Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Transcribe by Ear

Rethinking how to model polyphonic transcription formally, we frame it as a reinforcement learning task. Such a task formulation encompasses the notion of a musical agent and an environment containing an instrument as well as the sound source to be transcribed. Within this conceptual framework, the transcription process can be described as the agent interacting with the instrument in the environment, and obtaining reward by playing along with what it hears. Choosing from a discrete set of actions - the notes to play on its instrument - the amount of reward the agent experiences depends on which notes it plays and when. This process resembles how a human musician might approach the task of transcription, and the satisfaction she achieves by closely mimicking the sound source to transcribe on her instrument. Following a discussion of the theoretical framework and the benefits of modelling the problem in this way, we focus our attention on several practical considerations and address the difficulties in training an agent to acceptable performance on a set of tasks with increasing difficulty. We demonstrate promising results in partially constrained environments.
Exploring EFL Learners’ Perceived Self-efficacy and Beliefs on English Language Learning

Learners' perceived self-efficacy and beliefs on English language learning are important in education. Taking into consideration the important impact of individual variables on language learning, this study seeks to highlight the relationship between Turkish EFL learners' beliefs about language learning and their sense of self-efficacy. The participants are 210 Turkish EFL undergraduate students whose major is English. The subjects were questioned about their beliefs and self-efficacy as English language learners. The data gathered were analysed quantitatively. The findings demonstrated that EFL students have medium scores in their English self-efficacy and hold the strong belief that motivation factors have a great role on their learning process. Moreover, student's beliefs about language learning are affected by their English self-efficacy. It is recommended that teachers and teacher trainers enhance students' self-efficacy and help students hold correct beliefs about foreign language learning to motivate them. It is expected that the findings of the study will shed light on the relationship between EFL learners' beliefs and self-efficacy and provide guidelines for teachers to help their students become aware of and to evaluate their own beliefs in language learning and English self-efficacy during the teaching of a foreign language.
Detecting Work Stress in Offices by Combining Unobtrusive Sensors

Employees often report the experience of stress at work. In the SWELL project we investigate how new context aware pervasive systems can support knowledge workers to diminish stress. The focus of this paper is on developing automatic classifiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology). We address two methodological and applied machine learning challenges: 1) Detecting work stress using several (physically) unobtrusive sensors, and 2) Taking into account individual differences. A comparison of several classification approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90 percent accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable ‘mental effort’ can be better predicted from sensor data than, e.g., ‘perceived stress’. A comparison of several regression approaches showed that mental effort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We find that especially for estimating mental states it makes sense to address individual differences. When we train models on particular subgroups of similar users, (in almost all cases) a specialized model performs equally well or better than a generic model.
COMB: A Modular Low-Resolution Display to Support Electronic Musical Pre-Education

Displays are ubiquitous in our everyday lives. Since most smart phones or tablets try to avoid control elements, touch screens are primarily the only remaining component of interaction. Beyond the restriction of input methods, static screens define the frame for our applications. Designers and developers, as well as users have to align with standard manufactured screen sizes and resolutions. Modular displays are able to overcome this restriction. Their arrangement can be adapted to fit momentary needs, but an even greater potential lies in their capability to create meaningful objects. Derived from toddlers constructive play with blocks, we introduce COMB. Our prototype demo includes an interaction concept centered around the idea of modular low-resolution displays representing accessible functionality by their arrangement and provide educational benefits for children.
A 19-bit column-parallel folding-integration/cyclic cascaded ADC with a pre-charging technique for CMOS image sensors

A high-resolution column-parallel folding-integration/cyclic cascaded (FICC) ADC with a pre-charging technique for CMOS image sensors is presented in this paper. To achieve high-resolution data conversion with multiple sampling, a pre-charging technique is applied to the sampling circuits of the FICC ADC to reduce the influence of incomplete discharging of historical previous samples. This technique effectively reduces differential nonlinearity of the ADC. The prototype chip with 1504 columns FICC ADC array has been implemented and fabricated in 110 nm CMOS technology. The measured DNL of column-parallel FICC ADC with 128 times multiple sampling is −1/4.73 LSBs in sampling speed of 13KS/s and 19-bit resolution.
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction

Emojis are widely used on social media andunderstanding their meaning is important forboth practical purposes (e.g. opinion mining,sentiment detection) and theoretical purposes(e.g. how different L1 speakers use them, dothey have some syntax? ); this paper presents aset of experiments that aim to predict a singleemoji from a tweet. We built different mod-els and we found that the test results are verydifferent from the validation results.
Exosomes in Severe Asthma: Update in Their Roles and Potential in Therapy

