INTRODUCTION: Trust is an important indicator in the cloud computing environment for service selection and recommendation. It is a difficult task to create a composite value-added service from several candidate services for the desired objectives due to the dramatic gr

How the Suspend/Resume mechanism of smartphone influences the power consumption is examined in the dissertation. Specifically, various unimportant and not so urgent network packets keep awakening the operating system (OS) at the time it is under suspend mode, and switch it from suspend to resume mode continually, which results in more power consumption. Accordingly, an innovative optimization technique was suggested in this paper in order that the awakening of OS can be postponed and the lasting hour of suspend mode can be lengthened to decrease power consumption. Some experiments are also carried out, with the result data suggesting that such technique is an effective way to reduce power consumption by greater than 7.63%. It proves that this technique is workable.

Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three in

In recent years, raw security situation data cannot be utilized well by fully connected neural networks. Generally, a cyber infiltration is a gradual process and there are logical associations between future situation and historical information. Taking the factors into account, this paper proposes a framework to predict network security situation. According the needs of this framework, we improve Long Short-Term Memory (LSTM) with Cross-Entropy function, Rectified Linear Unit and appropriate layer stacking. Modules are designed in the framework to transform raw data into quantitative results. Finally, the performance is evaluated on KDD CUP 99 dataset and UNSW-NB15 dataset. Experiments prove that the framework built with the improved LSTM has better performance to predict network security situation in the near future. The framework achieves a relatively practical prediction of network security situation, helping provide advanced measures to improve network security.

A distributed Nash equilibrium seeking algorithm is presented for networked games. We assume an incomplete information available to each player about the other players' actions. The players communicate over a strongly connected digraph to send/receive the estimates of the other players' actions to/from the other local players according to a gossip communication protocol. Due to asymmetric information exchange between the players, a non-doubly (row) stochastic weight matrix is defined. We show that, due to the non-doubly stochastic property, the total average of all players' estimates is not preserved for the next iteration which results in having no exact convergence. We present an almost sure convergence proof of the algorithm to a Nash equilibrium of the game. Then, we extend the algorithm for graphical games in which all players' cost functions are only dependent on the local neighboring players over an interference digraph. We design an assumption on the communication digraph such that the players are able to update all the estimates of the players who interfere with their cost functions. It is shown that the communication digraph needs to be a superset of a transitive reduction of the interference digraph. Finally, we verify the efficacy of the algorithm via a simulation on a social media behavioral case.

In order to safely operate the electric transmission lines, to monitoring the crossed span distance of the overhead lines is one of the key measures. Since the current measurement methods of crossed span distance of overhead line has high cost, low accuracy, and traditional measurement methods has l

Introduction: With advancements in personalised medicine, healthcare delivery systems have moved away from the one-size-fits-all approach, towards tailored treatments that meet the needs of individuals and specific subgroups. As nearly one-third of those diagnosed with epilepsy are classed as refractory and are resistant to antiepileptic medication, there is need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more epilepsy related trigger as the causation behind their seizure onset. These triggers are person-specific and affect those diagnosed in different ways dependent on their idiosyncratic tolerance and threshold levels. Whilst these triggers are known to induce seizure onset, only a few studies have even considered their use as a preventive component, and whether they could be used as an additional sensing modality for non-EEG detection mechanisms. Objectives: 1. To record person-specific triggers (PST) from participants using IoT-enabled sensors and smart devices. 2. To train and test several dedicated machine learning models using a single participants data, 3. To conduct a comparative analysis and evaluate the performance of each model, 4. Formulate a conclusion as to whether PST could be used to improve on current methods of non-EEG seizure detection. Methodology: This study uses a precision approach combined with machine learning, to train and test several dedicated algorithms that can predict epileptic seizures. Each model is designed for a single participant, enabling a personalised method of classification unseen in non-EEG detection research. Results: Our results show accuracy, sensitivity, and specificity scores of 94.73%, 96.90% and 93.33% for participant 1 and 96.87%, 96.96% and 96.77% for participant 2, respectively. Conclusion: To conclude, this preliminary study has observed a noticeable correlation between the documented triggers and each participants seizure onset, indicating that PST have the potential to be used as an additional non-EEG sensing modality when classifying epileptic seizures.

Continuously extracting and integrating changing data from various heterogeneous systems based on an appropriate data extraction model is the key to data sharing and integration and also the key to building an incremental data warehouse for data analysis. The traditional data capture method based on timestamp changes is plagued with anomalies in the data extraction process, which leads to data extraction failure and affects the efficiency of data extraction. To address the above problems, this paper improves the traditional data capture model based on timestamp increments and proposes VTWM, an incremental data extraction model based on variable time-windows, based on the idea of extracting a small number of duplicate records before removing duplicate values. The model reduces the influence of abnormalities on data extraction, improves the reliability of the traditional data extraction ETL processes, and improves the data extraction efficiency.

This paper investigated the impact of Window to Wall Ratios (WWR) an the thermal comfort and energy lighting demand of a building in tropical zone. Simulations were carried out for a reference office proposed by Task 27 of IEA using three Window to Wall Ratios (WWR). Results were analyzed both in te