Question: Autoencoders, the principle of minimum description length, and the concept of Helmholtz free energy.

This paper explores the capability of multilayer perceptrons (MLPs) operating in auto-association mode as a tool for data compression and dimensionality reduction in information processing tasks. Contrary to common belief, the study reveals that nonlinearities in the hidden units are unnecessary for auto-association. Instead, optimal parameter values can be directly calculated using linear methods, specifically singular value decomposition (SVD) and low-rank matrix approximation. This technique is conceptually akin to the Karhunen-Loève transform, a well-established method for dimensionality reduction. By leveraging SVD, the proposed approach not only provides an efficient alternative to the error back-propagation algorithm typically used for training MLPs but also offers a clear understanding of the role of each parameter in the network. This linear perspective on auto-association by MLPs underscores the potential for simplifying training procedures and enhancing the interpretability of these networks in feature space compression applications.

Unsupervised learning algorithms are designed to extract structure from data s amples on the basis of a cost function for structures. For a reliable and robust inference proce ss, th unsupervised learning algorithm has to guarantee that the extracted structures ar e typical for the data source. In particular, it has to reject all structures where the infe renc is dominated by the arbitrariness of the sample noise and which, consequently, can be characterize d as overfitting in unsupervised learning. This paper summarizes an inference principle called Empirical Risk Approximationwhich allows us to quantitatively measure the overfitting effect and to derive a criterion as a saveguard against it. The crucial condition for learning is me t if (i) the empirical risk of learning uniformly converges towards the expected risk and if ( ii) the hypothesis class retains a minimal variety for consistent inference. Parameter s l ction of learnable data structures is demonstrated for the case of k-means clustering and Monte Carlo simulations are presented to support the selection principle. 1 PHILOSOPHY OF UNSUPERVISED LEARNING Learning algorithms are designed to extract structure from data. Two class es of algorithms have been widely discussed in the literature – supervised andunsupervised learning. The distinction between the two classes depends on supervision or teacher informati on which is either available to the learning algorithm or missing in the learning process. T hi paper describes a statistical learning theory of unsupervised learning. We summarize an induct ion principle for unsupervised learning which is refered to as Empirical Risk Approximation[1]. This principle is based on the optimization of a quality functional for structures in data and, most i portantly, it contains a safeguard against overfitting of structure to the noise in a sampl e set. The extracted structure of the data is encoded by a loss function and it is assumed to produce a lear ning risk below a predefined risk threshold. This induction principle is summarized by the fol lowing two inference steps: 1. Define a hypothesis class containing loss functions which evaluate candidate structures of the data and which measure their quality. Control the complexity of the hypothesis class by an upper bound on the costs. 2. Select an arbitrary loss function from the smallest subset which still guarantees c onsistent learning (in the sense of Statistical Learning Theory). The reader should note that the learning algorithm has to return a structure with c ostsbounded by a preselected cost threshold, but it is not required to return the structure w ith minimal empirical risk as in Vapnik’s “Empirical Risk Minimization” induction principle for classi fication and regression [2]. All structures in the data with risk smaller than the sel cted risk bound are considered to be equivalent in the approximation process without further distinction. Various cases of unsupervised learning algorithms are discussed in the literature wi h mphasis on the optimization aspect, i.e., data clustering or vector quantization, self-organi zing maps, principal or independent component analysis and principal curves, projection pursuit or algorithms for relational data as multidimensional scaling and pairwise clustering and histogram based clustering methods. How can we select a loss function from a hypothesis class with desired approxim ation quality or, equivalently, find a structure with bounded costs? The nested sequence of hypothesis classe suggests a tracking strategy of solutions which are elements of smaller and sm ller hypothesis classes in that sequence. The sequential sampling can be implemented by soluti on tracking, i.e., admissible solutions are incrementally improved by gradient descent if t he process samples from a subset with smaller risk bound than in the previous sampling step. Candidate procedure for the sampling of structures from the hypothesis class are stochastic search t chniques or continuation methods like simulated or deterministic annealing, although the theory does not refer to any particular learning algorithm. The general framework of Empirical Risk Approximationand its mathematical formulation is presented in Sec. 2, whereas a more detailed discussion can be found in [1]. Thi s theory of unsupervised learning is applied to the case of central clustering in Sec. 3. 2 MATHEMATICAL FRAMEWORK FOR UNSUPERVISED LEARNING The data samples X = xi 2 : IRd; 1 i l which have to be analysed by the unsupervised learning algorithm are elements of a suitable d-dimensional Euclidian space. The data are distributed according to a measure which is assumed to be known for the analysis. A mathematically precise statement of the Empirical Risk Approximationinduction principle requires several definitions which formalize the notion of searching for struct ure in the data. The quality of structures extracted from the data set X is evaluated by a learning cost function R̂( ;X ) = 1l l Xi=1 h(xi; ): (1) R̂( ;X ) denotes theempirical risk of learning a structure for an i.i.d. sample set X . The functionh(x; ) is known asloss function in statistics. It measures the costs for processing a generic datumx and often corresponds to the negative log-likelihood of a stochastic model. For example in vector quantization, the loss function quantifies what the costs are t o ssign a data pointx to a particular prototype or codebook vector. Each value 2 parametrizes an individual loss functions with denoting the parameter set. The parameter characterizes the different structures of the data set which are hypothetically considered candida te structures in the learning process and which have to be validated. Statistical learning t heory distinguishes between different classes of loss functions, i.e., 0 1 functions in classification problems, bounded functions and unbounded non-negative functions in regression problems. This paper is concerned with the class of unbounded non-negative functions since we are particularly interested in polynomially increasing loss function ( O kxkp)) as they occur in vector quantization. Note that the quality measurê R( ;X ) depends on the i.i.d. data set X and, therefore, is itself a random variable. The relevant quality measure for unsupervised learning, howev er, is the expectation value of this random variable, known as the expected risk of learning R( ) = Z h(x; ) d (x): (2) While minima ofR̂( ;X ) or solutions with bounded empirical risk are influenced by fluctuations in the samples, it is the expected risk R( ) which completely assesses the quality of learning results in a sound probabilistic fashion. The distribution is assumed to be known in the following analysis and it has to decay sufficiently fast such that all rth moments ( r > 2) of the loss functionh(x; ) are bounded byE fjh(x; ) R( )jrg r! r 2V fh(x; )g; 8 2 . E f:g andV f:g denote expectation and variance of a random variable, respectively. is a distribution dependent constant. The moment constraint on the distribution h lds for all distributions with exponentially fast decaying tails which include all distri butions with finite support. Empirical Risk Approximationis an induction principle which requires the learning algorithm to sample from the smallest consistently learnable subset of the hypothesis cla s . In the following, the hypothesis class H contains all loss functions h(x; ); 2 and the subsets of risk bounded loss functions HR are defined as HR = fh(x; ) : 2 ^ R( ) R g : (3) The subsetsHR are obviously nested since HR 1 HR 2 H forR 1 R 2 1 andH = limR !1HR . R induces a structure on the hypothesis class in the sense of Vapnik’s “Structural Risk” and essentially controls the complexity of the hypothesis clas . TheEmpirical Risk Approximationinduction principle requires to define a nested sequence of hypothesis classes with bounded expected risk and to sample from the hypothesis clas s with the desired approximation quality. Algorithmically, however, we select t he loss function according to the bounded empirical risk̂ R( ;X ) R . This induction principle is consistent if a bounded empirical risk implies in the asymptotic limit that the loss functi o has bounded expected riskR( ) R and, therefore, is an element of the hypothesis class HR , i.e., 8 2 lim l!1 R̂( ;X ) R =) h(x; ) 2 HR : (4) This implication essentially states that the expected risk asymptotica lly does not exceed the risk bound (R( ) R ) and, therefore, the inferred structure is an approximation of the optimal data structure with risk not worse than R . The consistency assumption (4) for Empirical Risk Approximationholds if the empirical risk uniformly converges towards the expected risk lim l!1P(sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) = 0; 8 > 0; (5) where denotes the hypothesis set of distinguishable structures. defines an -netH ;R on the set of loss functions HR or coarsens the hypothesis class with an -separated set if there does not exist a finite -net forHR . Using results from the theory of empirical processes the probability of an –deviation of empirical risk from expected risk can be bounded by Bernstein’s inequality ([3], Lemma 2.2.11) P( sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) 2jH j exp l 2 2(1 + = min) : (6) The minimal variance of all loss functions is denoted by 2 min = inf 2 V fh(x; )g. jH j denotes the cardinality of an -net constructed for the hypothesis class HR under the assumption of the measure . The confidence level limits the probability of large deviations [1]. The large deviation inequality weighs two competing effects in the learning probl em, i.e., the probability of a large deviation exponentially decreases with growing sample si ze l, whereas a large deviation becomes increasingly likely with growing cardinality of the hypothesis class. A compromise between both effects determines how reliable an estimate ac tually is for a given data set X . The sample complexity l0( ; ) is defined as the nec

Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers.

