Learning Language Representations for Typology Prediction

One central mystery of neural NLP is what neural models "know" about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one neural machine translation (NMT) system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages' geographic and phylogenetic neighbors.Comment: EMNLP 201

A Buffer Stocks Model for Stabilizing Price of Staple Food with Considering the Expectation of Non Speculative Wholesaler

This paper is a study of price stabilization in the staple food distribution system. All stakeholders experience market risks due to some possibility causes of price volatility. Many models of price stabilization had been developed by employing several approaches such as floor-ceiling prices, buffer funds, export or import taxes, and subsidies. In the previous researches, the models were expanded to increase the purchasing price for producer and decrease the selling price for consumer. Therefore, the policy can influence the losses for non-speculative wholesaler that is reflected by the descending of selling quantity and ascending of the stocks. The objective of this model is not only to keep the expectation of both producer and consumer, but also to protect non-speculative wholesaler from the undesirable result of the stabilization policy. A nonlinear programming model was addressed to determine the instruments of intervention program. Moreover, the result shows that the wholesaler behavior affects the intervention costs. Index Terms Buffer stocks, Price stabilization, Nonlinear programming, Wholesaler behavior

Investigation of Attitudes Towards Security Behaviors

Cybersecurity attacks have increased as Internet technology has proliferated. Symantec’s 2013 Internet Security Report stated that two out of the top three causes of data breaches in 2012 were attributable to human error (Pelgrin, 2014). This suggests a need to educate end users so that they engage in behaviors that increase their cybersecurity. This study researched how a user’s knowledge affects their engagement in security behaviors. Security behaviors were operationalized into two categories: cyber hygiene and threat response behaviors. A sample of 194 San José State University students were recruited to participate in an observational study. Students completed a card sort, a semantic knowledge quiz, and a survey of their intention to perform security behaviors. A personality inventory was included to see if there would be any effects of personality on security behaviors. Multiple regression was used to see how card sorting and semantic knowledge quiz scores predicted security behaviors, but the results were not significant. Despite this, there was a correlation between cyber hygiene behaviors and threat response behaviors, as well as the Big Five personality traits. The results showed that many of the Big Five personality traits correlated with each other, which is consistent with other studies’ findings. The only personality trait that had a correlation with one of the knowledge measures was neuroticism, in which neuroticism had a negative correlation with the semantic knowledge quiz. Implications for future research are discussed to understand how knowledge, cyber hygiene behaviors, and threat response behaviors relate

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Supply chain collaboration

In the past, research in operations management focused on single-firm analysis. Its goal was to provide managers in practice with suitable tools to improve the performance of their firm by calculating optimal inventory quantities, among others. Nowadays, business decisions are dominated by the globalization of markets and increased competition among firms. Further, more and more products reach the customer through supply chains that are composed of independent firms. Following these trends, research in operations management has shifted its focus from single-firm analysis to multi-firm analysis, in particular to improving the efficiency and performance of supply chains under decentralized control. The main characteristics of such chains are that the firms in the chain are independent actors who try to optimize their individual objectives, and that the decisions taken by a firm do also affect the performance of the other parties in the supply chain. These interactions among firms’ decisions ask for alignment and coordination of actions. Therefore, game theory, the study of situations of cooperation or conflict among heterogenous actors, is very well suited to deal with these interactions. This has been recognized by researchers in the field, since there are an ever increasing number of papers that applies tools, methods and models from game theory to supply chain problems

A Decision Model for E-commerce-enabled Partial Market Exit

Struggling retail chains often try to recover profitability by closing some of their stores. The challenge in this strategy lies in determining how many stores to close, as store exit has implications for both the customers and the supply chain. After a store closes, its customers are lost forever to the competition, unless there is a surviving open store nearby or an electronic alternative such as an e-store. From the supply chain perspective, after a store closes, its supporting regional distribution center is left with less business, and thus reduced viability. This paper develops a decision support model to study the profitability of alternative retail network structures by varying the proportion of stores that are closed, the average price sensitivity of demand, the price difference between the online store and the traditional retailers, and customer retention rates

Mesoscale mapping of sediment source hotspots for dam sediment management in data-sparse semi-arid catchments

Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.BMBF, 02WGR1421A-I, GROW - Verbundprojekt SaWaM: Saisonales Wasserressourcen-Management in Trockenregionen: Praxistransfer regionalisierter globaler Informationen, Teilprojekt 1DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli