Miracle-dz/UniFAR-FTUs huggingface dataset:
A model for measuring student persistence through collaborative learning

Institutions of higher education are being called upon to provide a more robust pathway to a college degree and improve upon the advanced workforce for the needs of the 21 st century. As 21 st century skills call for an employee to successfully work collaboratively in groups, an increase in technology adoption, globalization and increased competition are among the factors that make collaboration one of the most important skills that employers insist that individuals obtain today. An active learning environment through collaborative learning techniques has been encouraged in higher education as a means of improving student engagement (Freeman, Eddy, McDonough, Smith, Okoroafor, Jordt, & Wenderoth, 2014; Slavich & Zimbardo, 2012; Prince, 2004), but there is a gap in the literature when it comes to connecting the two research areas of collaborative learning and student intention to persist. Continued research is warranted to further understand the factors that may contribute to improving the situation of attrition, and to suggest ways that institutions can enhance engagement and ultimately improve student success. The purpose of this study is to create a model that will measure the factors that significantly influence a student’s persistence in a computer supported collaborative learning environment. Based on prior theoretical research, the model is developed to analyze how collaborative learning is mediated by campus connectedness and a sense of community, and subsequently how it impacts student persistence utilizing affective organizational commitment and turnover intention measures. A survey instrument was developed based on the factors of the research model, and was tailored to the terminology used for communities and academia. To test the model, a cohort of students across multiple institutions was invited to participate in a virtual learning community, and a total of 103 students participated. To test the entire model, partial least viii squares structural equation modeling (PLS-SEM) was used. In testing the design of the overall model utilizing structural equation modeling, the relationship between all factors but one were found to be statistically significant. In further analysis, the model design was also able to discern between two separate groups, adding to its versatility. Implications for research in this area include an expansion of student attrition research through turnover intention, scalability with the addition of more constructs, and ultimately a new model that contributes to future research that is not limited to a higher education domain.
Stock volatility prediction using multi-kernel learning based extreme learning machine

Stock price volatility prediction is regarded as one of the most attractive and meaningful research issues in financial market. Some existing researches have pointed out that both the prediction accuracy and the prediction speed are the most important facts in the process of stock prediction. In this paper, we focus on the problem of how to design a methodology which can improve prediction accuracy as well speed up prediction process, and propose a multi-kernel learning based extreme learning machine (MKL-ELM) model to enhance the prediction performance. ELM is a fast learning model and has been successfully applied in many research fields. Based on ELM, this MKL-ELM has the benefits of both multiple kernel learning and ELM, which can well balanced the requirements of both prediction accuracy and prediction speed. To validate the performance of the proposed MKL-ELM, we take experiments on HKEx 2001 stock market datasets. The market historical price and the market news are implemented in our MKL-ELM. We Compare our proposed MKL-ELM with Back-Propagation Neural Network(BP-NN), Support Vector Machine(SVM), Basic ELM and K-ELM. Experimental results show that, 1) MKL-ELM, K-ELM and SVM get higher prediction accuracy than BP-NN and B-ELM; 2) Both MKL-ELM and K-ELM can achieve faster prediction speed than SVM in most cases; 3) MKL-ELM has higher prediction accuracy in some cases than K-ELM and SVM.
An adaptive Kalman filter based traffic prediction algorithm for urban road network

Frequent traffic congestion and gridlocks are causing global economies staggering cost in terms of fuel consumption, time wastage, and public health. To rectify this problem, many advocates combining Information and Communication Technologies (ICT) and traffic engineering concepts for better traffic management. Timely and accurate traffic prediction and management are central to the ICT-based Intelligent Transportation Systems (ITS). In this paper, we presented a traffic prediction model based on Kalman filtering theory, which optimizes the prediction of speed by minimizing the variance between the real-time speed measurement and its estimation. The prediction model predicts the speed across high-level roadway segments using historical and real-time speed measurements (spot speed) reported by the vehicles traveling on the urban road network. The performance evaluation of the proposed prediction model includes a number of case studies. Each case study is conducted with different parametric settings to explain the different characteristic of the model. The results show that provided the spot speed measurements don't fluctuate significantly over the time, the proposed model is capable of predicting traffic with 54% more accuracy.
Sleep is associated with task-negative brain activity in fibromyalgia participants with comorbid chronic insomnia

