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11th International Conference on Language Resources and Evaluation, LREC 2018

作者： Lango, Mateusz Ševčíková, Magda Žabokrtský, Zdeněk Poznan University of Technology Faculty of Computing Institute of Computing Science United States Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic

ISBN: (纸本)9791095546009

The paper presents a semi-automatic method for the construction of derivational networks. The proposed approach applies a sequential pattern mining technique in order to construct useful morphological features in an unsupervised manner. The features take the form of regular expressions and later are used to feed a machine-learned ranking model. The network is constructed by applying resulting model to sort the lists of possible base words and selecting the most probable ones. This approach, besides relatively small training set and a lexicon, does not require any additional language resources such as a list of alternations groups, POS tags etc. The proposed approach is applied to the lexeme sets of two languages, namely Polish and Spanish, which results in the establishment of two novel word-formation networks. Finally, the network constructed for Polish is merged with the derivational connections extracted from the Polish WordNet and those resulting from the derivational rules developed by a linguist, resulting in the biggest word-formation network for that language. The presented approach is general enough to be adopted for other languages. © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.

关键词： Morphology

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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

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Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity

arXiv

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arXiv 2025年

作者： Najjar, Ayat A. Ashqar, Huthaifa I. Darwish, Omar A. Hammad, Eman Faculty of Modern Science Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States Information Security and Applied Computing Eastern Michigan University 900 Oakwood St YpsilantiMI48197 United States iSTAR Lab Engineering Technology & Industrial Distribution Texas A&M University 400 Bizzell St College StationTX77840 United States

This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contribution of this work is the generation of the CyberHumanAI dataset, which has 1000 observations, 500 of which are written by humans and the other 500 produced by ChatGPT. We evaluate various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) (i.e., ChatGPT). Results demonstrate that traditional ML algorithms, specifically XGBoost and Random Forest, achieve high performance (83% and 81% accuracies respectively). Results also show that classifying shorter content seems to be more challenging than classifying longer content. Further, using Explainable Artificial Intelligence (XAI) we identify discriminative features influencing the ML model's predictions, where human-written content tends to use a practical language (e.g., use and allow). Meanwhile AI-generated text is characterized by more abstract and formal terms (e.g., realm and employ). Finally, a comparative analysis with GPTZero show that our narrowly focused, simple, and fine-tuned model can outperform generalized systems like GPTZero. The proposed model achieved approximately 77.5% accuracy compared to GPTZero's 48.5% accuracy when tasked to classify Pure AI, Pure Human, and mixed class. GPTZero showed a tendency to classify challenging and small-content cases as either mixed or unrecognized while our proposed model showed a more balanced performance across the three classes. © 2025, CC BY.

关键词： Adversarial machine learning

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Early Detection of Chikungunya Virus Using Silver and Zinc Selenide Multilayer Structure Utilizing the Surface Plasmon Resonance: A Numerical Approach

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Plasmonics 2025年 1-12页

作者： Ansari, Gufranullah Oweis, Rami Baldaniya, Lalji Barakat, Muna S, Renuka Jyothi Kaur, Irwanjot Al-Hasnaawei, Shaker Maharana, Laxmidhar Chauhan, Ashish Singh Purohit, Bilakshan Uniyal, Arun Department of Electronics & Comm. Engg School of Engineering and Computing Dev Bhoomi Uttarakhand University Chakrata Road Uttarakhand Manduwala Naugaon 248007 India Modern College of Business and Science Muscat Oman Jordan University of Science and Technology Irbid 22110 Jordan Department of Pharmaceutical Sciences Faculty of Health Sciences Marwadi University Research Center Marwadi University Gujarat Rajkot India Faculty of Pharmacy Applied Science Private University Amman Jordan Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to Be University) Karnataka Bangalore India Department of Allied Healthcare and Sciences Vivekananda Global University Rajasthan Jaipur 303012 India College of Pharmacy the Islamic University Najaf Iraq Department of Medical Analysis Medical Laboratory Technique College the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Pharmaceutical Sciences Siksha’O’Anusandhan (Deemed to Be University) Odisha Bhubaneswar 751030 India Division of Research and Innovation Uttaranchal Institute of Pharmaceutical Sciences Uttaranchal University Uttarakhand Dehradun India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Center for Global Health Research Saveetha Medical College & Hospitals Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai 602105 India Depertment of Electronics and Communication Engineering Graphic Era Hill University Uttarakhand Dehradun 248002 India Department of Electronics and Communication Engineering Graphic Era (Deemed to Be University) Uttarakhand Dehradun 248002 India

