Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of the Extended Binaural MVDR Beamformer With Partial Noise Estimation

Besides reducing undesired noise sources and limiting speech distortion, another important objective of a binaural noise reduction algorithm is the preservation of the binaural cues of all sound sources in the acoustic scene. In this paper, we consider the binaural minimum variance distortionless response beamformer with partial noise estimation (BMVDR-N), which allows to trade off between noise reduction performance and binaural cue preservation of the noise component by mixing the output signals of the BMVDR beamformer with the noisy reference microphone signals. For a directional noise source, it has been shown that incorporating an external microphone in addition to the head-mounted microphones enables both the noise reduction performance as well as the interaural time and level difference cues of the noise component to be improved in the output signals. In this paper, we consider an arbitrary noise field and analytically show that incorporating an external microphone in the BMVDR-N beamformer enables 1) a larger output signal-to-noise ratio (SNR) for the same mixing parameter, 2) the same output SNR for a larger mixing parameter, and 3) the same desired output magnitude squared coherence (MSC) of the noise component for a smaller mixing parameter to be obtained. The derived analytical expressions are firstly validated using simulated anechoic acoustic transfer functions, where the listener’s head is modelled as a rigid sphere. Experimental results using recorded signals for a binaural hearing device setup in a reverberant environment also show that in a realistic scenario incorporating an external microphone in the BMVDR-N beamformer significantly improves the output SNR and reduces the mixing parameter that is required to obtain a desired output MSC of the noise component compared to using only the head-mounted microphones.
The BODYSOUND case. A tangible prototype for co-designing "intangible" healthcare solutions

This paper explores the role of tangible and intangible technologies in the development of healthcare solutions that actively involve patients and caregivers in the ideation and development phases through co-design and co-creation processes. In the first part of the document, we frame the characteristics of patient innovation the phenomenon of user-driven healthcare (Olivera et al. 2015) in relation to the category of solutions developed, processes and emerging technologies. The second part is focused on a case study called BODYSOUND (a pilot of a H2020 European research) and analyses the co-design process adopted to develop a product-service system for rehabilitation, based on a series of testing of tangible and nontangible technologies in an attempt to identify a range of opportunity and scenarios. The final part systematizes the results of the analysis and tries to identify a series of challenges that bring this kind of solutions to the market and to users.
Assessing Risk of Enterprise Bankruptcy by Indicators of Financial and Economic Activity Using Bayesian Networks

Financial problems and business failures can lead to a waste of resources and loss of investment opportunities. Forecasting bankruptcy will alert companies to the problem so they can take appropriate action to prevent bank-ruptcy. The purpose of this study is to develop a model for predicting the financial problems of enterprises. A Bayesian network has been developed for analyzing and predicting the financial condition of industrial enterprises. Financial statements were used to analyze 3000 industrial enterprises in Ukraine. Five integral financial indicators were identified for building Bayesian networks (ma-neuvering coefficient, debt-to-equity ratio, the coefficient of autonomy, current liquidity ratio, financial stability ratio). The developed banking network allows for situational analysis “What… if”. The results obtained in the study show the forecast of the quality and the practical application possibility of the developed Bayesian network in the decision support system for an intelligent assessment of forecasting bankruptcy probability of an enterprise.
POLITICAL AND RELIGIOUS UNDERSTANDING OF TERRORIST GROUPS IN INDONESIA by Gonda Yumitro, Dyah Estu Kurniawati, Saiman

The development of terrorism in Indonesia is highly influenced by the political and religious understanding of the members of terrorist groups. With this understanding, they are inspired and motivated to commit various acts of violence as a holy war (jihad). To understand the political and religious understanding of these terrorist groups, the authors conducted qualitative research by collecting data through indepth interview methods, focus group discussions, and literature reviews. The collected data were analyzed using Nvivo 12 plus. The authors found that the political and religious understanding of terrorist groups in Indonesia is based on limited comprehensive views. They have a high religious spirit, but they have a problem understanding and reading contextual issues, especially those related to politics. Moreover, in the religious aspect, terrorism groups feel like the only righteous people; they want to implement Islamic sharia; they legalize suicide bombings as a form of jihad, and have a political agenda.
Probabilistic Inverse Velocity Obstacles for Navigation under Uncertainty

