Miracle-dz/UniFAR-FTUs huggingface dataset:
PERANCANGAN SISTEM PELACAKAN SOFTWARE BERLISENSI DAN HELP DESK SYSTEM (Studi Kasus : PT. PUPUK KUJANG CIKAMPEK JAWA BARAT)

PT Pupuk Kujang merupakan Badan Usaha Milik Negara, dimana aktivitas bisnisnya terbilang kompleks. Salah satu software yang merupakan bagian dari sistem informasi yang mendukung aktivitas bisnisnya adalah penggunaan software berlisensi yang selalu ada audit secara berkala dari pihak vendor. Permasalahaccccnnya adalah terjadi penyalahgunaan software berlisensi oleh beberapa komputer client, misalnya penyalahgunaan jumlah lisensi, terjadinya penggunaan software bajakan. Permasalahan lain yaitu sedikitnya staf technical support di Biro Teknologi Informasi (TI). Hal ini menyebabkan terjadinya kelambatan penyelesaian masalah internal teknologi informasi di PT Pupuk Kujang. Berdasarkan permasalahan tersebut, maka penelitian ini bertujuan merancang sistem pelacakan software berlisensi dan Help desk. Dalam pengumpulan data, penulis menggunakan metode observasi, dan metode pengembangan sistem yaitu model prototype. Rancangan ini direkomendasikan untuk diimplementasikan menggunakan Java Virtual Machine (JVM), dan Oracle sebagai aplikasi basisdata dengan pertimbangan Data Base Management System yang sudah digunakan di PT. Pupuk Kujang adalah Oracle, Apache Tomcat untuk aplikasi web server. Diharapkan rancangan aplikasi yang dibuat dapat bermanfaat untuk pelacakan, pengidentifi kasian awal pnggunaan software berlisensi sebelum dilakukannya audit oleh pihak vendor, digunakan untuk pendukung keputusan akan jenis dan jumlah software licensed yang lebih dibutuhkan PT Pupuk Kujang. Help desk System diharapkan bisa mengefi siensikan penanganan masalah teknis di bidang TI di PT Pupuk Kujang.
Efficient Scheduling on Multiprogrammed Shared-Memory Multiprocessors

Shared-memory multiprocessors are often used as compute servers, with multiple users running applications in a multiprogrammed style. On such systems, naive time-sharing scheduling policies can result in poor performance for parallel applications. The reason is that many parallel applications are written assuming a model where applications are running uninterrupted on a fixed number of processors. In compute server environments, where new applications are continuously moving in and out of the system, the computing environment is much more unstable; processes are frequently preempted to allow other applications' processes to run, and both the number of processors and the set of processors running an application varies over time. The result is a decrease in performance due to a variety of factors, including processes being obliviously preempted inside critical sections and cached data being replaced by intervening processes. This thesis explores the problem of developing more sophisticated scheduling systems to avoid the performance problems raised by running parallel applications in a multiprogrammed environment. It begins with a study of two previously proposed solutions, cache affinity scheduling and gang scheduling. Effective new implementations of each have been developed that maintain good response time and fair processor allocation. Performance results from a suite of multiprogrammed workloads containing parallel and serial applications, run on a 4-processor Silicon Graphics workstation, show improvements of up to 16% and 15% (over a standard UNIX scheduler) for cache affinity and gang scheduling, respectively. The thesis then presents the design, implementation, and performance of a novel approach that offers high performance by combining the techniques of process control and processor partitioning. The process control approach is based on the principle that to maximize performance, a parallel application must dynamically match the number of runnable processes associated with it to the effective number of processors available. This avoids problems arising from frequent process preemption and allows applications to work at better operating points on their speedup curves. Performance results show speedups of up to 22% over the performance of a standard UNIX scheduler.
Different native languages as proxy for cultural differences in travel behaviour: insights from multilingual Switzerland.

