Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary School Students' Use of and Attitudes toward Online Mathematics Homework.

The purposes of this study were twofold: 1) to examine the influence of performance expectancy, and effort expectancy on secondary school students attitudes toward the use of a mathematics online homework package called MyiMaths; and 2) to predict the factor that best influences their attitudes. A 15 item, five-point Likertscale instrument was administered to 345 secondary school students, and data were analyzed using the Partial Least Squares-Structured Equation Modeling (PLS-SEM) approach. Findings revealed that there were significant relationships between performance expectancy, effort expectancy, and student attitude toward the use of an online mathematics homework tool. The findings also indicated a positive relationship between student attitudes and their actual use of online homework. Additionally, performance expectancy was found to be the best factor for predicting student attitude toward the use of online homework. The outcomes of this study will enable educators to design appropriate teaching and learning interventions for their students.
DYNAMIC SYSTEM DESCRIPTION AND CONTROL BASED ON DATA

Many real world intelligence systems are too complex to be modeled and controled by traditional mathematical mechanism models. The large amount of data from the systems and their environment is important information which objectively and truthfully reflects the nature of complex dynamic systems. Currently the advanced digital technology has made the digitized data easy to capture and cheap to store. However, raw digital data is rarely useful in practice and the capability to extract information from raw data is extremely important for description and understanding dynamic phenomenon of the data source. The description and control of complex dynamic systems without traditional mathematical mechanism models require new methods that can utilize the existing knowledge, human experience and data. In this paper, applying Fuzzy Set logic and Fuzzy Cognitive Map (FCM), we propose an innovative data based description and control of complex dynamic systems, which is totally different from the tradition mechanism mathematical model based approaches. In the FCM framework, all necessary information for description and control of the systems is directly extracted from the raw data and human experiences. In other words, as long as a few rough simple human experiences and sufficient data are given, the proposed FCMs can describe and control a complex dynamic system for which the traditional mathematical mechanism models can not be established. As an illustrative example, the simulation results of truck back-upper control problem verify the effectiveness, validity and advantageous characteristics of the learning techniques and control ability of the proposed methodology.
Characterization

Comparing implementations of various thread barrier algorithms is really an impossible task for the system with lots of processors. With brute force approach, we have to measure the performance of each algorithm in nCt times, where n is the maximum number of processors and t is the number of threads. This task is exponential in nature and impossible to accomplish for machine with huge number of processors. We designed an algorithm, which uses the symmetric characteristics of the underlying system architecture, and filter out the unnecessary and redundant measurements. This algorithm helps us to compare the performance of various thread barrier algorithms for the machine with any number of processors.
Revisit of Rule-Deletion Strategy for XCSAM Classifier System on Classification

The XCSAM classiﬁer system is an approach of evolutionary rule-based machine learning, which evolves rules advocating the highest-return actions at state, resulting in best classiﬁcation. This paper starts with claiming a limitation that XCSAM still fails to evolutionary generate adequate rules advocating the highest-return actions. Then, under our hypothesis that this limitation is caused from the rule-deletion mechanism of XCSAM, we revisit its rule-deletion strategy in order to promote the evolution of adequate rules. Diﬀerent from the existing deletion strategy which deletes two rules for each rule-evolution, our deletion strategy is modiﬁed to delete more than two rules as necessary. Experimental results on a benchmark classiﬁcation task validate our modiﬁcation powerfully works to evolve adequate rules, improving the performance of XCSAM. We further show our modiﬁcation robustly enables XCSAM to perform well on real world classiﬁcation task.
A FRAMEWORK FOR A WEBSITE SNAPSHOT MANAGEMENT SYSTEM

This paper presents a framework for a website snapshot management system. The objective of the system is to recreate webpage snapshots of every published webpage including their code and rendition upon users’ requests. We define four levels of snapshots and design software components to create snapshots that meet the requirement of each level. A website may choose to maintain the appropriate level of snapshot that fits its needs.
Gravitational fragmentation caught in the act: the filamentary Musca molecular cloud

