Miracle-dz/UniFAR-FTUs huggingface dataset:
Preface: Introduction to Domain Engineering: Product Lines, Languages, and Conceptual Models

A domain is an area of knowledge that uses common concepts for describing phenomena, requirements, problems, capabilities, and solutions. A domain is usually associated with well-defined or partially defined terminology. This terminology refers to the basic concepts in that domain, their definitions (i.e., their semantic meanings), and their relationships. It may also refer to behaviors that are desired, forbidden, or perceived within the domain. Domain engineering is a set of activities that aim to develop, maintain, and manage the creation and evolution of domains. Domain engineering has become of special interest to the information systems and software engineering communities for several reasons. These reasons include, in particular, the need to maintain and use existing knowledge, the need to manage increasing requirements for variability of information and software systems, and the need to obtain, formalize, and share expertise in different, evolving domains. Domain engineering as a discipline has practical significance as it can provide methods and techniques that may help reduce time-to-market, development cost, and projects risks, on the one hand, and help improve product quality and performance on a consistent basis, on the other hand. It is used, researched, and studied in various fields, predominantly: software product line engineering (SPLE), domain-specific language engineering (DSLE), and conceptual modeling. This book presents a collection of state-of-the-art research studies in the domain engineering field. About half of the chapters in this collection originated from a series of workshops, named domain engineering, which were associated with the Conference of Advanced Information Systems Engineering (CAiSE) during the years 2009–2011 and with the international conference on conceptual modeling (also known as the ER conference) in 2010. The authors of the other chapters were personally invited to contribute to this book. The chapters are organized in three parts. The first part includes research studies that deal with domain engineering in SPLE. The second part refers to domain engineering as a research topic within the field of DSLE. Finally, the third part presents research studies that deal with domain engineering within the field of conceptual modeling.
Alignment of the Polish-English Parallel Text for a Statistical Machine ＂Translation

Text alignment is crucial to the accuracy of MT (Machine Translation) systems, some NLP (Natural Language Processing) tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED (Translanguage English Database) talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with the described tool is shown.
A Large-scale Study of Failures in High-performance-computing Systems (CMU-PDL-05-112)

Designing highly dependable systems requires a good understanding of failure characteristics. Unfortunately, little raw data on failures in large IT installations is publicly available. This paper analyzes failure data recently made publicy available by one of the largest high-performance computing sites. The data has been collected over the past 9 years at Los Alamos National Laboratory and includes 23000 failures recorded on more than 20 different systems, mostly large clusters of SMP and NUMA nodes. We study the statistics of the data, including the root cause of failures, the mean time between failures, and the mean time to repair. We find for example that average failure rates differ wildly across systems, ranging from 20–1000 failures per year, and that time between failures is modeled well by a Weibull distribution with decreasing hazard rate. From one system to another, mean repair time varies from less than an hour to more than a day, and repair times are well modeled by a lognormal distribution.
Maloney and Smith Method for Modeling Debye-Media Thin Sheets in the FDTD Grid

The Maloney and Smith method used to account for sheets thinner than the cell size of the finite-difference time-domain (FDTD) method is extended to the case where both the sheet and the surrounding media are such dispersive Debye media as the Human Body tissues. The new method is then compared to the simpler Luebbers and Kuntz method that was previously extended to Debye media. The comparison relies on the reflection and transmission of plane waves and on 3-D experiments. It is concluded that the Maloney and Smith method permits a better accuracy to be achieved. It provides accurate reflection and transmission whatever may be the incidence angle of the wave that strikes the sheet.
Efficient processor management strategies for multicomputer systems

Multicomputers are cost-effective alternatives to the conventional supercomputers. Contemporary processor management schemes tend to underutilize the processors and leave many of the processors in the system idle while jobs are waiting for execution. Instead of designing faster processors or interconnection networks, a substantial performance improvement can be obtained by implementing better processor management strategies. This dissertation studies the performance issues related to the processor management schemes and proposes several ways to enhance the multicomputer systems by means of processor management. The proposed schemes incorporate the concepts of size-reduction, non-contiguous allocation, as well as job migration. Job scheduling using a bypass-queue is also studied. All the proposed schemes are proven effective in improving the system performance via extensive simulations. Each proposed scheme has different implementation cost and constraints. In order to take advantage of these schemes, judicious selection of system parameters is important and is discussed.
Space-variant picture coding

