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Power load analysis is important for optimizing resource allocation, planning the production of electricity, and predicting power markets. Yet, it is challenging, since load data exhibit both periodic and stochastic features, and is affected by a multitude of factors including social, economic, political, and climatic factors, as well as industrial structure, living standards, and user behaviors....
Sybil detection is an important task in cyber security research. Over past years, many data mining algorithms have been adopted to fulfill such task. Using classification and regression for sybil detection is a very challenging task. Despite of existing research made toward modeling classification for sybil detection and prediction, this research has proposed new solution on how sybil activity could...
As cloud computing becomes widespread, more and more users prefer to outsource their local sensitive data into the cloud. In order to protect data privacy, these sensitive data usually has to be encrypted before outsourcing, which makes effective data utilization a very difficult task. Although traditional searchable encryption techniques allow users to securely search over encrypted cloud data, they...
Emotion plays a significant role in consumer decision making. We recently conducted a study to explore how media-based information of aggregated market emotion influences consumers' expected demand of commodities, and how businesses can use media-based emotion indices to predict commodities' price. We implemented time series econometrics by analyzing a fourteen year daily observations of twelve major...
Twitter provides a platform for information sharing and diffusion, and has quickly emerged as a mechanism for organizations to engage with their consumers. A driving factor for engagement is providing relevant and timely content to users. We posit that the engagement via tweets offers a good potential to discover user interests and leverage that information to target specific content of interest....
Recent work in literary sentiment analysis has suggested that shifts in emotional valence may serve as a reliable proxy for plot movement in novels. The raw sentiment time series of a novel can now be extracted using a variety of different methods, and after extraction, filtering is commonly used to smooth the irregular sentiment time series. Using an adaptive filter, which is among the most effective...
Nominal compounds which constituted of two nouns together are very common in reading materials or web pages. The interpretation of these compounds can help us know the meaning of a text or sentences. Traditional approaches utilized the method based on verbs and rules to obtain the interpretation of compounds with low recall. So we investigate an interpretation method based on similarity which makes...
Contagion models have been used to study the spread of social behavior among agents of a population, such as information diffusion, social influence, and participation to collective action (e.g., protests). Key players, which are typically high-degree, -k-core or -centrality agents in a networked population, are considered important for spreading social contagions. In this paper, we ask whether contagions...
Increasingly more applications rely on crowd-sourced data from social media. Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical timelines of users. Nevertheless, determining the subset of "credible" users is crucial. While the majority of sampling approaches focus on individuals' static networks,...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
This paper aims at investigating home-based daily mobility patterns in Shanghai. The dataset consists of Data over Signaling (DoS) from 107,100 anonymous mobile phone subscribers in Shanghai over 9 days in different seasons, which contains spatial-temporal information of subscribers. Daily mobility pattern is characterized as motif of the individual's daily trajectory in this paper. Homes of subscribers...
The small companies become increasingly important in bank's lending business. But the challenge is how bank's credit assessment is made in a small amount of time and money. Compare with the big companies, the small companies often need a small amount of cash flow. They may not provide the complete certificates or documents, so that the bank has to collect information of the companies and evaluate...
There are many similarities on fluctuations between clothing styles and finance so that many theorists approach to analyze the relationship of them, the best known of which is the Hemline Index Theory. When the economy is flourishing, hemlines increase, and when the economic situation is deteriorating, the hemlines drop, perhaps even to the floor. In contrast with measuring the illustrations from...
Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models...
Re-identification of individuals has already drawn growing attentions due to the increasing intelligent visual surveillance. Human signature is quite different over a network of cameras and most related work devotes to selecting human features without any distinction. To address the problem, we propose a novel coupled feature space learning with joint graph regularization in this paper. The proposed...
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