VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201

Resolving the Chatbot Disclosure Dilemma: Leveraging Selective Self-Presentation to Mitigate the Negative Effect of Chatbot Disclosure

Chatbots are increasingly able to pose as humans. However, this does not hold true if their identity is explicitly disclosed to users—a practice that will become a legal obligation for many service providers in the imminent future. Previous studies hint at a chatbot disclosure dilemma in that disclosing the non-human identity of chatbots comes at the cost of negative user responses. As these responses are commonly attributed to reduced trust in algorithms, this research examines how the detrimental impact of chatbot disclosure on trust can be buffered. Based on computer-mediated communication theory, the authors demonstrate that the chatbot disclosure dilemma can be resolved if disclosure is paired with selective presentation of the chatbot’s capabilities. Study results show that while merely disclosing (vs. not disclosing) chatbot identity does reduce trust, pairing chatbot disclosure with selectively presented information on the chatbot’s expertise or weaknesses is able to mitigate this negative effect

The analysis of the effect of tax on profitability indices in listed companies of Tehran Stock Exchange

Profitability is considered as the most complicated feature for a company to be understood and evaluated. These ratios included in profitability are applied for evaluating business capabilities and making the wages in comparison with all cost during a specific period of time. In a more accurate way, the ratios indicate the profitability of a company, having calculated the total costs and tax on revenue, operational efficiency, company pricing policies, assets profitability and company’s shareholders. The approach applied in this research is descriptive-analytic. Using the data of 28 companies listed in Tehran Stock Exchange from 2004 to 2010 and using panel data approach, the tax effects over the paid profitability indices were studied in this paper. The results achieved from all estimation cases point out a negative significant effects on various profitability indices. It should be mentioned that in order to relate the taxes to the profitability indices, the costs and the debts of a corporation can be referred. Results of the study indicated that the debts ratio to asset and the type of the industry showed a negative effect on profitability and capital ratio to asset and the size of the company indicated positive significant effects on profitability index

Fullerene-based delivery systems

With the development of new drugs, there have been many attempts to explore innovative delivery routes. Targeted delivery systems are a desired solution designed to overcome the deficiency of routine methods. To transform this idea into reality, a wide range of nanoparticles has been proposed and studied. These nanoparticles should interact well with biological environments and pass through cell membranes to deliver therapeutic molecules. One of the pioneer classes of carbon-based nanoparticles for targeted delivery is the fullerenes. Fullerenes have a unique structure and possess suitable properties for interaction with the cellular environment. This short review concentrates on newly developed fullerene derivatives and their potential as advanced delivery systems for pharmaceutical applications. © 2019 Elsevier Lt

Efecto de la aplicación foliar de selenio y zinc para aumentar los rendimientos cuantitativos y cualitativos de colza en diferentes fechas de siembra

The sowing date is an important factor for expanding the cultivated area of rapeseed and affects seed yield, oil content, and fatty acid compounds. Micronutrient elements play an important role in improving the vegetative and reproductive growth of the plant, especially under conditions of biological and environmental stresses. A two-year experiment (2014-2016) was performed to study the response of rapeseed genotypes to foliar application of micronutrients on different sowing dates. The treatments were arranged as a factorial-split plot in a randomized complete block design with three replicates. Three sowing dates of 7 (well-timed sowing date), 17, and 27 (delayed sowing dates) October and two levels of foliar application with pure water (control), selenium (1.5%), zinc (1.5%), and selenium+zinc (1.5%) were factorial in the main plots and five genotypes of SW102, Ahmadi, GKH2624, GK-Gabriella, and Okapi were randomized in the subplots (a total of 30 treatments). Seed yield, oil yield and content, oleic acid, and linoleic acid were reduced when rapeseeds were cultivated on 17 and 27 October, while the contents in palmitic, linolenic, and erucic acids, and glucosinolate increased (p < 0.01). a selenium+zinc treatment improved seed yield, oil content and yield (p < 0.01). The oil quality increased due to increased contents of oleic and linoleic acids under the selenium+zinc treatment (p < 0.01). The GK-Gabriella and GKH2624 genotypes are recommended to be sown on well-timed (7 October) and delayed sowing dates (17 and 27 October) and treated with selenium+zinc due to the higher oil yield, linoleic and oleic acids.La fecha de siembra es un factor importante para expandir el área cultivada de colza que afecta el rendimiento de la semilla, el contenido de aceite y la composición en ácidos grasos. Los micronutrientes juegan un papel importante en la mejora del crecimiento vegetativo y reproductivo de la planta, especialmente en condiciones de estrés biológico y ambiental. Se realizó un experimento de dos años (2014-2016) para estudiar la respuesta de los genotipos de colza a la aplicación foliar de micronutrientes en diferentes fechas de siembra. Los tratamientos se organizaron como una parcela dividida factorial en un diseño de bloques completos al azar con tres repeticiones. Tres fechas de siembra del 7 (fecha de siembra en el momento oportuno), 17 y 27 (fechas de siembra retrasadas) de octubre y dos niveles de aplicación foliar con agua pura (control), selenio (1,5%), zinc (1,5%) y selenio + zinc (1.5%) fueron factoriales en las parcelas principales y cinco genotipos de SW102, Ahmadi, GKH2624, GK-Gabriella y Okapi fueron aleatorizados en las subparcelas (un total de 30 tratamientos). El rendimiento de semilla, el contenido y rendimiento de aceite, los ácidos grasos oleico y linoleico se redujeron cuando se cultivaron semillas de colza los días 17 y 27 de octubre, mientras que los contenidos de los ácidos grasos palmítico, linolénico y erúcico y glucosinolato aumentaron (p <0,01). El tratamiento con selenio + zinc mejoró el rendimiento de semillas, el contenido de aceite y el rendimiento (p <0,01). La calidad del aceite aumentó debido al mayor contenido de ácidos oleico y linoleico bajo tratamiento con selenio + zinc (p <0.01). Se recomiendan los genotipos GK-Gabriella y GKH2624 sembrados en fechas oportunas (7 de octubre) y tardía (17 y 27 de octubre) y tratados con selenio + zinc, respectivamente, debido al mayor rendimiento de aceite y contenido de los ácidos linoleico y oleico

Using Crowdsourcing for Fine-Grained Entity Type Completion in Knowledge Bases

Recent years have witnessed the proliferation of large-scale Knowledge Bases (KBs). However, many entities in KBs have incomplete type information, and some are totally untyped. Even worse, fine-grained types (e.g., BasketballPlayer) containing rich semantic meanings are more likely to be incomplete, as they are more difficult to be obtained. Existing machine-based algorithms use predicates (e.g., birthPlace) of entities to infer their missing types, and they have limitations that the predicates may be insufficient to infer fine-grained types. In this paper, we utilize crowdsourcing to solve the problem, and address the challenge of controlling crowdsourcing cost. To this end, we propose a hybrid machine-crowdsourcing approach for fine-grained entity type completion. It firstly determines the types of some “representative” entities via crowdsourcing and then infers the types for remaining entities based on the crowdsourcing results. To support this approach, we first propose an embedding-based influence for type inference which considers not only the distance between entity embeddings but also the distances between entity and type embeddings. Second, we propose a new difficulty model for entity selection which can better capture the uncertainty of the machine algorithm when identifying the entity types. We demonstrate the effectiveness of our approach through experiments on real crowdsourcing platforms. The results show that our method outperforms the state-of-the-art algorithms by improving the effectiveness of fine-grained type completion at affordable crowdsourcing cost.Peer reviewe