Filamentary structures are common in molecular clouds. Explaining how they fragment to dense cores is a missing step in understanding their role in star formation. We perform a case study of whether low-mass filaments are close-to hydrostatic prior to their fragmentation, and whether their fragmentation agrees with gravitational fragmentation models. For this, we study the 6.5 pc long Musca molecular cloud that is an ideal candidate for a filament at an early stage of fragmentation. We employ dust extinction mapping in conjunction with near-infrared data from the NEWFIRM instrument, and 870 um dust continuum emission data from the LABOCA instrument, to estimate column densities. We use the data to identify fragments from the cloud and to determine the radial density distribution of its filamentary part. We compare the cloud's morphology with 13CO and C18O line emission observed with the APEX/SHeFI instrument. The Musca cloud is pronouncedly fragmented at its ends, but harbours a remarkably well-defined, 1.6 pc long filament in its Center region. The line mass of the filament is 21-31 Ms pc^-1 and FWHM 0.07 pc. Its radial profile can be fitted with a Plummer profile that has the power-index of 2.6 \pm 11%, flatter than that of an infinite hydrostatic filament. The profile can also be fitted with a hydrostatic cylinder truncated by external pressure. These models imply a central density of 5-10 x 10^4 cm^-3. The fragments in the cloud have a mean separation of 0.4 pc, in agreement with gravitational fragmentation. These properties, together with the subsonic and velocity-coherent nature of the cloud, suggest a scenario in which an initially hydrostatic cloud is currently gravitationally fragmenting. The fragmentation has started a few tenths of a Myr ago from the cloud ends, leaving its center yet relatively non-fragmented, possibly because of gravitational focusing in a finite geometry.
802.11 Ad Hoc LANs with Realistic Channels

In this paper, we present an analytical method for estimating the saturation throughput of an 802.11 ad hoc LAN in the presence of noise distorting transmitted frames. This is the first method that allows studying analytically the 802.11 network performance with consideration of correlated channel failures usually inherent to realistic wireless channels. With the study, we consider the possible packet fragmentation that can be adopted to reduce the performance degradation caused by noise-induced distortions. In addition to the throughput, our method allows estimating the probability of a packet rejection occurring when the number of packet transmission retries attains its limit. The obtained numerical results of investigating 802.11 LANs by the developed method are validated by simulation and show high estimation accuracy as well as the method efficiency in determining the optimal fragmentation threshold.
On the Possible Automorphism Groups of a Steiner Quintuple System of Order 21

A Steiner system S(4,5,v) is called a Steiner quintuple systems of order v. The smallest order for which the existence, or otherwise, of a Steiner quintuple system is unknown is 21. In this article, we prove that, if an S(4, 5, 21) exists, the order of its full automorphism group is 1, 2, 3, 4, 5, 6, 7, or 10.
Use of Louisiana's Digital Cultural Heritage by Wikipedians

ABSTRACT Analysis of how cultural heritage institutions' (CHI) digital assets are being cited on Wikipedia can be beneficial to understanding user needs and interests as well as priorities for collection development and digitization. This case study details an analysis of Wikipedia links to online resources from Louisiana cultural heritage institutions in order to determine what types of cultural heritage resources users are citing on Wikipedia, what is the content of the Wikipedia articles with Louisiana CHI citations, and how this can influence the work of CHI. The results of the study include findings that digital library items and archival finding aids are the most cited sources from cultural heritage institutions on Wikipedia and are particularly popular for Louisiana-specific Wikipedia articles on society and the social sciences and culture and the arts. Some possible strategies for determining digitization and collection development priorities based on these findings are also detailed.
Semantic relation extraction from unstructured data in the business domain

Text analytics in the business domain is a growing field in research and practical applications. We chose to concentrate on Relation Extraction from unstructured data which was provided by a corporate partner. Analyzing text from this domain requires a different approach, counting with irregularities and domain specific attributes. In this thesis, we present two methods for relation extraction. The Snowball system and the Distant Supervision method were both adapted for the unique data. The methods were implemented to use both structured and unstructured data from the database of the company.
COPING WITH CLASS IMBALANCE IN CLASSIFICATION OF TRAFFIC CRASH SEVERITY BASED ON SENSOR AND ROAD DATA: A FEATURE SELECTION AND DATA AUGMENTATION APPROACH

This paper presents machine learning-based approaches to classification of historical traffic crashes in Kansas by severity, applied to a data set consisting of highway geometry, weather, and road sensor data. The goal of this work is to identify relevant features using a variety of loss measures and algorithms for feature selection. This is shown to facilitate the discovery of the most relevant sensors for the task of learning to predict severe crashes (those involving bodily injury). The key technical challenges are to cope with class imbalance (as a 75% majority of crashes are non-severe) and a highly correlated and redundant set of features from multiple coalesced sources. The major novel contributions of this work are the development of a random oversampling strategy for data augmentation, combined with the systematic application of multiple feature selection measures over a range of supervised inductive learning models and algorithms. Positive results from this approach, on a data set of 277 initial ground features and 20,000 vehicle crashes collected over 9 years (2007 – 2015) by the Kansas Department of Transportation (KDOT), included models trained using 30 features (out of 277) that achieve cross-validation precision and recall comparable to those obtained using the full set of features. These and other results point towards potential use of feature selection findings and the resultant models in planning future road construction.
IBConnect: Placing patient data at the heart of online communities

Personal Health Records (PHRs), electronic records of health-related information of an individual created and managed by the individual, represent an exciting opportunity for designers and the patients they serve. Tools that collect, analyze, and share personal health data may prove vital to the chronically ill, a population whose health is not managed by doctors but by patients themselves. However, existing PHRs mainly serve as repositories of static information. Less explored is the idea of a data-driven PHR, centered within an online patient community, enabling chronically ill patients to connect and share relevant knowledge. This thesis project explores design opportunities for next-generation personal health records. The result is IBConnect, a digital personal health record platform for patients with Inflammatory Bowel Disease. By navigating data within a personal health record as a means to explore online community knowledge, patients using IBConnect can manage their individualized illness experience by discovering actionable, relevant and contextualized knowledge. In turn, patients are encouraged to track and contribute more to the IBConnect community enabling access to new knowledge, a resource highly valued by patients.
Data fault detection algorithm based on multi-label classification in sensor network

Multiple data faults may occur at the same time in sensor network. In order to detect these data faults simultaneously,this paper modeled the data fault detection problem as a multi-label classification task. To improve the performance of multi-label classifiers in detecting data faults,it proposed a feature selection method based on multi-label Relief F and genetic algorithm( MLRG). The method extended the Relief F to the multi-label Relief F which could estimate the quality of feature subset. MLRG firstly searched for a feature subset and then evaluated the feature subset using the multi-label Relief F. It performed experiments on the MLRG using three multi-label classifiers and compared it with other feature reduction algorithms. The experimental results show that MLRG can promote the performance of multi-label classifiers significantly in data faults detection.
Data cleaning techniques by means of entity resolution

Real data are “dirty. ” Despite active research on integrity constraints enforcement and data cleaning, real data in real database applications are still dirty. To make matters worse, both diverse formats/usages of modern data and demands for large-scale data handling make this problem even harder. In particular, to surmount the challenges for which conventional solutions against this problem no longer work, we focus on one type of problems known as the Entity Resolution (ER)—the process of identifying and merging duplicate entities determined to represent the same real-world object. Despite the fact that the problem has been studied extensively, it is still not trivial to de-duplicate complex entities among a large number of candidates. In this thesis, we have studied three specialized types of ER problems: (1) the Split Entity Resolution (SER) problem, in which instances of the same entity type mistakenly appear under different name variants; (2) the Mixed Entity Resolution (MER) problem, in which instances of different entities appear together for their homonymous names; and (3) the Grouped Entity Resolution (GER) problem, in which instances of entities do not carry any name or description by which ER techniques can be utilized, and thus the contents of entities are exploited as a group of elements. For each type of problems, we have developed a novel scalable solution. Especially, for the GER problem, we have developed two graph theoretic algorithms—one based on Quasi-Clique and the other based on Bipartite Matching, and experimentally validate the superiority of the proposed solutions.
Hardware acceleration of n-body simulations for galactic dynamics

N-body methods are used to simulate the evolution and interaction of galaxies. These simulations are usually run on large-scale supercomputers or on very expensive full-custom hardare. This paper presents an alternative hardware method for acceleration of N-body simulations. The method yields a significant fraction of the performance of custom hardware and provides a great deal more flexibility. A protoype implementation is presented.
Non-photorealistic animation for immersive storytelling

Immersive media such as virtual and augmented reality pose some interesting new challenges for non-photorealistic animation: we must not only balance the screen-space rules of a 2D visual style against 3D motion coherence, but also account for stereo spatialization and interactive camera movement, at a rate of 90 frames per second. We introduce two new real-time rendering techniques: MetaTexture, an example-based multiresolution texturing method that adheres to the movement of 3D geometry while maintaining a consistent level of screen-space detail, and Edge Breakup, a method for roughening edges by warping with structured noise. We show how we have used these techniques, along with art-directable coloring, shadow filtering, and shader-based texture indication, to achieve the "moving illustration" style of the immersive short film "Age of Sail".
External Code Quality Model and Cross-Validation of the Model

