Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Confirmation Strategy for Large-scale Text Retrieval Systems with Spoken Dialogue Interface

Adequate confirmation for keywords is indispensable in spoken dialogue systems to eliminate misunderstandings caused by speech recognition errors. Spoken language also inherently includes out-of-domain phrases and redundant expressions such as disfluency, which do not contribute to task achievement. It is necessary to appropriately make confirmation for important portions. However, a set of keywords necessary to achieve the tasks cannot be predefined in retrieval for a largescale knowledge base unlike conventional database query tasks. In this paper, we describe two statistical measures for identifying portions to be confirmed. A relevance score represents the matching degree with the target knowledge base. A significance score detects portions that consequently affect the retrieval results. These measures are defined based on information that is automatically derived from the target knowledge base. An experimental evaluation shows that our method improved the success rate of retrieval by generating confirmation more efficiently than using a conventional confidence measure.
Pupils' environmental awareness of natural protected areas: The case of Zakynthos Island

ABSTRACT This study presents the results of a questionnaire-based survey, conducted in primary and secondary public schools of Zakynthos Island during 2013, referring to environmental awareness as well as pupils' perceptions and attitudes on specific environmental issues. The main aim of the study was to determine the knowledge and awareness that local pupils about the Loggerhead sea turtle (Caretta caretta) and bird species that are migrants and/or endemic in the National Marine Park of the island. The study area was selected because the percentage of pupils not attending compulsory education is one of the highest in Greece.
Fast and scalable minimal perfect hashing for massive key sets

Minimal perfect hash functions provide space-efficient and collision-free hashing on static sets. Existing algorithms and implementations that build such functions have practical limitations on the number of input elements they can process, due to high construction time, RAM or external memory usage. We revisit a simple algorithm and show that it is highly competitive with the state of the art, especially in terms of construction time and memory usage. We provide a parallel C++ implementation called BBhash. It is capable of creating a minimal perfect hash function of 10^{10} elements in less than 7 minutes using 8 threads and 5 GB of memory, and the resulting function uses 3.7 bits/element. To the best of our knowledge, this is also the first implementation that has been successfully tested on an input of cardinality 10^{12}. Source code: https://github.com/rizkg/BBHash
Nonparametric volatility density estimation

We consider a continuous-time stochastic volatility model. The model contains a stationary volatility process, the density of which, at a fixed instant in time, we aim to estimate. We assume that we observe the process at discrete instants in time. The sampling times will be equidistant with vanishing distance. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared processes is proposed to estimate the volatility density. An expansion of the bias and a bound on the variance are derived.
GTM Mixture through time for sequential data

Generative Topographic Mapping (GTM) is a popular probabilistic framework for modeling non-linear relationships in high-dimensional data as well as for unsupervised learning and visualization of such data. It is also known as to provide a principled probabilistic alternative to the well-known Self-Organizing Map (SOM) in the neural networks community, thanks to its flexible mixture model formulation and the desirable properties of the expectation-maximization (EM) algorithm. However, much attention has been focused on the use of GTM for multivariate data, in general assumed to be independent and identically distributed (i.i.d) and the problem of modeling sequences using GTM is less investigated. In this paper, we focus on GTM for unsupervised modeling and visualization of sequential data. We consider modeling sequences of continuous multidimensional observations and we propose a GTM through time (GTM-TT) approach based on hidden Markov models (HMM) where the observations are a sent of independent sequences, rather than a signle sequence. We further extend the model to the clustering of multiple sequences by proposing a GTM-TT mixture model. The model parameters are estimated by maximum likelihood via the EM algorithm. The proposed approach is evaluated using simulated data and real-world data.
Boundary effect of a partition in a quantum well

The paper wishes to demonstrate that, in quantum systems with boundaries, different boundary conditions can lead to remarkably different physical behaviour. Our seemingly innocent setting is a one-dimensional potential well that is divided into two halves by a thin separating wall. The two half wells are populated by the same type and number of particles and are kept at the same temperature. The only difference is in the boundary condition imposed at the two sides of the separating wall, which is the Dirichlet condition from the left and the Neumann condition from the right. The resulting different energy spectra cause a difference in the quantum statistically emerging pressure on the two sides. The net force acting on the separating wall proves to be nonzero at any temperature and, after a weak decrease in the low-temperature domain, to increase and diverge with a square-root-of-temperature asymptotics for high temperatures. These observations hold for both bosonic and fermionic type particles, but with quantitative differences. We work out several analytic approximations to explain these differences and the various aspects of the found unexpectedly complex picture.
Load-Dependent Roles of Frontal Brain Regions in the Maintenance of Working Memory

Brain imaging studies have suggested a critical role for prefrontal cortex in working memory (WM) tasks that require both maintainenance and manipulation of information over time in delayed-response WM tasks. In the present study, functional magnetic resonance imaging (fMRI) was used to examine whether prefrontal areas are activated when only maintenance is required in a delayed-response WM task, without the overt requirement to manipulate the stored information. In two scans, six subjects performed WM tasks in which, on each trial, they (1) encoded 1, 3, or 6 to-be-remembered letters, (2) maintained these letters across a 5-second unfilled delay, and (3) determined whether a single probe letter was or was not part of the memory set. Activation of left caudal inferior frontal gyrus was observed, relative to the 1-letter task, when subjects were required to maintain 3 letters in WM. When subjects were required to maintain 6 letters in WM, additional prefrontal areas, most notably middle and superior frontal gyri, were activated bilaterally. Thus, increasing the amount of to-be-maintained information, without any overt manipulation requirement, resulted in the recruitment of wide-spread frontal-lobe regions. Inferior frontal gyrus activation was left-hemisphere dominant in both the 3- and 6-letter conditions, suggesting that such activation reflected material-specific verbal processes. Activation in middle and superior frontal gyri appeared only in the 6-letter condition and was right-hemisphere dominant, suggesting that such activation reflected material-independent executive processes.
Salivary RNA Signatures in Oral Cancer Detection

Oral squamous cell carcinomas (OSCC) are common malignancies that affect almost a million people every year. The key issue in reducing mortality and morbidity associated with OSCC is to develop novel strategies to identify OSCC at an early stage. One such strategy is the identification of biomarkers. So far, more than 100 biomarkers are recognized in the detection of oral cancer and they range from proteins to nucleic acids (DNAs, RNAs). Detection of ribose nucleic acids in saliva is a recent trend in diagnosing oral cancer. Studies have shown statistically significant changes in the levels of salivary transcriptomes in patients with oral squamous cell carcinomas. These biomarkers have displayed high sensitivity and specificity. Also, new point-of-care platforms such as oral fluid nanosensor test are now available that will soon emerge as chair-side tools for early detection of oral cancer. The aim of this review is to highlight the importance of salivary transcriptomes in oral cancer detection.
Norepinephrine-induced human platelet activation in vivo is only partly counteracted by aspirin.