To describe the log-likelihood computation in our model, let us consider a two scale pyramid for the moment. Given a (vectorized) j × j image I , denote by l = d(I) the coarsened image, and h = I − u(d(I)) to be the high pass. In this section, to simplify the computations, we use a slightly different u operator than the one used to generate the images displayed in Figure 3 of the paper. Namely, here we take d(I) to be the mean over each disjoint block of 2× 2 pixels, and take u to be the operator that removes the mean from each 2× 2 block. Since u has rank 3d/4, in this section, we write h in an orthonormal basis of the range of u, then the (linear) mapping from I to (l, h) is unitary. We now build a probability density p on Rd2 by p(I) = q0(l, h)q1(l) = q0(d(I), h(I))q1(d(I)); in a moment we will carefully define the functions qi. For now, suppose that qi ≥ 0, ∫ q1(l) dl = 1, and for each fixed l, ∫ q0(l, h) dh = 1. Then we can check that p has unit integral: ∫

We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing -gram language models. Because of the mature body of research in -gram model smoothing and the close connection between ME and conventional -gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous -gram smoothing methods to explain its superior performance.

We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-theart classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation – rules for gradient backpropagation through stochastic variables – and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.

We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which carry no information. Although CEC, similarly like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is affine invariant, while the numerical complexity is comparable to that of k-means.

We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a “pretraining” step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.


Question: Regarding the preprocessing of the ALT-algorithm for student utilization.

Understanding the Class of Polynomial-Time Solvable Problems

To gain insight into the realm of polynomial-time solvable problems, we need a precise definition of what constitutes a "problem." An abstract problem, denoted as Q, can be conceptualized as a binary relation between a set I of problem instances and a set S of problem solutions. For instance, consider the SHORTEST-PATH problem: an instance is a triplet comprising a graph and two vertices, while a solution is a sequence of vertices in the graph, possibly including the empty sequence to indicate the absence of a path. The SHORTEST-PATH problem itself is the relation that links each graph and two vertices instance with the shortest path connecting the two vertices. Given that shortest paths are not always unique, a single problem instance may have multiple solutions.

This broad definition of an abstract problem, however, exceeds the scope of our interest. Specifically, the theory of NP-completeness focuses on decision problems, characterized by yes/no solutions. We can thus view an abstract decision problem as a function mapping the instance set I to the solution set {0, 1}. A decision problem related to SHORTEST-PATH, for example, is the PATH problem previously discussed. Given an instance i = 〈G,u, v,k〉 of the decision problem PATH, PATH(i) = 1 (yes) if the shortest path from u to v contains no more than k edges, and PATH(i) = 0 (no) otherwise.

Many abstract problems are not decision problems but optimization problems, where a certain value needs to be minimized or maximized. However, it is typically straightforward to reformulate an optimization problem as a decision problem of comparable difficulty.

This perspective provides a foundational understanding of the classification and characteristics of problems solvable in polynomial time, highlighting the distinction between decision and optimization problems while underscoring the versatility of reformulating problems to fit within the decision problem framework.

This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom’s workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both out-perform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming.

Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended action patterns in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain.

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner.While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Active Learning For Real Time Detection Of Polyps In Videocolonoscopy Quentin Angermann, Aymeric Histace, Olivier Romain

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.

The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. r 2006 Elsevier B.V. All rights reserved.

This study was planned to explore the impact of eLearning on the academic performance of student-teachers. The researchers' concern with e-Learning was sanctioned by the need for a teaching and learning strategy that can help institutions of learning address their problems and improve on their outcome. In this respect, the researchers conducted an experiment to find out whether student-teachers taught using the method of e-Learning (blended learning) perform better than studentteachers taught using the traditional method of teaching and learning. Findings offers new evidence that e-Learning has a significant influence on the performance of students as student-teachers taught using eLearning consistently performed better than student-teachers taught using the traditional method. Based on this result, recommendations are made to training institutions to embrace ICTs and become more flexible by adopting learning approaches that are dynamic and multidimensional as problems in education are becoming more complex.


Question: Regarding the preprocessing of the ALT-algorithm for students.

This paper provides an extensive survey of recent advancements in algorithms designed for route planning within transportation networks, showcasing methods that have achieved unprecedented speed improvements, up to three million times faster than the classic Dijkstra's algorithm. The evolution of these techniques has been driven by a combination of innovative approaches and optimizations. We delve into the details of these developments, highlighting the core methodologies that have propelled this progress. Additionally, the paper outlines the current boundaries of ongoing research, which is increasingly focusing on more complex variants of the route planning problem. These include scenarios involving dynamically evolving networks, where the topology changes over time, time-dependent routing where the optimal path can vary based on the departure time, and the integration of flexible objective functions that allow for customized route preferences. This review serves as a comprehensive guide for researchers and practitioners seeking to understand the state-of-the-art in route planning algorithms and the directions for future advancements in this field.

We propose a novel word embedding pretraining approach that exploits writing errors in learners’ scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.

This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade. 2015 Elsevier Ltd. All rights reserved.

Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics – a high-enrollment, highimpact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student’s cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students’ scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student’s cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning. 2012 Elsevier Ltd. All rights reserved.

An algorithm is presented that searches for the location, “<italic>i</italic>l” of the first occurrence of a character string, “<italic>pat</italic>,” in another string, “<italic>string</italic>.” During the search operation, the characters of <italic>pat</italic> are matched starting with the last character of <italic>pat</italic>. The information gained by starting the match at the end of the pattern often allows the algorithm to proceed in large jumps through the text being searched. Thus the algorithm has the unusual property that, in most cases, not all of the first <italic>i</italic> characters of <italic>string</italic> are inspected. The number of characters actually inspected (on the average) decreases as a function of the length of <italic>pat</italic>. For a random English pattern of length 5, the algorithm will typically inspect <italic>i</italic>/4 characters of <italic>string</italic> before finding a match at <italic>i</italic>. Furthermore, the algorithm has been implemented so that (on the average) fewer than <italic>i</italic> + <italic>patlen</italic> machine instructions are executed. These conclusions are supported with empirical evidence and a theoretical analysis of the average behavior of the algorithm. The worst case behavior of the algorithm is linear in <italic>i</italic> + <italic>patlen</italic>, assuming the availability of array space for tables linear in <italic>patlen</italic> plus the size of the alphabet.
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Drawing by learners can be an effective way to develop memory and generate visual models for higher-order skills in biology, but students are often reluctant to adopt drawing as a study method. We designed a nonclassroom intervention that instructed introductory biology college students in a drawing method, minute sketches in folded lists (MSFL), and allowed them to self-assess their recall and problem solving, first in a simple recall task involving non-European alphabets and later using unfamiliar biology content. In two preliminary ex situ experiments, students had greater recall on the simple learning task, non-European alphabets with associated phonetic sounds, using MSFL in comparison with a preferred method, visual review (VR). In the intervention, students studying using MSFL and VR had ∼50-80% greater recall of content studied with MSFL and, in a subset of trials, better performance on problem-solving tasks on biology content. Eight months after beginning the intervention, participants had shifted self-reported use of drawing from 2% to 20% of study time. For a small subset of participants, MSFL had become a preferred study method, and 70% of participants reported continued use of MSFL. This brief, low-cost intervention resulted in enduring changes in study behavior.

Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identify the function is through functional behavior assessment (FBA). Teachers conduct FBA in schools. However, the task load of recording the data manually is high, and the challenge of accurately identifying antecedents and consequences is significant while interacting with students. These issues often result in imperfect information capture. CareLog allows teachers more easily to conduct FBAs and enhances the capture of relevant information. In this paper, we describe the design process that led to five design principles that governed the development of CareLog. We present results from a five-month, quasi-controlled study aimed at validating those design principles. We reflect on how various constraints imposed by special education settings impact the design and evaluation process for HCI practitioners and researchers.

Speaker: Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document: http://www.cs.cmu.edu/~mrinmays/thesis_proposal.pdf Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.

This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

In this paper we propose the architecture of an Intelligent Tutoring System that considers the student's learning style and the competency-based education. We also describe the processes that have been implemented so far. Our architecture presents innovations in the representation of the tutor module and in the knowledge module; the tutor module incorporates a selector agent, which will choose the content to show, considering the teaching strategies that support the student's learning style.

The
  education
  of
  the
  poor
  and
  disadvantaged
  population
  has
  been
  a
  long-‐standing challenge
  to
  the
  education
  system
  in
  both
  developed
  and
  developing
  countries. Although
  computer-‐assisted
  learning
  (CAL)
  has
  been
  considered
  one
  alternative
  to improve
  learning
  outcomes
  in
  a
  cost-‐effective
  way,
  the
  empirical
  evidence
  of
  its
  impacts on
  improving
  learning
  outcomes
  is
  mixed.
  This
  paper
  intends
  to
  explore
  the
  nature
  of the
  effects
  of
  CAL
  on
  student
  academic
  and
  non-‐academic
  outcomes
  for
  underserved populations
  in
  a
  developing
  country.
  To
  meet
  this
  goal,
  we
  exploit
  a
  randomized
  field experiment
  of
  a
  CAL
  program
  involving
  over
  4000
  third-‐grade
  students,
  mostly
  from poor
  migrant
  families,
  in
  43
  migrant
  schools
  in
  Beijing.
  The
  main
  intervention
  is
  a
  math CAL
  program
  that
  is
  held
  out
  of
  regular
  school
  hours.
  The
  program
  is
  tailored
  to
  the regular
  school
  math
  curriculum
  and
  is
  remedial
  in
  nature.
  Our
  results
  show
  that,
  the
  CAL program
  improved
  the
  student
  standardized
  math
  scores
  by
  0.14
  standard
  deviations and
  most
  of
  the
  program
  effect
  took
  place
  within
  two
  months
  after
  the
  start
  of
  the program.
  Low-‐performing
  students
  and
  those
  with
  less-‐educated
  parents
  benefited more
  from
  the
  program.
  Moreover,
  CAL
  also
  significantly
  increased
  the
  levels
  of
  self-‐ efficacy
  of
  the
  students
  and
  their
  interest
  in
  learning.
  We
  observed
  at
  most
  a
  moderate program
  spillover
  in
  Chinese
  test
  scores.
  Our
  findings
  are
  robust
  to
  the
  Hawthorne effect
  and
  CAL
  program
  spillovers
  that
  might
  potentially
  bias
  the
  estimates
  of
  the program
  effects. Key
  Words:

  Education;
  Development;
  Computer
  Assisted
  Learning;
  Random Assignment;
  Test
  Scores;
  China;
  Migration JEL
  Codes:
  I20;
  I21;
  I28;
  O15


Question: Regarding the preprocessing of the ALT-algorithm for students.

This paper introduces an innovative approach to calculating the shortest path, blending A* search methodology with a novel graph-theoretic lower-bounding strategy grounded in landmarks and the triangle inequality. Our algorithms, designed to compute optimal shortest paths, are universally applicable to any directed graph. To validate the efficacy of our approach, we present empirical data that highlights the superior performance of our most efficient algorithm. Specifically, it significantly outpaces existing algorithms, including A* search augmented with Euclidean bounds, in a variety of contexts such as road networks and select synthetic problem sets. This advancement underscores the algorithm's potential for enhancing efficiency in pathfinding across diverse applications.

Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. In order to solve challenging tasks in problem situations, learners not only have to perform cognitive activities, e.g., activating existing cognitive structures or organizing new information, they also have to set specific goals, plan their activities, monitor their performance during the problem-solving process, and evaluate the efficiency of their actions. This paper reports an experimental study with 98 participants where effective instructional interventions for self-regulated learning within problemsolving processes are investigated. Furthermore, an automated assessment and analysis methodology for determining the quality of learning outcomes is introduced. The results indicate that generic prompts are an important aid for developing cognitive structures while solving problems.

CS Unplugged is a set of active learning activities designed to introduce fundamental computer science principles without the use of computers. The program has gained significant momentum in recent years, with proponents citing deep engagement and enjoyment benefits. With these benefits in mind, we initiated a one-year outreach program involving a local high school, using the CS Unplugged program as the foundation. To our disappointment, the results were at odds with our enthusiasm --- significantly. In this paper, we describe our approach to adapting the CS Unplugged materials for use at the high school level, present our experiences teaching it, and summarize the results of our evaluation.

