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The tension of usable safety, security and privacy

i-com 2025年第1期24卷 1-7页

作者： Kaufhold, Marc-André Mentler, Tilo Nestler, Simon Reuter, Christian Technical University of Darmstadt Darmstadt Germany Human Computer Interaction und User Experience Trier University of Applied Sciences Trier Germany Faculty of Computer Science Technische Hochschule Ingolstadt Ingolstadt Germany Technical University of Darmstadt Darmstadt Germany

Local disasters such as the Ahr Valley flood in Germany, the international backdrop of the Russo-Ukrainian War, or the global impact of the COVID-19 pandemic place high demands on the people and organisations that are involved in these situations and contexts to save lives, mitigate damage, provide comfort, or organise reconstruction. Novel technologies are constantly making their way into everyday life, such as artificial intelligence, big data, decentralised networks, internet of things, or virtual reality. Their adaptation, acceptance, usability, usefulness, and legal framework conditions for safety-critical systems must be researched and tested thoroughly. In this special issue, we investigate the use of computer-based solutions in areas and situations of direct relevance to people's lives and well-being (Usable Safety), as well as contributions to user-oriented resilience concepts of sociotechnical systems concerning potential attacks (Usable Security) and data protection mechanisms (Usable Privacy). © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： usable safety usable security safety-critical systems human-centered computing

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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A future prospect for European collaboration on advanced analytics in economy and society 6

A future prospect for European collaboration on advanced ana...

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6th Collaborative European Research Conference, CERC 2020

作者： Below, Nicola Burkhardt, Dirk Kaupp, Lukas Nazemi, Kawa Darmstadt University of Applied Sciences Human-Computer Interaction and Visual Analytics Group Germany

Analytical Reasoning by applying machine learning approaches, artificial intelligence, NLP and visualizations allow to get deep insights into the different domains of various stakeholders and enable to solve complex tasks. Thereby the tasks are very heterogenous and subject of investigation in the different areas of application. These tasks or challenges should be defined by the stakeholders themselves and lead through a deep investigation to advanced analytical approaches. We therefore set up a strategic alliance of research, enterprises and societal organization with the goal of a strong collaboration to identify in a first step these challenges and workout technological solutions for each application scenario. We give in this paper a first draft of current challenges and technological advancements. The main contribution of this paper is next to an accurate description of the current challenges in the analytics domain, also the description of an agenda how these challenges can be solved. Furthermore, a process is explained, how the strategic alliance should act and organize their work to realize beneficial and useful analytical solutions. Copyright © 2020 for this paper by its authors.

关键词： Artificial intelligence

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visual dashboards in trend analytics to observe competitors and leading domain experts 6

Visual dashboards in trend analytics to observe competitors ...

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6th Collaborative European Research Conference, CERC 2020

作者： Sina, Lennart Burkhardt, Dirk Nazemi, Kawa Darmstadt University of Applied Sciences Human-Computer Interaction and Visual Analytics Group Germany

The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements. Copyright © 2020 for this paper by its authors.

关键词： Commerce

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A mobile visual analytics approach for instant trend analysis in mobile contexts 5

A mobile visual analytics approach for instant trend analysi...

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5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Burkhardt, Dirk Nazemi, Kawa Kuijper, Arjan Ginters, Egils Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Riga Technical University Riga Latvia

ISBN: (纸本)9788885741423

The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Decision support systems

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Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks

arXiv

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arXiv 2025年

作者： Zhang, Liang Lin, Jionghao Sabatini, John Zapata-Rivera, Diego Forsyth, Carol Jiang, Yang Hollander, John Hu, Xiangen Graesser, Arthur C. Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Faculty of Information Technology Monash University ClaytonVIC3800 Australia Educational Testing Service PrincetonNJ08540 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72701 United States Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

