Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian semiparametric Gaussian copula approach to a multivariate normality test

Semiparametric copulas are useful tools for modeling a multivariate distribution whose dependence structure is induced by a known copula and whose marginal distributions are estimated. In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution . First, the Dirichlet process is constructed on the unknown marginal distributions of . Then a Gaussian copula model is utilized to capture the dependence structure of . As a result, a Bayesian multivariate normality test is developed by combining the relative belief ratio and the Energy distance. Various interesting theoretical results of the approach are derived. Several examples that cover the high dimensional case are discussed to illustrate the approach.
Attention Routing Between Capsules

In this paper, we propose a new capsule network architecture called Attention Routing CapsuleNet (AR CapsNet). We replace the dynamic routing and squash activation function of the capsule network with dynamic routing (CapsuleNet) with the attention routing and capsule activation. The attention routing is a routing between capsules through an attention module. The attention routing is a fast forward-pass while keeping spatial information. On the other hand, the intuitive interpretation of the dynamic routing is finding a centroid of the prediction capsules. Thus, the squash activation function and its variant focus on preserving a vector orientation. However, the capsule activation focuses on performing a capsule-scale activation function. We evaluate our proposed model on the MNIST, affNIST, and CIFAR-10 classification tasks. The proposed model achieves higher accuracy with fewer parameters (x0.65 in the MNIST, x0.82 in the CIFAR-10) and less training time than CapsuleNet (x0.19 in the MNIST, x0.35 in the CIFAR-10). These results validate that designing a capsule-scale operation is a key factor to implement the capsule concept. Also, our experiment shows that our proposed model is transformation equivariant as CapsuleNet. As we perturb each element of the output capsule, the decoder attached to the output capsules shows global variations. Further experiments show that the difference in the capsule features caused by applying affine transformations on an input image is significantly aligned in one direction.
A need for trust in conversational interface research

Across several branches of conversational interaction research including interactions with social robots, embodied agents, and conversational assistants, users have identified trust as a critical part of those interactions. Nevertheless, there is little agreement on what trust means within these sort of interactions or how trust can be measured. In this paper, we explore some of the dimensions of trust as it has been understood in previous work and we outline some of the ways trust has been measured in the hopes of furthering discussion of the concept across the field.
OWLS-MX:AHybridSemanticWebServiceMatchmaker forOWL-SServices

In this paper, we describe the first hybrid semantic Web service matchmaker for OWL-S services, called OWLSMX. It complements crisp logic-based semantic matching of OWL-S services with token-based syntactic similarity measurements in case the former fails. The results of the experimental evaluation of OWLS-MX provide strong evidence for the claim that logic-based semantic matching of OWL-S services can be significantly improved by incorporating non-logic-based information retrieval techniques. An additional analysis of false positives and false negatives of the hybrid matching filters of OWLS-MX led to an even further improved matchmaker version called OWLS-MX2.
Optimized Substrates and Measurement Approaches for Raman Spectroscopy of Graphene Nanoribbons

The on‐surface synthesis of graphene nanoribbons (GNRs) allows for the fabrication of atomically precise narrow GNRs. Despite their exceptional properties which can be tuned by ribbon width and edge structure, significant challenges remain for GNR processing and characterization. Herein, Raman spectroscopy is used to characterize different types of GNRs on their growth substrate and track their quality upon substrate transfer. A Raman‐optimized (RO) device substrate and an optimized mapping approach are presented that allow for the acquisition of high‐resolution Raman spectra, achieving enhancement factors as high as 120 with respect to signals measured on standard SiO2/Si substrates. This approach is well suited to routinely monitor the geometry‐dependent low‐frequency modes of GNRs. In particular, the radial breathing‐like mode (RBLM) and the shear‐like mode (SLM) for 5‐, 7‐, and 9‐atom‐wide armchair GNRs (AGNRs) are tracked and their frequencies are compared with first‐principles calculations.
Hydrogen Storage through Potassium adsorbed Germanene surface

Renewable energy is one of the most promising solution to the energy issues concerning the limited supply of energy and the environmental impact of the current energy industry. Hydrogen fuel is one possible source of renewable energy, and much research and interest has been directed to this. The key issue regarding the hydrogen fuel system was that there is inefficiency in the storage system. The perspective or approach of this paper in contributing to the hydrogen fuel industry is by finding more efficient hydrogen storage through hydrogen adsorption also known as materials-based hydrogen storage. In this paper, the capability of the potassium-decorated germanene, a germanium allotrope akin to graphene, as a hydrogen storage was investigated. The interaction between the hydrogen molecules and potassium-decorated Germanene was studied using spin-polarized ground state density functional theory (DFT) calculations. The calculations were performed using the Vienna Ab Initio Simulation Package (VASP), a plane wavebased code. The potential was described using the projector-augmented wave (PAW) method. The general gradient approximation (GGA) of Perdew-Burke-Ernzerhof formula (PBE) was adopted as the exchange correlation potential. The k points were gammacentered and automatically generated using the Monkhorst-Pack Scheme. The energy cutoff, number of k points, vacuum spacing, germanene lattice constant, and germanene buckling height were determined using multiple convergence tests. The potassium atom was used as the decoration. Relaxation and static calculations to determine the most stable adsorption site for the decoration is currently in progress.
High bit rate optical communication systems based on chaotic carriers

In this thesis, the potential of using chaotic optical carriers generated by non-linear optical oscillators as an encryption medium of high bit rate pseudorandom sequences and therefore their application in the development of an innovative platform of secure optical communications is studied. The operation of chaotic semiconductor laser emitters capable of hiding data and their application within an emitter-receiver system is simulated, underlying the decoding process that leads to a successful message recovery. A complete high bit rate optical communication system based on chaotic carriers is developed, followed by a successful encoding and decoding process. We confirm that the insertion of 100 km conventional optical fiber for telecommunication applications, as the transmission medium, has a minimal effect for bit rates of the order of 1 Gb/s. For the very first time, this experiment is also performed in real-world conditions, using an installed optical network of 120 km within the metropolitan area of Athens. The successful results are presented in a recent publication of the popular Nature magazine.
Similarity search in time series data sets

Similarity search on time-series data sets is of growing importance in data mining. With the increasing amount of data of time-series in many applications, from nancial to scienti c, it is important to study the methods of retrieving similarity patterns e ciently and user friendly for business decision making. The thesis proposes methods of e cient retrieval of all objects in the time-series database with a shape similar to a search template. The search template can be either a shape or a sequence of data. Two search modules, subsequence search and whole sequence search, are designed and implemented. We study a set of linear transformations that can be used as the basis for similarity queries on time-series data, and design an innovative representation technique which abstracts the shape notion so that the user can interactively query and answer the multi-level similarity patterns. The wavelet analysis technique and the OLAP technique used in knowledge discovery and data warehousing are applied in our system. The retrieval technique we propose is e cient and robust in the presence of noise, and can handle several di erent notions of similarity including changes in scale and shift.
MINING ASSOCIATION RULES FOR HIGH UTILITY ITEMSETS USING UP GROWTH+ ALGORITHM FROM TRANSACTIONAL DATABASES

Utility mining emerges as an important topic in data mining field. High utility item sets mining refers to importance or profitability of an item to users. Efficient mining of high utility itemsets plays an important role in many real-time applications and is an important research issue in data mining area. Number of Algorithms like apriori, FP Growth has been proposed in this area, but they cause the problem of generating a large number of candidate itemsets. That will lead to high requirement of space and time and hence the performance of mining will be less . It is not at all good when the database contains transactions having long size or high utility itemsets which also having long size. UP Growth+ algorithm is proposed in this paper for mining high utility itemsets. A UP Tree data structure is used for storing the information about high utility itemset such that by using only double scanning of database, candidate itemsets can be efficiently generate. These proposed algorithms will cause the reduction of the number of candidates effectively and also reduces the requirement for space and time, when a database contains large number of transactions.
Continuous positive airway pressure and cardiovascular outcomes in obstructive sleep apnoea patients : a systematic review and meta-analysis of randomized controlled trials

Background: Continuous positive airway pressure (CPAP) has been utilized to reduce blood pressure in obstructive sleep apnea (OSA) patients, but few studies are available in terms of mortality data and other cardiovascular outcomes. Methods: This study systematically searched PubMed, Embase, and Cochrane Library databases for studies investigating the effects of CPAP on OSA patients. Basic characteristics and outcomes were extracted. A random effects model was used to pool the overall size. Q and I2 statistics were employed to quantify between-group heterogeneity. Egger’s and Begg’s tests were employed to identify small-study effects. Results: Of 590 primary records, 9 randomized controlled trials, including 5,923 patients, were finally included in the meta-analysis. CPAP was not associated with all-cause mortality (relative risk [95% confidence interval]: 0.92 [0.65-1.30], I2 = 0.0%) or cardiovascular mortality (0.70 [0.27-1.80], I2 = 36.3%) in OSA population. Incidence of myocardial infarction (0.95 [0.53-1.70], I2 = 25.8%) or strokes (0.91 [0.68-1.24], I2 = 0.0%) was not significantly different between the CPAP group and non-CPAP group. Neither Egger’s nor Begg’s tests indicated any small-study effects. Conclusion: CPAP was not significantly associated with all-cause mortality, cardiovascular mortality, or occurrence of myocardial infarction and strokes in the OSA population.
CloudSpace: A Web Development Environment for CS1 Courses