Patients with chronic pain exhibit altered default mode network (DMN) activity. This preliminary project questioned whether comorbid disease states are associated with further brain alterations. Thirteen women with fibromyalgia (FM) only and 26 women with fibromyalgia with comorbid chronic insomnia (FMI) underwent a single night of ambulatory polysomnography and completed a sleep diary each morning for 14 days prior to performing a neuroimaging protocol. Novel imaging analyses were utilized to identify regions associated with significantly disordered sleep that were more active in task-negative periods than task-oriented periods in participants with FMI, when compared to participants with FM. It was hypothesized that core DMN areas (ie, cingulate cortex, inferior parietal lobule, medial prefrontal cortex, medial temporal cortex, precuneus) would exhibit increased activity during task-negative periods. Analyses revealed that significantly disordered sleep significantly contributed to group differences in the right cingulate gyrus, left lentiform nucleus, left anterior cingulate, left superior gyrus, medial frontal gyrus, right caudate, and the left inferior parietal lobules. Results suggest that FMI may alter some brain areas of the DMN, above and beyond FM. However, future work will need to investigate these results further by controlling for chronic insomnia only before conclusions can be made regarding the effect of FMI comorbidity on the DMN.
Do knobs have character?: exploring diversity in users' inferences

Physical controls are now ubiquitous in everyday interactions. Empirical studies of physical interactions have traditionally been exploring instrumental aspects such as error rate and experienced workload. Recently, affective aspects of physical interaction have attracted an increased interest. In this paper we further argue that physical controls might have a character. We describe an exploratory study that aimed at understanding whether individuals form character judgments of physical controls based on haptic information, and explored the diversity across individuals' inference processes.
User-based energy-efficient scalable video multicast over wireless networks

In this paper, we focus on designing energy-efficient opportunistic multicast scheduling method and user sleeping mechanism for scalable video transmission in a wireless network. The goal is to minimize the total energy consumption for receiving video data with bandwidth constraint and users' minimum QoS requirements. To utilize opportunistic multicast scheduling, unequal erasure protection (UXP) scheme is used for scalable video at the application layer. Considering the encoding characteristics and unequal protection scheme of scalable video, the UXP frame-level QoS is defined. And a low-complexity scheduling algorithm is proposed by transforming frame-level QoS requirement into slot-level QoS requirement at each time slot dynamically. Simulation results shows that our proposed algorithm can achieve better tradeoff between users' energy consumption and QoS requirements than conventional multicast scheduling schemes. Besides, multicast users' energy efficiency can be improved further if larger decoding delay is allowed at the receiver.
Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features.
On Hierarchical Knowledge Acquisition and Application

Classification is a famous branch of machine learning. We have tried many ways to invent and improve algorithms to get better results from given data. However, few have been done on how to revise data to adapt machine learning. In this paper, the same classifiers are implemented on same object sets which are different in the granularity of classification to show different classification can make great difference in the quality of classification first. Then the development of knowledge-base is studied. At last, a progressive knowledge acquisition method is advanced inspired by human’s cognition behavior.
Reversible Zero-Bit Watermark Based on Chaotic Encryption

A reversible watermarking algorithm is presented, which achieves content authentication and perfect restoration of the original image during extraction. The proposed algorithm is based on patchwork and chaotic encryption. The patchwork algorithm is used to generate zero-bit watermark for content authentication. The chaotic sequence is used to encrypt the general LSBs(Least Significant Bits) of image pixels. The results show this scheme is reversible and saf
Chinese keyword extraction based on max-duplicated strings of the documents

The corpus analysis methods in Chinese keyword extraction look on the corpus as a single sample of language stochastic process. But the distributions of keywords in the whole corpus and in each document are very different from each other. The extraction based on global statistical information only can get significant keywords in the whole corpus. Max-duplicated strings contain the local significant keywords in each document. In this paper, we designed an efficient algorithm to extract the max-duplicated strings by building PAT-tree for the document, so that the keywords can be picked out from the max-duplicated strings by their SIG values in the corpus.
Parsing Japanese with Information Structure

This paper argues for the need of integrating information structure with a parser for Japanese to derive the context-dependent information marked by morphology. Information structure is eeciently derived along syntactic structure due to the properties of Combinatory Categorial Grammar. The proposed grammar with type-raising and variables is shown to run in polynomial time with elimination of spurious ambiguities.
Comment on 'Discrepancies in dbSNP confirmations rates and allele frequency distributions from varying genotyping error rates and patterns'.

