Miracle-dz/UniFAR-FTUs huggingface dataset:
Shopping navigation system for visual impaired people based on proximity-based technology

At present, there are more than one million visually-impaired people in Thailand. This project has an objective for carrying out a system which helps and supports them for going shopping and products' property comparision by themselves. We developed an application with navigation function and barcode scanner in order to assist people with visual disability to solve the identified problem. From the experimental result for time spending on both navigation and barcode scanning, visually-impaired users spend most of the time on barcode scanning. After being trained how to use the application, the total time spent decreases to 29.94 %.
Information Technology and Organizational Design : Evidence from Micro Data October , 1998

While many theories about the relationship between information technology (IT) and organizational design have been proposed, there is little empirical evidence on the issue. We examine the influence of organizational design on the demand for IT and the productivity of IT investments, using data from approximately 380 US firms. We find greater demand for IT in firms with greater decentralization of decision rights (especially the use of self-managing teams), and greater investments in human capital, including training and screening by education. In addition, IT has a greater contribution to output in firms that adopt a more decentralized and human capital-intensive work system. This relationship is robust to alternative measures of IT and of work systems, as well as alternative specifications for demand and for productivity. These findings lend support to the idea that organizational practices are important determinants of IT demand and productivity.
Effect of Pain Neuroscience Education Combined With Cognition-Targeted Motor Control Training on Chronic Spinal Pain: A Randomized Clinical Trial

Importance Effective treatments for chronic spinal pain are essential to reduce the related high personal and socioeconomic costs. Objective To compare pain neuroscience education combined with cognition-targeted motor control training with current best-evidence physiotherapy for reducing pain and improving functionality, gray matter morphologic features, and pain cognitions in individuals with chronic spinal pain. Design, Setting, and Participants Multicenter randomized clinical trial conducted from January 1, 2014, to January 30, 2017, among 120 patients with chronic nonspecific spinal pain in 2 outpatient hospitals with follow-up at 3, 6, and 12 months. Interventions Participants were randomized into an experimental group (combined pain neuroscience education and cognition-targeted motor control training) and a control group (combining education on back and neck pain and general exercise therapy). Main Outcomes and Measures Primary outcomes were pain (pressure pain thresholds, numeric rating scale, and central sensitization inventory) and function (pain disability index and mental health and physical health). Results There were 22 men and 38 women in the experimental group (mean [SD] age, 39.9 [12.0] years) and 25 men and 35 women in the control group (mean [SD] age, 40.5 [12.9] years). Participants in the experimental group experienced reduced pain (small to medium effect sizes): higher pressure pain thresholds at primary test site at 3 months (estimated marginal [EM] mean, 0.971; 95% CI, –0.028 to 1.970) and reduced central sensitization inventory scores at 6 months (EM mean, –5.684; 95% CI, –10.589 to –0.780) and 12 months (EM mean, –6.053; 95% CI, –10.781 to –1.324). They also experienced improved function (small to medium effect sizes): significant and clinically relevant reduction of disability at 3 months (EM mean, –5.113; 95% CI, –9.994 to –0.232), 6 months (EM mean, –6.351; 95% CI, –11.153 to –1.550), and 12 months (EM mean, –5.779; 95% CI, –10.340 to –1.217); better mental health at 6 months (EM mean, 36.496; 95% CI, 7.998-64.995); and better physical health at 3 months (EM mean, 39.263; 95% CI, 9.644-66.882), 6 months (EM mean, 53.007; 95% CI, 23.805-82.209), and 12 months (EM mean, 32.208; 95% CI, 2.402-62.014). Conclusions and Relevance Pain neuroscience education combined with cognition-targeted motor control training appears to be more effective than current best-evidence physiotherapy for improving pain, symptoms of central sensitization, disability, mental and physical functioning, and pain cognitions in individuals with chronic spinal pain. Significant clinical improvements without detectable changes in brain gray matter morphologic features calls into question the relevance of brain gray matter alterations in this population. Trial Registration clinicaltrials.gov Identifier: NCT02098005
H∞ based robust observer for high-performance sensor-less direct vector control of AC driving systems

Due to the natural structure of the asynchronous motor, the estimation of the rotor or stator flux is essential for the synthesis of the control law for high-performance drive applications. Based on linear matrix inequality (LMI) framework, we propose, in this paper, an approach to provide a robust rotor flux observer of the three-phase induction motor. Furthermore, based upon the notion of quadratic H∞ performance, a robust self-gain scheduling observers design method results from the constraint satisfaction stability and takes into account certain performance specifications for all admissible trajectories of the rotor speed in the operating range. Finally, Digital simulations implemented in a Simulink environment demonstrate the validity and effectiveness of the designed state observer algorithm.
Designing and developing a literature-derived, population-based dietary inflammatory index

