Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS-aware discovery of web services

The web is evolving from a collection of pages to a collection of services. Different from original distributed computing system, Web Services are more dynamic because of its service discovery and run-time binding mechanism. As more and more web services are appearing on the web, Web Services' discovery mechanism becomes essential. However, the industry's standard service discovery protocol has limited support for value-added services. Its service discovery mechanism adopts keyword matching to locate published Web Services. It has neither well defined semantics nor QoS related information. Great efforts are required to manually compare and test on retrieved services, not to mention the automatic discovery, composition, execution and monitoring of the services. According to these analysis, our research mainly focuses on the QoS-Aware service discovery portion of Web Services system. First part of our research is non-ontology based QoS-Aware service discovery system. We investigate on the key component of industry standard service discovery framework the UDDI registry. We propose UDDI extension (UX), an enhancement for UDDI that facilitates requesters to discover services with QoS awareness. In this system the service requester invokes and generates feedback reports, which are received and stored in local domain's UX server for future usage. By sharing these experiences from those requesters in the local domain, the UX server summarizes and predicts the service's performance. A general federated service is designed to manage the service federation. The discovery between different cooperating domains is based on this general federated service. Therefore the links between domains are maintained dynamically. A prototype system is implemented to demonstrate the feasibility of the UX system. We then further investigate the problem of semantics in the QoS-Aware service discovery. Various Web Services have quite different requirements for their service quality 1 ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Mobile X-Space Design, Teaching Strategies and Undergraduate Students’ Collaborative Learning Behaviour: A Case Study in Taylor’s University, Malaysia

Purpose - The availability of new innovative learning spaces together with the introduction of new teaching and learning strategies have shifted the nature of the classroom from a place of instruction towards a place that produces authentic learning. Students in the 21st century are inclined to learn with peers, through the use technology to produce the information as the result of their collaboration. This research examines the effect of learning space design and teaching strategies on undergraduate students’ collaborative learning behaviour in the Mobile X-Space classroom. Methodology - This study used a quantitative survey research design to measure students’ learning experience in the Mobile X-Space classroom. A set of online self-reported questionnaire was posted through the official Learning Management System of the university to collect data from 467 undergraduate students in a Malaysian top private university. Through exploratory factor analysis, three factors were identified: (a) teaching strategies, (b) collaborative learning behaviour and (c) space design. Pearson’s correlation and multiple regression were run to assess the relationship between learning space design and teaching strategies conducted by the lecturers on the collaborative learning behaviour of the students. Two-way factorial ANOVA with Post Hoc tests were performed to determine the effects of gender and study semester of the students on their collaborative learning behaviour. Findings - The results from the multiple regression analysis revealed that both (i) teaching strategies, and (ii) space design were positively correlated with collaborative learning behaviour of the students. The results from Person’s correlation showed a significant, strong and positive relationship between teaching strategies conducted by the lecturers on the collaborative learning behaviour among the students. Also, there was a statistically significant, strong and positive relationship between learning space design on the teaching strategies of the lecturers. In addition, the results from two-way factorial ANOVA showed that collaborative learning behaviour was different for groups in different study semesters. However, there was no statistically significant difference for collaborative learning behaviour between different genders in all semesters. Significance - The outcomes of this research will be beneficial in enhancing the support and design of future learning spaces and add value to the present educational model. It is also beneficial in guiding academics in determining the practical teaching and learning approaches in the flexible learning spaces, which could be more suitable for the millennium youth who are more tech-savvy and favour Internet of things in their daily lives. This type of learning space will help to improve the students’ soft skills and collaborative skills, that are very useful in their future employability in the actual work settings.
A Methodology for Analyzing Public Opinion about Science and Technology Issues Using Text Analysis

