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Vast amounts of data available online and in other digital repositories make it challenging for users to find the right sources of information. In this paper, we present a novel approach for recommending documents to users by analyzing user browsing behavior, and demonstrate the effectiveness of our methods using an original data set. We conducted a study to collect a novel data set of document browsing...
A common approach to sentiment classification is to identify a set of sentiment-carrying words and then to use machine learning to build a classifier that can classify sentiment based on the presence/absence of those words. In this paper, we propose a Fourier-based extension of this approach. Specifically, we introduce a spectral learning algorithm that implicitly identifies sentiment-carrying words...
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the challenge of exploiting correlations between labels. While in existing work these correlations are often modelled globally, in this paper we use the divide-and-conquer approach of decision trees which enables taking local decisions about how best to model label dependency. The resulting...
This paper proposes a fast coding unit depth decision algorithm for High Efficiency Video Coding (HEVC) by utilizing the depth information of spatio-temporal relations to determine a depth search range (DSR). We make use of the coded block flag (CBF) as well as the information of quantization parameter (QP) to ensure the coding quality. We also analyze the characteristic of Merge mode and accordingly...
On social media, the user generated contents, e.g., Articles and images, can be assigned with multiple labels. In this paper, we focus on the problem of performing multi-label classification on social media data, where the user generated contents are associated with multiple labels. Multi-label learning studies the problem where each object is represented by a single instance and associated with a...
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able...
Micro-blog has become the most popular information sharing tool in our daily life. The retweet behavior is a main method of information propagation in micro-blog. So there tweet number prediction not only is an interesting research topic, but also has much practical significance. However, most of current researches only regard this problem as a classification or regression problem, and they did not...
Exploiting outdated channel quality indicators is crucial in most adaptive wireless communication systems. This is often done through channel prediction based on previous received indicators. In this paper, we analyze the case where the feedback delay experienced by the quality indicators is not constant, but random. Focusing on a single-pole IIR predictor, we obtain analytical expressions for the...
This work addresses the problem of sequential recovery of temporally correlated sparse vectors with common support from noisy under-determined linear measurements. The Kalman sparse Bayesian learning (SBL) algorithm is an efficient tool for solving the problem when the temporal correlation is modeled using a first order autoregressive model. However, this method processes the input data in a batch...
Chloroplasts are organelles in most green plant and some algal cells. Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations...
There is an important relationship between the stability of protein complex and hot region. Research has shown that in protein-protein interaction (PPI), residues are denser around the hot region. Therefore, this paper proposed an algorithm based on Gi statistics, regional division rule and regional amplification principle to form residue dense region (RDR); Then, according to the results of cascade...
The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. Most of the current security technologies do not provide the essential security features for cloud...
Legacy energy infrastructures are being replaced by modern smart grids. Smart grids provide bi-directional communications for the purpose of efficient energy and load management. In addition, energy generation is adjusted based on the load feedback. However, due to the dependency on the cyber infrastructure for load monitoring and reporting, generation control is inherently vulnerable to attacks....
Scalable Video Coding (SVC), provides different resolutions, different video quality and different video streaming rate after once compression according to various requirements of users. The characteristic performance can solve a series of video transmission problems encountered in the current complex and heterogeneous network environment conveniently and effectively, and provide a highly efficient...
Context Management Framework (CMF) for Ubiquitous Health (U-Health) Systems should be able to continuously gather raw data from observed entities to characterize their current situation (context). However, the death of battery-dependent sensors reduce their ability for detecting the context, which directly affects the availability of context-aware u-health services. This paper proposes the use of...
Random forests have been used as effective models to tackle a number of classification and regression problems. In this paper, we present a new type of Random Forests (RFs) called Red(uced)-RF that adopts a new voting mechanism called Priority Vote Weighting (PV) and a new dynamic data reduction principle which improve accuracy and execution time compared to Breiman's conventional RF. Red-RF also...
The classification with instances which can be tagged with any of the 2L possible subsets from the predefined L labels is called multi-label classification. Multi-label classification is commonly applied in domains, such as multimedia, text, web and biological data analysis. The main challenge lying in multi-label classification is the dilemma of optimising label correlations over exponentially large...
A new approach is presented for the detection of infrared small target in the single-frame cloud image, based on the block prediction of background. The new approach estimates the background of image with block prediction algorithm. Both the rule for reference window selecting and the method for weight matrix updating adopted by the new technique can improve the accuracy of background prediction,...
On-board data compression is a critical task that has to be carried out with restricted computational resources for remote sensing applications. This paper proposes an improved algorithm for onboard lossless compression of hyperspectral images, which combines low encoding complexity and high-performance. This algorithm is based on hybrid prediction. In the proposed work, the decorrelation stage reinforces...
With the development of mobile device and wireless networks, user location becomes increasingly valuable in enhancing user experience, system performance and resource allocation. Location-based services have been not only an important perspective of social media, but also a significant contributor to big data analysis. Location prediction, as an interesting topic, can help improve system performance...
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