10 research outputs found
A Survey on Legal Question Answering Systems
Many legal professionals think that the explosion of information about local,
regional, national, and international legislation makes their practice more
costly, time-consuming, and even error-prone. The two main reasons for this are
that most legislation is usually unstructured, and the tremendous amount and
pace with which laws are released causes information overload in their daily
tasks. In the case of the legal domain, the research community agrees that a
system allowing to generate automatic responses to legal questions could
substantially impact many practical implications in daily activities. The
degree of usefulness is such that even a semi-automatic solution could
significantly help to reduce the workload to be faced. This is mainly because a
Question Answering system could be able to automatically process a massive
amount of legal resources to answer a question or doubt in seconds, which means
that it could save resources in the form of effort, money, and time to many
professionals in the legal sector. In this work, we quantitatively and
qualitatively survey the solutions that currently exist to meet this challenge.Comment: 57 pages, 1 figure, 10 table
Comparative Analysis of Artificial Intelligence for Indian Legal Question Answering (AILQA) Using Different Retrieval and QA Models
Legal question-answering (QA) systems have the potential to revolutionize the
way legal professionals interact with case law documents. This paper conducts a
comparative analysis of existing artificial intelligence models for their
utility in answering legal questions within the Indian legal system,
specifically focusing on Indian Legal Question Answering (AILQA) and our study
investigates the efficacy of different retrieval and QA algorithms currently
available. Utilizing the OpenAI GPT model as a benchmark, along with query
prompts, our investigation shows that existing AILQA systems can automatically
interpret natural language queries from users and generate highly accurate
responses. This research is particularly focused on applications within the
Indian criminal justice domain, which has its own set of challenges due to its
complexity and resource constraints. In order to rigorously assess the
performance of these models, empirical evaluations are complemented by feedback
from practicing legal professionals, thereby offering a multifaceted view on
the capabilities and limitations of AI in the context of Indian legal
question-answering
Exploring the State of the Art in Legal QA Systems
Answering questions related to the legal domain is a complex task, primarily
due to the intricate nature and diverse range of legal document systems.
Providing an accurate answer to a legal query typically necessitates
specialized knowledge in the relevant domain, which makes this task all the
more challenging, even for human experts. QA (Question answering systems) are
designed to generate answers to questions asked in human languages. They use
natural language processing to understand questions and search through
information to find relevant answers. QA has various practical applications,
including customer service, education, research, and cross-lingual
communication. However, they face challenges such as improving natural language
understanding and handling complex and ambiguous questions. Answering questions
related to the legal domain is a complex task, primarily due to the intricate
nature and diverse range of legal document systems. Providing an accurate
answer to a legal query typically necessitates specialized knowledge in the
relevant domain, which makes this task all the more challenging, even for human
experts. At this time, there is a lack of surveys that discuss legal question
answering. To address this problem, we provide a comprehensive survey that
reviews 14 benchmark datasets for question-answering in the legal field as well
as presents a comprehensive review of the state-of-the-art Legal Question
Answering deep learning models. We cover the different architectures and
techniques used in these studies and the performance and limitations of these
models. Moreover, we have established a public GitHub repository where we
regularly upload the most recent articles, open data, and source code. The
repository is available at:
\url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}
OpenPose and its current applications in sports and exercise science: a review
The aim of this scoping review is to investigate current applications of a markerless human pose estimation (HPE) algorithm in sports and exercise science. 17 studies are selected for this pur-pose. Results show that HPE is applied already in a variety of sports for different aims and pur-poses. Even though it provides many advantages over marker-based approaches, it still comes with challenges that need to be tackled in future research.Ziel dieser Ăbersichtsarbeit ist es, die aktuellen Anwendungen eines markerlosen Algorithmus zur SchĂ€tzung der menschlichen Körperhaltung (HPE) in der Sport- und Bewegungswissenschaft zu untersuchen. Zu diesem Zweck wurden 17 Studien ausgewĂ€hlt. Die Ergebnisse zeigen, dass HPE bereits in einer Vielzahl von Sportarten mit unterschiedlichen Zielen und Zwecken eingesetzt wird. Obwohl sie viele Vorteile gegenĂŒber markerbasierten AnsĂ€tzen bietet, gibt es immer noch Herausforderungen, die in der zukĂŒnftigen Forschung angegangen werden mĂŒssen
Towards an Enforceable GDPR Specification
While Privacy by Design (PbD) is prescribed by modern privacy regulations
such as the EU's GDPR, achieving PbD in real software systems is a notoriously
difficult task. One emerging technique to realize PbD is Runtime enforcement
(RE), in which an enforcer, loaded with a specification of a system's privacy
requirements, observes the actions performed by the system and instructs it to
perform actions that will ensure compliance with these requirements at all
times. To be able to use RE techniques for PbD, privacy regulations first need
to be translated into an enforceable specification. In this paper, we report on
our ongoing work in formalizing the GDPR. We first present a set of
requirements and an iterative methodology for creating enforceable formal
specifications of legal provisions. Then, we report on a preliminary case study
in which we used our methodology to derive an enforceable specification of part
of the GDPR. Our case study suggests that our methodology can be effectively
used to develop accurate enforceable specifications
Pragmatic constraints on subject-oriented honorifics in Yaeyaman and Japanese
This paper explores cross-linguistic differences in the pragmatic constraints governing the use of subject-oriented honorific verb forms in Japanese and in three varieties of Yaeyaman (Southern Ryukyuan). I show that plural subjects with mixed honorific status give rise to different felicity patterns in these language varieties, and argue that these differences arise from different rankings of competing pragmatic constraints
spinfortec2022 : Tagungsband zum 14. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung fĂŒr Sportwissenschaft (dvs), Chemnitz 29. - 30. September 2022
Dieser Tagungsband enthĂ€lt die BeitrĂ€ge aller VortrĂ€ge und PosterprĂ€sentationen des 14. Symposiums der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung fĂŒr Sportwissenschaft (dvs) an der Technischen UniversitĂ€t Chemnitz (29.-30. September 2022). Mit dem Ziel, das Forschungsfeld der Sportinformatik und Sporttechnologie voranzubringen, wurden knapp 20 vierseitige BeitrĂ€ge eingereicht und in den Sessions Informations- und Feedbacksysteme
im Sport, Digitale Bewegung: Datenerfassung, Analyse und Algorithmen sowie SportgerÀteentwicklung: Materialien, Konstruktion, Tests vorgestellt.This conference volume contains the contributions of all oral and poster presentations of the 14th Symposium of the Section Sport Informatics and Engineering of the German Association for Sport Science (dvs) at Chemnitz University of Technology (September 29-30, 2022). With the goal of advancing the research field of sports informatics and sports technology, nearly 20 four-page papers were submitted and presented in the sessions Information and Feedback Systems in Sport, Digital Movement: Data Acquisition, Analysis and Algorithms, and Sports Equipment Development: Materials, Construction, Testing
Computational Methods for Medical and Cyber Security
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields