ピアアセスメントのための項目反応理論と整数計画法を用いたグループ構成最適化

In recent years, large-scale e-learning environments such as Massive Online Open Courses (MOOCs) have become increasingly popular. In such environments, peer assessment, which is mutual assessment among learners, has been used to evaluate reports and programming assignments. When the number of learners increases as in MOOCs, peer assessment is often conducted by dividing learners into multiple groups to reduce the learners’ assessment workload. In this case, however, the accuracy of peer assessment depends on the way to form groups. To solve the problem, this study proposes a group optimization method based on item response theory (IRT) and integer programming. The proposed group optimization method is formulated as an integer programming problem that maximizes the Fisher information, which is a widely used index of ability assessment accuracy in IRT. Experimental results, however, show that the proposed method cannot sufficiently improve the accuracy compared to the random group formulation. To overcome this limitation, this study introduces the concept of external raters and proposes an external rater selection method that assigns a few appropriate external raters to each learner after the groups were formed using the proposed group optimization method. In this study, an external rater is defined as a peer-rater who belongs to different groups. The proposed external rater selection method is formulated as an integer programming problem that maximizes the lower bound of the Fisher information of the estimated ability of the learners by the external raters. Experimental results using both simulated and real-world peer assessment data show that the introduction of external raters is useful to improve the accuracy sufficiently. The result also demonstrates that the proposed external rater selection method based on IRT models can significantly improve the accuracy of ability assessment than the random selection.近年，MOOCsなどの大規模型eラーニングが普及してきた．大規模な数の学習者が参加している場合には，教師が一人で学習者のレポートやプログラム課題などを評価することは難しい．大規模の学習者の評価手法の一つとして，学習者同士によるピアアセスメントが注目されている．MOOCsのように学習者数が多い場合のピアアセスメントは，評価の負担を軽減するために学習者を複数のグループに分割してグループ内のメンバ同士で行うことが多い．しかし，この場合，グループ構成の仕方によって評価結果が大きく変化してしまう問題がある．この問題を解決するために，本研究では，項目反応理論と整数計画法を用いて，グループで行うピアアセスメントの精度を最適化するグループ構成手法を提案する．具体的には，項目反応理論において学習者の能力測定精度を表すフィッシャー情報量を最大化する整数計画問題としてグループ構成問題を定式化する．実験の結果，ランダムグループ構成と比べて，提案手法はおおむね測定精度を改善したが，それは限定的な結果であることが明らかとなった．そこで，本研究ではさらに，異なるグループから数名の学習者を外部評価者として各学習者に割り当て外部評価者選択手法を提案する．シミュレーションと実データ実験により，提案手法を用いることで能力測定精度を大幅に改善できることを示す．電気通信大学201

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Development of a R package to facilitate the learning of clustering techniques

This project explores the development of a tool, in the form of a R package, to ease the process of learning clustering techniques, how they work and what their pros and cons are. This tool should provide implementations for several different clustering techniques with explanations in order to allow the student to get familiar with the characteristics of each algorithm by testing them against several different datasets while deepening their understanding of them through the explanations. Additionally, these explanations should adapt to the input data, making the tool not only adept for self-regulated learning but for teaching too.Grado en Ingeniería Informátic

Identifying Clinical Phenotypes of Type 1 Diabetes for the Co-Optimization of Weight and Glycemic Control

Obesity is an increasing concern in the clinical care of youth with type 1 diabetes (T1D). Standard approaches to co-optimize weight and glycemic control are challenged by profound population-level heterogeneity. Therefore, the goal of the dissertation was to apply novel analytic methods to understand heterogeneity in the co-occurrence of weight, glycemia, and underlying patterns of minute-to-minute dysglycemia among youth with T1D. Data from the SEARCH for Diabetes in Youth study were used to characterize subgroups of youth with T1D showing similar weight status and level of glycemic control as distinct ‘weight-glycemia phenotypes’ of T1D. Cross-sectional weight-glycemia phenotypes were identified at the 5+ year follow-up visit (n=1,817) using hierarchical clustering on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c), generated by reinforcement learning tree predictions. Longitudinal weight-glycemia phenotypes spanning eight years were identified with longitudinal k-means clustering using baseline and follow-up BMIz and HbA1c measures (n=570). Logistic regression modeling tested for differences in the emergence of early/subclinical diabetes complications across subgroups. Seven-day blinded continuous glucose monitoring (CGM) data from baseline of the Flexible Lifestyles Empowering Change randomized trial (n=234, 13-16 years, HbA1c 8-13%) was clustered with a neural network approach to identify subgroups of adolescents with T1D and elevated HbA1c sharing patterns in their CGM data as ‘dysglycemia phenotypes.’ We identified six cross-sectional weight-glycemia phenotypes, including four normal-weight, one overweight, and one subgroup with obesity. Subgroups showed striking differences in other sociodemographic and clinical characteristics suggesting underlying health inequity. We identified four longitudinal weight-glycemia phenotypes associated with different patterns of early/subclinical complications, providing evidence that exposure to co-occurring obesity and worsening glycemic control may accelerate the development and increase the burden of co-morbid complications. We identified three dysglycemia phenotypes with significantly different patterns in hypoglycemia, hyperglycemia, glycemic variability, and 18-month changes in HbA1c. Patient-level drivers of the dysglycemia phenotypes appear to be different from risk factors for poor glycemic control as measured by HbA1c. These studies provide pragmatic, clinically-relevant examples of how novel statistics may be applied to data from T1D to derive patient subgroups for tailored interventions to improve weight alongside glycemic control.Doctor of Philosoph

Image Partitioning based on Semidefinite Programming

Many tasks in computer vision lead to combinatorial optimization problems. Automatic image partitioning is one of the most important examples in this context: whether based on some prior knowledge or completely unsupervised, we wish to find coherent parts of the image. However, the inherent combinatorial complexity of such problems often prevents to find the global optimum in polynomial time. For this reason, various approaches have been proposed to find good approximative solutions for image partitioning problems. As an important example, we will first consider different spectral relaxation techniques: based on straightforward eigenvector calculations, these methods compute suboptimal solutions in short time. However, the main contribution of this thesis is to introduce a novel optimization technique for discrete image partitioning problems which is based on a semidefinite programming relaxation. In contrast to approximation methods employing annealing algorithms, this approach involves solving a convex optimization problem, which does not suffer from possible local minima. Using interior point techniques, the solution of the relaxation can be found in polynomial time, and without elaborate parameter tuning. High quality solutions to the original combinatorial problem are then obtained with a randomized rounding technique. The only potential drawback of the semidefinite relaxation approach is that the number of variables of the optimization problem is squared. Nevertheless, it can still be applied to problems with up to a few thousand variables, as is demonstrated for various computer vision tasks including unsupervised segmentation, perceptual grouping and image restoration. Concerning problems of higher dimensionality, we study two different approaches to effectively reduce the number of variables. The first one is based on probabilistic sampling: by considering only a small random fraction of the pixels in the image, our semidefinite relaxation method can be applied in an efficient way while maintaining a reliable quality of the resulting segmentations. The second approach reduces the problem size by computing an over-segmentation of the image in a preprocessing step. After that, the image is partitioned based on the resulting "superpixels" instead of the original pixels. Since the real world does not consist of pixels, it can even be argued that this is the more natural image representation. Initially, our semidefinite relaxation method is defined only for binary partitioning problems. To derive image segmentations into multiple parts, one possibility is to apply the binary approach in a hierarchical way. Besides this natural extension, we also discuss how multiclass partitioning problems can be solved in a direct way based on semidefinite relaxation techniques

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Variable Formulation and Neighborhood Search Methods for the Maximum Clique Problem in Graph

Doktorska disertacija se bavi temama rešavanja računarski teških problema kombinatorne optimizacije. Istaknut je problem maksimalne klike kao predstavnik određenih struktura u grafovima. Problem maksimalne klike i sa njim povezani problemi su formulisani kao nelinearne funkcije. Rešavani su sa ciljem otkrivanja novih metoda koje pronalaze dobre aproksimacije rešenja za neko razumno vreme. Predložene su varijante Metode promenljivih okolina na rešavanje maksimalne klike u grafu. Povezani problemi na grafovima se mogu primeniti na pretragu informacija, raspoređivanje, procesiranje signala, teoriju klasifikacije, teoriju kodiranja, itd. Svi algoritmi su implementirani i uspešno testirani na brojnim različitim primerima.This Ph.D. thesis addresses topics NP hard problem solving approaches in combinatorial optimization and according to that it is highlighted maximum clique problem as a representative of certain structures in graphs. Maximum clique problem and related problems with this have been formulated as non linear functions which have been solved to research for new methods and good solution approximations for some reasonable time. It has been proposed several different extensions of Variable Neighborhood Search method. Related problems on graphs could be applied on information retrieval, scheduling, signal processing, theory of classi_cation, theory of coding, etc. Algorithms are implemented and successfully tested on various different tasks

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Adaptive complex system modeling for realistic modern ground warfare simulation analysis based on evolutionary multi-objective meta-heuristic techniques

Dissertação apresentada à Escola Superior de Tecnologia e Gestão do IPL para obtenção do grau de Mestre em Engenharia Informática - Computação Móvel, orientada pelo Professor Silvio Priem Mendes.The battlefield is a harsh and inhuman environment, where deaths and destruction take lead role. Through many millennia there was blood shed all over the world, people who many time died in a battle that sometimes they didn‘t even care about. Today, the battle field is very different, machines take most damage and there are less casualties, this is because of the advancements made in the fields of aeronautics, weaponry, nautical, vehicles, armor, and psychology. Also there is another important party that throughout the last decades made a special and decisive advantage to the side which is more advanced in this field, it is intelligence and simulation. Intelligence today gives enormous advantage to one country as you ―see and feel‖ the battlefield hundreds or thousands kilometers away. Then, with the data provided by intelligence, countries can simulate the battle in order to deploy the most efficient units into battle. In this thesis we propose a warfare simulator analysis tool using a multi-objective approach and artificial intelligence. Further on, the 1991 Gulf war scenario is used to simulate and the results are presented and analyzed. The approach used in this thesis is difficult to be used in games due to its processing complexity and computing demands