Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Anti-money laundering and counter-terrorist financing measures: Australia - mutual evaluation report

This report examines how effective Australia\u27s anti-money laundering and counter-terrorist financing (AML/CFT) system is. Overview This report recognises that Australia has a good understanding of its money laundering risks, coordinates domestically to address these risks, and has highly effective mechanisms for international cooperation. However, the authorities focus more on the disruption of predicate crimes, rather than on the laundering of the proceeds of these crimes and their confiscation. Therefore, while the report recognises that Australia develops good quality financial intelligence which it shares with law enforcement bodies and other authorities, the report concludes that this information should lead to more ML/TF investigations

Temporal networks

Integrace plánování rozvrhování vyžaduje hledání nových přístupů problému rozvrhování. Rozvrhovací systém musí být schopen poskytnout užitečné informace plánovači, aby se zabránilo vytvářní neuskutečnitelných plánů. Pro rozvrhování založené na splňování omezujících podmínek je možné de novat vlastní fi ltrační pravidla a tak zefektivnit řešící algoritmus. Pokud filtrační pravidla využívají informace sdělené plánovačem a rozvrhovacím systémem (např. precedenční a nebo temporální podmínky), výstup těchto pravidel je mozné poskytnout plánovači, který je může s výhodou využít. V této práci je navržena filtrační metoda, která využívá temporální vztahy mezi aktivitami alokovanými na jeden nebo více disjunktivních zdrojů. Práce také popisuje sadu propagačnch pravidel založených na kombinaci ruzných fi ltračních technik.Integration of planning and scheduling requires new approaches to the scheduling problem. The scheduler must be able to provide useful information for the planner in order to avoid generation of unfeasible plans. In constraint-based scheduling it is possible to de ne custom ltering rules that improve the solving procedure. If the ltering rules exploit the information shared by the planner and the scheduler (e.g. precedence or temporal constraints), the outcome of these rules can be used to provide useful hints for the planner. This work presents a ltering technique that exploits temporal relations between a set of activities allocated to one or more disjunctive resources. The work also presents a set of propagation rules for constraint-based scheduling based on various ltering techniqes.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Proceedings of CSCLP 2007: Annual ERCIM Workshop on Constraint Solving and Constraint Logic Programming

Ce fichier regroupe en un seul document l'ensemble des articles acceptés pour la conférence CSCLP 2007Constraints are a natural way to represent knowledge, and constraint programming is a declarative programming paradigm that has been successfully used to express and solve many practical combinatorial optimization problems. Examples of application domains are scheduling, production planning, resource allocation, communication networks, robotics, and bioinformatics. These proceedings contain the research papers presented at the 12th International Workshop on Constraint Solving and Constraint Logic Programming (CSCLP'07), held on June 7th and 8th 2007, at INRIA Rocquencourt, France. This workshop, open to all, is organized as the twelfth meeting of the working group on Constraints of the European Research Consortium for Informatics and Mathematics (ERCIM). It continues a series of workshops organized since the creation of the working group in 1997, that have led since 2002 to the publication of a series of books entitled ”Recent Advances in Constraints” in the Lecture Notes in Artificial Intelligence, edited by Springer-Verlag. In addition to the contributed papers collected in this volume, two invited talks were given at CSCLP'07, one by Gilles Pesant, Ecole Polytechnique de Montreal, Canada, and one by Jean-Charles R égin, ILOG, France. The editors would like to take the opportunity to thank all the authors who submitted a paper, as well as the reviewers for their helpful work. CSCLP'07 has been made possible thanks to the support of the European Research Consortium for Informatics and Mathematics (ERCIM), the Institut National de la Recherche en Informatique et Automatique (INRIA) and the Association for Constraint programming (ACP)