The current work examines the theoretical aspects of a very sensitive surface plasmon resonance sensor for detecting the chikungunya virus. Silver and zinc selenide are the only material layers comprising the sensing structure. They are deposited one on top of the other on the surface of the BK7 glass prism. For best sensing performance, the geometric characteristics of each layer are tuned. The chikungunya virus detection capacity of the proposed sensor structure is demonstrated by its results. The obtained simulated results give a high sensitivity of 384.7 degree/RIU with a detection accuracy of 0.471 degree⁻¹, the figure of merit of 181.19 RIU⁻¹. These parameters support our assertion that we have designed a high-performance-based sensor to identify the chikungunya virus in the blood sample. The results are simulated using MATLAB programming based on the transfer matrix method (TMM), and they are then examined further to assess the sensor’s performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Chikungunya virus Figure of merit Sensitivity Surface plasmons Transfer matrix method

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Smart Skim Pinjaman Buku Teks (SBPT) School Operation System in Malaysia

Smart Skim Pinjaman Buku Teks (SBPT) School Operation System...

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2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Ismail, Nor-Syahidatul N Zakaria, Muhammad Ashraf Bin Muhammad, Nor Aishah Binti Yunus, Farizah Binti Faculty of Computing College of Computing and Applied Sciences Pahang Malaysia Wireless Communication Centre School of Electrical Engineering Faculty of Engineering Malaysia Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9781665403436

Skim Pinjaman Buku Teks (SPBT) is a textbook loan program for elementary and secondary schools in Malaysia. This program is an initiative system by the Malaysia Ministry of Education to help students in the school by providing them with textbooks for their learning purpose. The current SPBT system is handled by storing all the records into a book or a file that can cause data missing, data redundant and incorrect data stored in the system. This project presents a new system for the textbook loan system known as Smart Skim Pinjaman Buku Teks (SBPT) School Operation system (SSSOS). This system upgrades the conventional method that manually records the loan information to a systematic approach using a web-based management system. The rapid application development (RAD) cycle is used as a methodology to develop the SSSOS system. The proposed system can efficiently handle borrowing and returning processes and reduce the textbook loan process period. This system also provides a complete report related to the textbook, such as the current status of a book and the previous information of the borrower. The proposed system can be used to enhance the conventional SPBT system in handling the textbook loan program. The system has been tested in Sekolah Kebangsaan Gambang, Pahang, where an SPBT coordinator acts as a client for the system. © 2021 IEEE.

关键词： Textbooks

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Observation of giant nonlinear valley Hall effect

arXiv

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arXiv 2025年

作者： He, Pan Zhang, Min Cao, Jin Li, Jingru Liu, Hao Zhai, Jinfeng Wang, Ruibo Xiao, Cong Yang, Shengyuan A. Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Hefei National Laboratory Hefei230088 China Institute of Applied Physics and Materials Engineering Faculty of Science and Technology University of Macau China Fudan University Shanghai200433 China Department of Physics Fudan University Shanghai China Shanghai Research Center for Quantum Sciences Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving nonlinear valley responses undetected and nonlinear valleytronic devices undeveloped. Here, we report the experimental observation of a nonlinear VHE in a graphene-hBN moiré superlattice, evidenced by the generation of second-harmonic nonlocal voltages under AC currents. Remarkably, the nonlinear VHE has magnitude surpassing the linear VHE and is highly tunable via a gate voltage, which exhibits a pair of opposite peaks on the two sides of a Dirac gap. The nonlinear signal shows quadratic scaling with driving current and quartic scaling with local resistance, setting it apart from the linear counterpart. These experimental features are consistent with the theoretical picture of nonlocal transport mediated by nonlinear VHE and linear inverse VHE. We further reveal a nonlinear inverse VHE by observing the third- and fourth-harmonic nonlocal voltages. The nonlinear VHE provides a novel mechanism for valley manipulation and enables a novel valleytronic device—the valley rectifier—that converts AC charge current into DC valley current. Copyright © 2025, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI

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Scientific Reports 2025年第1期15卷 1-14页

作者： Hussain, Sayyed Shahid Shah, Pir Masoom Dawood, Hussain Degang, Xu Alshamayleh, Ahmad Khan, Muhammad Adnan Ghazal, Taher M. School of Automation Central South University Changsha 410083 China School of Computer Science and Engineering Central South University Changsha 410010 China Department of Computer Science Bacha Khan University Charsadda Pakistan School of Computing Skyline University College Sharjah United Arab Emirates Department of Data Science and Artificial Intelligence Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13120 South Korea Riphah School of Computing and Innovation Riphah International University Lahore Campus Lahore 54000 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Networks and Cybersecurity Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson’s disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the vision transformer (ViT) excels at capturing global features through its self-attention mechanism, but it compromises local feature representations. Additionally, the varying magnitude of MR data poses a challenge for ViT, potentially leading to the gradient vanishing problem. To address these limitations, this paper proposed a novel framework that combines the Swin-Transformer and CNN to capture both local and global features effectively. To mitigate the gradient vanishing issue in ViT, we used skipped connections and cosine attention mechanism in VIT that preserves the output distribution regardless of input magnitude variations. The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. Subsequently, the extracted features from both blocks are concatenated and fed to the dense block for decision-making. The proposed model achieved promesing results of 96%, 97%, 95%, and 95% in terms of accuracy, sensitivity, specificity, and area under the curve, respectively. © The Author(s) 2025.

