PRIVAFRAME: A Frame-Based Knowledge Graph for Sensitive Personal Data

The pervasiveness of dialogue systems and virtual conversation applications raises an important theme: the potential of sharing sensitive information, and the consequent need for protection. To guarantee the subject’s right to privacy, and avoid the leakage of private content, it is important to treat sensitive information. However, any treatment requires firstly to identify sensitive text, and appropriate techniques to do it automatically. The Sensitive Information Detection (SID) task has been explored in the literature in different domains and languages, but there is no common benchmark. Current approaches are mostly based on artificial neural networks (ANN) or transformers based on them. Our research focuses on identifying categories of personal data in informal English sentences, by adopting a new logical-symbolic approach, and eventually hybridising it with ANN models. We present a frame-based knowledge graph built for personal data categories defined in the Data Privacy Vocabulary (DPV). The knowledge graph is designed through the logical composition of already existing frames, and has been evaluated as background knowledge for a SID system against a labeled sensitive information dataset. The accuracy of PRIVAFRAME reached 78%. By comparison, a transformer-based model achieved 12% lower performance on the same dataset. The top-down logical-symbolic frame-based model allows a granular analysis, and does not require a training dataset. These advantages lead us to use it as a layer in a hybrid model, where the logical SID is combined with an ANNs SID tested in a previous study by the authors

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks

From Betterment to Bt maize

Agriculture has received renewed attention in poverty reduction efforts in Africa in recent years, and there are hopes that GM crops could have an important role in helping increase smallholder yields and reduce poverty. Drawing on critical discourse analysis (CDA) and livelihoods perspectives, this thesis examines the ideas governing the Massive Food Production Programme (MFPP), an agricultural development programme aiming to reduce poverty by raising agricultural production in Eastern Cape Province, South Africa, and its local effects when implemented in smallholder communities. In particular, the effects of introduction of Bt maize, genetically modified to be resistant to some potentially damaging insects in the region, were studied. The results reveal that the programme was not equipped to support the improvement of smallholders' livelihoods through agriculture. A core reason was the failure to break with a historically dominant unidirectional view of agricultural development, which was reinforced by a contemporary dominant neoliberal view of development as progress through growth. The programme thereby disregarded the effects of long-term marginalisation on smallholders' ability to engage in farming, and the associated need for substantial advisory, infrastructure and credit support to increase agricultural productivity. Local strategies for dealing with the effects of poverty were also unacknowledged; and practices and inputs originally developed for large-scale, capital-intensive farming were introduced without adaptation to smallholder conditions. The programme also failed to recognise the local heterogeneity of poverty, resulting in a bias towards comparatively better-off smallholders. The Bt maize variety introduced, like hybrid maize varieties introduced during pre-democracy interventions, was not adapted to smallholders' farming environments. It was input-demanding and sensitive to environmental dynamics, and it was promoted for planting in monoculture. Bans on saving and recycling seed resulting from patents, plant breeders' rights and new regulations to ensure the biosafety of GM crops were largely incompatible with smallholders' practices and further undermined strategies for dealing with resource shortage. It is suggested that cheaper, open-pollinated maize varieties, which can be recycled and are more tolerant to low-input conditions, could be better suited to smallholders' needs and practices

