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Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2020年第1期13卷 150-163页

作者： Milikic, Nikola Gasevic, Dragan Jovanovic, Jelena Univ Belgrade Fac Org Sci Belgrade 11000 Serbia Monash Univ Fac Educ Clayton Vic 3800 Australia Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

Learning design in a massive open online course (MOOC) intends to promote creativity, autonomy, and social networked learning, amongst other things. Students in a MOOC are required to self-regulate their learning to properly self-monitor their learning process and effectiveness of the adopted learning strategies. This paper presents the results of a study among 279 students enrolled in a MOOC that was enriched with a set of scaffolding interventions for social mirroring. The mirroring interventions supported social awareness and social embeddedness of learners. Associations between the use of the interventions and microlevel self-regulated learning processes were measured and analyzed. The extent to which those associations are affected by learner demographics and motivational characteristics was also investigated. Findings show that interventions that provide students, throughout the course, with learning updates and progress of peers are associated with the students' engagement with learning tasks and applying changes in strategies for completing those tasks. Social awareness scaffold influenced more students low in need for cognition, with a higher education degree, high in performance-approach orientation and low in grit, to engage with their learning tasks, while its effect on the change in learning strategies was higher with those early and towards the end of their careers and high in performance-approach strategy. The social comparison scaffold affected more students low in mastery goal orientation and high in grit to work on their learning tasks.

关键词： correlation and regression analysis education learning management systems learning technologies scaffolding mirroring scaffolds social technologies

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Training-Free, Generic Object Detection Using Locally Adaptive regression Kernels

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IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2010年第9期32卷 1688-1704页

作者： Seo, Hae Jong Milanfar, Peyman Univ Calif Santa Cruz Santa Cruz CA 95064 USA

We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge (learning) about objects being sought, and does not require any preprocessing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a pixel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. We illustrate optimality properties of the algorithm using a naive-Bayes framework. The algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the query and all patches in the target image. By employing nonparametric significance tests and nonmaxima suppression, we detect the presence and location of objects similar to the given query. The approach is extended to account for large variations in scale and rotation. High performance is demonstrated on several challenging data sets, indicating successful detection of objects in diverse contexts and under different imaging conditions.

关键词： Object detection image representation correlation and regression analysis

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Evaluation of variations and affecting factors of eco-environmental quality during urbanization

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2015年第5期22卷 3958-3968页

作者： Cui, Erqian Ren, Lijun Sun, Haoyu Shandong Univ Sch Environm Sci & Engn Jinan 2501000 Shandong Peoples R China

Regional eco-environmental quality is the foundation of economic sustainable development and rational utilization of resources. It is necessary to understand and evaluate the regional eco-environmental quality correctly. Based on national remote sensing land use data, normalized difference vegetation index (NDVI) data and some other statistical data, this paper established an eco-environmental quality index (EQI) model to evaluate the ecological status of Jinan from 2000 to 2011. The results of eco-environmental quality showed little variation, with EQI values ranged from 62.00 to 69.01. EQI of each region in Jinan firstly decreased sharply and then increased slowly with the development of local economy. Besides the spatial and temporal variations analysis, affecting factors of eco-environmental quality was also discussed in this article. According to the results of correlation and regression analysis, meteorological conditions (rainfall and sunshine duration) and industrial structure (the proportion of primary industry) had relatively high correlations with eco-environmental quality. To summarize, a better eco-environmental status is associated with increasing rainfall, shorter sunshine duration, and lower proportion of primary industry. This article aims to giving supporting data and decision-making bases to restore the ecological environment and promote the sustainable development of Jinan.

关键词： Ecological environment Remote sensing and geographic information system Eco-environmental quality index Affecting factors correlation and regression analysis

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Pseudo-3D RSSI-based WSN localization algorithm using linear regression

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2015年第9期15卷 1342-1354页

作者： Vanheel, Frank Verhaevert, Jo Laermans, Eric Moerman, Ingrid Demeester, Piet Univ Coll Ghent Fac Appl Engn Sci Ghent Belgium Ghent Univ iMinds Informat Technol INTEC Ghent Belgium

Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location-aware applications. For a final breakthrough these systems need fast deployment and easy auto-configuration. In this study, we use the real-life iMinds test bed to expand a two-dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three-dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non-parametric statistical tests, we show that our previously designed two-dimensional preprocessing performs equally well in pseudo-three dimensions: the preprocessing reduces the position error in a statistically significant way. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： Algorithm design and analysis correlation and regression analysis Wireless sensor networks Localization

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Pattern-Aided regression Modeling and Prediction Model analysis

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第9期27卷 2452-2465页

作者： Dong, Guozhu Taslimitehrani, Vahid Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA Wright State Univ Kno E Sis Ctr Dayton OH 45435 USA

This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) regression modeling applications often involve complex diverse predictor-response relationships, which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret;in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

关键词： correlation and regression analysis model validation and analysis error analysis data mining mining methods and algorithms

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Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2011年第1期2011卷 1-27页

作者： Vanheel, Frank Verhaevert, Jo Laermans, Eric Moerman, Ingrid Demeester, Piet Univ Coll Ghent Fac Appl Engn Sci Ghent Belgium Univ Ghent Dept Informat Technol INTEC B-9000 Ghent Belgium

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.

