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In this paper, we present the optimal strategy for green energy trading among three parties: user residents (URs), service provider (SP), and the grid. The URs make decisions about the amount of generated green energy for self-usage and trading. The SP collects green energy from URs and trade in the market and receives subsidy from the grid. The grid which is government owned subsidizes the SP to promote the adoption of green energy, so that the environment can be improved and social surplus benefits. To obtain insights of such a highly coupled system, we consider a monopoly market, where there are a group of URs trading with a single service provider. To find the optimal strategy of the three parties, we propose a three-stage Stackelberg game model. Besides, the grid’s optimal subsidy policy, the SP’s optimal pricing strategies, and URs’ optimal usage strategy are also provided in each stage. Both the analytical and simulation results show that, the optimal decisions such as the amount of green energy generated by UR and the subsidy given by the grid are all decreasing functions of URs’ average cost. High infrastructure and operation costs to obtain green energy deteriorate each party’s payoff and the whole system’s performance.

Residential demand response is vital for the efficiency of power system. It has attracted much attention from both academic and industry in recent years. Accurate short-term load forecasting is a fundamental task for demand response. While short-term forecasting for aggregated load data has been extensively studied, load forecasting for individual residential users is still challenging due to the dynamic and stochastic characteristic of single users’ electricity consumption behaviors, i.e., the variability of the residential activities. To address this challenge, this paper presents a short-term residential load forecasting framework, which makes use of the spatio-temporal correlation existing in appliances’ load data through deep learning. Multiple time series are conducted in the framework to describe electricity consumption behaviors and their internal spatio-temporal relationship. And a method based on deep neural network and iterative ResBlock is proposed to learn the correlation among different electricity consumption behaviors for short-term load forecasting. Experiments based on real world measurements have been conducted to evaluate the performance of the proposed forecasting approach. The results show that both the appliances’ load data and iterative ResBlocks can help to improve the forecasting performance. Compared with existing methods, measurements on Root Mean Squared Error, Mean Absolute Error and Mean Absolute Percentage Error for the proposed approach are reduced by 3.89%-20.00%, 2.18%-22.58% and 0.69%-32.78%. In addition, further experiments are conducted to evaluate the impact of using appliances’ load data, iterative ResBlocks as well as other factors for the proposed approach.

Recent detection method based on machine learning demonstrates significant advantages against varieties of network attacks, and has been widely deployed in cloud applications. However, novel attacks such as Advanced Persistent Threats (APTs) could evade detection of the intrusion detection system, which may lead to serious data leakage in cloud. Existing methods studied the countermeasures to defend against evasion attacks. However, a cloud service provider (CSP) also have to balance between its expect revenue and the security risk of system with limited resources. In this paper, we present the CSP’s optimal strategy for effective and safety operation, in which the CSP decides the size of users that the cloud service will provide and whether enhanced countermeasures will be conducted for discovering the possible evasion attacks. While the CSP tries to optimize its profit by carefully making a two-step decision of the defense plan and service scale, the attacker considers its expected revenue to launch evasion attacks or not. To obtain insights of such a highly coupled system, we consider a system with one CSP and one attacker with two attack choices of whether to launch an evasion attack. We propose a two-stage Stackelberg game, in which the CSP acts as the leader who decides the defense plan and service scale in Stage I, and the attacker acts as the follower that determines whether to make evasion attacks in Stage II. We derive the Nash Equilibrium by analyzing the attacker’s choices under different scenarios that the CSP selects. Then, we provide the CSP’s optimal strategies to maximize its revenue. The simulation results help to better understand the CSP’s optimal solutions under different situations.

