Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation

Survey of the State and Future Trends of Intelligent Systems

This paper presents an attempt to formalize objective macro model of the field of artificial intelligence (AI). We show that creation of this model is justified, and with the aid of information from World Wide Web it is possible now. With this aim in view, we propose a research method. To obtain a macro model of the artificial intelligence field, we made a survey of research groups in the world, including companies, applications, organizations as well as a general assessment of the state of Al technology. The survey is intended to show some benefits. Intelligent systems are becoming very useful and are starting to achieve many of the projected past promises. Important documents on future trends and roles of intelligent systems have been recently published, as well as interesting surveys in the field. We have assessed several methodologies of research of the state of the art in this field and identified their promises and limitations. Considering the present state of Al technology, research projects, important documents and trends in traditional information technologies, we have made a preliminary model of intelligent systems and their future surveys. In the era of second generation knowledge-based systems and growing complexity of the Al field, we believe that it is necessary to include macro model of Al in all scientific researches and application projects

A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions

© 1993-2012 IEEE. Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility

Lancet HIV

Background:HIV preexposure prophylaxis (PrEP) is effective but underutilized, in part because clinicians lack tools to identify PrEP candidates. We developed and validated an automated prediction algorithm using electronic health records (EHR) data to identify individuals at increased risk for HIV acquisition.Methods:We used machine learning algorithms to predict incident HIV infections using 180 potential predictors of HIV risk drawn from EHR data from 2007-2015 at Atrius Health, an ambulatory group practice in Massachusetts, USA. The best-performing model was validated prospectively using 2016 data from Atrius Health and externally using 2011-2016 data from Fenway Health, a community health center specializing in sexual healthcare in Boston, Massachusetts. We assessed the model\u2019s performance at identifying individuals with incident HIV and patients independently prescribed PrEP by clinicians using cross-validated area under the curve (cv-AUC).Findings:Cohorts included 1,155,966 Atrius Health patients from 2007-2015 (including 150 [<0\ub71%] patients with incident HIV), 537,257 patients in 2016 (16 [<0\ub71%] with incident HIV), and 33,404 Fenway Health patients from 2011-2016 (423 [1\ub73%] with incident HIV). The best-performing algorithm had a cv-AUC of 0\ub786 (95% CI 0\ub782-0\ub790) for identifying incident HIV infections in the development cohort, 0\ub791 (95% CI 0\ub781-1\ub700) on prospective validation, and 0\ub777 (95% CI 0\ub774-0\ub779) on external validation. The model successfully identified patients independently prescribed PrEP by clinicians at Atrius Health (cv-AUC 0\ub794, 95% CI 0\ub790-0\ub797) or Fenway Health (cv-AUC 0\ub779, 95% CI 0\ub778-0\ub780). HIV risk scores increased steeply at the 98th percentile. We designated patients with scores above this threshold as potential PrEP candidates and prospectively identified 9,515/536,384 (1\ub78%) new PrEP candidates at Atrius Health in 2016.Interpretation:Automated algorithms can efficiently identify patients at increased risk for HIV acquisition. Integrating these models into EHRs to alert providers about patients who may benefit from PrEP could improve PrEP prescribing and prevent new HIV infections.Funding:The Harvard University Center for AIDS Research, the Providence/Boston Center for AIDS Research, the Rhode Island IDeA-CTR [U54GM11567], and the US Centers for Disease Control and Prevention.P30 AI060354/AI/NIAID NIH HHS/United StatesU54 GM115677/GM/NIGMS NIH HHS/United StatesH25 PS004253/PS/NCHHSTP CDC HHS/United StatesK23 MH098795/MH/NIMH NIH HHS/United StatesP30 AI042853/AI/NIAID NIH HHS/United States2020-10-01T00:00:00Z31285182PMC75229198412vault:3604

Deploying artifical intelligence techniques in loan application processing

The granting of loans by a financial institution is one of the important decisions that require insubstantial care. The institution usually employs loan officers to make credit decisions or recommendations for that particular institution. These officers are given some hard roles in evaluating the worthiness of each application. Some researchers recognize that the capability of humans to judge the worthiness of a loan is rather poor. Since business data warehouses store historical data from previous application, it is likely that there is knowledge hidden in this data may be useful in decision making. Unfortunately the task of discovering hidden information and useful relationship from data is difficult for human. This is due the fact that the data to be examined is very large and the nature of the relationship within the data is not obvious. To this end, Artificial Intelligence (AI) techniques can be beneficial to assist the decision maker in making decisions regarding loan application. AI provides a variety of useful tool for discovering the non-obvious relationships in historical data, while ensuring those relationships discovered will generalize to the future data. This knowledge is important and can be used by the loan officer in determining whether to accept or reject an application. This study suggests that loan application processing system integrates two components of computer-based information system namely, office automation system and AI system that comprises of intelligent decision support system and knowledge based system. In essence, the potential use of such a system can be accelerated to promote any organizations as an efficient and effective organization that has competitive advantage

Création et mise à jour guidées d'objets dans une base RDF(S)

National audienceLa mise à jour des bases de connaissances existantes est cruciale pour tenir compte des nouvelles informations, régulièrement découvertes. Toutefois, en pratique, les données actuelles du Web Sémantique sont rarement mises à jour par les utilisateurs. Nous proposons UTILIS, une méthode pour aider les utilisateurs à ajouter de nouveaux objets. Un objet est une ressource de la base. Sa description correspond aux propriétés qu'il possède. Pendant la création d'un nouvel objet o, UTILIS recherche les objets similaires. Les propriétés des objets similaires sont utilisées comme suggestions pour complèter la description de o. Les objets similaires sont trouvés en appliquant des règles de relaxation à la description de o, prise comme une requête. Comparé avec l'état de l'art, la contribution est qu'UTILIS est à la fois incrémental, chaque nouvelle propriété est utilisée pour la recherche, et interactif, l'utilisateur a un rôle actif dans le processus