Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Cultural Tourism Development and Attractiveness in Transylvania, Romania. A Focus on the Counties of Cluj and Sibiu

Our paper aims at realizing a sketch of the manner how native and foreign visitors perceive and appreciate the cultural tourism offer of Romania and Transylvania, and also that of the counties of Cluj and Sibiu. Cluj is a major economic center of Romania. Sibiu was the European Capital of Culture of 2007. We have tried to identify the answers to several questions: Is Transylvania’s cultural heritage attractive to tourists? Do foreigners travel to Romania for cultural purposes? Are Romanians still interested in cultural activities? The findings of our paper are based on three different researches that we have carried out. Key-Words: County of Cluj, County of Sibiu, Transylvania, Romania, Cultural tourism, Cultural heritage, Economic crisis, Tourism development
Parallel Sparse LU Factorization of Power Flow Jacobian using GPU

Parallelizing the LU factorization of sparse Jacobian matrices reduces the execution time of the power flow algorithm which aids in real time control of the power system. In this paper, acceleration of LU factorization by employing different Graphical Processing Units(GPUs) is discussed. The hybrid column based right-looking LU factorization algorithm is found to be more amenable for GPU implementation. The structural symmetry property of non-zeros in the matrix is exploited to maximize the column/node level parallelization through a matrix re-ordering scheme obtained from Jess-Kees' algorithm. Also the influence of different fill-in reducing matrix re-orderings in the context of parallelization and execution time is discussed. The proposed method is tested on a set of power flow Jacobians with dimensions ranging from 4438 to 1136128 on different GPUs. Results from the current work demonstrate scalability of the proposed method with progressively powerful GPUs yielding better execution times. Efficacy of the proposed method is corroborated by improved speedup in execution when compared with CPU-based serial sparse system solvers like UMFPACK and KLU.
From Multi-Objective to Mono-Objective Refactoring via Developer's Knowledge Extraction

—Refactoring studies either aggregated quality metrics to evaluate possible code changes or treated them separately to ﬁnd trade-offs. For the ﬁrst category of work, it is challenging to deﬁne upfront the weights for the quality objectives since developers are not able to express them upfront. For the second category of work, the number of possible trade-offs between quality objectives is large which makes developers reluctant to look at many refactoring solutions. In this paper, we propose, for the ﬁrst time, a way to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on his preferences. The ﬁrst step consists of using a multi-objective search to generate different possible refactoring strategies by ﬁnding a trade-off between several conﬂicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and to reduce the number of refactoring options to explore. Finally, the extracted preferences from the developer are used to transform the multi-objective search into a mono-objective one by taking the preferred cluster of the Pareto front as the initial population for the mono-objective search and generating an evaluation function based on the weights that are automatically computed from the position of the cluster in the Pareto front. Thus, the developer will just interact with only one refactoring solution generated by the mono-objective search. We selected 32 participants to manually evaluate the effectiveness of our tool on 7 open source projects and one industrial project. The results show that the recommended refactorings are more accurate than the current state of the art.
Emotion Recognition from Raw Speech using Wavenet

This paper proposes the use of Wavenet architecture to the task of speech emotion recognition using raw speech signals. In contrast to the conventional deep learning methods, Wavenet utilises dilation filters, residual blocks and skip connections to model the long-term dependencies in speech signals and eliminates the need for LSTMs for the same. Wavenet is trained for the classification task on two popular datasets- EMO-DB and IEMOCAP. Experimental findings along with reasoning have been presented and compared with the standard baseline CNN+LSTM network for the four basic human emotions- Angry, Happy, Neutral and Sad.
The effect of musical training and music therapy on speech and music perception in cochlear-implant users

Music is reported to be the second most important acoustical stimulus by cochlear-implant (CI) users. Yet music is not well-perceived, nor enjoyed by CI users. Previously, normalhearing musicians have been shown to have better speech perception in noise, pitch perception in speech and music, as well as a better working memory and enhanced neural encoding of speech, compared to non-musicians. Based on these findings, we hypothesize that music therapy and musical training may have a positive effect on speech and music perception in CI users. Three groups of CI users were recruited for six weeks of musical training, music therapy and non-musical training for 2 hours per week. These different types of training were selected to vary in their level of experimental control and human interaction. Musical training involved individual computerized training with melodic contour identification and instrument recognition. Music therapy involved group exercises involving rhythm, musical speech, singing, emotion identification and improvisation. Non-musical training involved group activities involving writing, cooking, and woodworking. Before and after the training, all participants were tested behaviorally for speech and music-related tasks (emotion identification, melodic contour identification). Quality of life was quantified using the Nijmegen Cochlear Implant Questionnaire. In general, training effects were observed within domain (from musical training to better melodic contour identification), with little transfer across domains (no effect of any of the three training approaches on speech perception, but an effect of music therapy on emotion identification). The music therapy group also reported enhanced subjective perceptual skills. None of the training methods showed an effect on quality of life. Given the short duration of training, it is promising that music therapy already showed a cross-domain positive effect on emotion identification, along with subjective reports of benefit. It is possible that the interactive nature of music therapy was useful; however, further research is needed with more participants and with longer durations of training.
Microwave and RF Photonic Fractional Hilbert Transformer Based on a 50 GHz Kerr Micro-Comb

We report a photonic microwave and radio frequency (RF) fractional Hilbert transformer based on an integrated Kerr micro-comb source. The micro-comb source has a free spectral range (FSR) of 50 GHz, generating a large number of comb lines that serve as a high-performance multi-wavelength source for the transformer. By programming and shaping the comb lines according to calculated tap weights, we achieve both arbitrary fractional orders and a broad operation bandwidth. We experimentally characterize the RF amplitude and phase response for different fractional orders and perform system demonstrations of real-time fractional Hilbert transforms. We achieve a phase ripple of <0.15 rad within the 3-dB pass-band, with bandwidths ranging from 5 to 9 octaves depending on the order. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a new way to implement high-performance fractional Hilbert transformers with broad processing bandwidth, high reconfigurability, and greatly reduced size and complexity.
Ascorbic Acid Supplementation Protects Against Gasoline Fume-Induced Oxidative Stress and Thyroid Dysfunction in Rats

The protective effect of ascorbic acid against gasoline fume –induced oxidative stress and thyroid gland dysfunction was investigated in rats. Thirty-five (18 male and 17 female) rats weighing 200 gm to 250 gm were randomly segregated into five groups (n=7 per group). Rats in group 1 served as unexposed control, while rats in group 2 were exposed to GV alone. Rats in groups 3, 4 and 5 were exposed to Gasoline Vapor (GV) and orally administered ascorbic acid 100 mg/kg and 200 mg/kg and 280 mg/kg respectively) for 14 days. Thereafter, all animals were sacrificed and blood obtained was used for the determination of serum levels of Triiodothyronine (T3), Tetraiodothyronine (T4), Thyroid Stimulating Hormone (TSH), Catalase (CAT) and Malondialdehyde (MDA) using standard methods. Exposure to GV alone significantly (P<0.05) decreased serum levels of T3 and CAT and increased MDA in both male and female rats. Serum levels of T4, TSH significantly increased in male, but decreased in female rats compared to the corresponding values in the control group. The GVexposed thyroid section revealed follicular hypertrophy and hyperplasia, compacted follicles, congested blood vessels and almost extinct colloids. Co-administration of ascorbic acid caused a remarkable improvement in these parameters. Exposure to GV could be associated with oxidative stress and thyroid dysfunction which can be mitigated by the administration of ascorbic acid, a synthetic antioxidant.
Generative Localization With Uncertainty Estimation Through Video-CT Data for Bronchoscopic Biopsy

Robot-assisted endobronchial intervention requires accurate localization based on both intra- and pre-operative data. Most existing methods achieve this by registering 2D videos with 3D CT models according to a defined similarity metric with local features. Instead, we formulate the bronchoscopic localization as a learning-based global localisation using deep neural networks. The proposed network consists of two generative architectures and one auxiliary learning component. The cycle generative architecture bridges the domain variance between the real bronchoscopic videos and virtual views derived from pre-operative CT data so that the proposed approach can be trained through a large number of generated virtual images but deployed through real images. The auxiliary learning architecture leverages complementary relative pose regression to constrain the search space, ensuring consistent global pose predictions. Most importantly, the uncertainty of each global pose is obtained through variational inference by sampling within the learned underlying probability distribution. Detailed validation results demonstrate the localization accuracy with reasonable uncertainty achieved and its potential clinical value. A demonstration video demo can be found on the website https://youtu.be/ci9LMY49aF8.
Effects of Modifying the Input Features and the Loss Function on Improving Emotion Classification

