Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Bee Colony Algorithm for the Parallel Test Tasks Scheduling

Parallel test is the key technology of the NxTest technology and the parallel test tasks scheduling is one of the important part of parallel test. The mathematical model of the problem was introduced, according to the advantage of solving the problem of dynamic scheduling with artificial bee colony algorithm; an approach of parallel test scheduling based on artificial bee colony algorithm is brought forward. An example was given, the result of simulation shows that this algorithm’s constringency fast and the result has a high precision, it is an efficient way of solving the problem of optimized parallel test tasks scheduling.
Fuzzy combination of Kohonen's and ART neural network models to detect statistical regularities in a random sequence of multi-valued input patterns

A fuzzy simplified ART (SART) implementation is now proposed to combine SART architecture with a Kohonen-based soft learning strategy which employs a fuzzy membership function. Fuzzy SART consists of an attentional and an orienting subsystem. The fuzzy SART attentional subsystem is a self-organizing feedforward flat homogeneous network performing learning by examples. During the processing of a given data set, the fuzzy SART orienting subsystem: 1) adds a new neuron to the attentional subsystem whenever the system fails to recognize an input pattern; and 2) removes a previously allocated neuron from the attentional subsystem if the neuron is no longer able to categorize any input pattern. The performance of fuzzy SART is compared with that of the CGR fuzzy ART model when a 2D data set and the 4D IRIS data set are processed. Unlike the CGR fuzzy ART system, fuzzy SART: 1) requires no input data preprocessing; 2) features stability to small changes in input parameters and in the order of the input sequence; and 3) is competitive when compared to other neural network models found in the literature.
Machine vision for finding a joint to guide a welding robot

Summary This report contains a description on how it is possible to guide a robot along an edge, by using a camera mounted on the robot. If stereo matching is used to calculate 3Dcoordinates of an object or an edge, it requires two images from different known positions and orientations to calculate where it is. In the image analysis in this project, the Canny edge filter has been used. The result from the filter is not useful directly, because it finds too many edges and it misses some pixels. The Canny edge result must be sorted and finally filled up before the final calculations can be started. This additional work with the image decreases unfortunately the accuracy in the calculations. The accuracy is estimated through comparison between measured coordinates of the edge using a coordinate measuring machine and the calculated coordinates. There is a deviation of up to three mm in the calculated edge.
A State Transformation based Partitioning Technique using Dataflow Extraction for Software Binaries

Hardware-Software Partitioning and decompilation is a key issue in the Codesign of embedded systems. Partitioning in binary level helps in independent usage of software languages for the compilers. In this paper, the critical kernel of the software binary is relocated to the hardware and this is identified using instruction level profiling. The partitioned software binary is represented with initial and final state by a set of register value pairs. In the software binary the initial state to final state transformation is derived by equating the final state in terms of algebraic place holders, and then synthesized into hardware. A generalized decompiler is also designed to generate equivalent HDL for software binary block. The proposed method is applied to standard benchmarks and show significant speedup with lesser hardware resources. A source level partitioning is carried out for the scheduled elliptic wave filter and buffer size is estimated in binary and source level approach.
Motivation of staff in libraries

Identifies some of the issues that affect the motivation of staff in libraries. Argues that such motivation is central to a quality culture. As libraries become more sophisticated in their approach to quality, self‐motivation will become a central issue. Briefly reviews the rational‐economic model, the social model, the self‐actualizing model and the complex model as a basis for reviewing motivation of staff. Environmental factors that had an impact on motivation include: approaches to financial rewards, culture and the diversity of staff experience and roles. Possible strategies for motivation include developmental strategies such as appraisal, managing dissatisfiers and financial and social rewards.
Preventing Attacks on Social Networks UsingSanitization Technique

Online Social Networks for example Facebook are dynamically utilized by various people. These frameworks license customers to circulate experiences about themselves and to interface with their friends. A bit of the information uncovered inside these frameworks is planned to be private. Yet it is conceivable to utilize learning estimations on discharged information to imagine private data. In this venture, it is about how to dispatch determination ambushes utilizing discharged individual to individual correspondence information to suspect private data. It then devises three possible sanitization frameworks that could be used as a piece of diverse circumstances. By then, it explore the ampleness of these techniques and attempt to use frameworks for total inference to discover sensitive attributes of the data set. It exhibit that it can decrease the ampleness of both adjacent and social gathering computations by using the purification schedules it depicted.
On traffic flow modelling with phase transition: An energy concept

