@article{35620, abstract = {{Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.}}, author = {{Kucklick, Jan-Peter and Müller, Oliver}}, issn = {{2158-656X}}, journal = {{ACM Transactions on Management Information Systems}}, keywords = {{Interpretability, Convolutional Neural Network, Accuracy-Interpretability Trade-Of, Real Estate Appraisal, Hedonic Pricing, Grad-Ram}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}}}, doi = {{10.1145/3567430}}, year = {{2022}}, } @article{32560, abstract = {{Several methods are available to answer questions regarding similarity and accuracy, each of which has specific properties and limitations. This study focuses on the Latent Congruence Model (LCM; Cheung, 2009), because of its capacity to deal with cross-informant measurement invariance issues. Until now, no cross-national applications of LCM are present in the literature, perhaps because of the difficulty to deal with both cross-national and cross-informant measurement issues implied by those models. This study presents a step-by-step procedure to apply LCM to dyadic cross-national research designs controlling for both cross-national and cross-informant measurement invariance. An illustrative example on parent–child support exchanges in Italy and Germany is provided. Findings help to show the different possible scenarios of partial invariance, and a discussion related to how to deal with those scenarios is provided. Future perspectives in the study of parent–child similarity and accuracy in cross-national research will be discussed.}}, author = {{Tagliabue, Semira and Zambelli, Michela and Sorgente, Angela and Sommer, Sabrina and Hoellger, Christian and Buhl, Heike M. and Lanz, Margherita}}, issn = {{1664-1078}}, journal = {{Frontiers in Psychology}}, keywords = {{latent congruence model, measurement invariance, similarity, accuracy, cross-national, cross-informant, parent-child relationship, support exchanges}}, publisher = {{Frontiers Media SA}}, title = {{{Latent Congruence Model to Investigate Similarity and Accuracy in Family Members' Perception: The Challenge of Cross-National and Cross-Informant Measurement (Non)Invariance}}}, doi = {{10.3389/fpsyg.2021.672383}}, volume = {{12}}, year = {{2021}}, } @article{3585, abstract = {{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.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}}, issn = {{0026-2714}}, journal = {{Microelectronics Reliability}}, keywords = {{Approximate Computing, Framework, Pareto Front, Accuracy}}, pages = {{277--290}}, publisher = {{Elsevier}}, title = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}}, doi = {{10.1016/j.microrel.2019.04.003}}, volume = {{99}}, year = {{2019}}, } @unpublished{3586, abstract = {{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.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}}, booktitle = {{Third Workshop on Approximate Computing (AxC 2018)}}, keywords = {{Approximate Computing, Framework, Pareto Front, Accuracy}}, pages = {{6}}, title = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}}, year = {{2018}}, } @inproceedings{10598, abstract = {{Approximate computing has become a very popular design strategy that exploits error resilient computations to achieve higher performance and energy efficiency. Automated synthesis of approximate circuits is performed via functional approximation, in which various parts of the target circuit are extensively examined with a library of approximate components/transformations to trade off the functional accuracy and computational budget (i.e., power). However, as the number of possible approximate transformations increases, traditional search techniques suffer from a combinatorial explosion due to the large branching factor. In this work, we present a comprehensive framework for automated synthesis of approximate circuits from either structural or behavioral descriptions. We adapt the Monte Carlo Tree Search (MCTS), as a stochastic search technique, to deal with the large design space exploration, which enables a broader range of potential possible approximations through lightweight random simulations. The proposed framework is able to recognize the design Pareto set even with low computational budgets. Experimental results highlight the capabilities of the proposed synthesis framework by resulting in up to 61.69% energy saving while maintaining the predefined quality constraints.}}, author = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}}, booktitle = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}}, keywords = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}}, pages = {{219--224}}, title = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}}, doi = {{10.1109/VLSI-SoC.2018.8645026}}, year = {{2018}}, } @inproceedings{11753, abstract = {{This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data.