Autonomous navigation gained a lot of attention in the past decade. Motion planning and collision avoidance are the core things that drive these systems. Several advances resulted in different algorithms to tackle the problem of motion planning and collision avoidance. The crucial question at this stage is how reliable and robust are these systems in a real-life scenario. Recent works in the field have proposed algorithms that account for the uncertainties that arise in a real-life scenario. But the work is sparse. In an attempt to address the reliability and robustness of the navigation frameworks in real-life scenarios, we present two approaches that incorporate the uncertainty in the sensor measurements. First, we present a clever way, Inverse Velocity Obstacle (IVO), to achieve better collision detection based on the popularly used velocity obstacles. The proposed method stems from the concept of velocity obstacle and can be applied for both single-agent and multi-agent systems. It focuses on computing collision-free maneuvers without any knowledge or assumption on the position and the velocity of the robot. We achieve this by reformulating the velocity obstacle in an ego-centric frame. This is a significant step towards improving real-time implementations of collision avoidance in dynamic environments as there is no dependency on state estimation techniques to infer the robot’s position and velocity. We evaluate IVO for both single-agent and multi-agent cases in different scenarios and show it’s efficacy over the existing formulations. We also show the real-time scalability of the proposed methodology. Next, we present a probabilistic variant of the same to handle the state estimation and motion uncertainties that arise due to the other participants of the environment. We present an algorithmic framework that computes collision-free velocities for the robot dynamic and uncertain environments. We make no assumptions on the nature of the uncertainties and model them as non-parametric probability distributions. In our Probabilistic Inverse Velocity Obstacle (PIVO), we pose the collision-free navigation problem as an optimization problem by reformulating the velocity conditions of IVO as chance constraints that take the uncertainty into account. We also define a lower bound on the confidence of collision avoidance maneuvers that are a result of the optimization. We demonstrate the algorithm’s ability to generate safe trajectories under highly uncertain environments.
Improving User Interfaces for Physicians through New Materials, Tangible Interaction, and Tactile Feedback

In the ﬁ eld of medicine, the standard human-computer interaction is still widely based on classical WIMP interfaces with 2D screens. Due to the ever increasing number of computational systems in medical care this leads to a situation, in which these systems are hard to handle, especially as often a number of di ﬀ erent activities need to be executed in parallel. Furthermore, physicians have little time to familiarize themselves with software and hardware. Due to these reasons multimodal interaction approaches with devices that o ﬀ er clear a ﬀ ordances and that are directly integrated into existing medical activities – instead of introducing additional computer-based tasks – have the potential to signi ﬁ cantly improve the physicians’ work. Especially as medical treatments often are manual tasks, enhancing existing medical tools or using new materials and tangibles for interaction ﬁ ts the target group of medical professionals well. This ongoing research explores this design space. In this paper, we present three di ﬀ erent user interfaces incorporating electroluminescence displays, tactile feedback and 3D-printed tangibles in combination with virtual reality to support physicians.
KG-Agnostic Entity Linking Orchestration

The domain of Entity Linking (EL) has been researched thoroughly, resulting in numerous approaches and a level of maturity in the ﬁeld. In this dissertation, we aim at (1) creating an orchestrated, continuously-improving system through addition of approaches both existing and ones that are yet to come. On the same note, our framework shall (2) allow for Knowledge Graph (KG)- and potentially language-agnostic EL processes through cross-KG techniques, enabling further exploration of KG-dependence in the domain of EL. Finally, we will boost the ease of (3) reproducibility and (4) comparison between EL systems, their underlying techniques, as well as orchestration frameworks as a whole.
Impact of short-read sequencing on the misassembly of a plant genome

Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.
Photoluminescence of Cd1-xZnxTe crystals grown by high-pressure Bridgman technique

Photoluminescence (PL) spectra of detector-grade Cd 1-x Zn x Te crystals with x = 0.07 to 0.14 were measured as a function of the excitation laser power and over the temperature range from 12 to 295 K. Two bound exciton transitions at the energies of 13 and 24 meV below the band-gap E g and a number of phonon replicas dominate the low-temperature PL spectra. The transition at the energy of E g - 13 meV is attributed to a neutral donor-bound exciton D 0 X. The excitation power and temperature dependence of the peak at the energy of E g - 24 meV show that it is of excitonic origin with binding energy of 13 meV. The intensity of this A 0 X peak is found to depend on z, which suggests that it is related to structural defects. In some samples a donor-acceptor pair (DAP) transition at the energy of E g - 60 meV is observed. The PL spectra also exhibit a weak, defect-related broad peak at the energy of about E g - 200 meV. The PL of a large number of samples from different boules and locations in the boules is compared. The DAP-peak intensity was observed to depend strongly on the boule.
The structure of the solid-liquid interface: Atomic size effect

