Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Metric Aggregation for HDR/WCG Images

High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to display images with brighter and darker pixels with more vivid colors than ever. Automatically assessing the quality of these HDR/WCG images is of critical importance to evaluate the performances of image compression schemes. In recent years, full-reference metrics, such as HDR-VDP-2, PU-encoding metrics, have been designed for this purpose. However, none of these metrics consider chromatic artifacts. In this paper, we propose our own full-reference quality metric adapted to HDR and WCG content that is sensitive to chromatic distortions. The proposed metric is based on two existing HDR quality metrics and color image features. A support vector machine regression is used to combine the aforementioned features. Experimental results demonstrate the effectiveness of the proposed metric in the context of image compression.
Temporal Difference Learning for Recurrent Neural Networks

Truncated back-propagation through time(TBPTT) is one of the most common methods for training artificial recurrent neural networks(RNNs) for temporal credit assignment(TCA). There have been various proposed theories on how neural circuits in the brain might approximate back-propagation algorithm in solving credit assignment problem for feed forward neural networks, but it remains unclear how the equivalent of TBPTT could be implemented in brains. Temporal difference learning with eligibility traces is a key universal approach used in reinforcement learning for multi-step value prediction problems(TCA). In this work, we apply TD learning with eligibility trace to train RNNs which is biologically plausible as it encodes errors locally,does not need a separate backward computation and hence does not have an issue of weight symmetry.
Network structure of neural systems supporting cascading dynamics predicts stimulus propagation and recovery

Many neural systems display cascading behavior characterized by uninterrupted sequences of neuronal firing. When the distributions of cascade size and duration follow a power law, theoretical models suggest that such dynamics support optimal information transmission and storage. However, the unknown role of network structure on neural dynamics precludes an understanding of how variations in network structure either support or impinge upon information processing. Here, we develop a theoretical understanding of how network structure supports information processing through network dynamics and validate our theory with empirical data. Using a generalized spiking model and mathematical intuitions from linear systems theory, network control theory, and information theory, we show how network structure can be designed to temporally extend the propagation and recovery of certain stimulus patterns. Moreover, we observe cycles as structural and dynamic motifs that are prevalent in such networks. Broadly, our results demonstrate how network structure constrains cascading dynamics and supports persistent activation that could potentially contribute to cognitive faculties, such as working memory or attention.
Categorized Bandits

We introduce a new stochastic multi-armed bandit setting where arms are grouped inside ``ordered'' categories. The motivating example comes from e-commerce, where a customer typically has a greater appetence for items of a specific well-identified but unknown category than any other one. We introduce three concepts of ordering between categories, inspired by stochastic dominance between random variables, which are gradually weaker so that more and more bandit scenarios satisfy at least one of them. We first prove instance-dependent lower bounds on the cumulative regret for each of these models, indicating how the complexity of the bandit problems increases with the generality of the ordering concept considered. We also provide algorithms that fully leverage the structure of the model with their associated theoretical guarantees. Finally, we have conducted an analysis on real data to highlight that those ordered categories actually exist in practice.
A Deep Clustering Algorithm based on Gaussian Mixture Model

Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm.
Learner engagement, retention and success: why size matters in massive open online courses (MOOCs)

ABSTRACT Massive open online courses (MOOCs) are usually six to ten weeks long. Participation tends to decrease as the courses progress, leading to low completion rates. This led to the question: Could shorter MOOCs contribute to learners’ engagement, retention and success? This paper compares two versions of Study Skills MOOC, which shared the same content but were delivered in different length formats. One was deployed as a single six-week course and the other as two three-week blocks. In total, 617 people registered for the two versions. Data sources included learning analytics, surveys and the Spanish version of the General Self-Efficacy Scale. Both versions of the Study Skills MOOC resulted in increased participants’ self-efficacy. However, learners enrolled in the version composed of two three-week blocks were also more engaged with course content, other students and the facilitators. Their retention and completion rates were higher than those in the longer version of the course. Reasons linked to goal proximity, motivation, interactions and social modelling are discussed
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis

Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the words in the tail of a sentence are more heavily emphasized than those in the header for building sentence representations. Even tree-LSTM, with useful structural information, could not avoid the bias problem because the root node will be dominant and the nodes in the bottom of the parse tree will be less emphasized even though they may contain salient information. To overcome the bias problem, this study proposes a capsule tree-LSTM model, introducing a dynamic routing algorithm as an aggregation layer to build sentence representation by assigning different weights to nodes according to their contributions to prediction. Experiments on Stanford Sentiment Treebank (SST) for sentiment classification and EmoBank for regression show that the proposed method improved the performance of tree-LSTM and other neural network models. In addition, the deeper the tree structure, the bigger the improvement.
Research and Application of Data Mining in Chronic Diseases

In recent years, with the acceleration of people's pace of life, the number of chronic diseases in China is increasing. The attention and investment of the country to the medical industry is increasing year by year. At the same time, with the maturity and perfection of data mining technology, many countries have applied this technology to the research and mining of medical data. In this paper, the Apriori algorithm of data mining technology, and improve the data format of the Apriori algorithm is applied to the prediction of nephropathy, establish the association rules between chronic disease and a number of physical data by the algorithm, and the experimental results proved that Apriori algorithm is effective in the medical data mining.
Effects on cognition of the burn after reading principle in ephemeral media applications

ABSTRACT Ephemeral media platforms allow users to send content in a format that automatically deletes the content after the recipient has accessed it – a phenomenon known as the ‘burn after reading’ principle. This study investigates whether awareness of the burn after read principle results in improved recognition memory for pictures. An experiment is designed where participants interact with pictures using a social media application. In Version A, participants are made aware of the persistent nature of the pictures, in Version B, participants are made aware of the ephemeral nature. Thereafter, participants are presented with the pictures again and must identify whether or not they previously encountered the exact same picture. Results showed that the burn after read principle does have a significant impact on accuracy in recognising pictures and the time spent watching them. Awareness of the burn after read principle resulted in better recognition memory for pictures and longer viewing times in the ephemeral application. This finding is in accordance with existing findings on the relationship between ephemerality and recognition memory.
New models for product planning

The purpose of the present study is to develop the methodology of business planning through the establishment of new models for the formation of the product plan of a company. A set of methods such as strategic analysis and synthesis, differentiation method, grouping method, matrix planning method, graphic methods, etc. are used. The results of the study have enabled two models to be developed with the purpose to improve the product planning. The first model involves product allocation depending on their importance for the economy and the development of the company. The second model, created on the basis of the methodology of the Boston Consulting Group, presents the technology for determination of the level of significance of each product. The application of the new product planning models into the practice of the company improves the economic results and adds new value to the corporate
Neural Duplicate Question Detection without Labeled Training Data

Supervised training of neural models to duplicate question detection in community Question Answering (CQA) requires large amounts of labeled question pairs, which can be costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose two novel methods—weak supervision using the title and body of a question, and the automatic generation of duplicate questions—and show that both can achieve improved performances even though they do not require any labeled data. We provide a comparison of popular training strategies and show that our proposed approaches are more effective in many cases because they can utilize larger amounts of data from the CQA forums. Finally, we show that weak supervision with question title and body information is also an effective method to train CQA answer selection models without direct answer supervision.
Differences in back extensor muscles fatigability for smoking and non-smoking athletes

Background and objective: Empirical research on the influence of smoking on musculoske letal health in athletes is sparse. The objective of this study was to compare back extensor muscles' strength (BEMS) before and after a fatigue indu ction protocol among smoking and non smoking male athletes. A pretest-posttest design was used to determine the difference in BEMS between smoking (N = 52) and non smoking (N = 52) athletes aged 18 and 30. A back and leg dynamometer was used to quantify BEMS while repetitive prone chest raise test was used to induce fatigue of the back extensor muscles. The smoking (23.1± 2.9 years) and non-smoking athletes (23.2± 2.37 years) were comparable in age (p = 0.855). Smoking athletes exhibited a significantly higher rating of perceived exertion ( p = 0.007) and fatigue index (10.76% vs. 5.07%); and significant reduction in BEMS following fatigue induction (p < 0.05) compared with their non smoking counterparts. Smoking athletes have higher rates of muscular fatiguability and decreased back strengt h following back extensors tasks. Smoking is associated with increased muscle fatiguability and impairment of back strength in athletes.
Multiple Tags Positioning Algorithm using ESP8266 NODEMCU System

