Miracle-dz/UniFAR-FTUs huggingface dataset:
UNIVERSIDADE FEDERAL DE SANTA CATARINA DEPARTAMENTO DE INFORM ATICA E ESTAT ISTICA

There is interest in the simulation of clothes in any application where there is the presence of virtual humans. However, simulate the behavior of fabric on the body of a person is a complex task. In addition, there is a constant need to get faster results that allow the simulation of increasingly detailed garment. This work discusses techniques for different stages and aspects of the simulation process. We propose an optimization for a method used to generate effects of tearing and cutting. Also, we introduce an algorithm to generate seam lines for triangle meshes. The techniques described herein are used in the implementation of a simulator whose purpose is to facilitate the development of systems for designing and making clothes.
Staff Use of E-Learning and Graduate Competencies for the Knowledge Economy: A Study of the Misalignment between Rhetoric and Practice

E-learning is part of the wider debate on the changing role of higher education (HE). It is associated with the agenda on graduate employability and competencies for the knowledge economy (KE). Policy documents make explicit that participation in the KE is congruent with the acquisition of meta-skills. The role of HE is to provide for these competencies and e-learning is presented as assisting this objective. A primary prerequisite, however, is appropriate staff development. This qualitative study examines the relationship between the rhetoric and the practice of e-learning, and argues that issues associated with professional development exist at both institute and staff levels.
Using semantics for paragraph selection in question answering systems

Ejiciency of term-based Question Answering systems is limited to answering questions whose answer is expressed in documents by using mainly the same terms appearing in questions. The system presented in this paper overcomes this fact by performing open domain Question Answering (QA) from a semantic perspective. For this purpose, we define a general semantic model that represents the concepts referenced into the questions as well as a relevance measure that allows locating and ranking fragments of documents fiom whose content is possible to infer the answer to specific questions. mth the purpose of evaluation, this model has been embedded into a full QA system. Comparison of performance between our model and term-based approaches shows that QA measures improve signiJicantly when this model is applied to paragraph selection process.
Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts’ time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation. We approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.
Generative multi-robot task and motion planning over long horizons

The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically precomputed in advance. However, in order for robots to be able to act robustly and adapt to novel situations, they need to be able to plan sequences of behaviors and activities autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This greatly limits the expressivity of the problems that these approaches can solve. In this work we present Scotty, a planning system for hybrid activity and trajectory planning problems. Unlike other continuous time planners, Scotty can solve a broad class of expressive robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to efficiently generate practical plans for coordinated mobile robots over long horizons, our approach employs recent methods in convex optimization combined with methods for planning with relaxed planning graphs and heuristic forward search. The contributions of this thesis are threefold. First, we introduce a convex, goaldirected scheduling and trajectory planning problem. To solve this problem, we present the ScottyConvexPath planner, which reformulates the problem as a Second Order Cone Program (SOCP). Our formulation allows us to efficiently compute robot
Improvement of Pitch Motion Control of an Aircraft Systems

The movement of the aircraft pitch is very important to ensure the passengers and crews are in intrinsically safe and the aircraft achieves its maximum stability.The objective of this study is to provide a solution to the control system that features particularly on the pitch angle motion of aircraft systemin order to have a comfort boarding. Three controllers were developed in these projects which wereproportional integral derivative (PID), fuzzy logic controller (FLC), and linear quadratic regulator (LQR) controllers. These controllers will help improving the pitch angle and achievingthe target reference. By improving the pitch motion angle, the flight will be stabilized and in steady cruise (no jerking effect), hence provides all the passengers withthe comfort zone. Simulation results have been done and analyzed using Matlab software. The simulation results demonstrated LQR and FLC were better than PID in the pitch motion system due to the small error performance. In addition, withstrong external disturbances, a single controller is unable to control the system, thus, the combination of PID and LQR managed to stabilize the aircraft.
Unsupervised Deep Discriminative Feature Learning for Aerial Image Classification

