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Assessing responsible innovation training

Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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CARDINALITY MINIMIZATION, CONSTRAINTS, AND REGULARIZATION: A SURVEY

arXiv

引用

arXiv 2021年

作者： Tillmann, Andreas M. Bienstock, Daniel Lodi, Andrea Schwartz, Alexandra TU Braunschweig Institute for Mathematical Optimization Braunschweig Germany Columbia University Dept. of Industrial Engineering and Operations Research New YorkNY United States École Polytechnique Montréal CERC Data Science for Real-Time Decision-Making MontréalQC Canada TU Dresden Faculty of Mathematics Dresden Germany

We survey optimization problems that involve the cardinality of variable vectors in constraints or the objective function. We provide a unified viewpoint on the general problem classes and models, and give concrete examples from diverse application fields such as signal and image processing, portfolio selection, or machine learning. The paper discusses general-purpose modeling techniques and broadly applicable as well as problem-specific exact and heuristic solution approaches. While our perspective is that of mathematical optimization, a main goal of this work is to reach out to and build bridges between the different communities in which cardinality optimization problems are frequently encountered. In particular, we highlight that modern mixed-integer programming, which is often regarded as impractical due to commonly unsatisfactory behavior of black-box solvers applied to generic problem formulations, can in fact produce provably high-quality or even optimal solutions for cardinality optimization problems, even in large-scale real-world settings. Achieving such performance typically draws on the merits of problem-specific knowledge that may stem from different fields of application and, e.g., shed light on structural properties of a model or its solutions, or lead to the development of efficient heuristics;we also provide some illustrative examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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The enigmatic multiple star VV Ori

arXiv

引用

arXiv 2023年

作者： Budding, Edwin Southworth, John Pavlovski, Krešimir Rhodes, Michael D. Zihao, Wu Love, Tom Blackford, Mark G. Banks, Timothy S. Alexander, Murray Carter Observatory 40 Salamanca Road Kelburn Wellington6012 New Zealand School of Chemical & Physical Sciences Victoria University of Wellington PO Box 600 Wellington6140 New Zealand Astrophysics Group Keele University StaffordshireST5 5BG United Kingdom Department of Physics Faculty of Science University of Zagreb Bijenička cesta 32 Zagreb10000 Croatia Brigham Young University ProvoUT84602 United States Dept. Statistics & Data Science National University of Singapore 6 Science Drive 2 Singapore117546 Singapore Variable Stars South RASNZ PO Box 3181 Wellington6011 New Zealand Variable Stars South Congarinni Observatory CongarinniNSW2447 Australia Nielsen 675 6th Ave New YorkNY10011 United States Dept. of Physical Science & Engineering Harper College 1200 W Algonquin Rd PalatineIL60067 United States Physics Department University of Winnipeg 515 Portage Avenue WinnipegR3B 2E9 Canada

New photometry, including TESS data, have been combined with recent spectroscopic observations of the Orion Ib pulsating triple-star system VV Ori. This yields a revised set of absolute parameters with increased precision. Two different programs were utilized for the light curve analysis, with results in predictably close agreement. The agreement promotes confidence in the analysis procedures. The spectra were analysed using the FDBinary program. The main parameters are as follows: M1 = 11.6 ± 0.14 and M2 = 4.8 ± 0.06 (M). We estimate an approximate mass of the wide companion as M3 = 2.0 ± 0.3 M. Similarly, R1 = 5.11±0.03, R2 = 2.51±0.02, R3 = 1.8±0.1 (R);Te1 = 26600±300, Te2 = 16300±400 and Te3 = 10000 ± 1000 (K). The close binary’s orbital separation is a = 13.91 (R);its age is 8 ± 2 (Myr) and its photometric distance is 396 ± 7 pc. The primary’s β Cep type oscillations support these properties and confirm our understanding of its evolutionary status. Examination of the well-defined λ6678 He I profiles reveals the primary to have a significantly low projected rotation: some 80% of the synchronous value. This can be explained on the basis of the precession of an unaligned spin axis. This proposal can resolve also observed variations of the apparent inclination and address other longer-term irregularities of the system reported in the literature. This topic invites further observations and follow-up theoretical study of the dynamics of this intriguing young multiple star. Copyright © 2023, The Authors. All rights reserved.