frequency distributions from varying genotyping error rates and patterns” Christopher S. Carlson, Michael A. Eberle, Mark J. Reider, Leonid Kruglyak and Deborah A. Nickerson Department of Genome Sciences, University of Washington, 1705 NE Pacific, Seattle, Washington 98109. Division of Human Biology, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, Seattle, Washington 98109. Howard Hughes Medical Institute, Chevy Chase, Maryland.
Leapfrogging: A Theory of Cycles in National Technological Leadership

Much recent work has suggested that endogenous technological change tends to reinforce the position of the leading nations. Yet from time to time this leadership role shifts. We suggest a mechanism that explains this pattern of -leapfrogging- as a response to occasional major changes in technology. When such a change occurs, leading nations may have no incentive to adopt the new ideas; given their extensive experience with older technologies, the new ideas do not initially seem to be an improvement. Lagging nations, however, have less experience; the new techniques offer them an opportunity to use their lower wages, to break into the market. If the new techniques eventually prove to be more productive than the old, there is a reversal of leadership.
Reduced relatives judged hard require constraint-based analyses

The processing difficulty of sentences with reduced relative clauses (RRs) is strongly determined by the inherent lexical semantic class of the verbs used as passive participles in RRs: namely, the unaccusative vs. unergative class (see Stevenson and Merlo, 1997). Our main claim is that among the linguistic variables responsible for the relevant differences a crucial role is played by semantic variables, rather than just category-level syntactic complexity and/or complexity associated with word-internal lexical structure of verbs (see Hale and Keyser, 1993). First, we observe a considerable overlap in the distributions of acceptability judgments between sentences with RRs based on unaccusative verbs and those based on unergative verbs, and even more importantly, clear gradient effects with respect to acceptability judgments for both types of sentences that are influenced by the lexical semantics of the main verb in the matrix clause. Second, such data can be successfully motivated, if we characterize the crucial unaccusative-unergative distinction in terms of thematic Proto-Role properties (Dowty, 1988, 1991). Third, the linguistic analysis is consistent with recent constraint-based grammars, most notably HPSG, and our constraint-based model that uses the integration-competition architecture developed by Spivey (1996) and applied to reduced relatives by McRae et al. (1998) and Spivey and Tanenhaus (1998).
Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Based on the extracted contours of objects from images, one point to point (P2P) method is proposed for shape matching and image retrieval. Taking contour of one object as reference, contour of another object is transformed to reach their best match, during which the similarity is evaluated by comparison of two corresponding sets of contour points. Translation, scaling and rotation are all considered in transformation, thus the algorithm is robust to objects with different position, size and posture. The experimental results are presented and compared with those from two popular shape based techniques, Hu invariant moments and Zernike moments. Performance of our new approach has proved its efficiency in both ma tching accuracy and computational expense, and it can be used in related applications together with the other kinds of shape features or eve n color features, texture features, etc.
Motion Planning of Biped Robot Equipped with Stereo Camera Using Grid Map

Recognizing its surroundings is important for biped robots seeking a destination. In this paper, we propose a motion planning method of a biped robot including path planning and obstacle avoidance. The robot obtains distance information on its surrounding environment from images captured by a stereo camera system, and generates a 3D map, then, builds a 2D grid map that locates flat floor regions, obstacle regions, bump regions, gate regions, and un-measured regions to decide its path by using the 2D grid map. Experimental results confirm the effectiveness of the proposed method.
Focused Crawling in Depression Portal Search: A Feasibility Study

Previous work on domain specific search services in the area of depressive illness has documented the significant human cost required to setup and maintain closed-crawl parameters. It also showed that domain coverage is much less than that of whole-of-web search engines. Here we report on the feasibility of techniques for achieving greater coverage at lower cost. We found that acceptably effective crawl parameters could be automatically derived from a DMOZ depression category list, with dramatic saving in effort. We also found evidence that focused crawling could be effective in this domain: relevant documents from diverse sources are extensively interlinked; many outgoing links from a constrained crawl based on DMOZ lead to additional relevant content; and we were able to achieve reasonable precision (88%) and recall (68%) using a J48-derived predictive classifier operating only on URL words, anchor text and text content adjacent to referring links. Future directions include implementing and evaluating a focused crawler. Furthermore, the quality of information in returned pages (measured in accordance with the evidence based medicine) is vital when searchers are consumers. Accordingly, automatic estimation of web site quality and its possible incorporation in a focused crawler is the subject of a separate concurrent study.
Feedback effects of service extensions on the parent product brand image