BACKGROUND Epinephrine and mental stress may, via platelet stimulation, enhance the risk of thrombus formation. Norepinephrine is more likely than epinephrine to activate platelets in vivo because of higher levels in plasma but is less well studied in this respect. The antiplatelet drug of choice for patients with coronary artery disease, aspirin, may be less effective during sympathoadrenal activation. We therefore investigated platelet responses in vivo to exogenous norepinephrine with and without aspirin pretreatment. METHODS AND RESULTS Platelet aggregability in vivo was assessed in 11 healthy male subjects, by filtragometry ex vivo (which reflects platelet aggregability in vivo) and by measurements of plasma beta-thromboglobulin (beta-TG, which reflects platelet secretion). Norepinephrine infusions elevated venous plasma norepinephrine from 1.5 to 4 and 15 nmol/L, respectively, and enhanced platelet aggregability (filtragometry) concentration dependently (P < .001). Platelet secretion (beta-TG levels) increased during high-dose infusion (P < .01). Aspirin pretreatment (500 mg orally 12 hours earlier) reduced the excretion of 11-dehydrothromboxane B2 by 62 +/- 5% (P < .001) and attenuated platelet aggregability at rest (P < .05) but not the effect of norepinephrine infusion on platelet aggregability. Conversely, resting plasma beta-TG levels and the urinary excretion of high-molecular-weight beta-TG were not altered by aspirin pretreatment, whereas the norepinephrine-induced increase in plasma beta-TG was abolished. CONCLUSIONS Norepinephrine, at plasma levels easily attained during exercise, enhances platelet aggregability and platelet secretion in vivo in healthy humans. Aspirin may be less effective as an antithrombotic drug during sympathoadrenal activation in humans.
AN EMPIRICAL EVALUATION OF LEARNER PERFORMANCE IN E-LEARNING RECOMMENDER SYSTEMS AND AN ADAPTIVE HYPERMEDIA SYSTEM

This paper introduces a novel architecture for an e-learning recommender system which is based on good learners’ average ratings strategy and content-based filtering approach. The feasibility of the proposed system is conducted by comparing its performance against other recommender systems and an adaptive hypermedia system in order to measure the effectiveness of the proposed strategy in improving students’ learning performance. Experimental result has shown that the recommender strategy can improve students’ performance by at least 12.16%, as compared to other recommendation techniques. A performance evaluation with an adaptive hypermedia system that uses knowledge level as its adaptation feature also showed a positive increase of 14.99% in terms of students’ performance.
Vehicle Accident Detection and Identification Using Image Compression Analysis and RFID Traffic Cone Tracking System Module

This paper proposed an intelligent RFID traffic cone that is applied for vehicle accident detection and identifi cation based on image compressing analysis and RFID detection tracking in an accident clamming system and traffic reporting system. The gain benefit of this paper can reduce the waiting time of an accident clamming process that usually used along time with normally vehicle crash accessing and reporting systems, especially those related to an image processing and insurance techniques. This RFID technique deals with multi- vehicles, multi lane and multi road even or traffic junction area. It provides an efficiency time management scheme with enough correctly data reporting, in which a dynamic time schedule is worked out in real time for the driver or passengers of each accident situations. The accessing time operation of the RFID traffic cone system emulates the judgment of a traffic policeman on duty or user who may have the PDA nearby the RFID traffic cone that can be connected via by Wireless channels. The image compression present here is used along with the RFID information of each vehicle to get a precise event data picture that is composed of image encoding and decoding algorithms called wavelet transform with Principle Component Analysis (PCA) via Vector Quantization techniques (VQ). The small bit rates for high-speed data transmission with a small space for data storage area are required on wireless transmission channel. Simultaneously, the peak signal to noise ratio (PS NR) has to be maintained. The traffic management system model is constructed for testing this present idea that composed of traffic lights, vehicles transit, vehicles clash and traffic cone with RFID solution system. By applying the proposed technique, performance has been improved which indicated by lower bit rate an d better PS NR for an image compression algorithm. The RFID traffic cone mechanisms are work well while the RFID data tags that are recorded of each vehicle are enough correctly and can be sent the data to the traffic information center or an accident clamming center via on local area network (LAN) or wide area network (WAN) simultaneously. For scaling the large number of vehicles in a real situation, the simulation model is used to test this system and reviewed that this proposed technique may be work well.
Multiple-Biometric Fusion Methods using Support Vector Machine and Kernel Fisher Discriminant

This paper proposes multiple-biometric fusion methods, using Support Vector Machine (SVM) and Kernel Fisher Discriminant (KFD) in order to improve recognition accuracy. The proposed SVM and KFD are non-linear classification methods using Radial Basis Function (RBF) as a kernel function. The RBF kernel is effective with respect to the input data distribution. Experiments have been conducted on NIST BSSR1 (Biometric Scores Set - Release1) data set using SVM, KFD and weighted sum methods, and their performance of multiple biometric fusion has been presented by the HTER (Half Total Error Rate) and the ROC (Receiver Operating Characteristic) curves. The experimental results demonstrate that the non-linear fusion methods provide higher verification performance than linear fusion methods.
Protection cycles in mesh WDM networks

A fault recovery system that is fast and reliable is essential to today's networks, as it can be used to minimize the impact of the fault on the operation of the network and the services it provides. This paper proposes a methodology for performing automatic protection switching (APS) in optical networks with arbitrary mesh topologies in order to protect the network from fiber link failures. All fiber links interconnecting the optical switches are assumed to be bidirectional. In the scenario considered, the layout of the protection fibers and the setup of the protection switches is implemented in nonreal time, during the setup of the network. When a fiber link fails, the connections that use that link are automatically restored and their signals are routed to their original destination using the protection fibers and protection switches. The protection process proposed is fast, distributed, and autonomous. It restores the network in real time, without relying on a central manager or a centralized database. It is also independent of the topology and the connection state of the network at the time of the failure.
Measure of function in rheumatoid arthritis: individualised or classical scales?

Background: The Health Assessment Questionnaire Disability Index (HAQ-DI) is the most widely used measure of function in rheumatoid arthritis (RA). Objective: To evaluate individualised forms of the HAQ-DI and thus enhance the incorporation of patients’ views in outcome assessment. Patients and methods: HAQ-DI data were prospectively obtained from 370 outpatients with RA treated with leflunomide over a 6-month period. At baseline and final visits, patients had to rate the importance they attached to each activity addressed by the 20 HAQ-DI items, and to select the five activities they considered the most important. Different individualised scales were evaluated: scales preserving all domains, in which the score for each item is multiplied by or added to its importance; and scales involving, for each patient, only the five most important items. The psychometric properties of these scales were compared with those of the HAQ-DI. Results: For each HAQ-DI item, severity and importance scores were weakly correlated. Scores for all individualised scales were highly correlated with the HAQ-DI score (rs>0.75). All scales had a good internal consistency (Cronbach’s α 0.87–0.88). Compared with the HAQ-DI, individualised scales did not have better sensitivity to change (standardised response mean 0.64–0.69 vs 0.74). Conclusion: Individualised scales have similar properties to the HAQ-DI. However, individualised questionnaires measuring importance gave complementary information to the measure of disability. Individualisation is probably not needed for group assessment in all randomised controlled trials but, the use of individualised questionnaires may be clinically relevant for individual patients with RA.
Similarity Between Obesity and Drug Addiction as Assessed by Neurofunctional Imaging

SUMMARY Overeating in obese individuals shares similarities with the loss of control and compulsive drug taking behavior observed in drug-addicted subjects. The mechanism of these behaviors is not well understood. Our prior studies with positron emission tomography (PET) in drug-addicted subjects documented reductions in striatal dopamine (DA) D2 receptors. In pathologically obese subjects, we found reductions in striatal DA D2 receptors similar to that in drug-addicted subjects. Moreover, DA D2 receptor levels were found to have an inverse relationship to the body mass index of the obese subjects. We postulated that decreased levels of DA D2 receptors predisposed subjects to search for reinforcers; in the case of drug-addicted subjects for the drug and in the case of the obese subjects for food as a means to temporarily compensate for a decreased sensitivity of DA D2 regulated reward circuits. Understanding the mechanism in food intake will help to suggest strategies for the treatment of obesity.
IFDS operation mechanism and its implementation