Recently, many users frequently share their opinions on diverse issues using various social media. Therefore, many governments have attempted to establish or improve national policies according to the public opinions captured from the various social media. In this paper, we indicate several limitations of traditional approaches for analyzing public opinions about science and technology and provide an alternative methodology to overcome the limitations. First of all, we distinguish science and technology analysis phase and social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we apply a start list and a stop list successively to acquire clarified and interesting results. Finally, to identify most appropriate documents fitting to a given subject, we develop a new concept of logical filter that consists of not only mere keywords but also a logical relationship among keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discovering core issues and public opinions from 1,700,886 documents comprising SNS, blog, news, and discussion.
Methods and algorithms of adaptive designing for neuronetworking system of processing the data with non-stationary nature

The paper examines hybrid model of data neuronetworking processing system, in which the opportunities of a wide spectrum of methods and algorithms of neural network and statistical models are combined. The original ways and algorithms of adaptation during designing of neural network are developed by escalating, selection and adjustment of activation functions. The strategy and new principles are developed for neural network output quality control. The theoretical results were approved on the basis of Koxonen model; and as result conditions of well over reduction of output data approximation error are proved.
Teachers, textbooks, and orthographic choices in Quechua: comparing bilingual intercultural education in Peru and Ecuador across decades

ABSTRACT One of the central paradoxes of textbook authorship in Indigenous languages is that some of those for whom the textbooks are intended find it challenging to read them. Here, through examining cases of Quechua across the Andes in Peru and in Ecuador, we consider the role of orthography in this paradox. Textbook authors must decide on an alphabet for writing textbooks. Yet, the selection of a particular alphabet or changing the alphabet can cause unforeseen difficulties in reading and teaching with the books. Teachers often have experiences with the letters and with reading and writing that are linked to larger-scale questions about what ‘the Quechua language’ is and how one writes in Quechua. These experiences affect their own and others’ interactions with linguistic varieties and with textbooks, playing a significant role in what and how they teach.
A Spatially Explicit Comparison of Quantitative and Categorical Modelling Approaches for Mapping Seabed Sediments Using Random Forest

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods.
Participatory Community Education to Mitigate Human-Elephant Conflict in Botswana.

In northern Botswana, conflict between subsistence farmers and elephants can result in destroyed agricultural crops and death for both species. In June of 2016, students and faculty mentors from four universities traveled to the Okavango Delta region of Botswana to participate in a community education project designed to develop locally relevant solutions to mitigate human-elephant conflict. Local farmers and community members partnered with university students to design solutions and build prototypes of those solutions. In this paper, we present findings pertaining to the university students’ experiences, perceptions, and learning during and as a result of the workshop, including ways in which expectations and the actual experience were aligned and the experience of partnering university students with members of the local community. Findings indicate that future, similar projects should work to ensure an appropriate balance of instruction attention between the local and student participants. Successes include open dialogue and collaboration among all workshop attendees, application of university coursework to address issues of problem solving, design, working with diverse groups, and co-creation of prototypes of simple machines, tools, and devices ready for use and testing by local farmers.
Seven-year-olds’ imaginative engagement with play in non-virtual and virtual contexts

This thesis focuses on the imaginative activities that are present in middle childhood, and how children engage in the fictional worlds created during play in non-virtual and virtual activities. This was investigated in the context of the Cardiff Child Development Study, a UK-based prospective longitudinal study of first-born children. In Chapter 3, I analysed questionnaire data on children’s play activities. The children were reported by caregivers’ as enjoying a variety of playful and imaginative activities, including activities previously considered to be absent at this age, or neglected in previous research. Gender differences were reported for some activities, supporting those found in existing literature. In Chapters 4 and 5, I developed coding schemes of children’s engagement with the fictional world (play frame) created when children played with Playmobil figures, and their immersion in the virtual world of a bespoke video game. Children’s engagement with the play frame was considered to be in the role of an actor, manager, or narrator of the play. Children’s engagement with the video game was considered to reflect their immersive engagement with the virtual world or functional engagement with the mechanics of the game. Boys were more engaged in the role of an actor in the play frame and more immersed with the virtual world than girls. In Chapter 6, I examined links between the virtual and non-virtual tasks. Positive associations were found between children’s engagement as an actor and their immersion, even when controlling for gender. Children’s references to the internal states of the fictional characters were also compared as an indication of their engagement with the fictional worlds, and were associated across contexts when controlling for receptive vocabulary and gender. These findings add to knowledge regarding imagination in childhood, in supporting that children’s engagement in fictional worlds represents an expression of an imaginative characteristic.
The Sociopolitical Thesaurus as a resource for automatic document processing in Russian

This paper presents the structure and current state of the Sociopolitical thesaurus, which was developed for automatic document analysis and information-retrieval applications in Russian in a broad domain of public affairs. The scope of the Sociopolitical thesaurus resembles traditional information-retrieval thesauri for broad domains such as the EUROVOC or UNBIS thesauri, but the Sociopolitical thesaurus is intended as a tool for automatic document processing and this difference leads to considerable distinctions in the thesaurus structure and principles of its development. The knowledge representation in the Sociopolitical thesaurus is based on the combination of three existing traditions of developing information-retrieval thesauri, wordnets, and formal ontology research, which facilitates the consistent representation for such a broad scope of concepts and automatic document analysis of unstructured texts. The Sociopolitical thesaurus is used in such applications as conceptual indexing in information-retrieval systems, knowledge-based text categorization, automatic summarization of single and multiple documents, and question-answering. This paper presents an evaluation of the Sociopolitical thesaurus in automatic knowledge-based text categorization.
A Software Defect-Proneness Prediction Framework: A new approach using genetic algorithms to generate learning schemes.

Recently, defect prediction software is an important research topic in the software engineering field. The demand for development of good quality software has seen a rapid growth in the last few years. The software measurement data collected during the software development process include valuable information about software projects status, progress, quality, performance, and evolution. The software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. OBJECTIVE: This paper presents an approach that combines three phases: data preprocessing, attribute selector and learning algorithms using a genetic approach and select the best combination. METHOD: The framework is comprised of 1) scheme learning generator. This component evaluates performance of the learning schemes and suggests the best option according to each data set analyzed, 2) defect predictor component builds models according to the evaluated learning schemes and predicts software defects with new data agreed to the constructed model. CONCLUSIONS: The framework has considered more combinations of learning schemes than other proposals which select the model configuration manually, which means that there are more possibilities to find better learning schemes for each data set. The computational processing of the genetic approach was less costly than Song approach. Finally, The Genetic approach presented an improvement of 0.032 equivalent to 3.2% more than Song approach.
High resolution profiling of the atmospheric boundary layer

The University of Massachusetts Microwave Remote Sensing Laboratory developed a low cost, high-resolution S-band FM-CW radar profiler to study the structure and dynamics of clear-air turbulence in the atmospheric boundary layer. The radar system resolves the vertical reflectivity structure of the boundary layer with 2.5 m resolution. The radar employs a 250 W transmitter and a pair of parabolic dish antennas with 34 dB gain. In October 1999, the S-band profiler was deployed near Wichita, KS for the 1999 Cooperative Atmosphere-Surface Exchange Study (CASES'99) Nocturnal Boundary Layer experiment. Throughout this experiment it produced real-time imagery of a variety of boundary layer features including Kelvin-Helmholtz instabilities, gravity waves, and clear-air convection. Real-time images were used to guide research aircraft to sample these features with on-board instrumentation. In addition to clear-air (Bragg) returns, the radar often observed significant scattering from insects and birds. The distribution of insect scatter appears to reveal additional boundary layer structure not otherwise detectable at S-band. This paper describes the radar system and presents highlights of the profiler measurements collected during the experiment.
Sensing of complex buildings and reconstruction into photo-realistic 3D models

The 3D reconstruction of indoor and outdoor environments has received an interest only recently, as companies began to recognize that using reconstructed models is a way to generate revenue through location-based services and advertisements. A great amount of research has been done in the field of 3D reconstruction, and one of the latest and most promising applications is Kinect Fusion, which was developed by Microsoft Research. Its strong points are the real-time intuitive 3D reconstruction, interactive frame rate, the level of detail in the models, and the availability of the hardware and software for researchers and enthusiasts. A representative effort towards 3D reconstruction is the Point Cloud Library (PCL). PCL is a large scale, open project for 2D/3D image and point cloud processing. On December 2011, PCL made available an implementation of Kinect Fusion, namely KinFu. KinFu emulates the functionality provided in Kinect Fusion. However, both implementations have two major limitations: 1. The real-time reconstruction takes place only within a cube with a size of 3 meters per axis. The cube's position is fixed at the start of execution, and any object outside of this cube is not integrated into the reconstructed model. Therefore the volume that can be scanned is always limited by the size of the cube. It is possible to manually align many small-size cubes into a single large model, however this is a time-consuming and difficult task, especially when the meshes have complex topologies and high polygon count, as is the case with the meshes obtained from KinFu. 2. The output mesh does not have any color textures. There are some at-tempts to add color in the output point cloud; however, the resulting effect is not photo-realistic. Applying photo-realistic textures to a model can enhance the user experience, even when the model has a simple topology. The main goal of this project is to design and implement a system that captures large indoor environments and generates 3D photo-realistic large indoor models in real time. This report describes an extended version of the KinFu system. The extensions overcome the scalability and texture reconstruction limitations using commodity hardware and open-source software. The complete hardware setup used in this project is worth €2,000, which is comparable to the cost of a single professional laser scanner. The software is released under BSD license, which makes it completely free to use and commercialize. The system has been integrated into the open-source PCL project. The immediate benefits are three-fold: the system becomes a potential industry standard, it is maintained and extended by many developers around the world with no addition-al cost to the VCA group, and it can reduce the application development time by reusing numerous state-of-the-art algorithms.
On the Potential of Limitation-oriented Malware Detection and Prevention Techniques on Mobile Phones

The malware threat for mobile phones is expected to increase with the functionality enhancement of mobile phones. This threat is exacerbated with the surge in population of smart phones instilled with stable Internet access which provides attractive targets for malware developers. Prior research on malware protection has focused on avoiding the negative impact of the functionality limitations of mobile phones to keep the performance cost within the limitations of mobile phones. Being different, this paper investigates the positive impact of these limitations on suppressing the development of mobile malware. We study the state-of-the-art mobile malware, as well as the progress of academic research and industrial effort against mobile malware. Our study shows that the functionality limitations of mobile phones should be considered as advantages as they have significant impact on shrinking the living space of mobile malware. From this perspective, we propose and analyze three potential limitation-oriented techniques for effective malware detection and prevention on mobile phones.
A Review of Linear Regression and some Basic Proofs for Lasso

The goal of this paper is to do some basic proofs for lasso and have a deep understanding of linear regression. In this paper, firstly I give a review of methods in linear regression, and most concerns with the method of lasso. Lasso for ‘least absolute shrinkage and selection operator’ is a regularized version of method adds a constraint which uses norm less or equal to a given value t. By doing so, some predictor coefficients would be shrank and some others might be set to 0. We can attain good interpretation and prediction accuracy by using lasso method. Secondly, I provide some basic proofs for lasso, which would be very helpful in understanding lasso. Additionally, some geometric graphs are also given and one example is illustrated. 1 L
Automating Expertise in Collaborative Learning Environments.

We have developed a set of tools for improving online collaborative learning including an automated expert that monitors and moderates discussions, and additional tools to evaluate contributions, semantically search all posted comments, access a library of hundreds of digital books and provide reports to instructors. The technology behind these tools is Latent Semantic Analysis (LSA), a machine learning technology that understands the meaning of words and text in ways that agree highly with human judgments. These tools were evaluated in a series of studies with the U.S. Army War College and U.S. Air Force Academy. At the Army War College, we found that the automated monitor was as accurate at identifying discussion groups in trouble as trained human instructors, and has the potential to effectively reduce the amount of time instructors spend monitoring distance learning courses. At the Air Force Academy, the expert moderator significantly improved the quality of cadets’ discussion comments in a collaborative learning environment.