Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing.However,the resource allocation problem is NP-hard and cannot be solved in polynomial time.The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution;however,these algorithms have the disadvantages of low computational efficiency or low allocate accuracy.In this paper,we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions.By learning a small-scale training set,the prediction model can guarantee that the social welfare,allocation accuracy,and resource utilization in the feasible solution are very close to those of the optimal allocation solution.The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.

Establishment and validation of a method for cell irradiation in 96-well and 6-well plates using a linear accelerator

To establish and validate a method for cell irradiation in 96-well and 6-well plates using a linear accelerator, three irradiation methods(G0 B0 F40,G0 B1.5 F40, and G180 B1.5 F40) were designed to irradiate cell culture plasticware simulated with RW3 slab phantom and polystyrene. The difference between the actual physical measured dose and the preset dose was compared among the three methods under the preparatory conditions of 2, 4, 6, 8, and 10 Gy. MDA-MB-231 cells were analyzed by using a cell proliferation assay and a clonogenic assay to verify the difference between the three cell irradiation methods on cell radiosensitivity. For each preset dose, the difference between the actual measured dose and the preset dose was the lowest for Method G0 B1.5 F40, the second lowest for Method G180 B1.5 F40, and the maximum for Method GOB0 F40. The ranges of the differences were-0.28 to 0.02%,-2.17 to-1.80%, and-4.92 to-4.55%, and 0.31 to-0.12%,-3.42 to-2.86%, and-7.31 to-6.92%,respectively, for 96-well and 6-well plates. The cell culture experiments proved that Method G0 B1.5 F40 was an accurate, effective, simple, and practical irradiation method. The most accurate and effective cell irradiation method should always be used, as it will reduce dose differences and instability factors and provide improved accuracy and comparability for laboratories researching cellular radiosensitivity.

Extending Qualitative Probabilistic Network with Mutual Information Weights

Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. With much higher efficiency of inferences, QPNs are more suitable for real-time applications than BNs. However, the high abstraction level brings some inference conflicts and tends to pose a major obstacle to their applications. In order to eliminate the inference conflicts of QPN, in this paper, we begin by extending the QPN by adding a mutual-information-based weight (MI weight) to each qualitative influence in the QPN. The extended QPN is called MI-QPN. After obtaining the MI weights from the corresponding BN, we discuss the symmetry, transitivity and composition properties of the qualitative influences. Then we extend the general inference algorithm to implement the conflict-free inferences of MI-QPN. The feasibility of our method is verified by the results of the experiment.

Influence Maximization for Cascade Model with Diffusion Decay in Social Networks

Maximizing the spread of influence is to select a set of seeds with specified size to maximize the spread of influence under a certain diffusion model in a social network. In the actual spread process, the activated probability of node increases with its newly increasing activated neighbors, which also decreases with time. In this paper, we focus on the problem that selects k seeds based on the cascade model with diffusion decay to maximize the spread of influence in social networks. First, we extend the independent cascade model to incorporate the diffusion decay factor, called as the cascade model with diffusion decay and abbreviated as CMDD. Then, we discuss the objective function of maximizing the spread of influence under the CMDD, which is NP-hard. We further prove the monotonicity and submodularity of this objective function. Finally, we use the greedy algorithm to approximate the optimal result with the ration of 1 ? 1/e.

Selecting Seeds for Competitive Influence Spread Maximization in Social Networks

There exist two or more competing products in viral marketing, and the companies can exploit the social interactions of users to propagate the awareness of products. In this paper, we focus on selecting seeds for maximizing the competitive influence spread in social networks. First, we establish the possible graphs based on the propagation probability of edges, and then we use the competitive influence spread model (CISM) to model the competitive spread under the possible graph. Further, we consider the objective function of selecting k seeds of one product under the CISM when the seeds of another product have been known, which is monotone and submodular, and thus we use the CELF (cost-effective lazy forward) algorithm to accelerate the greedy algorithm that can approximate the optimal with 1 ? 1/e. Experimental results verify the feasibility and effectiveness of our method.

Discovering admissible Web services with uncertain QoS

Open and dynamic environments lead to inher- ent uncertainty of Web service QoS （Quality of Service）, and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empiri- cal distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services in- cluding alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance （SD） rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported pro- gressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be im- proved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scal- ability of the proposed algorithm.

Cardinality Estimator:Processing SQL with a Vertical Scanning Convolutional Neural Network

Although the popular database systems perform well on query optimization,they still face poor query execution plans when the join operations across multiple tables are complex.Bad execution planning usually results in bad cardinality estimations.The cardinality estimation models in traditional databases cannot provide high-quality estimation,because they are not capable of capturing the correlation between multiple tables in an effective fashion.Recently,the state-of-the-art learning-based cardinality estimation is estimated to work better than the traditional empirical methods.Basically,they used deep neural networks to compute the relationships and correlations of tables.In this paper,we propose a vertical scanning convolutional neural network(abbreviated as VSCNN)to capture the relationships between words in the word vector in order to generate a feature map.The proposed learning-based cardinality estimator converts Structured Query Language(SQL)queries from a sentence to a word vector and we encode table names in the one-hot encoding method and the samples into bitmaps,separately,and then merge them to obtain enough semantic information from data samples.In particular,the feature map obtained by VSCNN contains semantic information including tables,joins,and predicates about SQL queries.Importantly,in order to improve the accuracy of cardinality estimation,we propose the negative sampling method for training the word vector by gradient descent from the base table and compress it into a bitmap.Extensive experiments are conducted and the results show that the estimation quality of q-error of the proposed vertical scanning convolutional neural network based model is reduced by at least 14.6%when compared with the estimators in traditional databases.

High-temperature chlorination of gold with transformation of iron phase

Gold in cyanide tailings from Shandong Province is mainly encapsulated by hematite and magnetite at distribution rates of 76.49 % and 10.88 %, respectively.Chlorination-reduction one-step roasting of cyanide tailings was conducted under the following conditions： calcium chloride dosage of 6 %, bituminous coal dosage of 30 %, calcium oxide dosage of 10 %（all dosages are vs.the mass of cyanide tailings） at 1000 ℃ of roasting temperature. X-ray diffraction（XRD）, scanning electron microscopy（SEM）, and chemical-phase analysis were performed to investigate the effects of iron phase transformation on the high-temperature chlorination of gold.Results indicate that the lattice structure of hematite undergoes expansion, pulverization, and reorganization when hematite is reduced to magnetite, which leads to42.03 % gold exposure, and the high-temperature chlorination rate of gold is 41.17 % at the same time. The structure of wustite formed by the reduction in magnetite is porous and loose, and thus 44.02 % of gold is exposed. The high-temperature chlorination rate of gold is increased by41.98 percentage points. When wustite is reduced to metallic iron, 4.42 % of gold is exposed, and the hightemperature chlorination rate of gold is increased by3.38 percentage points. Accordingly, the high-temperature chlorination of gold mainly occurs in two stages, in which Fe_2O_3 is reduced to Fe_3O_4, and Fe_3O_4 is reduced to Fe_xO finally.

Associative categorization of frequent patterns based on the probabilistic graphical model

Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.