Continuous relaxation to over-constrained temporal plans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 165-168).When humans fail to understand the capabilities of an autonomous system or its environmental limitations, they can jeopardize their objectives and the system by asking for unrealistic goals. The objective of this thesis is to enable consensus between human and autonomous system, by giving autonomous systems the ability to communicate to the user the reasons for goal failure and the relaxations to goals that archive feasibility. We represent our problem in the context of temporal plans, a set of timed activities that can represent the goals and constraints proposed by users. Over-constrained temporal plans are commonly encountered while operating autonomous and decision support systems, when user objectives are in conflict with the environment. Over constrained plans are addressed by relaxing goals and or constraints, such as delaying the arrival time of a trip, with some candidate relaxations being preferable to others. In this thesis we present Uhura, a temporal plan diagnosis and relaxation algorithm that is designed to take over-constrained input plans with temporal flexibility and contingencies, and generate temporal relaxations that make the input plan executable. We introduce two innovative approaches within Uhura: collaborative plan diagnosis and continuous relaxation. Uhura focuses on novel ways of satisfying three goals to make the plan relaxation process more convenient for the users: small perturbation, quick response and simple interaction. First, to achieve small perturbation, Uhura resolves over-constrained temporal plans through partial relaxation of goals, more specifically, through the relaxation of schedules. Prior work on temporal relaxations takes an all-or-nothing approach in which timing constraints on goals, such as arrival times to destinations, are completely relaxed in the relaxations. The Continuous Temporal Relaxation method used by Uhura adjusts the temporal bounds of temporal constraints to minimizes the perturbation caused by the relaxations to the goals in the original plan. Second, to achieve quick responses, Uhura introduces Best-first Conflict-directed Relaxation, a new method that efficiently enumerates alternative options in best-first order. The search space of alternative options to temporal planning problems is very large and finding the best one is a NP-hard problem. Uhura empirically demonstrates fast enumeration by unifying methods from minimal relaxation and conflict-directed enumeration methods, first developed for model based diagnosis. Uhura achieves two orders of magnitude improvement in run-time performance relative to state-of-the-art approaches, making it applicable to a larger group of real-world scenarios with complex temporal plans. Finally, to achieve simple interactions, Uhura presents to the user a small set of preferred relaxations in best-first order based on user preference models. By using minimal relaxations to represent alternative options, Uhura simplifies the options presented to the user and reduces the size of its results and improves their expressiveness. Previous work either generates minimal relaxations or full relaxations based on preference, but not minimal relaxations based on preference. Preferred minimal relaxations simplify the interaction in that the users do not have to consider any irrelevant information, and may reach an agreement with the autonomous system faster. Therefore it makes communication between robots and users more convenient and precise. We have incorporated Uhura within an autonomous executive that collaborates with human operators to resolve over-constrained temporal plans. Its effectiveness has been demonstrated both in simulation and in hardware on a Personal Transportation System concept. The average runtime of Uhura on large problems with 200 activities is two order of magnitude lower compared to current approaches. In addition, Uhura has also been used in a driving assistant system to resolve conflicts in driving plans. We believe that Uhura's collaborative temporal plan diagnosis capability can benefit a wide range of applications, both within industrial applications and in our daily lives.by Peng Yu.S.M

The Convergence Of Dirty Money And Private To Private Corruption: Fact Or Fiction? How Efficient Are The Tools To Contain This? A Discourse from Anglo-American and Less Developed Countries’ Perspectives

The advent of globalisation and liberalization have necessitated and escalated a momentum amongst natural and legal persons in the quest to make profits. As a result, there have been activities, characterised by ways in which to evade the orderly manner of commercial interactions, thereby evading the regulatory radars. This has generated an inquest to ascertain the symbiotic nexus between dirty money and private to private corruption. The culprits generated funds via crime in both the private and public sectors and used money laundering to reintroduce the profits into the formal system. It is at this juncture, that the convergence question presents itself. Some mechanisms or tools directed towards curbing the problems are in the form of ‘‘soft laws’’ like the Financial Action Task Force (FATF), the Organization for Economic Cooperation and Development (OECD), the Basel Committee for Banking Supervision (BCBS) and the International Chamber of Commerce (ICC). The implementation is backed by the policy of ‘‘carrot and stick’’ mechanisms subtly introduced by global financial institutions exemplified by the International Monetary Fund (IMF) and the World Bank, exuding anti-money laundering and corruption characteristics. Some major Conventions and statutory mechanisms came in form of the United Nations Convention against Corruption 2003 (UNCAC), the United Nations Convention against Transnational Organised Crime (UNCTOC) 2000, the United Kingdom Bribery Act 2010 and US Foreign Corrupt Practices Act 1977. This has necessitated the examination of the efficiency of the combative mechanisms, as there are evidently differences in the approach to tackling the issues on account of diverse jurisdictional frameworks

An analysis of the Effectiveness of Anti-Money Laundering and Counter Terrorist Funding Legislation and its Administration in the UAE

This doctoral thesis develops a methodology to assess the effectiveness of anti money laundering and counter-­‐financing of terror efforts in the United Arab Emirates by applying five “markers of success” (as determined by AML/CFT professionals) to the UAE’s AML/CFT framework. The markers are Robust Regulatory Framework; AML Legislation Enforcement; AML Legislation Awareness; Private Sector Commitment and Cooperation; and Transparency. The analysis chapters attempts to measure these criteria using a variety of sources, including UAE laws of 2002 and 2004 and their amendments and replacements of 2014; various regulatory documents and policies enacted by the UAE Central Bank and other UAE bodies; expert opinion when available; and other materials from both official bodies and the country’s media. The findings show generally uneven progress and sometimes-­‐inconclusive results. During this period, the UAE, driven by external pressure coupled with internal self-­‐interest, established an AML/CFT regime. As part of its AML/CFT framework, the UAE also created an outreach program via official bodies, and there is some evidence of its effectiveness in encouraging private sector compliance. However, measuring enforcement is problematic given the lack of hard and publicly available statistical data for much of this period. Efforts made to encourage a culture of transparency and accountability have run up against limited availability and accessibility of data. Accordingly, the UAE remains perceived as a largely non-­‐transparent jurisdiction when it comes to financial crimes. Recent (2016) legislative developments underscore the UAE’s effort to change this, as it is due for a new FATF evaluation in 2019