Miracle-dz/UniFAR-FTUs huggingface dataset:
Finance time series prediction using complex-valued flexible neural tree model

In this paper, we present a novel time series prediction model based on complex-valued flexible neural tree (CVFNT) model to improve the forecasting accuracy. In a CVFNT model, data, parameters and activation functions are complex-valued. The evolutionary method based on the modified genetic algorithm (GP) and artificial bee colony (ABC) is used to evolve the CVFNT model. Two real time series datasets from Shanghai stock index and exchange rates between Euro and US Dollar are used to test CVFNT model. Results reveal that our proposed method can predict more accurately finance time series than real-valued classic neural networks (RVNN) and complex-valued neural network (CVNN).
Generative Models for Pose Transfer

We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding frames and generalizing outside the demonstrated action set. Our most salient contribution is determining a pipeline (pose detection, face detection, k-NN based pairing) that is effective at perform-ing the desired task. We also detail several iterative improvements and failure modes.
Characterization of neutralizing epitopes in antigenic site B of recently circulating influenza A(H3N2) viruses

Influenza A(H3N2) viruses are associated with outbreaks worldwide and can cause disease with severe complications. The impact can be reduced by vaccination, which induces neutralizing antibodies that mainly target the haemagglutinin glycoprotein (HA). In this study we generated neutralizing mouse monoclonal antibodies (mAbs) against A/Victoria/361/2011 and identified their epitopes by generating and sequencing escape viruses. The epitopes are located in antigenic site B, which is near the receptor-binding site and is immunodominant in humans. Amino acid (aa) substitutions at positions 156, 158, 159, 189, 190 and 193 in antigenic site B led to reduced ability of mAbs to block receptor-binding. The majority of A(H3N2) viruses that have been circulating since 2014 are antigenically distinct from previous A(H3N2) viruses. The neutralization-sensitive epitopes in antigenic site B of currently circulating viruses were examined with these mAbs. We found that clade 3C.2a viruses, possessing an additional potential glycosylation site at HA1 position N158, were poorly recognized by some of the mAbs, but other residues, notably at position 159, also affected antibody binding. Through a mass spectrometric (MS) analysis of HA, the glycosylated sites of HA1 were established and we determined that residue 158 of HA1 was glycosylated and so modified a neutralization-sensitive epitope. Understanding and monitoring individual epitopes is likely to improve vaccine strain selection.
Human Classification , Activity Recognition , Object Detection and Human Object Interaction

​—Human detection, tracking and activity recognition is an important area of research with applications ranging from entertainment, robotics, surveillance and elderly care. In this project, I implemented and developed human activity and object classification algorithms using Microsoft Kinect v1 and MATLAB R2016b/R2017a in Windows OS. The dataset used for activity classification was CAD-60 with 17 activities performed by 4 people and manually obtained dataset to detect microwave. Cascade object classifier using multiple stages of one-level decision trees was used while 10-layer neural network was used for activity classification in real time. The neural network has an accuracy of 99.9% and was tested with real data as well. The results of the detection are included in the report. The two classifiers were combined to detect human object interaction based on proximity. Keywords—Activity classification, Human classification, Object classification, Human-Object Interaction
Does Deeper Network Lead to Better Accuracy: A Case Study on Handwritten Devanagari Characters

Deep neural network architectures have been used successfully in various document analysis studies. Its strength in producing human like performance has already been explored in handwritten English numeral recognition task. In this context, a natural question that often arises in a practitioner's mind: does an increase in the depth of the network eventually lead to an improved recognition performance on unknown samples? A goal of the present work is to search for an answer of the same through a case study of a larger class handwriting recognition problem. Here, we have studied recognition of handwritten Devanagari characters. In this study, we have implemented convolutional neural network (CNN) architectures of five different depths. We have also implemented additional neural architectures by adding two Bidirectional Long Short Term Memory (BLSTM) layers between the convolutional stack and the fully connected part of each of these five CNN networks. Simulations have been performed on two different databases of handwritten Devanagari characters consisting of 30408 and 36172 samples and a combined set consisting of 58451 samples. The recognition accuracy obtained in the best case improves significantly the existing state-of-the-art of this handwriting recognition problem. Also, further analysis of our simulation results provides an answer to the above question. Additionally, we have trained a BLSTM network alone using the Histogram of Oriented Gradient (HOG) features. Performance of this architecture failed to compete with the performance of CNN-BLSTM hybrid architecture.
Phylogenetic divergence of cell biological features

Most cellular features have a range of states, but understanding the mechanisms responsible for interspecific divergence is a challenge for evolutionary cell biology. Models are developed for the distribution of mean phenotypes likely to evolve under the joint forces of mutation and genetic drift in the face of constant selection pressures. Mean phenotypes will deviate from optimal states to a degree depending on the effective population size, potentially leading to substantial divergence in the absence of diversifying selection. The steady-state distribution for the mean can even be bimodal, with one domain being largely driven by selection and the other by mutation pressure, leading to the illusion of phenotypic shifts being induced by movement among alternative adaptive domains. These results raise questions as to whether lineage-specific selective pressures are necessary to account for interspecific divergence, providing a possible platform for the establishment of null models for the evolution of cell-biological traits.
An in silico performance characterization of respiratory motion guided 4DCT for high-quality low-dose lung cancer imaging

This work aims to characterize the performance of an improved 4DCT technique aiming to overcome irregular breathing-related image artifacts. To address this, we have developed respiratory motion guided (RMG) 4DCT, which uses real-time breathing motion analysis to prospectively gate scans based on detection of irregular breathing. This is the first investigation of RMG-4DCT using a real-time software prototype, testing the hypothesis that it can reduce breathing irregularities during imaging, reduce image oversampling and improve image quality compared to a ‘conventional’ 4DCT protocol without breathing guidance. RMG-4DCT scans were simulated based on 100+ hours of breathing motion acquired for 20 lung cancer patients. Scan performance was quantified in terms of the beam on time (a surrogate for imaging dose), total scan time and the breathing irregularity during imaging (via RMSE of the breathing motion during acquisition). A conventional 4DCT protocol was also implemented using the same software prototype for a direct comparator to the RMG-4DCT results. We investigated the impact of key RMG-4DCT parameters such as gating tolerance, gantry rotation time and the use of baseline drift correction. Using a representative set of algorithm parameters, RMG-4DCT achieved significant mean reductions in estimated imaging dose (−17.8%, p < 0.001) and breathing RMSE during imaging (−12.6%, p < 0.001) compared to conventional 4DCT. These improvements came with increased scan times, roughly doubled on average (104%, p < 0.001). Image quality simulations were performed using the deformable digital XCAT phantom, with image quality quantified based on the normalized cross correlation (NCC) between axial slices. RMG-4DCT demonstrated qualitative image quality improvements for three out of 10 phase bins, however the improvement was not significant across all 10 phases (p = 0.08) at a population level. In choosing RMG-4DCT scan parameters, the trade-off between gating sensitivity and scan time may be optimized, demonstrating potential for RMG-4DCT as a viable pathway to improve clinical 4DCT imaging.
Assessing markers from ambulatory laboratory tests for predicting high-risk patients.

OBJECTIVES This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. STUDY DESIGN Observational study of a patient cohort over 2 years. METHODS We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced models. RESULTS Our study population included 120,844 patients. Adding laboratory markers to base models improved R2 predictions of costs by 0.1% to 3.7%, identification of high-cost patients by 3.4% to 121%, and identification of patients with inpatient admissions by 1.0% to 188% for the demographic model. The addition of laboratory risk markers to comprehensive risk models, compared with simpler models, resulted in smaller improvements in predictive power. CONCLUSIONS The addition of laboratory risk markers can significantly improve the identification of high-risk patients using models that include age, gender, and a limited number of morbidities; however, models that use comprehensive risk measures may be only marginally improved.
Conversation Trainings - Towards a Multidimensional Visualization of Learning Flows

Conversation trainings are an e-learning method designed for the transfer of communication-related skills. With increasing conversation length and number of learners, creating and evaluating conversations becomes a complex task that needs appropriate visual guidance and data visualization. In this paper we show the inherent complexities of conversation trainings and we will guide through an approach that eases the conversation creation. We will explain data collection with Experience API and finally we present a Sankey Diagrams based approach to visualize large amounts of collected learning data.
A novel approach for paper recommendation based on rough-fuzzy set theory

Nowadays, recommending relevant and valuable academic papers has drawn great attentions from academic researchers. However, most of time paper recommendation still relies on the keywords given by users as input. Previous studies have shown that this kind of method are not effective because they did not take semantic similarity into consideration. To address the problem, in this paper we conduct similarity analysis based on synoptic content including the titles and abstracts between the papers referred by users and the ones in paper dataset, and then recommend scientific papers to the users. We use the TF-IDF to pick out the important words of the titles and abstracts in papers, and apply rough-fuzzy set method as well as WordNet to calculate the similarity values of candidate papers. After ranking the papers based on the values, we can get the paper recommendation result. The experiments on the public available dataset have demonstrated the superiority of our proposed method over other baselines.
Data-driven and semantic-based pedestrian re-identification

