Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Filtering and Coding Method for Image Classification and Image Retrieval

The feature extraction-coding-pooling framework improves the performance of image classification because it generates robust and discriminative image representation. The proposed system uses saliency driven multi-scale nonlinear diffusion filtering with linear distance coding (LDC) method for image classification. The saliency driven multi-scale nonlinear diffusion filtering generates the images at small, mid and high scale and concatenation of information at these scales produces robust image classification using LDC. The class manifolds generated by this classification system are given as input to image retrieval system which finally retrieves all images those are relevant to supplied image.
Efficient speech enhancement technique by exploiting the harmonic structure of voiced segments

One of the most significant applications of the speech processing is speech enhancement. Several classical algorithms have been proposed and they suffer from one serious drawback of enhancing speech signal under very low signal to noise ratio. In this paper, this is accomplished by exploiting the harmonic structure of voiced segments. This method improves speech quality by suppressing the noise in the frequency domain with the use of a spectral weighting function. Two design parameters are included in the suppression gain, namely, the frequency-dependent noise flooring parameter (FDNFP) and the gain factor. Enhanced harmonic structures are incorporated into the FDFNP by time-domain processing of the linear prediction residuals of voiced speech. Further enhancement of the harmonics is achieved by adaptive comb filtering with a peak-picking algorithm. Simulation results prove that an admissible level of speech distortion is obtained as comparable to those caused by conventional noise reduction techniques. The performance of the enhancement method was evaluated by the modified bark spectral distance (MBSD), ITU-Perceptual Evaluation of speech Quality (PESQ) scores, composite objective measures and listening tests.
Neuropeptide Y Enhances Olfactory Mucosa Responses to Odorant in Hungry Rats

Neuropeptide Y (NPY) plays an important role in regulating appetite and hunger in vertebrates. In the hypothalamus, NPY stimulates food intake under the control of the nutritional status. Previous studies have shown the presence of NPY and receptors in rodent olfactory system, and suggested a neuroproliferative role. Interestingly, NPY was also shown to directly modulate olfactory responses evoked by a food-related odorant in hungry axolotls. We have recently demonstrated that another nutritional cue, insulin, modulates the odorant responses of the rat olfactory mucosa (OM). Therefore, the aim of the present study was to investigate the potential effect of NPY on rat OM responses to odorants, in relation to the animal's nutritional state. We measured the potential NPY modulation of OM responses to odorant, using electro-olfactogram (EOG) recordings, in fed and fasted adult rats. NPY application significantly and transiently increased EOG amplitudes in fasted but not in fed rats. The effects of specific NPY-receptor agonists were similarly quantified, showing that NPY operated mainly through Y1 receptors. These receptors appeared as heterogeneously expressed by olfactory neurons in the OM, and western blot analysis showed that they were overexpressed in fasted rats. These data provide the first evidence that NPY modulates the initial events of odorant detection in the rat OM. Because this modulation depends on the nutritional status of the animal, and is ascribed to NPY, the most potent orexigenic peptide in the central nervous system, it evidences a strong supplementary physiological link between olfaction and nutritional processes.
ONTOMOTIF: ONTOLOGI PENCARIAN INFORMASI KENDARAAN BERMOTOR

Ontologi sebagai tulang punggung web semantik diperlukan pada berbagai domain. Ontologi yang didesain secara khusus untuk menangani pencarian informasi kendaraan bermotor sejauh ini belum ditemukan. Paper ini membahas pengembangan ontologi pencarian informasi kendaraan bermotor (OntoMotif). Selain menjelaskan spesifikasi kendaraan, OntoMotif juga mengakomodasi kepentingan penyedia jasa penjualan dan persewaan kendaraan bermotor dalam mempromosikan produknya. Tahapan-tahapan metode pengembangan OntoMotif mengacu pada Ontology Development 101 . Dari hasil pengujian, dapat dibuktikan bahwa OntoMotif mampu menjawab kebutuhan pencari informasi yang menggunakan berbagai macam kalimat tanya. Dengan OntoMotif, query yang dibutuhkan untuk mendapatkan informasi relatif lebih sederhana. Oleh karena itu, OntoMotif dapat dijadikan sebagai salah satu acuan dalam rangka penyediaan data kendaraan bermotor dan propertinya yang sering kali ditanyakan dalam kehidupan sehari-hari.
Fast entropy based alert detection in super computer logs

