Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithm Based on Hilbert Transform and Rule Based Decision Tree Classification of Power Quality Disturbances

This research work presents an algorithm based on the Hilbert Transform and rule based decision tree for detection and classification of the single stage power quality disturbances. The power quality disturbances are generated in MATLAB software using mathematical relations. These signals are decomposed using Hilbert Transform. Features are extracted from output of the Hilbert Transform which is given as input to the rule based decision tree for classification purpose. The investigated single stage power quality disturbance include pure sine wave, voltage sag, voltage swell, momentary interruption (MI), oscillatory transient (OT), impulsive transient (IT), spike and notch. Effectiveness of the proposed approach is shown by calculating efficiency of the proposed algorithm on testing 50 data sets of each PQ disturbance obtained by varying the parameters of the disturbances.
Antibody Binding to Cryptococcus neoformans Impairs Budding by Altering Capsular Mechanical Properties

Abs to microbial capsules are critical for host defense against encapsulated pathogens, but very little is known about the effects of Ab binding on the capsule, apart from producing qualitative capsular reactions (“quellung” effects). A problem in studying Ab–capsule interactions is the lack of experimental methodology, given that capsules are fragile, highly hydrated structures. In this study, we pioneered the use of optical tweezers microscopy to study Ab–capsule interactions. Binding of protective mAbs to the capsule of the fungal pathogen Cryptococcus neoformans impaired yeast budding by trapping newly emerging buds inside the parental capsule. This effect is due to profound mAb-mediated changes in capsular mechanical properties, demonstrated by a concentration-dependent increase in capsule stiffness. This increase involved mAb-mediated cross-linking of capsular polysaccharide molecules. These results provide new insights into Ab-mediated immunity, while suggesting a new nonclassical mechanism of Ab function, which may apply to other encapsulated pathogens. Our findings add to the growing body of evidence that Abs have direct antimicrobial functions independent of other components of the immune system.
Low Cost Hand Gesture Recognition System Design and Implementation

The design and development of a low cost hand gesture recognition computer interface, using a standard laptop webcam is presented. The purpose of the system is to recognise both static and dynamic hand gestures from a user in real-time and perform basic macro instructions on the Windows operating system. The calibration and gesture recognition processes are discussed. The system is able to correctly classify 19 static hand gestures and recognise 6 dynamic hand gestures with greater than 95% accuracy. However, the system has a varying latency of between 50–500 milliseconds due to inefficient interfacing with the operating system. It was concluded that an optimised operating system interface would improve system performance and user experience dramatically.
SVM Tuned NARX Method for Wind speed & power Prediction in Electricity Generation

Due to continuous depleting of conventional energy reserves as well as global warming issues has diverted world attention towards non conventional energy sources. Out of different non conventional energy sources wind can be consider the cleanest source with minimum possible pollution or harmful emissions and has the potential to decrease the relying on conventional energy sources. Wind energy can play a major role to meet our energy demands, however, it faces various problems such as intermittent nature, instability in frequency. To reduce such issues the idea of futuristic wind speed trends and weather conditions are required. This paper suggests a method for wind speed, power forecasting using NARX and Support Vector Machine (SVM). This hybrid technique can select most suitable data segment from available data. These data segments are used for the training and validation of SVM model. The historical data used is from Kolkata region wind energy farm.
Self-Construals and Work/Family Conflict: A Monocultural Analysis in Taiwan

The aim of this research was to explore the impact of independent and interdependent self-construals on relations between work and family demands (workload and family conflict) and work/family conflict (WFC and FWC). Three hundred and ten Taiwanese employees were surveyed at two times (6 weeks apart) using structured questionnaires. For Taiwanese employees, workload was positively related to WFC (r .39), whereas family conflict was positively related to FWC (r .24). Furthermore, independent self-construal exacerbated the positive relation between workload and WFC (the interaction effect accounted for an additional 1.7% of variance in WFC), but buffered the positive relation between family conflict and FWC (the interaction effect accounted for an additional 2.6% of variance in FWC). However, interdependent self-construal did not have a moderating effect on either the workloadWFC or family conflictFWC relation. It is recommended that culture as construed at the individual level needs to be studied in order to understand its intricate effects on individuals’ perceptions of work and family demands and conflict.
INTEGRATING EVOLUTIONARY AND FUNCTIONAL APPROACHES TO INFER ADAPTATION AT SPECIFIC LOCI

Inferences about adaptation at specific loci are often exclusively based on the static analysis of DNA sequence variation. Ideally, population‐genetic evidence for positive selection serves as a stepping‐off point for experimental studies to elucidate the functional significance of the putatively adaptive variation. We argue that inferences about adaptation at specific loci are best achieved by integrating the indirect, retrospective insights provided by population‐genetic analyses with the more direct, mechanistic insights provided by functional experiments. Integrative studies of adaptive genetic variation may sometimes be motivated by experimental insights into molecular function, which then provide the impetus to perform population genetic tests to evaluate whether the functional variation is of adaptive significance. In other cases, studies may be initiated by genome scans of DNA variation to identify candidate loci for recent adaptation. Results of such analyses can then motivate experimental efforts to test whether the identified candidate loci do in fact contribute to functional variation in some fitness‐related phenotype. Functional studies can provide corroborative evidence for positive selection at particular loci, and can potentially reveal specific molecular mechanisms of adaptation.
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding

In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.
The multi-phase gaseous halos of star-forming late-type galaxies II. Statistical analysis of key parameters

Context. In a previous paper (Paper I) we showed that multi-phase gaseous halos of late-type spiral galaxies, detected in the radio continuum, in H α , and in X-rays, are remarkably well correlated regarding their morphology and spatial extent. Aims. In this work we present new results from a statistical analysis to specify and quantify these phenomenological relations in more detail. Methods. This is accomplished by investigating soft X-ray (0.3-2.0 keV) luminosities, FIR, radio continuum (1.4 GHz), H α , B -band, and UV (1550-1650 A) luminosities for a sample of 23 edge-on late-type spiral galaxies. Typical star formation indicators, such as star formation rates (SFRs), are determined and a statistical multi-parameter/frequency correlation analysis is carried out. Results. We find strong linear correlations, covering at least two orders of magnitude, between star formation indicators and integrated (disk+halo) luminosities in all covered wavebands. In addition to the well-established $L_{\rm FIR}/L_{\rm 1.4~GHz}$ relation, we show new and highly significant linear dependencies between integrated soft X-ray luminosities and FIR, radio continuum, H α , B -band, and UV luminosities. Moreover, integrated soft X-ray luminosities correlate well with SFRs and the energy input into the ISM by SNe. The same holds if these quantities are plotted against soft halo X-ray luminosities. Only a weak correlation exists between the dust mass of a galaxy and the corresponding X-ray luminosity. Among soft X-ray luminosities, baryonic, and $\ion{H}{i}$ gas masses, no significant correlations are found. There seems to exist a critical input energy by SNe into the ISM or a SFR threshold for multi-phase halos to appear. It is still not clear whether this threshold is a physical one or represents an instrument-dependent sensitivity limit. Conclusions. These findings strongly support our previous results that multi-phase gaseous galaxy halos in late-type spiral galaxies are created and maintained by outflowing gas produced in star formation processes in the disk plane. They conflict with the concept of halos being mainly due to infalling gas from the intergalactic medium.
GA Based Model for Web Content Mining

Several methods are available for mining frequent patterns in web data, but mostly they suffer from the problem of huge candidate generation and number of database scans. In view of above a genetic based model for mining frequent patterns in web content data. In the proposed genetic operator, crossing over method leads to offspring which must survive the certain fitness test or conditions to become frequent pattern and ancestor for next patterns. In this way the useless individuals or candidates are pruned out thereby reducing the number of candidates for next test. Also this approach requires only one scan of database. Thus this model is able to address the issues of large number of candidate generation and number of database scans.
Digital watermarking for telltale tamper proofing and authentication

