Commercial and Industrial Network Improvement and Programming Policy, Summary, 1991

The Commercial and Industrial Network improvement and programming policy reflected in this summary report was adopted for use in future highway programming by the Transportation Commission on November 5, 1991. The Iowa Department of Transportation, as directed by the Legislature, has established a 2,331-mile network of commercial and industrial highways and is directing a significant amount of primary construction funding resources toward improvements to this network. This summary outlines the technical needs assessment for improvements on the Commercial and Industrial Network for the next 20-year period. The portions of the network which require four-lane capacity, as well as major improvements to the two-lane sections, are graphically displayed. Detailed improvement needs and costs are listed in tabular form for the first two five-year periods (1992-1996 and 1997-2001). It is essential to note that these improvement needs are the result of a technical assessment and do not imply any funding commitment.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

Type-Aware Programming Models for Distributed Applications

Thesis (Ph.D.)--University of Washington, 2016-08

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Climbing Up the Leaderboard: An Empirical Study of Applying Gamification Techniques to a Computer Programming Class

Conventional taught learning practices often experience difficulties in keeping students motivated and engaged. Video games, however, are very successful at sustaining high levels of motivation and engagement through a set of tasks for hours without apparent loss of focus. In addition, gamers solve complex problems within a gaming environment without feeling fatigue or frustration, as they would typically do with a comparable learning task. Based on this notion, the academic community is keen on exploring methods that can deliver deep learner engagement and has shown increased interest in adopting gamification – the integration of gaming elements, mechanics, and frameworks into non-game situations and scenarios – as a means to increase student engagement and improve information retention. Its effectiveness when applied to education has been debatable though, as attempts have generally been restricted to one-dimensional approaches such as transposing a trivial reward system onto existing teaching materials and/or assessments. Nevertheless, a gamified, multi-dimensional, problem-based learning approach can yield improved results even when applied to a very complex and traditionally dry task like the teaching of computer programming, as shown in this paper. The presented quasi-experimental study used a combination of instructor feedback, real time sequence of scored quizzes, and live coding to deliver a fully interactive learning experience. More specifically, the “Kahoot!” Classroom Response System (CRS), the classroom version of the TV game show “Who Wants To Be A Millionaire?”, and Codecademy’s interactive platform formed the basis for a learning model which was applied to an entry-level Python programming course. Students were thus allowed to experience multiple interlocking methods similar to those commonly found in a top quality game experience. To assess gamification’s impact on learning, empirical data from the gamified group were compared to those from a control group who was taught through a traditional learning approach, similar to the one which had been used during previous cohorts. Despite this being a relatively small-scale study, the results and findings for a number of key metrics, including attendance, downloading of course material, and final grades, were encouraging and proved that the gamified approach was motivating and enriching for both students and instructors.

From Kandinsky to Java (The Use of 20th Century Abstract Art in Learning Programming)

At the University of Worcester we are continually striving to find new approaches to the learning and teaching of programming, to improve the quality of learning and the student experience. Over the past three years we have used the contexts of robotics, computer games, and most recently a study of Abstract Art to this end. This paper discusses our motivation for using Abstract Art as a context, details our principles and methodology, and reports on an evaluation of the student experience. Our basic tenet is that one can view the works of artists such as Kandinsky, Klee and Malevich as Object-Oriented (OO) constructions. Discussion of these works can therefore be used to introduce OO principles, to explore the meaning of classes, methods and attributes and finally to synthesize new works of art through Java code. This research has been conducted during delivery of an “Advanced OOP (Java)” programming module at final-year Undergraduate level, and during a Masters’ OO-Programming (Java) module. This allows a comparative evaluation of novice and experienced programmers’ learning. In this paper, we identify several instructional factors which emerge from our approach, and reflect upon the associated pedagogy. A Catalogue of ArtApplets is provided at the associated web-site.

Common Environment for Undergraduate Computer Programming

We present an Integrated Environment suitable for learning and teaching computer programming which is designed for both students of specialised Computer Science courses, and also non-specialist students such as those following Liberal Arts. The environment is rich enough to allow exploration of concepts from robotics, artificial intelligence, social science, and philosophy as well as the specialist areas of operating systems and the various computer programming paradigms.

An Integrated Programming Environment for Undergraduate Computing Courses

The Computing Division of the Business School at University College Worcester provides computing and information technology education to a range of undergraduate students. Topics include various approaches to programming, artificial intelligence, operating systems and digital technologies. Each of these has its own potentially conflicting requirements for a pedagogically sound programming environment. This paper describes an endeavor to develop a common programming paradigm across all topics. This involves the combined use of autonomous robots and Java simulations.

Applications of a Collaborative Open Object Oriented Learning Environment to Electronics, Computing and Mathematics Education

A lightweight Java application suite has been developed and deployed allowing collaborative learning between students and tutors at remote locations. Students can engage in group activities online and also collaborate with tutors. A generic Java framework has been developed and applied to electronics, computing and mathematics education. The applications are respectively: (a) a digital circuit simulator, which allows students to collaborate in building simple or complex electronic circuits; (b) a Java programming environment where the paradigm is behavioural-based robotics, and (c) a differential equation solver useful in modelling of any complex and nonlinear dynamic system. Each student sees a common shared window on which may be added text or graphical objects and which can then be shared online. A built-in chat room supports collaborative dialogue. Students can work either in collaborative groups or else in teams as directed by the tutor. This paper summarises the technical architecture of the system as well as the pedagogical implications of the suite. A report of student evaluation is also presented distilled from use over a period of twelve months. We intend this suite to facilitate learning between groups at one or many institutions and to facilitate international collaboration. We also intend to use the suite as a tool to research the establishment and behaviour of collaborative learning groups. We shall make our software freely available to interested researchers.

Database evolution on an orthogonal persistent programming system: A semi-transparent approach

In this paper the problem of the evolution of an object-oriented database in the context of orthogonal persistent programming systems is addressed. We have observed two characteristics in that type of systems that offer particular conditions to implement the evolution in a semi-transparent fashion. That transparency can further be enhanced with the obliviousness provided by the Aspect-Oriented Programming techniques. Was conceived a meta-model and developed a prototype to test the feasibility of our approach. The system allows programs, written to a schema, access semi-transparently to data in other versions of the schema.

The Visual Arts as a Canvas for Teaching Computer Programming

We explore the relationships between the construction of a work of art and the crafting of a computer program in Java and suggest that the structure of paintings and drawings may be used to teach the fundamental concepts of computer programming. This movement "from Art to Science", using art to drive computing, complements the common use of computing to inform art. We report on initial experiences using this approach with undergraduate and postgraduate students. An embryonic theory of the correspondence between art and computing is presented and a methodology proposed to develop this project further.

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.

Nonparametric tests of collectively rational consumption behavior: an integer programming procedure

We present an IP-based nonparametric (revealed preference) testing procedure for rational consumption behavior in terms of general collective models, which include consumption externalities and public consumption. An empirical application to data drawn from the Russia Longitudinal Monitoring Survey (RLMS) demonstrates the practical usefulness of the procedure. Finally, we present extensions of the testing procedure to evaluate the goodness-of-…t of the collective model subject to testing, and to quantify and improve the power of the corresponding collective rationality tests.

Diet and Probiotics during Pregnancy - Endorsing developmental Programming

Maternal obesity has been shown to increase the risk for adverse reproductive health outcomes such as gestational diabetes, hypertension, and preeclampsia. Moreover, several studies have indicated that overnutrition and maternal obesity adversely program the development of offspring by predisposing them to obesity and other chronic diseases later in life. The exact molecular mechanisms leading to developmental programming are not known, but it has recently been suggested that obesity-related low-grade inflammation, gut microbiota and epigenetic gene regulation (in particularly DNA methylation) participate in the developmental programming phenomenon. The aim of this thesis was to evaluate the effect of diet, dietary counseling and probiotic intervention during pregnancy in endorsing favorable developmental programming. The study population consisted of 256 mother-child pairs participating in a prospective, double-blinded dietary counselling and probiotic intervention (Lactobacillus rhamnosus GG and Bifidobacterium lactis Bb12) NAMI (Nutrition, Allergy, Mucosal immunology and Intestinal microbiota) study. Further overweight women were recruited from maternal welfare clinics in the area of Southwest Finland and from the prenatal outpatient clinic at Turku University Hospital. Dietary counseling was aimed to modify women’s dietary intake to comply with the recommended intake for pregnant women. Specifically, counseling aimed to affect the type of fat consumed and to increase the amount of fiber in the women’s diets. Leptin concentration was used as a marker for obesity-related low-grade inflammation, antioxidant vitamin status as an efficiency marker for dietary counselling and epigenetic DNA methylation of obesity related genes as a marker for probiotics influence. Results revealed that dietary intake may modify obesity-associated low-grade inflammation as measured by serum leptin concentration. Specifically, dietary fiber intake may lower leptin concentration in women, whereas the intakes of saturated fatty acids and sucrose have an opposite effect. Neither dietary counselling nor probiotic intervention modified leptin concentration in women, but probiotics tended to increase children’s leptin concentration. Dietary counseling was an efficient tool for improving antioxidant vitamin intake in women, which was reflected in the breast milk vitamin concentration. Probiotic intervention affected DNA methylation of dozens of obesity and weight gain related genes both in women and their children. Altogether these results indicate that dietary components, dietary counseling and probiotic supplementation during pregnancy may modify the intrauterine environment towards favorable developmental programming.

Functional Programming With Relations

While programming in a relational framework has much to offer over the functional style in terms of expressiveness, computing with relations is less efficient, and more semantically troublesome. In this paper we propose a novel blend of the functional and relational styles. We identify a class of "causal relations", which inherit some of the bi-directionality properties of relations, but retain the efficiency and semantic foundations of the functional style.