Brain network is a kind of biological networks, which could express complex connectivity among brain functional components. The node in brain network denotes region of interest, which executes specific function in the brain. The edge in brain network represents the connection relationship between nodes. Neuropsychiatric disorders can cause changes in the brain's nerves, which will further change the related characteristics of brain network. The detection for neuropsychiatric disorders with brain network could be treated as an anomaly detection problem. Recent researches have explored using complex networks or graph mining to deal with the detection problem. However, they neither ignore local structural features in critical regions nor fail to comprehensively extract the structural features for brain network. In this paper, we propose a feature learning method to build effective representations for brain network. By treating the closed frequent graph as a node, these representations contain both connection relationships in critical regions and local/global structural features for critical regions, which could benefit the detection with brain network. Experiments using real world data indicate that the proposed method could improve the detection ability of existing machine learning methods in the literatures.

In this paper, we proposed a Semantic Tensor Product (STP) model aimed at finding a more grammatically correct caption for a given image. We tried two kinds of ways to integrate semantic concepts (i.e. tags) into Tensor Product Representation (TPR) in the process of image caption generation, where Tensor Product Representation has the advantage of decoding a role embedding using a corresponding unbinding vector in any position directly, while semantic concepts could provide crucial clues for internal TPR prediction. We qualitatively analyze the two proposed STP based models, and quantitatively evaluate them on benchmark dataset: MSCOCO. Experiment result shows that our results outperform the other models, and generated captions are more natural.

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).

Due to the nonlinear, non-stationery, complex and stochastic characteristics of short-term traffic flow time series, traditional prediction methods do not work well. This paper presents a short-term traffic flow forecasting model based on the least square support vector regression (LSSVR) algorithm, which is optimized by a glowworm swarm optimization (GSO) algorithm. The GSO algorithm is used to determine two core parameters in the learning process, which significantly influence the predicting performance in the model. An actual example of traffic flow data on one section of highway in Chengdu, China is used to evaluate the performance of the proposed LSSVR-GSO model. The experimental results show that the proposed LSSVR-GSO model has more accurate predicting results than the LSSVR model optimized by the genetic algorithm and the back-propagation neural network model.

In backbone networks, due to the frequent exchange of data between adjacent routers, the characteristics of network behaviors caused by network distributed abnormal events are closely related with routers' spatial location, their connection relationships and other associated information. Based on this observation, this paper presents a novel graph-based abnormal event detection framework that uses a set of data mining techniques for facilitating the detection of distributed abnormal events in backbone networks. The experiment results using real traffic data collected from an ISP backbone network show the effective and scalable of the proposed framework.

Widely discussed in the mass media, Web 2.0, or social software, has also drawn the attention of researchers, developing into a whole new research area. With Web 2.0's further development, corporations aim to adopt its technologies and transfer its benefits, such as enhanced collaboration and knowledge sharing, to their organizations. Whether any of these benefits also apply in an organizational context and whether there are further, still uncovered, benefits remains unclear. Furthermore, research in this area is still in its early stages, thus hampering progress towards qualitative and quantitative models that could provide answers. In order to encourage further progress in this area, we reviewed the existing research on corporate blogging and identified 24 articles that investigate the topic. Using the framework by Ives et al. [18], we categorized the articles for further analysis. By means of process theory, we build a conceptual model and identify the antecedents and consequences of internal corporate weblog usage. Our findings suggest that usage is driven by organizational culture, as well as by attitudes towards blogging. In addition, the benefits of weblog usage are centered on community benefits.

Comprehensive collection and accurate description of traffic information are core problems in network traffic anomaly detection. Aiming at the lack of traffic anomaly detection in analyzing multi-time series, we propose a network traffic anomaly detection method based on graph mining. Our method accurately and completely describes the relationships among multi-time series which are used in traffic anomaly detection by time-series graph; by means of the support count of the patterns, our method mines all the frequent patterns ,which is conducive to detecting many kinds of abnormal traffic effectively; through mining the relationships among all itemsets, our method introduces weight coefficients of the itemsets, which is able to solve relationship quantification issues of multi-time series in traffic anomaly detection. The simulation results show that the proposed method can effectively detect the network traffic anomaly and achieve a higher accuracy than the CWT-based (Continuous Wavelet Transform-based) method in term of DDos attacks detection.