In this paper, we consider the problem of digital watermarking to ensure the credibility of multimedia. We specifically address the problem of fragile digital watermarking for the tamper proofing of still images. Applications of our problem include authentication for courtroom evidence, insurance claims, and journalistic photography. We present a novel fragile watermarking approach which embeds a watermark in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Tamper detection is possible in localized spatial and frequency regions. Unlike previously proposed techniques, this novel approach provides information on specific frequencies of the image that have been modified. This allows the user to make application-dependent decisions concerning whether an image, which is JPEG compressed for instance, still has credibility. Analysis is provided to evaluate the performance of the technique to varying system parameters. In addition, we compare the performance of the proposed method to existing fragile watermarking techniques to demonstrate the success and potential of the method for practical multimedia tamper proofing and authentication.
On the Origin of the $C_{\text{oss}}$ -Losses in Soft-Switching GaN-on-Si Power HEMTs

The unprecedented performance potential of gallium nitride-on-silicon (GaN-on-Si) high electron mobility transistors (HEMTs) is seen as the key enabler for the design of power converters featuring extreme power density figures, as demanded in next-generation power electronics applications. However, unexpected loss mechanisms, i.e. dynamic <inline-formula> <tex-math notation="LaTeX">$ {R}_{\text {ds,on}}$ </tex-math></inline-formula> phenomena and <inline-formula> <tex-math notation="LaTeX">$ {C}_{\text {oss}}$ </tex-math></inline-formula>-losses, are appearing in currently available GaN transistors and are compromising their operation. In this paper, measurements of <inline-formula> <tex-math notation="LaTeX">$ {C}_{\text {oss}}$ </tex-math></inline-formula>-losses are performed in a dedicated calorimetric measurement setup and, through a systematic approach, the root cause of the loss mechanism is potentially identified. Afterward, with the essential support of a manufacturer of power semiconductors, a novel transistor, featuring an enhanced multilayer III-N buffer, is developed according to the acquired knowledge. A significant reduction in terms of <inline-formula> <tex-math notation="LaTeX">$ {C}_{\text {oss}}$ </tex-math></inline-formula>-losses, i.e. of soft-switching losses, and the absence of dynamic <inline-formula> <tex-math notation="LaTeX">$ {R}_{\text {ds,on}}$ </tex-math></inline-formula> phenomena are verified experimentally on the new device. These achievements enable a significant performance improvement for future soft-switching power converters featuring GaN-on-Si HEMTs.
A comparison between fully connected and deconvolutional layers for road segmentation from satellite imagery

Semantic segmentation using fully convolutional networks has quickly become a popular solution as they provide very accurate per pixel classification. However, the implementation of deconvolutional layers and their mechanics differ greatly to those of patch based segmentation using convolutional neural networks. Both techniques have been used for road segmentation from satellite imagery but never compared. Thus we investigate the difference between fully connected and deconvolutional layers and provide an interpretation as to the correlation and differences between each methodology for road segmentation from satellite imagery.
Self-efficacy, self-esteem and gender as determinants of performance in speaking tasks

The study examines the effects of self efficacy, self esteem and gender on performances in speaking tasks among first year university students. It also analyzes correlations of and gender differences in the variables treated. In this study, 128 participants, who had been interested to participate, were included. To gather data, scales and tests were used. Both descriptive and inferential statistics, namely, t-test, Pearson product moment correlation coefficient, and multiple regression analyses were applied to analyze the data. The results reveal that male students surpass female students in all the variables treated. It also is evident that all the variables are interrelated positively. Moreover, the output of the first multiple regression analysis show that 55% of the variances in speaking performance is accounted for by the group effects of the independent variables entered in the regression equation. Among these, speaking self-esteem and speaking efficacy were found to have great effects on performance in speaking tasks. Such findings seem to make clearer that attention needs to be paid to the determinants of speaking performance. Key words: Self efficacy, global self esteem, academic self esteem, speaking self esteem, speaking performance.
Fuzzy Logic and Sliding Mode Control for a Grid-Connected Current Source Inverter Photovoltaic System

In this paper, a non-linear control technique and an artificial intelligent controller are presented and applied to a single-phase, grid-connected PV system based on the current source inverter (CSI) topology. These techniques are investigated in order to assure a good performance of CSI in term of total harmonic distortion (THD). The design of the non-linear controller is based on the Sliding mode control (SMC) approach to establish the inverter control laws. An analysis based on Lyapunov stability approach is developed to guarantee the global asymptotic stability of the system. The intelligent controller is based on the fuzzy logic controller (FLC) to generate a reference signal. A conventional regulator PI is used in input and output of the FLC to improve the performance. Both control techniques are used to control the injected current and synchronize it with the network. This paper performs a comparative performance evaluation of both approaches. The analytical expectations of the systems are verified using MATLAB/Simulink software environment.
Matrix Factorization Recommendation Algorithm Based on User Characteristics

