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作者： Saleem, Zeeshan Alhudhaif, Adi Qureshi, Kashif Naseer Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science College of Computer Engineering and Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context-aware text classification system to improve text quality. We use a content-aware recommendation system to extract the data from well-known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance. © 2021 John Wiley & Sons Ltd.

关键词： Classification (of information)

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Experimental observation of coherent-perfect-absorber and laser points in anti-PT symmetry

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Physical Review A 2024年第3期110卷 033504页

作者： Minye Yang Qi Zhong Zhilu Ye Şahin K. Özdemir Mohamed Farhat Ramy El-Ganainy Pai-Yen Chen State Key Laboratory for Manufacturing Systems Engineering School of Electronic Science and Engineering Department of Electrical and Computer Engineering Department of Engineering Science and Mechanics The Materials Research Institute Computer Electrical and Mathematical Science and Engineering Division Department of Physics Henes Center for Quantum Phenomena

The coexistence of coherent perfect absorber and laser or amplifier (CPAL) point is a peculiar spectral singularity associated with the scattering matrices of non-Hermitian systems. While the potential of CPAL systems for sensing application has been highlighted recently, the extreme sensitivity of parity-time (PT)-symmetric CPAL devices to the input signal deviations has so far impeded their practical utilization. Here we explore a strategy for implementing CPAL circuits by exploiting another type of non-Hermitian symmetry, namely anti-PT (APT) symmetry. We demonstrate that the condition for building CPAL in our proposed APT-symmetric electronic circuits additionally requires parity symmetry, which simplifies the circuit design and implementation. Additionally, we show that this newly proposed structure is 1.85 times more robust compared to previous CPAL devices studied in the literature.

关键词： Noise Quantum sensing Non-Hermitian systems PT-symmetry

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Log Preservation in Custody Dual Blockchain With Energy Regime and Obfuscation Shuffle

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IEEE Transactions on Network science and engineering 2024年第4期11卷 3495-3511页

作者： Chew, Chit-Jie Lee, Wei-Bin Chen, Tzu-Hao Lin, Iuon-Chang Lee, Jung-San Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan Hon Hai Research Institute Information Security Research Center Taipei236 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

The rise of cybercrime has led to an explosion of threats in recent years, resulting in the increasingly important of digital forensics. To achieve successful investigation and prosecution, the log preservation is a critical aspect of incident response. The blockchain technology is considered as one of the powerful log storage mechanism, which bringing data transparency and non-tempering property aside from taking off the hypothesis of a trusted third party. Nevertheless, the sensitivity of the data makes it vulnerable to malicious attacks, and the adoption of blockchain has significantly burdened participants regarding consensus time and storage. To address these challenges, we aim to develop a custody dual blockchain (CDB) to secure the confidentiality and integrity of log data. The design of reputation consensus attains that only attractive participants need to offer computation and storage for efficiency enhancing, while the energy regime constructs a positive feedback loop that invigorates users to contribute to CDB. Moreover, the obfuscation shuffle effectively obscures the explicit log plaintext to prevent illegal access. Thus, the confidential data on the CDB nodes can even be used as evidence of digital forensics. Simulation results have demonstrated that the ledger overhead can be significantly reduced, and CDB could pass the robust examination of two formal proof tools: Proverif (Blanchet et al. 2021) and AVISPA. © 2013 IEEE.

关键词： Blockchain

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Multilabel Aggressive Text Classification from Social Media using Transformer-based Approaches 26

Multilabel Aggressive Text Classification from Social Media ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hossain, Jawad Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

The prevalence of multilabel aggressive text content on social media has a detrimental societal impact attracting the attention of government agencies and tech corporations to undertake measures against the spread of it. Hitherto research has focused on high-resource languages like English, leaving low-resource languages like Bengali out of the spotlight. This work presents a transformer-based technique to classify multilabel aggressive texts in Bengali into their targets to aid research in this area. A dataset (EM-BAD) containing 13728 texts is developed into five target classes: Religious Aggression (ReAG), Political Aggression (PoAG), Verbal Aggression (VeAG), Gender Aggression (GeAG), and Racial Aggression (RaAG) to perform the aggressive texts classification. Experimental results demonstrate that the Bangla-BERT with adjusted pooling layer and fine-tuning outdoes all ML, DL, and transformer-base baselines and existing techniques. The Bangla-BERT shows the highest weighted f1-score of 0.89 in the multilabel aggressive text classification task. © 2023 IEEE.

关键词： Aggressive text classification Deep learning Natural language processing Text corpora Text processing

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Classifying Textual Sentiment Using Bidirectional Encoder Representations from Transformers 26

Classifying Textual Sentiment Using Bidirectional Encoder Re...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahsan, Shawly Tasnia, Fairooz Tabassum, Nafisa Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools are primarily tailored for English texts. The unique linguistic complexities of the Bengali language, coupled with a paucity of comprehensive resources and tools, pose distinctive challenges for TSA in Bengali. This paper introduces an intelligent approach, leveraging transformer-based learning techniques by harnessing the potent capabilities of self-attention mechanisms for dealing with Bengali sentences containing ungrammatical structures or local dialects. To tackle the downstream TSA task in Bengali, this work explores a range of machine learning (ML), deep learning (DL), and transformer-based baselines. Experimental results reveal that the Bangla BERT model outperforms the other baselines, achieving the highest weighted f1-score of 0.69. © 2023 IEEE.

