Information Inflation: Can The Legal System Adapt?

Information is fundamental to the legal system. Accordingly, lawyers must understand that information, as a cultural and technological edifice, has profoundly and irrevocably changed. There has been a civilization- wide morph, or pulse, or one might say that information has evolved. This article discusses the new inflationary dynamic, which has caused written information to multiply by as much as ten thousand-fold recently. The resulting landscape has stressed the legal system and indeed, it is becoming prohibitively expensive for lawyers even to search through information. This is particularly true in litigation

Artificial Intelligence as Evidence

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal profession. The article underscores the importance of determining the validity of an AI application (i.e., how accurately the AI measures, classifies, or predicts what it is designed to), as well as its reliability (i.e., the consistency with which the AI produces accurate results when applied to the same or substantially similar circumstances), in deciding whether it should be admitted into evidence in civil and criminal cases. The article further discusses factors that can affect the validity and reliability of AI evidence, including bias of various types, “function creep,” lack of transparency and explainability, and the sufficiency of the objective testing of AI applications before they are released for public use. The article next provides an in-depth discussion of the evidentiary principles that govern whether AI evidence should be admitted in court cases, a topic which, at present, is not the subject of comprehensive analysis in decisional law. The focus of this discussion is on providing a step-by-step analysis of the most important issues, and the factors that affect decisions on whether to admit AI evidence. Finally, the article concludes with a discussion of practical suggestions intended to assist lawyers and judges as they are called upon to introduce, object to, or decide on whether to admit AI evidence

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic

We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.Plan Propio de la Universidad de Sevill

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science.</p

Study of document retrieval using Latent Semantic Indexing (LSI) on a very large data set.

The primary purpose of an information retrieval system is to retrieve all the relevant documents, which are relevant to the user query. The Latent Semantic Indexing (LSI) based ad hoc document retrieval task investigates the performance of retrieval systems that search a static set of documents using new questions/queries. Performance of LSI has been tested for several smaller datasets (e.g., MED, CISI abstracts etc) however, LSI has not been tested for a large dataset. In this research, we concentrated on the performance of LSI on large dataset. Stop word list and term weighting schemes are two key parameters in the area of information retrieval. We investigated the performance of LSI by using three different set of stop word lists and, also, without removing the stop words from the test collection. We also applied three different term-weighting (raw term frequency, log-entropy, and tf-idf) schemes to measure retrieval performance of LSI. We observed that, firstly, for a LSI based ad hoc information retrieval system, a tailored stop word list must be assembled for every unique large dataset. Secondly, the use of tf-idf term weighting scheme shows better retrieval performance than log-entropy and raw term frequency weighting schemes even when the test collection became large. --P. ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b165965

Town of Wilmot, New Hampshire annual reports of the town officers, boards and other agencies for the year ending December 31, 2018.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire

Sense and reference on the web

This thesis builds a foundation for the philosophy of theWeb by examining the crucial question: What does a Uniform Resource Identifier (URI) mean? Does it have a sense, and can it refer to things? A philosophical and historical introduction to the Web explains the primary purpose of theWeb as a universal information space for naming and accessing information via URIs. A terminology, based on distinctions in philosophy, is employed to define precisely what is meant by information, language, representation, and reference. These terms are then employed to create a foundational ontology and principles ofWeb architecture. From this perspective, the SemanticWeb is then viewed as the application of the principles of Web architecture to knowledge representation. However, the classical philosophical problems of sense and reference that have been the source of debate within the philosophy of language return. Three main positions are inspected: the logicist position, as exemplified by the descriptivist theory of reference and the first-generation SemanticWeb, the direct reference position, as exemplified by Putnamand Kripke’s causal theory of reference and the second-generation Linked Data initiative, and a Wittgensteinian position that views the Semantic Web as yet another public language. After identifying the public language position as the most promising, a solution of using people’s everyday use of search engines as relevance feedback is proposed as a Wittgensteinian way to determine sense of URIs. This solution is then evaluated on a sample of the Semantic Web discovered by via using queries from a hypertext search engine query log. The results are evaluated and the technique of using relevance feedback from hypertext Web searches to determine relevant Semantic Web URIs in response to user queries is shown to considerably improve baseline performance. Future work for the Web that follows from our argument and experiments is detailed, and outlines of a future philosophy of the Web laid out