140 research outputs found
Quantification and Comparison of Degree Distributions in Complex Networks
The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
The degree distribution is an important characteristic of complex networks.
In many data analysis applications, the networks should be represented as
fixed-length feature vectors and therefore the feature extraction from the
degree distribution is a necessary step. Moreover, many applications need a
similarity function for comparison of complex networks based on their degree
distributions. Such a similarity measure has many applications including
classification and clustering of network instances, evaluation of network
sampling methods, anomaly detection, and study of epidemic dynamics. The
existing methods are unable to effectively capture the similarity of degree
distributions, particularly when the corresponding networks have different
sizes. Based on our observations about the structure of the degree
distributions in networks over time, we propose a feature extraction and a
similarity function for the degree distributions in complex networks. We
propose to calculate the feature values based on the mean and standard
deviation of the node degrees in order to decrease the effect of the network
size on the extracted features. The proposed method is evaluated using
different artificial and real network datasets, and it outperforms the state of
the art methods with respect to the accuracy of the distance function and the
effectiveness of the extracted features.Comment: arXiv admin note: substantial text overlap with arXiv:1307.362
A Model Based Approach on Multi-Agent System and Genetic Algorithm to Improve the Process Management in Service Oriented Architecture
Service oriented architecture is based on the provision of services. To enhance the performance of the systems by providing a better combination of services, it is necessary to extract more information compared to the one in the service registry. In this regard, the accomplished works have been focusing on the basic concepts of service-oriented architecture. The service composition is based on the information in service registry, provided by the service provider. Further, centralized combination with insufficient information does not meet the system performance requirements. This solution helps to facilitate resource distribution and reduces tasks of the central unit. In this paper, efforts have been made to use the agents in the proposed model to enhance the processes of a system. In the proposed model, agents have been used in order to extract and manage the essential information in service registry table. This information forms the basis of monitoring and selection of services. Agents are used for selecting and making efficient composite services. Finally, different communications and configuration mechanisms are implemented using multi-agent systems that can perform service-oriented architecture. Moreover, genetic algorithm is used to enhance architectural processes. In the proposed model, the genetic algorithm and multi-agent system has enhanced productivity of the system and its important quality attributes. The system runs in the international conference of computer society of Iran (ICCSI). Implementation of this model in the real environment and its comparison with its implementation on the prototype could be helpful to justify better efficiency and accuracy for future applications
A Hybrid Three Layer Architecture for Fire Agent Management in Rescue Simulation Environment
This paper presents a new architecture called FAIS for imple- menting
intelligent agents cooperating in a special Multi Agent environ- ment, namely
the RoboCup Rescue Simulation System. This is a layered architecture which is
customized for solving fire extinguishing problem. Structural decision making
algorithms are combined with heuristic ones in this model, so it's a hybrid
architecture
Feature Model Configuration Based on Two-Layer Modelling in Software Product Lines
The aim of the Software Product Line (SPL) approach is to improve the software development process by producing software products that match the stakeholders’ requirements. One of the important topics in SPLs is the feature model (FM) configuration process. The purpose of configuration here is to select and remove specific features from the FM in order to produce the required software product. At the same time, detection of differences between application’s requirements and the available capabilities of the implementation platform is a major concern of application requirements engineering. It is possible that the implementation of the selected features of FM needs certain software and hardware infrastructures such as database, operating system and hardware that cannot be made available by stakeholders. We address the FM configuration problem by proposing a method, which employs a two-layer FM comprising the application and infrastructure layers. We also show this method in the context of a case study in the SPL of a sample E-Shop website. The results demonstrate that this method can support both functional and non-functional requirements and can solve the problems arising from lack of attention to implementation requirements in SPL FM selection phase
Predicting Subjective Features from Questions on QA Websites using BERT
Community Question-Answering websites, such as StackOverflow and Quora,
expect users to follow specific guidelines in order to maintain content
quality. These systems mainly rely on community reports for assessing contents,
which has serious problems such as the slow handling of violations, the loss of
normal and experienced users' time, the low quality of some reports, and
discouraging feedback to new users. Therefore, with the overall goal of
providing solutions for automating moderation actions in Q&A websites, we aim
to provide a model to predict 20 quality or subjective aspects of questions in
QA websites. To this end, we used data gathered by the CrowdSource team at
Google Research in 2019 and a fine-tuned pre-trained BERT model on our problem.
Based on the evaluation by Mean-Squared-Error (MSE), the model achieved a value
of 0.046 after 2 epochs of training, which did not improve substantially in the
next ones. Results confirm that by simple fine-tuning, we can achieve accurate
models in little time and on less amount of data.Comment: 5 pages, 4 figures, 2 table
- …