Narrow Results

In calculating band structure, the local density approximation and density functional theory are widely popular and do reproduce a lot of the basic physics. Regrettably, without some fine tuning, the local density approximation and density functional theory do not generally get the details of the experimental band structure correct, in particular the band gap in semiconductors and insulators is generally found to be too small when compared with experiment. For experimentalists using commercial packages to calculate the electronic structure of materials, some caution is indicated, as some long-standing problems exist with the local density approximation and density functional theory.

The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1s to 12s segments, was 12s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.

Traditional journal analyses of topic trends in IS journals have manually coded target articles from chosen time periods. However, some research efforts have been made to apply automatic bibliometric approaches, such as cluster analysis and probabilistic models, to find topics in academic articles in other research areas. The purpose of this study is thus to investigate research topic trends in Engineering Management from 1998 through 2017 using an LDA analysis model. By investigating topics in EM journals, we provide partial but meaningful trends in EM research topics. The trend analysis shows that there are hot topics with increasing numbers of articles, steady topics that remain constant, and cold topics with decreasing numbers of articles.

Online shopping is becoming more prevalent, with consumers turning to e-commerce platforms to search for information about the goods and services they need. Users will usually check other consumer reviews on the platform as a reference while shopping. Online retailers can collect and analyze these online reviews to monitor consumer opinions about product quality, logistics services, packaging and other attributes to provide an accurate basis for product improvement and service optimization. This paper applies the Latent Dirichlet Allocation (LDA) algorithm to extract the critical factors that affect consumer satisfaction. More than 30,000 reviews of seven kinds of 3C (computer, communication, and consumer electronic) product categories obtained by crawler technology are analyzed. Then, the DEMATEL-ANP (DANP) method is applied to the extracted framework to build a cause-and-effect diagram of 3C product satisfaction model. The innovative LDA-DANP hybrid model clarifies the causal influence of the evaluation dimensions for 3C products sold online. The results show that brand value is the most important dimension affecting consumer online product satisfaction. Appearance design, logistics awareness service and product performance also have a positive influence on perceived service and brand value. Finally, some management implications and practical suggestions are proposed.

The electronic structure and density of states (DOS) of BaMnO₃ compound are studied in the framework of density functional theory (DFT) using the generalized gradient approximation (GGA) and local density approximation (LDA). A number of different exchange-correlation functionals including hybrid (PBE, PZ and BLYP) exchange techniques have been used. The results show that in ambient conditions, the compound has metallic structure. It has been found from DOS calculations that the overlapping of bands near the Fermi energy are mainly due to the 3d state of Mn atoms.

The phonon spectra, band structure and density of states of cubic perovskite SnTiO₃ were investigated using first-principles density functional theory (DFT) computation. The potential energy curves of cations displacement and the formation energy of Sn substitution to B-site were calculated to estimate the structure stability. The results indicate that perovskite SnTiO₃ is a promising ferroelectric end member for lead-free piezoelectric materials and applications.

In Finger-Knuckle-Print (FKP) recognition, feature extraction plays a very important role in the overall system performance. This paper merges two types of the histograms of oriented gradients (HOG)-based features extracted from reflectance and illumination images for FKP-based identification. The Adaptive Single Scale Retinex (ASSR) algorithm has been used to extract the illumination and the reflectance images from each FKP image. Serial feature fusion is used to form a large feature vector for each user, and extract the distinctive features in the higher-dimension vector space. Finally, the cosine similarity distance measure is used for classification. The Hong Kong Polytechnic University (PolyU) FKP database is used during all of the tests. Experimental results show that our proposed system achieves better results than other state-of-the-art system.

Word adjacency networks constructed from written works reflect differences in the structure of prose and poetry. We present a method to disambiguate prose and poetry by analyzing network parameters of word adjacency networks, such as the clustering coefficient, average path length and average degree. We determine the relevant parameters for disambiguation using linear discriminant analysis (LDA) and the effect size criterion. The accuracy of the method is 74.9 ± 2.9% for the training set and 73.7 ± 6.4% for the test set which are greater than the acceptable classifier requirement of 67.3%. This approach is also useful in locating text boundaries within a single article which falls within a window size where the significant change in clustering coefficient is observed. Results indicate that an optimal window size of 75 words can detect the text boundaries.

