Structure of computations in parallel complexity classes

Issued as Annual report, and Final project report, Project no. G-36-67

Publications from NIAS: January 1988-June 2013 (NIAS Report No. R23-2014)

This report has a bibliographic listing of all the publications from NIAS since inception till June 201

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

Knowledge Management, Trust and Communication in the Era of Social Media

The article entitled "Selected Aspects of Evaluating Knowledge Management Quality in Contemporary Enterprises" broadens the understanding of knowledge management and estimates select aspects of knowledge management quality evaluations in modern enterprises from theoretical and practical perspectives. The seventh article aims to present the results of pilot studies on the four largest Information Communication Technology (ICT) companies' involvement in promoting the Sustainable Development Goals (SDGs) through social media. Studies examine which communication strategy is used by companies in social media. The primary purpose of the eighth article is to present the relationship between trust and knowledge sharing, taking into account the importance of this issue in the efficiency of doing business. The results showed that trust is vital in sharing knowledge and essential in achieving a high-performance efficiency level. The ninth article presents the impact of social media on consumer choices in tourism and tourist products' specificity. The study's main purpose was to indicate the most commonly used social media in selecting a tourist destination and implementing Generation Y's journey. The 10th article aims to identify the most critical purposes of using social media by responding to women's attitudes according to age and their respective countries' economic development. The research was done through an online survey in 2017–2018, followed by an analysis of eight countries' results. The article entitled "Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster" presents the framework of a question-answering system that was developed using a Twitter dataset containing more than 9 million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. The authors also study the structure of the questions posed and develop methods for classifying them into particular categories to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The book provides a theoretical and practical background related to trust, knowledge management, and communication in the era of social media. The editor believes that the collection of articles can be relevant to professionals, researchers, and students' needs. The authors try to diagnose the situation and show the new challenges and future directions in this area

Outsourcing of IT Services: Studies on Diffusion and New Theoretical Perspectives

abstract: Information technology (IT) outsourcing, including foreign or offshore outsourcing, has been steadily growing over the last two decades. This growth in IT outsourcing has led to the development of different hubs of services across nations, and has resulted in increased competition among service providers. Firms have been using IT outsourcing to not only leverage advanced technologies and services at lower costs, but also to maintain their competitive edge and grow. Furthermore, as prior studies have shown, there are systematic differences among industries in terms of the degree and impact of IT outsourcing. This dissertation uses a three-study approach to investigate issues related to IT outsourcing at the macro and micro levels, and provides different perspectives for understanding the issues associated with IT outsourcing at a firm and industry level. The first study evaluates the diffusion patterns of IT outsourcing across industries at aggregate level and within industries at a firm level. In addition, it analyzes the factors that influence the diffusion of IT outsourcing and tests models that help us understand the rate and patterns of diffusion at the industry level. This study establishes the presence of hierarchical contagion effects in the diffusion of IT outsourcing. The second study explores the role of location and proximity of industries to understand the diffusion patterns of IT outsourcing within clusters using the spatial analysis technique of space-time clustering. It establishes the presence of simultaneous space and time interactions at the global level in the diffusion of IT outsourcing. The third study examines the development of specialized hubs for IT outsourcing services in four developing economies: Brazil, Russia, India, and China (BRIC). In this study, I adopt a theory-building approach involving the identification of explanatory anomalies, and propose a new hybrid theory called- knowledge network theory. The proposed theory suggests that the growth and development of the IT and related services sector is a result of close interactions among adaptive institutions. It is also based on new knowledge that is created, and which flows through a country's national diaspora of expatriate entrepreneurs, technologists and business leaders. In addition, relevant economic history and regional geography factors are important. This view diverges from the traditional view, wherein effective institutions are considered to be the key determinants of long-term economic growth.Dissertation/ThesisPh.D. Business Administration 201

A review of literature on parallel constraint solving

As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation