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Towards Completeness: A Generalizable Action Proposal Generator for Zero-Shot Temporal Action Localization 27th

Towards Completeness: A Generalizable Action Proposal Genera...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Du, Jia-Run Lin, Kun-Yu Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031784439

To address the zero-shot temporal action localization (ZSTAL) task, existing works develop models that are generalizable to detect and classify actions from unseen categories. they typically develop a category-agnostic action detector and combine it with the Contrastive language-Image Pre-training (CLIP) model to solve ZSTAL. However, these methods suffer from incomplete action proposals generated for unseen categories, since they follow a frame-level prediction paradigm and require hand-crafted post-processing to generate action proposals. To address this problem, in this work, we propose a novel model named Generalizable Action Proposal generator (GAP), which can interface seamlessly with CLIP and generate action proposals in a holistic way. Our GAP is built in a query-based architecture and trained with a proposal-level objective, enabling it to estimate proposal completeness and eliminate the hand-crafted post-processing. Based on this architecture, we propose an Action-aware Discrimination loss to enhance the category-agnostic dynamic information of actions. Besides, we introduce a Static-Dynamic Rectifying module that incorporates the generalizable static information from CLIP to refine the predicted proposals, which improves proposal completeness in a generalizable manner. Our experiments show that our GAP achieves state-of-the-art performance on two challenging ZSTAL benchmarks, i.e., thumos14 and ActivityNet1.3. Specifically, our model obtains significant performance improvement over previous works on the two benchmarks, i.e., +3.2% and +3.4% average mAP, respectively. the code is available at https://***/Run542968/GAP. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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A Study of Contrastive Learning Methods for Bengali Social Analysis

A Study of Contrastive Learning Methods for Bengali Social A...

引用

international conference on Electrical Engineering and information & Communication technology (ICEEICT)

作者： Jannatim Maisha Farhana Hossain Swarnali M. Saymon Islam Iftikar Sarkar Bulbul Ahammed Faisal Muhammad Shah Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Across various domains, Contrastive Learning (CL) has already proven to be a powerful technique but using the Bengali language in the domain of Natural language processing (NLP) its' application is still unexplored. In this research, we introduce the implementation of CL in Bengali NLP by presenting a comprehensive benchmark study on two distinct datasets: Bengali Hate Speech, and Rokomari Book Review. the efficiency of the supervised contrastive techniques is emphasized by our methodology. We have implemented a contrastive learning technique through Bangla Bert. the superiority of supervised contrastive learning techniques over traditional Cross Entropy (CE) methods has been showcased by our result. Detailed experiments reveal performance variations across datasets, models, and hyperparameters but ensure the superiority of CL techniques over CE. Our findings show the significance of contrastive learning methods in low-resource settings, contributing to the advancement of Bengali NLP research.

关键词： Hate speech Benchmark testing Natural language processing Entropy Communications technology Book reviews Task analysis

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Learning to Automatically Generating Genre-Specific Song Lyrics: A Comparative Study 1

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4th Iberoamerican conference and 3rd Indo-American conference Knowledge Graphs and Semantic web conference (KGSWC)

作者： Tee, Tze Huat Yeap, Belicia Qiao Bei Gan, Keng Hoon Tan, Tien Ping Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

ISBN: (数字)9783031214226

ISBN: (纸本)9783031214219;9783031214226

the impact of music on the many dimensions of human life can be partly attributed to its linguistic component - the lyrics. In hopes of helping songwriters reach their full potential, researchers have implemented advanced artificial intelligence (AI) technology to automatically generate song lyrics. these efforts, however, were met with challenges that accompany the distinctive qualities of song lyrics, such as word repetition, structural pattens, and line breaks;all of which are dependent on the music genre. Seeing as most previous research either focuses on a given approach or genre, or performs the task without consideration of lyric variation among genres, this study attempts to address the gap by exploring and comparing the capabilities of three promising methods, specifically Markov chains, long short-term memory (LSTM), and gated recurrent units (GRU), in algorithmically generating lyrics for six selected music genres, namely rock, pop, country, hip-hop, electronic dance music (EDM), and rhythm and blues (R&B). Our findings show that LSTM scored better in the average readability index in overall, however, GRU produced the overall highest Rhyme Density score.

