Perception Visualization: Seeing Through the Eyes of a DNN

Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to apply these powerful models leads us to focus on their performance and deprioritises our ability to understand them. Current research in the field of explainable AI tries to bridge this gap by developing various perturbation or gradient-based explanation techniques. For images, these techniques fail to fully capture and convey the semantic information needed to elucidate why the model makes the predictions it does. In this work, we develop a new form of explanation that is radically different in nature from current explanation methods, such as Grad-CAM. Perception visualization provides a visual representation of what the DNN perceives in the input image by depicting what visual patterns the latent representation corresponds to. Visualizations are obtained through a reconstruction model that inverts the encoded features, such that the parameters and predictions of the original models are not modified. Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available, thus easing the debugging and deployment of deep models as trusted systems.Comment: Accepted paper at BMVC 2021 (Proceedings not available yet

Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter

Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given the ongoing COVID-19 outbreak, it is essential for governments to have access to reliable data on policy-adherence with regards to mask wearing, social distancing, and other hard-to-measure quantities. In this paper we investigate whether it is possible to obtain such data by aggregating information from images posted to social media. The paper presents VisualCit, a pipeline for image-based social sensing combining recent advances in image recognition technology with geocoding and crowdsourcing techniques. Our aim is to discover in which countries, and to what extent, people are following COVID-19 related policy directives. We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative. Preliminary results shows that social media images can produce reliable indicators for policy makers.Comment: 10 pages, 9 figures, to be published in Proceedings of ICSE Software Engineering in Society, May 202

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations.ISSN:2169-353

Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100)

MAGIS-100 is a next-generation quantum sensor under construction at Fermilab that aims to explore fundamental physics with atom interferometry over a 100-meter baseline. This novel detector will search for ultralight dark matter, test quantum mechanics in new regimes, and serve as a technology pathfinder for future gravitational wave detectors in a previously unexplored frequency band. It combines techniques demonstrated in state-of-the-art 10-meter-scale atom interferometers with the latest technological advances of the world's best atomic clocks. MAGIS-100 will provide a development platform for a future kilometer-scale detector that would be sufficiently sensitive to detect gravitational waves from known sources. Here we present the science case for the MAGIS concept, review the operating principles of the detector, describe the instrument design, and study the detector systematics.Comment: 65 pages, 18 figure

What Is the Evidence to Support the Use of Therapeutic Gardens for the Elderly?

Horticulture therapy employs plants and gardening activities in therapeutic and rehabilitation activities and could be utilized to improve the quality of life of the worldwide aging population, possibly reducing costs for long-term, assisted living and dementia unit residents. Preliminary studies have reported the benefits of horticultural therapy and garden settings in reduction of pain, improvement in attention, lessening of stress, modulation of agitation, lowering of as needed medications, antipsychotics and reduction of falls. This is especially relevant for both the United States and the Republic of Korea since aging is occurring at an unprecedented rate, with Korea experiencing some of the world's greatest increases in elderly populations. In support of the role of nature as a therapeutic modality in geriatrics, most of the existing studies of garden settings have utilized views of nature or indoor plants with sparse studies employing therapeutic gardens and rehabilitation greenhouses. With few controlled clinical trials demonstrating the positive or negative effects of the use of garden settings for the rehabilitation of the aging populations, a more vigorous quantitative analysis of the benefits is long overdue. This literature review presents the data supporting future studies of the effects of natural settings for the long term care and rehabilitation of the elderly having the medical and mental health problems frequently occurring with aging

INVASIVESNET towards an International Association for Open Knowledge on Invasive Alien Species

In a world where invasive alien species (IAS) are recognised as one of the major threats to biodiversity, leading scientists from five continents have come together to propose the concept of developing an international association for open knowledge and open data on IAS—termed “INVASIVESNET”. This new association will facilitate greater understanding and improved management of invasive alien species (IAS) and biological invasions globally, by developing a sustainable network of networks for effective knowledge exchange. In addition to their inclusion in the CBD Strategic Plan for Biodiversity, the increasing ecological, social, cultural and economic impacts associated with IAS have driven the development of multiple legal instruments and policies. This increases the need for greater co-ordination, co-operation, and information exchange among scientists, management, the community of practice and the public. INVASIVESNET will be formed by linking new and existing networks of interested stakeholders including international and national expert working groups and initiatives, individual scientists, database managers, thematic open access journals, environmental agencies, practitioners, managers, industry, non-government organisations, citizens and educational bodies. The association will develop technical tools and cyberinfrastructure for the collection, management and dissemination of data and information on IAS; create an effective communication platform for global stakeholders; and promote coordination and collaboration through international meetings, workshops, education, training and outreach. To date, the sustainability of many strategic national and international initiatives on IAS have unfortunately been hampered by time-limited grants or funding cycles. Recognising that IAS initiatives need to be globally coordinated and ongoing, we aim to develop a sustainable knowledge sharing association to connect the outputs of IAS research and to inform the consequential management and societal challenges arising from IAS introductions. INVASIVESNET will provide a dynamic and enduring network of networks to ensure the continuity of connections among the IAS community of practice, science and management

Employing document dependency in blog search

The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State-of-the-art approaches view it as an expert search or resource selection problem. We investigate the effect of content-based similarity between posts on the performance of the retrieval system. We test two different approaches for smoothing (regularizing) relevance scores of posts based on their dependencies. In the first approach, we smooth term distributions describing posts by performing a random walk over a document-term graph in which similar posts are highly connected. In the second, we directly smooth scores for posts using a regularization framework that aims to minimize the discrepancy between scores for similar documents. We then extend these approaches to consider the time interval between the posts in smoothing the scores. The idea is that if two posts are temporally close, then they are good sources for smoothing each other's relevance scores. We compare these methods with the state-of the-art approaches in blog search that employ Language Modeling-based resource selection algorithms and fusion-based methods for aggregating post relevance scores. We show performance gains over the baseline techniques which do not take advantage of the relation between posts for smoothing relevance estimates

Inducing Source Descriptions for Automated Web Service Composition

We introduce a framework for learning Local-as-View (LAV) source definitions for the operations provided by a Web Service