Mobile edge computing (MEC) enables terminals to migrate their tasks to edge servers instead of the central cloud for efficient execution. However, most researches on task offloading are limited to binary offloading for atomic tasks with a single edge server, while in practice, serial tasks of computation-intensive applications are more important. Therefore, we jointly study the task offloading and resource allocation for serial tasks in the multi-terminal multi-server scenario. A serial task is divided into multiple sub-tasks that are executed sequentially, leading to a better utilization of fragmented resources. The offloading mechanism of inter-coupled terminals is formulated as a noncooperative stochastic game, with evaluation indexes defined by the joint task priority, average task delay and energy consumption. Aiming at minimizing the long-term cost of the whole system, we adopt a multi-agent reinforcement learning (MARL) algorithm with dynamically adjusted offloading strategies, subchannels, transmit power, and allocated resources with only the partial state information. Simulation results demonstrate the feasibility of the proposed algorithm to solve the formulated problem in a distributed way. Compared with the other five benchmark algorithms, it has better system cost performance, and can schedule delay-sensitive tasks with higher priority earlier on the basis of a lower task failure rate.

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.

Software clone detection is an active research area, which is very important for software maintenance, bug detection, etc. The two pieces of cloned code reflect some similarities or equivalents in the syntax or structure of the code representations. There are many representations of code like AST, token, PDG, etc. The PDG (Program Dependency Graph) of source code can contain both syntactic and structural information. However, most existing PDG-based tools are quite time-consuming and miss many clones because they detect code clones with exact graph matching by using subgraph isomorphism. In this paper, we propose a novel PDG-based code clone detector, CCGraph, that uses graph kernels. Firstly, we normalize the structure of PDGs and design a two-stage filtering strategy by measuring the characteristic vectors of codes. Then we detect the code clones by using an approximate graph matching algorithm based on the reforming WL (Weisfeiler-Lehman) graph kernel. Experiment results show that CCGraph retains a high accuracy, has both better recall and F1-score values, and detects more semantic clones than other two related state-of-the-art tools. Besides, CCGraph is much more efficient than the existing PDG-based tools.

A grid system is a dynamic and distributed environment which contains mass entities and resources, and is involved in large-scale data exchanges and resource sharing. Thus in grid system, information security is a prime concern. Trust mechanism is a main part of grid security. Taking the fuzziness of behavior trust into consideration, this paper introduces fuzzy theory to grid trust and proposes a novel trust model in order to better manage trust relationships between grid entities, presenting the calculation of direct and recommended trust based on fuzzy comprehensive evaluation. Especially, the paper adds the definition mechanism of trust class based on fuzzy cluster analysis to the trust model, achieving dynamic and better visualized definition of trust class. Case study shows that the proposed model can efficiently evaluate entity trust and give clear, accurate judgment upon entities' trust class, which contributes to the establishment of access control policies.

BACKGROUND: It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure. RESULTS: The statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure. CONCLUSION: Search algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These preliminary findings demonstrate that graph-theoretic quantifiers utilized in fields such as computer network design hold significant promise as an added tool for genomics and proteomics.