Purpose – This study aims to study different native languages as proxy for cultural differences in travel behaviour in multilingual Switzerland. Differences in travel behaviour between tourists from different nations (implying different cultural backgrounds) have been investigated in several studies. However, most studies do not reveal the potential confounding effects associated with cross-nation studies, such as differences in the economic, legal/regulatory or subcultural contexts of their residents. This exploratory study tries to overcome this gap by investigating differences in travel behaviour between language groups within one country: Switzerland. By holding potential confounding effects constant, our results reveal differences in travel behaviour associated with language, including travel motivation, choice of destination, choice of type of accommodation, duration of trip and travel expenses. Design/methodology/approach – The study is based on data from one of the most comprehensive outbound trav.
Extraction of height information from target shadow for applications in ATC

Physical attributes, e.g. length and width of a target can be extracted from its radar signature for target characterization. However, an additional dimension, i.e. height can provide value added information to distinguish between targets, e.g. between tanks and APCs, which may have similar lengths and widths and which exhibit similar intensity characteristics. For a non-interferometric data collection system, the height of a target can be accurately extracted from its shadow at low grazing angles, fine resolution and a cluttered background.
Snowfall and rainfall forecasting from weather radar images with artificial neural networks

Discusses problems of the weather forecasting technique with artificial neural networks and describes some solutions. The authors show that the computational time for learning with an acceleration learning algorithm can be reduced by about 10 percent. To overcome the problem of overtraining, a pruning method is introduced and the prediction error is decreased by about 20 percent. Using the data obtained over a winter, the neural weather forecasting technique is more effective than the cross correlation method in producing a substantial reduction of prediction error.
Classification of web pages in Yioop with active learning

CLASSIFICATION OF WEB PAGES IN YIOOP WITH ACTIVE LEARNING by Shawn C. Tice This thesis project augments the Yioop search engine with a general facility for automatically assigning “class” meta words (e.g., “class:advertising”) to web pages based on the output of a logistic regression text classifier. Users can create multiple classifers using Yioop’s web-based interface, each trained first on a small set of labeled documents drawn from previous crawls then improved over repeated rounds of active learning using density-weighted pool-based sampling. The classification system’s accuracy when classifying new documents was found to be comparable to published results for a common dataset, approaching 82% for a corpus of advertisements to be filtered from content-providers’ web pages. In agreement with previous work, logistic regression was found to provide greater accuracy than Naive Bayes for training sets consisting of more than two hundred documents. Active learning with density-weighted pool-based sampling was found to offer a small accuracy boost over random document sampling for training sets consisting of less than one hundred documents. Overall, the system was shown to be effective for the proposed task of allowing users to create novel web page classifiers, but the active learning component will require more work if it is to provide users with a salient benefit over random sampling.
Integrating the Sense of Smell in Virtual Reality for Second Language Learning

Although audiovisual virtual reality (VR) has been successfully researched and employed in educational settings, the sense of smell has recently been integrated to virtual reality for second language learning (LL2). Past research has found that olfactory information can serve to supplement or complement learners' auditory, visual or tactile human information channels, as well as to stimulate their affection and cognition. This paper reviews studies about how the sense of smell has been used in VR for LL2. This paper also describes an exploratory usability study carried out with computer science students, to see if mint odor assists listening comprehension in an English language learning exercise when using a VR environment. Results shown student acceptance for using a smell in VR, and there were technical issues about incorporating an odor in VR to improve its usability in a computer room.
Singularity analysis of a 6 DOF Stewart platform using genetic algorithm

A new singularity analysis method for a general 6 degree-of-freedom (DOF) Stewart platform using genetic algorithm (GA) is proposed in the paper. The Jacobian matrix of the Stewart platform is first deduced, then the square of determinant of the Jacobian matrix is selected as the objective function, and the minimum of this objective function is searched in the workspace of the Stewart platform by a real-coded GA. The singularity of the Stewart platform depends on this minimum objective function, if this value is zero, the singularity of Stewart platform will happen, otherwise, the Stewart platform is singularity-free. The effectiveness of this new genetic singularity analysis method is validated by numerical simulation.
An Empirical Evaluation of Force-Directed Graph Layout

