Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring data augmentation methods in reverberant human-robot voice communication

Collecting training data is not an easy task especially in situation involving robots that require tremendous physical effort. The ability to augment data through synthetic means is a convenient tool to solve this problem. Therefore it is important to evaluate the extent of the usefulness of augmented data. In this paper, we will explore data augmentation schemes in reverberant environment and investigate a method to effectively select data. We experiment in a real reverberant environment condition and investigate both the traditional automatic speech recognition (ASR) system based on gaussian mixture model-hidden markov model (GMM-HMM) and the most current system based on Deep Neural Networks (i.e, HMM-DNN). Our results show that the combination of data augmentation and data selection, further improves system performance. In our experiments, we used real test data in a reverberant hands-free human-robot communication scenario.
A Data Retrieval Method Based on AGCN-WGAN

Traditional knowledge graph retrieval techniques ignore node relationship weights, making it difficult to achieve targeted retrieval. Therefore, a nonlinear model AGCN-WGAN is proposed to solve retrieval tasks in relational networks, fully utilizing the advantages of Graph Convolutional Networks (GCN) and Generative Adversarial Networks (GAN). Firstly, AGCN is used to capture the local topological features of a single node; In addition, the use of GAN enhances the ability of AGCN models to generate reasonable weight distribution maps, effectively extracting correlations between nodes, thereby improving the performance of the model in handling large-scale data retrieval tasks. In order to verify the effectiveness of the method, the dispatching operation data in a real business scenario of a city power grid is used for experiments. The experimental results show that the proposed data retrieval method has significantly improved accuracy compared to existing methods.
[1,4-Bis(diphenylphosphanyl)butane-κ2 P,P′]chlorido(η5-indenyl)ruthenium(II)

Facile ligand substitution is observed when the ruthenium chloride complex [Ru(η5-C9H7)Cl(PPh3)2] is treated with 1,4-bis(diphenylphosphanyl)butane in refluxing toluene yielding the title compound, [Ru(C9H7)Cl(C28H28P2)]. The RuII atom has a typical piano-stool coordination, defined by the indenyl ligand, one Cl atom and two phosphanyl P atoms. The Ru—P bond lengths are 2.2502 (9) and 2.2968 (8) Å.
Development of Design Principles of Group Awareness Tool for Facilitating Computer-supported Collaborative Argumentation

As one of the forms of collaborative learning, collaborative argumentation can improve students’ argumentation skills, promote effective content learning and cultivate critical thinking. Especially in the field of Computer-supported Collaborative Learning (CSCL), such as conflicts between group members, free-riding behavior, and unequal participation occur frequently. At the same time, in the CSCL environment, it is difficult to coordinate one’s actions with other group members’ actions. These problems are mainly caused by learners who cannot obtain group awareness information about accompanying learners and the current group. Therefore, this study according to the previous research, the initial design principles and initial specific guidelines for the group awareness tool were identified. After that, three educational technology experts reviewed the initial design principles and guidelines. The modified specific guidelines were applied to the two-week classroom teaching of 45 first-year students in a class. In addition, a learners’ response survey was conducted on the students who actually participated in the computer-supported collaborative argumentation activities. Finally, the results of questionnaire surveys (n=45) and interviews (n=5) were analyzed, and the final specific guidelines were developed. According to the research results, collaborative argumentation based on group awareness tool solves the problem that general collaborative argumentation activities can’t obtain timely information on the cognitive, behavioral, and social awareness of accompanying learners and the current group study situation. It can continuously maintain their study motivation and promote interaction among learners to improve the quality of computer-supported collaborative learning.
Molecular Active and Passive Membrane Properties and Intrinsic Kinetics Shape Synaptic Inhibition in Hippocampal CA 1 Pyramidal Neurons

The impact of synaptic inhibition depends on the passive and active properties of the neuronal membrane as well as on the characteristics of the underlying synaptic conductances. Here, we evaluated the contributions of these different factors to the IPSPs produced by two kinetically and anatomically distinct inhibitory synapses onto hippocampal CA1 pyramidal neurons: somatic GABAA,fast and dendritic GABAA,slow. Using combined current-clamp and voltage-clamp recordings from neurons in hippocampal brain slices, we found that despite pronounced differences in kinetics and only weak voltage dependence of the underlying synaptic conductances, there were much smaller differences in duration but strong voltage dependence of IPSPs arising from somatic and dendritic synapses. Pharmacologic tests and compartmental modeling showed that these effects were produced by the hyperpolarization-activated cation current, IH , which accelerated IPSP decay over a broad range of membrane potentials and reduced IPSP amplitudes at hyperpolarized potentials, and the persistent sodium current, INaP , which prolonged and amplified IPSPs at depolarized subthreshold potentials. The relative magnitudes of their influences depended on the kinetics of the underlying synaptic conductances: the effect on duration was greater for GABAA,fast and on amplitude was greater for GABAA,slow. Passive and active factors thus influence the impact of synaptic inhibition in a locationand voltage-dependent manner.
POLITECNICO DI TORINO Repository ISTITUZIONALE SeLeCT : Self-Learning Classifier for Internet Traffic

