On learning and visualizing lexicographic preference trees

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of categorical attributes. Visualizing preferences is essential to provide users with insights into the process of decision making, while learning preferences from data is practically important, as it is ineffective to elicit preference models directly from users. The results obtained from this thesis are two parts: 1) for preference visualization, aweb- basedsystem is created that visualizes various types of lexicographic preference tree models learned by a greedy learning algorithm; 2) for preference learning, a genetic algorithm is designed and implemented, called GA, that learns a restricted type of lexicographic preference tree, called unconditional importance and unconditional preference tree, or UIUP trees for short. Experiments show that GA achieves higher accuracy compared to the greedy algorithm at the cost of more computational time. Moreover, a Dynamic Programming Algorithm (DPA) was devised and implemented that computes an optimal UIUP tree model in the sense that it satisfies as many examples as possible in the dataset. This novel exact algorithm (DPA), was used to evaluate the quality of models computed by GA, and it was found to reduce the factorial time complexity of the brute force algorithm to exponential. The major contribution to the field of machine learning and data mining in this thesis would be the novel learning algorithm (DPA) which is an exact algorithm. DPA learns and finds the best UIUP tree model in the huge search space which classifies accurately the most number of examples in the training dataset; such model is referred to as the optimal model in this thesis. Finally, using datasets produced from randomly generated UIUP trees, this thesis presents experimental results on the performances (e.g., accuracy and computational time) of GA compared to the existent greedy algorithm and DPA

A comparison of body height in Crucian carp (Carassius carassius) in lakes with- and without predators

Induced morphometrical defenses have been reported in crucian carp (Carassius carassius), and these defenses are presumed to be induced by predators. Crucian carp was sampled during 2018 and 2019 in 12 lakes in southeast Norway. In three of these lakes there were no piscivorous predators, while in the remaining nine lakes there were top predators such as trout (Salmo trutta), perch (Perca flavescens) and pike (Esox lucius). I observed how crucian carp develop a greater body height in the presence of predators and other abiotic variables. Results confirm what other studies have found, that crucian carp grow a higher body with the presence of predators, but that also the abiotic factor lake size have a major impact on the growth. The size of the lake has been poorly reported in the literature as a factor that can drive changes in body height. A larger lake can compromise of a much more complex biological system than a small lake, which maybe can explain the growth in body height

Agriculture in the Face of Changing Markets, Institutions and Policies: Challenges and Strategies

Since the late 1980s, agriculture in Central and Eastern European Countries (CEECs) has been under considerable adjustment pressure due to changing political, economic and institutional environments. These changes have been linked to the transition process, as well as the ongoing integration into the European Union and the world market. Reduced subsidies, increased environmental and food quality demands, as well as structural changes in the supply, processing and food retailing sector call for major structural adjustments and the improvement of farmersâ managerial abilities. Though such changes always carry significant threats to farms, they also offer new opportunities for the farms' entrepreneurial engagement. Upcoming changes in the agricultural environment and their possible consequences for farm structures across Europe are thus still timely subjects. The objective of the IAMO Forum 2006 is to contribute to the success of agriculture in the CEECs, as well as their neighboring countries, in todayâs increasingly competitive environment. Concrete questions the conference focuses on are: What are the most suitable farm organizations, cooperative arrangements and contractual forms? How to improve efficiency and productivity? Where do market niches lie and what are the new product demands? This book contains 33 invited and selected contributions. These papers will be presented at the IAMO Forum 2006 in order to offer a platform for scientists, practitioners and policy-makers to discuss challenges and potential strategies at the farm, value chain, rural society and policy levels in order to cope with the upcoming challenges. IAMO Forum 2006, as well as this book, would not have been possible without the engagement of many people and institutions. We thank the authors of the submitted abstracts and papers, as well as the referees, for their evaluation of the abstracts from which the papers were selected. In particular, we would like to express our thanks to OLIVER JUNGKLAUS, GABRIELE MEWES, KLAUS REINSBERG and ANGELA SCHOLZ, who significantly contributed to the organization of the Forum. Furthermore, our thanks goes to SILKE SCHARF for her work on the layout and editing support of this book, and to JIM CURTISS, JAMIE BULLOCH, and DÃNALL Ã MEARÃIN for their English proof-reading. As experience from previous years documents, the course of the IAMO Forum continues to profit from the support and engagement of the IAMO administration, which we gratefully acknowledge. Last but not least, we are very grateful to the Robert Bosch Foundation, the Federal Ministry of Nutrition, Agriculture and Consumer Protection (BMELV), the German Research Foundation (DFG), the Haniel Foundation and the Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO) for their respective financial support.Agribusiness, Community/Rural/Urban Development, Farm Management, Industrial Organization, International Development, Labor and Human Capital, Land Economics/Use, Productivity Analysis,

Agriculture in the face of changing markets, institutions and policies: Challenges and strategies

Since the late 1980s, agriculture in Central and Eastern European Countries (CEECs) has been under considerable adjustment pressure due to changing political, economic and institutional environments. These changes have been linked to the transition process, as well as the ongoing integration into the European Union and the world market. Reduced subsidies, increased environmental and food quality demands, as well as structural changes in the supply, processing and food retailing sector call for major structural adjustments and the improvement of farmers' managerial abilities. Though such changes always carry significant threats to farms, they also offer new opportunities for the farms' entrepreneurial engagement. Upcoming changes in the agricultural environment and their possible consequences for farm structures across Europe are thus still timely subjects. The objective of the IAMO Forum 2006 is to contribute to the success of agriculture in the CEECs, as well as their neighboring countries, in today's increasingly competitive environment. Concrete questions the conference focuses on are: What are the most suitable farm organizations, cooperative arrangements and contractual forms? How to improve efficiency and productivity? Where do market niches lie and what are the new product demands? CONTENT: Preface; Jarmila Curtiss, Alfons Balmann, Kirsti Dautzenberg, Kathrin Happe. The success of gradualism: Empirical evidence from China's agricultural reform; Jikun Huang, Johan F. M. Swinnen, Scott Rozelle. Land reform and farm restructuring in Moldova, Azerbaijan and Kazakhstan: A stocktaking; David Sedik. Land market developments, imperfections, and effects in transition countries; Johan F. M. Swinnen, Pavel Ciaian, Liesbet Vranken. Farmland markets, boom/bust cycles, and farm size; Charles B. Moss, Andrew Schmitz. Duality of farm structure in transition agriculture: The case of Moldova; Zvi Lerman, Dragos Cimpoies. Organizational restructuring of the agrarian sector in Bulgaria during the pre-accession period; Julia M. Doitchinova, Ivan St. Kanchev, Albena Miteva. Governance of Bulgarian farming - Modes, efficiency, impact of EU accession; Hrabrin Bachev. Leadership may have a decisive influence on the successful transition of production cooperatives - A social capital approach; Csaba Forgács. Contractual arrangement and enforcement in transition agriculture: Theory and evidence from China; Hongdong Guo. Contractrual relationships in the Hungarian horticultural sector; Imre Ferto. Contract farming in China: Perspectives of smallholders; Hongdong Guo, Robert W. Jolly, Jianhua Zhu. Are macro policies adjusted to institutional arrangements at the micro level? Some evidence from Polish Agriculture during transition; Jan Falkowski, Dominika Milczarek. The Austrian private foundation as a legal form in farm management, with special emphasis on tax issues; Hermann Peyerl, Günter Breuer. Credit as a tool of integration between the Polish farms and buyers of their products; Alina Danilowska. Who, why and how: Problems of farmers' interest representation in Poland; Aldona Zawojska. How competitive is milk production in the Central and Eastern European countries in comparison to Western Europe? Mikhail Ramanovich, Torsten Hemme. Production and trade of animal products in selected ECO countries; Farhad Mirzaei, Olaf Heidelbach. European agriculture without direct payments - A partial equilibrium analysis; Oliver Balkhausen, Martin Banse. Measuring the degree of market power in the Ukrainian milk processing; Oleksandr Perekhozhuk, Michael Grings. Determinants of foreign direct investments in the food processing industry: An empirical analysis for Ukraine; Oksana Luka. Allocative efficiency of corporate farms in the Leningrad region; David Epstein. Pathways towards efficient levels of machinery investments needed for the sustainable development of arable farms in Bulgaria; Nikolay Naydenov. Small-scale farming in Romania - Shadow prices and efficiency; Johannes Sauer, Borbala Balint. How large is the marginal product of land in the Moscow region? Natalia Il'ina, Nikolay Svetlov. Spatial price transmission on the Turkish wheat market - An initial application; Enno-Burghard Weitzel, Ahmet Bayaner. Farm to retail price transmission on the pork market: A German-Hungarian comparison; Lajos Zoltán Bakucs, Imre Ferto, Heinrich Hockmann, Oleksandr Perekhozhuk. The nature of selected price transmissions in the agri-food chain and their consequences; Lukáš Čechura. Labor mobility in transition countries and the impact of institutions; Thomas Herzfeld, Thomas Glauben. Choosing to migrate or migrating to choose: Migration and labor choice in Albania; Carlo Azzarri, Gero Carletto, Benjamin Davis, Alberto Zezza. Rural non-farm employment in Ukraine; Oleg Nivyevskiy, Stephan von Cramon-Taubadel. Opportunities and challenges for farm household livelihood strategies: Pluriactivity in Finland and the UK; Claire Newton. Territorial aspects of enterprise development in remote rural areas of Europe; Zuzana Bednarikova, Tomas Doucha, Zdenek Travnicek. New policy approaches for rural development: The experience of two case regions in Eastern Germany; Theodor Fock --

Functional requirements document for the Earth Observing System Data and Information System (EOSDIS) Scientific Computing Facilities (SCF) of the NASA/MSFC Earth Science and Applications Division, 1992

Five scientists at MSFC/ESAD have EOS SCF investigator status. Each SCF has unique tasks which require the establishment of a computing facility dedicated to accomplishing those tasks. A SCF Working Group was established at ESAD with the charter of defining the computing requirements of the individual SCFs and recommending options for meeting these requirements. The primary goal of the working group was to determine which computing needs can be satisfied using either shared resources or separate but compatible resources, and which needs require unique individual resources. The requirements investigated included CPU-intensive vector and scalar processing, visualization, data storage, connectivity, and I/O peripherals. A review of computer industry directions and a market survey of computing hardware provided information regarding important industry standards and candidate computing platforms. It was determined that the total SCF computing requirements might be most effectively met using a hierarchy consisting of shared and individual resources. This hierarchy is composed of five major system types: (1) a supercomputer class vector processor; (2) a high-end scalar multiprocessor workstation; (3) a file server; (4) a few medium- to high-end visualization workstations; and (5) several low- to medium-range personal graphics workstations. Specific recommendations for meeting the needs of each of these types are presented

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services