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检索条件"丛书名=Communications in Computer and Information Science"

共 42977 条记录，以下是91-100 订阅

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ExARN: Target Speaker Extraction with Attentive Recurrent Networks 19th

ExARN: Target Speaker Extraction with Attentive Recurrent Ne...

引用

19th National Conference on Man-Machine Speech Communication

作者： Shen, Pengjie He, Shulin Zhang, Xueliang Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9789819610440;9789819610457

Target Speaker Extraction (TSE) isolates target speech from multi-speaker recordings, aiding applications like speaker verification. A significant challenge lies in effectively leveraging enrollment utterances to favor the target speaker, which, if not addressed properly, can result in speaker misidentification (referred to as speaker confusion, SC), leading to adverse user experiences. Although previous studies have investigated methods like concatenation and attention mechanisms to merge speaker embeddings from enrollment with mixed speech, these approaches have not fully capitalized on the potential of enrollment utterances. To overcome these limitations, We introduce ExARN, a novel TSE architecture with a custom self-attention mechanism and RNNs for better speaker embedding integration and extended sequence modeling. ExARN demonstrates unparalleled performance on benchmark datasets, significantly improving TSE.

关键词： Target speaker extraction self-attention speaker embedding

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Preliminary Analysis of Empathy-Driven Design and Inclusive Cybersecurity Education: The Initial Phase of the uSucceed Project's Virtual Reality Curriculum for Neurodiverse Adults in STEM 10th

Preliminary Analysis of Empathy-Driven Design and Inclusive ...

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10th International Conference of the Immersive Learning Research Network

作者： Glaser, Noah Calyam, Prasad Duan, Yupei Li, Shangman Nuguri, Sai Shreya Ousley, Cannon Kambhampati, Anirudh Parishani, Zeinab Joshi, Amogh Chetankumar Yang, Mohan Univ Missouri Columbia MO 65211 USA Texas A&M Univ College Stn TX USA

ISBN: (纸本)9783031804748;9783031804755

The uSucceed project seeks to mitigate employment disparities among neurodiverse adults in the U.S., especially in the STEM fields. Utilizing Virtual Reality (VR), the project delivers a specialized curriculum in computational technology and advanced computer engineering. This paper delves into the project's initial phase, employing a Design-Based Research (DBR) approach framed within the Learner Experience Design paradigm. Methods include empathy interviews, empathy mapping, and persona development to inform design principles. Preliminary findings, drawn from qualitative analysis of empathy maps and personas, have led to key design considerations in user experience, pedagogy, and technology. These considerations serve as a framework for the development of inclusive VR games in cybersecurity education. As the project evolves, it is moving towards the implementation and evaluation stages, with an AI-driven pedagogical agent under development to offer real-time, adaptive learning support.

关键词： Virtual Reality Game-based Learning Cybersecurity Learning Experience Design Neurodiverse Learners

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Deep Reinforcement Learning for Arabic Machine Translation: A Study on Reward Signals 8th

Deep Reinforcement Learning for Arabic Machine Translation: ...

引用

8th International Conference on Arabic Language Processing

作者： Zouidine, Mohamed Khalil, Mohammed El Farouk, Abdelhamid Ibn Hassan II Univ Casablanca FSTM LMCSA Casablanca Morocco Hassan II Univ Casablanca FLSH LLEC Casablanca Morocco

ISBN: (纸本)9783031791635;9783031791642

Machine translation training with reinforcement learning has shown improvement in the translation quality. In previous works, the BLEU score was used as a reward function for reinforcement learning being the metric the most widely employed for evaluating machine translation models. Nevertheless, BLEU is not the only metric available to evaluate machine translation models. In this work, we use four different measures as reward functions, and we study their impact on the translation quality. We use these rewards to train a transformer model with reinforcement learning to perform translation between Arabic and English. Experimental results with the IWSLT dataset reveal that the GLEU score enhances the Arabic-to-English translation quality and the BLEU score improves the English-to-Arabic one.

关键词： Machine translation Transformer Reinforcement Learning BLEU GLEU METEOR TER

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Transformer-Based Framework for Versatile Analysis of Events Data in Soccer 11th

Transformer-Based Framework for Versatile Analysis of Events...