Abstract Objective To design and develop a literature-derived, population-based dietary inflammatory index (DII) to compare diverse populations on the inflammatory potential of their diets. Design Peer-reviewed primary research articles published through December 2010 on the effect of diet on inflammation were screened for possible inclusion in the DII scoring algorithm. Qualifying articles were scored according to whether each dietary parameter increased (+1), decreased (−1) or had no (0) effect on six inflammatory biomarkers: IL-1β, IL-4, IL-6, IL-10, TNF-α and C-reactive protein. Setting The Dietary Inflammatory Index Development Study was conducted in the Cancer Prevention and Control Program of the University of South Carolina in Columbia, SC, USA from 2011 to 2012. Results A total of ≈6500 articles published through December 2010 on the effect of dietary parameters on the six inflammatory markers were screened for inclusion in the DII scoring algorithm. Eleven food consumption data sets from countries around the world were identified that allowed individuals’ intakes to be expressed relative to the range of intakes of the forty-five food parameters observed across these diverse populations. Qualifying articles (n 1943) were read and scored based on the forty-five pro- and anti-inflammatory food parameters identified in the search. When fit to this composite global database, the DII score of the maximally pro-inflammatory diet was +7·98, the maximally anti-inflammatory DII score was −8·87 and the median was +0·23. Conclusions The DII reflects both a robust literature base and standardization of individual intakes to global referent values. The success of this first-of-a-kind attempt at relating intakes of inflammation-modulating foods relative to global norms sets the stage for use of the DII in a wide variety of epidemiological and clinical studies.
An Effective Document Search Technique by Semantic Relationship Approach

Office applications are becoming a major pillar of today’s organizations since they are used to edit a vast amount of digital documents. Finding these office documents in large databases that fit users’ needs is becoming increasingly important. Traditional search tools that employ keyword and phrase matching between the query and search index alone tend to offer high recall and low precision. The search users are faced with too many irrelevant results. In order to solve this problem, we propose a novel search system that effectively searches the target documents by the search query whose definition is based on the document type, search terms and the semantic relationship between the search terms and the target documents. We present a technique that collects search terms and their semantic relationship from the documents of some office applications to generate the XML-based search indices that can effectively locate the office documents. Furthermore, our query optimization algorithm is also presented.
Generalized Theoretical Model of Relationship Between Frame-Rate and Bit-Rate Considering Low Pass Filtering Induced by Shutter Opening

Higher frame-rates are being considered to achieve more realistic representations. Since increasing the frame-rate increases the total amount of information, efficient coding methods are required. However, the statistical properties of such data has not been clarified. This paper establishes, for high frame-rate video, a mathematical model of the relationship between frame-rate and bit-rate. The model incorporates the effect of the low-pass filtering induced by shutter open. By incorporating the open interval of shutter, our model can be extended to describes the various cases of downsampling frame-rates. A coding experiment confirms the validity of the mathematical model.
Research on algorithm of Chinese BBS topic detection based on content analysis

Through analyzing and studying the BBS topic model, topic similarity, topic inspection, topic evaluation standards and topic developing trends, This paper designs and implements the Chinese BBS topic detection algorithm based on the content analysis, which includes obtaining BBS information by web crawler, processing BBS information based on the URL and Xpath page templates, realizing BBS information participle by ICTLAS, clustering BBS topic by Carrot2, analyzing hot topic based on the power spectrum and predicting of BBS topic based on time series. Finally, this paper developed the Chinese BBS Topic detection system used J2EE development kit, based on the eclipse integrated development environment, combined with Hibernate and GWT technology, and getting good results by tested in various BBS forums.
Online public deliberation in China: evolution of interaction patterns and network homophily in the Tianya discussion forum

ABSTRACT Internet discussion platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique context. Adopting the theoretical lens of public deliberation, this paper investigates the evolution of patterns of similar-minded and different-minded interactions over time on a Chinese online discussion forum. We analyse the content and reply networks of 18,000+ messages on four highly debated topics on the Bulletin Board System (BBS) platform Tianya. Findings provide nuanced evidence to the phenomenon of increased network homophily over time, mitigated in discussions where the difference between opinion sides is smaller, and participants have positive opinions. Providing empirical foundation for testing and refining the tenets of public deliberation in the unique socio-cultural and institutional environment of China, this study lays ground for future investigation on independent variables for understanding dynamics of online discussions, and for studies comparing cases across different contexts.
Neuro-Fuzzy Nonlinear Dynamical System Approximations using High Order Neural Networks