Since a massive decline of computer science graduates in 2002, computer science departments have been unable to reach previous graduation rates. In wake of this dramatic loss of graduates, researchers have been searching for the reasons students are avoiding computer science and choosing other majors. To combat this decrease in computer science graduates, the CloudSpace environment provides additional context to entry level computer science courses. This shift in context removes boring assignments from the early computer science curriculum and replaces them with more engaging web centric assignments. The CloudSpace environment presents a model that maintains student’s focus on core computer science competencies while providing a highly simplified web development toolkit to develop feature rich AJAX web applications. This thesis includes the rational and implementation of a cloud based hosting service and a highly abstracted web tool kit that enables students to replicate modern web applications. That this work received support from the National Science Foundation
Development of Smart Multiplatform Game App using UNITY3D Engine for CPR Education

The purpose of this study was to spread CPR education to the public and to promote the effects of training using an app design that applied the gamification theory based on UNITY3D engine. The results of this study are expected to contribute to the advancement of the online smart SNG market through the promotion of the net functional effects of games by applying the gamification theory on various functional games, and by using UNITY3D to convert web-based games and contents into ‘IOS’ or ‘ANDROID’ and widely developing them into smartphone-use services. Also the clients in community would likely be equipped with an ability to respond to emergency situations, by receiving CPR training in a fun and easy manner at anytime, anywhere.
R e g r esso r Global Discriminators Landmark Consistency Conditional Eyes LocalUnconditional Local Discriminators Nose Local Mouth

In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones. A major challenge of this task is that the structures of real and cartoon faces are in two different domains, whose appearance differs greatly from each other. Without explicit correspondence, it is difficult to generate a high quality cartoon face that captures the essential facial features of a person. In order to solve this problem, we propose landmark assisted CycleGAN, which utilizes face landmarks to define landmark consistency loss and to guide the training of local discriminator in CycleGAN. To enforce structural consistency in landmarks, we utilize the conditional generator and discriminator. Our approach is capable to generate high-quality cartoon faces even indistinguishable from those drawn by artists and largely improves state-of-the-art.
Evaluation of NMT and SMT systems: A study on uses and perceptions

Statistical and neural approaches have permitted fast improvement in the quality of machine translation, but we are yet to discover how those technologies can best “serve translators and end users of translations” (Kenny, 2017). To address human issues in machine translation, we propose an interdisciplinary approach linking Translation Studies, Natural Language Processing and Philosophy of Cognition. Our collaborative project is a first step in connecting sound knowledge of Machine Translation (MT) systems to a reflection on their implications for the translator. It focuses on the most recent Statistical MT (SMT) and Neural MT (NMT) systems, and their impact on the translator's activity. BTEC-corpus machine translations, from in-house SMT and NMT systems, are subjected to a comparative quantitative analysis, based on BLEU, TER (Translation Edit Rate) and Meteor. Then, we qualitatively analyse translation errors from linguistic criteria (Vilar, 2006) using LIG tools, to determine for each MT system, which syntactic patterns imply translation errors and which error type is mainly made. We then assess translators’ interactions with the main error types in a short evaluati on task, completed by participants in the Master's degree in Multilingual Specialized Translation of Grenoble Alps
Goal Ambiguity in Public Organizations: A Systematic Literature Review

In the literature on the public-private organizations is well known that public organizations have more ambiguous elements than private ones, such as ideology and objectives, that negatively influence the enthusiasm (Brunsson, 1986), motivation and performance of public employees (Pandey & Raney, 2006), as well as organizational performance (Chun & Raney, 2005). The close relationship with performance led many public administration scholars to deepen the concept of goal ambiguity in public management. However, given the lack of a univocal conceptualization of the phenomenon, the present work aims to contextualize goal ambiguity in the public administration, carrying out a systematic literature review. The results bring out goal ambiguity methods of measurement, its antecedents and consequences and the relationship between goal ambiguity and performance. From a theoretical point of view, the study allows systematizing the contributions on goal ambiguity, while from a practical point of view, a thorough knowledge of the concept allows public managers and policy-makers to obtain valuable information for the achievement of good organizational performance.
Progressive Failure of a Cut in London Clay due to Strain Softening

Introduction Potts et al. (1997) used a finite element analysis to explore a link between strain softening and delayed failures of old railway slopes cut into stiff plastic clays. Strain softening causes a soil’s strength to reduce from a peak to residual state. The strength loss does not occur simultaneously along a potential rupture zone because the rate of softening is linked with the magnitude of shear straining. As a result, the failure is progressive in the sense that the rupture surface propagates through the soil profile over time. Potts et al. (1997) simulated the progressive failure by using an elastic-plastic model in which strain softening was accommodated by allowing the effective strength properties to vary with deviatoric plastic strain.
A Semi-supervised Pipelined Deep Learner for an Adaptive and Efficient Biometric System