Abstract Using the fluid mechanics and thermodynamics laws to describe the traffic flow reality, a new second order model is proposed, which represents different traffic flow phases. The conditions for transition between phases become clear. In addition, our approach suggests solutions to a number of problems still open. Finally, the proposed model highlights different types of possible actions for traffic flow control.
Information security management based on intelligent technologies

Представлено аналіз р озвитку інформаційно-телекомунікаційних мереж нового покоління. Метою проведених досліджень була розробка наукових основ ситуаційного управління безпекою в ІТМ на основі інтелектуальних технологій. Наведена узагальнена структура системи інтелектуального управління безпекою. Представлено структуру нейросистеми оцінки рівня безпеки в ІТМ та описано принцип її функціонування.
Fractional gaussian fields for modeling and rendering of spatially-correlated media

Transmission of radiation through spatially-correlated media has demonstrated deviations from the classical exponential law of the corresponding uncorrelated media. In this paper, we propose a general, physically-based method for modeling such correlated media with non-exponential decay of transmittance. We describe spatial correlations by introducing the Fractional Gaussian Field (FGF), a powerful mathematical tool that has proven useful in many areas but remains under-explored in graphics. With the FGF, we study the effects of correlations in a unified manner, by modeling both high-frequency, noise-like fluctuations and k-th order fractional Brownian motion (fBm) with a stochastic continuity property. As a result, we are able to reproduce a wide variety of appearances stemming from different types of spatial correlations. Compared to previous work, our method is the first that addresses both short-range and long-range correlations using physically-based fluctuation models. We show that our method can simulate different extents of randomness in spatially-correlated media, resulting in a smooth transition in a range of appearances from exponential falloff to complete transparency. We further demonstrate how our method can be integrated into an energy-conserving RTE framework with a well-designed importance sampling scheme and validate its ability compared to the classical transport theory and previous work.
Symbolic execution-based approach to extracting a micro state transition table

During embedded system development, developers frequently change and reuse the existing C source code for the development of a new but behaviorally similar product. Such frequent changes generally decrease the understandability of C source code although the developers have to understand how it behaves and how to reuse it. So far, much research has been done on symbolic execution techniques that statically analyze the behavioral aspect of given source code. In this paper, we propose a symbolic execution-based approach to extracting a Micro State Transition Table (MSTT) that helps developers understanding the behavioral aspect of embedded C source code based on a fine-grained state transition model. As a case study, we applied the proposed approach to a collection of source files and then confirmed the correctness of the extracted MSTTs.
Tangent-space optimization for interactive animation control

Character animation tools are based on a keyframing metaphor where artists pose characters at selected keyframes and the software automatically interpolates the frames inbetween. Although the quality of the interpolation is critical for achieving a fluid and engaging animation, the tools available to adjust the result of the automatic inbetweening are rudimentary and typically require manual editing of spline parameters. As a result, artists spend a tremendous amount of time posing and setting more keyframes. In this pose-centric workflow, animators use combinations of forward and inverse kinematics. While forward kinematics leads to intuitive interpolations, it does not naturally support positional constraints such as fixed contact points. Inverse kinematics can be used to fix certain points in space at keyframes, but can lead to inferior interpolations, is slow to compute, and does not allow for positional contraints at non-keyframe frames. In this paper, we address these problems by formulating the control of interpolations with positional constraints over time as a space-time optimization problem in the tangent space of the animation curves driving the controls. Our method has the key properties that it (1) allows the manipulation of positions and orientations over time, extending inverse kinematics, (2) does not add new keyframes that might conflict with an artist's preferred keyframe style, and (3) works in the space of artist editable animation curves and hence integrates seamlessly with current pipelines. We demonstrate the utility of the technique in practice via various examples and use cases.
Computational design of fabric formwork

We present an inverse design tool for fabric formwork - a process where flat panels are sewn together to form a fabric container for casting a plaster sculpture. Compared to 3D printing techniques, the benefit of fabric formwork is its properties of low-cost and easy transport. The process of fabric formwork is akin to molding and casting but having a soft boundary. Deformation of the fabric container is governed by force equilibrium between the pressure forces from liquid fill and tension in the stretched fabric. The final result of fabrication depends on the shapes of the flat panels, the fabrication orientation and the placement of external supports. Our computational framework generates optimized flat panels and fabrication orientation with reference to a target shape, and determines effective locations for external supports. We demonstrate the function of this design tool on a variety of models with different shapes and topology. Physical fabrication is also demonstrated to validate our approach.
The “half‐life” of some scientific and technical literatures

A consideration of the analogy between the half-life of radioactive substances and the rate of obsolescence of scientific literature. The validity of this analogy suggests the possibility of more accurate prognostications concerning the period of time during which scientific literature may be used and hence might help to guide the planning of library collections and technical information services.
Online Learning Adoption: Effects of Perceived Value and the Moderating role of Neuroticism

Perceived values and neurotic personality have been used to explain online learning adoption. However, there is a lack of understanding of how neuroticism interact with perceived values resulting in online learning adoption. This study explores the moderating role of neuroticism in the relationships between perceived value (functional value, emotional value and social value) and adoption intention of online learning. The results show that neuroticism strengthens the positive effect of perceived emotional value on adoption intention of online learning. The adoption intention of online learning is higher for low neuroticism when perceived emotional value increases. Neuroticism does not moderate the effects of functional value and social value on adoption intention of online learning. This study fills the gap in online learning adoption literature by generalizing the effects of perceived values on online learning adoption to a specific personality trait. It also provides guidance for universities to shape their online learning strategy for neurotic students who are difficult to cope with new learning phenomenon. The paper discusses the study limitations and directions for future research.
Semantic Feature Clustering for Sentiment Analysis of English Reviews

ABSTRACT Sentiment analysis research has increased tremendously in recent times due to the wide range of business and social applications. Motivation behind sentiment analysis is that it provides companies’ methods to determine the product acceptance and ways to improve its quality. It also helps users to take purchasing decisions. Various parsing schemes/feature extraction methods have been proposed in the literature to process unstructured text to extract patterns that may help machine learning model to learn. The main limitation of the existing feature extraction techniques is the sparseness of the data and inability to incorporate semantic information. In this paper, a new feature extraction method is proposed, namely clustering features. Proposed feature extraction technique focuses on alleviating the data sparsity faced by supervised sentiment analysis by clustering of semantic features. Proposed clustering features are capable of including semantic information and alleviating data sparseness for machine learning algorithm. In all the experiments, support vector machine and Boolean Multinomial Naive Bayes (BMNB) machine learning algorithms are used for classification. Experimental results show that the proposed clustering features significantly outperform other features for document-level sentiment classification. All the experiments are performed on standard movie review data-set and product review data-sets, namely book, electronics, kitchen appliances.
Diffusion Kernel based Fast Adaptive Clustering of Single Cell RNA-seq Data

Recently, with the advent of high throughput single-cell technologies, it has become possible to quantify the whole transcriptome of individual cells; however, it remains challenging to discover intrinsic rare cell-types from high throughput genes expression data. To overcome this challenge, various unsupervised clustering based approaches have been proposed such as GiniClust, SC3 and SIMLR clustering. These approaches identified appropriate rare cell types based on the ambiguous parametric settings and employed clustering algorithms are inefficient to discover meaningful clusters adaptively. However, the appropriate signal of interest can be observed along with the robust filtration, normalization, and transformation of raw count samples of single-cell data. Filtration, normalization, and transformation have become the essential primary procedure for down-stream analysis of single-cell data and to eliminate the risk of biological variation and technical noise. In this paper, we will evaluate the various methods to detect the rare cell-types from a large population and develop fast novel diffusion kernel based unsupervised framework (DKBUF) to identify rare cell types from single-cell RNA-seq data, more in an adaptive and attractive fashion. The DKBUF filters the non-stable genes, normalizes the genes, attractively detects subpopulations within single-cell datasets, and visualizes the discovered distinct subpopulations. Extensive experiments on single-cell datasets and comparisons with state-of-the-art methods validate the robustness of the DKBUF.
The Use of a Code-generating System for the Derivation of the Equations for Wind Turbine Dynamics

For many reasons the size of wind turbines on the rapidly growing wind energy market is increasing. Relations between aeroelastic properties of these new large turbines change. Modifications of turbine designs and control concepts are also influenced by growing size. All these trends require development of computer codes for design and certification. Moreover, there is a strong desire for design optimization procedures, which require fast codes. General codes, e.g. finite element codes, normally allow such modifications and improvements of existing wind turbine models. This is done relatively easy. However, the calculation times of such codes are unfavourably long, certainly for optimization use. The use of an automatic code generating system is an alternative for relevance of the two key issues, the code and the design optimization. This technique can be used for rapid generation of codes of particular wind turbine simulation models. These ideas have been followed in the development of new versions of the wind turbine simulation code VIDYN. The equations of the simulation model were derived according to the Lagrange equation and using Mathematica®, which was directed to output the results in Fortran code format. In this way the simulation code is automatically adapted to an actual turbine model, in terms of subroutines containing the equations of motion, definitions of parameters and degrees of freedom. Since the start in 1997, these methods, constituting a systematic way of working, have been used to develop specific efficient calculation codes. The experience with this technique has been very encouraging, inspiring the continued development of new versions of the simulation code as the need has arisen, and the interest for design optimization is growing. Copyright © 2003 John Wiley & Sons, Ltd.
Motion Capture based Dynamic Assessment of Hip Joint Cartilage Contact Pressure during Daily Activities

Hip joint cartilage contact stresses are clinically relevant and necessary to improve our understanding of hip osteoarthritis. Therefore, the objective of this paper is to assess the contact pressure changes during series of dynamic postures such as slow walking, normal walking, fast walking, descending stairs and ascending stairs. A standard anatomical model is built from CT images and twenty kinematical models are constructed using a motion capture system. A two-step adjusted-iterative closest point method is proposed to register the anatomical model with the motion capture recorded kinematical model. After the registration, acetabular cartilage contact pressure is analyzed by a finite element method. According to simulation results among twenty subjects, the contact pressure distribution of walking and stairs movements are mostly uniform during a cycle of movement. The peak of contact pressure appears at the transition location from superior to posterior. The peak of contact pressure happens almost at the time of heel-strike. The contact area is changing from anterior to superior-posterior and ending at anterior for all the activities. These results demonstrate the trends for normal hip contact pressure in cartilage during daily activities. These results also provide guidance for the diagnosis of osteoarthritis. The location at transition from superior to posterior should be paid more attention in the diagnosis of osteoarthritis. And the osteoarthritis patient should try to avoid the movement of ascending and descending stairs.
Heuristic scheduling of resource‐constrained, multiple‐mode and repetitive projects

An alternative heuristic method for scheduling repetitive projects in which resources are limited and activities may be executed with multiple modes of resource demands associated with different durations is proposed. Unlike general heuristic methods that separately analyze each competing activity and schedule only one at a time, the proposed heuristic algorithm ranks possible combinations of activities every time and simultaneously schedules all activities in the selected combination leading to minimal project duration. All alternative combinations of activities in consideration of resource constraints, multiple modes and characteristics of the repetitive projects are determined through a permutation tree‐based procedure. The heuristic method is implemented based on the corresponding framework. An example is presented to demonstrate the efficiency of the proposed heuristic method. The study is expected to provide an efficient heuristic methodology for solving the project scheduling problem.
Harmonic triangulations

We introduce the notion of harmonic triangulations: a harmonic triangulation simultaneously minimizes the Dirichlet energy of all piecewise linear functions. By a famous result of Rippa, Delaunay triangulations are the harmonic triangulations of planar point sets. We prove by explicit counterexample that in 3D a harmonic triangulation does not exist in general. However, we show that bistellar flips are harmonic: if they decrease Dirichlet energy for one set of function values, they do so for all. This observation gives rise to the notion of locally harmonic triangulations. We demonstrate that locally harmonic triangulations can be efficiently computed, and efficiently reduce sliver tetrahedra. The notion of harmonic triangulation also gives rise to a scalar measure of the quality of a triangulation, which can be used to prioritize flips and optimize the position of vertices. Tetrahedral meshes generated by optimizing this function generally show better quality than Delaunay-based optimization techniques.
Computer Aided Detection of Normal and Abnormal Heart Sound using PCG

A PCG (phonocardiogram) is a method of plotting of heart sounds and murmurs during a cardiac cycle, with the help of machine called phonocardiograph. A PCG can be visually represented. PCG recordings comprise of bio-acoustic statistics indicating the functional condition of the heart. Intelligent and automated analysis of the PCG is therefore very important not only in detection of cardiac diseases but also in monitoring the effect of certain cardiac drugs on the condition of the heart. PCG analysis includes segmentation of the PCG signal, feature extraction from the segmented signal and then classification. We used Kaggle data sets [10] and have extracted feature sets of different domains i.e. Time domain, frequency domain and statistical domain. We used 8 features of 118 recordings and train our different classifiers (Bagged Tree, subspace Discriminant, Subspace KNN, LDA, Quadratic SVM and Fine Tree) to obtain and compare accuracy and results. We use only two classes for classification i.e. normal and abnormal. Out of these 6 classifiers Bagged tree gave highest accuracy of 80.5%.