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Visual Grounding Helps Learn Word Meanings in Low-Data Regimes

Visual Grounding Helps Learn Word Meanings in Low-Data Regim...

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhuang, Chengxu Fedorenko, Evelina Andreas, Jacob McGovern Institute for Brain Research MIT United States Department of Brain and Cognitive Sciences MIT United States The Program in Speech and Hearing Bioscience and Technology Harvard University United States Computer Science and Artificial Intelligence Laboratory MIT United States

ISBN: (纸本)9798891761148

Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with representations of language in the human brain. But to achieve these results, LMs must be trained in distinctly un-human-like ways-requiring orders of magnitude more language data than children receive during development, and without perceptual or social context. Do models trained more naturalistically-with grounded supervision-exhibit more human-like language learning? We investigate this question in the context of word learning, a key sub-task in language acquisition. We train a diverse set of LM architectures, with and without auxiliary visual supervision, on datasets of varying scales. We then evaluate these models’ learning of syntactic categories, lexical relations, semantic features, word similarity, and alignment with human neural representations. We find that visual supervision can indeed improve the efficiency of word learning. However, these improvements are limited: they are present almost exclusively in the low-data regime, and sometimes canceled out by the inclusion of rich distributional signals from text. The information conveyed by text and images is not redundant-models mainly driven by visual information yield qualitatively different from those mainly driven by word co-occurrences. However, our results suggest that current multimodal modeling approaches fail to effectively leverage visual information to build human-like word representations from human-scale data. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Blockchain Developer Experience: A Multivocal Literature Review

Blockchain Developer Experience: A Multivocal Literature Rev...

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ICSE Workshop on Cooperative and Human Aspects on Software Engineering, CHASE

作者： Pamella Soares Allysson Allex Araújo Giuseppe Destefanis Rumyana Neykova Raphael Saraiva Jerffeson Souza Graduate Program in Computer Science State University of Ceara Fortaleza Brazil Science and Technology Center Federal University of Cariri Juazeiro do Norte Brazil Department of Computer Science Brunel University London United Kingdom

ISBN: (数字)9798331538712

ISBN: (纸本)9798331502539

The rise of smart contracts has expanded blockchain's capabilities, enabling the development of innovative decentralized applications (dApps). However, this advancement brings its own challenges, including the management of distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging to support development in this domain. Developer Experience (DEx) is central to this effort, focusing on the usability, productivity, and overall satisfaction of tools and frameworks from the engineers' perspective. Despite its importance, research on Blockchain Developer Experience (BcDEx) remains limited, with no systematic mapping of academic and industry efforts. To bridge this gap, we conducted a Multivocal Literature Review analyzing 62 to understand the distribution of BcDEx sources, practical implementations, and their impact. Our findings revealed that academic focus on BcDEx is limited compared to the coverage in gray literature, which primarily includes blogs (41.8%) and corporate sources (21.8%). Particularly, development efficiency, multi-network support, and usability are the most addressed aspects in tools and frameworks. In addition, we found that BcDEx is being shaped through five key perspectives: complexity abstraction, adoption facilitation, productivity enhancement, developer education, and BcDEx evaluation.

关键词： Productivity Systematics Smart contracts Focusing Decentralized applications Blockchains Complexity theory Usability Systematic literature review Software engineering

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The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education 24

The Impact of Middle and High School Students Participation ...

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24th International Arab Conference on Information technology, ACIT 2023

作者： Mustafa, Dheya Zriqat, Samah Mustafa, Intisar Ghazi Althebyan, Qutaibah College of Engineering The Hashemite University Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Dept. of Information Technology and Computer Science Irbid Jordan College of Education Qatar University Dept. of Educational Sciences Doha Qatar Al Ain University Cybersecurity Program Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350384307

The Innovative Nation competition is a Jordanian initiative that uses workshops, training camps, and competitions to improve students' general and soft skills, problem-solving, creativity, and invention. The curriculum is divided into three parts, each of which has the potential to increase interest in science, technology, Engineering, and Mathematics (STEM). The goal of this research is to determine how much the STEM competition has influenced students' education and decision to pursue jobs in science and technology. Personal interviews with students and mentors were employed in this study to assess their skill sets, prospective career paths, and interests. The collected data was reviewed, and it was found that participation in the Innovation Nation competition increased students' interest in STEM vocations. © 2023 IEEE.

关键词： Artificial intelligence education Innovation non-curricular learning PictoBlox STEM

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Blockchain Developer Experience: A Multivocal Literature Review

arXiv

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arXiv 2025年

作者： Soares, Pamella Araújo, Allysson Allex Destefanis, Giuseppe Neykova, Rumyana Saraiva, Raphael Souza, Jerffeson Graduate Program in Computer Science State University of Ceará Fortaleza Brazil Science and Technology Center Federal University of Cariri Juazeiro do Norte Brazil Department of Computer Science Brunel University London United Kingdom

The rise of smart contracts has expanded blockchain’s capabilities, enabling the development of innovative decentralized applications (dApps). However, this advancement brings its own challenges, including the management of distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging to support development in this domain. Developer Experience (DEx) is central to this effort, focusing on the usability, productivity, and overall satisfaction of tools and frameworks from the engineers’ perspective. Despite its importance, research on Blockchain Developer Experience (BcDEx) remains limited, with no systematic mapping of academic and industry efforts. To bridge this gap, we conducted a Multivocal Literature Review analyzing 62 to understand the distribution of BcDEx sources, practical implementations, and their impact. Our findings revealed that academic focus on BcDEx is limited compared to the coverage in gray literature, which primarily includes blogs (41.8%) and corporate sources (21.8%). Particularly, development efficiency, multi-network support, and usability are the most addressed aspects in tools and frameworks. In addition, we found that BcDEx is being shaped through five key perspectives: complexity abstraction, adoption facilitation, productivity enhancement, developer education, and BcDEx evaluation. © 2025, CC BY.

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An Emotion Recognition Model Augmented a Small Amount of Individual EEG by Domain Adaptation

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2022年第7期142卷 771-780页

作者： Furukawa, Shoya Sakuma, Takuto Kato, Shohei Creative Engineering Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Computer Science Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan

In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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Audiovisual Groupcast QoE Enhancement by Switching Unsolicited Retry and Block Ack over a Wireless LAN

Audiovisual Groupcast QoE Enhancement by Switching Unsolicit...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Minoru Ishida Toshiro Nunome Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

In this paper, we propose a switching scheme of GCR Block Ack and GCR Unsolicited Retry, standardized in IEEE 802.11aa, according to network conditions for video and audio groupcast over wireless LANs. We utilize three transmission modes in the proposed method: GCR Block Ack with four retries, GCR Block Ack with two retries, and GCR Unsolicited Retry with twice transmission. The proposed method is compared with the three individual methods by computer simulation under various network conditions to evaluate application-level QoS. We then assess QoE by a subjective experiment. We show that the proposed method can choose an appropriate mode and achieve better QoE than the individual methods.

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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A Study of Enhancing Computational Thinking Skills through STEAM Robotics Activities 2

A Study of Enhancing Computational Thinking Skills through S...

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2nd International Annual Meeting on STEM Education, IAMSTEM 2019

作者： Phadung, Muneeroh Yokkhun, Atcharaporn Persoh, Sulaiman Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Information Technology Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Computer Science Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand

STEAM robotics activities are designed to be an enhancement on students' computational thinking skills, which are claimed to be essential for the 21st century. The aims of this study were to examine the learning outcomes of secondary students' computational thinking skills through the use of STEAM robotics activities, and to investigate the students' satisfaction after the implementation of STEAM robotics activities. A quasi-experimental research design was deployed in this study. The research participants were 37 eleventh-grade students of Private Islamic Schools in Yala province, Thailand. A quiz with pre- and post-test and satisfaction questionnaires were employed as data. Descriptive statistics - including mean, standard deviation and a pair sample t-test - was used to analyse the collected data. The findings were that students' learning outcomes indicated a significant improvement in their computational skills after the use of STEAM robotics activities as compared with before, with the statistical level of significance of 0.05. In response to the second research question, the overall satisfaction of the students after the use of such STEAM robotics activities was high (mean=4.01;SD=0.54). © Published under licence by IOP Publishing Ltd.

关键词： Robotics

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Simulation of Photonic Metasurfaces using a Surface Integral Equation Solver with Generalized Sheet Transition Conditions

Simulation of Photonic Metasurfaces using a Surface Integral...

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Antennas and Propagation (LACAP), IEEE Latin American Conference on

作者： Sebastian Celis Sierra Partha Mondal Ran Zhao Hakan Bagci Electrical and Computer Engineering (ECE) Program King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331527587

ISBN: (纸本)9798331527594

Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. GSTCs connect the electromagnetic fields on both sides of the metasurface using equivalent susceptibility tensors and avoid modelling of geometrically intricate metasurface unit cells by the SIE solver. Susceptibility tensors are obtained from the transmission and the reflection coefficients associated with a unit cell. A numerical example, involving a metasurface that supports high-quality factor resonances and excited by a Gaussian beam, is presented to demonstrate the accuracy of the proposed approach.

关键词： Tensors Accuracy Integral equations Metasurfaces Mathematical models Numerical models Reflection coefficient Electromagnetic fields Dispersion Photonics

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