Research on Data Theory of Value

This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observation of data peculiar features,it draws the conclusion that data have the epochal characteristics of non-competitiveness and non-exclusivity,decreasing marginal cost and increasing marginal return,non-physical and intangible form,and non-finiteness and non-scarcity.It is the epochal characteristics of data that undermine the traditional theory of value and innovate the“production-exchange”theory,including data value generation,data value realization,data value rights determination and data value pricing.From the perspective of data value generation,the levels of data quality,processing,use and connectivity,data application scenarios and data openness will influence data value.From the perspective of data value realization,data,as independent factors of production,show value creation effect,create a value multiplier effect by empowering other factors of production,and substitute other factors of production to create a zero-price effect.From the perspective of data value rights determination,based on the theory of property,the tragedy of the private outweighs the comedy of the private with respect to data,and based on the theory of sharing economy,the comedy of the commons outweighs the tragedy of the commons with respect to data.From the perspective of data pricing,standardized data products can be priced according to the physical product attributes,and non-standardized data products can be priced according to the virtual product attributes.Based on the epochal characteristics of data and theoretical innovation,the“production-exchange”paradigm has undergone a transformation from“using tangible factors to produce tangible products and exchanging tangible products for tangible products”to“using intangible factors to produce tangible products and exchanging intangible products for tangible products”and ultimately to“using intangible factors to produce intangible products and exchanging intangible products for intangible products”.

Multi-bit soft error tolerable L1 data cache based on characteristic of data value

Due to continuous decreasing feature size and increasing device density, on-chip caches have been becoming susceptible to single event upsets, which will result in multi-bit soft errors. The increasing rate of multi-bit errors could result in high risk of data corruption and even application program crashing. Traditionally, L1 D-caches have been protected from soft errors using simple parity to detect errors, and recover errors by reading correct data from L2 cache, which will induce performance penalty. This work proposes to exploit the redundancy based on the characteristic of data values. In the case of a small data value, the replica is stored in the upper half of the word. The replica of a big data value is stored in a dedicated cache line, which will sacrifice some capacity of the data cache. Experiment results show that the reliability of L1 D-cache has been improved by 65% at the cost of 1% in performance.

Estimation of Parameters of Boundary Value Problems for Linear Ordinary Differential Equations with Uncertain Data

In this paper we construct optimal, in certain sense, estimates of values of linear functionals on solutions to two-point boundary value problems (BVPs) for systems of linear first-order ordinary differential equations from observations which are linear transformations of the same solutions perturbed by additive random noises. It is assumed here that right-hand sides of equations and boundary data as well as statistical characteristics of random noises in observations are not known and belong to certain given sets in corresponding functional spaces. This leads to the necessity of introducing minimax statement of an estimation problem when optimal estimates are defined as linear, with respect to observations, estimates for which the maximum of mean square error of estimation taken over the above-mentioned sets attains minimal value. Such estimates are called minimax mean square or guaranteed estimates. We establish that the minimax mean square estimates are expressed via solutions of some systems of differential equations of special type and determine estimation errors.

The Analysis of Seismic Data Structure and Oil and Gas Prediction

In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

Big Data Security and Privacy： A Review

While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.

Consumers'privacy data sharing between the seller and the e-commerce platform

Due to the fact that consumers'privacy data sharing has multifaceted and complex effects on the e-commerce platform and its two sided agents,consumers and sellers,a game-theoretic model in a monopoly e-market is set up to study the equilibrium strategies of the three agents(the platform,the seller on it and consumers)under privacy data sharing.Equilibrium decisions show that after sharing consumers'privacy data once,the platform can collect more privacy data from consumers.Meanwhile,privacy data sharing pushes the seller to reduce the product price.Moreover,the platform will increase the transaction fee if the privacy data sharing value is high.It is also indicated that privacy data sharing always benefits consumers and the seller.However,the platform's profit decreases if the privacy data sharing value is low and the privacy data sharing level is high.Finally,an extended model considering an incomplete information game among the agents is discussed.The results show that both the platform and the seller cannot obtain a high profit from privacy data sharing.Factors including the seller's possibility to buy privacy data,the privacy data sharing value and privacy data sharing level affect the two agents'payoffs.If the platform wishes to benefit from privacy data sharing,it should increase the possibility of the seller to buy privacy data or increase the privacy data sharing value.

Performance Evaluation of Different Data Value Prediction Schemes

Data value prediction has been widely accepted as an effective mechanism to break data hazards for high performance processor design. Several works have reported promising performance potential. However, there is hardly enough information that is presented in a clear way about performance comparison of these prediction mechanisms. This paper investigates the performance impact of four previously proposed value predictors, namely last value predictor, stride value predictor, two-level value predictor and hybrid （stride-t-two-level） predictor. The impact of misprediction penalty, which has been frequently ignored, is discussed in detail. Several other implementation issues, including instruction window size, issue width and branch predictor are also addressed and simulated. Simulation results indicate that data value predictors act differently under different configurations. In some cases, simpler schemes may be more beneficial than complicated ones. In some particular cases, value prediction may have negative impact on performance.

Why Do Applications Request My Contacts Data？ A Large-Scale Study on Openness and Control of User Contacts Permission in Android Mobile Applicaitons Marketplace

To understand the value of data transfer, this paper uses consumers＇ contact information in mobile application that resides in their mobile devices as a proxy to evaluate the value of user data to application developer and platform owner. Based on a data set of 567,730 Android mobile applications, our research results show that using user contacts can increase application popularity, enable developer to become more influential, and have a positive effect on developers＇ productivity when developing future applications. In addition to examining the use and access of a consumers extending contact information on their device, we broaden our research to examine the context and appropriateness of using this information in the context of privacy, and to a less extent education. We conclude our research with a summary of guidance to developers, consumerss, and platform owners in order to maintain a healthy application ecosystem.

Enhanced CMOS image sensor by flexible 3D nanocone anti-reflection film

Complementary metal oxide semiconductor(CMOS) image sensors(CIS) are being widely used in digital video cameras, web cameras, digital single lens reflex camera(DSLR), smart phones and so on, owing to their high level of integration, random accessibility, and low-power operation. It needs to be installed with the cover glass in practical applications to protect the sensor from damage, mechanical issues,and environmental conditions, which, however, limits the accuracy and usability of the sensor due to the reflection in the optical path from air-to-cover glass-to-air. In this work, the flexible 3D nanocone anti-reflection(AR) film with controlled aspect ratio was firstly employed to reduce the light reflection at air/cover glass/air interfaces by directly attaching onto the front and rear sides of the CIS cover glass.As both the front and rear sides of cover glass were coated by the AR film, the output image quality was found to be improved with external quantum efficiency increased by 7%, compared with that without AR film. The mean digital data value, root-mean-square contrast, and dynamic range are increased by45.14%, 38.61% and 57, respectively, for the output image with AR films. These results provide a novel and facile pathway to improve the CIS performance and also could be extended to rational design of other image sensors and optoelectronic devices.

Immediate regularization of measure-type population densities in a two-dimensional chemotaxis systemwith signal consumption

This paper deals with the Neumann initial-boundary value problem for a classical chemotaxis system with signal consumption in a disk.In contrast to previous studies which have established a comprehensive theory of global classical solutions for suitably regular nonnegative initial data,the focus in the present work is on the question to which extent initially prescribed singularities can be regularized despite the presence of the nonlinear cross-diffusive interaction.The main result in this paper asserts that at least in the framework of radial solutions immediate regularization occurs under an essentially optimal condition on the initial distribution of the population density.More precisely,it will turn out that for any radially symmetric initial data belonging to the space of regular signed Borel measures for the population density and to L^(2) for the signal density,there exists a classical solution to the Neumann initial-boundary value problem,which is smooth and approaches the given initial data in an appropriate trace sense.