Network visibility is a critical part of traffic engineering, network management, and security. The most popular current solutions Deep Packet Inspection (DPI) and statistical classification, deeply rely on the availability of a training set. Besides the cumbersome need to regularly update the signatures, their visibility is limited to classes the classifier has been trained for. Unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. To address the above issues, we propose SeLeCT, a SelfLearning Classifier for Internet Traffic. It uses unsupervised algorithms along with an adaptive seeding approach to automatically let classes of traffic emerge, being identified and labeled. Unlike traditional classifiers, it requires neither a-priori knowledge of signatures nor a training set to extract the signatures. Instead, SeLeCT automatically groups flows into pure (or homogeneous) clusters using simple statistical features. SeLeCT simplifies label assignment (which is still based on some manual intervention) so that proper class labels can be easily discovered. Furthermore, SeLeCT uses an iterative seeding approach to boost its ability to cope with new protocols and applications. We evaluate the performance of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents. Our experiments show that SeLeCT achieves excellent precision and recall, with overall accuracy close to 98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to discover new protocols and applications in an almost automated fashion.
On MR3-type Algorithms for the Tridiagonal Symmetric Eigenproblem and the Bidiagonal SVD

Die Arbeit "On MRRR-type Algorithms for the Tridiagonal Symmetric Eigenproblem and the Bidiagonal SVD" behandelt neue und verbesserte Ansatze zur Berechnung von Eigenzerlegungen symmterisch tridiagonaler Matrizen (Problem TSEP) und Singularwertzerlegungen von bidiagonalen Matrizen (BSVD). Diese zwei eng verwandten Aufgaben sind ein Kernthema der Numerischen Linearen Algebra, da sie die jeweiligen schwierigsten Teilaufgaben darstellen, die der Standardalgorithmus zur Berechnung von Eigen-/Singular-Systemen allgemeiner dichtbesetzter Matrizen losen muss. Das Werkzeug im Hauptfokus dieser Arbeit ist der Algorithmus "Multiple Relatively Robust Representations (MRRR)" von Inderjit Dhillon und Beresford Parlett. In einem ersten Teil werden der Algorithmus und seine begleitende Theorie in einer uberarbeiteten modularisierten und Rahmenwerk-orientierten Form prasentiert. Darauf basierend wird eine Reihe von Weiterentwicklungen am Kernalgorithmus selbst vorgestellt. Der zweite, und zentrale, Teil dieser Arbeit behandelt, wie Algorithmus MRRR eingesetzt werden kann, das Problem BSVD zu losen. Aufbauend auf vorangegangenen Arbeiten von Benedikt Groser und Bruno Lang wird eine umfangreiche Theorie entwickelt, auf welche drei weitere Beitrage aufbauen: ein besseres Verstandnis, warum die sogenannten "Black-Box" Ansatze nicht funktionieren konnen, eine rigorose Fehleranalyse des kopplungsbasierten Algorithmus von Groser und Lang, und schliesslich ein neuer Losungsansatz uber die Golub-Kahan Matrix. Der dritte Teil der Arbeit stellt einen neuen Algorithmus zum Verschieben (shiften) symmetrischer Tridiagonalmatrizen in block-faktorisierter Form vor, und zwar mit gemischter komponentenweiser relativer Stabilitat. Letzteres ist entscheidend, um die Benutzung der Methode im Rahmen von MRRR uberhaupt erst zu erlauben. Der grose Vorteil von Block-Faktorisierungen liegt darin, dass sie eine Moglichkeit zur Kontrolle von Elementwachstum bieten, und daher stark zur Robustheit von MRRR-basierten Methoden beitragen konnen. Die in der Arbeit entwickelten Varianten von MRRR wurden als Software-Prototypen implementiert und in einer Reihe von numerischen Experimenten wird belegt, dass diese entsprechenden Losern aus der LAPACK Software Bibliothek ebenburtig und teilweise sogar uberlegen sind.
Controlling health care costs.