A new definition of Adaptive Neuro Fuzzy Systems is presented in this paper for the identification of unknown nonlinear dynamical systems. The proposed scheme uses the concept of Adaptive Fuzzy Systems (AFS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an adaptive fuzzy system and in the sequel the fuzzy rules are approximated by appropriate HONNFs. Thus the identification scheme leads up to a Recurrent High Order Neural Network, which however takes into account the fuzzy output partitions of the initial AFS. The proposed scheme does not require a-priori experts’ information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Weight updating laws for the involved HONNFs are provided, which guarantee that the identification error reaches zero exponentially fast. Simulations illustrate the potency of the method and comparisons with well known benchmarks are given.
Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions

Background: Social media have drawn attention for their potential use in Pharmacovigilance. Recent work showed that it is possible to extract information concerning adverse drug reactions (ADRs) from posts in social media. The main objective of the Vigi4MED project was to evaluate the relevance and quality of the information shared by patients on web forums about drug safety and its potential utility for pharmacovigilance. Methods: After selecting websites of interest, we manually evaluated the relevance of the content of posts for pharmacovigilance related to six drugs (agomelatine, baclofen, duloxetine, exenatide, strontium ranelate, and tetrazepam). We compared forums to the French Pharmacovigilance Database (FPVD) to (1) evaluate whether they contained relevant information to characterize a pharmacovigilance case report (patient’s age and sex; treatment indication, dose and duration; time-to-onset (TTO) and outcome of the ADR, and drug dechallenge and rechallenge) and (2) perform impact analysis (nature, seriousness, unexpectedness, and outcome of the ADR). Results: The cases in the FPVD were significantly more informative than posts in forums for patient description (age, sex), treatment description (dose, duration, TTO), and outcome of the ADR, but the indication for the treatment was more often found in forums. Cases were more often serious in the FPVD than in forums (46% vs. 4%), but forums more often contained an unexpected ADR than the FPVD (24% vs. 17%). Moreover, 197 unexpected ADRs identified in forums were absent from the FPVD and the distribution of the MedDRA System Organ Classes (SOCs) was different between the two data sources. Discussion: This study is the first to evaluate if patients’ posts may qualify as potential and informative case reports that should be stored in a pharmacovigilance database in the same way as case reports submitted by health professionals. The posts were less informative (except for the indication) and focused on less serious ADRs than the FPVD cases, but more unexpected ADRs were presented in forums than in the FPVD and their SOCs were different. Thus, web forums should be considered as a secondary, but complementary source for pharmacovigilance.
Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks

Head pose estimation and tracking is useful in variety of medical applications. With the advent of RGBD cameras like Kinect, it has become feasible to do markerless tracking by estimating the head pose directly from the point clouds. One specific medical application is robot assisted transcranial magnetic stimulation (TMS) where any patient motion is compensated with the help of a robot. For increased patient comfort, it is important to track the head without markers. In this regard, we address the head pose estimation problem using two different approaches. In the first approach, we build upon the more traditional approach of model based head tracking, where a head model is morphed according to the particular head to be tracked and the morphed model is used to track the head in the point cloud streams. In the second approach, we propose a new multi-scale convolutional neural network architecture for more accurate pose regression. Additionally, we outline a systematic data set acquisition strategy using a head phantom mounted on the robot and ground-truth labels generated using a highly accurate tracking system.
Explore the associated MiRNAs with emphysema severity of chronic obstructive pulmonary disease

MicroRNAs (MiRNA) are small non-coding RNAs that regulate gene expression. Up to date, seventy miRNAs have been found differentially expressed in lung whole tissue between smoking patients affected by Chronic Obstructive Pulmonary Disease (COPD) and smokers. The aim of this study was to explore the associated miRNAs with emphysema severity of COPD. Firstly, we identified miRNAs differentially expressed between mild and moderately emphysematous lung. Secondly, we used a new mutual information estimation method to construct the miRNA-miRNA interaction network based on these differentially expressed miRNAs. Then we performed the network structure analysis and extracted the COPD-related hub miRNAs. Finally, we compared the classification accuracy of the identified hub miRNAs and the differentially expressed miRNAs. As a result, we identified 4 hub miRNAs: miR-200b, miR-92b, miR-511 and miR-449b. miR-200b and miR-92b are up-regulated in mild emphysema patients whereas miR-511 and miR-449b are down-regulated in mild emphysema patients. In addition, as the predictors to classify samples, the results also showed that the classification accuracy rate of 4 identified hub miRNAs was higher than 156 differentially expressed miRNAs.
A Generalization of LSB Matching