Matrix Factorization is a popular and successful method. It is already a common model method for collaborative filtering in recommendation systems. As most of the scoring matrix is sparse and the dimensions are increasing rapidly, the prediction accuracy and calculation time of the current matrix decomposition are limited. In this paper, a matrix decomposition model based on user characteristics is proposed, which can effectively improve the accuracy of predictive scoring and reduce the number of iterations. By testing the actual data and comparing it with the existing recommendation algorithm, the experimental results show that the method proposed in this paper can predict user's score well.
Artificial Visual Cortex and Random Search for Object Categorization

Brain modeling is a research area within computer science devoted to the study of complex and dynamic computing algorithms that imitate brain function regarding the information processing properties of the structures that make up the nervous system. The computational and mathematical structures are composed of interacting modules, whose coordination aims to enhance their problem-solving capabilities. The computational models of the visual cortex use non-trivial interactions between a large number of components. In this paper, we propose a hierarchical structure that mimics the information flow and transformations that take place in the human brain. This paper describes a virtual system composed of an artificial dorsal pathway–or “where” stream–and an artificial ventral pathway–or “what” stream–both are fused to recreate an artificial visual cortex. In previous work, the model was refined through genetic programming to enhance its performance over challenging object recognition tasks. The system finds good solutions during the initial stage of the genetic and evolutionary search. In this paper, the goal is to show that a random search can discover numerous heterogeneous functions that are applied to a hierarchical structure of our virtual brain. Thus, the proposal presents two key ideas: 1) the concept of function composition in combination with a hierarchical structure leads to outstanding object recognition programs, and; 2) multiple random runs of the search process can discover optimal functions. The experimental results provide evidence that high recognition rates could be achieved in well-known object categorization problems; consequently, this paper corroborates the importance of the hierarchical computational structure described in the neuroscience literature.
Implementation of Multipath and Multiple Description Coding in OLSR

In this paper we discussed the application and the implementation of multipath routing and multiple description coding (MDC) extension of OLSR, called MP-OLSR. It is based on the link state algorithm and employs periodic exchange of messages to maintain topology information of the networks. In the mean time, it updates the routing table in an on-demand scheme and forwards the packets in multiple paths which have been determined at the source. If a link failure is detected, the algorithm recovers the route automatically. Concerning the instability of the wireless networks, the multiple description coding is used to improve reliability of the network transmission, and several methods are proposed to allocate the redundancy in different paths. The simulation in NS2 shows that the new protocol can effectively improve the performance of the networks. The implementation of MP-OLSR is also proposed in the end.
A PWM-based sliding mode voltage control of DC-DC boost converter for DC microgrid

DC Microgrids are currently replacing AC Microgrids for distributed generation owing to their inherent benefits like the compatible sources, green environment, high efficiency and solidity. The prime responsibility of successful operations of DC Microgrids is to maintain / regulate DC bus voltage within the adequate bounds amid entire functioning conditions. In this paper, a second order SM control is utilized to generate persistent control inputs conceptualized as duty cycles of the dc-dc boost converter (DDBC). This work is designed to supply both DC and AC loads in remote areas where the utility cannot be accessed. Simulations have been accomplished in Simulink settings and results show effectiveness of designed controller.
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey

Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimization problem could potentially exist. In fact, the process of sequential computation of optimal component weights that maximize the portfolio's expected return subject to a certain risk budget can be reformulated as a discrete-time Markov Decision Process (MDP) and hence as a stochastic optimal control, where the system being controlled is a portfolio consisting of multiple investment components, and the control is its component weights. Consequently, the problem could be solved using model-free Reinforcement Learning (RL) without knowing specific component dynamics. By examining existing methods of both value-based and policy-based model-free RL for the portfolio optimization problem, we identify some of the key unresolved questions and difficulties facing today's portfolio managers of applying model-free RL to their investment portfolios.