Force-directed graph layout is a widely used algorithm for the automatic layout of graphs. Little experimental work has been done exploring the behaviour of the algorithm under a variety of conditions. This thesis carries out three large-scale metric-based experiments. The first explores how the core algorithm behaves under changes to initial conditions. The second looks at extending the force-directed layout algorithm with additional forces to reduce overlaps. The third develops a novel symmetry metric for graphs and uses that to explore the symmetries of graphs. This thesis also carries out a user study to show that the differences reported by metrics in the graphs are reflected in a difference in user performance when using graphs for a free-form selection task.
Management of E-Resources Cataloging Workflows at the University of Maryland, College Park

In 2011, one of the authors, a staff member of the Metadata Services Department at the University of Maryland, College Park, created an electronic resources cataloging management database (ERCM) to manage the details of MARC record set loads to the online catalog. After attending the NISO Standards update session entitled â€œThe NISO ERM Data Standards and Best Practices Reviewâ€ presentation at the 2012 Annual Conference of the American Library Association, at which cataloging workflow support was referred to as a problem area in electronic resources management, she decided to follow up with an investigation of the nature of the problem and to explore its relevancy to the ERCM. This article will inform metadata services departments about the management of constantly changing electronic resources cataloging workflows and also discuss cataloging workflow as it pertains to Electronic Resources Management System (ERMS) development.
AN INVESTIGATION INTO THE APPLICATION OF SYSTEMATIC SOFTWARE REUSE IN A PROJECT-CENTRIC ORGANISATION

The software development continues to become more competitive and demanding, placing pressure on developers. Changes in the international political climate have resulted in shrinking military budgets, putting developers of defence software under further pressure. At present, systematic reuse is probably the most realistic way of addressing this pressure by improving software development productivity and quality. Software product line (SPL) engineering provides a comprehensive approach to systematic software reuse and is becoming widely accepted. The focus of this interpretive case study was ground station software development in a small multidisciplinary project-centric company which produces avionics systems for military aircraft. The purpose of the study was to investigate the potential implementation of systematic software reuse in the company. The study consisted of three phases, a literature study, a contextualisation and a set of field interviews, and used elements of the Carnegie-Mellon Software Engineering Institute (SEI) Product Line Practice Framework to examine the suitability of SPL engineering for the company. The findings of the study highlight the potential challenges that SPL engineering poses for the company, and emphasise how the company’s project-centric structure could impede its implementation of systematic software reuse.
How Shallow is a Bug? Open Source Communities as Information Repositories and Solving Software Defects

A central tenet of the open source software development methodology is that the community of users and developers is instrumental in improving the quality of software. I conceptualize open source communities as information repositories to understand how community members help in improving software quality. The information repository contains the accumulated experiences and encoded knowledge of a community that reflects the software architecture. I investigate how community members contribute to the development of this information repository and how the information repository influences the repair time of software defects. Using a 10-year longitudinal dataset from the Firefox community, I find empirical evidence that user contributions and comprehension of the information repository shorten the repair time of software defects. Increased comprehension of the information repository allows a community to offset detrimental effects of community churn.
An Algorithm for Parsing Flow Graphs

Abstract : This report describes research about flow graphs - labeled, directed, acyclic graphs which abstract representations used in a variety of Artificial Intelligence applications. Flow graphs may be derived from flow grammars much as strings may be derived from string grammars; this derivation process forms a useful model for the stepwise refinement processes used in programming and other engineering domains. The central result of this report is a parsing algorithm for flow graphs. Given a flow grammar and a flow graph, the algorithm determines whether the grammar generates the graph and, if so, finds all possible derivations for it. The author has implemented the algorithm in LISP. The intent of this report is to make flow-graph parsing available as an analytic tool for researchers in Artificial Intelligence. The report explores the intuitions behind the parsing algorithm, contains numerous, extensive examples of its behavior, and provides some guidance for those who wish to customize the algorithm to their own uses.
A new adaptive detection algorithm for power quality improvement

