HIEF: a holistic interpretability and explainability framework - Kucklick, Jan-Peter (Journal of Decision Systems)
Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.
Keywords: Explainable AI (XAI); machine learning; interpretability; real estate appraisal; framework; taxonomy

Introducing FUM: A Framework for API Usage Constraint and Misuse Classification - Schlichtig, Michael; Sassalla, Steffen; Narasimhan, Krishna; Bodden, Eric (Software Engineering 2023)
Application Programming Interfaces (APIs) are the primary mechanism developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and how they are caused, is important to prevent them, eg, with API misuse detectors. However, definitions for API misuses and related terms in literature vary. This paper presents a systematic literature review to clarify these terms and introduces FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To address this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detector’s capabilities, we performed a case study on the state-of the-art misuse detection tool CogniCrypt. The study showed that FUM can be used to properly assess CogniCrypt’s capabilities, identify weaknesses and assist in deriving mitigations and improvements.
Keywords: API misuses API usage constraints; classification framework; API misuse detection; static analysis

FUM - A Framework for API Usage constraint and Misuse Classification - Schlichtig, Michael; Sassalla, Steffen; Narasimhan, Krishna; Bodden, Eric (2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER))
Application Programming Interfaces (APIs) are the primary mechanism that developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and for what reasons they are caused, is important to prevent them, e.g., with API misuse detectors. However, definitions and nominations for API misuses and related terms in literature vary and are diverse. This paper addresses the problem of scattered knowledge and definitions of API misuses by presenting a systematic literature review on the subject and introducing FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To capture this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detectors' capabilities, we performed a case study on CogniCrypt, a state-of-the-art misuse detector for cryptographic APIs. The study showed that FUM can be used to properly assess CogniCrypt's capabilities, identify weaknesses and assist in deriving mitigations and improvements. And it appears that also more generally FUM can aid the development and improvement of misuse detection tools.
Keywords: API misuses; API usage constraints; classification framework; API misuse detection; static analysis

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation - Witschen, Linus Matthias; Wiersema, Tobias; Ghasemzadeh Mohammadi, Hassan; Awais, Muhammad; Platzner, Marco (Microelectronics Reliability)
Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we ﬁrst analyze and classify related approaches and then present CIRCA, our ﬂexible framework for search-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.
Keywords: Approximate Computing; Framework; Pareto Front; Accuracy

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation - Witschen, Linus Matthias; Wiersema, Tobias; Ghasemzadeh Mohammadi, Hassan; Awais, Muhammad; Platzner, Marco (Third Workshop on Approximate Computing (AxC 2018))
Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we ﬁrst analyze and classify related approaches and then present CIRCA, our ﬂexible framework for search-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.
Keywords: Approximate Computing; Framework; Pareto Front; Accuracy

„A coupled phase field/diffusion model for upper and lower bainitic transformation” - Düsing, M.; Mahnken, R. (International Journal of Solids and Structures)
Bainite is a steel microstructure consisting of three phases, bainitic ferrite, austenite and carbides. It forms in two different morphologies, upper and lower bainite, where different diffusion mechanisms are dominant. The aim of this work is to simulate both transformations within a unified model. To this end, we extend an own previously published model for lower bainite with diffusion across the phase interface. As a central idea we introduce weighted Helmholtz energy functions and a weighted mobility tensor, respectively. The individual Helmholtz energy functions and mobility terms are related to the different diffusion mechanisms which are responsible for the formation of both morphologies. Two representative examples illustrate the capability of the coupled phase field/diffusion model and show the expected behaviour.
Keywords: Coupled phase field/diffusion model; Bainite; Multiphase field method; Cahn–Hilliard diffusion; Diffusion across the interface; Lower bainitic transformation; Upper bainitic transformation; Thermodynamic framework; Microforce balance

Risk Reduction in Natural Disaster Management Through Information Systems: A Literature review and an IS design science research agenda - Schryen, Guido; Wex, Felix (International Journal of Information Systems for Crisis Response and Management (IJISCRAM))
Natural disasters, including earthquakes, Tsunamis, floods, hurricanes, and volcanic eruptions, have caused tremendous harm and continue to threaten millions of humans and various infrastructure capabilities each year. In their efforts to take countermeasures against the threats posed by future natural disasters, the United Nations formulated the ?Hyogo Framework for Action?, which aims at assessing and reducing risk. This framework and a global review of disaster reduction initiatives of the United Nations acknowledge the need for information systems research contributions in addressing major challenges of natural disaster management. In this paper, we provide a review of the literature with regard to how information systems research has addressed risk assessment and reduction in natural disaster management. Based on the review we identify research gaps that are centered around the need for acquiring general knowledge on how to design IS artifacts for risk assessment and reduction. In order to close these gaps in further research, we develop a research agenda that follows the IS design science paradigm.
Keywords: Natural Disaster Management; Risk Reduction; Hyogo Framework; IS Design Science; Literature review

GMM-based significance decoding - Abdelaziz, Ahmed H.; Zeiler, Steffen; Kolossa, Dorothea; Leutnant, Volker; Haeb-Umbach, Reinhold (Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on)
The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios.
Keywords: Bayes methods; Gaussian processes; convolution; decision theory; decoding; noise; reverberation; speech coding; speech recognition; Bayesian decision rule; GMM; Gaussian mixture models; additive noise scenarios; automatic speech recognition systems; convolutive noise scenarios; decoding approach; mathematical framework; reverberant environments; significance decoding; speech feature estimation; uncertainty-of-observation techniques; Hidden Markov models; Maximum likelihood decoding; Noise; Speech; Speech recognition; Uncertainty; Uncertainty-of-observation; modified imputation; noise robust speech recognition; significance decoding; uncertainty decoding

MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations - Chinaev, Aleksej; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.
Keywords: Gaussian noise; maximum likelihood estimation; parameter estimation; GMM parameter; Gaussian mixture model; MAP estimation; Map-based estimation; maximum a-posteriori estimation; maximum likelihood technique; noisy observation; sequential estimation framework; white Gaussian noise; Additive noise; Gaussian mixture model; Maximum likelihood estimation; Noise measurement; Gaussian mixture model; Maximum a posteriori estimation; Maximum likelihood estimation

Numerical Representations and User Behaviour in Social Networking Sites: Towards a Multi- Theoretical Research Framework - Sjöklint, Mimmi; Constantiou, Ioanna; Trier, Matthias (ECIS 2013 Proceedings)
The new technological enhancements and the accessibility to varieties of online applications, enable users to collect personal data and perform self-evaluation through test, comparison and experimentation. The sparked interest in numbers and numbers as self-representative visualisations is prominent in social networking sites, which are the empirical setting for the present study. This paper sets out to establish a multi-theoretical framework which enables the investigation of emerging phenomena of the role of numbers in social networking sites. The proposed framework rests on three theoretical pillars: self-determination theory, heuristic decision making and behavioural economics. A discussion departs from these convictions to investigate user reactions and behaviour when faced with numerical representations in the SNS.
Keywords: User Behaviour; Social Networking Sites; Numerical Representations; Multi-Theoretical Framework; Quantified Self; Pointification