Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Sound Event Detection Metrics: Insights from DCASE 2020

The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point. This paper compares conventional event-based and segment-based criteria against the Polyphonic Sound Detection Score (PSDS)'s intersection-based criterion, over a selection of systems from DCASE 2020 Challenge Task 4. It shows that, by relying on collars, the conventional event-based criterion introduces different strictness levels depending on the length of the sound events, and that the segment-based criterion may lack precision and be application dependent. Alternatively, PSDS's intersection-based criterion overcomes the dependency of the evaluation on sound event duration and provides robustness to labelling subjectivity, by allowing valid detections of interrupted events. Furthermore, PSDS enhances the comparison of SED systems by measuring sound event modelling performance independently from the systems' operating points.
Graph-Based Universal Dependency Parsing in the Age of the Transformer: What Works, and What Doesn't

Current state-of-the-art graph-based dependency parsers differ on various dimensions. Among others, these include (a) the choice of pre-trained word embeddings or language models used for representing token, (b) training setups performing only parsing or additional tasks such as part-of-speech-tagging, and (c) their mechanism of constructing trees or graphs from edge scores. Because of this, it is difficult to estimate the impact of these architectural decisions when comparing parsers. In this paper, we perform a series of experiments on STEPS, a new modular graph-based parser for basic and enhanced Universal Dependencies, analyzing the effects of architectural configurations. We find that pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance. The choice of factorized vs. unfactorized architectures and a multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs). Our parser achieves new state-of-the-art results for a wide range of languages on both basic as well as enhanced Universal Dependencies, using a unified and comparatively simple architecture for both parsing tasks.
Neglecting Long-Term Risks: Self-Disclosure on Social Media and Its Relation to Individual Decision-Making Tendencies and Problematic Social-Networks-Use

Social media including social-networking sites (SNS) encourage people to disclose personal information via profiles and posts. It is assumed that positive short-term effects and immediate feedback (e.g., getting Likes) have a rewarding nature and may complicate the rational weighing of possible negative long-term consequences related to self-disclosure. Dual-process theories assume risky behaviors to result from more impulsive/short-term oriented compared to reflective/long-term oriented decision making. The current laboratory study investigates whether the extent of online self-disclosure is explained by the general tendency to choose short-term rewards by neglecting long-term risks as well as by tendencies toward a problematic social-networks-use. Participants (N = 88) were asked to log into their Facebook account to answer questions about their actual self-disclosing behavior. Furthermore, they performed an experimental decision-making task and answered a questionnaire assessing problematic social-networks-use. The quantity of self-disclosure via posts was negatively associated with advantageous decision making and positively with tendencies toward a problematic social-networks-use. The findings indicate that high self-disclosure via posts is associated with a general tendency to neglect long-term risks. Moreover, a problematic social-networks-use can additionally increase individual’s self-disclosure via posts.
Genistein-attenuated hepatic steatosis and inflammation in nonalcoholic steatohepatitis with bilateral ovariectomized rats