Question-answering (QA) websites supply a quickly growing source of useful information in numerous areas. These platforms present novel opportunities for online users to supply solutions, they also pose numerous challenges with the ever-growing size of the QA community. QA sites supply platforms for users to cooperate in the form of asking questions or giving answers. Stack Overflow is a massive source of information for both industry and academic practitioners, and its analysis can supply useful insights. Topic modeling of Stack Overflow is very beneficial for pattern discovery and behavior analysis in programming knowledge. In this paper, we propose a framework based on the Latent Dirichlet Allocation (LDA) algorithm and fuzzy rules for question topic mining and recommending highlight latent topics in a community question-answering (CQA) forum of developer community. We consider a real dataset and use 170,091 programmer questions in the R language forum from the Stack Overflow website. Our result shows that LDA topic models via novel fuzzy rules can play an effective role for extracting meaningful concepts and semantic mining in question-answering forums in developer communities.

Following the Basel II Accord, with the increased focus on operational risk as an aspect distinct from credit and market risk, quantification of operational risk has been a major challenge for banks. This paper analyzes implications of the advanced measurement approach to estimate the operational risk. When modeling the severity of losses in a realistic manner, our preliminary tests indicate that classic distributions are unable to fit the entire range of operational risk data samples (collected from public information sources) well. Then, we propose a piecewise-defined severity distribution (PSD) that combines a parameter form for ordinary losses and a generalized Pareto distribution (GPD) for large losses, and estimate operational risk by the loss distribution approach (LDA) with Monte Carlo simulation. We compare the operational risk measured with piecewise-defined severity distribution based LDA (PSD-LDA) with those obtained from the basic indicator approach (BIA), and the ratios of operational risk regulatory capital of some major international banks with those of Chinese commercial banks. The empirical results reveal the rationality and promise of application of the PSD-LDA for Chinese national commercial banks.

We present results from ab-initio, self-consistent local density approximation (LDA) calculations of electronic and related properties of zinc blende indium phosphide (InP) and gallium phosphide (GaP). We employed a LDA potential and implemented the linear combination of atomic orbitals (LCAO) formalism. This implementation followed the Bagayoko, Zhao and Williams (BZW) method, as enhanced by Ekuma and Franklin (BZW–EF). This method searches for the optimal basis set that yields the minima of the occupied energies. This search entails increases of the size of the basis set and the related modifications of angular symmetry and of radial orbitals. Our calculated, direct band gap of 1.398 eV (1.40 eV), at the Γ point, is in excellent agreement with experimental values, for InP, and our preliminary result for the indirect gap of GaP is 2.135 eV, from the Γ to X high symmetry points. We have also calculated electron and hole effective masses for both InP and GaP. These calculated properties also agree with experimental findings. We conclude that the BZW–EF method could be employed in calculations of electronic properties of high-T_c superconducting materials to explain their complex properties.

Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.

Eye movement analysis provides a new way for disease screening, quantification and assessment. In order to track and analyze eye movement scanpaths under different conditions, this paper proposed the Gaussian mixture-Hidden Markov Model (G-HMM) modeling the eye movement scanpath during saccade, combing with the Time-Shifting Segmentation (TSS) method for model optimization, and also the Linear Discriminant Analysis (LDA) method was utilized to perform the recognition and evaluation tasks based on the multi-dimensional features. In the experiments, 800 real scene images of eye-movement sequences datasets were used, and the experimental results show that the G-HMM method has high specificity for free searching tasks and high sensitivity for prompt object search tasks, while TSS can strengthen the difference of eye movement characteristics, which is conducive to eye movement pattern recognition, especially for search tasks.

Classification techniques are routinely utilized on satellite images. Pansharpening techniques can be used to provide super resolved multispectral images that can improve the performance of classification methods. So far, these pansharpening methods have been explored only as a preprocessing step. In this work we address the problem of adaptively modifying the pansharpening method in order to improve the precision and recall figures of merit of the classification of a given class without significantly deteriorating the performance of the classifier over the other classes. The validity of the proposed technique is demonstrated using a real Quickbird image.