ABSTRACT The emergence of the Internet of things (IoT) shaped the future of many domains where connectivity became a hot topic in electronic engineering. Recently, IoT was being a part of Indoor positing systems where devices need to communicate continuously at a defined time slot leading to various problematics for the conventional positioning algorithm. We propose an ESP8266 NodeMCU tracking system of multiple tags using a Wireless communication. The proposed implemented algorithm consists of two-phase in the first stage, a master anchor sends a signal to tags in order to establish the communication. In the second one, the master anchor initiates distance ranging and positioning. This two-phase based system aims to minimize reading time and increase the accuracy of positioning by reducing distance ranging errors, which we validate in our proposed model and algorithm by an experiment in an indoor environment.
3D Body Pose and Shape Estimation from Multi-View Images With Limb Geometric Constraint

The estimation of 3D body pose and shape has always been a challenging problem due to various reasons, such as the ambiguity in 2D images and complex articulated structure of the human body. In order to solve the ill-conditioned problems, in this paper, we bring up an end-to-end method to estimate 3D human shape and pose from multi-view RGB images. In the proposed framework, we first implement a CNN embedded with attention module to extract the image feature and design the view-pooling layer to combine the features from multiple views. Then we adopt a regression network with a novel geometric constraint of body limbs to estimate 3D human pose and shape. Additionally, during the training process, we employ the idea of adversarial learning in our model to help regress accurate pose and shape parameters. Extensive experiments are conducted on Human3.6M and MPI-INF-3DHP datasets, and our method achieves competitive results in the 3D pose and shape estimation task.
Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Loss function plays an important role in CNNs. However, the recent loss functions either do not apply weight and feature normalisation or do not explicitly follow the two targets of improving discriminative ability: minimising intra-class variance and maximising inter-class variance. Besides, all of them consider only the feedback information from the current mini-batch instead of the distribution information from the whole training set. In this paper, we propose a novel loss function – Global Information-based Cosine Optimal loss (Gico loss). Gico loss is applied with weight and feature normalisation, designed explicitly following the aforementioned two targets of improving discriminative ability, and is guided by the distribution information from the whole training set. Extensive experiments are conducted on multiple public datasets, which confirms the effectiveness of the proposed Gico loss and shows that we achieve state-of-the-art performance.
Transfer Learning based Multi-label Classification of Images

: Multi-label classification task is concerned with classifying an image into one or more classes(categories) based on the content of the image itself. Multi-label classification is different from binary or multi-class classification wherein the aim of the classifier built is to classify the image into a single class from a set number of classes. Existing methods utilize feature extraction techniques such as colour histograms, SIFT which are limited by their representational ability. We propose to overcome this problem by leveraging the rich features that can be extracted from CNN that have been trained on million images. The features are then fed into an Artificial Neural Net, which is trained on the image features and multi-label tags. By utilising transfer learning, we harness the feature representational ability combined with reduced training time. We benchmark the model with dataset obtained from Flickr (FLICKR-25K). The evaluation metrics utilised here include mAP, Training accuracy and Training Loss.
Data Augmentation via Image Registration

Data augmentation helps improve generalization of deep neural networks, and can be perceived as implicit regularization. It is pivotal in scenarios in which the amount of ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a common problem in medical image analysis, especially tumor delineation—in this paper, we focus on brain-tumor segmentation from magnetic resonance imaging (MRI), and propose a novel augmentation technique which exploits image registration to benefit from subtle spatial and/or tissue characteristics captured within the training set. We used a set of MRI scans of 44 low-grade glioma patients, augmented it using the proposed technique, and exploited it to train U-Net-based deep networks. The results show that our augmentation delivers statistically important boost of performance without sacrificing inference speed.
Sports Medicine: Principles of Primary Care

OVERVIEW: General Overview. Basic Science. Cardiopulmonary System. Neurologic System. SPECIFIC INJURIES: Skin. Craniofacial. Cervical Spine. Thoracolumbar Spine. Shoulder. Elbow and Forearm. Wrist. Hand. Pelvis and Hip. Knee. Lower Leg. Foot and Ankle. SPECIAL TOPICS: Rehabilitation. Nutrition. Gastrointestinal System. Genitourinary System. The Pediatric Athlete. Women in Sports. Preparticipation Evaluation. Emergencies.
It is difficult to make people use condoms - or is it?

