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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ashraf, Shaina Bezzaoui, Isabel Andone, Ionut Markowetz, Alexander Fegert, Jonas Flek, Lucie Department of Mathematics and Computer Science University of Marburg Germany FZI Forschungszentrum Informatik Germany Murmuras GmbH Germany Lab Germany University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (纸本)9782493814104

In today's rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset "DeFaktS", designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing 105,855 posts with 20,008 meticulously labeled tweets, serves as a rich platform for in-depth exploration of disinformation's diverse characteristics. A key attribute that sets DeFaktS apart is, its fine-grain annotations based on polarized categories. Our annotation framework, grounded in the textual characteristics of news content, eliminates the need for external knowledge sources. Unlike most existing corpora that typically assign a singular global veracity value to news, our methodology seeks to annotate every structural component and semantic element of a news piece, ensuring a comprehensive and detailed understanding. In our experiments, we employed a mix of classical machine learning and advanced transformer-based models. The results underscored the potential of DeFaktS, with transformer models, especially the German variant of BERT, exhibiting pronounced effectiveness in both binary and fine-grained classifications. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

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Neutrosophic Encoding and Decoding Algorithm for ASCII Code System

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Neutrosophic Sets and Systems 2024年 63卷 105-129页

作者： Salama, A.A. Tarek, Zahraa Darwish, Eman Yousif Elseuofi, Sherif Shams, Mahmoud Y. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Computer Science Faculty of Computers and information Mansoura University Egypt Dept. of Information System Higher Institute for Computers & Specific Studies Damietta Ras El-Bar Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt

Context and Background: This paper addresses the challenge of encoding and decoding numerical data by introducing innovative algorithms utilizing Neutrosophic ASCII codes and ASCII Neutrosophic codes. These codes serve to represent uncertain or imprecise character values through the incorporation of neutrosophic numbers, encompassing degrees of truth, falsity, and indeterminacy. Motivation: The study stems from the necessity to effectively represent uncertain or imprecise character values in numerical data. This is crucial in diverse applications where handling uncertain or ambiguous data is a prevalent concern. Hypothesis: We hypothesize that employing Neutrosophic ASCII codes and ASCII Neutrosophic codes in encoding and decoding numerical data can provide a robust solution for representing uncertain or imprecise character values. Methods: The encoding algorithm in this study transforms each character in the ASCII string into its corresponding ASCII code, utilizing either 7 or 8 bits based on the code type. This algorithm calculates the degree of truth, falsity, and indeterminacy for each bit, considering the uncertainty or ambiguity associated with the character. The resulting neutrosophic numbers are appended to create the Neutrosophic ASCII code or ASCII Neutrosophic code. The decoding algorithm partitions the code into groups of neutrosophic numbers, calculates the associated degrees of truth, falsity, and indeterminacy for each ASCII bit, and converts the neutrosophic numbers back to ASCII codes, reconstructing the original ASCII character string. Results: Our study yields a novel and effective approach for encoding and decoding numerical data, demonstrating the potential of Neutrosophic ASCII codes and ASCII Neutrosophic codes in representing uncertain or imprecise character values. Conclusions: The proposed algorithms offer a promising solution for handling uncertain or ambiguous data in numerical encoding and decoding. The specific and quantitative results

关键词： Decoding

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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Virtual Scribe for Visually Impaired Learners Through Interactive Speech-Based Application 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Sanjeetha, R. Peter, T. John Kallimani, Jagadish S. Sambhram Institute of Technology Bangalore India Department of Computer Science and Engineering Sambhram Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Blind people face various challenges every day, something that people take for granted. One such problem that blind students face is the need for a scribe to write their exam. The onus falls on the blind student to find themselves a scribe which in itself is a difficult task. But even if a blind student has found a scribe, the scribes come with their own set of problems, such as linguistic capability and illegible handwriting. To solve this problem, V-Scribe or virtual scribe is being proposed which is a web application that would completely replace a human scribe. The application uses speech recognition, text-to-speech and a natural language understanding module to understand the requests given by the blind student and give back an appropriate response. The DIET classifier developed by Rasa is trained, obtaining an accuracy of 96.7% for intent recognition and 94.7% for entity recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Echo Insights: YouTube Sentiment Analysis App

Echo Insights: YouTube Sentiment Analysis App

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Vijay, K. Sharan Adhitya, C.D. Senthilnaathan, K. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