关键词： Natural language processing Sentiment analysis Sentiment corpus Text processing Transformer learning

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Implementing Data science and Big Data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting Data-Driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Arthy, M. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. Ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big Data Analytics Data Governance Data Infrastructure Data Literacy Data Quality Data science Data-Driven Decision Making Data-Driven Innovation Ethical Considerations Machine Learning Natural Language Processing Predictive Modeling Strategic Advantage

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A Novel Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer for Satellite Image Classification with Google Earth Engine 4

A Novel Dual Temporal Channel Convolutional Network with Exp...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Ragavendiran, S. D. Prabu Gowthami, N.R. Gayathri, C. Ramya, D. Padmapriya, D. Aparna, S.V. Suji Builders Engineering College Department of Computer Science and Engineering Tamil Nadu Tirupur638108 India Annamacharya University Department of Civil Engineering Andhra Pradesh Rajampet516126 India Mahendra Institute of Technology Tamil Nadu Namakkal637503 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Telangana Hyderabad501401 India

ISBN: (纸本)9798331529635

Classification of satellite images using Google Earth Engine entails identifying features on the satellite images to classify the land cover in a particular region through the use of deep learning algorithms. Such a process contributes to monitoring the environment and geographical distribution of resources, planning of cities, and management of resources. However, in many existing approaches, some challenges are worth noting. To address these challenges, this study introduces a new approach, the Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer (ANDualTCCNet+EDO) for satellite image classification using Google Earth Engine. This approach uses satellite imagery from the Landsat 8 Surface Reflectance dataset, with the input images preprocessed through the Adaptive Tri-Plateau Limit Tri-Histogram Equalization Algorithm (ATri-PLTri-HEA) technique. Following preprocessing, features are extracted from the images using the DenseNet201 method. Then, classification is carried out by A Novel Dual Temporal Channel Convolutional Network (ANDualTCCNet), optimized by the Exponential Distribution Optimizer (EDO) for precise satellite image classification in Google Earth Engine (GEE). The ANDualTCCNet+EDO method is implemented in Python. The proposed method was evaluated using the Landsat 8 Surface Reflectance dataset, achieving a high accuracy of 99.8%, surpassing existing methods. The results presented in this paper show that the use of this method allows for the accurate classification of satellite images in Google Earth Engine. The outcomes of the proposed method improve the method of satellite image classification in Google Earth Engine. This model captures both short- and long-duration temporal characteristics, therefore enhancing spatial-temporal feature acquisition compared to the previous method used in the classification of land cover, especially for temporal dynamics of land covers with different seasonal changes. © 2024 IEEE.

关键词： Equalizers

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Decentralized Edge-Based Detection of Label Flipping Attacks in Federated Learning 17th

Decentralized Edge-Based Detection of Label Flipping Attack...

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17th International Conference on Computational Intelligence in Security for Information systems, CISIS 2024 and the 15th International Conference on EUropean Transnational Education, ICEUTE 2024

作者： AlOtaibi, Nourah Felemban, Muhamad Mahmood, Sajjad Information and Computer Science Department KFUPM Dhahran Saudi Arabia Dammam Saudi Arabia Computer Engineering Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

ISBN: (纸本)9783031750151

Federated learning (FL) enhances data privacy by enabling decentralized model training without sharing local data. However, FL is vulnerable to Label-Flipping-Attacks (LFA), where malicious clients flip data labels. Traditionally, cloud-centric centralized defenses against LFA pose security and efficiency challenges. We propose a novel edge-based decentralized LFA detection method using edge servers for efficient detection. The proposed method considers the overall and class-wise accuracy in identifying suspicious clients. Initially, we adopt a strict zero-tolerance approach by excluding the entire update from detected malicious clients. We then experiment with four aggregation techniques-subtracting, masking, clipping, and reweighting–to handle the malicious parts of updates by focusing on the final layer neurons corresponding to specific classes. Experiments using three datasets demonstrate the effectiveness, robustness, and efficiency of our method, showing improved model performance and reduced latency under adversarial conditions. This approach improves the security and reliability of FL systems while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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Enhancing Pre-Tertiary Students Decision-Making using a Web-Based Admission Recommender System

Enhancing Pre-Tertiary Students Decision-Making using a Web-...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Famodimu, Oluwasefunmi Akande, Adeyemi Fapohunda, Kofoworola Faluyi, Samuel Balogun, Temitayo Babcock University Department of Computer Science Ilisan Nigeria Louisiana Tech University Department of Electrical and Electronics Engineering Ruston United States Louisiana Tech University Department of Biomedical Engineering Ruston United States Ekiti State Polytechnic Department of Computer Science Isan Ekiti Nigeria Federal University of Technology Department of Information Systems Akure Nigeria

ISBN: (纸本)9798350358155

Education plays a pivotal role in individual development, imparting growth, values, and cultural understanding. In the realm of education, university education emerges as a transformative phase crucial for professional life. The selection of the right course during these formative years significantly shapes one's life trajectory. Amidst the complexities of this decision-making stage, exacerbated by societal pressures, pre-tertiary students often grapple with confusion. This research addresses the unique challenges faced by pre-tertiary students through the introduction of a Web-Based Admission Recommender System. Unlike existing systems, our recommender system empowers students to autonomously make well-informed decisions about their course of study, considering their distinctive abilities. The system integrates three crucial parameters: preferred core subjects' combination, Intelligence Quotient, and Career Interest. Implemented with the Catboost Classification utilizing the Gradient Boosting Algorithm, and featuring a user interface designed with Bootstrap 3, Python, and Flask, the system underwent rigorous testing with primary data from 346 secondary school students in their final year. The evaluation showcased commendable accuracy, with a notable 86.71% accuracy rate, 74.4% precision, 80% recall, and an 83% f1 score rate. This research makes distinctive contributions to the field by significantly enhancing the decision-making process for pretertiary students. The recommender system emerges as a reliable tool, uniquely positioned to guide students in selecting courses aligned with their individual capabilities and aspirations. By addressing the nuanced needs of pre-tertiary students, our research sets a new standard in personalized educational guidance. © 2024 IEEE.

关键词： Students

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