As learners engage with Intelligent Tutoring Systems (ITSs) by responding to a series of questions, their performance data, such as correct or incorrect responses, is crucial for assessing and predicting their knowledge states through analysis and modeling. However, data sparsity, often arising from skipped or incomplete responses, poses challenges for accurately assessing learning and delivering personalized instruction. To address this, we propose a generative data imputation method based on Generative Adversarial Imputation Networks (GAIN) to complete missing learning performance data. Our approach employs a three-dimensional (3D) framework structured by learners, questions, and attempts, with an adaptable design along the attempts dimension to manage varying sparsity levels. Enhanced by convolutional neural networks in the input and output layers and optimized with a least squares loss function, our GAIN-based method aligns the input and output shapes with the dimensions of question-attempt matrices across the learners’ dimension. Extensive experiments on datasets from three types of ITSs, including AutoTutor Adult Reading Comprehension (ARC), ASSISTments and MATHia, demonstrate that our approach generally outperforms baseline methods, e.g., tensor factorization-based methods and other Generative Adversarial Network (GAN) variants, in imputation accuracy across different setting of maximum attempts. Bayesian Knowledge Tracing (BKT) modeling further validates the imputed data’s efficacy by estimating learning parameters, including initial knowledge P(L0), learning rate P(T), guess rate P(G), and slip rate P(S). Results reveal that the imputed data not only enhances model fit but also closely aligns with the original sparse distributions by capturing underlying learning behaviors, indicating greater reliability in learner assessments. Kullback-Leibler (KL) divergence measurements of all these learning parameters confirm that the imputed data effectively preserve e

关键词： Generative adversarial networks

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Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks

SSRN

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SSRN 2025年

作者： Zhang, Liang Lin, Jionghao Sabatini, John Zapata-Rivera, Diego Forsyth, Carol Jiang, Yang Hollander, John Hu, Xiangen Graesser, Arthur C. Institute for Intelligent Systems University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Centre for Learning Analytics Monash University MelbourneVIC Australia Department of Psychology University of Memphis MemphisTN United States Educational Testing Service PrincetonNJ United States Department of Psychology and Counseling Arkansas State University JonesboroAR United States Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

As human learners respond to questions in Intelligent Tutoring Systems (ITSs), their performance data (correct or incorrect answers) is crucial for assessing and predicting their knowledge states. However, data sparsity, often arising from skipped or incomplete responses, hampers accurate assessment and personalized instruction. To address this challenge, we propose a Generative Adversarial Imputation Networks (GAIN) method using a three-dimensional representation (learners, questions, and attempts) with Convolutional Neural Networks (CNNs) in the input and output layers. We evaluate our method on three ITS datasets, AutoTutor ARC, ASSISTments, and MATHia, where it outperforms baselines, including tensor factorization methods and other GAN variants, across multiple maximum attempt settings. To confirm imputation effectiveness for learner modeling, we applied Bayesian Knowledge Tracing (BKT) and observed significant reductions in RMSE compared to original sparse data, indicating substantial improvements in modeling accuracy. Moreover, analyzing divergence of key BKT parameters (initial knowledge \(P(L_0)\), learning rate \(P(T)\), guess rate \(P(G)\), and slip rate \(P(S)\)) via Kullback-Leibler (KL) divergence shows that the imputed data preserves essential learning characteristics. Our findings highlight GAIN’s potential for mitigating data sparsity in ITSs, enabling more adaptive, individualized instruction and fostering more effective AI-driven education. © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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visual analytics for Systematic Reviews According to PRISMA

Visual Analytics for Systematic Reviews According to PRISMA

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International Conference on Information visualisation (IV)

作者： Lennart B. Sina Kawa Nazemi Human-Computer Interaction and Visual Analytics Darmstadt University of Applied Sciences Darmstadt Germany

ISBN: (纸本)9781665490085

Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a visual analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel visual analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.

关键词： Systematics Protocols visual analytics Face recognition Layout Data visualization Information retrieval

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visual analytics for Forecasting Technological Trends from Text

Visual Analytics for Forecasting Technological Trends from T...

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International Conference on Information visualisation (IV)

作者： Cristian A. Secco Lennart B. Sina Midhad Blazevic Kawa Nazemi Human-Computer Interaction and Visual Analytics Darmstadt University of Applied Sciences Darmstadt Germany

Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel visual analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest.

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visual Collaboration - An Approach for visual Analytical Collaborative Research

Visual Collaboration - An Approach for Visual Analytical Col...

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International Conference on Information visualisation (IV)

作者： Midhad Blazevic Lennart B. Sina Kawa Nazemi Human-Computer Interaction and Visual Analytics Darmstadt University of Applied Sciences Darmstadt Germany

ISBN: (纸本)9781665490085

Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative visual analytics with enhanced collaboration techniques to support researchers from different disciplines.

关键词： Learning systems visualization visual analytics Collaboration Machine learning Feature extraction Natural language processing

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