The task of alert detection in event logs is very important in preventing or recovering from downtime events. The ability to do this automatically and accurately provides significant savings in the time and cost of downtime events. The Nodeinfo algorithm, which is currently in production use at Sandia National Laboratories, is an entropy based algorithm for alert detection in event logs. Automatic alert detection needs to be fast for it to be practical in a production environment. In this work we show that with Message Type Indexing (MTI) the computational effort required for alert detection can be reduced by up to 99%. This can be achieved without a drop in detection performance. Our proposed method has special significance because it provides a framework for alert detection which requires little or no human input, due to message type extraction required for MTI being carried out automatically using the Iterative Partitioning Log Mining (IPLoM) algorithm.
Evaluation of MCNPX and GATE with HotSpot for Internal Dosimetry (Simulation Study)

Objective: Different categories are available for estimating of radiation dose. Radioisotopes with the same energy and almost the same physical properties have similar effects. Monte Carlo technique is a computerized method based on mathematical simulation of physical processes. The main purpose of this study is to show that beta particles are not able to penetrate deep into the water. also difference between GATE and MCNP code is not significant. Methods: In this study, a digital form of the cylinder mathematical phantom was constructed and used with GATE and MCNPX to calculate the phantom dose. The voxel-based anthropomorphic Zubal phantom was used to model a typical adult male. The equivalent effective dose derived for the electrons of Er-169, P-32, and Y-90 with GATE and MCNPX. The results were compared to the HOTSPOT data. Results: The GATE and MCNPX difference was negligible. However, difference at this level is acceptable and we can conclude that GATE produces almost similar results as MCNPX. In this study, we tried to set the physical framework, calculate the penetration depth via dosimetry, using the results of the two simulator codes. In the first part, we applied the results of the HotSpot dosimetry software for validation. Conclusion: The GATE and MCNPX difference was negligible.
A Universal Grid-Connected Fuel-Cell Inverter for Residential Application

This paper describes a universal fuel-cell-based grid-connected inverter design with digital-signal-processor-based digital control. The inverter has a direct power conversion mechanism with a high-frequency zero-voltage-switched dc/ac primary-side converter followed by a pair of ac/ac cycloconverters that operates either in parallel or in series to simultaneously address the issues of universal output and high efficiency. The critical design issues focus on the impact and optimization of transformer leakage inductance with regard to effectiveness of zero voltage switching of a primary-side converter, duty-cycle loss, resonance, and voltage spike that has effect on the breakdown voltage rating of the cycloconverter devices. An additional concept of dynamic transformer tapping has been explored to address the impact of varying input voltage on secondary-side voltage spike and inverter efficiency. Finally, detailed grid-parallel and grid-connected results are presented that demonstrate satisfactory inverter performances.
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data

The generation of motion, and specifically locomotion, encompasses widely accepted techniques like blending or scattered data interpolation. To ensure a real-time simulation, each frame of the resulting animation is computed only when it has to be displayed, even if user parameters have not been modified. Thus, existing methods cannot reduce their computation cost in case of complex animation framework. Additionally, as the motion is computed frame by frame, no motion prediction can be performed. In this paper, we propose an on-line reactive method that generates the whole new locomotion sequence only at each user parameters update. Using experimental data of locomotion capture in a coherent framework, the resulting engine is able to animate on the fly humanlike characters of any size and proportions, through the control of high-level parameters. Moreover, we improve existing techniques for a better and quantitative extrapolation of physical parameters of different locomotion types.
Effects of problem solving after worked example study on secondary school children’s monitoring accuracy

Abstract Monitoring accuracy, measured by judgements of learning (JOLs), has generally been found to be low to moderate, with students often displaying overconfidence, and JOLs of problem solving are no exception. Recently, primary school children’s overconfidence was shown to diminish when they practised problem solving after studying worked examples. The current study aimed to extend this research by investigating whether practising problem solving after worked example study would also improve JOL accuracy in secondary education. Adolescents of 14–15 years old (N = 143) were randomly assigned to one of five conditions that differed in timing of JOLs, whether practice problems were provided, and timing of the practice problems provided: (1) worked examples – JOL, (2) worked examples – delay – JOL, (3) worked examples – practice problems – JOL, (4) worked examples – practice problems – delay – JOL or (5) worked examples – delay – practice problems – JOLs. Results showed that practice problems improved absolute accuracy of JOLs as well as regulation accuracy. No differences in final test performance were found.
Urban monitoring and change detection of central Tokyo using TerraSAR-X images