Die vorliegende Masterarbeit beschaftigt sich mit den Auswirkungen von Markenerweiterungen auf das Markenimage und wie sich das Markenimage als Konsequenz von solchen Entscheidungen verandert. Hierfur wurden vier verschiedene Dienstleistungsszenarien entwickelt, die von der bekannten Produktmarke Nespresso fiktiv angeboten werden. Diese vier Dienstleistungsszenarien stellen zwei unterschiedliche Stufen der Kundenzufriedenheit und zwei diverse Stufen von ‚Fit’ dar. Unter ‚Fit’ ist die Ahnlichkeit der Dienstleistung im Vergleich zur Ursprungsmarke zu verstehen. Das Besondere an dieser Arbeit ist, dass der Faktor ‚Dienstleistungsqualitat’ zum ersten Mal in die Evaluierung von Markenerweiterungen integriert wird, genauso, wie dessen Auswirkungen auf das Markenimage. Daher konnen die Ergebnisse der vorliegenden Masterarbeit als einzigartig und noch nie dagewesene Erkenntnis im Rahmen der Forschung fur Markendehnungen und fur die Veranderung des Markenimages gesehen werden. Die Auswertungen der empirischen Studie belegen, dass der Grad an Dienstleistungsqualitat einen signifikanten Einfluss auf die Entstehung von Markenimage-Veranderungen hat. Es kann bewiesen werden, dass hohe Dienstleistungsqualitat zur Kundenzufriedenheit fuhrt und sich hieraus positive Einflusse auf das Image einer Marke entwickeln. Auf der anderen Seite fuhrt niedrige Dienstleistungsqualitat zur Kundenunzufriedenheit und zu einer Verschlechterung des Markenimages. Daher konnen Dienstleistungsqualitat und Kundenzufriedenheit als die wichtigsten Faktoren fur die Bewertung von Markenerweiterungen und der daraus resultierenden Veranderung des Markenimages betrachtet werden. Auserdem kommt man in der vorliegenden Arbeit zu der Erkenntnis, dass negative Erfahrungen in Bezug auf Markenerweiterungen eine weitaus starkere Veranderung des Markenimages nach sich ziehen als Positive. Aber die Starke der Veranderung ist auch abhangig vom Faktor ‚Fit’. Die Ergebnisse zeigen, dass je ahnlicher die Markenerweiterung der Muttermarke ist, umso starker ist die positive Veranderung des Markenimages bei einer hohen Dienstleistungsqualitat. Wahrend je unahnlicher die Markenerweiterung zur Ursprungsmarke ist, umso starker ist die negative Veranderung des Images bei niedriger Dienstleistungsqualitat. %%%%This master thesis examines the topic of feedback effects on the parent brand image and potential drivers of those feedback effects by developing, presenting and analysing four different service extension scenarios based on quasi-experiences of a famous brand in the FMCG market. These four different service scenarios represent two different levels of service quality and two diverse levels of fit. As the level of service quality is for the first time directly integrated into the evaluation of brand extensions and into the formation of feedback effects, this work represents a unique study in the field of brand extension research. The author finds strong support that the level of service quality substantially influences the creation of feedback effects on the parent brand image…
Best Kronecker Product Approximation of The Blurring Operator in Three Dimensional Image Restoration Problems

In this paper, we propose a method to find the best Kronecker product approximation of the blurring operator which arises in three dimensional image restoration problems. We show that this problem can be reduced to a well known rank-1 approximation of the scaled three dimensional point spread function (PSF) array, which is much smaller. This approximation can be used as a preconditioner in solving image restoration problems with iterative methods. The comparison of the approximation by the new scaled PSF array and approximation by the original PSF array that is used in [J. G. Nagy and M. E. Kilmer, IEEE Trans. Image Process. , 15 (2006), pp. 604--613], confirms the performance of the new proposed approximation.