Health Misinformation in Search and Social Media

People increasingly rely on the Internet in order to search for and share health-related information. Indeed, searching for and sharing information about medical treatments are among the most frequent uses of online data. While this is a convenient and fast method to collect information, online sources may contain incorrect information that has the potential to cause harm, especially if people believe what they read without further research or professional medical advice. The goal of this thesis is to address the misinformation problem in two of the most commonly used online services: search engines and social media platforms. We examined how people use these platforms to search for and share health information. To achieve this, we designed controlled laboratory user studies and employed large-scale social media data analysis tools. The solutions proposed in this thesis can be used to build systems that better support people's health-related decisions. The techniques described in this thesis addressed online searching and social media sharing in the following manner. First, with respect to search engines, we aimed to determine the extent to which people can be influenced by search engine results when trying to learn about the efficacy of various medical treatments. We conducted a controlled laboratory study wherein we biased the search results towards either correct or incorrect information. We then asked participants to determine the efficacy of different medical treatments. Results showed that people were significantly influenced both positively and negatively by search results bias. More importantly, when the subjects were exposed to incorrect information, they made more incorrect decisions than when they had no interaction with the search results. Following from this work, we extended the study to gain insights into strategies people use during this decision-making process, via the think-aloud method. We found that, even with verbalization, people were strongly influenced by the search results bias. We also noted that people paid attention to what the majority states, authoritativeness, and content quality when evaluating online content. Understanding the effects of cognitive biases that can arise during online search is a complex undertaking because of the presence of unconscious biases (such as the search results ranking) that the think-aloud method fails to show. Moving to social media, we first proposed a solution to detect and track misinformation in social media. Using Zika as a case study, we developed a tool for tracking misinformation on Twitter. We collected 13 million tweets regarding the Zika outbreak and tracked rumors outlined by the World Health Organization and the Snopes fact-checking website. We incorporated health professionals, crowdsourcing, and machine learning to capture health-related rumors as well as clarification communications. In this way, we illustrated insights that the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with targeted and timely action. From identifying rumor-bearing tweets, we examined individuals on social media who are posting questionable health-related information, in particular those promoting cancer treatments that have been shown to be ineffective. Specifically, we studied 4,212 Twitter users who have posted about one of 139 ineffective ``treatments'' and compared them to a baseline of users generally interested in cancer. Considering features that capture user attributes, writing style, and sentiment, we built a classifier that is able to identify users prone to propagating such misinformation. This classifier achieved an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention

Communications : methods and applications for financial managers

https://egrove.olemiss.edu/aicpa_guides/2670/thumbnail.jp

The concept of 'Genetic Modification' in a Descriptive Translation Study (DTS) of an English-Spanish corpus of Popular Science Books on Genetic Engineering: Denominative Variation, Semantic Prosody and Ideological Aspects of Translation Strategies

El objetivo general consiste en examinar el concepto de 'modificación genética' a través de tres fenómenos lingüísticos: la variación denominativa, la prosodia semántica y los aspectos ideológicos de las principales estrategias de traducción. Para estudiar la variación denominativa se han seleccionado dos términos técnicos 'DNA' y 'gene/s' y dos subtécnicos 'food/s' y 'crop/s'. Para el estudio de la prosodia semántica se han analizado las concordancias de 'genetic' + N y 'genetically'`+ Adj. La comparación de las variantes denominativas y las prosodias semánticas en un corpus paralelo inglés-español de ingenería genética arrojan resultados sobre los aspectos ideológicos de las principales estrategias de traducción encontradas en el corpus.Departamento de Filología Ingles

Intellectual Property Management in Health and Agricultural Innovation: Executive Guide

Prepared by and for policy-makers, leaders of public sector research establishments, technology transfer professionals, licensing executives, and scientists, this online resource offers up-to-date information and strategies for utilizing the power of both intellectual property and the public domain. Emphasis is placed on advancing innovation in health and agriculture, though many of the principles outlined here are broadly applicable across technology fields. Eschewing ideological debates and general proclamations, the authors always keep their eye on the practical side of IP management. The site is based on a comprehensive Handbook and Executive Guide that provide substantive discussions and analysis of the opportunities awaiting anyone in the field who wants to put intellectual property to work. This multi-volume work contains 153 chapters on a full range of IP topics and over 50 case studies, composed by over 200 authors from North, South, East, and West. If you are a policymaker, a senior administrator, a technology transfer manager, or a scientist, we invite you to use the companion site guide available at http://www.iphandbook.org/index.html The site guide distills the key points of each IP topic covered by the Handbook into simple language and places it in the context of evolving best practices specific to your professional role within the overall picture of IP management