Chlamydia is the most common infection among the sexually transmitted infections (STI) included in Swedish law. A previous history of an STI is a strong risk factor for re-infection with chlamydia. The aim of this study was to elicit from patients their motivation for using condoms and for starting to practise safer sex more frequently, which reduces re-infection with chlamydia. A total of 100 patients diagnosed with chlamydia infection at the STI clinic received information about condoms and counselling, based on a motivational interviewing technique. At follow-up 6–8 months later by a telephone call from the medical social worker, in general, the patients used condoms more frequently and had sex with fewer partners compared with before. The re-infection rate with CT was 2.2%.
Scalable Deep Generative Relational Models with High-Order Node Dependence

In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring. Given the possible feature information for the nodes in a network, our model builds up a deep architecture that can approximate to the possible nonlinear mappings between the nodes' feature information and latent representations. For each node, we incorporate all its neighborhoods' high-order structure information to generate latent representation, such that these latent representations are ``smooth'' in terms of the network. Since the latent representations are generated from Dirichlet distributions, we further develop a data augmentation trick to enable efficient Gibbs sampling for Ber-Poisson likelihood with Dirichlet random variables. Our model can be ready to apply to large sparse network as its computations cost scales to the number of positive links in the networks. The superior performance of our model is demonstrated through improved link prediction performance on a range of real-world datasets.
Social Network Threat Detection

Various government agencies have a stake in knowing when bad actors cross the United States’ borders, or how bad actors may be involved in the flow of people across borders. Interviews conducted at border checkpoints with individuals who intend to cross the border can contain valuable information. The quantity of interviews is such that intelligence analysts could benefit greatly from an automation system that extracts the information they are looking for from within the interviews. This would allow them to focus more of their time on analyzing what is extracted as opposed to inspecting all interviews themselves. The information extracted can be written to an SQL database, allowing the information to then be easily and efficiently queried for valuable insight and analysis.
A fuzzy-based routing scheme for network-on-chip with honeycomb topology

Network-on-chip (NoC) paradigm, which is based on a modular packet-switched mechanism, effectively addresses many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth of many-core systems. In designing an efficient NoC, topology and routing algorithm are the most important challenging issues that have significant impact on area, latency and power consumption. The goal of this paper is designing a fuzzy-based routing algorithm for a NoC architecture with honeycomb topology. The proposed algorithm is a livelock and deadlock free routing algorithm based on fuzzy logic for hexagonal zones with flat triple coordinate system. The analysis of simulation results demonstrates that the proposed algorithm, provides higher performance in terms of latency, power consumption, throughput and area than a traditional fuzzy-based routing algorithm for mesh-based NoC architectures. Comparing to a non-fuzzy routing algorithm for honeycomb NoCs, the proposed scheme performs faster with higher throughput with a negligible area and power consumption overhead.
A Two-Phase Segmentation Method for Drosophila Olfactory Glomeruli

In order to understand olfactory coding and functional connectome within the fly brain, scientists need not only a 3D stereotypical brain atlas but also anatomical local landmarks to describe hard-wiring circuits of olfactory neurons. Olfactory glomeruli are such indispensable local landmarks; however, it is hard to segment them from confocal microscopy images. We propose in this paper a systematic approach for semi-automatic olfactory glomerulus segmentation. This method consists of two phases. The former phase aims to highlight within-glomerulus regions, appraise the amount of recognizable glomeruli in each z-slice, and to suggest a seed used to generate an initial contour for every recognizable glomerulus. The latter phase, starting from the initial contour of each recognizable glomerulus, propagates the initial contour information to adjacent slices recursively until the glomerulus’ contours on all z-slices are obtained. Experiment results shows that our results are similar to those of manual segmentation. The proposed strategy is effective.