Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of power quality by optimized SAPF system

Introduction The proliferation of nonlinear loads such as diode/thyristor rectifiers, nonsinusoidal currents degrade power quality in power transmission/distribution systems. Power Quality (PQ) is an important measure of an electrical power system. The term PQ means to maintain purely sinusoidal current wave form in phase with a purely sinusoidal voltage wave form. The power generated at the generating station is purely sinusoidal in nature. The deteriorating quality of electric power is mainly because of current and voltage harmonics due to wide spread application of static power electronics converters, zero and negative sequence components originated by the use of single phase and unbalanced loads, reactive power, voltage sag, voltage swell, flicker, voltage interruption etc. Different theories have been introduced such as the instantaneous power theory (p-q theory), notch algorithm, synchronous reference method and kalman filter method. The proposed optimized shunt active power filter system is a great tool for the compensation not only of current harmonics produced by distorting loads, but also of reactive power of non-linear loads. The DC-link voltage of SAPF can be adjusted to a great extent so as to provide easy control and high performance Fuzzy theory was first proposed and investigated by Prof. Zadeh in 1965.The mamdani fuzzy inference system was presented to control a steam engine and boiler combination by linguistic rules [4]-[5]. Fuzzy logic is expressed by means of if-then rules with the human language. In the design of a fuzzy logic controller, the mathematical model is not necessary. The ant colony algorithm has been used to find the optimal values and parameters of our fuzzy logic controller . W Ant Colony Algorithm was proposed by Dorigo in the year 1991[3]. After observation people found that between the individual ants there is a material known as the phenomenon for the transmission of information. When ants were in moving, phenomenon was left in the way the Ants passed and ants can feel the concentration of the phenomenon, to lead their actions and tend to choose a higher concentration of pheromone way to move. As a result, the behavior of a group of ants is feedback: the way which the more Ants passed, the greater probability it was chose. The ants find the food by exchange of information between the individual ants. The ACO algorithms are applied to look for globally optimal parameters of fuzzy logic. The best ranges of membership functions, the greatest shapes of membership functions and the best fuzzy inference rules. Moreover, the two different fuzzy logic controllers are compared and then simulations results are offered to show the efficacy of fuzzy logic controller.
Energy Aware Relay GAF algorithm for WSN using Improved Conservative schemes

Wireless sensor systems (WSN) is the system of Sensor Nodes (SNs) in which every hub have detecting, correspondence and calculation office. The fundamental impediment of WSN is that SNs have restricted vitality. So the fundamental focal point of research in WSN is to improve the Network Lifetime by falling vitality utilization. A few areas mindful directing convention has been proposed. Geographic Adaptive Fidelity (GAF) is one of the most famous vitality mindful steering conventions. It moderates vitality by recognizing equality between sensors from a steering point of view and after that killing superfluous sensors, while keeping up the availability of the system. Anyway conventional GAF can't achieve the ideal vitality use. It requires progressively number of jumps to transmit information from source to sink.so that it prompts higher bundle delay. The underlying issue of essential GAF is information can be sent in just flat and vertical. The issues which are being worked in this undertaking are minimization of jump check, parcel deferral and separation secured by the bundle postponement steering utilizing vitality mindful Relay GAF calculation Both the conventions are actualized in MATLAB. Investigation and reproduction results show critical enhancements of the projected work contrasting with customary GAF in the part of absolute jump check, arrange lifetime vitality utilization, all out separation secured by the information bundle before achieving the sink, and parcel delay
Solar Based High Gain Power Converter with Efficient Controller for LED Application

The representation of this paper is a different approach to get efficient high brightness of LED lighting operating with high gain buck boost converter controlled by efficient incremental conductance MPPT controller algorithm of solar PV module. The mathematical modeling associated with process of simulation for solar panel, high gain buck boost topology and efficient MPPT controller is discussed clearly in this paper. Conventional converters are applied and analyzed for the LED lighting system in literature, but these are not preferred due to lack of efficient performance, high harmonic distortion and low power factor. The projected buck-boost structure is modified structure of high gain buck-boost structure. The comparison between projected converter and existing power converter is clearly summarized in this paper. The switching as well as conduction losses are very low due to single switch operating without transformer. The cost of the solar based LED lighting system with high conversion ratio buck-coost converter is very cheap because the number of semiconductor devices and passive components are using low. In this paper 120W and 30W of LED lighting system is planned and executed in MATLAB/SIMULINK. The clear summary of outcomes will prove the system is efficient
Overeducation Among Saudi Graduates in the Labour Market: Incidence and Determinants Across Two Self-Assessment Measures

