Jensbymorales0115

We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.While there exist a plethora of datasets on the Internet related to Food, Energy, and Water (FEW), there is a real lack of reliable methods and tools that can consume these resources. This hinders the development of novel decision-making applications utilizing knowledge graphs. In this paper, we introduce a novel software tool, called FoodKG, that enriches FEW knowledge graphs using advanced machine learning techniques. Our overarching goal is to improve decision-making and knowledge discovery as well as to provide improved search results for data scientists in the FEW domains. Given an input knowledge graph (constructed on raw FEW datasets), FoodKG enriches it with semantically related triples, relations, and images based on the original dataset terms and classes. FoodKG employs an existing graph embedding technique trained on a controlled vocabulary called AGROVOC, which is published by the Food and Agriculture Organization of the United Nations. AGROVOC includes terms and classes in the agriculture and food domains. As a result, FoodKG can enhance knowledge graphs with semantic similarity scores and relations between different classes, classify the existing entities, and allow FEW experts and researchers to use scientific terms for describing FEW concepts. The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset. We observed that this model outperformed its competitors based on the Spearman Correlation Coefficient score.Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engaging in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provides privacy-conscious organizations with a way to monitor the usage of the application data made available to third parties in terms of privacy. The proposed approach, without loss of generality, is applicable to BigSQL-style technologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of the privacy score.From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model for agricultural drought monitoring. This will be done by examining the standardized soil moisture anomaly index. The skill of the SMAP-enhanced Palmer model is assessed over three agricultural regions that have experienced major drought since the launch of SMAP in early 2015 (1) the 2015 drought in California (CA), USA, (2) the 2017 drought in South Africa, and (3) the 2018 mid-winter drought in Australia. During these three events, the SMAP-enhanced Palmer soil moisture estimates (PM+SMAP) are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and Normalized Difference Vegetation Index (NDVI) products. Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving U.S. Department of Agriculture-Foreign Agricultural Service root-zone soil moisture information generated using the Palmer model. In particular, PM+SMAP soil moisture estimates are shown to enhance the spatial variability of Palmer model root-zone soil moisture estimates and adjust the Palmer model drought response to improve its consistency with ancillary CHIRPS precipitation and NDVI information.Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing similar surrounding words are semantically close. We argue that local contexts can only partially define word semantics in the unsupervised word embedding learning. Global contexts, referring to the broader semantic units, such as the document or paragraph where the word appears, can capture different aspects of word semantics and complement local contexts. We propose two simple yet effective unsupervised word embedding models that jointly model both local and global contexts to learn word representations. We provide theoretical interpretations of the proposed models to demonstrate how local and global contexts are jointly modeled, assuming a generative relationship between words and contexts. We conduct a thorough evaluation on a wide range of benchmark datasets. Nirmatrelvir datasheet Our quantitative analysis and case study show that despite their simplicity, our two proposed models achieve superior performance on word similarity and text classification tasks.Understanding user privacy expectations is important and challenging. General Data Protection Regulation (GDPR) for instance requires companies to assess user privacy expectations. Existing privacy literature has largely considered privacy expectation as a single-level construct. We show that it is a multi-level construct and people have distinct types of privacy expectations. Furthermore, the types represent distinct levels of user privacy, and, hence, there can be an ordering among the types. Inspired by expectations-related theory in non-privacy literature, we propose a conceptual model of privacy expectation with four distinct types - Desired, Predicted, Deserved and Minimum. We validate our proposed model using an empirical within-subjects study that examines the effect of privacy expectation types on participant ratings of privacy expectation in a scenario involving collection of health-related browsing activity by a bank. Results from a stratified random sample (N = 1,249), representative of United States online population (±2.