95638
MU-MAQAS: A P2P based Model Updatable Mobile Automatic Q/A System
Along with the development of wireless technology and especially 3Gpsilas throw into commercial, the mobile value-added services gradually popular used, such as mobile automatic question and answer (Q/A) system. But the current mobile Q/A system mainly based on FAQ database, the question and answer can not be validated and updated by itself. So this paper attempts to implement a p2p based model updatable automatic Q/A system named MU-MAQAS for mobile phones using SMS interface. MU-MAQAS mainly includes: question & answer information collecting module and model manage & use module. It can automatically collect Q/A information from internet and update the Q/A model in time. A simulated Q/A system is also coded to validate the design. Results of simulation proved that under ideal conditions the systempsilas average responding time is less than 64 ms and precision is above 60% when there are no more than 3000 simultaneous asking queries, and so the completeness and feasibility of the systempsilas design is proved.
11099012
A Pattern Matching Algorithm in P2P Mobile QA System Answer Extracting Process
Mobile automatic question and answer (QA) system is more and more popular as a mobile value-added business. But the current mobile QA systems always have low query precision and recall ratio. In this paper, a new algorithm by pattern matching in p2p mobile QA system answer extracting process is designed. It expresses the document as triad and extracts the question and answer sentence from the triad based documents. Simulation experimental analysis results show that under ideal conditions the systempsilas average responding time is less than 58 ms and precision is above 66%, and the recall ratio is upper than 52% when there are no more than 3000 simultaneous query nodes, and so the completeness and feasibility of the systempsilas design is proved.
14879086
The Algorithm of Obtaining Best Rules Based on Rough Set
With the fast development of the technology in Database and Internet, excessive information has become a problem that decision makers engage in electronic commerce have to affront. This paper firstly establishes the decision table for excessive data, then reduces knowledge of the decision table, and obtains the best rules. This can reduce the redundancy of the data, and improve the efficiency of knowledge obtaining. Because of the high time complexity and spatial complexity the general algorithm has, this paper proposed a new algorithm based on analogical matrix, and carried out the algorithm. The practice proves that the algorithm can improve the efficiency of knowledge obtaining, and the rules is tidy and best.
15186113
Association based immune network for multimodal function optimization
For the problem of serious resources waste, indeterminate direction of local search and degeneration in the original opt-aiNet, a novel association based immune network is proposed for multimodal function optimization. The hexabasic model mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune association. The antibody population scale is semi-fixed reducing the time and space required to execute it. The information of the antibody population and the memory cells population is effective utilized to point out the direction of local search, to regulate the ratio between local search and global search, and to enhance the affinity of new antibodies. The elitist selection mechanism is adopted to ensure the convergence and stability of our algorithm respectively. The experiments on 10 benchmark functions show that when compared with opt-aiNet method, the new algorithm is capable of improving the search performance significantly in global convergence, convergence speed, computational cost, search ability, solution quality and algorithm stability.
14386113
P2P Recommendation Trust Model
Security trust model is an important research topic for the P2P application. This paper firstly analyzes the existing P2P network security trust models, and then puts forward a new P2P recommendation trust model based on the network of social relationships. The model provides different types of services to show the userpsilas interest in different areas, and gives recommendation based on the interested area. Feedback mechanism and punishment mechanism have been used to improve the model. The simulation shows that the model can truly evaluate entity behavior and effectively isolated from malicious nodes.
10848491
Detecting and Eliminating Potential Violations of Sequential Consistency for Concurrent C/C++ Programs
When a concurrent shared-memory program written with a sequential consistency (SC) model is run on a machine implemented with a relaxed consistency (RC) model, it could cause SC violations that are very hard to debug. To avoid such violations, programmers need to provide explicit synchronizations or insert fence instructions. In this paper, we propose a scheme to detect and eliminate potential SC violations by combining Shasha/Snir's conflict graph and delay set theory with existing data race detection techniques. For each execution, we generate a race graph, which contains the improperly synchronized conflict accesses, called race accesses, and race cycles formed with those accesses. As a race cycle would probably lead to a non-sequential-consistent execution, we call it a potential violation of sequential consistency (PVSC) bug. We then compute the race delays of race cycles, and suggest programmers to insert fences into source code to eliminate PVSC bugs. We further convert a race graph into a PC race graph, and improves cycle detection and race delay computation to O(n^2), where n is the number of race access instructions. We evaluate our approach with the SPLASH-2 benchmarks, two large real-world applications (MySQL and Apache), and several multi-threaded Cilk programs. The results show that (1) the proposed approach could effec-tively detect PVSC bugs in real-world applications with good scalability; (2) it retains most of the performance of the concurrent program after inserting required fence instructions, with less than 6.3% performance loss; and (3) the additional cost of our approach over traditional race detection techniques is quite low, with 3.3% on average.
15857389
A novel genetic algorithm based on immunity
A novel algorithm, the immune genetic algorithm (IGA), is proposed based on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps: 1) a vaccination and 2) an immune selection. IGA proves theoretically convergent with probability 1. Strategies and methods of selecting vaccines and constructing an immune operator are also given. IGA is illustrated to be able to restrain the degenerate phenomenon effectively during the evolutionary process with examples of TSP, and can improve the searching ability and adaptability, greatly increase the convergence rate.
12607701
An AIS-Based Service Match Model
Aiming at the problem that self-learning ability of the current system usually appears weak during service discovery, a novel artificial immune system (AIS) based service match model is proposed, which is based on the reference to the principles of cellpsilas mutation, evaluation and the quadratic response abilities, as well as simulation on the antibody-antigen identification mechanism. Theoretical analysis and simulations both show that the model can not only increasing the recall ratio and matching speed, but also realizing the function that deriving similar service request from known service request and then searching the best matching service. Furthermore, this service matching model is able to improve systempsilas adaptive learning, memorizing and dynamic evaluating abilities in service matching process.