The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
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...
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...
We develop a novel approach to predict certain type of stability events (battles, battles won by a government, riots/protests, violence against civilians) in countries by monitoring the content of a mix of traditional news, blog, and social media data. Specifically, we show that by monitoring sentiment on both pro- and anti-government entities within a country, even with a relative paucity of longitudinal...
Data aggregation is an efficient way to prolong the lifetime of wireless sensor networks (WSNs) by reducing communication traffic. However, sensor nodes are usually deployed in harsh or hostile environments. They are easy to be malfunctioning or become compromised nodes, which makes the sensor data unreliable and affects the accuracy of data aggregation. This paper proposes a secure data aggregation...
Phenology is the study of periodic natural phenomena and their relationship to climate. Usually, phenology studies consider the identification of patterns on temporal data. In those studies, several phenological change patterns are often encoded in time series for analysis and knowledge extraction. In this paper, we evaluate the effectiveness of several time series similarity functions in the task...
In partial duplicate image retrieval systems, min-Hash algorithms are widely used because of its high efficiency and robustness. In most of min-Hash algorithms, min-Hash functions are considered independent and grouped into tuples called sketches, the discriminative power of sketches are limited. By modeling correlations of min-Hash functions, we propose a novel sketch construction method called Nonpara-metric...
In multi-media and social media communities, web topic detection poses two main difficulties that conventional approaches can barely handle: 1) there are large inter-topic variations among web topics; 2) supervised information is rare to identify the real topics. In this paper, we address these problems from the similarity diffusion perspective among objects on web, and present a clustering-like pattern...
This paper presents a comparison among three methods for Steady-State Visually Evoked Potentials (SSVEP) detection. These techniques are based on Power Spectral Density Analysis (PSDA) and Canonical Correlation Analysis (CCA). The first method estimates the signal-to-noise ratio of the power spectrum in each stimulus frequency using PSDA, which is called Traditional-PSDA. The second analysis estimates...
In recent years, based on the steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) have generated significant interest, due to their shorter calibration times and higher information transfer rates. Target identification is the core signal processing task in BCIs. Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are the most popular and widely...
This study examines the behavioral and neural mechanisms by which somatosensory training affects the human motor system. We have developed a technique, which combines psychophysical and neuroimaging procedures to examine plasticity in the resting-brain following perceptual learning in a somatosensory-discrimination task. We show that effects of perceptual learning are not local to sensory areas of...
To minimize slice excitation leakage to adjacent slices, interleaved slice acquisition is nowadays performed regularly in fMRI scanners. In interleaved slice acquisition, the number of slices skipped between two consecutive slice acquisitions is often referred to as the ‘interleave parameter’ the loss of this parameter can be catastrophic for the analysis of fMRI data. In this article we present a...
Query-URL relevance (QUR) is an important criterion to measure the quality of commercial search engines. However, the traditional way to collect high-quality QURs is time-consuming and labor-intensive since it is primarily based on human judges. To address these issues, numerous models have been studied to automatically infer the QURs. Unlike the prior studies in this literature, we first empirically...
The computation of the three-point correlation function (3PCF) is a critical challenge in astrophysics. An algorithm, named as RCSF (recursive convolution for scalar fields), has been proposed to solve 3PCF by using a filter matrix to reduce the computation load. In this paper, we accelerate the 3PCF by parallel implementation of RCSF. The proposed parallel algorithm, denoted as p-RCSF, splits the...
In this paper, a ensemble learning classification algorithm based on the novel feature selection method is proposed. The feature selection method takes full account of the discrimination and class information of each feature by calculating the scores. Specially, the scores are fused for getting a weight for each feature. We select the significant features according to the weights. The result of feature...
The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress...
Overproduce-and-select (OPAS) is a frequently used paradigm for building ensembles. In static OPAS, a large number of base classifiers are trained, before a subset of the available models is selected to be combined into the final ensemble. In general, the selected classifiers are supposed to be accurate and diverse for the OPAS strategy to result in highly accurate ensembles, but exactly how this...
Online reviews on a service are important sources for service providers to improve their service delivery and service consumers to obtain information for decision making before their service acquisition. However, in the real situation, there are several points of view (dimensions) in service assessment using online reviews. This paper shows an empirical study to apply classification-based sentiment...
Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder which is quite common in childhood, with an estimated prevalence of 5–8%, and often persists into adolescence and adulthood. It is further characterized as inappropriate developmentally symptoms of inattention, impulsiveness, motor over-activity and restlessness. The aim of this study is to evaluate the feasibility of diagnosing...
Sequence-based localization is a novel RF localization technique. The algorithm is achieved by constituting RSSI-based constraint tables and comparing data between two tables. But, the definitions of the constraint relation and the centroid in the algorithm are imperfect. In this paper, we present a new sequence localization method that involves with correlation metric and centroid. First, we use...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.