Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of protein structures by using fuzzy KNN classifier and protein voxel-based descriptor

Protein classification is among the main themes in bioinformatics, for the reason that it helps understand the protein molecules. By classifying the protein structures, the evolutionary relations between them can be discovered. The knowledge for protein structures and the functions that they might have could be used to regulate the processes in organisms, which is made by developing medications for different diseases. In the literature, plethora of methods for protein classification are offered, including manual, automatic or semiautomatic methods. The manual methods are considered as precise, but their main problem is that they are time consuming, hence by using them a large number of protein structures stay uncategorized. Therefore, the researchers intensively work on developing methods that would afford classification of protein structures in automatic way with acceptable precision. In this paper, we propose an approach for classifying protein structures. Our protein voxel-based descriptor is used to describe the features of protein structures. For classification of unclassified protein structures, we use a k nearest neighbors classifier based on fuzzy logic. For evaluation, we use knowledge for the classification of protein structures in the SCOP database. We provide some results from the evaluation of our approach. The results show that the proposed approach provide accurate classification of protein structures with reasonable speed.
Functions of Car GPS Information System

GPS is called Global Positioning System for short, its aim is fixed position and supervise the things on the ground and in the air accurately of the globe. The essay introduced the principle of GPS and the development direction. The system has keep a large developing space on fininshing the models of GPS,GPRS,combining the soft with the hardware of computer system and mak-ing the car GPS funtions happen. It can be quadratic developing. It also introduces the use of the GPS.
On Simultaneously and Approximately Simultaneously Diagonalizable $m$-tuples of Matrices

In this paper, the problem of simultaneous diagonalization of $m$-tuples of $n$-order square complex matrices, is analyzed and some necessary and some necessary and sufficient conditions for this property to be fulfilled are presented. This study has an interest in its applications in different areas as for example in engineering and physical sciences. For example, they appear founding when we must give the instanton solution of Yang-Mills field presented in an octonion form, and it can be represented by triples of traceless matrices. In the case where the $m$-tuple does not simultaneously diagonalize, the possibility of to find near of the given $m$-tuple, an m-tuple that diagonalize simultaneously is studied.
Dual Sentiment Analysis 3: Considering Three Sides of One Review

In statistical machine learning approaches, Bag-of-words (BOW) is the most popular way to model text in sentiment analysis. In handling the polarity shift problem, the performance of BOW sometimes remains limited due to some fundamental deﬁciencies. To address this problem for sentiment classiﬁcation, we propose a model called dual sentiment analysis (DSA). For each training and test review, we ﬁrst propose a novel data expansion technique by creating a sentiment-reversed review. Due to this, we propose a dual training and dual prediction algorithm to use original and reversed reviews in pairs for learning sentiment classiﬁer, and to classify the test reviews by considering three sides of one review respectively. We extend the DSA framework from polarity classiﬁcation (positive-negative) to 3-class classiﬁcation (positive-negative-neutral), by also considering the neutral reviews. Finally, we develop a corpus-based method to construct a data dictionary, which removes DSA3’s dependency on an external data dictionary for review reversion. We conduct a wide range of experiments including tasks, datasets, data dictionaries, classiﬁcation algorithms, and features. The results demonstrate the effectiveness of DSA3 in supervised sentiment classiﬁcation.
Device vs Edge Computing for Mobile Services: Delay-Aware Decision Making to Minimize Power Consumption

A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60 percent for large bit stream size compared to local computing baseline.
ÇÜ-eDS: Çukurova University E-Learning Support System

Ongoing developments in the field of information and communication technologies, as in many other fields, have brought new opportunities to education and instruction. Among these opportunities, e-learning technologies are of primary concern. On the other hand, open source software has brought in both an economic and parametric dimension to e-learning systems. However, despite all of these positive developments, the use and popularisation of these technologies by educational institutions in Turkey is not as widespread as it should be. Although there may be several reasons for this, one of the major causes is the problems encountered during developing and publishing e-learning courses. In this study, an e-learning support system (ÇÜ-eDS–Çukurova University e-Learning Support System) capable of supporting the entire procedure from preparation to publication of economic and useful e-learning courses has been developed. The system consists of four main modules, namely a learning objects portal, a content development editor, a learning management system, and a single sign-on system. The system was actively used by the second-year and third-year students for one semester and three semesters respectively. Then, in order to test the system usability, a system usability scale was applied to 30 second-year and 30 thirdyear undergraduate students of Çukurova University. According to the system usability score (65.17 out of 100), it can be said that the system is moderately usable, but requires some improvements.
Pricing an American Option by Approximating its Early Exercise Boundary as a Piece-Wise Exponential Function

