Parimutuel wagering is a significant source of revenue for many state governments. MonitorPlus is a surveillance system for parimutuel operators and regulators. Using industry expertise and best practices, MonitorPlus examines each and every wager and account transaction for evidence of fraud, crime, and money laundering. Alerts are generated in real-time. In forensic discovery mode, MonitorPlus is designed to collaborate with skilled analysts to discover more complex suspicious wagering patterns. MonitorPlus utilizes machine learning, so its risk profiles are current: its knowledge base improves with time. Each alert is accompanied by an automatically generated, rule-based explanation. This is critically important if an event rises to the level where legal action is required. Our development and deployment strategy is based on a new paradigm of a secure surveillance utility, where real-time alerts and dataintensive forensics support multiple regulatory jurisdictions. We believe this surveillance paradigm can be applied to other application domains such as lotteries, casinos, online gaming, and financial services.

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

This paper discusses a research based on analyzing criminal sentences on criminal trials on organized crime activity in Sicily pronounced from 2000 through 2006. Large criminal sentences related dataset collection activity in Italy is severely constrained for various reasons such as difficulty of data collection at the courthouses, unavailability of data in digital format, and classification criteria used in the public archives. Thus, in general, judicial statistics suffer from lack of reliability and informativeness. The objective of this research is to analyze the text of criminal sentences in a revisable and verifiable way, so that information is extracted on the trial leading to the sentence, the socio-economic environment in which the relevant events occurred, and the differences between the various districts conducting the trials. The purpose is to elaborate a tool of automated analysis of the text of the sentences that is generalizable to other areas of jurisprudence, and, outside of jurisprudence, to other temporal and geographical contexts. The 726 criminal sentences that have been converted into text files have been pronounced at all judicial levels in the four Sicilian districts for mafia-related crimes. This research is relevant because, for the first time in Italy, we aim to empirically describe the juridical response to the phenomenon of organized crime, by using a large and extendable database of criminal sentences that can be analyzed with data mining techniques, rather than deriving general conclusions from a focused small set of sentences.

The applicability conditions of legal Norms regulating computer systems can be modelled in different rules very often refer to these institutional concepts, rather ways, see, for example, (Boella, van der Torre, and than to so called brute facts. To simplify the notation we refer Verhagen 2008). If norms are represented by hard constraints, to the former as constitutive rules, and the latter simply then computer systems are designed to avoid violations.

In this paper we introduce dialectical frameworks, a powerful generalization of Dung-style argumentation frameworks where each node comes with an associated acceptance condition. This allows us to model different types of dependencies, e.g. support and attack, as well as different types of nodes within a single framework. We show that Dung's standard semantics can be generalized to dialectical frameworks, in case of stable and preferred semantics to a slightly restricted class which we call bipolar frameworks. We show how acceptance conditions can be conveniently represented using weights respectively priorities on the links and demonstrate how some of the legal proof standards can be modeled based on this idea.

Some of the top legal ontologies developed so far include the Functional Ontology for Law [FOLaw] The increasing need for legal information and content (Valente 1995), the Frame-Based Ontology (van Kralingen management caused by the growing amount of 1995), the LRI-Core ontology (Breuker 2004), unstructured (or poorly structured) legal data managed by DOLCE CLO [Core Legal Ontology] (Gangemi et al. legal publishing companies, law firms and public 2003), or the Ontology of Fundamental Concepts (Rubino administrations, or the increasing amount of legal et al. 2006, Sartor 2006) the basis for the LKIF-Core information directly available on the World Wide Web, Ontology (Breuker et al. 2007). Nevertheless, most legal have created an urgent need to construct conceptual ontologies are domain specific ontologies, which represent structures for knowledge representation to share and particular legal domains towards search, indexing and manage intelligently all this information, whilst making reasoning in a specific domain of national or European law human-machine communication and understanding (e.g. the IPRONTO ontology by Delgado et al. 2003, the possible.

One of the most significant challenges in achieving digital privacy is incorporating privacy policy directly in computer systems. While rule systems have long existed, translating privacy laws, regulations, policies, and contracts into processor amenable forms is slow and difficult because the legal text is scattered, run-on, and unstructured, antithetical to the lean and logical forms of computer science. We are using and developing intermediate isomorphic forms as a Rosetta Stone-like tool to accelerate the translation process and in hopes of providing support to future domain-specific Natural Language Processing technology. This report describes our experience, thoughts about how to improve the form, and discoveries about the form and logic of the legal text that will affect the successful development of a rules tool to implement real-world complex privacy policies.

While there is a plethora of mechanisms to ensure lawful access to privacy-protected data, additional research is required in order to reassure individuals that their personal data is being used for the purpose that they consented to. This is particularly important in the context of new data mining approaches, as used, for instance, in biomedical research and commercial data mining. We argue for the use of computational workflows to ensure and enforce appropriate use of sensitive personal data. Computational workflows describe in a declarative manner the data processing steps and the expected results of complex data analysis processes such as data mining (Gil et al. 2007b; Taylor et al. 2006). We see workflows as an artifact that captures, among other things, how data is being used and for what purpose. Existing frameworks for computational workflows need to be extended to incorporate privacy policies that can govern the use of data.

Functional or end user requirements are the tasks that the system - Protection and control of consolidated data under development is expected to perform. However, nonfunctional - Data retrieval requirements are the qualities that the system is - Equitable treatment of users to adhere to. Functional requirements are not as difficult - Data retention and disposal to tackle, as it is easier to test their implementation in the - User monitoring and protection against unauthorized system under development. Security and privacy requirements monitoring are considered nonfunctional requirements, although in many instances they do have functionality. To identify Several laws and regulations provide a set of guidelines privacy risks early in the design process, privacy requirements that can be used to assess privacy risks. For example, engineering is used (Chiasera et al. 2008). However, the Health Insurance Portability and Accountability Act unlike security requirements engineering, little attention is (HIPAA) addresses privacy concerns of health information paid to privacy requirements engineering, thus it is less mature systems by enforcing data exchange standards.