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A Study on Knowledge Network Acceptability Facilitated by a computer-Based Translation Learning Platform 21st

A Study on Knowledge Network Acceptability Facilitated by a ...

引用

21st International Conference on Web-Based Learning / 7th International Symposium on Emerging Technologies for Education (ICWL/SETE)

作者： Mu, Yuanyuan Du, Lizhen Li, Qianyu Chaohu Univ Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)9783031330223;9783031330230

The research aims to prove knowledge network acceptability in/between Chinese and English translation learning by interviewing 82 junior EFL undergraduates who have used a computer-based platform for one semester. With the assistance of Likert Scale and statistical tools, it reveals that students can widely accept the concept, translation knowledge network, and this computer-based translation learning strategy, and they even consider this strategy more efficient than traditional offline teaching-learning activity. Based upon exploration of translation learning strategy on the platform, experiment design and analysis of experiment data, the study finds how the knowledge network acceptability can enrich students' translation learning strategies, learning resources management strategies.

关键词： Knowledge network Translation learning Learning strategy

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VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection 17th

VideoPatchCore: An Effective Method to Memorize Normality f...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Ahn, Sunghyun Jo, Youngwan Lee, Kijung Park, Sanghyun Department of Computer Science Yonsei University Seoul Korea Republic of

ISBN: (纸本)9789819609079

Video anomaly detection (VAD) is a crucial task in video analysis and surveillance within computer vision. Currently, VAD is gaining attention with memory techniques that store the features of normal frames. The stored features are utilized for frame reconstruction, identifying an abnormality when a significant difference exists between the reconstructed and input frames. However, this approach faces several challenges due to the simultaneous optimization required for both the memory and encoder-decoder model. These challenges include increased optimization difficulty, complexity of implementation, and performance variability depending on the memory size. To address these challenges, we propose an effective memory method for VAD, called VideoPatchCore. Inspired by PatchCore, our approach introduces a structure that prioritizes memory optimization and configures three types of memory tailored to the characteristics of video data. This method effectively addresses the limitations of existing memory-based methods, achieving good performance comparable to state-of-the-art methods. Furthermore, our method requires no training and is straightforward to implement, making VAD tasks more accessible. Our code is available online at ***/SkiddieAhn/Paper-VideoPatchCore. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Machine learning Video Anomaly Detection

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Enhancing Anchor-Based Weakly Supervised Referring Expression Comprehension with Cross-Modality Attention 17th

Enhancing Anchor-Based Weakly Supervised Referring Expressio...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Chu, Ting-Yu Lin, Yong-Xiang Huang, Ching-Chun Hua, Kai-Lung Department of CSIE National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Yang-Ming Chiao Tung University Hsinchu Taiwan Microsoft Taiwan Taipei Taiwan

ISBN: (纸本)9789819609079

Weakly supervised Referring Expression Comprehension (REC) tackles the challenge of identifying specific regions in an image based on textual descriptions without predefined mappings between the text and target objects during training. The primary obstacle lies in the misalignment between visual and textual features, often resulting in inaccurate bounding box predictions. To address this, we propose a novel cross-modality attention module (CMA) module that enhances the discriminative power of grid features and improves localization accuracy by harmonizing textual and visual features. To handle the noise from incorrect labels common in weak supervision, we also introduce a false negative suppression mechanism that uses intra-modal similarities as soft supervision signals. Extensive experiments conducted on four REC benchmark datasets: RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame. Our results show that our model consistently outperforms state-of-the-art methods in accuracy and generalizability. Our source code is publicly available at https://***/t22786959/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cross-modality false negative suppression Referring expression comprehension similarity regularization

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Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics 25th

Predator-Prey Q-Learning Based Collaborative Coverage Path P...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Watson, Michael Ren, Hanchi Arvin, Farshad Hu, Junyan Department of Computer Science Durham University Durham United Kingdom Department of Computer Science Swansea University Swansea United Kingdom

ISBN: (纸本)9783031720611

Coverage Path Planning (CPP) is an effective approach to let intelligent robots cover an area by finding feasible paths through the environment. In this paper, we focus on using reinforcement learning to learn about a given environment and find the most efficient path that explores all target points. To overcome the limitations caused by standard Q-learning based CPP that often fall into a local optimum and may be in-efficient in large-scale environments, two methods of improvement are considered, i.e., the use of a robot swarm working towards the same goal and the augmenting of the Q-learning algorithm to include a predator-prey based reward system. Existing predator-prey based reward systems provide rewards the further away an agent is from its predator, the paper adapts this concept to work within a robot swarm by simulating each agent of the swarm as both predator and prey. Simulation case studies and comparisons with the standard Q-learning show that the proposed method has a superior coverage performance in complicated environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Toward a Method for Design science Research Meta-Studies to Improve the Reusability of Design Principles 19th

Toward a Method for Design Science Research Meta-Studies to ...

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19th International Conference on Design science Research in Information Systems and Technology (DESRIST) - Design science Research for a Resilient Future

作者： Khosrawi-Rad, Bijan Grogorick, Linda Strohmann, Timo Robra-Bissantz, Susanne TU Braunschweig Muhlenpfordtstr 23 D-38106 Braunschweig Germany

ISBN: (纸本)9783031611742;9783031611759

Design science research (DSR) is an established approach to create innovative artifacts while considering relevance and rigor. Design principles (DPs) are the most common knowledge contribution. However, DPs are often not reused in the research and practice community. In this methodology paper, we propose an approach to conduct meta-studies for design knowledge to tackle the issue of low DP reusability. We developed a process model for meta-studies based on DSR-specific terms, evaluated it in an expert workshop with DSR scholars, and demonstrated its application to the example of virtual reality literature. Our metastudy model contributes to the research discussion on the reusability of DPs and is a new approach to gain more transparency about existing DPs in research and practice, enabling the community to better guide future research.

关键词： Meta Study DSR Design Principle Reusability

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Deaf in STEM: A New Approach to Measuring Problem Solving, Deductive Reasoning, Creativity, and Ability 18th

Deaf in STEM: A New Approach to Measuring Problem Solving, D...

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18th International Conference on Universal Access in Human-computer Interaction (UAHCI) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Hafeez, Shireen Deaf Kids Code W Lafayette IN 47906 USA

ISBN: (纸本)9783031608834;9783031608841

Deaf Kids Code (DKC), founded in 2015 by Shireen Hafeez, is a national organization aimed at equipping Deaf, deaf, and hard of hearing students with computer science, technology, and design thinking skills to enhance their societal and economic participation. Utilizing Deaf role models from diverse professional backgrounds, DKC has reached over 15,000 participants across 50 locations nationally, extending its impact internationally as well. The organization adopts a kinesthetic learning model, emphasizing experiential learning and fostering a growth mindset among students, positioning them as creators, not just consumers, of technology. DKC's objectives include expanding STEM/CS learning opportunities, inspiring students towards STEM careers, and advocating for integrating such education into deaf education curricula. Collaborations with industry giants like Scratch Foundation, Microsoft, Google, AWS, and Dell Tech Crew bolster DKC's efforts, while interventions like ASL-accessible digital tools and partnerships with educational institutions and corporations further bridge the digital divide for the Deaf community. Through internships, shadowing opportunities, and specialized workshops, DKC facilitates hands-on learning experiences and career pathways in technology for Deaf individuals, paving the way for greater inclusivity and empowerment in the tech industry.

关键词： Deaf Education Disability and computer science Workforce Development Inclusive Education Vocational Rehab Disability Services Disability Career Training

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Exploring Programming Concepts and Robotic Sensors: How to Organise Them Within Educational Content 15th

Exploring Programming Concepts and Robotic Sensors: How to O...

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15th Conference on Robotics in Education (RiE)

作者： Stenova, Barbora Mikova, Karolina Comenius Univ Bratislava 84248 Slovakia

ISBN: (纸本)9783031670589;9783031670596

In this paper, we present a set of six activities using the Lego Spike Prime robotics kit. The primary learning objectives of these activities are to develop students' programming and algorithmic skills, computational thinking, and soft skills. Our research introduces one of the possible appropriate command sequences combined with the specific sensors of the selected kit. This approach aims to respect the cognitive difficulty of the tasks based on the age of the students and, simultaneously, exploit the motivational potential of educational robotics. Unlike traditional programming, this allows teachers to incorporate hands-on activities as a modern element of teaching. The activities are designed with consideration for the framework and needs of the Slovak education system, specifically for a 90 min computer science lesson in lower secondary school. We conducted qualitative research in three different schools with students aged 10 to 15. These students were not necessarily robotics enthusiasts. Each task in every activity is briefly described, along with the commands taught. These activities are the result of our iterative research and multiple implementations in the educational process, encompassing the design idea, used sensors, and commands.

关键词： LEGO SPIKE Prime Activities Programming Lower Secondary Education computer science Classes

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Enhancing Deepfake Detection via Adversarial Generative Learning 18th

Enhancing Deepfake Detection via Adversarial Generative Lea...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Chen, Zengqiang Wang, Xudong Li, Yuezun School of Computer science and Technology Ocean University of China Qingdao China

ISBN: (纸本)9789819610679

Deepfake technology generates highly realistic videos effortlessly, raising serious concerns about privacy violations, misinformation, and financial fraud. Detecting Deepfakes is the most effective solution to address these issues. While existing detection methods perform well on standard protocols, they struggle with real-world scenarios due to constantly emerging unknown forgery types. To enhance detection generalization, recent methods augment training images by synthesizing diverse forged faces (pseudo-fake faces) and identifying common forgery features. In this paper, we describe a new augmentation-based method to further improve the detection generalization. Our method leverages adversarial generative learning, which adaptively synthesizes effective pseudo-fake faces based on a generator network, a face synthesizer, a face reconstruction, and a discriminator. Extensive experiments on several public datasets demonstrate the efficacy of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Theoretical Framework for computer Vision Learning Environment 21st

A Theoretical Framework for Computer Vision Learning Environ...

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21st International Conference on Web-Based Learning / 7th International Symposium on Emerging Technologies for Education (ICWL/SETE)

作者： Laura, Luigi Miloso, Antonio Uninettuno Univ Corso Vittorio Emanuele 239 I-00186 Rome Italy

ISBN: (纸本)9783031330223;9783031330230

Nowadays, the World Wide Web gives access to most of the world's knowledge, nevertheless, people require competence to interpret and comprehend most of that knowledge. The Lifelong Learning approach introduces a new way of constantly pursuing knowledge consistently. Nevertheless, learners need to test and carry out projects to reinforce the study of theoretical concepts. Mostly in computer science, where they often need to experiment with large amounts of sensitive data to generalize beyond small training samples. However, in the past years, the European government enacted several regulations concerning privacy after catching up with sectors such as AI and Data Mining. The study illustrates a theoretical framework that includes hardware and software characteristics to avoid privacy violations and comply with European privacy regulations. The resulting framework could be applied for learning purposes as well as IoT applications regarding computer vision technologies to ensure correct and compliant communications between different devices.

关键词： computer Vision GDPR Lifelong Learning

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Dialectical Tensions in Design Theorizing: Exploring the Selection, Use, and Development of Kernel Theory 19th

Dialectical Tensions in Design Theorizing: Exploring the Sel...

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19th International Conference on Design science Research in Information Systems and Technology (DESRIST) - Design science Research for a Resilient Future

作者： Moeller, Frederik Schoormann, Thorsten Ciriello, Raffaele Hansen, Magnus Rotvit Pent TU Braunschweig Data Driven Enterprise Braunschweig Germany Fraunhofer ISST Dortmund Germany Univ Sydney Sydney NSW Australia Roskilde Univ Dept People & Technol Roskilde Denmark

ISBN: (纸本)9783031611742;9783031611759

Theory is a pivotal component in Information Systems (IS) research and no less so in Design science Research (DSR) projects, which are typically expected to select and use kernel theories to develop theoretical contributions. However, the actual application and utilization of kernel theories remain challenging and heterogeneous - from producing theoretical outcomes in each project to using theory for the justification of design activities. This is problematic since academic journals have high expectations for selecting, using, and contributing to (kernel) theory. As a consequence, DSR researchers, especially novice ones, face challenges in navigating the high expectations of journals with a blurry research component. In this paper, we explore a set of tensions prevalent in the selection, use, and development of kernel theory to then elaborate on possible pathways to respond to them.

关键词： Kernel Theory Justificatory Knowledge Design science Research

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