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11th International Workshop on Machine Learning and Data Mining for Sports Analytics-MLSA

作者： Rovshitz, Aviv Puzis, Rami Ben Gurion Univ Negev Software & Informat Syst Engn Beer Sheva Israel

ISBN: (纸本)9783031866913;9783031866920

In sports analytics, machine learning models are used for players' appointment analysis, strategic planning, etc. Multiple rating systems were developed to rank the player performance based on statistical measurements, machine learning, and deep learning analysis of actions and sequences. However, capturing the complex relationships between actions within open play sequences, affected by tactical game planning and team deployment, is a major challenge. Inspired by language models, we developed SoccerTransformer, a transformer-based deep learning framework tailored to soccer analytics to address this challenge. The proposed framework includes (1) self-supervised pre-training to capture generic in-game dynamics, (2) a downstream sequence classification task to predict the attack phase outcomes, and (3) a player rating system. Empirical results demonstrate that SoccerTransformer accurately captures player roles and predicts goals with F1 of 0.814 to 0.862 on previously unseen games. SoccerTranformer framework stands out by providing a greater correlation with market values than other state-of-the-art rating systems.

关键词： sports analytics soccer transformers language model market value

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Qur'an Passage Ranking Using Transformer Models 8th

Qur'an Passage Ranking Using Transformer Models

引用

8th International Conference on Arabic Language Processing

作者： Alnefaie, Sarah Atwell, Eric Alsalka, Mohammed Ammar Univ Leeds Sch Comp Leeds W Yorkshire England King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia

ISBN: (纸本)9783031791635;9783031791642

Passage ranking is arguably one of the most important natural text-processing tasks. It is based on retrieving a passage from a larger corpus that contains an answer to a specific question. This task has not been fully explored in the Classical Arabic (CA) language. Because the size of the only available CA corpus was small, we created a new, larger CA dataset. This dataset was used to train eight Arabic pre-trained transformer-based models, which had been used in different approaches to passage ranking. The highest result we achieved was in using AraBERT Base (Arabic bidirectional encoder representations from transformers) as a bi-encoder model in the dense passage retrieval approach. This model outperformed BM25 (best match 25), which is considered a traditional approach. It obtained a 0.244 mean average precision (MAP) and 0.413 mean reciprocal rank (MRR), compared to the 0.170 MAP and 0.313 MRR obtained by BM25.

关键词： Arabic Passage Retrieval Arabic Transformer Models Quran Retrieval Model

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Enhancing Arabic Handwritten Recognition System Based CNN-BLSTM Using Generative Adversarial Networks 8th

Enhancing Arabic Handwritten Recognition System Based CNN-BL...

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8th International Conference on Arabic Language Processing

作者： Rabi, Mouhcine Amrouche, Mustapha Ibn Zohr Univ IMIS Lab Fac Appl Sci AM Agadir Morocco Ibn Zohr Univ IRF SIC Lab Fac Sci Agadir Agadir Morocco

ISBN: (纸本)9783031804373;9783031804380

Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.

关键词： Arabic handwriting recognition (AHR) Generative Adversarial Networks (GANs) Convolutional Neural Networks (CNN) Bi-Dimensional Long Short-Term Memory (BLSTM)

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Enhancing Machine Translation Across Multiple Domains and Languages with Large Language Models 20th

Enhancing Machine Translation Across Multiple Domains and La...

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20th China Conference on Machine Translation

作者： Lu, Hao Zhang, Rui Huang, Hui Song, Fuhai Liu, Junkai Ye, Yican Lang, Lang Zhao, Ziqing Yang, Muyun Cong, Rui Harbin Inst Technol Dept Comp Sci & Engn Harbin Peoples R China Harbin Inst Technol Sch Foreign Languages Harbin Peoples R China

ISBN: (纸本)9789819622917;9789819622924

Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. In this paper, we describe our LLM-based machine translation system submitted to CCMT 2024 evaluation task. We investigated the effects of pre training data ratio, language quantity, instruction fine-tuning data ratio, and instruction construction method on the machine translation performance of large language models. We found that large language models have greater potential for multi-domain machine translation compared to traditional machine translation models. Within limits, increasing the number of newly added languages can enhance the overall translation capabilities for these new languages.

关键词： Large language models Multi-domain Multilingual

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ARAP-IRONY: A Multi-dialectal Arabic Irony Corpus for Irony Detection 8th

ARAP-IRONY: A Multi-dialectal Arabic Irony Corpus for Irony ...

