Workflow model performance is an importance problem of workflow modeling. The paper proposed the definition of Workflow Performance Analysis Net after expounding the correlation research on workflow model performance analysis. Based on the definition of workflow performance analysis net, a simulation algorithm for solving the average trigger time of active nodes in workflow model is brought up. The error of the simulation algorithm and the design principles of mainly parameter are discussed in detail. Then the performance indicators of workflow model can be computed based on the theory of open Markov queuing network and the average trigger time of active nodes. The usability of the research results presented in the paper is illustrated by the experimental data of a processing of complaints.
Community Question Answering (CQA) websites have become valuable knowledge repositories. Millions of internet users resort to CQA websites to seek answers to their encountered questions. CQA websites provide information far beyond a search on a site such as Google due to (1) the plethora of high-quality answers, and (2) the capabilities to post new questions toward the communities of domain experts. While most research efforts have been made to identify experts or to preliminarily detect potential experts of CQA websites, there has been a remarkable shift toward investigating how to keep the engagement of experts. Experts are usually the major contributors of high-quality answers and questions of CQA websites. Consequently, keeping the expert communities active is vital to improving the lifespan of these websites. In this article, we present an algorithm termed PALP to predict the activity level of expert users of CQA websites. To the best of our knowledge, PALP is the first approach to address a personalized activity level prediction model for CQA websites. Furthermore, it takes into consideration user behavior change over time and focuses specifically on expert users. Extensive experiments on the Stack Overflow website demonstrate the competitiveness of PALP over existing methods.
When the applications are running in the virtual machine (VM), the virtual address of VM are translated into physical address in host. To improve the quality of address translation, huge page mechanism is introduced to increase Translation Lookaside Buffer (TLB) hit rates and reduce page faults. In this paper, we discuss and investigate the impact ofTHP(transparent huge page) on VM.
Federated Recommendation (FR) has received considerable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from private user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.
Put forward a SNMP extensible agent based network simulator design architecture, which applies SNMP Extendible Agent architecture to current SNMP agent simulator systems. It creates one master-agent and several sub-agents. Every sub-agent simulates a network device, and communicates with the SNMP management side via the master-agent. Thus it implements the simulation of a certain size of network and meets the requirement for a test environment during developing network management project.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)