Pedestrian Re-identification faces many difficulties in training of supervised model because of limited number of labeled data of surveillance videos. Besides, applications of pedestrian re-identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data-driven pedestrian re-identification model based on hierarchical semantic representation is proposed, this model extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid-level attributes. Firstly, CNNs, well-trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes-classes mapping relations’, final result can be calculated. Our method is proved to significantly outperform the state of the art on the VIPeR and i-LIDS data set in the aspects of accuracy and semanteme.
A double-blind crossover study in moderately hypercholesterolemic men that compared the effect of aged garlic extract and placebo administration on blood

A double-blind crossover study comparing the effect of aged garlic extract with a placebo on blood lipids was performed in a group of 41 moderately hypercholesterolemic men [cholesterol concentrations 5.7-7.5 mmol/L (220-290 mg/dL) ]. After a 4-wk baseline period, during which the subjects were advised to adhere to a National Cholesterol Education Program Step I diet, they were started on 7.2 g aged garlic extract per day or an equivalent amount of placebo as a dietary supplement for a period of 6 mo, then switched to the other supplement for an additional 4 mo. Blood lipids, blood counts, thyroid and liver function measures, body weight, and blood pressure were followed over the entire study period. The major findings were a maximal reduction in total serum cholesterol of 6. 1 % or 7.0% in comparison with the average concentration during the placebo administration or baseline evaluation period, respectively. Low-density-lipoprotein cholesterol was also decreased by aged garlic extract, 4% when compared with average baseline values and 4.6% in comparison with placebo period concentrations. In addition, there was a 5.5% decrease in systolic blood pressure and a modest reduction of diastolic blood pressure in response to aged garlic extract. We conclude that dietary supplementation with aged garlic extract has beneficial effects on the lipid profile and blood pressure of moderately hypercholesterolemic subjects. Am J Clin Nutr 1996;
Differences in Maturity, Morphological, and Fitness Attributes Between the Better- and Lower-Ranked Male and Female U-14 Portuguese Elite Regional Basketball Teams.

Ramos, S, Volossovitch, A, Ferreira, AP, Barrigas, C, Fragoso, I, and Massuça, L. Differences in maturity, morphological and fitness attributes between the better- and lower-ranked male and female U-14 Portuguese elite regional basketball teams. J Strength Cond Res XX(X): 000-000, 2018-During a national championship, the anthropometric, physiological, and maturation attributes of 13-14-year-old players of regional selection basketball teams and their association with team's final classification were analyzed. Body parameters (weight, height, skinfolds, and lengths) were measured and physiological capacities assessed by sprint (20 m), agility, jump, and upper-body strength tests. Chronological age, maturity offset (years from age at peak height velocity; YAPHV), and predicted age at peak height velocity were calculated. In both sexes, no significant differences were found for maturity. Anthropometric and physiological analysis indicated that male players from finalist's teams were significantly faster, were more agile, threw the medicine ball longer, and showed lower percentages of body fat than players from lower-ranked teams. Further, semifinalists were faster than lower-ranked players. In the same sense, female players from semifinalist teams demonstrated to be significantly faster, more agile, and threw the medicine ball longer than female players from lower-ranked teams. In addition, discriminant analysis showed that speed (in boys) and the combination of speed and upper limb strength (in girls) could successfully discriminate players with different performances in a championship. In conclusion, speed and upper limb strength appear to be the physical attributes associated with better team performance in youth basketball. Coaches should be aware of the relationship between the improvement of these physical attributes and the teaching-learning process of the basketball game.
Threshold algorithm for pancreas segmentation in Dixon water magnetic resonance images

Pancreas segmentation is crucial for a computer-aided diagnosis (CAD) system to provide cancer detection and radiation therapy of pancreatic cancer. Because of anatomically high-variability between subjects, achieving high accuracies in pancreas segmentation remains a challenging task. In this work, based on Otsu threshold method and morphological method, we first proposed a segmentation pipeline for pancreas, using Dixon water magnetic resonance image (MRI) data from five healthy volunteers. The threshold method was used to obtain the approximate outline of the pancreas, and the morphological method was used to separate the pancreas from the surrounding tissues. The segmentation results were compared with manual contours using Dice Index (DI) and we achieved DI: 0.80 ± 0.08 which was better than the level Set Methods (LSMs) DI: 0.64 ± 0.08. The proposed method was simple and easy to integrate with the Medical Imaging Interaction Toolkit (MITK) workbench, so it provided an efficient and simple segmentation method for processing large clinical datasets.
Research on influential factors of E-commerce recommendation user behavior intention

