Information Extraction in Illicit Domains

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.Comment: 10 pages, ACM WWW 201

An evaluation methodology for concept maps mined from lecture notes: an educational perspective

Revised Selected Papers from 6th International Conference, CSEDU 2014 Barcelona, Spain, April 1–3, 2014Concept maps are effective tools that assist learners in organising and representing knowledge. Recent efforts in the area of concept mapping work toward semi- or fully automated approaches to extract concept maps from various text sources such as text books. The motivation for this research is twofold: novice learners require substantial assistance from experts in constructing their own maps, introducing additional hurdles, and alternatively, the workload required by academics in manually constructing expert maps is substantial and repetitive. A key limitation of an automated concept map generation is the lack of an evaluation framework to measure the quality of concept maps. The most common evaluation mechanism is measuring the overlap between machine-generated elements (e.g. concepts) with expert maps using relevancy measures such as precision and recall. However, in the educational context, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information being retrieved for knowledge organisation. Therefore, this paper introduces a machine-based approach to evaluate the relative importance of knowledge by comparing with human judgment. We introduce three ranking models and conclude that the structural features are positively correlated with human experts (rs ~ 1) for courses with rich content and good structure (well-fitted).Thushari Atapattu, Katrina Falkner, and Nickolas Falkne

Conception d’un Comparateur Phase-Fréquence pour les PLLs de haute Performance

Ce Papier décrit la conception d’une nouvelle architecture d’un comparateur phase-fréquence (PFD1) pour les boucles de verrouillage de phase (PLL), la dudit conception est basée sur la combinaison de deux techniques de réduction de consommation énergétique en hautes fréquences à savoir la technique DTCMOS (Dynamic Threshold CMOS) et la technique GDI (Gate Diffusion Input). Ce Comparateur conçu présente l’avantage d’être capable de commander une pompe de charge sur des fréquences allant jusqu’à 5 GHz et sur une petite surface d’intégration avec absence totale de la zone morte et sans avoir besoin de voltage externe. Une pompe de charge CP1 compatible avec PFD1 à base d’un étage miroir de courant en technologie CMOS a été proposée. Le Comparateur PFD1 et la pompe CP1 ont été simulé sous Tspise (modèle BSIM4) avec la technologie 0.25 um et un voltage de 2.5 volt

Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning