Coupling analysis in educational data

University of Technology Sydney. Faculty of Engineering and Information Technology.Educational data analysis refers to techniques, tools, and research designed to automatically extract meaning from large repositories of data generated by or related to people’s learning activities in educational environments. It is a research field which focus on helping policymakers and administrators understand how analytics and data mining can be applied for the purposes of educational improvement. Unfortunately, most research on educational data only by applying the existing machine learning or data mining algorithms, very few publications have discussed the character of the data itself. Traditional data mining algorithms have disadvantages, in that most of them assume the independent and identically distributed (IID) of data objects, attributes, and values. However, real world data usually contains strong couplings among values, attributes and data objects, and this represents a considerable challenge to existing methods and tools. This thesis focuses on utilizing coupling analysis in educational data analysis tasks. In particular, it focuses on two educational data analysis tasks: student performance prediction, and student social media sentiment analysis. The student performance prediction task is firstly examined. This thesis begins with the most straightforward method which integrates coupling similarities as the distance for a weighted k-nearest centroid classifier. This method considers not only the intra-coupled similarity within an attribute but also the inter-coupled similarity between attributes. Computational cost is high for coupling analysis. Hence, a more efficient method is proposed that selects the centroid objects instead of all objects in the nearest neighbor search process. Furthermore, integrating support vector machines with coupled similarity. The original SVMs is designed for numerical data. This thesis develops a novel pairwise SVMs that use the coupled similarity metric as a kernel between data objects with nominal attributes. The experiment result shows the two proposed methods outperform the traditional SVMs and other popular classification methods on various public data sets, and the student performance prediction task. Secondly, the student social media sentiment analysis is examined. Unlike linguistic methods, this thesis learns how to classify student sentiment by applying data mining on the labeled historical data. Most previous research employs the vector-space model for text representation and analysis, however, the vector-space model does not utilize the information about the term to term relationships. In other words, the traditional text mining techniques assume the relations between term to term are independent and identically distributed (IID). This thesis introduces a novel term representation by involving coupling relations between neighbors. This coupling representation provide much richer information which enables us to create a coupled similarity metric from document to document, and a coupling document similarity based k-nearest centroid classifier applied to the classification task. Experiments verify that the proposed approach outperforms the classic vector-space based classifier and displays distinct advantages and richness in terms of student social media sentiment analysis tasks. Finally, due to the complexity of the proposed algorithm and the enormous amount of the educational related data source, a scalable educational data mining platform is in great demand. Hence, with the help of the Spark cluster, a novel coupling similarity based learning approach has been proposed to cater for the big data learning problem by parallelizing the coupled similarity calculation process. Further, the parallel k-NN for classification and k-Means for the clustering task has been proposed. Compared to the original algorithms, the experimental results show that the proposed methods not only outperforms the clustering and classification performance of the baselines, but also represent a huge improvement on the data scale in terms of the time efficiency. Accordingly, the proposed framework has already been implemented, a scalable educational data analysis platform with coupling analysis will serve to meet a host of future challenges

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limited resources among VMs. Many techniques such as VM scheduling and resource provisioning are proposed to maximize the cost-efficiency of the computing servers while alleviating the performance inference between VMs. However, these management techniques require accurate performance prediction of the application running inside the VM, which is challenging to get in the public cloud due to the black-box nature of the VMs. From this perspective, this paper proposes a novel machine learning-based performance prediction approach for applications running in the cloud. To achieve high accuracy predictions for black-box VMs, the proposed method first identifies the running application inside the virtual machine. It then selects highly-correlated runtime metrics as the input of the machine learning approach to accurately predict the performance level of the cloud application. Experimental results with state-of-the-art cloud benchmarks demonstrate that our proposed method outperforms the existing prediction methods by more than 2x in terms of worst prediction error. In addition, we successfully tackle the challenge in performance prediction for applications with variable workloads by introducing the performance degradation index, which other comparison methods fail to consider. The workflow versatility of the proposed approach has been verified with different modern servers and VM configurations.Comment: 15 pages, 11 figures, summited to IEEE Transactions on Sustainable Computin

Machine learning assisted abstraction of photonic integrated circuits in fully disaggregated transparent optical networks

10sipartially_openopenKhan, Ihtesham; Chalony, Maryvonne; Ghillino, Enrico; Masood, Muhammad Umar; Patel, Jigesh; Richards, Dwight; Mena, Pablo; Bardella, Paolo; Carena, Andrea; Curri, VittorioKhan, Ihtesham; Chalony, Maryvonne; Ghillino, Enrico; Masood, Muhammad Umar; Patel, Jigesh; Richards, Dwight; Mena, Pablo; Bardella, Paolo; Carena, Andrea; Curri, Vittori

iQPP: A Benchmark for Image Query Performance Prediction

To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). First, we establish a set of four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and estimate the ground-truth difficulty of each query as the average precision or the precision@k, using two state-of-the-art image retrieval models. Next, we propose and evaluate novel pre-retrieval and post-retrieval query performance predictors, comparing them with existing or adapted (from text to image) predictors. The empirical results show that most predictors do not generalize across evaluation scenarios. Our comprehensive experiments indicate that iQPP is a challenging benchmark, revealing an important research gap that needs to be addressed in future work. We release our code and data as open source at https://github.com/Eduard6421/iQPP, to foster future research.Comment: Accepted at SIGIR 202

A Survey on Surrogate-assisted Efficient Neural Architecture Search

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve the major limitation of NAS, improving the efficiency of NAS is essential in the design of NAS. This paper begins with a brief introduction to the general framework of NAS. Then, the methods for evaluating network candidates under the proxy metrics are systematically discussed. This is followed by a description of surrogate-assisted NAS, which is divided into three different categories, namely Bayesian optimization for NAS, surrogate-assisted evolutionary algorithms for NAS, and MOP for NAS. Finally, remaining challenges and open research questions are discussed, and promising research topics are suggested in this emerging field.Comment: 18 pages, 7 figure

Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling

A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs. We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis

04511 Abstracts Collection -- Architecting Systems with Trustworthy Components

From 12.12.04 to 17.12.04, the Dagstuhl Seminar 04511 ``Architecting Systems with Trustworthy Components\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data

For the LMRT task at ImageCLEFlifelog 2020, LIFER 3.0, a new version of the LIFER system with improvements in the user interface and system affordance, is used and evaluated via feedback from a user experiment. In addition, since both tasks share a common dataset, LIFER 3.0 borrows some features from the LifeSeeker system deployed for the Lifelog Search Challenge; which are free-text search, visual similarity search and elastic sequencing filter. For the SPLL task, we proposed a naive solution by capturing the rate of change in running speed and weight, then obtain the target changes for each subtask using average computation and linear regression model. The results presented in this paper can be used as comparative baselines for other participants in the ImageCLEFlifelog 2020 challenge.publishedVersio

Data Analysis and Neuro-Fuzzy Technique for EOR Screening : Application in Angolan Oilfields

This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscriptPeer reviewedPublisher PD