Nonnegative sparse coding (NSC) is widely utilized as an image representation model. However, conventional NSC methods usually cause large representation errors, lack of spatial information and weak discriminative. In order to overcome these drawbacks, this paper proposes an unsupervised deep discriminative feature learning framework for aerial image classification which is based on Fisher Discriminative Nonnegative Sparse Coding (FDNSC) and Deep Belief Network (DBN). First, image features are extracted by using scale-invariant feature transform (SIFT). Then fisher discriminative analysis is added to construct a NSC with fisher discriminative criterion constraint, thus to obtain the discriminative sparse representation of images. Finally, DBN is combined to perform aerial image classification. The proposed method is applied to OT data set and UC Merced data set. Experimental results show that the proposed method efficiently utilizes spatial information of images and can promote the spatial separability of sparse coefficients, thus improves the classification performance and is more suitable for aerial image classification.
Klasifikasi Sinyal Ecg Menggunakan Deep Learning Dengan Stacked Denoising Autoencoders

Aritmia merupakan kondisi jantung yang berdetak tidak sebagaimana mestinya, bisa berdetak terlalu cepat, lambat, atau berdetak dengan pola yang tidak beraturan. Akibat jantung yang tidak berdetak dengan normal, maka peredaran darah pun menjadi tidak normal juga. Sehingga berpengaruh terhadap kondisi kesehatan tubuh. Untuk mendiagnosa aritmia, salah satu metode yang digunakan ialah dengan membaca pola sinyal jantung yang disebut dengan Electrocardiogram (ECG). Deep Learning merupakan suatu metode pembelajaran mesin yang memungkinkan komputasi dalam level abstraksi bertingkat. Salah satu jenis deep learning yaitu Stacked Denoising Autoencoders (SDAE) [7]. SDAE digunakan untuk merekonstruksi suatu data yang telah dirusak agar tetap memiliki informasi yang mendekati data yang sebenarnya. Pada tugas akhir ini, penulis merancang sebuah sistem yang dapat mendeteksi aritmia menggunakan metode SDAE sebagai tahap pretraining, dan Softmax Regression untuk melakukan fine tuning. Akurasi tertinggi yang dihasilkan dalam mengklasifikasikan sinyal ECG yaitu sebesar 98.91%. Kata kunci : Aritmia, Electrocardiogram, Deep Learning, Stacked Denoising Autoencoders, Softmax Regression, Fine Tuning.
Self-adaptive grasp analysis of a novel under-actuated cable-truss robotic finger

This paper presents the self-adaptive grasp of a novel cable-driven finger, which is an underactuated finger comprising cable-truss units. The underactuated cable-truss finger uses tendon-pulley transmission and parallel four-linkage mechanism to realize grasps. The working principle of the underactuated cable-truss finger and the self-adaptation at the grasp-closing stage are introduced. A self-adaptive grasping model is constructed to analyze the self-adaptation, and a new analysis method that considers the position and posture of grasping point in distal phalange is proposed. A new generalized coordinate, which directly shows the contacting position and the relative angular displacement in the distal phalange, is established. The expression of general static grasping force is established by using the virtual work principle, which reveals the relationship among the driving force, the equivalent torques on joints and the grasping forces. The workspace of the underactuated cable-truss finger and distributions of grasping force in new generalized coordinate are assessed through numerical analysis. The balance conditions of the self-adaptive grasp and the corresponding statuses are theoretically illustrated. Valid and adequate self-adaptive grasping experiments are conducted to verify the accuracy of self-adaptive grasping analysis.
Tendency of Creation of “driverless” Vehicles Abroad

1 Head of Department, Doctor of Engineering. 2 Deputy CEO for Science, Doctor of engineering, professor. 3 Director of Centre, Doctor of philosophy. 4 Director of Centre. 5 Head of department, Doctor of philosophy. 6 Chief specialist. Federal State Unitary Enterprise Central Scientific Research Automobile and Automotive Institute “NAMI” (FSUE «NAMI»), Avtomotornaya street, 2, Moscow, Russia, 125438.
A Defense-in-depth Cybersecurity for Smart Substations

The increase of cyber-attacks on industrial and power systems in the recent years make the cybersecurity of supervisory control and data acquisition and substation automation systemsa high important engineering issue. This paper proposes a defense in depth cybersecurity solution for smart substations in different layers of the substation automation system. In fact, it presents possible vulnerabilities in the substation automation system and propose a multiple layer solution based on best practice in cyber security such as the hardening ofdevices, whitelisting, network configuration, network segmentation, role-based account management and cyber security management and deployement.
Inside the Java Virtual Machine