Objective: The objective was to investigate the anti-lipidemic and anti-inflammatory effects of genistein on rats with estrogen deficiency and nonalcoholic steatohepatitis (NASH). Materials and Methods: Sprague-Dawley female rats (n = 48) were randomly divided into ovariectomized (OVX) and non-OVX groups, then again divided into three subgroups as follows: controls, rats fed with high-fat high-fructose (HFHF) diet (NASH group), and rats fed with HFHF diet plus daily 16 mg/kg genistein (genistein group). Liver tissues were used for histology, liver tissues were used for histology and measured of hepatic free fatty acid (FFA) by colorimeter, and nuclear factor kappa B (NFκB) expression by immunohistochemistry. Serum tumor necrosis factor-α (TNF-α) was evaluated by enzyme-linked immunosorbent assay. Results: NASH group had increased serum TNF-α (171.62 ± 22.34 vs. 58.47 ± 14.83 pg/mL), %NFκB-positive cells (53.94 ± 11.89 vs. 13.73 ± 3.40), and hepatic FFA (9.07 ± 2.27 vs. 3.62 ± 0.77 nmol/mg tissue) when compared with control (P < 0.01). The most severe hepatic fat accumulation and inflammation was found in OVX with NASH group. Genistein treatment decreased serum TNF-α compared with NASH groups in both non-OVX and OVX groups (105.84 ± 29.77 vs. 171.62 ± 22.34 pg/mL and 73.07 ± 19.31 vs. 124.12 ± 16.04 pg/mL, respectively) (P < 0.01). Genistein reduced %NFκB-positive cells in NASH rats (31.84 ± 10.60 vs. 53.94 ± 11.89) and decreased hepatic FFA levels in OVX with NASH rats (6.50 ± 0.60 vs. 13.11 ± 1.65 nmol/mg tissue) when compared with NASH group, respectively (P < 0.01). Conclusion: Estrogen deficiency is the contributing factor that worsens NASH. Genistein attenuated hepatic fat accumulation and inflammation. Moreover, genistein demonstrated to be more effective in estrogen deficiency with NASH than ovary-intact rats. Abbreviations used: NAFLD: Nonalcoholic fatty liver disease; NASH: Nonalcoholic steatohepatitis; non-OVX: Nonovariectomized; OVX: ovariectomized; HFHF: High-fat high-fructose; FFA: Free fatty acid; NFκB: Nuclear factor kappa B; TNF-α: Tumor necrosis factor-alpha; IL-6: Interleukin-6; PPARγ: Peroxisome proliferator-activated receptor gamma; ERα: Estrogen receptor alpha; ERβ: Estrogen receptor beta; LDL: Low-density lipoprotein; TBARS: Thiobarbituric acid-reactive substances; ELISA: Enzyme-linked immunosorbent assay; DMSO: Dimethyl sulfoxide; DAB: Diaminobenzidine.
Exploring Multi-Channel Features for Speaker Verification with Joint VAD and Speech Enhancement.

To improve multi-channel Speaker Verification, we propose to bring together advancements made in multi-channel speech features. For robust far-field scenario, we combine so-called spectral, spatial, and directional features. Using a simple feature concatenation scheme, we incrementally introduce new features on top of single-channel features. Experimented features include Inter-channel Phase Difference, multi-channel sinc convolutions, directional power ratio features and angle features. To maximally leverage supervised learning, our framework is also equipped with multi-channel speech enhancement and Voice Activity Detection. On a challenging language-mismatched test set, we obtain consistent improvements on various degradation levels. On noisy condition, we achieve a 32% relative reduction in Equal Error Rate (EER) w.r. t. single-channel baseline. We find the improvements from speaker-dependent directional features more consistent in noisy condition than clean. This is established via an analysis on several background disturbance levels. Lastly, we investigate if the learned multi-channel speaker embedding space can be made more discriminative through a constrastive loss based fine-tuning. With a simple choice of Triplet loss, we observe a further 8.3% relative reduction in EER.
Deep Learning for Radio-Based Human Sensing: Recent Advances and Future Directions

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.
Towards automating the construction of recommender systems for low-code development platforms

Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations. To alleviate these problems, in this paper, we present the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms. As a proof of concept, we present some preliminary results obtained from an offline evaluation of our framework on three datasets of class diagrams. The results show that the proposed framework is capable of providing relevant recommendations in the given context.
Fast and Accurate Light Field Saliency Detection through Feature Extraction

Light field saliency detection---important due to utility in many vision tasks---still lack speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a "memorizing" tasks, existing approaches consume unnecessarily large amounts of computational resources for (training and) testing leading to execution times is several seconds. We solve this by aggressively reducing the large light-field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes $0.4$ s to process a light field of size $9\times9\times512\times375$ in a CPU and is significantly faster than existing systems, with better or comparable accuracy. Our work shows that extracting features from light fields through aggressive size reduction and the attention results in a faster and accurate light-field saliency detector.
A model-driven approach for cobotic cells based on Petri nets