引用

8th International Conference on Arabic Language Processing

作者： Charfi, Anis Mehdi, Syed Hassan Mohamed, Esraa Bessghaier, Mabrouka Carnegie Mellon Univ Qatar Doha Qatar

ISBN: (纸本)9783031791635;9783031791642

In this paper, we present ARAP-IRONY, a multi-dialectal Arabic irony corpus including 21,120 tweets covering 11 Arabic dialects, which we developed to enable the detection of irony in dialectal Arabic using machine learning. In fact, Arabic dialects exhibit important differences in pronunciation, grammar, and vocabulary so that it is quite hard from someone from the Gulf region for instance to understand someone from the Maghreb region. Our irony corpus is balanced in terms of gender and age groups. It includes 20 ironic and 20 non-ironic tweets from each of the 48 users per dialect region that are manually annotated. We conducted various machine learning experiments using this corpus to identify irony in Dialectal Arabic. The highest accuracy and F1-score of 70% was achieved using a Logistic Regression classifier with character n-gram feature extraction.

关键词： Irony Dialectal Arabic Corpus Irony Detection Natural Language Processing

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Exploiting the TARC Framework: The Relations Between Educators' Attitudes Towards AR, Innovativeness, Digital Skills, and AR Skills in Education 10th

Exploiting the TARC Framework: The Relations Between Educato...

引用

10th International Conference of the Immersive Learning Research Network

作者： Nikou, Stavros A. Perifanou, Maria Economides, Anastasios A. Univ Strathclyde Strathclyde Inst Educ Glasgow Lanark Scotland Univ Macedonia SMILE Lab Thessaloniki Greece Univ Macedonia Dept Econ Thessaloniki Greece

ISBN: (纸本)9783031804748;9783031804755

Augmented Reality (AR) can offer benefits to education however, its successful implementation heavily relies on teachers' competencies personal characteristics, attitudes, and skills. The Teachers' Augmented Reality Competences (TARC) framework defines the main AR capacity areas for educators to employ in their teaching practice: creating, using, and managing AR resources. The current study proposes and validates a model about the impact of teachers' personal innovativeness, attitudes towards AR and digital skills on the TARC defined competence areas. Quantitative data was collected from 150 educators worldwide. Structured equation modeling was used for the analysis. The model explains and predicts approximately 73% of the capacity of teachers to use AR for teaching. The ability to create (design, develop and modify) and manage AR resources (search, evaluate, organize as well as address and resolve ethics and safety) directly impact teachers' capacity to use AR for teaching. Teachers' personal innovativeness, attitudes towards AR and digital skills also have found to have an impact. Study findings deemed important for educators considering their continuous professional development towards improving their digital and specifically Augmented Reality competences.

关键词： Augmented Reality Teachers Augmented Reality Competences Framework Education Attitudes Personal Innovativeness

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Immersive Learning of Cerebral Visual Impairment: Understanding Vision Through Dynamic Immersive Simulations 10th

Immersive Learning of Cerebral Visual Impairment: Understand...

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10th International Conference of the Immersive Learning Research Network

作者： Cassidy, Catherine Anne Oliver, Iain Oles, Kamila Tracy, Helen St Clair Blaikie, Andrew Miller, Alan Univ St Andrews St Andrews Fife Scotland

ISBN: (纸本)9783031804717;9783031804724

Many different brain-based impairments of vision can affect how someone experiences the world. Those conditions, called Cerebral Visual Impairments (CVI) are often difficult to explain and understand as each individual has their own unique experience of how they see and how their conditions affect them. However, people with CVI benefit substantially if those close to them understand their condition well enough to know which simple steps will make their world easier to navigate, interpret and understand. This paper summarises efforts to create immersive virtual reality visualisations which help those without CVI to better understand various CVI conditions. Two environments types are explored;classrooms and a climate change museum. A visual interface has been created which simulates various effects of CVI. Through this mechanism the user tailors their experience by selecting different impairments and their level of severity which then reacts dynamically to the environment chosen. Through experiential learning from a perspective of the world differing from their own, users gain exposure to CVI and potentially understand changes they can make to better support those affected. Evaluation and feedback demonstrates the simulation's effectiveness as a research platform in its ability to help refine understanding of the dynamic nature of the impairments. The simulation is also effecting in establishing change in understanding, potentially leading to broader accessibility standards in schools, public spaces and beyond.

关键词： Cerebral Visual Impairment Virtual Reality Immersive Learning Wellbeing Sustainable Development

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