the proceedings contain 79 papers. the topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language mod...
ISBN:
(纸本)9798331511272
the proceedings contain 79 papers. the topics discussed include: an interactive approach for query-based multi-document scientific text summarization;enhancing Persian word sense disambiguation with large language models techniques and applications;assessing users' influence on respondents in conversation quality: a quantitative study on reddit based on the cooperative principle;non-negative matrix factorization improves residual neural networks;cluster sampling: a cluster-driven sampling strategy for deep metric learning;a scalable blockchain-based educational network for data storage and assessment;towards efficient capsule networks through approximate squash function and layer-wise quantization;automated software design using machine learning with natural language processing;evaluation of efficient electrocardiomatrix-based identification using deep learning methods;disturbance rejection in quadruple-tank system by proposing new method in reinforcement learning;and an improved and accurate measure for mining correlated high-utility itemsets.
the users of assistive technologies are often insufficiently represented in design processes. As a consequence, designed systems can allow too few adjustments to specific contexts of use. the paper describes insights ...
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ISBN:
(纸本)9783031616877;9783031616884
the users of assistive technologies are often insufficiently represented in design processes. As a consequence, designed systems can allow too few adjustments to specific contexts of use. the paper describes insights and challenges from a project on robotic-assisted rehabilitation therapies. It is suggested to use two level personas to better cover two groups of users in therapeutic sessions. Patients are end-users of the therapeutic robots;they are assisted by therapists who should also be provided with means to act as designer-users. It is argued that proven user-centered design methods and representations need to be adapted or revised to better cope withthe specific challenges of new technologies and consequences of their use.
Defective chips may cause huge losses (even disasters), and thus ensuring the reliability of chips is fundamentally important. To ensure the functional correctness of chips, adequate testing is essential on the chip d...
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ISBN:
(纸本)9798350300376
Defective chips may cause huge losses (even disasters), and thus ensuring the reliability of chips is fundamentally important. To ensure the functional correctness of chips, adequate testing is essential on the chip design implementation (CDI), which is the software implementation of the chip under design in hardware description languages, before putting on fabrication. Over the years, while some techniques targeting CDI functional testing have been proposed, there are still a number of hard-to-cover functionality points due to huge input space and complex constraints among variables in a test input. We call the coverage of these points last-mile functional coverage. Here, we propose the first technique targeting the significant challenge of improving last-mile functional coverage in CDI functional testing, called LMT, which does not rely on domain knowledge and CDI internal information. LMT first identifies the relevant variables in test inputs to the coverage of last-mile functionality points inspired by the idea of feature selection in machine learning, so as to largely reduce the search space. It then incorporates Generative Adversarial Network (GAN) to learn to generate valid test inputs (that satisfy complex constraints among variables) with a larger possibility. We conducted a practical study on two industrial CDIs in Huawei to evaluate LMT. the results show that LMT achieves at least 49.27% and 75.09% higher last-mile functional coverage than the state-of-the-art CDI test input generation techniques under the same number of test inputs, and saves at least 94.24% and 84.45% testing time to achieve the same functional coverage.
the increase in irrigated crop areas lead to the requirements for freshwater will in fact rise significantly. Considering this kind of need, this study aims to develop a low cost irrigation system that provide the mon...
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Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Follow...
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ISBN:
(纸本)9781665457019
Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional softwareengineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.
Traffic prediction is essential for intelligent transportation systems and smart city applications, yet existing spatio-temporal models face limitations. these include inadequate spatial feature extraction, neglect of...
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Drug-target Interaction (DTI) prediction provides strong support for drug repurposing. Most of the existing models are designed for specific protein / drug datasets to identify decoys, but there is still room for impr...
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Entity prediction and relation prediction are the two major tasks of temporal knowledge graph (TKG) reasoning. the key to answering queries about future events is to understand historical trends and extract the inform...
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As HCI has become more mainstream, introductory HCI courses have transitioned in many universities from more specialised elective courses taught in postgraduate degree programmes to compulsory courses taught in the fi...
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ISBN:
(纸本)9783031616877;9783031616884
As HCI has become more mainstream, introductory HCI courses have transitioned in many universities from more specialised elective courses taught in postgraduate degree programmes to compulsory courses taught in the first or second year of undergraduate degree programmes. At many universities, this transition means that class sizes can jump from one or two dozen students to many hundreds of students. this paper collects some of my experiences and advice for teaching HCI to such large class sizes, including redesigning the course to an online environment during the COVID pandemic.
Withthe rapid development of the software industry, software supply chains have become increasingly complex and diverse. Critical software domains such as operating systems, databases and web servers extensively adop...
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