devices to analyze the traffic and transmit the relevant packets. It is quite beneficial for the internet of Things (IoT) where the huge number of devices share the packets which cannot be handled by the conventional networks. SDN is also essential to manage congestion scenarios during massive communication especially in video streaming. The main problem arises when the residual queues on the network path are left underutilized whereas the bandwidth is reduced as per the minimum bandwidth link between any two nodes in the path. It occurs based on the feedback based congestion avoidance mechanism. This work presents a proposed mechanism to utilize the residual queue in the communication path to enhance the minimum bandwidth. It maintains the average queue length to analyze the percentage of queue utilized and then decide about the packet dropping probability. In proposed scheme, the congestion can be managed earlier whereas the base scheme applies a constraint of double the size of previous threshold which may result in identifying the congestion at a later stage where the retreat is hardly possible. According to the findings in results after simulations, the proposed IAGRED dominates the counterparts in terms of queue delay, queue length, link utilization and throughput. © 2024 IEEE.

关键词： Bandwidth

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Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

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computer Systems science & Engineering 2023年第1期44卷 845-858页

作者： Muhammad Shahzeb Ali Ali Alqahtani Ansar Munir Shah Adel Rajab Mahmood Ul Hassan Asadullah Shaikh Khairan Rajab Basit Shahzad Department of Computer Science Institute of Southern PunjabMultan5932Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajran61441Saudi Arabia Department of Engineering National University of Modern LanguagesIslamabad44000Pakistan

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of *** routing protocols are available,but the issue is still *** is one of the most important techniques in the existing routing *** the clustering-based model,the important thing is the selection of the cluster *** this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each ***,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small *** proposed scheme performs hierarchal routing and direct routing with some energy *** simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its ***,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

关键词： Bubble sort algorithm gateway energy thresholds wireless sensor networks

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APFormer: Anti-Phishing Transformer for Website-Phishing Detection Via Joint Feature Learning

APFormer: Anti-Phishing Transformer for Website-Phishing Det...

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2024 International Conference on Engineering and Computing, ICECT 2024

作者： Memon, Anam Manjotho, Ali Asghar National University of Modern Languages Department of Computer Science Hyderabad Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan

ISBN: (纸本)9798350349719

Phishing is a cybercrime activity where the perpe-Trator tricks users into believing that a fraudulent website is authentic, revealing sensitive information. Recently, the frequency of phishing attacks has increased, targeting both individuals and corporate sectors, thereby rendering traditional methods of detecting phishing websites less effective. These methods often produce high false positive rates due to the complexities involved in creating a strong correlation between brand identity and website features. Furthermore, sophisticated attacks, such as DOM element manipulation tricks, evade the existing methods and convince the user with visual and behavioural similarity. We propose a transformer-based approach for website phishing detection using joint feature learning. The approach jointly learns textual, visual, and heuristic multi-modal features to establish a stronger connection between brand identity and the webpage. The proposed model comprises a feature extraction module and a transformer network. The feature extraction module extracts textual, visual, and heuristic features from the DOM tree, while the transformer module classifies the input page as legitimate or phishing by learning the feature mapping to brand identity. Our method outperforms current state-of-The-Art methods on benchmark datasets by achieving a 3.5% increase in recall and a 5.9% reduction in false positive rate. © 2024 IEEE.

关键词： Websites

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