Miracle-dz/UniFAR-FTUs huggingface dataset:
Accidents at rural t junctions

This report concerns traffic accidents occurring during the 5 years, 1979-1983, at a sample of some 300 T-junctions on rural single carriageway roads subject to a speed limit of 50 mile/h or higher and covering 40 English counties. The objectives of the study were to investigate the frequency and character of the accidents in relation to traffic flow, geometric layout and other factors, such as traffic speed and gradient. The study examined in particular, the safety performance of three design features, ghost islands, merging and diverging lanes, and islands in the minor road. The study examined not only those accidents occurring within 20 m of the T-junction (the 'Stats 19' definition of junction accidents) but also accidents up to 100 m from the junction. Multiple regression analysis was used to relate accident frequency at the junctions to a range of explanatory variables to provide a model for examining the effect of the geometric and other features. (A)
Animated Infographics in Digital Educational Publishing : Case Study of Educational Animated Infographics

Tampereen ammattikorkeakoulu Tampere University of Applied Sciences Degree Programme in Media LIEVEMAA, JOHANNA: Animated Infographics in Digital Educational Publishing Case Study of Educational Animated Infographics Bachelor's thesis 42 pages May 2017 The purpose of this thesis was to study the possibilities of animated infographics in the technologically developing field of educational publishing. Digital books can be enhanced in many ways, including animations. This thesis explored the advantages and disadvantages of animated infographics in digital schoolbooks in Finland. First the current state of Finnish educational materials was charted examining the potential for digital learning. The history of infographics and animation were then studied in the context of educational use. The theory was applied to case animations in order to create effective animated infographics. The commissioner of this thesis was e-Oppi Oy, a publisher of educational books that are primarily made for digital platforms. Three technically diverse infographics from different subjects were chosen to be animated. It was concluded that animations as infographics have unique benefits in learning materials. They should be used with consideration. Whether a specific infographic should be animated or not depends on the subject matter and if there are enough resources to be used. More animations will be used in the books of e-Oppi Oy as a result of this study. More research needs to be done on the effectiveness of animation as an educational tool as technology opens up possibilities for even more immersive applications.
Optimal dispatchable DG allocation in a distribution network considering load growth with a mixed-PSO algorithm

In recent years, one of the major issues faced by distribution utilities is integrating distributed generation (DG) units in distribution networks. This paper proposes a population-based method called particle swarm optimization (PSO) for optimal planning of the location and sizing of different types of DG units in the distribution network, considering different loading conditions. The objective of this application is power loss minimization. In order tond the optimal location and size of DG units, continuous and discrete forms of PSO are deployed, respectively (mixed PSO). In addition, the optimal locations and sizes of the DG units are determined in the areas of signicant feeder load growth. This meta- heuristic approach makes little or no assumption about the issue being optimized and can search large spaces of possible solutions. The presented method is tested on the standard IEEE 33-bus and IEEE 69-bus test systems. In order to show the effectiveness of the proposed methodology, the results are compared with another method of DG allocation and another loss minimization technique.
Software Defect Prediction Using Supervised Learning Algorithm and Unsupervised Learning Algorithm

Software defect prediction has recently attracted attention of many software quality researchers. One of the major areas in current project management software is to effectively utilize resources to make meaningful impact on time and cost. A pragmatic assessment of metrics is essential in order to comprehend the quality of software and to ensure corrective measures. Software defect prediction methods are majorly used to study the impact areas in software using different techniques which comprises of neural network (NN) techniques, clustering techniques, statistical method and machine learning methods. These techniques of Data mining are applied in building software defect prediction models which improve the software quality. The aim of this paper is to propose various classification and clustering methods with an objective to predict software defect. To predict software defect we analyzed classification and clustering techniques. The performance of three data mining classifier algorithms named J48, Random Forest, and Naive Bayesian Classifier (NBC) are evaluated based on various criteria like ROC, Precision, MAE, RAE etc. Clustering technique is then applied on the data set using k-means, Hierarchical Clustering and Make Density Based Clustering algorithm. Evaluation of results for clustering is based on criteria like Time Taken, Cluster Instance, Number of Iterations, Incorrectly Clustered Instance and Log Likelihood etc. A thorough exploration of ten real time defect datasets of NASA[1] software project, followed by various applications on them finally results in defect prediction.
Luminance Contrast Influences Reaction Time in Young and Older Adults

Age-specific design principles for three dimensional virtual environment systems are sparse. Given that sensorimotor control systems change across the lifespan, understanding age differences in motor performance within virtual environments is crucial to designing effective, usable interfaces. This paper investigates the effect of luminance contrast level on reaction time to a visual stimulus in both young and senior adults. Results indicate that young adults have faster reaction times than seniors, but both groups improved reaction times with increasing luminance contrast of the target. Young adults improved at lower levels of contrast than seniors. Implications for age-specific design of virtual environments are discussed. Keywordsvirtual environment; aging; motor control; reach to grasp; luminance contrast
State of the Art on Semantic IS Standardization, Interoperability & Quality

This book contains a broad overview of relevant studies in the area of semantic IS standards. It includes an introduction in the general topic of standardization and introduces the concept of interoperability. The primary focus is however on semantic IS standards, their characteristics, and the quality of standards. Sidesteps are made to the economics of standards, the development & adoption of semantic IS standards, and the latest trends in the area of standardization. The reader will get a basic understanding of a huge amount of literature in this area and is invited to look for the original sources for in-depth understanding. In addition, the audience can make the next step by building new research on these sources to advance the area of semantic IS standardization. It is also useful for practitioners or policy makers that are mainly interested in a quick overview of the state-of-the-art on semantic IS standards in the current scientific literature.
ESTRUCTURAS DE DATOS SUCINTAS PARA RECUPERACIÓN DE DOCUMENTOS

Document retrieval consists in, given a collection of documents and a query pattern, obtaining documents relevant for the query. Having the documents available on advance allows one to build an index that obtains relevant documents within a reasonable amount of time. Indexes to solve such a fundamental problem are required in many fields, like Information Retrieval, data mining, bioinformatics, and so on. When the text to be indexed is natural language, the paradigmatic solution is the inverted index. However, document retrieval problems arise also in scenarios where text and pattern can be general sequences of symbols, such as Oriental languages, multimedia databases, and genomic sequences. In those scenarios the classical inverted indexes cannot be successfully applied. Even though there exist linear-space solutions for this general text scenario, the space required is a serious concern in practice: those indexes may require more than 20 times the size of the collection. This thesis introduces novel algorithms and data structures to solve some important document retrieval problems on general text collections in reduced space. More specifically, we provide new solutions for document listing with term frequencies and for top-k document retrieval. As a byproduct, we obtain a new compression scheme for repetitive bitmaps that might be of independent interest. We implemented our proposals, as well as most relevant previous work, and studied their practicality. Our experiments show that our proposals for document listing reduce the space of the best previous solution by 40% with little impact on query times. For top-k document retrieval our solutions allow one to reduce the space by 40% in practice, with no impact on the query time. In addition, we were able to reduce query times up to a factor of 100, as the cost of about 1 extra bit per character. Our solutions are able to retrieve from top-10 to top-100 documents within milliseconds. Our new combinations dominate most of the space/time tradeoff, aiming to become the reference standard with which subsequent research should compare.
Extended cardiac tachyarrhythmias in guinea pigs

Guinea pigs are frequently used as models for ventricular tachyarrhythmias, including polymorphic ventricular tachycardia and ventricular fibrillation. However, applications of the model for the development of new cardioversion and defibrillation technology are limited because the arrhythmias typically last less than five seconds. This study demonstrates that a brief period of rapid pacing facilitates the initiation of consistent, sustained ventricular tachyarrhythmias in large guinea pigs, eliminating spontaneous termination as a confounding factor in the study of acute therapies.
Profiling the Reading Comprehension Strategies of Malaysian College TESL Students

This paper will present an inventory of reading comprehension strategies of Malaysian College TESL students. More specifically, it outlines the strategies these students commonly use when they read expository texts. The paper is based on a study conducted in a teacher training college in the East Coast of Peninsula Malaysia. The sample of the study comprised 14 students who were pursuing the Bachelor in Education (B.Ed.) TESL foundation course in the teacher training college. Of the 14 college TESL students seven were proficient readers and the rest less proficient readers. Altogether, they read two culturally familiar texts and two culturally unfamiliar texts. The think-aloud method was used to elicit reading comprehension strategies when they read the two different text types. The data were subjected to both qualitative and quantitative analyses. The findings of the study are discussed in relation to past reading strategy research and in the context of current theoretic views about strategy use in reading comprehension. (Author's abstract)
ON THE EVALUATION OF THE PARAMETERS OF THE BASIC STAGES OF THE HUMAN BODY GROWTH. II. ANALYSIS OF THE HIGH-VOLUME STATISTICS DATA

Starting from the results obtained in the frame of the first part of this study, this paper reports the: a) specific features of the existing high-volume statistics growth data, b) proposed criterion of for the evaluation of the compatibility of theoretical models relative to the studied experimental data, c) results obtained by means of the analysis of the high-volume statistics growth data, compared with the results obtained by means of some low-volume statistics growth data.
Statistical Shape Analysis for Computer Aided Spine Deformity Detection

In this paper we describe a medical application where we exploit surface properties (measured in form of 3D-Range scans of the human back) to derive a-priori unknown additional properties of the proband, that otherwise can only be acquired using multiple x-ray recordings or volumetric scans as CT or MRI. On the basis of 274 data sets, we perform classification using statistical shape analysis methods. Consistent parameterization and alignment is achieved on the basis of only few anatomic landmarks. As our choice of landmarks is easy to detect on the human body, our approach is feasible for screening applications that can be expected to have much impact on the early detection and later treatment of spine deformities, in particular scoliosis.
Application-driven Development of Flexible Packet-oriented Communication Interfaces

Today's heterogeneous SoCs deploy concurrent processing cores in increasing numbers. But their diverse IO interfaces remain dedicated non-programmable modules. Aimed at fully programmable and homogeneous platforms in network and multimedia domains, this work explores the feasibility of programmable approaches for packet-oriented IO interfaces in 3 steps: 1) Modeling and analysis of IO interfaces with respect to a common structure and elementary functions, 2) Design of a Y-chart-based methodology and a set of domain-specific tools for the application-driven development (SystemClick) and later deployment (CRACC) of flexible IO interfaces, and 3) the quantitative exploration of the programmable interface design space based on a strictly modular platform (NOVA). Case studies of PCIe, Hypertransport, RapidIO, Ethernet, and wLAN demonstrate feasibility and current limitations of a common protocol-agnostic and truly programmable implementation.
Sequential Decision-Making under Non-stationary Environments via Sequential Change-point Detection

Reinforcement Learning (RL) has been mainly interested in computing an optimal policy for an agent acting in a stationary environment. However, in many real world decision problems the assumption on the stationarity does not hold. One can view a non-stationary environment as a set of contexts (also called modes or modules) where a context corresponds to a possible stationary dynamics of the environment. Even most approaches assume that the number of modes is known, a RL method-Reinforcement Learning with Context Detection (RLCD)-has been recently proposed to learn an a pirori unknown set of contexts and detect context changes. In this paper, we propose a new approach by adapting the tools developed in statistics and more precisely in sequential analysis for detecting an environmental change. Our approach is thus more theoretically founded and necessitates less parameters than RLCD. We also show that our parameters are easier to interpret and therefore easier to tune. Finally, we show experimentally that our approach out-performs the current methods on several application problems.
RBF Neural network based on ART neural network

For the problem that it is difficult to determine the hidden layer structure of the radial basis function(RBF) neural network, based on the good online classified characteristic of adaptive resonance theory(ART) neural network, a self-organizing RBF neural network structure design algorithm is proposed. The algorithm uses the clustering characteristic of ART neural network to design the RBF neural network structure. Through the similarity comparison of input vector, the number of the hidden layer nodes and initial parameters are determined, so that the network has simplified structure. The experiment results show that the proposed structure has a smaller number of nodes, fast learning speed and better approximation ability.
Study on DAPODIK Information System: User Satisfaction as Mediation of System Quality and Information Quality on Net Benefit

The purpose of this study was to describe the system quality, information quality, user satisfaction and net benefit, and analyze the influence of system quality and information quality on net benefit through user satisfaction. The number of the population in this study were 70 operators DAPODIK Information System in Public Senior High School on Malang Regency, Indonesia. The researcher uses the census technique by asking questions on the questionnaire to collect the data. This research use quantitative and explanatory approach. For descriptive and regression analysis SPSS version 20 were applied while path analysis used to test the hypothesis. Based on the research result, the conclusions were as follows: 1) ease of use, ease of learning, response time, reliability, flexible, personalizable, and security contributed on system quality. 2) completeness, presentation of information, relevance, accuracy, and timeliness contributed to information quality. 3) repeat purchase and repeat visit contributed to user satisfaction. 4) learning, decision quality, decision time, productivity, and task performance contributed to net benefit. 5) system quality does not influence toward user satisfaction, but information quality had a positive influence toward user satisfaction. 6) system quality and information quality do not influence toward net benefit. 7) user satisfaction had a positive influence toward net benefit. 8) information quality had a positive influence toward net benefit through user satisfaction. 9) the implication of this study that information system plays an important role in creating user satisfaction and net benefit. Keywords: System quality, information quality, user satisfaction, net benefit
How to Read Less: On the Benefit of Human-in-the-loop Incremental Learning for Systematic Literature Review

Systematic literature reviews (SLRs) are the primary method for aggregating and synthesizing evidence in evidence-based software engineering (SE). Primary study selection is a critical and time-consuming SLR step in which reviewers use titles, abstracts, or even full texts to evaluate thousands of studies to find the dozens of them that are relevant to the research questions. We seek to reduce the effort of primary study selection in SE SLRs by exploring and refactoring the state-of-the-art human-in-the-loop incremental learning techniques from evidence-based medicine and legal electronic discovery. By refactoring those methods, we discovered FASTREAD, which is a new state-of-the-art in human-in-the-loop incremental learning for SE SLRs. Tested on two data sets generated from existing SE SLRs of Hall, Wahono, et al., FASTREAD outperforms the current state-of-the-art methods. Our results suggest that FASTREAD is able to find 90% of the studies found by standard manual methods, by only reading less than 10% of the candidate studies.
Optimal Distributed Control of Multi-agent Systems in Contested Environments via Reinforcement Learning

This paper presents a model-free reinforcement learning (RL) based distributed control protocol for leader-follower multi-agent systems. Although RL has been successfully used to learn optimal control protocols for multi-agent systems, the effects of adversarial inputs are ignored. It is shown in this paper, however, that their adverse effects can propagate across the network and impact the learning outcome of other intact agents. To alleviate this problem, a unified RL-based distributed control frameworks is developed for both homogeneous and heterogeneous multi-agent systems to prevent corrupted sensory data from propagating across the network. To this end, only the leader communicates its actual sensory information and other agents estimate the leader state using a distributed observer and communicate this estimation to their neighbors to achieve consensus on the leader state. The observer cannot be physically affected by any adversarial input. To further improve resiliency, distributed H-infinity control protocols are designed to attenuate the effect of the adversarial inputs on the compromised agent itself. An off-policy RL algorithm is developed to learn the solutions of the game algebraic Riccati equations arising from solving the H-infinity control problem. No knowledge of the agent dynamics is required and it is shown that the proposed RL-based H-infinity control protocol is resilient against adversarial inputs.
Test factoring with amock: generating readable unit tests from system tests

Automated unit tests are essential for the construction of reliable software, but writing them can be tedious. If the goal of test generation is to create a lasting unit test suite (and not just to optimize execution of system tests), it is essential that generated tests can be understood by the developers that will be running them, so that they can tell the difference between real and spurious failures. amock is a system which automatically generates human-readable JUnit regression tests that use mock objects to simulate the behavior of individual objects dynamically observed during a system test execution. Thesis Supervisor: Michael D. Ernst Title: Associate Professor
FORMATTING THREADED DISCUSSIONS FOR ENHANCED E-LEARNING: EXPLORING BEST PRACTICES

This paper explores which of several formats in threaded discussions seem to work best for student learning. Student surveys were conducted over a period of three years asking which of four types of threaded discussions were most valuable to them in the online learning environment. The first study was conducted in 2013-2014, followed by a second study conducted in 2015-2016. Each study was composed of multiple student surveys administered in the fall, spring, and summer terms. Graduate and undergraduate students in Business and Education classes were surveyed. Courses were taught both in the partially online and fully online formats. The setting was a mid-size private university in Southwestern Pennsylvania. Results differed. In each of the surveys, students were asked to choose which of the four formats for threaded discussions they preferred. More than a quarter of the participants in the first study responded that they liked all of the formats “about the same. ” In the second study, almost a third of the students said they preferred discussion formats that allowed them to express an opinion on a scenario or an example provided by the instructor. This paper examines the possible basis for the differences.
Interactive Participatory Dramas for Language Learning

Interactive participatory dramas are software applications that engage the users by involving them as a character in a story. Although these dramas have been part of computer-assisted language learning (CALL) for more than a decade, they have not become a widespread genre. However, recent advances in video, graphics, and speech recognition, as well as the spread of interactive participatory dramas in other areas of education and training, make the proliferation of such applications in CALL considerably more likely. As there is presently little in the way of research or agreed-on standards of practice to inform the process of creating participatory dramas, this article offers some preliminary guidelines for software developers and evaluators engaged in their design. Among other points, it argues for the use of engaging characters and plots, the use of speech recognition for verbal interaction, the presentation of choice points with consequences for the learners, and the abandonment of the common use of full text prompts that are simply read out loud. It concludes by observing that most of these language learning considerations are equally relevant for simulations and role plays.