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A semantic model for integrated content management, localisation and language technology processing

A semantic model for integrated content management, localisa...

2nd International Workshop on the Multilingual Semantic Web 2011, MSW 2011 - In Conjunction with the International Semantic Web Conference, ISWC 2011

作者： Jones, Dominic O'Connor, Alexander Abgaz, Yalemisew M. Lewis, David Centre for Next Generation Localisation School of Computer Science and Statistics Trinity College Dublin Ireland Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland School of Computing Dublin City University Dublin Ireland

Providers of products and services are faced with the dual challenge of supporting the languages and individual needs of the global customer while also accommodating the increasing relevance of user-generated content. As a result, the content and localisation industries must now evolve rapidly from manually processing predicable content which arrives in large jobs to the highly automated processing of streams of fast moving, heterogeneous and unpredictable content. This requires a new generation of digital content management technologies that combine the agile flow of content from developers to localisers and consumers with the data-driven language technologies needed to handle the volume of content required to feed the demands of global markets. data-driven technologies such as statistical machine translation, cross-lingual information retrieval, sentiment analysis and automatic speech recognition, all rely on high quality training content, which in turn must be continually harvested based on the human quality judgments made across the end-to-end content processing flow. This paper presents the motivation, approach and initial semantic models of a collection of research demonstrators where they represent a part of, or a step towards, documenting in a semantic model the multi-lingual semantic web.

关键词： Semantic Web

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Retraction notice to “An intrusion identification and prevention for cloud computing: From the perspective of deep learning” [Optik 270 (2022) 170044]

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Optik 2025年

作者： Anil Kumar Rajabov Sherzod Umurzoqovich Nguyen Duc Duong Pratik Kanani Arulmani Kuppusamy M. Praneesh Minh Ngyen Hieu Data Science Research Group School of Computing DIT University Dehradun India The Head of the Department of Quality Control of Education Tashkent Institute of Finance Tashkent Uzbekistan East Asia University of Technology Hanoi Viet Nam Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai 400056 India SASTRA Deemed to be University Thanjavur India PG & Research Department of Computer Science Sri Ramakrishna College of Arts & Science Coimbatore Tamil Nadu India Pusa Research Lab Viet Nam

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Self-explaining deep models with logic rule reasoning

arXiv

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arXiv 2022年

作者： Lee, Seungeon Yi, Xiaoyuan Wang, Xiting Xie, Xing Han, Sungwon Cha, Meeyoung KAIST School of Computing IBS Data Science Group Korea Republic of Social Computing Group Microsoft Research Asia China Microsoft Research Asia China

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust and allows users to collaborate closely with the model. We demonstrate that logic rule explanations naturally satisfy human precision with the expressive power required for good predictive performance. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350313604

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2024 IEEE.

关键词： Decision making

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Female sex hormones and the oral contraceptive pill modulate asthma severity through GLUT-1

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Mucosal Immunology 2025年

作者： Brown, Alexandra C. Carroll, Olivia R. Mayall, Jemma R. Zounemat-Kermani, Nazanin Vinzenz, Samantha L.E. Gomez, Henry M. Mills, Ed F. Kim, Richard Y. Donovan, Chantal Baines, Katherine J. Williams, Evan J. Berthon, Bronwyn S. Wynne, Katie Scott, Hayley A. Pinkerton, James W. Guo, Yike Hansbro, Philip M. Foster, Paul S. Wark, Peter A.B. Dahlen, Sven-Erik Adcock, Ian M. Wood, Lisa G. Horvat, Jay C. The University of Newcastle and Immune Health Program Hunter Medical Research Institute Newcastle NSW Australia The Data Science Institute Department of Computing Imperial College London United Kingdom School of Life Sciences Faculty of Science University of Technology Sydney Sydney NSW Australia Respiratory Pharmacology & Toxicology Group National Heart & Lung Institute Imperial College London London United Kingdom Centre for Inflammation Centenary Institute and Faculty of Science University of Technology Sydney Sydney NSW Australia Clinical Lung and Allergy Research Unit Department of Medicine Huddinge Karolinska Institutet and Department of Respiratory Medicine and Allergy Karolinska University Hospital Huddinge and Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden The Airway Disease Section National Heart & Lung Institute Imperial College London London United Kingdom Woolcock Institute of Medical Research Sydney NSW Australia School of Translational Medicine Monash University Melbourne VIC Australia Woolcock Institute of Medical Research and Faculty of Medicine Health and Human Sciences Macquarie University Sydney NSW Australia Hong Kong University of Science and Technology Hong Kong