关键词： algorithm design and analysis correlation and regression analysis wireless sensor networks localization

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Additive Log-Logistic Model for Networked Video Quality Assessment

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第4期22卷 1534-1545页

作者： Zhang, Fan Lin, Weisi Chen, Zhibo Ngan, King Ngi Technicolor China Technol Co Ltd Dept Res & Innovat Beijing 100192 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Modeling subjective opinions on visual quality is a challenging problem, which closely relates to many factors of the human perception. In this paper, the additive log-logistic model (ALM) is proposed to formulate such a multidimensional nonlinear problem. The log-logistic model has flexible monotonic or nonmonotonic partial derivatives and thus is suitable to model various uni-type impairments. The proposed ALM metric adds the distortions due to each type of impairment in a log-logistic transformed space of subjective opinions. The features can be evaluated and selected by classic statistical inference, and the model parameters can be easily estimated. Cross validations on five Telecommunication Standardization Sector of International Telecommunication Union (ITU-T) subjectively-rated databases confirm that: 1) based on the same features, the ALM outperforms the support vector regression and the logistic model in quality prediction and, 2) the resultant no-reference quality metric based on impairment-relevant video parameters achieves high correlation with a total of 27 216 subjective opinions on 1134 video clips, even compared with existing full-reference quality metrics based on pixel differences. The ALM metric wins the model competition of the ITU-T Study Group 12 (where the validation databases are independent with the training databases) and thus is being put forth into ITU-T Recommendation P.1202.2 for the consent of ITU-T.

关键词： correlation and regression analysis feature evaluation image quality assessment multivariate statistics

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Continuous Cotemporal Probabilistic Modeling of Systems Biology Networks from Sparse Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第5期8卷 1208-1222页

作者： John, David J. Fetrow, Jacquelyn S. Norris, James L. Wake Forest Univ Dept Comp Sci Winston Salem NC 27106 USA Wake Forest Univ Dept Phys Winston Salem NC 27106 USA Wake Forest Univ Dept Math Winston Salem NC 27106 USA

Modeling of biological networks is a difficult endeavor, but exploration of this problem is essential for understanding the systems behavior of biological processes. In this contribution, developed for sparse data, we present a new continuous Bayesian graphical learning algorithm to cotemporally model proteins in signaling networks and genes in transcriptional regulatory networks. In this continuous Bayesian algorithm, the correlation matrix is singular because the number of time points is less than the number of biological entities (genes or proteins). A suitable restriction on the degree of the graph's vertices is applied and a Metropolis-Hastings algorithm is guided by a BIC-based posterior probability score. Ten independent and diverse runs of the algorithm are conducted, so that the probability space is properly well-explored. Diagnostics to test the applicability of the algorithm to the specific data sets are developed;this is a major benefit of the methodology. This novel algorithm is applied to two time course experimental data sets: 1) protein modification data identifying a potential signaling network in chondrocytes, and 2) gene expression data identifying the transcriptional regulatory network underlying dendritic cell maturation. This method gives high estimated posterior probabilities to many of the proteins' directed edges that are predicted by the literature;for the gene study, the method gives high posterior probabilities to many of the literature-predicted sibling edges. In simulations, the method gives substantially higher estimated posterior probabilities for true edges and true subnetworks than for their false counterparts.

关键词： Biological system modeling statistical computing multivariate statistics correlation and regression analysis signal transduction networks transcriptional regulatory networks biological network modeling

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Principal Uncertainty Quantification With Spatial correlation for Image Restoration Problems

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IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2024年第5期46卷 3321-3333页

作者： Belhasin, Omer Romano, Yaniv Freedman, Daniel Rivlin, Ehud Elad, Michael Verily Life Sci South San Francisco CA 94080 USA Technion Israel Inst Technol Dept Comp Sci IL-3200003 Haifa Israel

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions. Using recent advancements in generative models, we derive uncertainty intervals around principal components of the empirical posterior distribution, forming an ambiguity region that guarantees the inclusion of true unseen values with a user-defined confidence probability. To improve computational efficiency and interpretability, we also guarantee the recovery of true unseen values using only a few principal directions, resulting in more informative uncertainty regions. Our approach is verified through experiments on image colorization, super-resolution, and inpainting;its effectiveness is shown through comparison to baseline methods, demonstrating significantly tighter uncertainty regions.

关键词： Uncertainty Task analysis Inverse problems correlation Superresolution Image restoration Standards Uncertainty and probabilistic reasoning probability and statistics restoration inverse problems stochastic processes correlation and regression analysis

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Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2006年第9期18卷 1181-1193页

作者： Pan, Jeffrey Junfeng Kwok, James T. Yang, Qiang Chen, Yiqiang Hong Kong Univ Sci & Technol Dept Comp Sci Kowloon Hong Kong Peoples R China Chinese Acad Sci Comp Technol Inst Shanghai Branch Shanghai Peoples R China

In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts Kernel Canonical correlation analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matern kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods.

关键词： location-dependent and sensitive correlation and regression analysis pervasive computing

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