Challenges of E-Learning Management Within the Croatian Higher Education System

For the past few years, e-learning has become synonymous with different learning and teaching techniques based on information and communication technologies. Generally speaking, elearning has been increasingly present in the Croatian higher education system, gradually changing its traditional character. However, this modern learning and teaching concept has not been equally accepted throughout student population. There are numerous reasons for this state of affairs, one of the most important ones being disproportion, i.e. unequal pace of its introduction at different university and vocational studies in Croatia. These discrepancies cannot be eliminated without active support by all the actors participating in the education process. The greatest responsibility, nevertheless, lies with the people directly in charge of the e-learning process. To fulfil its task more efficiently, e-learning management requires relevant information on different aspects of its usage, as well as its acceptance among students. With this aim in mind, we conducted a survey of student attitudes at Josip Juraj Strossmayer University of Osijek. This paper presents the results of this research, which are based on application of various statistical methods, primarily cluster analysis.e-learning management, attitudes of students, relevant information, cluster analysis

Unsupervised Active Learning For Video Annotation

International audienceWhen annotating complex multimedia data like videos, a human expert usually annotates them manually. However, labeling these immense quantities of videos manually is a labor-intensive and time-consuming process. Therefore, computational methods, such as active learning are used to help annotate. In this study, we propose a cluster based unsupervised active learning approach and a new active learning method for un-supervised active learning on REPERE (Giraudel et al., 2012) video dataset, which is created for the problem of person identification in videos. Our study aims to identify who is speaking and who is on screen by using multi-modal data

Balancing the Communication Load of Asynchronously Parallelized Machine Learning Algorithms

Stochastic Gradient Descent (SGD) is the standard numerical method used to solve the core optimization problem for the vast majority of machine learning (ML) algorithms. In the context of large scale learning, as utilized by many Big Data applications, efficient parallelization of SGD is in the focus of active research. Recently, we were able to show that the asynchronous communication paradigm can be applied to achieve a fast and scalable parallelization of SGD. Asynchronous Stochastic Gradient Descent (ASGD) outperforms other, mostly MapReduce based, parallel algorithms solving large scale machine learning problems. In this paper, we investigate the impact of asynchronous communication frequency and message size on the performance of ASGD applied to large scale ML on HTC cluster and cloud environments. We introduce a novel algorithm for the automatic balancing of the asynchronous communication load, which allows to adapt ASGD to changing network bandwidths and latencies.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0495

Graph-based Semi-Supervised & Active Learning for Edge Flows

We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices. We derive bounds for our method's reconstruction error and demonstrate its strong performance on synthetic and real-world flow networks from transportation, physical infrastructure, and the Web. Furthermore, we provide two active learning algorithms for selecting informative edges on which to measure flow, which has applications for optimal sensor deployment. The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free. The second approach clusters the graph and selects bottleneck edges that cross cluster-boundaries, which works well on flows with global trends

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

ANALISA KUALITAS PRODUK PIRING MELAMIN DENGAN METODE SIX SIGMA DI PT.SEMESTA RAYA ABADI JAYA

This research is to perform clustering of activity in the central library book borrowers UPN "Veteran" East Java from a variety of majors. Based on borrowing books at the library circulation center UPN "Veteran" East Java, for 3 months are June, July and September is 1922 data. The center’s library UPN "Veteran" East Java don’t know of any department that perform activities as a borrower of books and book groups which are much borrowed. So it can’t be recommended priority groups to be reproduced. Given these problems , then conducted research grouping and group borrower are many books borrowed by k-means clustering method to support the teaching and learning process. This research were obtained 3 clusters. Data in cluster 1 (less active) there are 778 students , cluster 2 (moderately active) there are 267 students and cluster 3 (active) there are 877 students. For groups that are often borrowed books from the 3 cluster is a applied technology in technology management especially accounting and general management. Keywords : borrowers, library books, cluster, k-means clusterin

PENGELOMPOKAN PEMINJAM BUKU DENGAN METODE K-MEANS DI PERPUSTAKAAN PUSAT UPN “VETERAN” JAWA TIMUR

This research is to perform clustering of activity in the central library book borrowers UPN "Veteran" East Java from a variety of majors. Based on borrowing books at the library circulation center UPN "Veteran" East Java, for 3 months are June, July and September is 1922 data. The center’s library UPN "Veteran" East Java don’t know of any department that perform activities as a borrower of books and book groups which are much borrowed. So it can’t be recommended priority groups to be reproduced. Given these problems , then conducted research grouping and group borrower are many books borrowed by k-means clustering method to support the teaching and learning process. This research were obtained 3 clusters. Data in cluster 1 (less active) there are 778 students , cluster 2 (moderately active) there are 267 students and cluster 3 (active) there are 877 students. For groups that are often borrowed books from the 3 cluster is a applied technology in technology management especially accounting and general management. Keywords : borrowers, library books, cluster, k-means clusterin

Pengaruh Strategi Think Pair Share Dan Numbered Heads Together Terhadap Hasil Belajar Matematika Ditinjau Dari Keaktifan Siswa Kelas X Sma N 1 Kartasura Tahun Ajaran 2015/2016

The aims of research to understanding: (1) the effect of mathematics learning with think pair share (TPS) strategy and numbered heads together (NHT) strategy toward mathematics learning outcomes, (2) the effect of active students toward mathematics learning outcomes, (3) the interaction between learning strategy and active students toward mathematics learning outcomes. The type of the research is experiment with quasi experimental design. The population of the research was all students of X grade of SMA N 1 Kartasura of academic year 2015/2016. The research sample consisted of two classes. The sampling technique use cluster random sampling. Methods of data collection use documentation, questionnaires and test. Data analyzed by analysis of variance with two different cell lines. The results of data analysis with significance level of 5% was obtained: (1) there is effect of mathematics learning outcomes through teaching with think pair share (TPS) strategy and numbered heads together (NHT) strategy, with (2) the effect in the active levels of students towards methematics learning outcomes, with (3) there is no interaction between think pair share (TPS) strategy and numbered heads together (NHT) strategy based on active students toward mathematics learning outcome, with