From the Book: My primary goal in writing this book was to explain the Java virtual machine-and several core Java APIs closely related to the virtual machine-to Java programmers. Although the Java virtual machine incorporates technologies that have been tried and proven in other programming languages, prior to Java, many of these technologies had not yet entered into common use. As a consequence, many programmers will encounter these technologies for the first time as they begin to program in Java. Garbage collection, multi-threading, exception handling, dynamic extension---even the use of a virtual machine itself-might be new to many programmers. The aim of this book is to help programmers understand how all these things work, and in the process we hope to help them become more adept at Java programming. Another goal I had in mind as I wrote this book was to experiment a bit with the changing nature of text. Web pages have three interesting characteristics that differentiate them from paper-based text: they are dynamic (can evolve over time), they are interactive (especially if you embed Java applets in them), and they are interconnected (you can easily navigate from one to another). Besides the traditional text and figures, this book includes several Java applets (in a mini-Web site on the CD-ROM) that serve as interactive illustrations of the concepts presented in the text. In addition, I maintain a Web site at art ima. com on the Internet that serves as a launching point for readers to find more (and more current) information about the topics covered in the book. This book is composed of all of these components: text, figures, interactive illustra. tions, and constantly evolving links tofurther reading.
Teaching physiology through simulation of hemodynamics

A user-friendly interface to a mathematical model for teaching cardiovascular physiology is developed. The underlying model, a lumped-parameter approximation of cardiovascular hemodynamics and control, 'comes to life' in this real-time simulation. Students interact with the model through the X Window System interface, using the mouse to create continuously updating, realistic graphs of blood pressure, volume, or flow for any of the compartments. Students perform experiments on a 'virtual patient' in order to understand the intrinsic properties of the system as it responds dynamically to normal stresses and pathological insults. Instructors may set up patient cases with simulated diseases and ask students to examine the hemodynamics in order to make a diagnosis. Using this program, students are able to explore the function of the cardiovascular system in greater depth than would be possible through conventional teaching methods. <<ETX>>
Beanz: An R package for Bayesian analysis of heterogeneous treatment effects with a graphical user interface

In patient-centered outcomes research, it is essential to assess the heterogeneity of treatment effects (HTE) when making health care decisions for an individual patient or a group of patients. Nevertheless, it remains challenging to evaluate HTE based on information collected from clinical studies that are often designed and conducted to evaluate the efficacy of a treatment for the overall population. The Bayesian framework offers a principled and flexible approach to estimate and compare treatment effects across subgroups of patients defined by their characteristics. In this paper, we describe the package beanz which facilitates the conduct of Bayesian analysis of HTE by allowing users to explore a wide range of Bayesian HTE analysis models and produce posterior inferences about HTE. The package beanz also provides a web-based graphical user interface (GUI) for users to conduct the Bayesian analysis of HTE in an interactive and user-friendly manner. With the GUI feature, package beanz can also be used by analysts not familiar with the R environment. We demonstrate package beanz using data from a randomized controlled trial on angiotensin converting enzyme inhibitor for treating congestive heart failure (N = 2569).
Tabular minimal redundant modular structures for fast and high-precision computations using general-purpose computers

The present paper is a continuation of research in parallel information processing based on the tabular modular computing structures. We deal with the methodology of using a minimal redundant modular number system for high-speed and high-precision computation by means of modern universal multicore processors. Advantages of formal computing mode on the base of modular arithmetic are demonstrated by the example of implementation of digital signal processing procedures. The additive and additive-multiplicative formal computing schemes with the obtained estimations of the cardinality of working ranges for the realization of calculations are presented in the article.
Organizational Factors as Predictors of Knowledge Management Practices in Federal University Libraries in Nigeria