Exosomes are nanosized vesicles and have recently been recognized as important players in cell-to-cell communication. Exosomes contain different mediators such as proteins, nucleic acids (DNA, mRNA, miRNAs, and other ncRNAs), and lipid mediators and can shuttle their exosomal content to both neighboring and distal cells. Exosomes are very effective in orchestrating immune responses in the airways and all cell types can contribute to the systemic exosome pool. Intracellular communication between the broad range of cell types within the lung is crucial in disease emphasizing the importance of exosomes. In asthma, exosomes affect the inflammatory microenvironment which ultimately determines the development or alleviation of the pathological symptoms. Recent studies in this area have provided insight into the underlying mechanisms of disease and led to interest in using exosomes as potential novel therapeutic agents.
Detection of Malicious and Abusive Domain Names

The Domain Name System (DNS) is a critical component of the Internet, and as such it is widely relied upon by a large part of the world. Consequently, it can be abused for multiple purposes, with financial gain being perhaps the most obvious, and important. An important countermeasure to such criminal and malicious activity is to identify involved domains, in order to blacklist or otherwise disable them. In this paper we provide the results of studying existing work on detecting malicious domains and analyse the findings. We identify an approach which is promising but has received surprisingly little attention; Pre-registration detection. We identify the following gaps between the problem of domain abuse, and the described state-of-the-art: Existing work on Pre-registration is strictly focused on a single form of abuse, spam, hence it must be explored if Pre-registration detection can be applied to other forms of abuse as well. Existing work, on both Pre- and Post-registration detection, is focused on a few Top-Level domains (TLDs) and Registries, prompting for studies with other TLDs and Registries. There is relevant information, including Registrant-based features, that has not yet been used for Pre-registration detection - which also calls for investigation. Finally, a study of a real-world deployment of Pre- registration detection at a Registry has not yet been presented, despite the potential of the approach. We contribute with an analysis of existing work, by identifying the state-of-the-art, and by identifying important areas of future work.
Model&Motion based Shape Tracking in Large-Scale Cellular Datasets

This material proposes a model&motion based new cell tracking framework, where two main tasks are targeted, including shape segmentation in low contrast images, and estimation of cell trajectories in clutter environment. To be concrete, the parametric snake model and the optic-flow technique are seamlessly combined to cooperate with each other. By that means, the shortcomings of each side are addressed in the process of simultaneously tracking cell deformation and movement. By experiments on real cellular datasets, the proposed approach has been demonstrated to be well applicable for jointly segmenting and tracking cells, particularly under multiple challenges, such as low quality images, ambiguous cell boundaries, and closely touching cells in high concentration.
Research area classification using wikipedia and information retrieval

The research areas1 of researchers can be identified using their publication that reflects their current interest. However, the full text of these publications is not always available. Other data regarding these publications, such as titles, are available in many bibliographic databases, but these data are usually short, sparse, and multidimensional. In this paper, we propose an approach for identifying the research areas of faculty members using some of their publication, information retrieval and Wikipedia content. Wikipedia is one of the largest encyclopedias to date. It is collaboratively written and kept up to date. A prototype system was implemented and evaluated using real data. Accuracy of 89.3% was achieved. The results are encouraging and show that keywords achieve the highest accuracy in identifying research areas.
miRNA122a regulation of gene therapy vectors targeting hepatocellular cancer stem cells

In this study, we report a miRNA122a based targeted gene therapy for hepatocellular cancer stem cells (CSCs). First, we assessed the levels of miRNA122a in normal human hepatocytes, a panel of hepatocellular carcinoma (HCC) cell lines and hepatocellular CSCs observing its significant downregulation in HCC and CSCs. The miRNA122a binding site was then incorporated at the 3’-UTR of reporter genes gaussia luciferase (GLuc) and eGFP which resulted in significant hepatocyte detargeting. Using this strategy for the delivery of gene directed enzyme prodrug therapy (GDEPT) utilizing the cytosine deaminase/5-fluorocytosine (CD/5-FC) system, we showed significant killing in cells with low or no miRNA122a while those cells, such as hepatocytes with high miRNA122a were largely spared. Next, we showed that CSC enriched tumorspheres exhibit a significant downregulation of miRNA122a expression providing a rational to exploit its binding site for targeted gene delivery. Using plasmids harboring reporters GLuc and eGFP with or without miR122a binding sites, we showed high reporter expression in the CSCs and little reported expression in the non-enriched cultures. Finally, we demonstrate the efficacy of miRNA122a based post-transcriptionally targeted GDEPT for hepatocellular CSCs.
GRFusion: Graphs as First-Class Citizens in Main-Memory Relational Database Systems