This work proposes a semi-supervised pipelined deep learner for efficient and adaptive biometric authentication. A semi-supervises based schema namely Error Driven Incremental Model (EDIM) is used for the online learning of the semisupervised learner. Deep generative network, based on Convolutional Neural Network has been used for the deep learning phase. Coupled with back propagation based Markov Chaining (MC) to take care of the error calculation for the neural net for judging the incremental learning. In order to access the effectiveness of the conceived learning mechanism for adaptive biometrics (biometric characteristics can change either temporarily or permanently, perhaps due to ageing, diseases or treatment for diseases or maybe accidents, adaptability of the system with such changes is termed as adaptive biometrics), we have considered the multimodal ocular trait (combination of iris and sclera) to assess its applicability in biometric domain. Initial investigation using an in-house data solicits appreciable result of the learning mechanism in biometric
N-3 polyunsaturated fatty acids ( PUFAS ) dietary improves the metabolic syndrome induce by high fat food

Objective: Metabolic syndrome is affected by the interaction of multiple genes and diverse environmental factors. Among environmental factors, food composition is a major one in modulating the development of metabolic diseases. Using diabetic hamster model, in the present study, we investigated the effects of different n-3 fatty acids diet (n-3 PUFAs) on metabolic disease. Methods: 40 healthy male depuratory hamsters were divided into 4 groups: control group (10% lard), aerobic exercise (EX) group (10% lard), high α-linolenic acid (HALA) group (n6/n3, 12.4:1) and low α-linolenic acid (LALA) group (n6/n3, 18.4:1). 10 weeks later, the hamsters were fasted for 12 hours and then anaesthetized. After blood samples were collected from femoral artery, all animals were sacrificed, and tissue samples, including the internal organs, skeletal muscles as well as amphi-organs adipose, were harvested. Serum glucose, insulin and lipids were determined. The levels of free fatty acids (FFA), and triacylglycerol (TG) in serum and tissues were also measured. Hepatic lipase (HL) and lipoprotein lipase (LPL) were determined in the tissues of liver, skeletal muscle and adipose. Furthermore, we measured the levels of leptin and TNF-α in adipose tissue, PPARs in liver by RT-PCR. Results: N-3 PUFAs did not affect body weight gain induced by high-fat food. However, it significantly reduced the mass of intermuscular adipose, not other fat tissues. More importantly, n-3 fatty acid dramatic decreased the fasting glucose (FPG) and fasting insulin (FINS), alleviating insulin resistance index (HOMA-IR). We further found that, after supplementation with n-3 PUFAs, hepatic LPL dramatically elevated. PPARs, including PPAR-α, γ and δ, significantly increased in n-3 PUFAs groups, especially in HALA group. Conclusion: N-3 fatty acids is able to reduce body fat masses and improve insulin resistance (IR) possibly through increasing hepatic LPL and elevating the expression levels as well as activity of PPARs. Supplement of n-3 fatty acids in dietary might help prevent diseases associated with metabolic syndrome, including diabetes, coronary heart disease, stroke etc.
Izvirni znanstveni prispevek DERIVING ECONOMIC WEIGHTS BY APPLYING THE PROFIT EQUATION METHOD TO A HYPOTHETICAL HUNGARIAN LARGE WHITE PIG POPULATION

The profit equation method was used in order to derive economic weights on a hypothetical Hungarian Large White pig population. The size of the population was held constant at the number of 100 sows which constructed four equal age categories (12, 18, 24, 30 months of age). Average farrowing interval was supposed to be 6 months, all of the sows were fertilised each year. Conception rate and weaning rate was 85% and 90%, respectively. The average litter size was set to be 10 thus total number of the weaned piglets per year was 1530. After replacement 25 culled sows and 1505 pigs could be sold in every year. Economic weight for number of piglets born was 1.1 EURO. Comparing the economic weight of the born piglet with that of the unit of product it can be concluded that reproductive performance receives strong emphasis in Hungary.
Image Compression Based on Clustering Fuzzy Neural Network

The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithm that compress images to lower data rates with better quality. This research present, a new approach to image compression based on clustering. This new approach includes new objective function, and its minimization by energy function based on unsupervised two dimensional fuzzy Hopfield neural network. New objective function consists of a combination of classification entropy function and average distance between image pixels and cluster centers. After applying new method on gray scale sample images at different number of clusters, better compression ratio and signal to noise ratio was observed. The new method is also a new clustering analysis method, and it provides more compact and separate clustering.