关键词： Photometry

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Optimal resampling for learning small models

arXiv

引用

arXiv 2019年

作者： Ghose, Abhishek Ravindran, Balaraman Dept. of Computer Science and Engineering IIT Madras Dept. of Computer Science and Engineering Robert Bosch Centre for Data Science and AI IIT Madras

Models often need to be constrained to a certain size for them to be considered interpretable, for e.g., a decision tree of dept. 5 is much easier to make sense of than one of dept. 30. This suggests a trade-off between interpretability and accuracy. Our work tries to minimize this trade-off by suggesting the optimal distribution of the data to learn from, that surprisingly, may be different from the original distribution. We use an Infinite Beta Mixture Model (IBMM) to represent a specific set of sampling schemes. The parameters of the IBMM are learned using a Bayesian Optimizer (BO). While even under simplistic assumptions a distribution in the original d-dimensional space would need to optimize for O(d) variables - cumbersome for most real-world data - our technique lowers this number significantly to a fixed set of 8 variables at the cost of some additional preprocessing. The proposed technique is model-agnostic;it can be applied to any classifier. It also admits a general notion of model size. We demonstrate its effectiveness using multiple real-world datasets to construct decision trees, linear probability models and gradient boosted models. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

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Public Kaggle Competition "IceCube – Neutrinos in Deep Ice"

arXiv

引用

arXiv 2023年

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Tjus, J. Becker Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Meneguolo, C. Boscolo Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, Philipp Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Jeong, M. Technical University of Munich Munich Data Science Institute Walther-von-Dyck-Strasse 10 Garching85748 Germany III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen Copenh

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube – Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutrino event reconstruction. Participants worked with a dataset of simulated neutrino events and were tasked with creating a suitable model to predict the direction vector of incoming neutrinos. From January to April 2023, hundreds of teams competed for a total of $50k prize money, which was awarded to the best performing few out of the many thousand submissions. In this contribution I will present some insights into the organization of this large outreach project, and summarize some of the main findings, results and takeaways. Copyright © 2023, The Authors. All rights reserved.

关键词： Neutrons

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Self-attention capsule networks for object classification

arXiv

引用

arXiv 2019年

作者： Hoogi, Assaf Wilcox, Brian Gupta, Yachee Rubin, Daniel L. Dept. of Biomedical Data Science Stanford University Dept. of Computer Science and Applied Math Weizmann Institute of Science Dept. of Electrical Engineering Stanford University

We propose a novel architecture for object classification, called Self-Attention Capsule Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as an integral layer within the Capsule Network (CapsNet). While the Self-Attention mechanism supplies a longrange dependencies, results in selecting the more dominant image regions to focus on, the CapsNet analyzes the relevant features and their spatial correlations inside these regions only. The features are extracted in the convolutional layer. Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task. The attention map is then fed into the CapsNet primary layer that is followed by a classification layer. The proposed SACN model was designed to solve two main limitations of the baseline CapsNet - analysis of complex data and significant computational load. In this work, we use a shallow CapsNet architecture and compensates for the absence of a deeper network by using the Self-Attention module to significantly improve the results. The proposed Self-Attention CapsNet architecture was extensively evaluated on six different datasets, mainly on three different medical sets, in addition to the natural MNIST, SVHN and CIFAR10. The model was able to classify images and their patches with diverse and complex backgrounds better than the baseline CapsNet. As a result, the proposed Self-Attention CapsNet significantly improved classification performance within and across different datasets and outperformed the baseline CapsNet, ResNet-18 and DenseNet-40 not only in classification accuracy but also in robustness. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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LEARNING GEOGRAPHICAL DISTRIBUTION of VACANT HOUSES USING CLOSED MUNICIPAL data: A CASE STUDY of WAKAYAMA CITY, JAPAN 5

LEARNING GEOGRAPHICAL DISTRIBUTION of VACANT HOUSES USING CL...