Space-variant picture coding techniques exploit the strong spatial non-uniformity of the human visual system in order to increase coding efficiency in terms of perceived quality per bit. This thesis extends space-variant coding research in two directions. The first of these directions is in foveated coding. Past foveated coding research has been dominated by the single-viewer, gaze-contingent scenario. However, for research into the multi-viewer and probability-based scenarios, this thesis presents a missing piece: an algorithm for computing an additive multi-viewer sensitivity function based on an established eye resolution model, and, from this, a blur map that is optimal in the sense of discarding frequencies in least-noticeable-first order. Furthermore, for the application of a blur map, a novel algorithm is presented for the efficient computation of high-accuracy smoothly space-variant Gaussian blurring, using a specialised filter bank which approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced cost compared to the brute force approach of employing a separate low-pass filter at each image location. The second direction is that of artificially increasing the depth-of-field of an image, an idea borrowed from photography with the advantage of allowing an image to be reduced in bitrate while retaining or increasing overall aesthetic quality. Two synthetic depth of field algorithms are presented herein, with the desirable properties of aiming to mimic occlusion effects as occur in natural blurring, and of handling any number of blurring and occlusion levels with the same level of computational complexity. The merits of this coding approach have been investigated by subjective experiments to compare it with single-viewer foveated image coding. The results found the depth-based preblurring to generally be significantly preferable to the same level of foveation blurring.
生成型アプローチによるLatent Words Language ModelのN-gram近似

概要:今日の大語彙連続音声認識において,デコーディングとの相性から,N-gramモデルが最も実用的な 言語モデルとして利用されている. N-gramモデルは,膨大なモデルパラメータに起因するデータスパー スネスの問題を持つことが知られており,この問題を解決するために,スムージングや次元削減に基づ く様々なアプローチが検討されてきた. これに対して我々は,学習データ自体を新たに生成し,生成した データに基づき N-gramモデルを推定する生成型アプローチを検討する. 本稿では,高性能な生成モデル である Latent Words Language Model(LWLM)をデータ生成のためのモデルとして利用する. LWLM自 体を音声認識に直接利用することは,計算コストの問題から非現実的ではある. しかし,確率過程に基づ きデータ生成を行うことは比較的容易である. ベイズ推定に基づく LWLMは,スムージングと次元削減を 同時に考慮した非常に柔軟なモデル構造を持つ. よって,生成されたデータから推定した N-gramモデル は,学習データから直接推定した N-gramモデルとは異なる性質を持ち,様々なタスクで頑健に動作する ことが期待できる. そこで本稿では,複数タスクで様々な言語モデルとの比較実験を行うことで,LWLM を用いた生成型アプローチの有効性を検証する.
ERP Components: The Ups and Downs of Brainwave Recordings

Th e goal of this chapter is to provide a framework for understanding, interpreting, and using eventrelated potential (ERP) components in the broad domain of mind, brain, and behavior sciences. Researchers in other areas such as political science, economics, law, and medicine may also fi nd this overview useful as a guide to a broad understanding of ERP components. Event-related potentials have been used for decades to uncover aspects of the sensory, cognitive, and motor processes that underlie human thought and behavior. Th e excellent temporal resolution of the technique provides a narration of neural processes as they unfold millisecond by millisecond, adding whole pages to the story of the mind that behavioral and imaging techniques leave blank. However, the ERP technique is not without limitations. As refl ected in the title of this chapter, there are both advantages and limitations of the ERP technique, and we will explore both the ups and the downs of ERPs in this chapter. Th e fi rst section of the chapter is aimed at defi ning the term ERP component , describing the neural events that give rise to ERP components and explaining how multiple components sum together to form the observed ERP waveform. Th e next section describes the problems involved in isolating individual ERP components from the observed waveform, which is often much more diffi cult than researchers realize. Th is is followed by a discussion of the challenges involved in linking an ERP component with a specifi c neural or psychological process and then using this link to answer broader questions about the mind and brain. Th ese challenges may seem insurmountable, but researchers have developed experimental and analytic approaches that can overcome them in many cases. Th e key to using ERPs eff ectively is to understand what questions can be answered by ERP experiments and how the limitations of the technique can be avoided. Indeed, despite its limitations, the ERP technique is often Abstract
PAUP* 4.0 : Phylogenetic Analysis Using Parsimony

PAUP* 4.0 Beta is a major upgrade of the bestselling software for the inference of evolutionary trees, for use in Macintosh or Windows/DOS-based formats. This version is for use in Macintosh. Please note it is currently only available as a Beta version. For more information (and the Linux/UNIX version), visit www.sinauer. com/detail.php? id=8060
Quantitative description of the relationship between the enhancement of distraction-suppression and brain local state alteration after transcranial direct current stimulation

Anodal transcranial direct current stimulation (tDCS) over the left dorsal lateral prefrontal cortex (lDLPFC) can improve distraction suppression ability, possibly by distantly regulating the connection properties of several large-scale brain networks and local brain state changes. However, little is known about the local state alteration that tDCS can induce in distant but task-related regions and the relationship between performance enhancement and local state alteration in potentially related regions, resulting in inefficient and uncertain tDCS regulation. We aimed to examine the alteration of brain local state before and after tDCS and its relationship with performance enhancement. With the within-subject design, the participants received anodal (1.5 mA) and sham tDCS at F3 (lDLPFC) for 20 min. The visual search task and resting-state functional magnetic resonance imaging (rsfMRI) were performed before and after stimulation. Anodal tDCS significantly enhanced distraction suppression. The amplitude of low-frequency fluctuation (ALFF) in the left parietal region significantly decreased, the decrement significantly positively correlated with performance enhancement after anodal tDCS. As well, the regional homogeneity (ReHo) in the left precuneus significantly increased, and the increasement significantly positively correlated with performance enhancement. Anodal tDCS over the lDLPFC can distantly modulate the local state of the brain and improve the distraction suppression ability. These two aspects are closely related and provide a direct and efficient approach to enhancing performance.
A JavaScript Mode for Yi

Yi is a text editor written in the lazy functional programming language Haskell, which makes it possible to define precise editing modes using an abstract syntax tree provided online using the lazy and incremental parser library in Yi. We have developed a JavaScript mode for this editor using this parser library to accurately point out possible errors in the source code. The mode accurately highlights syntactical errors as the user types and provides a verifier to check the semantics of the source code. It supports most of the syntax from JavaScript 1.8 and can readily be extended with more functionality. The mode can also be used as a starting point for future developers of C-like modes for Yi. Writing a responsive parser for Yi proved non-trivial, because of the trade-off between parser performance and accuracy. This report describes how to write a mode for Yi and the different problems encountered during the working process of the JavaScript mode. It also describes in what ways the problems were solved. Sammanfattning Yi ar en textredigerare skriven i det lata funktionella programspraket Haskell, som gor det mojligt att definiera noggranna redigeringslagen med hjalp av ett abstrakt syntaxtrad som tillhandahalls av det lata och inkrementella parsningsbiblioteket i Yi. Vi har utvecklat ett JavaScript-lage till denna redigerare med hjalp av detta parsningsbibliotek for att exakt utpeka mojliga fel i kallkoden. Laget markerar syntaktiska fel medan anvandaren skriver och tillhandahaller en verifierare for att kontrollera semantiken i kallkoden. Det stodjer storre delen av syntaxen i JavaScript 1.8 och kan enkelt utokas med mer funktionalitet. Laget kan aven anvandas som en utgangspunkt for framtida utvecklare av C-lika lagen till Yi. Det visade sig att det inte ar trivialt att skriva en parser till Yi p.g. a. avvagningen mellan prestanda och noggrannhet. Denna rapport beskriver hur lagen till Yi skrivs och de olika problemen vi stotte pa under utvecklingen av JavaScript-laget. Den beskriver aven hur vi loste problemen.
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications

1. Introduction. 2. Neural Network Architectures and Learning Schemes. 3. ELEANNE: Efficient LEarning Algorithms for Neural NEtworks. 4. Fast Learning Algorithms for Neural Networks. 5. ALADIN: Algorithms for Learning and Architecture DetermINation. 6. Performance Evaluation of Single-Layered Neural Networks. 7. High-Order Neural Networks and Networks with Composite Key Patterns. 8. Applications of Neural Networks: A Case Study. 9. Applications of Neural Networks: A Review. 10. Future Trends and Directions. References. Subject Index. Author Index.
Learning environments and tutoring systems

The articles here combine breadth of perspectives with a focus on the field's two major areas of work: learning environments and tutoring systems. The section on learning environments addresses issues of cognitive style, metatheoretic objectives, domain analysis, and a variety of implemented prototype systems: programming by example, Object-orientated Logo and the educational use of constraint based language. The domains of application range from single-word learning to algebra and architectural design. The section on intelligent tutoring systems (ITS) presents a spectrum of work grown out of the classic knowledge engineering paradigm, discussing principles and emerging methodologies for ITS construction: authoring shells, user modelling, and the role of machine learning. The applications reported range from elementary number knowledge to electronic circuits and complex industrial processes.
Feature selection by AdaBoost for SVM-based face detection

In this paper, we present a three-stage method to speed up a SVM-based face detection system. In this proposed system, a large number of simple non-face patterns are rejected quickly by two first stage cascaded classifiers using flexible sizes of analyzed windows while the last stage uses a non linear SVM classifier to robustly classify complex 24x24 pixel patterns as either faces or non-faces. For all stage classifiers, an optimal subset of overcomplete Haar wavelet feature set selected by AdaBoost learning is used to achieve both fast and high detection rate. Experimental results show that our system can achieve comparable results to state of the art face detection systems.
Telling the Mobile Libraries Story: Collecting the Past To Build a Future.

This paper discusses mobile libraries in Thailand. Topics addressed include: (1) justification for the mobile library project; (2) the different types of mobile library services (i.e., mobile train, bus, and boat libraries), including the objectives of each type of library; (3) how the mobile services were formed; (4) the uniqueness of mobile services; (5) planning the service for target groups; and (5) approaches to user education through mobile services. (MES) Reproductions supplied by EDRS are the best that can be made from the original document. Telling the mobile libraries story.nference Programme and Proceedings http://www.ifla. org/IV/ifla66/papers/102-175e.htm
Forensics Image Background Matching Using Scale Invariant Feature Transform (SIFT) And Speeded Up Robust Features (SURF)

Abstract : In criminal investigations, it is not uncommon for investigators to obtain a photograph or image that shows a crime being committed. Additionally, thousands of pictures may exist of a location, taken from the same or varying viewpoints. Some of these images may even include a criminal suspect or witness. One mechanism to identify criminals and witnesses is to group the images found on computers, cell phones, cameras, and other electronic devices into sets representing the same location. One or more images in the group may then prove the suspect was at the crime scene before, during,and/or after a crime. This research extends three image feature generation techniques, the Scale Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), and the Shi-Tomasi algorithm, to group images based on location. The image matching identifies keypoints in images with changes in the contents, viewpoint, and individuals present at each location. After calculating keypoints for each image, the algorithm stores the strongest features for each image are stored to minimize the space and matching requirements.
Enhanced atmospheric oxidizing capacity in simulating air quality with updated emission inventories for power plants especially for haze periods over East China

Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting Model with Chemistry (WRF-Chem), two simulations were executed to assess the atmospheric environmental change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that (1) compared to the power emissions of MEIC, PM 2.5 , PM 10 , SO 2 and NO x were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs were higher in the UEIPP, reflecting a large discrepancy in the power emissions over East China; (2) In accordance with the changes of UEIPP, the modeled concentrations were reduced for SO 2 and NO 2 , and increased for most areas of primary OC, BC and CO, whose concentrations in atmosphere are highly dependent on emission changes. (3) Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced, reflecting by increased oxidizing agents, e.g. O 3 and OH, thus directly strengthened the chemical production from SO 2 and NO x to sulfate and nitrate, which offset the reduction of primary PM 2.5 emissions especially in the haze days. This study indicated the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with the implications on air quality and environmental changes.
Development of a novel single-phase auto-reclosing scheme for distribution network with integrated distributed generation.

According to G59 recommendation, the common practice adopted by utilities in the UK is to disconnect Distributed generators (DGs) immediately once a network fault is detected to prevent islanding operation and disruption of the operation of conventional auto-reclosing schemes. However, with the continuous increase of penetration of DGs into existing distribution networks, it is becoming increasingly important that DGs must support power networks during steady-state and fault conditions. It has been recognised that 80% of faults in distribution networks are temporary and most of them are single-line-to-ground (SLG) faults. This should give a strong incentive to find ways for maintaining DGs in service during temporary SLG faults as far as possible which would benefit utilities, DG developers and customers. The literature survey shows that no research work has been carried out to investigate a fault identification and phase selection algorithm suitable to be used in a novel single-phase auto-reclosing (SPAR) scheme, specifically suitable to distribution networks with DGs, for maintaining the continued operation of DGs during fault conditions. The work in this PhD has focused on investigating feeder protection requirements for continued operation of DGs in radial and ring overhead distribution networks with DGs, particularly the new requirements for operating an auto-reclosing scheme in networks with DGs. Simulation results obtained from this investigation have revealed that it is a possible to maintain continued operation of DGs that are based on DFIG and FSIG during temporary SLG faults in radial and ring operated networks. This is followed by investigating the development of an adaptive fault identification and phase selection algorithm suitable for a SPAR scheme in a power system with DGs. The proposed fault identification and phase selection algorithm uses only the three line currents measured at the relay point. The waveform pattern of phase angle and symmetrical components of the three line currents during the transient period following a fault condition is analysed using IF-THEN condition-based rules in order to determine the type of SLG fault. The verification test results have revealed that the proposed method can correctly detect the faulty phase within one cycle in a distribution network with DGs under various network operating and fault conditions. The work in this PhD has resulted in publication of papers (see Appendix I).