Monitoring urban growth and change is an important issue for urban planning and disaster management. In this study, two different approaches are conducted to monitor the urban areas. One is that a TerraSAR-X image is compared with an ALOS/PALSAR image to investigate the characteristics of urban land cover in different radar wavelengths. Also, a very high resolution optical QuickBird image is used as the ground truth. The other is that two temporal TerraSAR-X images are used to detect urban changes. Difference and correlation coefficient between two images are calculated with a sliding window. A new factor that composites difference and correlation coefficient is proposed to detect the changed areas. An aerial photograph is introduced to verify the accuracy of detection results.
A Service Oriented Framework for Efficient Resource Orchestration in Future Optical Networks

The future Internet evolution is driven by applications that require simultaneous real-time access to multiple heterogeneous IT (e.g. computing and storage) resources interconnected by high-speed optical networks. Service oriented architectural frameworks have been considered to improve service delivery in this environment by organising distributed heterogeneous resources owned by multiple providers. In this paper, we discuss a service oriented architectural framework tailored to the needs of future Internet applications. The framework introduces the capability to orchestrate the provisioning of distributed heterogeneous resources owned by multiple providers. Central to the framework is a novel resource orchestration model. This model focuses on the optimized selection of heterogeneous IT and network resources owned by different Infrastructure Providers (InPs). The proposed model aims to achieve a global optimum from both the end-users’ and InPs’ point of view across different administrative domains. An integer linear program (ILP) is formulated to obtain optimal results that maximizes the number of accepted requests while minimising resource usage. It is compared against a co-scheduling ILP model, whose objective is to maximise the number of accepted requests only. We then propose a heuristic solution for scalability. Its performance is compared against (i) our proposed ILP (ii) a co-scheduling heuristic that aim to maximise the number of accepted requests only and (iii) an algorithm that does not take into account cross-domain optimisations. Finally, two online algorithms for operational scenarios is proposed which outperforms three benchmark algorithms.
A polynomial time algorithm for computing all minimal decompositions of a polynomial

The composition of two polynomials g(h) = goh is a polynomial. For a given polynomial f we are interested in finding a functional decomposition f = goh. In this paper an algorithm is described, which computes all minimal decompositions in polynomial time. In contrast to many previous decomposition algorithms this algorithm works without restrictions on the degree of the polynomial and the characteristic of the ground field. The algorithm can be iteratively applied to compute all decompositions. It is based on ideas of Landau & Miller (1985) and Zippel (1991). Additionally, an upper bound on the number of minimal decompositions is given.
Distributed under Creative Commons Cc-by 4.0 Artificial Neural Network Cascade Identifies Multi-p450 Inhibitors in Natural Compounds

Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450) enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC) model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS). Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were obtained for 8,148 compounds from the Cytochrome P450 In-hibitors Database (CPID). The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman's ρ = 0.684, p < 0.0001). Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Tenfold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure.
On the solution of the modified Ginzburg–Landau type equation for a one-dimensional superconductor in the presence of a normal layer

We perform an analytical and numerical study of the crossover from the Josephson effect to that of bulk superconducting flow for a one-dimensional superconductor containing a sandwich layer of normal material. A generalized Ginzburg–Landau (GL) model, proposed in Chapman & Gunzburger [1] is used in modelling the whole structure. When the thickness of the normal layer is very small, the introduction of three effective $\delta$-function potentials of specified strength leads to an exact analytical solution of the modified stationary GL equation. The resulting current density-phase offset relation is analyzed numerically. We show that the critical Josephson current density $j_c$ corresponds to a bifurcation of the solutions of the nonlinear boundary value problem for the modified GL-equation. The influence of the second term in the Fourier-decomposition of the supercurrent density-phase relation is also investigated. We derive also a simple analytical formula for the critical Josephson current.
Scalable AOI-Cast for Peer-to-Peer Networked Virtual Environments

