3,586 research outputs found
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Recent developments in large language models (LLMs) have been impressive.
However, these models sometimes show inconsistencies and problematic behavior,
such as hallucinating facts, generating flawed code, or creating offensive and
toxic content. Unlike these models, humans typically utilize external tools to
cross-check and refine their initial content, like using a search engine for
fact-checking, or a code interpreter for debugging. Inspired by this
observation, we introduce a framework called CRITIC that allows LLMs, which are
essentially "black boxes" to validate and progressively amend their own outputs
in a manner similar to human interaction with tools. More specifically,
starting with an initial output, CRITIC interacts with appropriate tools to
evaluate certain aspects of the text, and then revises the output based on the
feedback obtained during this validation process. Comprehensive evaluations
involving free-form question answering, mathematical program synthesis, and
toxicity reduction demonstrate that CRITIC consistently enhances the
performance of LLMs. Meanwhile, our research highlights the crucial importance
of external feedback in promoting the ongoing self-improvement of LLMs.Comment: ICLR 202
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Automatic speech recognition (ASR) has recently become an important challenge
when using deep learning (DL). It requires large-scale training datasets and
high computational and storage resources. Moreover, DL techniques and machine
learning (ML) approaches in general, hypothesize that training and testing data
come from the same domain, with the same input feature space and data
distribution characteristics. This assumption, however, is not applicable in
some real-world artificial intelligence (AI) applications. Moreover, there are
situations where gathering real data is challenging, expensive, or rarely
occurring, which can not meet the data requirements of DL models. deep transfer
learning (DTL) has been introduced to overcome these issues, which helps
develop high-performing models using real datasets that are small or slightly
different but related to the training data. This paper presents a comprehensive
survey of DTL-based ASR frameworks to shed light on the latest developments and
helps academics and professionals understand current challenges. Specifically,
after presenting the DTL background, a well-designed taxonomy is adopted to
inform the state-of-the-art. A critical analysis is then conducted to identify
the limitations and advantages of each framework. Moving on, a comparative
study is introduced to highlight the current challenges before deriving
opportunities for future research
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
PSYCHOMETRIC EVALUATIONS OF AN ACCULTURATION SCALE AND AN ACCULTURATION STRESS SCALE WITH AMERICAN INDIAN UNIVERSITY STUDENTS
Acculturation and acculturation stress are constructs studied with various minority groups. This study tests the factor loadings for both the Native American Acculturation Scale (N.A.A.S.) and the Social, Attitudinal, Familial, and Environmental Acculturation Stress Scale (S.A.F.E.), as well as the reliability of these measures with American Indian undergraduate and graduate students in a university setting. The first hypothesis was that the factor loadings of the N.A.A.S. with this sample would not resemble the original factor structure found by Garrett and Pichette (2000). It was also believed that the N.A.A.S. would exhibit robust reliability with this sample, based upon a previous, unpublished study by this author. The second hypothesis was that the factor loadings of the S.A.F.E. scale with this sample would not approximate the original factor loadings by Mena, Padilla, and Maldonaldo (1987). It was also believed that the S.A.F.E. would exhibit robust reliability with this sample. The final hypothesis was that a significant, negative correlation would exist between the N.A.A.S. and the S.A.F.E. Factor loadings were found utilizing exploratory factor analysis with promax rotation. The results showed that both measures loaded differently with this sample. A significant, negative relationship was also found for the N.A.A.S. and the S.A.F.E., in which lower scores attained on the N.A.A.S. were associated with higher scores on the S.A.F.E. scale. Participants who scored lower on the N.A.A.S. (low acculturation), reported higher levels of acculturative stress on the S.A.F.E. scale. Limitations and future directions are discussed
Continuous User Authentication Using Multi-Modal Biometrics
It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security.
This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion.
This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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