The advance in the development of industrial robots has accelerated significantly in recent years. One of the driving forces behind this is collaboration between humans and robots in shared working areas, resulting in increased productivity and thus reduced cost. Unfortunately, the development of software for collaborating robots is a complex, time-intensive, and demanding task. This is because robotic technology changes fast and knowledge is hard to make available for reuse. Hence, a model-driven approach is necessary to facilitate the development of software for collaborating robots. This work will introduce such a model-driven approach based on hybrid Petri nets, as a formal technique for modeling the various aspects of robotic software. Therefore, specific challenges of employing Petri net based models are discussed, focusing on the management, usage and adaptation of the different models required for applications of collaborating robots. Building on that, a model-driven architecture is proposed, which solves the identified challenges of application development for collaborating robots.
Effectivity of Progressive Taxation from the Micro- and Macroeconomic Perspective

Abstract Many empiric analyses on the topic of the progression exist and despite their often contradictory results governments still include their conclusions in to their tax policies. Many questions arise in relation to the issue: What is the theoretical justification of progressive taxation? What are the effects of the progression from the microeconomic and macroeconomic perspectives? Which conditions should be met in the real-world for progressive taxation to be effective? The purpose of the article is to find the answers to these questions. We aim to identify a set of requirements that shall be observed if the efficiency of progressive taxation in real conditions is the goal. We conclude that the taxation as a built-in stabilizer have an impact on economic stability under certain conditions. We described the mechanism of how the taxation act in the counter-cyclic and pro-social way and we defined the factors which influence the efficiency of progressive taxation in the real economy.
Automated generation of test scenario models for the system-level safety assurance of autonomous vehicles

Autonomous vehicles controlled by advanced machine learning techniques are significantly gaining in popularity. However, the safety engineering practices currently used in such vehicles are not capable of justifying that AI techniques would prevent unsafe situations with a designated level of confidence and reliability. One related challenge is the perpetually changing environment that autonomous vehicles must interact with, which must be taken into consideration when deriving test suites for their safety assurance. As a result, a common approach for testing autonomous vehicles involves subjecting them to test scenarios and evaluating their system-level quality of service. As it stands, such system-level testing approaches do exist but only at a prototypical and conceptual level: these approaches cannot handle complex system-level traffic scenarios and related coverage criteria. I plan to address this challenge through my PhD studies by (1) defining situation coverage as an abstract coverage criteria for autonomous vehicle testing, (2) evaluating situation coverage of existing test suites obtained by off-the-shelf simulation tools, and (3) proposing a test suite generation approach that provides test scenarios with increasing situation coverage as output.
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.
Effect of tobacco use on disease activity and DMT discontinuation in multiple sclerosis patients treated with dimethyl fumarate or fingolimod

Background Tobacco exposure is a modifiable risk factor for multiple sclerosis (MS). Studies evaluating the relationship between tobacco, disease activity, and disease modifying therapy (DMT) persistence yielded conflicting results. We sought to address this issue with data from clinical practice. Objective To compare 24-month disease outcomes in tobacco versus non-tobacco users treated with dimethyl fumarate (DMF) or fingolimod (FTY) in clinical practice. Methods We retrospectively identified 659 MS patients treated with DMF or FTY, stratified by patient-reported tobacco use. DMT discontinuation and measures of disease activity at 24 months were assessed using propensity score (PS) weighting. Outcome estimates were calculated as tobacco vs non-tobacco use. Results 164 tobacco users (DMF n = 101; FTY n = 63) and 495 non-tobacco users (DMF n = 294; FTY n = 201) were identified. Tobacco (39.4%) and non-tobacco (34.4%) users were equally likely to discontinue DMT (OR = 1.17, 95% CI 0.79, 1.75), but tobacco users discontinued therapy earlier (HR = 1.53, 95% CI 1.06, 2.43). There were no differences in ARR (rate ratio = 1.39, 95% CI 0.97, 1.96). However, tobacco users had decreased odds of NEDA-2 (OR = 0.61, 95% CI 0.44, 0.83). Conclusion Our findings suggest that tobacco is a negative risk factor for inflammatory disease activity and earlier DMF and FTY discontinuation.
Word2vec Conjecture and A Limitative Result

Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by vector spaces. Unlike many previous works that focus on the distributional semantic aspect of \texttt{word2vec}, we study the purely \emph{representational} question: can \emph{all} semantic word-word relations be represented by differences (or directions) of vectors? We call this the word2vec conjecture and point out some of its desirable implications. However, we will exhibit a class of relations that cannot be represented in this way, thus falsifying the conjecture and establishing a limitative result for the representability of semantic relations by vector spaces over fields of characteristic 0, e.g., real or complex numbers.
Automated video game world map synthesis by model-based techniques

World maps contribute a significant part of the interactivity and entertainment to modern video games. While large-scale industrial world map generation tools exist, their use usually implies a substantial learning curve, and the cost of licences restricts the accessibility of these tools to individual game developers. In this paper, we introduce a world map generator for Unity-based games that exploits model-based techniques. After the game-specific concepts of the world map are captured and turned into a metamodel, the world map generation problem is first formulated as a consistent graph generation problem solved by a state-of-the-art model generator. This graph model is subsequently refined into a concrete world within the Unity game engine by (1) mapping the abstract graph elements into Unity game objects and (2) creating a height map based on user-defined properties with the Perlin Noise technique. Demonstration video: https://youtu.be/03BbD61EKpk
Correction

SUSTAINABILITY SCIENCE Correction for “Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn–soybean croplands,” by Lisa A. Schulte, Jarad Niemi, Matthew J. Helmers, Matt Liebman, J. Gordon Arbuckle, David E. James, Randall K. Kolka, Matthew E. O’Neal, Mark D. Tomer, John C. Tyndall, Heidi Asbjornsen, Pauline Drobney, Jeri Neal, Gary Van Ryswyk, and Chris Witte, which was first published October 2, 2017; 10.1073/ pnas.1620229114 (Proc Natl Acad Sci USA 114:11247–11252). The authors note that the following statement should be added to the Acknowledgments: “The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the US Fish and Wildlife Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by USDA. ”
Knowledge-creation Processes in Crafts-based HCI Research: Introducing a Sympoietic Framework

Crafts-based approaches in Human-Computer Interaction (HCI) blend analog crafts and materials with digital technologies. In addition to introducing novel ways of creating artifacts, they also present us with alternative modes of inquiry and knowledge creation but we still lack frameworks for understanding the forms of knowledge created through them. We therefore introduce a sympoietic framework for articulating and analyzing knowledge generation in crafts-based research projects in HCI, which integrates concepts from craft theory with HCI. The framework extends from knowledge processes in the making of an artifact to encompass the wider research process. This includes processes that occur within the ‘lab’, e.g. research question articulation and experimentation, as well as what happens when an artifact is deployed in and enters into dialogue with the world. We exemplify the potentials of the framework with analyses of two cases, a photonic fabric and a kinetic wearable.
Somatic Genomic Changes in the Formation of Differentiated Thyroid Carcinoma

Abstract Globally, the diffuse goiter affects more than 10% of the population and in some regions is endemic. Thyroid nodules are found in approximately 5% of the population using the oldest method for thyroid examination – palpation. When performing ultrasound screening, this percentage increases significantly and reaches between 20 and 75% of the total population. Thyroid carcinoma is a rare malignancy and accounts for up to 1% of all malignant tumors. It is the most common endocrine cancer and is clinically manifested as a thyroid nodule. Somatic mutations play an important role in its development. Differentiation of benign and malignant thyroid nodules is of great importance due to the different therapeutic approach. Therefore, new diagnostic tools are sought to help distinguish the two. Despite the progress in our knowledge of carcinogenesis in recent years, a number of key issues still remain unanswered. The establishment of new rare somatic mutations can improve pre-surgical diagnosis and optimize post-operative strategies for the treatment of thyroid carcinoma. Next-generation sequencing (NGS) allows for extensive mutation and genome rearrangements tracking. The results obtained with NGS provide the basis for the development of new approach for systematic genetic screening, at prevention, early diagnosis, accurate prognosis, and targeted therapy of this disorder.