Networked virtual environments (NVEs) are computer-generated virtual worlds where users interact by exchanging messages via network connections. Each NVE user often pays attention to only a limited visibility sphere called area of interest (AOI) where interactions occur. The dissemination of messages to other users within the AOI (i.e., the AOI neighbors) thus is a fundamental NVE operation referred to as AOI-cast. Existing studies on NVE scalability have focused on system scalability, or the ability for the system to handle a growing number of total users, by using multicast or peer-to-peer (P2P) architectures. However, another overlooked, yet important form of scalability relates to the handling of a growing number of users within the AOI (or AOI scalability). In this paper, we propose two AOI-cast schemes, called VoroCast and FiboCast, to improve the AOI scalability of P2P-based NVEs. VoroCast constructs a spanning tree across all AOI neighbors based on Voronoi diagrams, while FiboCast dynamically adjusts the messaging range by a Fibonacci sequence, so that AOI neighbors would receive updates at frequencies based on their hop counts from the message originator. Simulations show that the two schemes provide better AOI scalability than existing approaches.
Single-Channel Multi-Speaker Separation Using Deep Clustering

Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.
Functional Minimum Storage Regenerating Codes For Multi-Cloud Failure

This paper reduces the network repair traffic and recovery from multiple node failures in a cloud storage environment. Suppose if a cloud suffers from permanent failure and loses all its data by more than one node, then we need to regenerate the lost data with the help of the other neighboring clouds. We design a proxy based storage system for fault tolerant multiple cloud storage called NC cloud, which achieves reduction in repair traffic for a permanent multi cloud failure. Functional minimum storage regenerating [FMSR] codes is used for regenerating data in multicloud failure. we also prove that FMSR codes reduces repair traffic and incur less monitory cost during data transfer than Erasure [RAID-6] codes and EMSR [exact minimum storage regenerating] codes.
Labour Supply, Unemployment and Income Taxation: An Empirical Application for Finnish Females

Tyon tarjontafunktion estimointi on useimmissa tapauksissa perustunut olettamukseen, etta yksilot voivat valita haluamansa tyotuntimaaran. Toisen vahvan olettamuksen mukaan yksilot, jotka eivat ole tyoskennelleet, eivat halua tyoskennella. Molemmat oletukset ovat kumottavissa tarkastelemalla tarkemmin mikrotason aineistoa. Tassa tutkimuksessa sovelletaan Blundellin, Hamin ja Meghirin kehittamaa empiirista mallia, jossa otetaan tyon tarjontafunktiota estimoitaessa huomioon tyottomat ja tyota etsivat yksilot. Tyossa yleistetaan ym. mallia ottamalla huomioon myos ei-lineaarinen tuloverojarjestelma. Aineistona kaytetaan tyovoimatutkimuksen vuosihaastatteluja vuosilta 1987, 1989, 1991 ja 1993. Saadut estimointitulokset indikoivat, etta aikaisemmissa tutkimuksissa saadut palkka- ja tulojoustojen arvot voivat olla ylospain harhaisia kaytetyn ekonometrisen lahestymistavan vuoksi.
A New Video Quality Predictor based on Decoder Parameter Extraction

In the mobile communication area there is a demand for reference free perceptual quality measurements in video applications. In addition low complexity measurements are required. This paper proposes a method for prediction of a number of well known quality metrics, where the inputs to the predictors are readily available parameters at the decoder side of the communication channel. After an investigation of the dependencies between these parameters and between each parameter and the quality metrics, a set of parameters is chosen for the predictor. This predictor shows good results, especially for the PSNR
Iterative Coding for Network Coding

We consider communication over a noisy network under randomized linear network coding. Possible error mechanisms include node- or link-failures, Byzantine behavior of nodes, or an overestimate of the network min-cut. Building on the work of Kötter and Kschischang, we introduce a systematic oblivious random channel model. Within this model, codewords contain a header (this is the systematic part). The header effectively records the coefficients of the linear encoding functions, thus simplifying the decoding task. Under this constraint, errors are modeled as random low-rank perturbations of the transmitted codeword. We compute the capacity of this channel and we define an error-correction scheme based on random sparse graphs and a low-complexity decoding algorithm. By optimizing over the code degree profile, we show that this construction achieves the channel capacity in complexity which is jointly quadratic in the number of coded information bits and sublogarithmic in the error probability.