关键词： Lyrics Text generation Natural language processing (NLP) Markov chains Long Short-Term Memory (LSTM) Gated Recurrent Units (GRU)

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Automatized Educational Chatbot using Deep Neural Network 6

Automatized Educational Chatbot using Deep Neural Network

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Joint 6th international conference on Digital Arts, Media and technology with 4th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunication Engineering, ECTI DAMT and NCON 2021

作者： Jaiwai, Mathawan Shiangjen, Kanokwatt Rawangyot, Surangkana Dangmanee, Sakpan Kunsuree, thanakrit Sa-Nguanthong, Autchadaporn University of Phayao School of Information and Communication Technology Computer Science Department Phayao Thailand

ISBN: (纸本)9781665415699

Most of the time, students must visit universities or web pages to collect various information e.g. semester schedule, grade, curriculum, advisor info, etc. this process is time consuming and requires manpower providing such required information. Nowadays, social media and online services such as email, direct message, or live chat are becoming popular. Many students use live chat service because it is easy, fast and comfortable. However, there are some issues occurred an extensive amount of valuable time to answer by admin are wasted. Hence, in order to solve the problem properly, this article proposes an approach for building a question-Answering system via chatbot technology. Because chatbots is an enable human-machine interactions with the system by the human language, which is very intuitive and user-friendly. this research objective was to develop a question-Answer system in thai using chatbot technology, which was integrated with the LINE mobile application the experiment added a keyword in the educational domain to the dictionary and comparing word segmentation for 3 methods with text classification, combination of natural language processing (NLP) and Deep Neural Network (DNN) are used in the form of intent for text classification in this article. © 2021 IEEE.

关键词： Deep neural networks

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An Improved Topic Extraction Method Based on Word Frequency information Entropy for Multilingual Topic Attentional Division

An Improved Topic Extraction Method Based on Word Frequency ...

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Yue Yuan Huaping Zhang Beijing Institute of Technology Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In the contemporary era of ubiquitous global information dissemination, a myriad of news articles are generated worldwide on a daily basis. the topics that capture the attention of different countries diverge due to variances in culture, values, and other influential factors. Analyzing these discrepancies in topic preferences across languages within specific timeframes holds paramount importance for comprehensively understanding and delineating the nuances of diverse national cultures. this paper proposes a novel statistical analysis methodology for extracting multi-language news topic keywords, leveraging the concept of word frequency information entropy. Our approach facilitates the identification of shared topics across different languages, as well as language-specific concerns, within extensive news datasets. Furthermore, we address a prevalent challenge encountered in existing topic modeling methodologies, namely output redundancy. through the aggregation of synonymous terms, we effectively alleviate redundancy, thereby enhancing the quality of extracted topic keywords. Experimental evaluations are conducted on a meticulously collected multinational news dataset, wherein we assess the effectiveness of our approach in partitioning common and language-specific focus topics across multiple languages, while also quantifying the efficacy of redundancy elimination.

关键词： Vocabulary Statistical analysis Computational modeling Redundancy Merging Signal processing Vectors Robustness Data mining information entropy

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SPEAR: Design and Implementation of an advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert Systems (ICSES), international conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural language processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. these findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants Machine learning information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors web search

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Patent Classification for Business Strategy with BERT 6th

Patent Classification for Business Strategy with BERT

引用

6th international conference on Intelligent Computing and Optimization, ICO 2023

作者： Higashi, Masaki Utsumi, Yoshimasa Nakata, Kazuhide Tokyo Institute of Technology Tokyo Japan Rakuten Group Inc. Tokyo Japan