BACKGROUND: Many believe that computerization will improve health care quality, reduce costs, and increase administrative efﬁciency. However, no previous studies have examined computerization’s cost and quality impacts at a diverse national sample of hospitals. METHODS: We linked data from an annual survey of computerization at approximately 4000 hospitals for the period from 2003 to 2007 with administrative cost data from Medicare Cost Reports and cost and quality data from the 2008 Dartmouth Health Atlas. We calculated an overall computerization score and 3 subscores based on 24 individual computer applications, including the use of computerized practitioner order entry and electronic medical records. We analyzed whether more computerized hospitals had lower costs of care or administration, or better quality. We also compared hospitals included on a list of the “100 Most Wired” with others. RESULTS: More computerized hospitals had higher total costs in bivariate analyses ( r (cid:31) 0.06, P (cid:31) . 001) but not multivariate analyses ( P (cid:31) . 69). Neither overall computerization scores nor subscores were consistently related to administrative costs, but hospitals that increased computerization faster had more rapid administrative cost increases ( P (cid:31) . 0001). Higher overall computerization scores correlated weakly with better quality scores for acute myocardial infarction ( r (cid:31) 0.07, P (cid:31) . 003), but not for heart failure, pneumonia, or the 3 conditions combined. In multivariate analyses, more computerized hospitals had slightly better quality. Hospitals on the “Most Wired” list performed no better than others on quality, costs, or administrative costs. CONCLUSION: As currently implemented, hospital computing might modestly improve process measures of quality but does not reduce administrative or overall costs. ©
Improving Learning Outcomes of Catholic Religious Education for Grade III Students Through The Storytelling Method

Learning outcomes are the mastery and abilities that students have achieved about the material and skills regarding the subject after receiving their learning experience. Learning outcomes can be measured from their own abilities, where in the learning process in the classroom students are more active and the ability of Catholic Religious Educator teachers to apply skills to the teaching and learning process can improve student learning outcomes. In teaching and learning activities at SDK Puubheto, especially in grade III Catholic Religious Education lessons, problems were found in teaching and learning activities, namely; Students feel bored because the classroom atmosphere is not conducive, teachers do not understand the right learning methods according to the age of students and are not skilled in applying learning methods, students are not actively involved in teaching and learning activities, teachers tend to only give assignments so that students lack understanding of the material, and student learning outcomes are low. The formulation of the problem in this study is how the influence of the storytelling method on improving student learning outcomes. The purpose of this study was to determine how the influence of the application of storytelling learning methods on Catholic Religious Education subjects in improving learning outcomes. In improving learning outcomes, researchers use the application of storytelling methods in Catholic Religion subjects. The storytelling method is a learning strategy where the delivery is through oral speech by telling stories or events and information that is packaged creatively to attract children's attention. The results of the study found by researchers showed that the learning outcomes of students in each cycle increased from the pre-test results with a percentage of 35.71%, the percentage of post-test cycle I of 50%, and the percentage of post-test cycle II of 100%. It can be said that there is an increase in the learning outcomes of Catholic Religious Education through the application of storytelling methods in grade III of Puubheto Catholic Elementary School.
LCC-LCC Compensation based Dual Active Bridge Bidirectional WPT for Electric Vehicle Application

Bidirectional Wireless Power Transfer (BWPT) for EV applications is a viable answer to the impending power grid volatility and charging concerns. The Dual Active Bridge (DAB) stands out because to its properties, which include bidirectional operation (vehicle to grid, vehicle to vehicle, and grid to vehicle) and comparably high efficiency, despite the fact that various isolated DC-DC converter topologies find applications in BWPT. Compensation network topologies like SP, PP, and PS can offer either constant current operation (CCO) or constant voltage operation (CVO). However, these topologies comprise harmonics that lead to high power consumption during charging. This study reports on the LCC-LCC hybrid correction, which aims to offer a constant output with minimal total harmonic distortion (THD) and a smooth transition between CCO and CVO. The DAB with LCC compensation, which offers a broad working range, high power density, and the previously described benefits, is presented in the article. The proposed DAB and LCC-LCC compensation are simulated in the MATLAB environment for a power rating of 3.7kW and achieve an efficiency of 96%. An experimental model was developed for a 3.7kW power rating for comparative analysis.
An Efficient Resource Allocation Algorithm for Traffic of Content Streaming in Non-Standalone OFDM Based 5G NR

The 5G NR network provides support for a wide range of service types, including content traffic through large number of wireless connections. To ensure efficient resource allocation for specific service types, such as video streaming, a Traffic Differentiator stage is implemented at the user level. This stage segregates content queues from active users into distinct service queues, enabling tailored resource allocation. In order to investigate the performance of differentiated resource allocation in a content delivery scenario, this research paper focuses on the physical and cross-layer resource allocation of the non-standalone 5G NR network. The paper introduces a Pseudo-Inverse-based Traffic Differentiator algorithm in the TD Scheduler stage, which aims to allocate optimal radio resources to real-time and non-real-time services, while allocating the remaining resources to background services. By segregating users based on their requested services and prioritizing them differently within the service priority-specific queues, the Traffic Differentiator algorithm enhances throughput fairness among all users using Pseudo- Inverse learning. Additionally, the FD Scheduler stage utilizes our proposed optimal Channel Quality Indicator (CQI) selection algorithm to leverage Frequency Domain (FD) Multi-User (MU) diversity for resource allocation.
Artificial Intelligence based Algorithm for Detecting Android Obfuscated Applications