In this work, we show that the discriminative power of a deep neural network can be improved at three different levels: (i) inputting discriminative features, (ii) designing an optimal network architecture and (iii) changing the loss function. This work suggests that only increasing the depth or the width of a deep network may not always be the best solution while requiring computationally efficient models. Here, we show that there is scope for improving the classifier accuracy at each of the three levels. We have carried out all our experiments on the FERplus dataset and show that the facial emotion recognition accuracy can be independently improved up to 2.5% by adding better features, 1.8% by modifying the loss function and up to 3.1% by combining the two ideas. In separate experiments, we show that the computational complexity can be reduced by a factor of 24.3, while simultaneously increasing the FER by 0.95% by modifying the architecture of the model, input features and the loss function.
Ab initio Theoretical Investigation of Dopants for Ultrahigh Conductivities in Single Wall Carbon Nanotubes

Study of highly effective dopants of carbon nanotube is essential to understand the mechanism and produce ultrahigh conductive materials for high-power or high-current electrical applications. Though there are several experimental reports of chemically doped high electrical conductivity carbon nanotubes, influence of doping on quantum conduction is not studied well. Here, we investigated the impact of dopant such as $I_{2}$ and $AuCl_{3}$ on the electronic structure and quantum conduction using ab initio theoretical calculations. For both $I_{2}$ and $AuCl_{3}$, they are adsorbed in carbon nanotube molecules and act as p-type dopants. Our study reveals that −1.61 eV shift in Fermi level occurs for $AuCl_{3}$ doping in CNT, which implies possibility of ultrahigh conductivity. Furthermore, transmission function calculations confirm significant increase of available quantum channels for conduction in $AuCl_{3}$ doped CNT.
A Study on Use of Two Doses of Mifepristone Followed by Misoprostol in Pregnancies with Intrauterine Foetal Death in Mid and Late Trimester

Objective: To assess the efficacy, safety and acceptability of this new regimen for induction of labour in IUFD in mid and late trimester pregnancies. Design: Observational study. Setting: Umaid Hospital Jodhpur (Rajasthan, India). Methods: A total of 100 women with IUFD of gestational age more than 12 weeks were studied.Once the termination of pregnancy was decided, Tab.Mifepristone 200mg was administered per orally followed again by Tab. Mifepristone 200mg 24 hours later, regardless of gravid status and mode of previous deliveries.48 hours after the second dose of Tab. Mifepristone, Tab Misoprostol was administered per vaginally. For gestational age less than 28 weeks 200mg Misoprostol PV every 4 hours (maximum 4 doses), and for gestational age more than 28 weeks 25mg Misoprostol PV every 4 hours ( maximum 4 doses).
Applying the UTAUT Model to Explain the Students’ Acceptance of Mobile Learning System in Higher Education

Mobile learning applications have been growing in demand and popularity and have become a common phenomenon in modern educational systems, especially with the implementation of mobile learning projects. This study applies the Unified Theory of Acceptance and Use Technology (UTAUT) model to examine the effects of different factors that were identified from the literature on students’ acceptance of mobile learning applications in higher education. The data was collected from a 697 university students responded to an online questionnaire. SEM method was used for data analysis. The results showed that perceived information quality, perceived compatibility, perceived trust, perceived awareness, and availability of resources, self-efficacy, and perceived security are the main motivators of students’ acceptance of mobile learning system, and consequently success the implementation of mobile learning projects. Results from this study provide the necessary information as to how higher education institutions can enhance students’ acceptance of mobile learning system in order to support the usage of mobile technologies in learning and teaching process. These results offer important implications for mobile learning acceptance and usage.
Disaggregated edge-enabled C+L-band filterless metro networks

The recent interest in the upgrade and enhancements of metro transport networks and the availability of transponder cards with coherent receivers is opening the way to filterless solutions employing only passive splitters/couplers and optical amplifiers, potentially achieving significant capital expeditures and operating expenditures savings. However, the filterless option suffers from inefficiencies, mainly due to the broadcasting constraint and the reduced optical reach. To overcome such limitations, this paper proposes three complementary strategies to upgrade optical filterless metro networks (FMN). First, the number of supported channels is incremented by exploiting the full ${\rm C}{+}{\rm L}$C+L-band. To this end, two design architectures (i.e., Single and Dual Region) are proposed and evaluated, targeting double capacity with respect to the standard C-band and an upgrade to cost reduction. Second, we investigate a dual-architecture solution, extending metro deployments with a low-cost filterless and unamplified L-band system. Its design trade-offs are evaluated to determine its suitability in providing direct low-latency connectivity between metro-access nodes with the aim of supporting edge-computing platforms. Finally, the flexibility of the FMN is extended by introducing disaggregated transponders with different bitrates (i.e., 100 Gbps and 400 Gbps) and configurable transmission parameters, such as the modulation format and the forward error correction). Such flexibility is exploited through the extension of the OpenConfig YANG model of the optical line system, thus enabling automatic spectrum and transmission parameter assignment by means of a centralized software-defined-network controller and achieving better resource utilization. Simulation and experimental results are provided, showing the effectiveness and the potential impact of filterless metro solutions in future deployments and low-cost network upgrades supporting edge/fog clusters and 5G.
Characterization of Electromagnetic Nonlinearities of RFI/EMI Gasket: A Microstrip Based Approach

