| 31. |
- Isaksson, Martin, et al.
(författare)
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Adaptive Expert Models for Federated Learning
- 2023
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Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
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Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
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| 32. |
- Huhnstock, Nikolas Alexander, 1988-, et al.
(författare)
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An Infinite Replicated Softmax Model for Topic Modeling
- 2019
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Ingår i: Modeling Decisions for Artificial Intelligence. - Cham : Springer. - 9783030267728 - 9783030267735 ; , s. 307-318
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.
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| 33. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena
- 2016
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116
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Tidskriftsartikel (refereegranskat)abstract
- Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.
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| 34. |
- Petersson, Jesper, 1974, et al.
(författare)
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Off the record: The invisibility work of doctors in a patient-accessible electronic health record information service.
- 2021
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Ingår i: Sociology of health & illness. - : Wiley. - 1467-9566 .- 0141-9889. ; 43:5, s. 1270-1285
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we draw on Michael Lipsky's work on street-level bureaucrats and discretion to analyse a real case setting comprising an interview study of 30 Swedish doctors regarding their experiences of changes in clinical work following patients being given access to medical records information online. We introduce the notion of invisibility work to capture how doctors exercise discretion to preserve the invisibility of their work, in contrast to the well-established notion of invisible work, which denotes work made invisible by parties other than those performing it. We discuss three main forms of invisibility work in relation to records: omitting information, cryptic writing and parallel note writing. We argue that invisibility work is a way for doctors to resolve professional tensions arising from the political decision to provide patients with online access to record information. Although invisibility work is understood by doctors as a solution to government-initiated visibility, we highlight how it can create difficulties for doctors concerning accountability towards patients, peers and authorities.
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| 35. |
- Picazo-Sanchez, Pablo, 1985, et al.
(författare)
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Are chrome extensions compliant with the spirit of least privilege?
- 2022
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Ingår i: International Journal of Information Security. - : Springer Science and Business Media LLC. - 1615-5262 .- 1615-5270. ; 21:6, s. 1283-1297
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Tidskriftsartikel (refereegranskat)abstract
- Extensions are small applications installed by users and enrich the user experience of browsing the Internet. Browsers expose a set of restricted APIs to extensions. To be used, extensions need to list the permissions associated with these APIs in a mandatory extension file named manifest. In particular, Chrome’s permission ecosystem was designed in the spirit of the least privilege. Yet, this paper demonstrates that 39.8% of the analyzed extensions provided by the official Web Store are compliant with the spirit of least privilege. Also, we develop: (1) a browser extension to make aware regular users of the permissions the extensions they install; (2) a web app where extensions developers can check whether their extensions are compliant with the spirit of the least privileged; and (3) a set of scripts that can be part of the vendors’ acceptance criteria such that when developers upload their extensions to the official repositories, the scripts automatically analyze the extensions and generate a report about the permissions and the usage.
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| 36. |
- Rexhepi, Hanife, 1984-, et al.
(författare)
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Cancer patients’ information seeking behavior related to online electronic healthcare records
- 2021
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Ingår i: Health Informatics Journal. - : Sage Publications. - 1460-4582 .- 1741-2811. ; 27:3, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Patients’ online access to their EHR together with the rapid proliferation of medical information on the Internet has changed how patients use information to learn about their health. Patients’ tendency to turn to the Internet to find information about their health and care is well-documented. However, little is known about patients’ information seeking behavior when using online EHRs. By using information horizons as an analytical tool this paper aims to investigate the information behavior of cancer patients who have chosen to view their EHRs (readers) and to those who have not made that option (non-readers). Thirty interviews were conducted with patients. Based on information horizons, it seems that non-reading is associated with living in a narrower information world in comparison to readers. The findings do not suggest that the smallness would be a result of active avoidance of information, or that it would be counterproductive for the patients. The findings suggest, however, that EHRs would benefit from comprehensive linking to authoritative health information sources to help users to understand their contents. In parallel, healthcare professionals should be more aware of their personal role as a key source of health information to those who choose not to read their EHRs.
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| 37. |
- Falkman, Göran, 1968-, et al.
(författare)
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SOMWeb - Towards an Infrastructure for Knowledge Sharing in Oral Medicine
- 2005
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Ingår i: Connecting Medical Informatics and Bio-Informatics: Proceedings of MIE2005 - The XIXth International Congress of the European Federation for Medical Informatics. - Amsterdam : IOS Press. - 1586035495 ; 116, s. 527-32, s. 527-532
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Konferensbidrag (refereegranskat)abstract
- In a net-based society, clinicians can come together for cooperative work and distance learning around a common medical material. This requires suitable techniques for cooperative knowledge management and user interfaces that are adapted to both the group as a whole and to individuals. To support distributed management and sharing of clinical knowledge, we propose the development of an intelligent web community for clinicians within oral medicine. This virtual meeting place will support the ongoing work on developing a digital knowledge base, providing a foundation for a more evidence-based oral medicine. The presented system is founded on the use and development of web services and standards for knowledge modelling and knowledge-based systems. The work is conducted within the frame of a well-established cooperation between oral medicine and computer science.
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| 38. |
- John, Meenu Mary, et al.
(författare)
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Towards an AI-driven business development framework: A multi-case study
- 2023
-
Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 35:6
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.
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| 39. |
- Peldszus, Sven, et al.
(författare)
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Secure Data-Flow Compliance Checks between Models and Code Based on Automated Mappings
- 2019
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Ingår i: Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019. ; , s. 23-33
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Konferensbidrag (refereegranskat)abstract
- During the development of security-critical software, the system implementation must capture the security properties postulated by the architectural design. This paper presents an approach to support secure data-flow compliance checks between design models and code. To iteratively guide the developer in discovering such compliance violations we introduce automated mappings. These mappings are created by searching for correspondences between a design-level model (Security Data Flow Diagram) and an implementation-level model (Program Model). We limit the search space by considering name similarities between model elements and code elements as well as by the use of heuristic rules for matching data-flow structures. The main contributions of this paper are three-fold. First, the automated mappings support the designer in an early discovery of implementation absence, convergence, and divergence with respect to the planned software design. Second, the mappings also support the discovery of secure data-flow compliance violations in terms of illegal asset flows in the software implementation. Third, we present our implementation of the approach as a publicly available Eclipse plugin and its evaluation on five open source Java projects (including Eclipse secure storage).
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| 40. |
- Lindén, Joakim, et al.
(författare)
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Evaluating the Robustness of ML Models to Out-of-Distribution Data Through Similarity Analysis
- 2023
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Ingår i: Commun. Comput. Info. Sci.. - : Springer Science and Business Media Deutschland GmbH. - 9783031429408 ; , s. 348-359, s. 348-359
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Konferensbidrag (refereegranskat)abstract
- In Machine Learning systems, several factors impact the performance of a trained model. The most important ones include model architecture, the amount of training time, the dataset size and diversity. We present a method for analyzing datasets from a use-case scenario perspective, detecting and quantifying out-of-distribution (OOD) data on dataset level. Our main contribution is the novel use of similarity metrics for the evaluation of the robustness of a model by introducing relative Fréchet Inception Distance (FID) and relative Kernel Inception Distance (KID) measures. These relative measures are relative to a baseline in-distribution dataset and are used to estimate how the model will perform on OOD data (i.e. estimate the model accuracy drop). We find a correlation between our proposed relative FID/relative KID measure and the drop in Average Precision (AP) accuracy on unseen data.
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