In this study, we present an award-wining software environment for groundwater education and research. The software functions as a ‘‘digital laboratory’’ in which students can freely explore: visually creating an aquifer of desired configurations and then immediately investigating and visualizing the groundwater system. Students learn by active exploration and interaction. The software allows introducing routinely research and complex problem-solving into the classroom. 2004 Wiley Periodicals, Inc. Comput Appl Eng Educ 11: 179 202, 2003; Published online in Wiley InterScience (www.interscience.wiley.com.); DOI 10.1002/cae.10052

In education system, students' feedback is important to measure the quality of teaching. Students' feedback can be analyzed using lexicon based approach to identify the students' positive or negative attitude. In most of the existing teaching evaluation system, the intensifier words and blind negation words are not considered. The level of opinion result isn't displayed: whether positive or negative opinion. To address this problem, we propose to analyze the students' text feedback automatically using lexicon based approach to predict the level of teaching performance. A database of English sentiment words is created as a lexical source to get the polarity of words. By analyzing the sentiment information including intensifier words extracting from students' feedback, we are able to determine opinion result of teachers, describing the level of positive or negative opinions. This system shows the opinion result of teachers that is represented as to whether strongly positive, moderately positive, weakly positive, strongly negative, moderately negative, weakly negative or neutral.

The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based on data collected during student team project development. These data include over 100 objective and quantitative Team Activity Measures (TAM) obtained from monitoring and measuring activities of student teams during the creation of their final class project in our joint software engineering classes which ran concurrently at San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). In this paper we provide the first RF analysis results done at SFSU on our full data set covering four years of our joint SE classes. These data include 74 student teams with over 380 students, totaling over 30000 discrete data points. These data are grouped into 11 time intervals, each measuring important phases of project development during the class (e.g. early requirement gathering and design, development, testing and delivery). We briefly elaborate on the methods of data collection and describe the data itself. We then show prediction results of the RF analysis applied to this full data set. Results show that we are able to detect student teams who are bound to fail or need attention in early class time with good (about 70%) accuracy. Moreover, the variable importance analysis shows that the features (TAM measures) with high predictive power make intuitive sense, such as late delivery/late responses, time used to help each other, and surprisingly statistics on commit messages to the code repository, etc. In summary, we believe we demonstrate the viability of using ML on objective and quantitative team activity measures to predict student learning of software engineering teamwork, and point to easy-to-measure factors that can be used to guide educators and software engineering managers to implement early intervention for teams bound to fail. Details about the project and the complete ML training database are downloadable from the project web site.

Computational thinking skills will be important for the next generation of students. However, there is a disparity in the populations joining the field. Integrating computational thinking into artistic fields has shown to increase participation in computer science. In this paper, we present our initial design prototype for TunePad, a sound composition tablet application controlled by a block-based programming environment. TunePad is designed to introduce learners to computational thinking and to prepare them for text-based coding environments. From our preliminary testing, with children ages 7-14, we observed that our design actively engages learners and communicates how the programming blocks control the sounds being played. This testing is a prelude to more formal studies as we continue to improve the design and interface of TunePad. With this work, we intend to engage students in computational thinking who may not have otherwise been exposed, giving the opportunity to more people to enter the computer science field.

We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.

Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining

algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems. Successful applications range from text categorization to handwriting recognition to classification of geneexpression data.

In a series of three experimental studies, we investigate the understanding achieved by learners when studying a complex domain in computer science, that is, the subject of memory management concepts in operating systems. We use three different media combinations to explain the subject of memory management concepts - Animation and text, animation and voice and a combination of animation, voice and text. The students who had no prior knowledge on the subject where given a test on recall and transfer knowledge after viewing the treatments. This paper reports the results of the experiment which may provide useful guidance for instructional designers specifically in the area of computer science.


Question: Regarding the preprocessing of the ALT-algorithm for students.

This paper presents a dynamic method for efficient route planning in extensive road networks, catering to the requirements of modern navigation systems. Our technique is the first to effectively manage scenarios that are common in today's navigation environments: In the event of an edge weight modification (such as a traffic congestion), we can refresh the preprocessed data within a time frame of 2–40 milliseconds, enabling subsequent rapid queries at an average of about one millisecond. For scenarios where only a single query is required, we can bypass the relatively costly update phase and execute a judicious query that inherently accounts for the altered conditions. When the overall cost function shifts (for instance, due to a change in vehicle type), the preprocessed information can be recalculated in less than two minutes on average. The core of our dynamic approach is a novel static method that synthesizes and extends multiple previous speedup techniques. A significant feature of this new static approach is its remarkably low memory consumption, requiring just a few bytes per node.

Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended action patterns in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain.

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.

With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.

As more secondary students with learning disabilities (LD) enroll in advanced content-area classes and are expected to pass state exams, they are faced with the challenge of mastering difficult concepts and abstract vocabulary while learning content. Once in these classes, students must learn from lectures that move at a quick pace, record accurate and complete notes, and then demonstrate their mastery of the content on tests. This article provides an overview of the challenges faced by students with LD in content-area classes and discusses the problems that students have learning from lectures and recording notes. Further, the article discusses theory and research related to note-taking and presents note-taking interventions that teachers can use to help students improve their note-taking skills, and ultimately, improve their achievement in these classes.

By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.

When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons. * Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence © 2012 Elsevier B.V. All rights reserved.

Probably the most important, problem in machine learning is the preliminary biasing of a learner’s hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt, In this paper a mechanism for automailcall,y learning or biasing the learner’s hypothesis space is introduced. It works by first learning an appropriate [nterna! represenlaiton for a learning en~-ironment and then using that representation to hiss the learner’s hypothesis space for the learning of future tasks drawn from the same environment. Jln internal represent at ion must be learnt by sampling from many s2mtiar tasks, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples m per task required to ensure good generalisation from a representation learner obeys }n = O(a + b/n) where n is the number of tasks being learnt, and a and b are constants. If the tasks are learnt independently (/. e. without a common representation ) then m = 0( a + b). It is argued that for learning environments such as speech and character recognition b >> a and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if n = O(b) (with m = O(a+b/n ) ) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to O(a) (as opposed to {)( a + b) if no represent ation is used). It, is shown that gradient descent can be used to train neural network representations and the results of an experiment are reported in which a neural network representation was learnt for an environment consisting of transJaf ion ail,y in uarlanf Boolean functions. The experiment, provides strong qualitative support for the theoretical results.

Data Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student’s learning behavior. The student’s evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.

Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users’ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.

In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.


Question: Regarding the preprocessing of the ALT-algorithm for student use.

This paper explores an innovative technique to significantly expedite point-to-point shortest-path computations in extensive, sparsely connected, directed graphs with nonnegative arc weights. The proposed method, known as the arc-flag approach, builds upon Dijkstra's algorithm, a foundational algorithm in graph theory. The arc-flag method permits a preprocessing phase, during which additional data is generated to optimize shortest-path query execution times. This preprocessing involves segmenting the graph into distinct regions and compiling information on whether a particular arc is part of a shortest path leading into any given region. When combined with an effective partitioning strategy and bidirectional search, the arc-flag method achieves a remarkable average speedup factor exceeding 500 times that of the standard Dijkstra's algorithm on large-scale networks, featuring a million nodes and 2.5 million arcs. This optimization drastically reduces the search space of Dijkstra's algorithm to nearly the size of the corresponding shortest path for long-distance queries. To determine the most suitable partitioning methods for the arc-flag technique, an extensive experimental analysis was conducted. This study specifically evaluated partitioning algorithms derived from computational geometry and a multiway arc separator partitioning approach, using German road networks as the testbed. The results elucidate the impact of varying partitions on the acceleration of the shortest path algorithm. Additionally, the paper introduces a multilevel variant of the speedup technique, which maintains the same acceleration factors while requiring less memory. This variant can be viewed as a compression method for the precomputed data, preserving the accuracy of the computed shortest paths while enhancing efficiency.

The Experimental Study of Adaptive User Interfaces Pat Langley (langley@isle.org) Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, CA 94306 Michael Fehling (fehling@odc.stanford.edu) Organizational Dynamics Center, EES/OR Department Stanford University, Stanford, CA 94305 Abstract In this paper we examine some issues that arise in the experimental evaluation of adaptive user interfaces, which are computational decision aids that use machine learning to improve their interaction with users. We begin by reviewing the notion of an adaptive interface and presenting examples for a number of application areas. After this, we discuss plausible dependent measures of their behavior, including solution speed, solution quality, user satisfaction, and predictive accuracy. Next we turn to independent variables that are likely to in uence these measures, including the number of user interactions and characteristics of the system, the user, and the task at hand. In closing, we comment on the role that experimentation plays in the larger scienti c process.

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.

The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students’ summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.

Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users’ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Several standard text-categorization techniques were applied to the problem of automated essay grading. Bayesian independence classifiers and knearest-neighbor classifiers were trained to assign scores to manually-graded essays. These scores were combined with several other summary text measures using linear regression. The classifiers and regression equations were then applied to a new set of essays. The classifiers worked very well. The agreement between the automated grader and the final manual grade was as good as the agreement between human graders.