}}, author = {{Drude, Lukas and Chinaev, Aleksej and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014)}}, keywords = {{Accuracy, Acoustics, Estimation, Mathematical model, Soruce separation, Speech, Vectors, Bayes methods, Blind source separation, Directional statistics, Number of speakers, Speaker diarization}}, pages = {{213--217}}, title = {{{Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models}}}, year = {{2014}}, } @inproceedings{37040, abstract = {{Refinement of untimed TLM models into a timed HW/SW platform is a step by step design process which is a trade-off between timing accuracy of the used models and correct estimation of the final timing performance. The use of an RTOS on the target platform is mandatory in the case real-time properties must be guaranteed. Thus, the question is when the RTOS must be introduced in this step by step refinement process. This paper proposes a four-level RTOS-aware refinement methodology that, starting from an untimed TLM SystemC description of the whole system, progressively introduce HW/SW partitioning, timing, device driver and RTOS functionalities, till to obtain an accurate model of the final platform, where SW tasks run upon an RTOS hosted by QEMU and HW components are modeled by cycle accurate TLM descriptions. Each refinement level allows the designer to estimate more and more accurate timing properties, thus anticipating design decisions without being constrained to leave timing analysis to the final step of the refinement. The effectiveness of the methodology has been evaluated in the design of two complex platforms.}}, author = {{Becker, Markus and Di Guglielmo, Giuseppe and Fummi, Franco and Müller, Wolfgang and Pravadelli, Graziano and Xie, Tao}}, booktitle = {{Proceedings of DATE’10}}, keywords = {{Timing, Hardware, Operating systems, Process design, Accuracy, Standards development, Context modeling, Real time systems, Communication channels, Microprogramming}}, location = {{Dresden}}, publisher = {{IEEE}}, title = {{{RTOS-Aware Refinement for TLM2.0-based HW/SW Design}}}, doi = {{10.1109/DATE.2010.5456965}}, year = {{2010}}, } @article{11870, abstract = {{We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidean distance of the respective class means. We generalize upon LDA by introducing a different weighting function}}, author = {{Loog, M. and Duin, R.P.W. and Haeb-Umbach, Reinhold}}, journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, keywords = {{approximate pairwise accuracy, Bayes error, Bayes methods, error statistics, Euclidean distance, Fisher criterion, linear dimension reduction, linear discriminant analysis, pattern classification, statistical analysis, statistical pattern classification, weighting function}}, number = {{7}}, pages = {{762--766}}, title = {{{Multiclass linear dimension reduction by weighted pairwise Fisher criteria}}}, doi = {{10.1109/34.935849}}, volume = {{23}}, year = {{2001}}, } @inproceedings{11869, abstract = {{Amongst several data driven approaches for designing filters for the time sequence of spectral parameters, the linear discriminant analysis (LDA) based method has been proposed for automatic speech recognition. Here we apply LDA-based filter design to cepstral features, which better match the inherent assumption of this method that feature vector components are uncorrelated. Extensive recognition experiments have been conducted both on the standard TIMIT phone recognition task and on a proprietary 130-words command word task under various adverse environmental conditions, including reverberant data with real-life room impulse responses and data processed by acoustic echo cancellation algorithms. Significant error rate reductions have been achieved when applying the novel long-range feature filters compared to standard approaches employing cepstral mean normalization and delta and delta-delta features, in particular when facing acoustic echo cancellation scenarios and room reverberation. For example, the phone accuracy on reverberated TIMIT data could be increased from 50.7\% to 56.0\%}}, author = {{Lieb, M. and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2000)}}, keywords = {{acoustic echo cancellation algorithms, adverse environmental conditions, automatic speech recognition, cepstral analysis, cepstral features, cepstral mean normalization, command word task, delta-delta features, delta features, echo suppression, error rate reductions, feature vector components, FIR filters, LDA derived cepstral trajectory filters, linear discriminant analysis, long-range feature filters, phone accuracy, real-life room impulse responses, reverberant data, spectral parameters, speech recognition, standard TIMIT phone recognition task}}, pages = {{II1105--II1108 vol.2}}, title = {{{LDA derived cepstral trajectory filters in adverse environmental conditions}}}, doi = {{10.1109/ICASSP.2000.859157}}, volume = {{2}}, year = {{2000}}, }