Education expansion has prompted an extensive body of literature on the issue of overeducation, particularly in developed countries. However, as is the case for many developing countries, little, if any, empirical evidence from Saudi Arabia has emerged on this topic. Using cross-sectional survey data, this study examined the prevalence and possible determinants of overeducation among Saudi graduates in the labour market on the basis of two different self-assessment measures. Results indicated that nearly 50% of Saudi graduates in the study were considered overeducated based on each measure, while about 41% were consistently considered overeducated based on both measures. Using logistic regression models, several individual and job characteristics were deemed as major determinants of the probability of being overeducated across both measures. Furthermore, the two measures largely overlapped and yielded somewhat similar conclusions in terms of both the estimates and determinants of overeducation among graduates. The plausible implications of the results for education and labour market policies are discussed.
Studies on Cadmium Sulfide / Porous Silicon ( CdS / PS ) Nanocomposite Structure

In this work Cadmium Sulphide/Porous Silicon (CdS/PS) nanocomposite structure was successfully synthesized by depositing CdS film over PS substrate at room temperature. CdS was incorporated into the pores of PS and thereby making a protecting layer. The XRD, SEM, FTIR and PL analysis were carried out to characterize the CdS/PS structure. XRD result suggests the formation of CdS hexagonal structure on PS surface. The diffraction peaks pertaining to PS along with those corresponding to CdS were observed. The surface morphology of the prepared CdS/PS layer was studied by SEM analysis. The information about the vibration and rotation of molecular groups and the bonding of inorganic molecules on silicon surfaces are confirmed by transmission FTIR spectra. The PL measurements on CdS/PS structure show emission peaks at around 620 & 660 nm with 2.0 & 1.9 eV band gap respectively. These results show the incorporation of CdS into the pores of PS and capable of utilizing for device applications. © 2016 Elixir All rights reserved. Elixir Thin Film Tech. 91 (2016) 38073-38076 Thin Film Technology Available online at www.elixirpublishers. com (Elixir International Journal) I. Rathinamala et al./ Elixir Thin Film Tech. 91 (2016) 38073-38076 38074 evaporate the solvent; the sample was then annealed in a furnace at 400 o C for 1 hour. Results and Discussions X-ray diffraction Study The crystallographic structure of film was analyzed with X-ray diffractometer (PANalytical XPERTPRO) using CuKα (λ=1.54Å) radiation. Diffraction patterns have been recorded over the 2θ range of 20 o to 80 o at the scan rate of 10 o min -1 . The crystal structure and purity of the prepared samples were identified by using X-ray diffraction analysis. Figure 1 shows the XRD pattern of CdS coated PS sample. Figure 1. XRD pattern for CdS/PS nanocomposite structure The diffraction along (0 0 2) (1 0 1) (1 0 3) (0 0 4) and (1 0 4) planes have been observed for CdS. (0 0 2) plane is the major plane which corresponds to CdS with hexagonal structure. The results are in good agreement with the standard JCPDS File No: 06 – 0314 data. Thambidurai et al reported that (0 0 2) is the major plane for CdS prepared via sol – gel spinning method [13]. XRD pattern shows that the PS structure (Figure 1) exhibited a dominant diffraction peak at 2θ = 69.2° corresponding to the PS (4 0 0) plane (JCPDS File No: 89 – 2955). No other peaks were present which indicates the purity of the samples. The particle size has been calculated along the major plane by using Debye-Scherrer formula. 0.9 λ D = (nm) βCosθ (1) where θthe diffraction angle, β Full Width at Half Maximum, λ -Wavelength of the X-ray. The calculated crystallite size was 51.66 nm for the CdS coated PS sample. From the XRD data the lattice parameters ‘a’ & ‘c’, was evaluated using the relation [14],
Fault Identification by Extreme Pressure in Oil Pipelines Using Artificial Neural Network