: As technology continues to advance, so does the landscape of Android; based on its open-source nature which renders it vulnerable to various risks. Therefore, the developers need to deploy and employ obfuscation techniques in their newly developed android applications. In this paper, we present an investigation into Android obfuscation detection. Our work encompasses a comprehensive examination of Android obfuscation techniques and an exploration of their intersection with machine learning. We conducted extensive experiments involving various machine learning models to detect obfuscation. The results show that Random Forest is the one with the most promising results with accuracy 99.5% in detecting Android Obfuscation. The used dataset comprises both malicious and benign samples, where different obfuscation techniques have been applied.
Parallel Support Vector Machines: The Cascade SVM

We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a 'Cascade' of SVMs, until the global optimum is reached. The Cascade SVM can be spread over multiple processors with minimal communication overhead and requires far less memory, since the kernel matrices are much smaller than for a regular SVM. Convergence to the global optimum is guaranteed with multiple passes through the Cascade, but already a single pass provides good generalization. A single pass is 5x - 10x faster than a regular SVM for problems of 100,000 vectors when implemented on a single processor. Parallel implementations on a cluster of 16 processors were tested with over 1 million vectors (2-class problems), converging in a day or two, while a regular SVM never converged in over a week.
Specification Sensitivity in Right‐Tailed Unit Root Testing for Explosive Behaviour

This article aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focusing on the recursive right-tailed ADF test of Phillips et al. (2011b). We analyze and compare the limit theory of the recursive test under different hypotheses and model specifications. The size and power properties of the test under various scenarios are examined and some recommendations for empirical practice are given. Some new results on the consistent estimation of localizing drift exponents are obtained, which are useful in assessing model specification. Empirical applications to stock markets illustrate these specification issues and reveal their practical importance in testing.
Construction, characterization and FISH mapping of a bacterial artificial chromosome library of Chinese pangolin (Manis pentadactyla)

Chinese pangolins as a representative species in the order Pholidota have highly specified morphological characters and occupy an important place in the mammalian phylogenetic tree. To obtain genomic data for this species, we have constructed a bacterial artificial chromosome (BAC) library of Chinese pangolin. The library contains 208,272 clones with an average insert size of 122.1 kb and represents approximately eight times the Chinese pangolin haploid genome (if we assume that the Chinese pangolins have a genome size similar to human). One hundred and twenty randomly-selected BAC clones were mapped onto Chinese pangolin chromosomes by fluorescence in situ hybridization (FISH), showing a largely unbiased chromosomal distribution. Several clones containing repetitive DNA and ribosomal DNA genes were also found. The BAC library and FISH mapped BAC clones are useful resources for comparative genomics and cytogenetics of mammals and in particular, the ongoing genome sequencing project of Chinese pangolins.
The forest-based Tree Sequence to String SMT System for CWMT-2009

This paper reports IR’s SMT System for CWMT-2009 MT evaluation. In the CWMT-2009 MT evaluation, we use our forest-based tree sequence to string translation system to participate in the ChineseEnglish single system evaluation track. In this paper, we give an overall introduction of our translation system and then report the experiment details including how we pre-process the training data, system configuration and post-processing procedures.
An empirical comparison of case-control and trio based study designs in high throughput association mapping

Motivated by high throughput genotyping technology, our aim in this study was to experimentally compare the power and accuracy of case-control and family trio based approaches for haplotype based, large scale, association gene mapping. We compared trio based and case-control study designs in different disease models, and partitioned the performance differences into separate components: those from the sample ascertainment, the effective sample size, and the haplotyping approaches. For systematic and controlled tests, we simulated a rapidly expanding and relatively young isolated population. The experiments were also replicated with real asthma data. We used computationally efficient methods that scale up to large amounts of both markers and individuals. Mapping is based on a haplotype association test for haplotypes of 1–10 markers. For population based haplotype reconstruction, we use HaploRec, and compare it to both a simple trio based inference and true haplotypes. Firstly and surprisingly, statistically inferred population based haplotypes can be equally powerful as true haplotypes. Secondly, as expected, the effective sample size has a clear effect on both gene detection power and mapping accuracy. Thirdly, the sample ascertainment method does not have much effect on mapping accuracy. Finally, an interesting side result is that the simple haplotype association test clearly outperformed exhaustive allelic transmission disequilibrium tests. The results suggest that the case-control design is a powerful alternative to the more laborious family based ascertainment approach, especially for large datasets, and wherever population stratification can be controlled.