Nous etudions par dynamique moleculaire la structure atomique de l'interface solide-liquide constituee de deux elements differentes. Deux types de systemes sont etudies. Le premier est constitue de cuivre cristallin de surface orientee (100) et (111) et d'aluminium liquide. Le second est constitue de particules de type Lennard-Jones de parametres de taille et d'energie differents de part et d'autre de l'interface solide-liquide. Nous montrons qu'a l'interface le liquide se structure en couches quelle que soit l'orientation de la surface du cristal. Quelle que soit la difference de taille entre atomes dans les phases solide et liquide, le liquide est structure. En outre la structure atomique de la premiere couche de liquide est de type epitaxique et depend fortement du desaccord de tailles entre atomes dans le solide et le liquide.
Reducing Digital Exclusion of Seniors - Exploring the Lasting Effects of Collaborative Training Sessions

: Today's digital society is developing rapidly and the number of smart and innovative digital services is increasing every day. However, the rapid digitalization may also lead to the exclusion of some groups. Senior citizens are one of these groups at risk. The most common way of combating digital exclusion among seniors is to offer education and hands-on training. The short term effects of such education initiatives have shown positive results but the long term lasting effects of digital training have not been explored to any greater extent. Data was collected via semi structured interviews with seniors who had previously participated in training sessions targeting Swedish seniors with a desire to become more digital. The results show that although the immediate effect of participating in training is positive, more individualized training is needed to achieve a lasting effect.
Personalized Deep Learning in Human Activity Recognition from Inertial Signals: a Preliminary Study on its Effectiveness (short paper)

In the recent years there has been a growing interest in techniques able to automatically recognize activities performed by people. This field is known as Human Activity recognition (HAR). HAR can be crucial in monitoring the wellbeing of the people, with special regard to the elder population and those people affected by degenerative conditions. One of the main challenges concerns the population diversity problem, that is, the natural differences between users’ activity patterns, which implies that executions of the same activity performed by different people are different. Previous experiments have shown that personalization based on similarity between subjects and signals can increase the accuracy of recognition models of human activities obtained by traditional machine learning techniques. In this article, we investigate whether personalization applied to deep learning techniques can lead to more accurate models with respect to those obtained both by applying personalization to machine learning models, and to traditional deep learning models. In particular, the experiments have been done on two public domain datasets and using the AdaBoost classifier and two Convolutional Neural Networks. Preliminary results show that, on average, traditional deep learning outperforms both personalized deep learning and personalized machine learning techniques.
Dimensional Analysis and Similarity Method Driving Self-similar Solutions of the First and Second Kind Inducing Energy

Nearly all scientists, at conjunction with simplifying a differential equation, have probably used dimensional analysis. Dimensional analysis (also called the Factor-Label Method or the Unit Factor Method) is an approach to the problem that uses the fact that one can multiply any number or expression without changing its value. This is a useful technique. However, the reader should take care to understand that chemistry is not simply a mathematics problem. In every physical problem, the result must match the real world. In physics and science, dimensional analysis is a tool to find or check relations among physical quantities by using their dimensions. The dimension of a physical quantity is the combination of the fundamental physical dimensions (usually mass, length, time, electric charge, and temperature) which describe it; for example, speed has the dimension length/time, and may be measured in meters per second, miles per hour, or other units. Dimensional analysis is necessary because a physical law must be independent of the units used to measure the physical variables in order to be general for all cases. One of the most derivation elements from dimensional analysis is scaling and consequently arriving at similarity methods that branch out to two different groups namely self-similarity as the first one, and second kind that through them one can solve the most complex none-linear ODEs (Ordinary Differential Equations) and PDEs (Partial Differential Equations) as well. These equations can be solved either in Eulearian or Lagrangian coordinate systems with their associated BCs (Boundary Conditions) or ICs (Initial Conditions). Exemplary ODEs and PDEs in the form of none-linear can be seen in strong explosives or implosives scenario, where the results can easily be converted to induction of energy in a control forms for a peaceful purpose (i.e., fission or fusion reactions).
Doing Agile vs. Being Agile - Understanding Their Effects to Improve Agile Work

Companies show a growing demand in corporate agility. The effects of so-called doing and being agile are the key for positive outcomes in agile work. Scrum is used by companies to make them more agile. The present study examines the interaction of doing agile (leadership, work design) and being agile (individual characteristics, empowerment) in relation to satisfaction and commitment based on an online survey of 129 Scrum development team members. The results show that work design and leadership impact empowerment and that the latter leads to both job satisfaction and commitment. Team orientation has no effect. Openness for new actions moderates the relationship between work design and the outcome variables. The results can be used in practice to increase the satisfaction and commitment within Scrum. They provide an insight into the impact structure within Scrum and are thus also the basis for future research.
Endohedral (X@ZniSi)i=4-160,± Nanoclusters, X = Li, Na, K, Cl, Br

Endohedral (X@ZniSi)q structures have been characterized, with X being alkali metals such as Li, Na, and K or halogens such as Cl and Br, 4 ≤ i ≤ 16, and q = −1, 0, 1. In these structures, the atoms are trapped inside previously characterized spheroid hollow structures with positively charged Zn atoms and negatively charged S atoms. Moreover, although the radii of all atoms are similar, Zn atoms are located more inside the structure. The alkali metals are found to be trapped inside a larger number of spheroid structures than the halogens. The parameters determining the stability of the endohedral structures are the charge and size of the trapped atom, along with the sphericity of the cluster.
Scaling Graph Neural Networks Using Diﬀerentiable Trees

The number of pairwise relations between object scales quadratically with the number of objects. For classical problems such as finding the nearest neighbor, we have developed fast algorithms that trade some precision for speed. Unfortunately, in many Machine Learning (ML) problems these approximations have yet to be developed. For example, in the field of Natural Language Processing (NLP), the attention layer between the encoder and the decoder is usually the main bottleneck of the neural network [1]–[4]. Similarly, in Graph Neural Networks (GNNs), the number of possible edges scales quadratically with the number of graph nodes. This limits GNNs to predefined and fixed sparse graphs or a small number of nodes. The goal of this project is to develop a new type of architecture for neural networks that takes a list of n feature vectors as input and outputs a relational pattern, i.e. a graph with a computational cost smaller than O ( n2 ) . To achieve this, a special learnable tree which can be backpropagated through will be constructed and studied. The student will test the architecture first on a simple toy problem, then on standard graph benchmarks and, if the time allows for it, on an NLP problem.
Single-grating interferometer for high-resolution phase-contrast imaging at synchrotron radiation sources

Synchrotron X-ray imaging is constantly achieving higher spatial resolution. In the field of grating-based phase- contrast imaging, these developments allow to directly resolve the interference patterns created by a phase grating without need for a analyzer grating. In this study we analyzed the performance of a single-grating interferometer and compared it to a conventional double-grating interferometer. Based on simulations and measurements of a test phantom we evaluated the sensitivity, resolution and signal to noise ratios of different setup configurations.
Computer-Based Reading Texts to Support Fourth Graders’ Reading Comprehension

In an experimental research design, elementary school fourth grade students (n = 60) were randomly assigned to either computer-based reading or traditional reading conditions in this study. The groups completed before-reading, during-reading and after-reading activities on computer or in traditional format, respectively. The computer-based reading texts included static and animated visuals and background sounds. Students in each group completed a pretest, four reading texts and a posttest over a six-week period: the pretest in the first week, the four texts in the following four weeks, and the posttest in the sixth week. The results showed that although no significant difference was found between group’s pretest scores, computer-based group has significantly better posttest scores than the traditional group. Additionally, the fourth graders in the computer-based reading group significantly improved their reading comprehension. However, there was no significant difference between students’ reading comprehension results from pretest to posttest in traditional group. As a result, presenting students multimedia supported reading activities has a positive impact on their reading comprehension. Thus, we recommend teachers use individual and visually-supported computer-based reading texts to improve students’ reading comprehension.