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20th Conference on Natural Language Processing, KONVENS 2024

作者： Petersen-Frey, Fynn Biemann, Chris Hub of Computing and Data Science Language Technology Group Universität Hamburg Germany

The task of quotation detection and attribution deals with identifying quotation spans together with their associated role spans such as the speaker. We describe an approach to solve the task of fine-grained quotation detection and attribution using a sequence-to-sequence transformer model with constrained decoding. Our model improves vastly upon the existing baselines on the German news articles quotation dataset, thereby making it feasible for a first time to automatically extract attributed quotations from German news articles. We provide an extensive description of our method, discuss alternative approaches, performed experiments using multiple foundation language models and method variants, and analyzed our model’s prediction errors. Our source code and trained models are available1,. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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From GPT to BERT: Benchmarking Large Language Models for Automated Quiz Generation 2024

From GPT to BERT: Benchmarking Large Language Models for Aut...

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1st ACM Virtual Global computing Education Conference V. 2, SIGCSE Virtual 2024

作者： Folajimi, Yetunde School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9798400706042

This study evaluates the effectiveness of four leading large language models (LLMs), GPT-3, GPT-4, GPT-4o, and BERT, in generating quiz questions for Java and Python programming courses. We aim to recognize how LLMs can effectively produce educationally valuable questions that meet specific pedagogical criteria, including technical precision, relevance to course objectives, linguistic clarity, and pedagogical appropriateness. Each model was prompted to generate 200 Java and 200 Python quiz questions, totaling 1600 unique questions. These questions are currently being evaluated based on both quantitative and qualitative assessments by a team of computer science educators. Preliminary findings suggest that GPT-4 outperforms BERT in terms of technical precision. Further analysis is ongoing to assess the performance of the models in generating contextually appropriate and educationally useful questions, offering insights into their potential integration into computer science curricula. This work seeks to contribute to the broader discourse on the utility of LLMs in educational settings, specifically within the scope of automated content creation to enhance teaching and assessment methodologies in computer science education. © 2024 Copyright is held by the owner/author(s).

关键词： Java programming language

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A Comprehensive Study on Current Trends in Unsupervised Machine learning Algorithms and Challenges in Real World Applications 14

A Comprehensive Study on Current Trends in Unsupervised Mach...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Velvadivu, P. Sujithra, M. Priyadharshini, R. Department of Computing - Data Science Coimbatore Institute of Technology India

In this technology fueled world, everyone is using smart devices, electronic gadgets, wireless products etc. and huge amounts of data are being generated, collected, and stored in the databases. To efficiently process and intelligently analyze the huge amount of data, the knowledge about subfield of Artificial Intelligence that is, Particularly Machine learning (ML) is required. There are various types of machine learning and its algorithms have been introduced to handle real world scenarios. This paper discusses the Comprehensive survey based on methodologies, techniques, algorithms, applications, and challenges faced by unsupervised machine learning and how unsupervised learning techniques can be helpful in real world business and environment. Thus, this study’s key contribution is explaining the principles of different unsupervised machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. © Grenze Scientific Society, 2023.

关键词： Unsupervised learning

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

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Journal of Computer science & Technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

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A Comprehensive Analysis of Image Processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews Image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis 1

Deep Neural Networks Based Feature Extraction with CBIR: An ...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Praveena, S. Veeramakali, T. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamilnadu Kattankulathur India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

The need for having sufficient space to store and retrieve data has grown in importance in today's fast-paced worlde. Hence, end-users struggle to access particular facts, leading to difficulties in obtaining reliable statistics. The text-based image retrieval (TBIR) system is being enhanced by researchers to address these issues. Based on the user's query or annotation, TBIR finds relevant facts. This is the best method for users to get all the important data. The database unfortunately produces more unwanted data due to the complexity and length of the operation. A technology known as content-based image retrieval (CBIR), offers a way around this problem. The user's query or input images are used to determine the relevant output images. Machine learning and deep learning algorithms are highly suited for CBIR- based image classification and prediction. The notion of machine learning as a potential remedy for the semantic gap problem has been extensively studied. Given the recent successes of deep learning, there is evidence to suggest that we are capable of bridging the semantic gap in CBIR. The various techniques for feature extraction and similarity measurement are thoroughly reviewed in this paper. The benefits and drawbacks of deep learning networks and CBIR-based machine learning techniques are further investigated through comparison analysis portion. Consequently, detailed explanations of techniques utilizing deep learning for identifying skin disorders are provided. Following to that, a variety of CBIR approaches and the Corel 5K dataset are used to assess the various performance measures. © 2024 IEEE.

关键词： Feature extraction

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