The knowledge of the electromagnetic nonlinearities of materials is of paramount importance for a wide variety of application. RFI/EMI shielding gasket is comparatively a new material used in this domain, and its electromagnetic nonlinearities are not very renowned. This research focused to develop a microstrip-based technique to study the electromagnetic nonlinearities of RFI shielding gaskets, which involved third & fifth-order passive intermodulation (PIM), and second & third- order harmonic distortion (HD). A bit of nickel and two types of gaskets were use as material-under-test (MUT) at 700MHz frequency band. The output of this research might help in the material selection for wide variety of applications.
How You See Me: Understanding Convolutional Neural Networks

Convolutional Neural networks(CNN) are one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data effectively. This paper answers the question, “How does a CNN look at an image? ”. Visual results are also provided to strongly support the proposed method. The proposed algorithm exploits the basic math behind CNN to backtrack the important pixels. This is a generic approach which can be applied to any architecture of a neural network. This doesn't require any additional training or architectural changes. In literature, few attempts have been made to explain how learning happens in CNN internally, by exploiting the convolution filter maps. This is a simple algorithm as it does not involve any cost functions, filter exploitation, gradient calculations or probability scores. Further, we demonstrate that the proposed scheme can be used in some important computer vision tasks such as object detection, salient region proposal, etc.
Food Image Categorization Using Attentional Bilinear Model

Nowadays, many food images are posted on various social network platforms without identification labels. An automatic food categorization application would greatly help to identify and classify food categories. Food categorization is a complex problem since the number of category types can be more than one hundred. Many kinds of food are similar with only subtle differences in taste and presentation and this can lead to a problem called “finegrained issue”. Recently, a bilinear model was employed which showed good accuracy and generated excessive features to capture details among different food categories, albeit with limited performance. Diverse food categories require disparate sets of features. Here, an attention mechanism was applied to capture suitable features and specifically identify each food category. Furthermore, the performance of a bilinear backbone was also enhanced by applying Inception in correlation with Inception-ResNet-v2 and Inception-v3 networks. The experiment was conducted on the Wongnai dataset containing various images that were separated into 83 classes. Results showed that our attentional model outperformed the traditional bilinear model, with an average of 16% improvement showing 3% and 44% as min-max performance values, respectively.
End-to-end Deep Reinforcement Learning for Multi-agent Collaborative Exploration

Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture. The model combines convolutional neural network to process multi-channel visual inputs, curriculum-based learning, and PPO algorithm for motivation based reinforcement learning. Evaluations show that the proposed method can learn more efficient strategy for multiple agents to explore the environment than the conventional frontier-based method.
Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Normal Based on Neuropsychological Data via Supervised Learning

Dementia is neurodegenerative or vascular disorder which is characterized by declining mental function including a combination of symptoms for abnormal activity, behavior and cognitive. Alzheimer's disease (AD) is the most common type of dementia, it accounts for 60 to 80 percent of dementia cases. Their brain neuron are die and loss the connection with each other neurons which stop firing and establishing the brain network, and it is easily considered as normal aging processes when the subject is actually in the early stages of AD. This paper proposed a machine learning algorithm based on neuropsychological data to classify subjects into Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN). We acquired neuropsychological data from 678 participants with clinical diagnosis information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and we focus on Mini-Mental State Examination (MMSE) which is the most extensively used psychometric examination in the clinical practice. Through computer-aided diagnosis (CAD) system which is called as “smart doctor”, this algorithm can help doctors diagnose patients with Alzheimer's disease. The medical AI brings accurate and rapid diagnosis and prediction. As to follow up the patient's care and social resources can be given faster. The main result of this paper is that we found two valuable features from MMSE, orientation and recall, which have the same ability as the entire MMSE, to detect the Alzheimer's disease.