引用

5th International Conference on Smart data and Smart Cities, SDSC 2020

作者： Baba, H. Akiyama, Y. Tokudomi, T. Takahashi, Y. Center for Spatial Information Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba Japan Dept. of Urban and Civil Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo Japan Wakayama Data Utilization Promotion Center 3-17 Higashikuramaecho Wakayama Wakayama Japan Statistics Bureau of Japan 3-17 Higashikuramaecho Wakayama Wakayama Japan

Vacant housing detection is an urgent problem that needs to be addressed. It is also a suitable example to promote utilisation of smart data that are stored in municipalities. This study proposes a vacant housing detection model that uses closed municipal data and considers accelerating the use of public data to promote smart cities. Employing a machine learning technique, this study ensures high predictive power for vacant housing detection. The model enables us to handle complex municipal data that include non-linear feature characteristics and substantial missing data. In particular, handling missing data is important in the practical use of closed municipal data because not all of the data are necessarily absorbed to a building unit. Consequently, the model in this analysis showed that the accuracy and false positive rate are 95.4 percent and 3.7 percent, respectively, which are high enough to detect vacant houses. However, the true positive rate is 77.0 percent. Although the rate is not low to some extent, selection of features and further collection of extra samples may improve the rate. Geographic distribution of vacant houses further enabled us to check the difference between the actual and estimated number of vacant houses, and more than 80 percent of 500-meter grid data are with below 10 errors, which we think, provides city planners with informative data to roughly grasp geographical tendencies. © 2020 Copernicus GmbH. All rights reserved.

关键词： Machine learning

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Detection of Careless Mistakes during Programming Learning using a Simple Electroencephalograph

Detection of Careless Mistakes during Programming Learning u...

引用

International Conference on Computer science & Education (ICCSE)

作者： Katsuyuki Umezawa Makoto Nakazawa Manabu Kobayashi Yutaka Ishii Michiko Nakano Shigeichi Hirasawa Dept. of Information Science Shonan Institute of Technology Kanagawa Japan Dept. of Industrial Information Science Junior College of Aizu Fukushima Japan Center for Data Science Waseda University Tokyo Japan Faculty of Education Chiba University Chiba Japan Faculty of Education and Integrated Arts and Sciences Waseda University Tokyo Japan Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

There are several difficulties encountered by learners during learning such as good or bad learning content, the difficulty level of learning content, and the degree of learning proficiency. It is possible to detect these difficulties by measuring the browsing history, editing history, and biological information such as brain waves or eye-tracking information. In this paper, we measure electroencephalograph (EEG) information during programming learning. We focus on the relationship between task response time and EEG, and try to detect careless mistakes due to the lack of attention. The results show that careless mistakes during programming learning can be detected by experiments.

关键词： Electroencephalography Task analysis Atmospheric measurements Particle measurements Programming Headphones Education

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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A ROBUST REPUTATION-BASED GROUP RANKING SYSTEM AND ITS RESISTANCE TO BRIBERY

arXiv

引用

arXiv 2020年

作者： Saúde, João Ramos, Guilherme Boratto, Ludovico Caleiro, Carlos Department of Electrical and Computer Engineering Carnegie-Melon University Pittsburgh United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal Data Science and Big Data Analytics EURECAT Barcelona Spain SQIG - Instituto de Telecomunicações Dept. of Mathematics Instituto Superior Técnico University of Lisbon Lisbon Portugal

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case. Copyright © 2020, The Authors. All rights reserved.

关键词： data mining

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