关键词： CNN Cosine similarity MRI Parkinson’s disease Swin-transformer

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AI-Enhanced Intrusion Detection in Smart Renewable Energy Grids: A Novel Industry 4.0 Cyber Threat Management Approach

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International Journal of Critical Infrastructure Protection 2025年

作者： Umar Islam Hanif Ullah Naveed Khan Kashif Saleem Iftikhar Ahmad Department of Computer Science IQRA National University Swat Campus KPK Pakistan School of Computing Ulster University Belfast UK Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh 11362 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The rapid adoption of Industry 4.0 technologies in renewable energy grids has significantly improved efficiency and scalability. However, this integration has also amplified cybersecurity risks, making conventional Intrusion Detection Systems (IDS) insufficient against evolving cyber threats. This study proposes a novel AI-enhanced Intrusion Detection System (IDS) tailored for smart renewable energy grids, leveraging a multi-stage detection framework that integrates both supervised and unsupervised learning techniques. The proposed IDS combines Random Forest for signature-based detection and Autoencoders for anomaly-based threat identification, enabling real-time detection of both known and zero-day cyber threats. A comprehensive evaluation using real-world cyberattack datasets demonstrates that the system achieves a detection accuracy of 97.8%, significantly reducing false positives compared to traditional IDS solutions. This work not only enhances the security and resilience of smart grids but also offers a scalable and adaptable cybersecurity framework for Industry 4.0 applications. The findings contribute to the advancement of AI-driven security mechanisms, ensuring the reliability of critical energy infrastructure in the face of sophisticated cyber threats.

关键词： AI-enhanced intrusion detection smart grids renewable energy Industry 4.0 cybersecurity machine learning anomaly detection

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Hilbert-Peano and Zigzag: Two Approaches Mapping Pattern of Digital Watermarking for Text Images Authentication 7

Hilbert-Peano and Zigzag: Two Approaches Mapping Pattern of ...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Ghani, Aqilah Abd. Izhar Hisham, Syifak Arshad, Nurul Wahidah Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan26600 Malaysia Faculty of Electrical and Electronic Engineering Technology College of Engineering Technology Universiti Malaysia Pahang Pekan26600 Malaysia

ISBN: (纸本)9781665414074

The wide increment of information and communication technology nowadays in line with the usage of digital documents, the user from different organizations such as education, military, medical, business, and others tend to transfer any official file through various digital platforms. Thus, to secure this confidential data, a digital watermarking technique was chosen. This paper proposed an improved mapping pattern method of a fragile watermarking authentication algorithm for text images. There are various methods for watermark embedding, which mapping pattern is one of them. The aim is to validate and compare the SCAN pattern for digital watermarking in order to produce a fast and efficient authentication algorithm. We proposed a Zigzag SCAN pattern algorithm and compared it with the proposed Hilbert-Peano scheme. The result from this paper shows that Zigzag SCAN algorithm contributed to a superior performance in regard to processing time, while PSNR and MSE are similar. © 2021 IEEE.

关键词： Authentication

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Semantic parsing of speech using grammars learned with weak supervision

Semantic parsing of speech using grammars learned with weak ...

引用

Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015

作者： Gaspers, Judith Cimiano, Philipp Wrede, Britta Semantic Computing Group CITEC Bielefeld University Germany Applied Informatics Faculty of Technology Bielefeld University Germany

ISBN: (纸本)9781941643495

Semantic grammars can be applied both as a language model for a speech recognizer and for semantic parsing, e.g. in order to map the output of a speech recognizer into formal meaning representations. Semantic speech recognition grammars are, however, typically created manually or learned in a supervised fashion, requiring extensive manual effort in both cases. Aiming to reduce this effort, in this paper we investigate the induction of semantic speech recognition grammars under weak supervision. We present empirical results, indicating that the induced grammars support semantic parsing of speech with a rather low loss in performance when compared to parsing of input without recognition errors. Further, we show improved parsing performance compared to applying n-gram models as language models and demonstrate how our semantic speech recognition grammars can be enhanced by weights based on occurrence frequencies, yielding an improvement in parsing performance over applying unweighted grammars. © 2015 Association for Computational Linguistics.

关键词： Semantics

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