Kiel Declarative Programming Days 2013

This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems

Beyond Quantity: Research with Subsymbolic AI

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

Security Analysis: A Critical Thinking Approach

Security Analysis: A Critical-Thinking Approach is for anyone desiring to learn techniques for generating the best answers to complex questions and best solutions to complex problems. It furnishes current and future analysts in national security, homeland security, law enforcement, and corporate security an alternative, comprehensive process for conducting both intelligence analysis and policy analysis. The target audience is upper-division undergraduate students and new graduate students, along with entry-level practitioner trainees. The book centers on a Security Analysis Critical-Thinking Framework that synthesizes critical-thinking and existing analytic techniques. Ample examples are provided to assist readers in comprehending the material. Newly created material includes techniques for analyzing beliefs and political cultures. The book also functions as an introduction to Foreign Policy and Security Studies.https://encompass.eku.edu/ekuopen/1005/thumbnail.jp

Forecasting financial markets using linear, nonlinear & model combination methods

In this thesis we investigate the question of asset price predictability. The two major themes that we focus on are firstly; whether machine learning and statistical modelling techniques, which impose less restrictive assumptions on asset price dynamics than do classical linear methods, can be used to forecast and trade financial markets to a degree greater than that which traditional asset pricing models would lead us to expect and secondly; to what extent model combination/ensemble strategies can add value in this pursuit. The approaches used include support vector regression (SVR), k-nearest neighbours (KNN), trading rules, linear regression (LR) and the random subspace ensemble method. We investigate these two themes using inherently data-driven models across datasets of sufficient size to render statistically meaningful results in three self-contained contexts. The first piece of empirical work compares the relative forecasting performance of SVR, KNN and LR models when applied to predicting daily returns of 58 UK stocks in the FTSE 100 over 4000 days. Bootstrap simulations are used to shed further statistical light on model performance. Secondly, we investigate the extent to which model combinations can improve forecasting performance with the use of the random subspace ensemble method for constructing ensembles of linear regression models to predict the returns of a portfolio of FTSE 100 stocks. The primary ensemble consists of 62500 component models estimated by randomly sampling subsets of the feature set and the final result combined via a majority vote. Lastly, we conduct an in-depth study of the channel break-out trading rule over a portfolio of 37 futures markets. We borrow a page from the book of modern portfolio theory where it is the performance of individual markets in the context of a portfolio that is ultimately of interest rather than on an individual basis. This approach is rarely used in the literature but is able to shed more light on the question of trading rule efficacy. Bootstrap resampling is employed to derive robust performance statistics. Our results show the Sharpe Ratio of the portfolio to be three times greater than of individual markets as a result of diversification in addition to being greater than that of S&P500 benchmark. We did not set out in an attempt to refute the weak form of Fama's (1970) classic taxonomy of information sets or, colloquially, "to beat the market"; nonetheless, some of our results suggest economically significant returns

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Design and Instantiation of an Interactive Multidimensional Ontology for Game Design Elements – a Design and Behavioral Approach

While games and play are commonly perceived as leisure tools, focus on the strategic implementation of isolated gameful elements outside of games has risen in recent years under the term gamification. Given their ease of implementation and impact in competitive games, a small set of game design elements, namely points, badges, and leaderboards, initially dominated research and practice. However, these elements reflect only a small group of components that game designers use to achieve positive outcomes in their systems. Current research has shifted towards focusing on the game design process instead of the isolated implementation of single elements under the term gameful design. But the problem of a tendency toward a monocultural selection of prominent design elements persists in-game and gameful design, preventing the method from reaching its full potential. This dissertation addresses this problem by designing and developing a digital, interactive game design element ontology that scholars and practitioners can use to make more informed and inspired decisions in creating gameful solutions to their problems. The first part of this work is concerned with the collation and development of the digital ontology. First, two datasets were collated from game design and gamification literature (game design elements and playing motivations). Next, four explorative studies were conducted to add user-relevant metadata and connect their items into an ontological structure. The first two studies use card sorting to assess game theory frameworks regarding their suitability as foundational categories for the game design element dataset and to gain an overview of different viewpoints from which categorizations can be derived. The second set of studies builds on an explorative method of matching dataset entries via their descriptive keywords to arrive at a connected graph. The first of these studies connects items of the playing motivations dataset with themselves, while the second connects them with an additional dataset of human needs. The first part closes with the documentation of the design and development of the tool Kubun, reporting on the outcome of its evaluation via iterative expert interviews and a field study. The results suggest that the tool serves its preset goals of affording intuitive browsing for dedicated searches and serendipitous findings. While the first part of this work reports on the top-down development process of the ontology and related navigation tool, the second part presents an in-depth research of specific learning-oriented game design elements to complement the overall research goal through a complementary bottom-up approach. Therein, two studies on learning-oriented game design elements are reported regarding their effect on performance, long-term learning outcome, and knowledge transfer. The studies are conducted with a game dedicated to teaching correct waste sorting. The first study focuses on a reward-based game design element in terms of its motivatory effect on perfect play. The second study evaluates two learning-enhancing game design elements, repeat, and look-up, in terms of their contribution to a long-term learning outcome. The comprehensive insights gained through the in-depth research manifest in the design of a module dedicated to reporting research outcomes in the ontology. The dissertation concludes with a discussion on the studies’ varying limitations and an outlook on pathways for future research