Adversarial Attacks in Natural Language Question-Answering

Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based on human-in-the-loop adversarial example generation is explored. In recent published work on adversarial question-answering, perturbations are made on the questions without changing the background context on which the question is based. In the current work, the complementary idea of perturbing the background context while keeping the same question is examined. From the user study, novel adversarial examples crafted by humans exposed the weaknesses of the models. This thesis puts forth a typology of the successful attacks as a baseline for stress-testing QA systems. It also describes a system to automatically generate adversarial examples based on the identified attacks

Which "Apple" are you talking about ?

In a higher level task such as clustering of web results or word sense disambiguation, knowledge of all possible dis-tinct concepts in which an ambiguous word can be expressed would be advantageous, for instance in determining the num-ber of clusters in case of clustering web search results. We propose an algorithm to generate such a ranked list of dis-tinct concepts associated with an ambiguous word. Con-cepts which are popular in terms of usage are ranked higher. We evaluate the coverage of the concepts inferred from our algorithm on the results retrieved by querying the ambigu-ous word using a major search engine and show a coverage of 85 % for top 30 documents averaged over all keywords

Development of a Multilevel Intervention to Increase Colorectal Cancer Screening in Appalachia

Background Colorectal cancer (CRC) screening rates are lower in Appalachian regions of the United States than in non-Appalachian regions. Given the availability of various screening modalities, there is critical need for culturally relevant interventions addressing multiple socioecological levels to reduce the regional CRC burden. In this report, we describe the development and baseline findings from year 1 of “Accelerating Colorectal Cancer Screening through Implementation Science (ACCSIS) in Appalachia,” a 5-year, National Cancer Institute Cancer MoonshotSM-funded multilevel intervention (MLI) project to increase screening in Appalachian Kentucky and Ohio primary care clinics. Methods Project development was theory-driven and included the establishment of both an external Scientific Advisory Board and a Community Advisory Board to provide guidance in conducting formative activities in two Appalachian counties: one in Kentucky and one in Ohio. Activities included identifying and describing the study communities and primary care clinics, selecting appropriate evidence-based interventions (EBIs), and conducting a pilot test of MLI strategies addressing patient, provider, clinic, and community needs. Results Key informant interviews identified multiple barriers to CRC screening, including fear of screening, test results, and financial concerns (patient level); lack of time and competing priorities (provider level); lack of reminder or tracking systems and staff burden (clinic level); and cultural issues, societal norms, and transportation (community level). With this information, investigators then offered clinics a menu of EBIs and strategies to address barriers at each level. Clinics selected individually tailored MLIs, including improvement of patient education materials, provision of provider education (resulting in increased knowledge, p = .003), enhancement of electronic health record (EHR) systems and development of clinic screening protocols, and implementation of community CRC awareness events, all of which promoted stool-based screening (i.e., FIT or FIT-DNA). Variability among clinics, including differences in EHR systems, was the most salient barrier to EBI implementation, particularly in terms of tracking follow-up of positive screening results, whereas the development of clinic-wide screening protocols was found to promote fidelity to EBI components. Conclusions Lessons learned from year 1 included increased recognition of variability among the clinics and how they function, appreciation for clinic staff and provider workload, and development of strategies to utilize EHR systems. These findings necessitated a modification of study design for subsequent years. Trial registration Trial NCT04427527 is registered at https://clinicaltrials.gov and was registered on June 11, 2020

4MOST: Project overview and information for the First Call for Proposals

We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs ($R = \lambda/\Delta\lambda \sim 6500$), and 812 fibres transferring light to the high-resolution spectrograph ($R \sim 20\,000$). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations

Event Detection Using “Variable Module Graphs” for Home Care Applications

Technology has reached new heights making sound and video capture devices ubiquitous and affordable. We propose a paradigm to exploit this technology for home care applications especially for surveillance and complex event detection. Complex vision tasks such as event detection in a surveillance video can be divided into subtasks such as human detection, tracking, recognition, and trajectory analysis. The video can be thought of as being composed of various features. These features can be roughly arranged in a hierarchy from low-level features to high-level features. Low-level features include edges and blobs, and high-level features include objects and events. Loosely, the low-level feature extraction is based on signal/image processing techniques, while the high-level feature extraction is based on machine learning techniques. Traditionally, vision systems extract features in a feed-forward manner on the hierarchy, that is, certain modules extract low-level features and other modules make use of these low-level features to extract high-level features. Along with others in the research community, we have worked on this design approach. In this paper, we elaborate on recently introduced V/M graph. We present our work on using this paradigm for developing applications for home care applications. Primary objective is surveillance of location for subject tracking as well as detecting irregular or anomalous behavior. This is done automatically with minimal human involvement, where the system has been trained to raise an alarm when anomalous behavior is detected