In this dissertation, a new adaptive harmonic selective detection algorithm is proposed for power quality improvement applications. The adaptive gains of the proposed method can be chosen relatively large to obtain faster convergence. The stability of the proposed method is guaranteed. The adaptive harmonic selective algorithm is analyzed then compared to a popular d-q method. This proposed adaptive method is simple and effective in extracting fundamental and harmonic current information from harmonic load currents. The extracted fundamental or harmonic currents therefore can be used as the reference signals for power quality improvement applications such as harmonic selective cancellation or reactive power compensation. The proposed adaptive algorithm can estimate time varying power system frequency and it also can identify a dc offset in a load current. These are two conditions that the d-q transformation based detection algorithm is incapable of. This adaptive detection method is phase independent and therefore it can be easily applied to three phase systems. Simulation and experimental results verify the good performance of the proposed new adaptive detection method.
A fast gradient method for embedded linear predictive control

Abstract This work considers the fast solution of model predictive control problems for linear systems with input constraints and a quadratic cost criterion. If the resulting optimization problem arising from the model predictive control is solved online using the Fast Gradient method one needs to determine the gradient of the cost function. We propose a method, tailored for embedded control purposes, that efficiently calculates the gradient taking the underlying structure of the system into account. Moreover, we discuss how the stability of the plant influences the required number of iterations to obtain a solution within a prescribed accuracy.
Multi-frequency radiowave propagation measurements in the portable radio environment

Time-delay-spread and signal-level measurements were made in two dissimilar office buildings at 850 MHz, 1.7 GHz, and 4.0 GHz. No significant statistical difference in time-delay spread was found. The maximum RMS time-delay spread at the three frequencies did not exceed 270 ns at the larger building and 100 ns at the smaller building. Attenuation, although very nearly the same, decreased slightly with increasing frequency, within the limits of the experiment's accuracy. The variation of the received signal level with distance could be characterized by free-space propagation, together with attenuation per unit distance. This attenuation coefficient decreased slightly with increasing frequency. The fractional error was found to be a useful measure of the spread of the data. <<ETX>>
Signal processing strategies and clinical outcomes for gain and waveform compression in hearing aids

Compressive hearing aids automatically reduce their gain with increasing sound level to accommodate reduced dynamic ranges in sensorineural hearing loss. Hearing-impaired people can also benefit from waveform compression that strengthens consonant sounds relative to vowels (E. Villchur, 1973). The normal ear accomplishes both forms of compressive signal processing with instantaneously compressing bandpass nonlinearity (BPNL) filters (R.R. Pfeiffer, 1970) under efferent control. Guided by the development of this basic knowledge (J.L. Goldstein, 2001), we have implemented a real-time PC simulation of a multi-channel DSP hearing aid with programmable gain and waveform compression options. Prescription fitting is being developed through clinical study of patient preferences and speech intelligibility.
MR brain image segmentation using an enhanced fuzzy C-means algorithm

This paper presents a new algorithm for fuzzy segmentation of MR brain images. Starting from the standard FCM and its bias-corrected version BCFCM algorithm, by splitting up the two major steps of the latter, and by introducing a new factor, the amount of required calculations is considerably reduced. The algorithm provides good-quality segmented brain images a very quick way, which makes it an excellent tool to support virtual brain endoscopy.
Book Reviews: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Jurafsky and Martin’s long-awaited text sets a new gold standard that will be difficult to surpass, as attested by the flurry of glowing reviews that accompanied its publication early this year. In a nutshell, J&M have successfully integrated knowledge-based and statistical methods with linguistic foundations and computational models in a book that is at once balanced, comprehensive, and, above all, eminently readable. My review will focus primarily on the format and content of Speech and Language Processing (SLP) as well as its use in a course.
Quantitative Study of Attractor Neural Network Retrieving at Low Spike Rates: I

We discuss the conversion of the description of the dynamics of a neural network from a temporal variation of synaptic currents driven by point spikes and modulated by a synaptic structure to a description of the current dynamics driven by spike rates. The conditions for the validity of such a conversion are discussed in detail and are shown to be quite realistic in cortical conditions. This is done in preparation for a discussion of a scenario of an attractor neural network, based on the interaction of synaptic currents and neural spike rates.The spike rates are then expressed in terms of the currents themselves to provide a closed set of dynamical equations for the currents. The current-rate relation is expressed as a neuronal gain function, converting currents into spike rates. It describes an integrate-and-fire element with noisy inputs, under explicit quaniitatve conditions which we argue to be plausible in a cortical situation In particular, it is shown that the gain of the current to rate transduct.
Learning to Recognize Actions with Weak Supervision

L'accroissement rapide des donnees numeriques videographiques fait de la comprehension automatiquedes videos un enjeu de plus en plus important. Comprendre de maniere automatique une video recouvrede nombreuses applications, parmi lesquelles l'analyse du contenu video sur le web, les vehicules autonomes,les interfaces homme-machine. Cette these presente des contributions dans deux problemes majeurs pourla comprehension automatique des videos : la detection d'actions supervisee par des donnees web, et la localisation d'actions humaines.La detection d'actions supervisees par des donnees web a pour objectif d'apprendre a reconnaitre des actions dans des contenus videos sur Internet, sans aucune autre supervision. Nous proposons une approche originaledans ce contexte, qui s'appuie sur la synergie entre les donnees visuelles (les videos) et leur description textuelle associee, et ce dans le but d'apprendre des classifieurs pour les evenements sans aucune supervision. Plus precisement, nous telechargeons dans un premier temps une base de donnees videos a partir de requetes construites automatiquement en s'appuyant sur la description textuelle des evenements, puis nous enlevons les videos telechargees pour un evenement, et dans laquelle celui-ci n'apparait pas. Enfin, un classifieur est appris pour chaque evenement. Nous montrons l'importance des deux etapes principales, c'est-a-dire la creations des requetes et l'etape de suppression des videos, par des resutatsquantitatifs. Notre approche est evaluee dans des conditions difficiles, ou aucune annotation manuelle n'est disponible, denotees EK0 dans les challenges TrecVid. Nous obtenons l'etat de l'art sur les bases de donneesMED 2011 et 2013.Dans la seconde partie de notre these, nous nous concentrons sur la localisation des actions humaines, ce qui implique de reconnaitre a la fois les actions se deroulant dans la video, comme par exemple "boire" ou "telephoner", et leur etendues spatio-temporelles. Nous proposons une nouvelle methode centree sur la personne, traquant celle-ci dans les videos pour en extraire des tubes encadrant le corps entier, meme en cas d'occultations ou dissimulations partielles. Deux raisons motivent notre approche. La premiere est qu'elle permet de gerer les occultations et les changements de points de vue de la camera durant l'etape de localisation des personnes, car celle-ci estime la position du corps entier a chaque frame. La seconde est que notre approche fournit une meilleure grille de reference que les tubes humains standards (c'est-a-dire les tubes qui n'encadrent que les parties visibles) pour extraire de l'information sur l'action. Le coeur de notre methode est un reseau de neurones convolutionnel qui apprend a generer des propositions de parties du corps humain. Notre algorithme de tracking connecte les detections temporellement pour extraire des tubes encadrant le corps entier. Nous evaluons notre nouvelle methode d'extraction de tubes sur une base de donnees difficile, DALY, et atteignons l'etat de l'art.