This paper proposes to price an American option by approximating its early exercise boundary as a piece-wise exponential function. Closed-form formulas are obtained in terms of the bases and exponents of the piece-wise exponential function. It is demonstrated that a three-point extrapolation scheme has the accuracy of an 800-time-step binomial tree, but about 130 times faster. An intuitive argument is given to indicate why this seemingly crude approximation works so well. Our method is very simple and easy to implement. Comparisons with other leading competing methods are also included.
Analyzing Meaning in Big Data: Performing a Map Analysis Using Grammatical Parsing and Topic Modeling

Social scientists have recently started discussing the utilization of text-mining tools as being fruitful for scaling inductively grounded close reading. We aim to progress in this direction and provide a contemporary contribution to the literature. By focusing on map analysis, we demonstrate the potential of text-mining tools for text analysis that approaches inductive but still formal in-depth analysis. We propose that a combination of text-mining tools addressing different layers of meaning facilitates a closer analysis of the dynamics of manifest and latent meanings than is currently acknowledged. To illustrate our approach, we combine grammatical parsing and topic modeling to operationalize communication structures within sentences and the semantic surroundings of these communication structures. We use a reliable and downloadable software application to analyze the dynamic interlacement of two layers of meaning over time. We do so by analyzing 15,371 newspaper articles on corporate responsibility published in the United States from 1950 to 2013.
A Neural Network Control System for the Segway Robotic Mobility Platform

FORREST JR., CHARLES EDWARD. A Neural Network Control System for the Segway Robotic Mobility Platform. (Under the direction of Edward Grant). An Artificial Neural Network (ANN) is a network of simple processing elements that emulate neurons in the brain. The behavior of such a network is characterized by the synaptic connections between the input data and the processing elements. Here, an ANN was generated and used as part of a control system for a Segway Robotic Mobility Platform (RMP) being trained in obstacle avoidance behavior. The single sensor input to the control system is a SICK laser, a range-finding sensor; the control output is Pulse Width Modulation commands to the RMP’s motors. The Segway RMP, neural network maps input sensor data directly to appropriate motor output commands for obstacle avoidance. Obstacle avoidance training was accomplished in a simulated LabView world using supervised reinforcement learning and practices from evolutionary robotics. Synaptic connection strengths were stored in an array called the artificial “chromosome”. The chromosome was randomly modified, and the response of the network was compared to a pre-defined desired output. The goal of the genetic algorithm training was to minimize the error between the desired and actual outputs, yet to ensure that local minima were avoided. Once the ANN was trained in simulation, it was transferred to an actual RMP for obstacle avoidance testing in the real world . The benefits of training ANN’s for obstacle avoidance tasks in simulation are demonstrated here. In the simulated world, training and testing can be done in virtual environments: offering greater control over environment complexity, testing the robustness of the controllers generated, and filtering the training data set. All of the foregoing reduces the cost of training and lead to the development of an optimized ANN controller for RMP obstacle avoidance. The ANN provided input pattern generalization for smooth motion, improved computational speeds, and added to the body of knowledge for RMP controller development. A Neural Network Control System for the Segway Robotic Mobility Platform By Charles E. Forrest Jr. A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree Master of Science Computer Science Raleigh July 14, 2006 APPROVED BY: ________________________________ _________________________________ David J. Thuente John Muth Co-chair of Advisory Committee Co-chair of Advisory Committee ____________________________________ Edward Grant Co-chair of Advisory Committee
Handbook of item response theory: volume two, statistical tools

There are three volumes in the series on this topic: item response theory (IRT). What is it? Based on scores in a battery of questions in Psychometrics, IRT is a paradigm for designing, analyzing, and interpreting the individual’s abilities. The volume one describes models that are used in IRT. This book is a collection of articles about IRT versus statistical tools. This is volume two covers mainly probability distributions, models with intentional and nuisance parameters, information criteria, and model identification issues, among others. Volume three provides the applications of IRT. The IRT ideas and methods are highly utilized in economics, sociology, education, marketing research, political science, criminology, demography, and psychology. To read through and comprehend the contents of this volume two, one needs calculus and mathematical statistics background. There are 20 well written chapters covering a range of topics: logit, probit and other response functions, discrete distributions, multivariate normal distribution, exponential family of distributions, loglinear models for observed score distributions, distribution of sums of nonidentical random variables, information theory in testing in Section I,model identification issues, dealing with nuisance and incidental parameters, missing responses in Section II, and finally maximum likelihood estimation, expectation maximization algorithm, Bayesian estimation, variational approximation methods, Markov Chain Monte Carlo methods, optimal design theory in Section III, and frequentist model fit tests, information criteria, Bayesian model comparisons, residual analyses in Section IV of this book. These chapters were written by experts from all continents. The references in the chapters are exhaustive and up-to-date. I enjoyed reading this book. I recommend this book highly to economists, psychometricians, sociologists, marketing researchers, statistics and computing professionals.
Biased random walks in biology.

Random walks are used to describe the trajectories of many motile animals and microorganisms. They are a useful tool for both qualitative and quantitative descriptions of the behaviour of such creatures. Simple diffusive random walk models, or position jump processes, are unrealistic as they allow for effectively infinite propagation and do not take into account correlation between steps. Othmer et al. (1988) use a generalised transport equation to model biased and correlated velocity jump processes where the speed of movement is finite. In two dimensions an equation for the underlying spatial distribution of the velocity jump process model cannot be found, so Othmer et al. use a method of calculating moments to derive and solve differential equations for the statistics of interest. We extend the velocity jump process and method of moments used by Othmer et al. to include reorientation models where the mean turning angle is dependent on the previous direction of movement, as observed by Hill & Häder (1997) in experiments on algae. Closure assumptions are made in order to derive and solve a system of differential equations for the higher order moments and statistics of the underlying spatial distribution. Numerical simulations are then used to compare the asymptotic solutions with simulated data, and the fit is good for biologically realistic parameter values. Numerical simulations of velocity jump processes are also used to investigate the method used by Hill & Häder to calculate the reorientation parameters from the angular statistics of a random walk, and also to investigate the effect of spatially dependent parameters or a changing preferred direction on the spatial statistics. We look at the ratio between the root of the mean squared displacement and the mean dispersal distance in both unbiased and biased random walks and demonstrate how this can give us more information about the spatial distribution. We give an application of the velocity jump process model to the movement and recruitment of reef fish larvae. The variability in the movement is found to be important if there is a low survival probability, while in simple reef environments the survival probability appears to be highly sensitive to the reorientation parameters and corresponding swimming behaviour.
A Novel Hyperspectral Anomaly Detection Algorithm for Real-Time Applications With Push-Broom Sensors

Most practical hyperspectral anomaly detection (AD) applications require real-time processing for detecting complex targets from their background. This is especially critical in defense and surveillance domains, but also in many other scenarios, in which a rapid response is mandatory to save human lives. Dealing with such a high dimensionality of data requires the conception of new algorithms to ease the demanding computing performance. Push-broom scanning represents the mainstream in hyperspectral imaging, introducing added complexity to the equation as there is no information of future pixels. In this paper, a novel technique named line-by-line anomaly detection (LbL-AD) algorithm, is presented as a way of performing real-time processing with a push-broom sensor. The sensor has been mounted on an unmanned aerial vehicle, and the acquired images, together with others from the scientific literature and synthetic ones, have been used to extensively validate the proposed algorithm in terms of accuracy, based on different metrics and processing time. Comparisons with state-of-the-art algorithms were accomplished in order to evaluate the goodness of the LbL-AD, giving as a result an outstanding performance.
Sensorless Landing Control Strategy of Bistable Permanent Magnet Actuator

Bistable permanent magnet actuator (BPMA) has been widely used in on/off application. However, the response time and landing performance of on/off actuator are contradictory. Through analyzing the landing control goal of on/off actuator and sensorless technology features of the BPMA, a new sensorless landing control strategy was proposed using armature position detection instead of real-time displacement estimation or displacement sensor. The feasibility and effectiveness of the method are verified by experiment and simulation, the operation quality of BPMA is effectively improved. The results show that the first bounce decreases by 85.7% and the second bounce is avoided when the iteration converged, compared to the condition without landing control, while the response time increased by 6.8%. Meanwhile, the landing velocity decreased more than 50% and sound intensity at different frequencies reduced more than 4dB with sensorless landing control strategy. Although in this paper the control strategy was employed to a BPMA, its structure can be used to analyze and design a wide range of on/off actuator.
Understanding the effect of vitamin B6 and PEG functionalization on improving the performance of carbon nanotubes in temozolomide anticancer drug transportation

Density functional theory (DFT) and molecular dynamics (MD) simulation are employed to explore the efficiency of functionalized carbon nanotubes with polyethylene glycol (CNT-PEG) in temozolomide (TMZ) delivery. The obtained results from DFT calculations reveal that the interaction of the TMZ molecule with a nanocarrier has a physical nature. It is found that π − π stacking interaction between the drug and carbon nanotube rings has a principal role in the stability of the TMZ on the carrier surface. Moreover, the hydrogen bond formed between the drug molecule and PEG group leads to greater stability in the resulting complexes. The electronic structure analysis of TMZ shows that the structure of the drug molecule does not change considerably during the adsorption process. Also, it is observed that during the complexation, the charge is transferred from the carrier to drug. Moreover, MD simulation is performed to study the dynamics and diffusion of the drug molecule. The obtained results indicated that there are strong interactions between TMZ molecules and CNT-PEG in a biological environment. These interactions become stronger with the presence of vitamin B6. Therefore, it seems vitamin B6 can be used to improve the adsorption behavior of the TMZ drug molecule.
Quantized Control of Networked Control Systems Under Stochastic Clock Offsets

In this paper, the quantized control of networked control systems under stochastic clock offsets is considered. We assume that the clock offsets are caused by asynchronous clocks between sensors and controllers. A stochastic variable with a specific probability density function is used to describe the stochastic clock offsets. A quantized controller is designed to deal with quantization error such that the corresponding systems are stochastically stable. Finally, two numerical examples are used to show the validity of the proposed design method.
Weighted Distance Weighted Discrimination and Pairwise Variable Selection for Classification

XINGYE QIAO: Weighted Distance Weighted Discrimination and Pairwise Variable Selection for Classification (Under the direction of Professor J. S. Marron and Professor Yufeng Liu) Statistical machine learning has attracted a lot of attention in recent years due to its broad applications in various fields. The driving statistical problem that is common throughout this dissertation is classification. This dissertation covers two major topics in classification. The first topic is weighted Distance Weighted Discrimination (weighted DWD or wDWD), an improved version of a recently proposed classification method. We show significant improvements are available in several situations. Using our proposed optimal weighting schemes, we show that wDWD is Fisher consistent under the overall misclassification criterion. In addition, we propose three alternative criteria and provide the corresponding optimal weights or adaptive weighting schemes for each of them. Mathematical validation of these ideas is established through the High-Dimensional, Low Sample-Size (HDLSS) asymptotic properties of wDWD. An important contribution is the weakening of the assumptions from Hall et al. (2005) and Ahn et al. (2007). We then extend the results to two classes. The HDLSS asymptotic properties of wDWD that we discuss here contain two results, one is about the misclassification rate of wDWD, the other explores the angle between the DWD direction and the optimal classification direction. The second topic of this dissertation is variable selection for classification. The goal is to find those variables that have weak marginal effects, but can lead to good classification results when they are viewed jointly. To accomplish this, we use a within-class permutation test called Significance test of Joint Effect (SigJEff). The resulting object of SigJEff is a set of pairs of variables with statistically significant joint effects. To extend our scope to joint effects with more than two variables, we introduce a new visualization approach