The maturity of RDBMSs has motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing, where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this demonstration, we present GRFusion, an in-memory relational database system, where graphs are managed as first-class citizens. GRFusion is realized inside VoltDB. The SQL and query engines of VoltDB are empowered to declaratively define graphs and execute cross-data-model query plans that consist of relational operators and newly-introduced graph operators. Using a social network and a real continental-sized road network covering the entire U.S., we demonstrate the functionality and the performance of GRFusion in evaluating queries that reference both relational tables and graphs seamlessly in the same query execution pipeline. GRFusion shows up to four orders-of-magnitude speed-up in query-time w.r. t. state-of-the-art approaches.
Supramolecular Nucleoside-Based Gel: Molecular Dynamics Simulation and Characterization of Its Nanoarchitecture and Self-Assembly Mechanism

Among the diversity of existing supramolecular hydrogels, nucleic acid-based hydrogels are of particular interest for potential drug delivery and tissue engineering applications because of their inherent biocompatibility. Hydrogel performance is directly related to the nanostructure and the self-assembly mechanism of the material, an aspect that is not well-understood for nucleic acid-based hydrogels in general and has not yet been explored for cytosine-based hydrogels in particular. Herein, we use a broad range of experimental characterization techniques along with molecular dynamics (MD) simulation to demonstrate the complementarity and applicability of both approaches for nucleic acid-based gelators in general and propose the self-assembly mechanism for a novel supramolecular gelator, N4-octanoyl-2′-deoxycytidine. The experimental data and the MD simulation are in complete agreement with each other and demonstrate the formation of a hydrophobic core within the fibrillar structures of these mainly water-containing materials. The characterization of the distinct duality of environments in this cytidine-based gel will form the basis for further encapsulation of both small hydrophobic drugs and biopharmaceuticals (proteins and nucleic acids) for drug delivery and tissue engineering applications.
Implementation of active learning method in transportation engineering seminar course: Case study at San Diego State University

The challenges of retiring workforce, attracting new students, and high educational requirements for dealing with transportation systems complexities have been highlighted in the past decades. In response, transportation engineering (TE) education has been developing both curricula and teaching practices. However, the need for further development and implementation of active leaning techniques remains, especially with regard to specific learning context of the graduate level education. The meaningful implementation of known active learning techniques can make effective use of opportunities arising from information-communication technologies. In particular, this research focuses on development of writing-based in-class activity in a seminar graduate course at San Diego State University. The research builds upon the theoretical framework of learning in engineering education, emphasizing the learner-centered methods. Implementation of in-class activity explains the writing tool deployed and the details of the learning context. Evaluation methodology includes mid- and end-term student survey as well as assessment rubric. Overall, results indicate that this innovation is instrumental in evaluating students' learning in the class, and in helping them with development of motivation and metacognition practices. Concluding implications provide lessons for further implementation and development of active-learning techniques in TE and engineering graduate education in general.
Spectres, virtual ghosts, and hardware support

Side-channel attacks, such as Spectre and Meltdown, that leverage speculative execution pose a serious threat to computing systems. Worse yet, such attacks can be perpetrated by compromised operating system (OS) kernels to bypass defenses that protect applications from the OS kernel. This work evaluates the performance impact of three different defenses against in-kernel speculation side-channel attacks within the context of Virtual Ghost, a system that protects user data from compromised OS kernels: Intel MPX bounds checks, which require a memory fence; address bit-masking and testing, which creates a dependence between the bounds check and the load/store; and the use of separate virtual address spaces for applications, the OS kernel, and the Virtual Ghost virtual machine, forcing a speculation boundary. Our results indicate that an instrumentation-based bit-masking approach to protection incurs the least overhead by minimizing speculation boundaries. Our work also highlights possible improvements to Intel MPX that could help mitigate speculation side-channel attacks at a lower cost.
Fusing Laser Scanner and Stereo Camera in Evidential Grid Maps

Automation driving techniques have seen tremendous progresses these last years, particularly due to a better perception of the environment. In order to provide safe yet not too conservative driving in complex urban environment, data fusion should not only consider redundant sensing to characterize the surrounding obstacles, but also be able to describe the uncertainties and errors beyond presence/absence (be it binary or probabilistic). This paper introduces an enriched representation of the world, more precisely of the potential existence of obstacles through an evidential grid map. A method to create this representation from 2 very different sensors, laser scanner and stereo camera, is presented along with algorithms for data fusion and temporal updates. This work allows a better handling of the dynamic aspects of the urban environment and a proper management of errors in order to create a more reliable map. We use the evidential framework based on the Dempster-Shafer theory to model the environment perception by the sensors. A new combination operator is proposed to merge the different sensor grids considering their distinct uncertainties. In addition, we introduce a new long-life layer with high level states that allows the maintenance of a global map of the entire vehicle's trajectory and distinguish between static and dynamic obstacles. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach.
Integrating lexical-conceptual and distributional semantics : a case report . ∗

By means of a case study on German verbs prefixed with the preposition über (‘over’) we compare alternation-based lexical-conceptual and usage-based distributional approaches to verb meaning. Our investigation supports the view that when distributional vectors are rendered human-interpretable by approximation of their representation with its nearest neighbour words in the semantic vector space, they reflect conceptual commonalities between verbs similar to those targeted in lexical-conceptual semantics. Moreover, our case study shows that distributional representations reveal conceptual features of verb meaning that are difficult if not impossible to detect and represent in theoretical frameworks of lexical semantics and thus that a general theory of word meaning requires a combination and complementation of lexical and distributional methods.
GeneXpert MTB/RIF Outperforms Mycobacterial Culture in Detecting Mycobacterium tuberculosis from Salivary Sputum

GeneXpert MTB/RIF (Xpert) assay has been endorsed for the diagnosis of pulmonary TB due to its high sensitivity and specificity for culture positive TB. There is no doubt that Xpert could not be more sensitive than mycobacterial culture, while the positive rate of Xpert among sputum samples was higher than that of mycobacterial culture in our laboratory. We therefore carried out a prospective study to determine a potential explanation for this unexpected result regarding the clinical use of Xpert. Overall, a total of 558 patients meeting inclusion criteria were enrolled in final analysis between August 2017 and September 2017 in Beijing Chest Hospital. The overall positive rate of Xpert among sputum samples was 45.9% (256/558), which was significantly higher than that of liquid culture (33.4%, 184/558; P < 0.01). The percentage of culture negative result in salivary sputum was significantly higher than that in mucoid sputum [odds ratio (OR): 5.04, 95% confidence interval (95% CI): 2.74–9.28; P < 0.01]. In addition, the TB cases having previous treatment history had a higher proportion of culture negative result than new cases (OR: 4.26, 95% CI: 1.61–11.28; P = 0.01). In conclusion, the results of this study demonstrate that Xpert outperforms mycobacterial culture in detecting MTB from salivary sputum. In addition, the previously treated patients are more likely to yield negative culture results. Our data will provide important hints to formulate an appropriate diagnostic algorithm for pulmonary tuberculosis based on the appearance of sputum samples.
Sensor Array Optimization for Seismic Estimation via Structured Sparse Inversion

This paper presents a distributed sensor array optimization algorithm for Autonomous Underwater Vehicles (AUV)-based seismic surveying. The algorithm is based on a sparse formulation of bottom layer reflection resulting on a structured design matrix with the bottom return field. Since the design matrix structure depends also on the receiving system characteristics, field coherence is used as optimization criteria for determining sensor array position. The receiver positions are constrained by actual array characteristics and AUV relative position physical constraints. Simulated results based on actual physical propagation model data are provided for a three AUV 1D geometry case. These results show that a clear improvement can be reached regarding bottom layer resolution in depth and range. The developed methodology may be useful for the resource planning and setup of seismic surveying experiments involving moving sensing arrays such as those under test in the EU WiMUST project.
Optimal Control of Partial Differential Equations with Nonsmooth Cost Functionals

Over the last decade significant progress was made in the analysis and numerical treatment of optimal control problems with nonsmooth cost functionals. Such functionals are in the context of optimal control with sparsity constraints, for switching control and for multi-bang optimal control problems. The natural setting of such problems is given by non-reflexive Banach spaces, which leads to new analytical challenges. The lack of smoothness, on the other hand, demands novel numerical methods for practical solution of the resulting infinite dimensional optimization problems.