Recently, a significant improvement of the well-known least significant bit (LSB) matching steganography has been proposed, reducing the changes to the cover image for the same amount of embedded secret data. When the embedding rate is 1, this method decreases the expected number of modification per pixel (ENMPP) from 0.5 to 0.375. In this letter, we propose the so-called generalized LSB matching (G-LSB-M) scheme, which generalizes this method and LSB matching. The lower bound of ENMPP for G-LSB-M is investigated, and a construction of G-LSB-M is presented by using the sum and difference covering set of finite cyclic group. Compared with the previous works, we show that the suitable G-LSB-M can further reduce the ENMPP and lead to more secure steganographic schemes. Experimental results illustrate clearly the better resistance to steganalysis of G-LSB-M.
The Fast and Easy Way for Double-Lumen Tube Intubation: Individual Angle-Modification

To find the faster and easier way than the existing intubating technique for double-lumen tube, we modified the angle of double-lumen tube according to an individual’s upper airway anatomy and compared the time needed and the number of attempts for successful intubation between individually angle-modified and non-modified double-lumen tubes. Adult patients undergoing elective thoracic surgery were randomly allocated in either non-angle-modified (Group N, n = 54) or angle-modified (Group M, n = 54) groups. During mask ventilation in the sniffing position, angle-modification was performed in Group M as follows: the distal tip of the tube was placed at the level of the cricoid cartilage and the shaft was bent at the intersection of the oral and pharyngeal axes estimated from the patient’s surface anatomy. The time needed and the number of attempts for successful intubation and Cormack and Lehane (C-L) grade were recorded. Overall median intubation time (sec) was significantly shorter in Group M than in Group N [10.2 vs. 15.1, P<0.001]. In addition, Group M showed the shorter median intubation time (sec) in C-L grades I-III [8.2 vs. 11.1 in C-L grade I, (P = 0.003), 10.3 vs. 15.3 in II, (P = 0.001), and 11.8 vs. 27.9 in III, (P<0.001), respectively]. Moreover, all intubation was successfully performed at the first attempt in patients with C-L grades I-III in Group M (P = 0.027). Our study showed an individual angle-modification would be useful for the fast and easy intubation of double-lumen tube in patients with C-L grades I-III. Trial Registration: ClinicalTrials.gov NCT02190032
Exercise-Induced Synaptogenesis in the Hippocampus Is Dependent on UCP2-Regulated Mitochondrial Adaptation

Mitochondria are essential organelles in neurons providing appropriate energetic needs to maintain resting and action potentials as well as to modulate synaptic plasticity. Although neuronal events underlie various behavioral events, the behavior itself, such as voluntary exercise, feeds back to affect neuronal morphology and function as well as glial morphology and function. The hippocampal formation is a main site of synaptic plasticity induced by voluntary exercise. Here we show that voluntary exercise induces uncoupling protein 2 (UCP2) mRNA expression and mitochondrial oxygen consumption in coupled as well as uncoupled respiratory states in the hippocampus. These changes in mitochondrial metabolism coincided with an increase in mitochondrial number and dendritic spine synapses in granule cells of the dentate gyrus and the stratum radiatum of the CA1 region and were dependent on UCP2 expression, because in UCP2 knock-out mice such changes were not observed. Together, these observations reveal that a mitochondrial mechanism related to UCP2 function is essential for appropriate bioenergetic adaptation of neurons to increased neuronal activity and synaptic plasticity in response to exercise.
Client-Server Password Recovery (Extended Abstract)

Human memory is not perfect - people constantly memorize new facts and forget old ones. One example is forgetting a password, a common problem raised at IT help desks. We present several protocols that allow a user to automatically recover a password from a server using partial knowledge of the password. These protocols can be easily adapted to the personal entropy setting, where a user can recover a password only if he can answer a large enough subset of personal questions. We introduce client-server password recovery methods, in which the recovery data are stored at the server, and the recovery procedures are integrated into the login procedures. These methods apply to two of the most common types of password based authentication systems. The security of these solutions is significantly better than the security of presently proposed password recovery schemes. Our protocols are based on a variation of threshold encryption that may be of independent interest.
Linked Data Games: Simulating Human Association with Linked Data

Teaching machines to understand human communication is one of the central goals of artificial intelligence. Psychological research indicates that human associations are an essential requirement to understand human communication. In this paper the hypothesis is presented that simulating human associations with the help of Linked Data could improve text understanding capabilities of machines. To investigate whether human associations can be simulated with Linked Data, two preliminary problems are identified: (i) A reasonable ground truth for human associations is lacking and (ii) human associations have different strengths while Linked Data treats all triples equally and does not provide edge weights. To overcome these problems, two ideas for web games in accordance with Luis von Ahn’s Games with a Purpose are proposed trying to turn the tedious acquisition processes into fun games. The resulting datasets are then to be used for quantitative comparisons of human associations and Linked Data.