Abstract Objective – University libraries in Nigeria are facing challenges arising from poor funding, increasing user demands, and a competitive information environment. Knowledge management has been accepted by information professionals as a viable management tool, but issues surrounding its application require empirical investigation. The aim of this study is to determine the organizational factors that are correlates and predictors of knowledge management practices in federal university libraries in Nigeria. Methods – The study was based on a correlational research design. Twenty heads of university libraries in Nigeria responded to a structured questionnaire developed by the researcher. The questionnaire was validated by experts and its internal reliability was 0.78 obtained through Cronbach’s alpha procedures. The data collected were analyzed using Mean, Standard Deviation, One-Way ANOVA, Pearson’s Product Moment Correlation Coefficient, and regression analysis. Results – The study found that management support and collaboration were the most significant predictors of knowledge management practices in federal university libraries in Nigeria. Even though human resources policy and rewards systems had positive correlations with knowledge management practices, their correlation coefficients were not significant. Conclusion – The success of knowledge management in university libraries in Nigeria depends on some contextual factors such as the support given by the management staff and the extent of collaboration among staff.
Standard compliant communication of motion data in a telemonitoring system

Interoperability is regarded as one of the key factors for the high acceptance of a telemonitoring system. In a general telemonitoring scenario Neltje Emma Piro Lisa Baumann for motion tracking, data recorded by inertial sensors should be sent Tibor Kesztyüs from the patient's home to a central server for medical assessment by Ronald Archibald Blechschmidt-Trapp a physician. The objective of this work is to elaborate a concept on how continuously measured motion data can be transferred according to the guidelines of the Continua Health Alliance and IHE, and which adaptations are reasonable to achieve high efficiency. 1 Institute of Medical Engineering and For the communication between sensor systems and a smartphone, the X73 standards family was applied. To cover the interface between Mechatronics, Ulm University the smartphone and a central information system, the proposed HL7 of Applied Sciences, Ulm, Germany Version 2.6 was used as message exchange format. Because we are dealing with continually measured data, the HL7message construction 2 Institute of Computer Science, Ulm University of is oriented towards the IHE PCDWaveform ContentModule. Two variants of the transmission of binary motion data with an HL7 message were Applied Sciences, Ulm, Germany implemented. An evaluation with regard to relevant criteria led to the decision to use REST-based web services for message transport instead of the SOAP-based variant proposed by the Continua Design Guidelines. To summarize, the developed approach uses extended standards for the transmission of motion data. The detailed results can support other work groups with comparable implementation needs. In order to achieve a fully interoperable system, the communication of motion data should be included in the guidelines of IHE and Continua Health Alliance, since motion sensors can be used in various monitoring scenarios such as in patients with multiple sclerosis or for rehabilitation in general.
A freehand interface for computer aided drawing systems based on the fuzzy spline curve identifier

This paper proposes a freehand interface for computer aided drawing (CAD) systems, which is based on an online primitive curve identification technique called fuzzy spline curve identifier (FSCI). FSCI, which has been introduced by Saga and Makino (1993), identifies a freehand curve as a sequence of curve segments, each of which is categorized into one of seven kinds of primitive curves: line, circle, circular arc, ellipse, elliptic arc, closed free curve or open free curve. The interface in this paper is built by a bottom-up approach based on the primitive curve identification. Therefore, the interface handles sufficient varieties of curves for common CAD use. The prototype of the interface is composed of three pen-based interactive user interfaces over the FSCI's primitive curve identification functions. Experimental results from the prototype show the effectiveness of the approach to a freehand CAD interface, which allows a user to input any of the seven kinds of primitive curves simply by drawing a curve and modifying it on a small scale.
DSP-based fuzzy logic control of a positioning system-experimental results

This paper presents some experimental results of a fuzzy logic control system for a high-performance positioning system with real-time digital implementation using digital signal processors (DSP). The PI-like fuzzy logic controller is developed using the fuzzy logic system with fuzzifier and defuzzifier. Seven fuzzy sets with normalized triangular membership functions are used for both input and output variables of the controller. The proposed control system is implemented using a DSP-based real time control testbed. Experimental results for two-dimensional contour following control are presented. The effects of sampling frequency, speed and payload variations are discussed. The results demonstrate the effectiveness of the proposed control system, especially at high speed with payload variations.
Developing Academic Language: Some Hypotheses

This paper presents a simple hypothesis and some related sub ⴀhypotheses. The simple hypothesis is this: We develop academic language by reading. Nearly all of the conventions of academic language, its special vocabulary, grammar and discourse style are subconsciously absorbed, or acquired, from reading texts written in the academic style that are relevant to us. If this hypothesis is true, it means a profound