Exiting study on E-Commerce recommendation system focus on addressing its low adoption from from a technical point of view, and less analyzing from Chinses users' behavior in B2C E-Commerce website where the users' habits and behavior is special. So we propose the user adoption model of e-commerce recommendation system based on TAM model. Fistly, we use the behavior intention to express the customer adoption behavior for online shopping behavior is difficult to. Then, we use the concept of compatibility in the IDT model, which is the influence factor on usefulness and ease of use. The study found that, as external factor, compatibility affects the user perceived usefulness and ease of use, perceived ease of use significantly affect the usefulness, usefulness and ease of use are important factors affecting the user adoption of e-commerce recommendation system, then we provide comments and suggestions on the recommendation system for the e-commerce business.
Object classification in underwater images using adaptive fuzzy neural network

Classification in underwater imagesis a challenging task as images are often captured inextreme environmental conditions with poor illumination, hazy background, etc. Ocean scientists who are involved in such analysis, prefer automatic classification as manual classification is costly and time consuming. Techniques based on intensity information alone may not result in accurate segmentation of underwater objects. Statistical features representing the texture information of the object and background is needed. A set of 14 texture features was computed for underwater images and features like autocorrelation, sum average, sum variance and sum entropy were able to accurately classify object of interest from background. A fuzzy neural network was designed and texture features were trained and tested for classification. The proposed adaptive fuzzy neural network obtained a maximum classification accuracy of 97%.
Hyperspectral image classification based on deep auto-encoder and hidden Markov random field

Hyperspectral Image (HSI) classification is one of the most persistent issue in remote sensing field. Recently, deep learning has attracted attention in HSI Classification field due to its accuracy and stronger generalization. This paper proposes a new spectral-spatial HSI classification approach developed on the deep learning concept of stacked-auto-encoders (SAE) based deep feature extraction and hidden Markov random field based segmentation. Specifically, First the SAE model is implemented as a spectral information-based classifier to extract the deep spectral features. Second, spatial information is obtained by using effective Hidden Markov random field (HMRF) based segmentation technique. Finally, maximum voting based criteria is employed to merge the extracted spectral and spatial information, which results in the precise spectral-spatial HSI classification. The characterization of the HSI with spectral spatial features results into more comprehensive analysis of HSI and to a more accurate classification. In general, use of spectral information resulted from the SAE process and spatial information by means of HMRF based segmentation and merging of spectral and spatial information by means of maximum voting based criteria, has a significant effect on the accuracy of the HSI classification. Experiments on real diverse hyperspectral data sets with different contexts and resolutions acquired by AVIRIS and ROSIS sensors show the accuracy of the proposed method and confirms that results of the proposed classification approach are comparable to several recently proposed HSI classification techniques.
Social Cues, Social Biases: Stereotypes in Annotations on People Images

Human computation is often subject to systematic biases. We consider the case of linguistic biases and their consequences for the words that crowdworkers use to describe people images in an annotation task. Social psychologists explain that when describing oth- ers, the subconscious perpetuation of stereotypes is in- evitable, as we describe stereotype-congruent people and/or in-group members more abstractly than others. In an MTurk experiment we show evidence of these bi- ases, which are exacerbated when an image’s “popular tags” are displayed, a common feature used to provide social information to workers. Underscoring recent calls for a deeper examination of the role of training data quality in algorithmic biases, results suggest that it is rather easy to sway human judgment.
Towards a semi-automatic method for building Chinese tax domain ontology

Domain Ontology engineering is the key challenge for knowledge based intelligent systems. This research focus on identifying potential opportunities and problems that may arise in applying ontology engineering techniques to the Chinese tax domain. In this paper we have practiced a middle-out method of modeling Chinese tax domain ontology, and presented a framework to build an ontology in Chinese tax domain, especially on domain ontology learning. We have developed a platform that provides 1) a layered ontology architecture for Chinese tax domain, 2) an integrated method to ontology learning, 3) a collaborate platform for experts interacting with extracted terms and relations, 4) an evaluation results for proposed methodology.