Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

A Comparative Study of Cost Estimation Models for Web Hypermedia Applications

Abstract: Software cost models and effort estimates help project managers allocate resources, control costs and schedule and improve current practices, leading to projects finished on time and within budget. In the context of Web development, these issues are also crucial, and very challenging given that Web projects have short schedules and very fluidic scope. In the context of Web engineering, few studies have compared the accuracy of different types of cost estimation techniques with emphasis placed on linear and stepwise regressions, and case-based reasoning (CBR). To date only one type of CBR technique has been employed in Web engineering. We believe results obtained from that study may have been biased, given that other CBR techniques can also be used for effort prediction. Consequently, the first objective of this study is to compare the prediction accuracy of three CBR techniques to estimate the effort to develop Web hypermedia applications and to choose the one with the best estimates. The second objective is to compare the prediction accuracy of the best CBR technique against two commonly used prediction models, namely stepwise regression and regression trees. One dataset was used in the estimation process and the results showed that the best predictions were obtained for stepwise regression.

Optimal Demand Response Using Device-Based Reinforcement Learning

Abstract: Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated energy management system (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS’s rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation does not require explicitly modeling the user’s dissatisfaction on job rescheduling, enables the EMS to self-initiate jobs, allows the user to initiate more flexible requests, and has a computational complexity linear in the number of device clusters. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.

Data synchronization and device handling

Abstract: In one example, a synchronization server includes logic operable to engage in a first synchronization session with a client device, wherein client modifications and server modifications may be exchanged based, at least in part, on synchronization data stored locally. The synchronization server further includes logic operable to initiate a query of a remote database (e.g., having data associated with the synchronization data) to determine differences between the synchronization data stored locally and associated data stored remotely. The synchronization server is further operable to initiate an exchange of further server modifications based on the differences between the synchronization data stored locally and the associated data stored remotely. In one example, the server may engage in a second synchronization session with the client device to update the client device with differences to the synchronization data stored locally and the remote database.

Role of Silicon in Mitigation of Heavy Metal Stresses in Crop Plants.

Abstract: Over the past few decades, heavy metal contamination in soil and water has increased due to anthropogenic activities. The higher exposure of crop plants to heavy metal stress reduces growth and yield, and affect the sustainability of agricultural production. In this regard, the use of silicon (Si) supplementation offers a promising prospect since numerous studies have reported the beneficial role of Si in mitigating stresses imposed by biotic as well as abiotic factors including heavy metal stress. The fundamental mechanisms involved in the Si-mediated heavy metal stress tolerance include reduction of metal ions in soil substrate, co-precipitation of toxic metals, metal-transport related gene regulation, chelation, stimulation of antioxidants, compartmentation of metal ions, and structural alterations in plants. Exogenous application of Si has been well documented to increase heavy metal tolerance in numerous plant species. The beneficial effects of Si are particularly evident in plants able to accumulate high levels of Si. Consequently, to enhance metal tolerance in plants, the inherent genetic potential for Si uptake should be improved. In the present review, we have discussed the potential role and mechanisms involved in the Si-mediated alleviation of metal toxicity as well as different approaches for enhancing Si-derived benefits in crop plants.