One goal of this paper is to empirically explore the relationships between existing object-oriented (OO) structural measures (both, class-level and package-level) and external code quality in the context of different development methods (solo/pair programming and test-first/testlast programming) and based on 122 projects. It appeared that external code quality can be better predicted based on OO measures (class-level CBO measure, package-level NOT measure), and size related LOCC measure than development methods. About 37 per cent of the external code quality variance can be explained by the model based on only aforementioned three measures. The second goal is to answer the question how accurate can these models be considering the unavoidable differences that may exist across projects and systems. This paper attempts to answer this question by means of cross-validation of the model. Twothirds of the projects (81 projects) were randomly picked to build our prediction model and the remaining one-third (41 projects) were used to verify the efficacy of the built model. Generalizability of the model, as currently captured by existing measures, seems to be limited as about 19 per cent of the variance can be explained by the model. Therefore, possible explanations and improvements are suggested.
Current-Aware Flash Scheduling for Current Capping in Solid State Disks

Solid state disks (SSDs) employ internal parallelism to boost their input/output (I/O) performance, but a high degree of flash parallelism inevitably consumes a high level of current. To budget power or support multiple power sources, system software may force an SSD into a new power mode that has a specific current supply limit. This paper introduces a firmware approach to optimize SSD internal parallelism subject to a current supply limit. The proposed method involves two steps. First, we constructed current models of flash operations on the basis of real-world measurement results. Second, we designed a firmware scheduler to determine the actual starting time of each flash operation. The proposed scheduler accounted for flash aging, process variation, and internal resource contention, and it avoided any current cap violation by checking a few time points instead of every unit of time. Our experimental results indicated that the proposed approach outperformed existing methods with respect to I/O response time and throughput under realistic workloads.
Process Maturity Models – Applicability and Usability Review

Maturity models are tools enabling application of structured approach for process improvement and assessment of business process management capabilities (BPM) of the organizations. In fact, the number of maturity models is so large that it is difficult or even impossible to identify leading standard. The aim of this paper is therefore to provide a review of BPM maturity models. Design/methodology/ approach the article follows research process of literature review. This work focuses in particular on the applicability and usability of process maturity models that were reviewed according to general design principles. Research Limitations/implications The author is convinced that this review covers the main publicly available BPM maturity models. As the number of models is constantly increasing, paper is not claiming completeness.
Integrating a Tutoring Model into the Training of Prospective Computer Science Teachers

This paper offers a tutoring model whose objective is to develop and establish the pedagogical-disciplinary knowledge of prospective computer science teachers with respect to guiding learners in problem-solving processes. The tutoring model focuses on the tutor, who is a prospective computer science (CS) teacher. During the tutoring process, tutor-students, who have already acquired CS knowledge, support the learning processes of novice CS students. The paper presents the tutoring model and the evaluation research that accompanied its implementation. The research findings indicate that the main objectives of the tutoring model were accomplished. The prospective teachers changed their perspective vis a vis the teaching process; became aware of the importance of identifying learner difficulties and emphasizing this in their teaching process; emphasized problem-solving processes and their impact; adopted reflective processes of thinking and encouraged these processes among the learners as well; reinforced their own self-confidence regarding their ability and place in the disciplinary teaching process; and came to appreciate the contribution of the tutoring model to their training as future teachers. Accordingly, we conclude that the proposed tutoring model has the potential to foster the prospective CS teachers' skills on three levels: pedagogical-disciplinary professional skills, pedagogical professional skills and disciplinary knowledge. Further, we suggest that the tutoring model can be applied in other disciplines in which problem solving is an inherent part of the discipline.
How to Become More Effective in Discussions? Four Studies.

Based on literature and research on discussion and on educational techniques to improve discussion moderators behaviors, a training program was developed for preand in-service teacher educators, business and industry executives and university professors (23 to 28 hours, three and a half days). This program was tested for its effectiveness with both low inference as well as high inference observation tools in four studies. Results from these studies show that this minimal training achieved significant improvements on low inference measures of effective discussion and general communicative competence, leading to the identification and validation of effective discussion techniques for teacher training and other settings. Evaluation of the training program in the four and in additional 22 studies was very favorable.