In an era where social media is getting a wider attraction seeing an improvement in content creators than consumers, ECHO INSIGHTS is a tool for analyzing YouTube video sentiments. This mainly focusses on creating an analytical tool that inputs a YouTube video link from user and uses YouTube API V3 to fetch comments from the video and organize them into a dataframe using pandas. For global audacity, these extracted comments are translated into English using Google trans library in Python. Subsequently, each of the fetched results are assigned with sentiments using Vader, a pre trained sentiment analysis model with a better accuracy of 96%. Then the analyzed results are displayed in a intuitive dashboard designed using streamlit enabling users to interpret sentiment patterns in the content. The dashboard offers various visualization contents like, good and bad comments based on Vader sentiment providing a clear view on sentiments of comments. Additionally, a summarized sentiment score is also displayed, providing general tone of the video. One of the additional features of out webapp is Wordcloud, a python library that plots most comments used or relevant words in the fetched comments representing a quicker glance in video engagement and users' opinions on video. ECHO INSIGHTS aims to empower content creators with better analysis and a healthier component and understanding of audience feedback and engagement with videos. Overall enabling users and creators with continuous improvement and optimization of their content, thus acting as a goof asset by translating raw comments till overall sentiment facilitating deeper connection with audience and crafting contents more efficiently. © 2024 IEEE.

关键词： Sentiment Analysis Streamlit Vader Wordcloud YouTube API

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Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-trust Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2312-2327页

作者： Wang, Tingting Fang, Kai Cai, Jijing Wang, Lina Tian, Jinyu Feng, Hailin Li, Jianqing Guizani, Mohsen Wang, Wei Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering Macau999078 China Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Machine Learning Department United Arab Emirates Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%. © 1983-2012 IEEE.

关键词： Economic and social effects

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Automatic Modulation Classification for CR Using Deep learning

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SN computer science 2024年第8期5卷 1061页

作者： Solanki, Surendra Brahma, Banalaxmi Singh, Yadvendra Pratap Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Dehmi Kalan Rajasthan Jaipur 303007 India Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Automatic Modulation Classification holds significant importance in the realm of wireless communication systems. The proliferation of modulation schemes, coupled with the escalating complexity of channel environments, necessitates a heightened accuracy in discerning modulated signals. Traditional methods struggle to effectively classify this array of modulation schemes. In response, this paper advocates the employment of deep learning techniques for precise modulation classification. A deep learning architecture is introduced, comprising a convolutional neural network, and a long short-term memory neural network leveraging the power of transfer learning. The convolutional neural network adeptly extracts spatial features from the received signal, while a bidirectional long short-term memory neural network is engineered to capture temporal features. Transfer learning is employed for efficient parameter sharing. Empirical evaluations were conducted using the RadioML2016.10b dataset, affirming the efficacy of the proposed mechanism. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolutional neural network Long short term memory Modulation modes Transfer learning

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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GroundingMate: Aiding Object Grounding for Goal-Oriented Vision-and-Language Navigation

GroundingMate: Aiding Object Grounding for Goal-Oriented Vis...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liu, Qianyi Zhang, Siqi Qiao, Yanyuan Zhu, Junyou Li, Xiang Guo, Longteng Wang, Qunbo He, Xingjian Wu, Qi Liu, Jing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Tongji University Department of Computer Science and Technology China Australian Institute for Machine Learning The University of Adelaide Australia

ISBN: (纸本)9798331510831

Goal-Oriented Vision-and-Language Navigation (VLN) aims to enable agents to navigate to specified locations and identify designated target objects following natural language instruction. This approach has gained popularity due to its close alignment with real-world scenarios. However, existing studies have predominantly focused on enhancing navigation performance, neglecting the ability to locate objects at the navigation endpoint. This oversight has resulted in a significant discrepancy between the success rates of navigation and object grounding. The challenge is compounded by the complex reasoning required by the instructions and the necessity to synthesize multiperspective images of objects, which overwhelms traditional object grounding methods. We leverage the Multi-Modal Large Language Model (MLLM) to bridge this gap, allowing agents to seek assistance from these models when struggling to locate the target object. The agent conducts a multi-stage evaluation to discern the cause of its confusion and promptly extracts and updates the most relevant information for MLLM to assess. Our method is plug-and-play and model-agnostic, facilitating integration with numerous existing VLN strategies without the need for retraining. Implementing our approach across four distinct methods has improved performance on the REVERIE and SOON datasets, demonstrating the effectiveness and generalizability of our technique. © 2025 IEEE.

关键词： Electric grounding

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