Highly accurate model for prediction of lung nodule malignancy with CT scans.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN).

Personalized response generation by Dual-learning based domain adaptation.

Open-domain conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. The goal of this paper is to further improve the response generation, using personalization criteria. We propose a novel method called PRGDDA (Personalized Response Generation by Dual-learning based Domain Adaptation) which is a personalized response generation model based on theories of domain adaptation and dual learning.

Is it Suitable for a Journal to Bid for Publishing a Review That is Likely to be Highly Cited?

By following a recently published paper entitled "The effect of publishing a highly cited paper on a journal's impact factor: a case study of the Review of Particle Physics" in Learned Publishing, we argue that it is not suitable for journals to bid for the right to publish a review that is likely to be highly cited. A few suggestions are also provided to deal with the special case of the Review of Particle Physics phenomenon.

SparRec: An effective matrix completion framework of missing data imputation for GWAS.

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new.

Identification of serum miRNAs by nano-quantum dots microarray as diagnostic biomarkers for early detection of non-small cell lung cancer.

Circulating microRNAs (miRNAs) are potential noninvasive biomarkers for cancer detection. We used preoperative serum samples from non-small cell lung cancer (NSCLC) patients and healthy controls to investigate whether serum levels of candidate miRNAs could be used as diagnostic biomarkers in patients with resectable NSCLC and whether they were associated with clinicopathologic characteristics. We initially detected expression of 12 miRNAs using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in preoperative serum samples of 94 NSCLC patients and 58 healthy controls.

Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing.

Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer's loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer's satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer.

Explore Awareness of Information Security: Insights from Cognitive Neuromechanism.

With the rapid development of the internet and information technology, the increasingly diversified portable mobile terminals, online shopping, and social media have facilitated information exchange, social communication, and financial payment for people more and more than ever before. In the meantime, information security and privacy protection have been meeting with new severe challenges. Although we have taken a variety of information security measures in both management and technology, the actual effectiveness depends firstly on people's awareness of information security and the cognition of potential risks.

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs.

In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial.

New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images.

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position.

A global optimization approach to multi-polarity sentiment analysis.

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis.

Information analytics for healthcare service discovery.

The concept of being 'patient-centric' is a challenge to many existing healthcare service provision practices. This paper focuses on the issue of referrals, where multiple stakeholders, such as General Practitioners (GPs) and patients, are encouraged to make a consensual decision based on patients' needs. In this paper, we present an ontology-enabled healthcare service provision, which facilitates both patients and GPs in jointly deciding upon the referral decision.

Modeling and computing of stock index forecasting based on neural network and Markov chain.

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events.

Information spread of emergency events: path searching on social networks.

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm.