Miracle-dz/UniFAR-FTUs huggingface dataset:
User acceptance of personal health records in Malaysia: pilot test

The importance of Personal health record (PHRs) can be illustrated from its ability to improve information exchange among the patients, physicians, and healthcare service providers. PHRs enroll the patients directly to their health decision making through giving them the authority to control and share their health information. The user acceptance of PHRs is a vital issue. A combination of UTAUT2 and PMT models is going to be tested in this study in order to test the user acceptance of PHRs in Malaysia in the future. This paper involves the pilot testing of the proposed model in order to find out its validity and reliability using Smart PLS.
Channel Pruning for Efficient Convolution Neural Networks

In this paper, we propose a novel pruning method using feature maps Ln-norm to compress deep convolutional neural networks (CNNs) with large model size and long inference time. Different from existing pruning methods, which are mainly based on convolution kernels L1-norm, the proposed method uses output feature maps Ln-norm, where n is a variable. This method can accurately identify the redundant convolution filters and has good compression performance. The experiments on ImageNet show that the proposed method outperforms previous methods and is generally suitable for any CNNs models.
CUSTOMER SEGMENTATION ANALYSIS BASED ON THE CUSTOMER LIFETIME VALUE METHOD

Companies need to understand the customers’ data better in all aspects. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities tocustomers became very important for customer-company engagement. Companies need a database of customer that contains customer information in detail, one of which is data about the potential value of each customer.Customer Lifetime Value (CLV) measures the potential value of each customer from the perspective of a service or product provider.This study aims to analyze Customer Lifetime Value (CLV) of the customer and clustering it into customer segmentation using the K-means cluster method.The results showed the highest average CLV value with is Rp 19,170,991,- and the lowest average value is -Rp 112,566,-. The customer clustering produced in this study isfour segmentswith the majority of customers at cluster 3 segment low with 51 unit population.
Weakly-Supervised Learning for Attention-Guided Skull Fracture Classification In Computed Tomography Imaging

We propose a novel attention-guided deep learning framework for image classification, with the goal of predicting both image and pixel-level labels for input images. While the training images are with either positive or negative labels, we do not assume that each training image is with annotated pixel-level ground truth information, and thus our method can be realized in such a weakly supervised setting. Our proposed module can be easily combined with standard CNN architectures with no extra parameter needed. With the above advantages, we evaluate our model on a skull CT dataset, and the experimental results confirm the effectiveness and robustness of our approach over recent popular CNN architectures.
Mining negation and uncertainty in social healthcare networks

More Internet users, particularly patients are seeking medical information from others through healthcare social networks (HSNs). Extensive amount of online comments by patients (or patient generated data (PGD)) can be a potential source for other medical research. Novel studies and findings have been achieved by the researcher in processing natural language from healthcare-related texts in HSNs. However, one aspect of PGD from social media that has not been widely studied are negations and uncertainty of medical concepts. Here, we introduced a social healthcare miner (SchMiner), a framework in identifying negation and uncertainty of medical concepts. In this thesis, we analysed and presented a novel conceptual model for negation expression in PGD. The model introduced and defined complete and incomplete negations of medical concepts. We also studied the types of negation modifiers that negates medical concepts into complete and incomplete classes. Negation identifier was trained using machine-learning algorithm that rely on engineered lexical, syntactic and semantic domain specific features. We applied scope-based approach in extracting the features at sentence level. The negation classifier trained with support vector machine algorithm achieved 90% accuracy and 84% F-Measure score. In addition, we also conducted an analysis of uncertainty in medical entities in PGD and mapped an existing state-of-the-art uncertainty model for medical entities. Identification of uncertainty from PGD in recent studies only focused on classification of sentence and document level. Specific work on identification of uncertainty at conceptual level is still very limited. Hence in this thesis, we implement a social healthcare uncertainty identifier (SchTnty). The identifier were modelled using machine-learning algorithm trained using scoped-based engineered features. The identifier achieved 83% accuracy, 65% F-Measure with 68% sensitivity and 88% specificity score performance on support vector machine algorithm. The framework provides state-of-the-art method that can be used to support other research work related to patient generated data from social media specifically.
Identifiability of Gaussian Bayesian bandit models

The Kalman filter, combined with heuristic choice rules such as softmax, UCB, and Thompson sampling, has been a popular model to identify the role of uncertainty in exploration in human reinforcement learning. Here we show that the Kalman filter combined with a softmax or UCB choice rule is not fully identifiable. By this structural identifiability, we mean that with unlimited data, the true parameter values are determinable. Perhaps surprisingly, the Kalman filter with Thompson sampling is fully identifiable.
Semi-Supervised Cross-Modality Action Recognition by Latent Tensor Transfer Learning

Microsoft’s Kinect sensors are receiving an increasing amount of interests by security researchers since they are cost-effective and can provide both visual and depth modality data at the same time. Unfortunately, depth or RGB modalities are unavailable in training or testing procedures in some realistic scenarios. Therefore, we explore a new problem focusing on the arbitrary absence of modality, which is completely different from the conventional action recognition. The new problem in action recognition aims to deal with cross modality data (e.g., RGB training and depth testing data), “missing” modality data (e.g., RGB training and RGB-D test data), and single-modality data (e.g., RGB/depth in both phases). Accordingly, our method aims to borrow some information (e.g., correlation between two modalities) from the well-established RGB-D dataset and apply it to the existing dataset to recover some latent information to improve the performance of recognition. For instance, a cross-modality regularizer is used to preserve the correlation of RGB and depth modalities. The “missing” knowledge is considered as latent information, which is recovered by low-rank learning in our model. In the real world, the target data are usually sparsely labeled or completely unlabeled; however, we could exploit the pseudolabels of the target as prior knowledge for “supervised” learning in the target domain. Accordingly, we propose a semi-supervised model for transfer learning. The experiments on three widely used RGB-D action datasets show that our method performs better than that of the state-of-the-art transfer learning methods in most cases in terms of accuracy and time efficiency.
Proposed Workflow and Conceptual Implementation for Logistics Automation Using Block Chain Technology

Digital currency a well-known term in past few years that grabbed the attention all over specially in financial sectors. Bit coin is a modern name use for digital currency in today’s world. Many names can be found in the market but Bit coin was first to show trust in digital currency and adopting it as a legal currency. Transactions made using block chain are well secure and transparent to user for monitoring. All signatures are stored in a distributed ledger that keeps the record for transactions happened. Decentralized network is use in block chain technology that connects all the nodes in a network. In this paper, the concept of block chain technology has been incorporated to improve the logistics process using secure digital currency and automate the payment process using bit coins and eliminating third parties interactions. Proposed workflow and conceptual model will help the supply chain management for secure and monitored delivery of goods to end customers. Model will provide advantages for logistics department that includes tracking, transactions updates, transparency, less documentation, on time delivery etc. SHA 256 is used in hashing for data encryption. Customer can also monitor their ordered material status. Proposed conceptual model can be used for all kind of shipment methods i.e. by air, by road, by sea. Paper includes comparison table that will highlight importance of block chain technology.
Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm

Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing.
Significant implication of unified power quality conditioner in power quality problems mitigation

This paper presents an analysis of a three-phase unified power quality conditioner (UPQC) in terms of design and performance. A back to back connection of a series compensator and a shunt compensator with a common DC-bus is utilized to build the UPQC model. The series compensator compensates the power quality problems such as grid voltage sags/swells for the grid side. During sag and swell condition, the compensated voltage is injected by the series compensator in phase with the point of common coupling (PCC) or out of phase with PCC. The load current harmonics is compensated by using the shunt compensator. The dynamic performance and the steady state of the designed model are analyzed by using MATLAB-Simulink under several disturbances such as PCC voltage harmonics, voltage sags/swells and load unbalancing using a nonlinear load.
Reconstructing Endovascular Catheter Interaction Forces in 3D using Multicore Optical Shape Sensors

Catheterization instruments are increasingly being improved to accurately diagnose and treat cardiovascular conditions. However, current catheter systems provide limited information about the shape of the catheter and tissue-instrument interaction forces during an intervention. Furthermore, relying on inconsistent feedback of such interaction forces during an intervention may result in tissue injury. This paper presents the first steps to estimate the interaction forces between a catheter and a mock-up arterial environment. We base the proposed method on a Pseudo-Rigid Body approximation of the catheter and integrate three-dimensional shape information provided by Fiber Bragg Grating sensors inside the catheter. The reconstructed forces along the catheter body can be fed back to the surgeon in visual and/or haptic form. In this work, the estimated forces are displayed in real-time in a graphical user interface with the reconstructed catheter shape. Experimental validation demonstrates a root mean square error of 0.03 N and a mean reconstruction error of 0.02 N.
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge

We propose new BERT-based methods for recognizing event causality such as “smoke cigarettes” –> “die of lung cancer” written in web texts. In our methods, we grasp each annotator’s policy by training multiple classifiers, each of which predicts the labels given by a single annotator, and combine the resulting classifiers’ outputs to predict the final labels determined by majority vote. Furthermore, we investigate the effect of supplying background knowledge to our classifiers. Since BERT models are pre-trained with a large corpus, some sort of background knowledge for event causality may be learned during pre-training. Our experiments with a Japanese dataset suggest that this is actually the case: Performance improved when we pre-trained the BERT models with web texts containing a large number of event causalities instead of Wikipedia articles or randomly sampled web texts. However, this effect was limited. Therefore, we further improved performance by simply adding texts related to an input causality candidate as background knowledge to the input of the BERT models. We believe these findings indicate a promising future research direction.
A deep-learning framework to detect sarcasm targets

In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.
Heritage language acquisition: What it reveals and why it is important for formal linguistic theories

Funding information Research Council of Norway for the project MiMS (Microvariation in Multilingual Acquisition & Attrition Situations), Grant/ Award Number: 250857 Abstract This paper discusses the interplay between acquisition and theory construction. It endeavors to show how a more direct and crucially bi-directional relationship between formal linguistic theory and the study of heritage language bilingualism can provide mutual benefit. It will be argued that data from acquisition—not exclusively but indeed especially from heritage language bilingualism—provide windows into the tenability of formal linguistic proposals. As a result, acquisition evidence—both developmental and in ultimate attainment—should be welcomed by all formal theorists, as they can confirm or disconfirm otherwise seemingly unverifiable theoretical arguments. Our claims will be illustrated by three research areas: language change, differences between functional and lexical features, and the interplay between core syntax and other components of the grammar.
Quadratic video interpolation

Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the complex motion in the real world. To address these issues, we propose a quadratic video interpolation method which exploits the acceleration information in videos. This method allows prediction with curvilinear trajectory and variable velocity, and generates more accurate interpolation results. For high-quality frame synthesis, we develop a flow reversal layer to estimate flow fields starting from the unknown target frame to the source frame. In addition, we present techniques for flow refinement. Extensive experiments demonstrate that our approach performs favorably against the existing linear models on a wide variety of video datasets.
Image Classification Using Convolutional Neural Networks

Convolutional neural networks (CNNs), consist of multiple processing layers to learn the representations of data with multiple abstract levels. They are the most successful machine learning models in recent years. Within this paper, we will know the usage of a trained deep convolutional neural network model to extract the features of the images, and then classify the images. We will study image processing and understand image classification. It has good application prospects. Keywords— Convolutional Neural Networks, Deep Learning, Image Pre-processing, Image Classification, Overfitting.
Random Forests for Simultaneous-Multislice (SMS) Undersampled HARDI Reconstruction and Uncertainty Estimation

In high-angular-resolution diffusion imaging (HARDI), simultaneous multislice (SMS) acquisition incorporated in multi-coil parallel imaging offers speedups in addition to the speedup obtained from undersampling gradient directions. We propose a novel learning-based method for reconstructing direction-undersampled SMS HARDI data. Our method relies on random-forest regression that also informs on the uncertainty in the reconstructions stemming from noise and artifacts. Results on a large clinical HARDI dataset show that our method significantly improves over the state of the art on SMS HARDI reconstruction qualitatively and quantitatively.
Using a Bayesian approach to evaluate the accuracy of economies of scope: Examples from Australian public universities

The point estimate of economies of scope is a nonlinear combination of estimated coefficients from an empirical model. This estimate usually involves out-of-sample predictions when calculating the separate costs (as a part of the calculation of economies of scope). These difficulties make it hard to give the precise prediction and to calculate the standard deviation of this estimate along with its confidence intervals. In this paper, we demonstrate methods for constructing the confidence interval for economies of scope to allow researchers to draw inferences from estimated economies of scope. We review the common approaches such as delta method or bootstrap adopted by previous studies. In contrast to the above approximation methods, this study also proposes an alternative method, Bayesian approach, to produce full predictive distribution for this measure with the posterior distribution. To demonstrate these three approaches, we use a balanced panel data including 37 Australian public universities over the period 2003-12. Our Bayesian approach uses a quadratic cost function with two outputs. Estimates of economies of scope will be calculated with the sample data and estimated parameters from the model.
Asynchronous Advantage Actor-Critic Algorithms Based on Residual Networks

Deep reinforcement learning is one of the fastest-growing technologies in machine learning. The Asynchronous Advantage Actor-Critic algorithm completely uses the actor-critic framework and utilizes the idea of asynchronous training, which greatly speeds up the training and improves performance. Although A3C algorithm puts actor-critic into multiple threads to train synchronously, effectively utilizes computer resources and improves training effectiveness, it is still difficult to train in deep neural network. Deep networks have proved to be capable of extending to thousands of layers and still have improved performance. However, every one percent increase in accuracy almost doubles the cost of layers, so it is not easy for A3C to train both actor and critic networks. In response to this problem, we innovatively utilize the residual network to apply to the asynchronous advantage actor-critic algorithm and has achieved improvement greatly in the inverted pendulum problem.
Job satisfaction, stress and burnout in haemodialysis nurses

Haemodialysis nurses provide health care for people with end stage kidney disease leading to a unique, intense and complex interaction between nurses and patients. This study involved the development of a model which explains the relationships between the work environment, job satisfaction, stress and burnout of haemodialysis nurses in Australia and New Zealand. Results from this study identified that haemodialysis nurses, while being satisfied by their jobs, were also experiencing high levels of burnout. This study's novel contribution could lead to improving the retention of the nursing workforce which is crucial due to the growing global burden of chronic disease.
MÉTODOS DE MAPPING FEATURES APLICADOS AO PROCESSAMENTO DE SINAIS EEG

The electroencephalogram (EEG) is a medical examination that aims to record the individual's brain activity for further analysis. Several applications are currently emerging for the same, and a major factor for any application is finding patterns and groups in the signals and relating them to the actions. Currently, there are several classifiers used for this, and these classifiers are applied directly to EEG signals. However, another theme uses Mapping Features methods in signal processing and then performs the classification on the resulting signals for better results.
Estimating Human Shape Under Clothing from Single Frontal View Point Cloud of a Dressed Human

Estimating human shape under clothing is a challenging task. We propose the first method to estimate accurate shape parameters from single-frame frontal view point cloud. To account for casual clothing, we personalize the original SMPL model to describe clothing as deviation from naked human parametric model, define a novel method to search for corresponding vertex pairs, and design a novel objective function that enforces point cloud vertices to remain outside of the naked body shape and tightly cling the personalized shape. Consolidating these three parts, our method integrates the advantages of free deformation method and model-based method. Our method is more effective than previous works in dealing with casual clothing situation. We evaluate the accuracy of estimated shape on noisy point cloud data captured by a commodity depth sensor.
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r. t. state-of-the-art supervised and semi-supervised approaches.