ISBN: (纸本)9783031501579

Recent developments in deep learning have greatly improved the accuracy of various natural language processing tasks. Patent classification using patent documents has also seen a significant improvement in accuracy based on the internationally defined IPC as well as the region-specific CPC prediction methods. Such an automated patent classification capability can reduce the burden on examiners and improve efficiency. It can also be used to organize patents, such as in prior art searches. However, such a classification cannot be used to support the formulation of management strategies, such as capturing the fields in which the predicted patent classification will be used in the management of a company. To this end, this study aims to classify patents based on technological field instead of the existing IPC classification such that the developed automated patent classification is useful for companies. To elaborate, this study investigates the differences between IPC and the proposed classification based on technological field, selects documents to be used, and proposes a classification model using IPC information. the proposed classification model is comprised of two distinct components, namely a model for document input and another for IPC input. BERT is employed for the document input model, while skip-connections are used for the IPC input model. Finally, the improvement in accuracy is examined compared to existing IPC using actual data;moreover, solutions are proposed to the problems identified in this study. As a result, our proposed model significantly improves precision, recall, F1, and AP over existing models. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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A Comprehensive Study for the Hindi language to Implement Supervised Text Classification Techniques 6

A Comprehensive Study for the Hindi Language to Implement Su...

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6th IEEE international conference on Signal processing, Computing and Control, ISPCC 2021

作者： Soni, Vijay Kumar Selot, Smita Chandigarh University Computer Science And Engineering Punjab India

ISBN: (纸本)9781665425520

A large amount of feedback, comments, and postings made every second in social networking is rapidly growing the social database. Now, enormous data must be analyzed to determine the direction of people's opinions about a certain business and its products. the bulk of evaluations on the internet are in English, however as technology develops and people's knowledge expands, Also the amount of online information available in Hindi languages grows. To comprehend people's sentiments around real-world things, due to this Hindi language sentiment analysis is necessary;their reviews are equally important to us. For categorization accuracy, we used the Hindi language resource for general news headlines from several news sources. For text categorization, we utilized machine learning (ML) classification methods such as Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR), and the accuracy varied. © 2021 IEEE.

关键词： Support vector machines

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Low-Light Image Enhancement Algorithm for Unmanned Aerial Vehicle Imagery via Color Distribution

Low-Light Image Enhancement Algorithm for Unmanned Aerial Ve...

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Natural language processing (ICNLP), international conference on

作者： Xiaotong Bai Dianwei Wang Jie Fang Yuanqing Li Jiayin Wen Zhijie Xu School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Unmanned aerial vehicle (UAV) imagery captured under low-light conditions are often suffering from uneven illumination and lack of detail. To address these issues, this paper proposes a low-light image enhancement algorithm for UAV imagery, which locate different illumination regions with local color distribution. Firstly, a bright channel prior(BCP) is utilized to train the enhancement network by unsupervised learning approach. Secondly, a color distribution module is constructed to locate and enhance different illumination regions. Experimental results show that the proposed method can preserve the detailed information in images while enhancing image brightness, and enhance different illumination regions in low-light UAV images.

关键词： Measurement Image color analysis Brightness Lighting Autonomous aerial vehicles Natural language processing Image enhancement Unsupervised learning

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A Prompt-based Few-shot Machine Reading Comprehension Model for Intelligent Bridge Management 6

A Prompt-based Few-shot Machine Reading Comprehension Model ...

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6th international conference on Electronic information technology and Computer Engineering, EITCE 2022

作者： Zhang, Luyi Li, Ren Xiao, Qiao School of Information Science and Engineering Chongqing Jiaotong University Chongqing China

ISBN: (纸本)9781450397148

Bridge inspection reports are an important data source in the bridge management process, and they contain a large amount of fine-grained information. However, the research on machine reading comprehension (MRC) methods for this field is insufficient, and annotating large scale domain-specific corpus is time-consuming. this paper presented a novel prompt-based few-shot MRC approach for intelligent bridge management. the proposed model uses the pretrained model MacBERT as backbone. the prompt templates are designed based on some domain-specific heuristic rules. the experimental results show that our model outperforms the baseline models in different few-shot settings. the proposed model can provide technical support for the construction of automatic question answering system in the field of bridge management. © 2022 Association for Computing Machinery.

关键词： Natural language processing systems

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