In order to deal better with the changeful market demand, based on IFDS for modern instrument manufacturing, people make researches on the product family and the operation mechanism of IFDS. In the design process, first, people determine the variants of products through grid dividing markets; and then find main related design parameters in the product family. In order to establish flexible product platform, people have to find out the core structure of a product and refine its public module and flexible module by the aid of IFDS operation mechanism; and finally add specific elements to the platform. In the end, the author takes the development of the product family of concentrator solar energy system for example to prove the effectiveness of this method.
AN ANALYSIS OF POLITENESS STRATEGIES USED BY HARRY POTTER IN J.K. ROWLING’S NOVEL “HARRY POTTER AND THE SORCERER’S STONE”

IRMA RATNA NINGSIH: AN ANALYSIS OF POLITENESS STRATEGIES USED BY HARRY POTTER IN J.K. ROWLING’S NOVEL “HARRY POTTER AND THE SORCERER’S STONE” Politeness is a strategy of people in being polite to build a harmony in term of communication. Politeness is one of the communication strategies that emphasize on how to be polite in words and actions. People usually use politeness in order to maintain and develop their relationship. If the individual can use politeness, automatically the individual can reach a running well and going smoothly of life. The aims of this research are to know what politeness strategies that used by Harry Potter in Harry Potter and the Sorcerer‟s Stone novel by J.K. Rowling, to know to whom Harry Potter uses the politeness strategies and to know how Harry Potter uses the politeness strategies in his utterances. The method of this research is discourse analysis. It used because it is concerning with describing instances of language use in context. From the data can be explored in a systematic way, the relation between language use from text and the context of occurrence of politeness strategies that used by Harry Potter. The data of this research taken from a novel entitled Harry Potter and the Sorcerer‟s Stone by J.K. Rowling. The main theory to analyze the data taken from the book entitled Politeness: Some Universals in Language Usage by Penelope Brown and Stephen C. Levinson (1987). As results of this research, the writer found 90 data which contain politeness strategies and about 16 data has been analyzed by the writer. All the data belong to the four politeness strategies as being proposed by Brown and Levinson, namely bald on record, positive politeness, negative politeness and off record. Bald on record strategy is used by Harry in the situation of urgency. In positive politeness, Harry uses it to people around him in order to satisfying their positive face. Negative politeness also used by Harry as he wants to satisfy the hearer‟s negative face. While off record strategy used by Harry because he wants to be indirect and gives the hearer clues. Harry used those strategies to his friends, teachers and character around him. Finally, the researcher expects this research will be able to help the next researcher who conducts the research in the same field. It is suggested to further researchers to be able to investigate other objects which might contain politeness strategy and use another theory of politeness strategy.
EDVD — Enhanced descriptor for visual and depth data

Many problems in computer vision and robotics rely on automatically determining point correspondences from two images. Due to issues such as illumination variations, uncontrolled acquisition conditions and noise, this is a challenging problem. This work presents a method that combines visual and shape information to perform point correspondences which is invariant to rotation and scaling transformations in the image and geometry domains. Experimental results show that our approach is a robust and computationally efficient technique compared with classic descriptors in the literature.
AN INTELLIGENT CONTROL POLICY FOR FUEL INJECTION CONTROL OF CNG ENGINES

Abstract– This paper proposes an intelligent control technique for fuel injection control of Compressed Natural Gas (CNG) engines. Recurrent Neuro-Fuzzy Networks are used to estimate and control air to fuel ratio (AFR) of CNG engines. To reasonably handle such a complicated control problem, a precise experimental test has been done on a real CNG fuelled vehicle and the process input output data have been collected by running the vehicle in transient conditions. To determine the proper amount of gas to be injected, a controller has been designed based on nonlinear inverse dynamics of AFR. The results show that the predicted results are in line with the measured fuel injection commands produced by the real electronic control unit (ECU). This evaluated and validated the efficiency of the controller. The control strategy has the advantage that control actions can be calculated analytically, avoiding the costly and time-consuming calibration efforts required in conventional fuel injection control strategies.