Deep speech inpainting of time-frequency masks

Transient loud intrusions, often occurring in noisy environments, can completely overpower speech signal and lead to an inevitable loss of information. While existing algorithms for noise suppression can yield impressive results, their efficacy remains limited for very low signal-to-noise ratios or when parts of the signal are missing. To address these limitations, here we propose an end-to-end framework for speech inpainting, the context-based retrieval of missing or severely distorted parts of time-frequency representation of speech. The framework is based on a convolutional U-Net trained via deep feature losses, obtained using speechVGG, a deep speech feature extractor pre-trained on an auxiliary word classification task. Our evaluation results demonstrate that the proposed framework can recover large portions of missing or distorted time-frequency representation of speech, up to 400 ms and 3.2 kHz in bandwidth. In particular, our approach provided a substantial increase in STOI & PESQ objective metrics of the initially corrupted speech samples. Notably, using deep feature losses to train the framework led to the best results, as compared to conventional approaches.Comment: Accepted to InterSpeech202

User-created persona: Namibian rural Otjiherero speakers

Persona is a communicative artefact for usability that currently functions under the umbrella of User-Centred Design (UCD). Since we argue usability methods differ across cultures, this project presents a cross-cultural research probe on persona generated by indigenous Otjiherero speakers in Namibia. The objective is to find out how participants in this milieu take on, understand and portray persona artefacts, what goals of User Experience (UX) emerge from the inquiry, and whether the artefacts created simulate or differ from those in literature. Tentative methods scaffold from benefits attained by persona in the attempt to advance persona technical communication in cross-cultural design. This experience report presents initial findings on narrative content, rhetorical preferences, and the physical layout of persona artefacts as so-far constructed by Otjiherero speakers in rural Namibia. The report draws to a close reflecting on present challenges and advances, and indicating upcoming pathways

Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202

Human-computer interaction for development (HCI4D):the Southern African landscape

Human-Computer interaction for development (HCI4D) research aims to maximise the usability of interfaces for interacting with technologies designed specifically for under-served, under-resourced, and under-represented populations. In this paper we provide a snapshot of the Southern African HCI4D research against the background of the global HCI4D research landscape.We commenced with a systematic literature review of HCI4D (2010-2017) then surveyed Southern African researchers working in the area. The contribution is to highlight the context- specific themes and challenges that emerged from our investigation

A framework to maximise the communicative power of knowledge visualisations

Knowledge visualisation, in the field of information systems, is both a process and a product, informed by the closely aligned fields of information visualisation and knowledg management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, knowledge visualisations are infrequently deployed due to a lack of evidence-based guidance. To improve this situation, we carried out a systematic literature review to derive a number of “lenses” that can be used to reveal the essential perspectives to feed into the visualisation production process.We propose a conceptual framework which incorporates these lenses to guide producers of knowledge visualisations. This framework uses the different lenses to reveal critical perspectives that need to be considered during the design process. We conclude by demonstrating how this framework could be used to produce an effective knowledge visualisation

Technological Change in the Retirement Transition and the Implications for Cybersecurity Vulnerability in Older Adults

Retirement is a major life transition, which leads to substantial changes across almost all aspects of day-to-day life. Although this transition has previously been seen as the normative marker for entry into older adulthood, its influence on later life has remained relatively unstudied in terms of technology use and cybersecurity behaviours. This is problematic as older adults are at particular risk of becoming victims of cyber-crime. This study aimed to investigate which factors associated with the retirement transition were likely to increase vulnerability to cyber-attack in a sample of 12 United Kingdom based older adults, all of whom had retired within the past 5 years. Semi-structured, one to one interviews were conducted and subsequently analysed using thematic analysis. Six themes were identified referring to areas of loss in: social interaction, finances, day-to-day routine, feelings of competence, sense of purpose, and technology support structures. We discuss the implications of these losses for building cyber-resilience in retirees, with suggestions for future research

Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-{\em i.i.d} nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past $\tau$ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.Comment: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 201