Females are disproportionately affected by asthma. An increased understanding of how female sex hormones influence key pathophysiological processes that underpin asthma may identify new, more effective asthma therapies, particularly for females with severe, poorly controlled asthma. We assessed the effects of oral ethinylestradiol/levonorgestrel (representing OCP use) and depot-medroxyprogesterone acetate (DMPA) and estradiol injections on key features of experimental asthma, and determined their effects on glucose transporter-1 (GLUT-1). The effects of OCP use on clinical asthma outcomes, and the relationships between estrogen receptors and type 2 (T2), non-T2, and GLUT-1 responses, in clinical asthma were also determined. OCP and DMPA reduce T2 responses, disease features, and lung expression of GLUT-1, whereas estradiol increases lung expression of GLUT-1, and results in severe, corticosteroid-insensitive, neutrophil-enriched disease, in experimental asthma. OCP use is associated with reduced T2 cytokine and GLUT-1 responses in clinical asthma. GLUT-1 expression is increased in sputum of severe asthmatics, and positively correlates with estrogen receptor expression and both T2 and non-T2 inflammatory responses. Significantly, OCP or GLUT-1 inhibition protects against obesity-associated or estradiol-induced, severe, experimental asthma, respectively. Together, these data show how female sex hormones and the OCP likely modulate asthma severity by modifying GLUT-1 responses in the airways. © 2025 The Authors

关键词： Asthma Estrogen GLUT-1 oral contraceptive pill (OCP) Progesterone

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm

arXiv

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arXiv 2023年

作者： Lima, Gabriel Grgić-Hlača, Nina Cha, Meeyoung School of Computing KAIST & Data Science Group IBS Korea Republic of Max Planck Institute for Software Systems Max Planck Institute for Research on Collective Goods Germany Data Science Group IBS & School of Computing KAIST Korea Republic of

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people’s reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people’s reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople’s reactions to AI-caused harm. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Sequential Modeling for Automatic Interpretation of Pressure Transient Test 9

Sequential Modeling for Automatic Interpretation of Pressure...

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9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Pandey, Rakesh Kumar Kumar, Anil Ganju, Rahul DIT University Department of Petroleum and Energy Studies Dehradun248009 India School of Computing DIT University Data Science Research Group Dehradun248009 India DIT University Department of Petroleum and Energy Studies Dehradun248009 India

ISBN: (纸本)9781665417037

Reservoir characterization is a crucial step in developing the oil and gas fields. In this research, a new technique has been demonstrated for analyzing pressure transient tests conducted in oil wells using deep learning. The pressure data recorded at varying time periods from well sites are provided in the prediction model for characterizing the reservoir. The proposed model allows automatic interpretation of homogeneous reservoirs with circular drainage area supported by active aquifers. The constant rate pressure drawdown tests have been simulated using the available analytical model for wells with well-bore storage and formation damage. The noise was added to simulated data to replicate the real-time environment during training of the model. The model showcases significantly high-performance accuracy in automatically characterizing the reservoir. © 2021 IEEE.

关键词： Analytical models

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Large data visualization on distributed memory multi-GPU clusters 2

Large data visualization on distributed memory multi-GPU clu...

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2nd ACM SIGGRAPH / Eurographics on High-Performance Graphics, HPG 2010

作者： Fogal, Thomas Childs, Hank Shankar, Siddharth Krüger, Jens Bergeron, R. Daniel Hatcher, Philip Scientific Computing and Imaging Institute University of Utah United States Department of Computer Science University of New Hampshire United States Lawrence Berkeley National Laboratory United States Interactive Visualization and Data Analysis Group DFKI Germany

ISBN: (纸本)9783905674262

data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets. The Eurographics Association 2010.

关键词： Graphics processing unit

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Evaluating Psychological Safety of Large Language Models

Evaluating Psychological Safety of Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Xingxuan Li, Yutong Qiu, Lin Joty, Shafiq Bing, Lidong DAMO Academy Alibaba Group Singapore College of Computing and Data Science NTU Singapore School of Social Sciences NTU Singapore Department of Psychology CUHK Hong Kong Salesforce Research Singapore Hupan Lab Hangzhou310023 China

ISBN: (纸本)9798891761643

In this work, we designed unbiased prompts to systematically evaluate the psychological safety of large language models (LLMs). First, we tested five different LLMs by using two personality tests: Short Dark Triad (SD-3) and Big Five Inventory (BFI). All models scored higher than the human average on SD-3, suggesting a relatively darker personality pattern. Despite being instruction fine-tuned with safety metrics to reduce toxicity, InstructGPT, GPT-3.5, and GPT-4 still showed dark personality patterns;these models scored higher than self-supervised GPT-3 on the Machiavellianism and narcissism traits on SD-3. Then, we evaluated the LLMs in the GPT series by using well-being tests to study the impact of fine-tuning with more training data. We observed a continuous increase in the well-being scores of GPT models. Following these observations, we showed that fine-tuning Llama-2-chat-7B with responses from BFI using direct preference optimization could effectively reduce the psychological toxicity of the model. Based on the findings, we recommended the application of systematic and comprehensive psychological metrics to further evaluate and improve the safety of LLMs. Our code is available at https://***/DAMONLP-SG/PsychSafety. © 2024 Association for Computational Linguistics.

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