Conventional Artificial Neural Network approaches such as Feed-Forward Networks has been used in diverse applications but are not naturally predictive and also require supervised learning. Feed-forward Artificial Neural Network also trained by backpropagation poses the problem of varnishing gradient. Algorithm using Gaussian membership function with a context-decision gate for detection operations has been proposed as an alternative to the traditional Feed Forward Architecture. The AI monitoring System shows promising results in solving many recurrent problems, particularly those requiring long-term storage dependencies - the Vanishing Gradient problem (VGP) and has the ability to use contextual information when mapping between input and output sequences. The Oil monitoring system employs dynamic data flow modeling to simulate the behavior of probably militant behaviors. The contextual information (context data) includes such context as Pressure from the lab scale experimental setup of oil pipeline system. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. Dynamic simulations were performed using a real-time data obtained from the Radial Bias Kernel Network. The data is tested using AI system in MATLAB-SIMULINK environment to verify the performance of the proposed system. The results were promising indicating the real state of fault identification in oil pipeline system caused by extreme pressure during transportation.
SDN-Ddos - Detecting Ddos Attack in SDN using Sflow Mitigation Technology

The evolution of Software Defined Network (SDN) and its guarantees in systems administration innovation has gotten each partner energized. Notwithstanding, it is trusted that each innovative advancement accompanies its own difficulties of which the most conspicuous for this situation is network security. This paper displays a continuous recognition of the disseminated forswearing of administration (DDoS) assaults on the SDN and a control strategy dependent on the sFlow moderation innovation. sFlow investigations tests of bundles gather information from the system traffic and creates dealing with guidelines to be sent to the central administrator in the event of an assault recognition method. The execution was finished by copying the system in Minimum which keeps running on a Virtual Machine (VM) and it was demonstrated that the proposed technique successfully distinguishes and mitigates DDoS assaults.
Retrieval of Context and Content based Images using Enhanced Crow Search Optimization

In late decades, Content-Based Image Retrieval (CBIR) has been one on the most distinctive research zones in the field of Computer applications. The critical goal of this examination is to improve the recovering presentation of CBIR framework by fusing advancement strategies to foresee suitable centroid in Fuzzy C-Means (FCM). The expectation of consolidating streamlining method to anticipate FCM centroids positively decrease intricacy and computational time. The outcomes clear that consolidation of ECSO with FCM uncovers better outcomes over challenge procedures when compared with existing procedures like PWO, SSO and CSO.
O riginal A rticle Clinico-pathological Analysis of 68 cases of Ameloblastoma-A Retrospective Study

NTRODUCTION Ameloblastoma is a rare, benign or cancerous tumor of odontogenic epithelium (ameloblasts, or outside portion, of the teeth during development) much more commonly appearing in the lower jaw than the upper jaw. It was recognized in 1827 by Cusack. This type of odontogenic neoplasm was designated as an adamantinoma in 1885 by the French physician Louis-Charles Malassez. It was finally renamed to the modern name ameloblastoma in 1930 by Ivey and Churchill. 1
Towards Solving Inefﬁciency of Self-supervised Representation Learning

Self-supervised learning (especially contrastive learning) has attracted great interest due to its tremendous potentials in learning discriminative representations in an un-supervised manner. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efﬁciency, e.g., taking about ten times more training epochs than supervised learning for comparable recognition accuracy. In this paper, we discover two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efﬁciency. Under-clustering means that the model cannot efﬁciently learn to discover the dissimilarity between inter-class samples when the negative sample pairs for contrastive learning are insufﬁcient to differentiate all the actual object categories. Over-clustering implies that the model cannot efﬁciently learn the feature representation from excessive negative sample pairs, which enforces the model to over-cluster samples of the same actual categories into different clusters. To simultaneously overcome these two problems, we propose a novel self-supervised learning framework using a median triplet loss. Precisely, we employ a triplet loss tending to maximize the relative distance between the positive pair and negative pairs to address the under-clustering problem; and we construct the negative pair by selecting the negative sample of a median similarity score from all negative samples to avoid the over-clustering problem, guaranteed by the Bernoulli Distribution model. We extensively evaluate our proposed framework in several large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The re-sults
Mixing dynamics and group imbalance lead to degree inequality in face-to-face interaction

Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-toface situations can enable the emergence of degree inequality. We present a mechanism that integrates group mixing dynamics with individual preferences, which reproduces group degree inequality found in six empirical data sets of face-to-face interactions. We uncover the impact of group-size imbalance on degree inequality, revealing a critical minority group size that changes social gatherings qualitatively. If the minority group is larger than this ‘critical mass’ size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens degree inequality. Finally, we expose the underrepresentation of social groups in degree rankings due to mixing dynamics and propose a way to reduce such biases.
Machine learning for risk assessment in gender-based crime

Gender-based crime is one of the most concerning scourges of contemporary society. Governments worldwide have invested lots of economic and human resources to radically eliminate this threat. Despite these efforts, providing accurate predictions of the risk that a victim of gender violence has of being attacked again is still a very hard open problem. The development of new methods for issuing accurate, fair and quick predictions would allow police forces to select the most appropriate measures to prevent recidivism. In this work, we propose to apply Machine Learning (ML) techniques to create models that accurately predict the recidivism risk of a gender-violence offender. The relevance of the contribution of this work is threefold: (i) the proposed ML method outperforms the preexisting risk assessment algorithm based on classical statistical techniques, (ii) the study has been conducted through an official specific-purpose database with more than 40,000 reports of gender violence, and (iii) two new quality measures are proposed for assessing the effective police protection that a model supplies and the overload in the invested resources that it generates. Additionally, we propose a hybrid model that combines the statistical prediction methods with the ML method, permitting authorities to implement a smooth transition from the preexisting model to the ML-based model. This hybrid nature enables a decision-making process to optimally balance between the efficiency of the police system and aggressiveness of the protection measures taken.
A Drift Homotopy Implicit Particle Filter Method for Nonlinear Filtering problems

In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.
Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.
Voltage Sensors and Calcium Channels of Excitation-Contraction Coupling

ABSTRACT Three mechanisms are proposed for the transduction from action potential to Ca2+ release from the sarcoplasmic reticulum in skeletal muscle: Chemical mediation, a mechanical connection between transverse tubular membrane and sacroplasmic reticulum, and Ca2+-induced release of Ca2+. New biochemical, biophysical, and structural data favor a mechanical connection and add the possibility that Ca2+-induced Ca2+-release is working in parallel.
Fourier Transform Approximation as an Auxiliary Task for Image Classification

Image reconstruction is likely the most predominant auxiliary task for image classification, but we would like to think twice about this convention. In this paper, we investigated “approximating the Fourier Transform of the input image” as a potential alternative, in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction. We experimented with five popular classification architectures on the CIFAR-10 dataset, and the empirical results indicated that our proposed auxiliary task generally improves the classification accuracy. More notably, the results showed that in certain cases our proposed auxiliary task may enhance the classifiers’ resistance to adversarial attacks generated using the fast gradient sign method.
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models

Tensor network methods have been a key ingredient of advances in condensed matter physics and have recently sparked interest in the machine learning community for their ability to compactly represent very high-dimensional objects. Tensor network methods can for example be used to efficiently learn linear models in exponentially large feature spaces [Stoudenmire and Schwab, 2016]. In this work, we derive upper and lower bounds on the VC dimension and pseudo-dimension of a large class of tensor network models for classification, regression and completion. Our upper bounds hold for linear models parameterized by arbitrary tensor network structures, and we derive lower bounds for common tensor decomposition models~(CP, Tensor Train, Tensor Ring and Tucker) showing the tightness of our general upper bound. These results are used to derive a generalization bound which can be applied to classification with low rank matrices as well as linear classifiers based on any of the commonly used tensor decomposition models. As a corollary of our results, we obtain a bound on the VC dimension of the matrix product state classifier introduced in [Stoudenmire and Schwab, 2016] as a function of the so-called bond dimension~(i.e. tensor train rank), which answers an open problem listed by Cirac, Garre-Rubio and P\'erez-Garc\'ia in [Cirac et al., 2019].