OF THE THESIS Building Simple Annotation Tools

Despite the fact that e-Learning exists for a relatively long time, it is still in its infancy. Current eLearning systems on the market are limited to technical gadgets and organizational aspects of teaching, instead of supporting the learning. As a result the learner has become deindividualized and demoted to a noncritical homogenous user. One way out of this drawback is the creation of individual e-Learning materials. For this purpose a flexible multidimensional data model and the generation of individual content are the solution. It is necessary to enable the interaction between the learners and the content in e-Learning systems in the same manner.

A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen’s self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.

Background Since Big Data mainly aims to explore the correlation between surface features but not their underlying causality relationship, the Big Mechanism 2 program has been proposed by DARPA to find out “why” behind the “Big Data”. However, the pre-requisite for it is that the machine can read each document and learn its associated knowledge, which is the task of Machine Reading (MR). Since a domain-independent MR system is complicated and difficult to build, the math word problem (MWP) [1] is frequently chosen as the first test case to study MR (as it usually uses less complicated syntax and requires less amount of domain knowledge). According to the framework for making the decision while there are several candidates, previous MWP algebra solvers can be classified into: (1) Rule-based approaches with logic inference [2-7], which apply rules to get the answer (via identifying entities, quantities, operations, etc.) with a logic inference engine. (2) Rule-based approaches without logic inference [8-13], which apply rules to get the answer without a logic inference engine. (3) Statistics-based approaches [14, 15], which use statistical models to identify entities, quantities, operations, and get the answer. To our knowledge, all the statistics-based approaches do not adopt logic inference. The main problem of the rule-based approaches mentioned above is that the coverage rate problem is serious, as rules with wide coverage are difficult and expensive to construct. Also, since they adopt Go/No-Go approach (unlike statistical approaches which can adopt a large Top-N to have high including rates), the error accumulation problem would be severe. On the other hand, the main problem of those approaches without adopting logic inference is that they usually need to implement a new handling procedure for each new type of problems (as the general logic inference mechanism is not adopted). Also, as there is no inference engine to generate the reasoning chain [16], additional effort would be required for


Question: Enhancement of low-light video images using a multiscale Retinex-like algorithm approach.

This paper presents an enhanced multiscale Retinex algorithm aimed at narrowing the discrepancy between color images and human visual perception of scenes. Often, direct human observation of scenes and their recorded color images exhibit significant differences, primarily due to the human visual system's ability to process vivid colors and details in shadows while adjusting to changes in scene illumination. An effective color image processing algorithm should emulate this capability by incorporating dynamic range compression, color consistency akin to human vision's color constancy, and accurate color and lightness rendition. We have expanded upon a previously developed single-scale center/surround Retinex model to create a multiscale variant that concurrently achieves dynamic range compression, color consistency, and lightness rendition. However, this approach struggles with color rendition in images where the gray-world assumption, a fundamental premise of the Retinex theory, is violated. To address this issue, we introduce a color restoration technique that compensates for this shortcoming, albeit with a slight compromise on color consistency. Comprehensive testing of the multiscale Retinex with color restoration on various test scenes and a large dataset of over a hundred images has not uncovered any significant flaws or inconsistencies in its performance.

Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.

Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.

We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill-posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to non-smooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.

In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods. & 2013 Elsevier B.V. All rights reserved.

Underwater images are degraded due to scatters and absorption, resulting in low contrast and color distortion. In this paper, a novel self-similarity-based method for descattering and super resolution (SR) of underwater images is proposed. The traditional approach of preprocessing the image using a descattering algorithm, followed by application of an SR method, has the limitation that most of the high-frequency information is lost during descattering. Consequently, we propose a novel high turbidity underwater image SR algorithm. We first obtain a high resolution (HR) image of scattered and descattered images by using a self-similarity-based SR algorithm. Next, we apply a convex fusion rule for recovering the final HR image. The super-resolved images have a reasonable noise level after descattering and demonstrate visually more pleasing results than conventional approaches. Furthermore, numerical metrics demonstrate that the proposed algorithm shows a consistent improvement and that edges are significantly enhanced.

Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.

The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may lead to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.

We propose a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.

We propose a multi-focus image fusion method. The energy of Laplacian of input images is obtained to decide which portions of the input images are in better focus, and the majority filter offers a means of spreading the focused regions to neighborhood. The edge-preserving guided image filter is applied to reduce the block effect. We conduct several experiments which demonstrate that it provided a better performance than the state of the art fusion methods in visual and quantitative evaluations.