Reviewerly/exHarmony huggingface dataset:
Investigating classification supervised learning approaches for the identification of critical patients’ posts in a healthcare social network

Nowadays, Healthcare Social Networks (HSNs) offer the possibility to enhance patient care and education. However, they also present potential risks for patients due to the possible distribution of poor-quality or wrong information along with their bad interpretation. On one hand doctors and practitioners want to promote the exchange of information among patients about a specific disease, but on the other hand they do not have enough time to read patients’ posts and moderate them when required. In this paper, we investigate and compare different supervised learning classifiers that we adopted for the classification of critical patients’ posts who can trigger the intervention of the medical personnel. In particular, by considering different Bayesian, Linear and Support Vector Machine (SVM) classifiers we analyze their accuracy considering different n-grams datasets preparation approaches in order to identify the best approach for the identification of critical patients’ posts in a Healthcare Social Network.
Deep multi-label learning for image distortion identification

Image Distortion Identification is important for image processing system enhancement, image distortion correction and image quality assessment. Although images may suffer various number of distortions while going through different systems, most of the previous researches of image distortion identification were focus on identifying single distortion in image. In this paper, we proposed a CNN-based multi-label learning model (called MLLNet) to identify distortions for different scenarios, including images having no distortion, single distortion and multiple distortions. Concretely, we transform the multi-label classification for image distortion identification to a number of multi-class classifications and use a deep multi-task CNN model to train all associated classifiers simultaneously. For unseen image, we use the trained CNN model to predict a number of classifications at same time and fuse them to final multi-label classification. The extensive experiments demonstrate that the propose algorithm can achieve good performance on several databases. Moreover, the network architecture of the CNN model can make flexible adjustment according to the different requirements.
A Novel Camera Calibration Method Based on Multilevel-Edge-Fitting Ellipse-Shaped Analytical Model

In this paper, a novel camera calibration method based on a multilevel-edge-fitting ellipse-shaped analytical model (CCM-MEF-ESAM) is proposed. First, an ellipse-shaped analytical model (ESAM) is derived which considers distortion error of the spherical image. Second, multilevel-edge-fitting (MEF) is presented to select the edge points to fit the ellipse. Then the actual projection of the sphere is calculated by CCM-MEF-ESAM, which can be utilized to obtain the accurate external parameters of the camera. Finally, the robustness and accuracy of CCM-MEF-ESAM are validated by simulations and experiments. Within the environments of incomplete edges and complex illumination, CCM-MEF-ESAM can realize an improvement of calibration accuracy of 74% compared to the traditional method, which indicates that CCM-MEF-ESAM holds great potential in the field of vision navigation.
Outlier Detection in Object Counting based on Hue and Distance Transform using Median Absolute Deviation (MAD)

Object counting based on image data has been developed in many research. It is fast, automatic and noncontact solution that is applied in health, microbiology, object tracking, robotics and industry. During the counting, object that differs from majority object might be presented in a frame. This outlier object should be detected and not be counted. This study present an algorithm to detect outliers in object counting based on color and shape information. The color was based on Hue whilst the shape was based on distance transform. Both features were chosen as it is invariant to position and rotation in plane. Outlier detection utilized Median Absolute Deviation (MAD) on Histograms of both features. The testing shows promising result (accuracy of 94.3%) in 35 images with simple background.
Hierarchical Class Incremental Learning of Anatomical Structures in Fetal Echocardiography Videos

This paper proposes an ultrasound video interpretation algorithm that enables novel classes or instances to be added over time, without significantly compromising prediction abilities on prior representations. The motivating application is diagnostic fetal echocardiography analysis. Currently in clinical practice, recording full diagnostic fetal echocardiography is not common. Diagnostic videos are typically available in varying length and summarize a number of diagnostic sub-tasks of varying difficulty. Although large clinical datasets may be available at onset to build ultrasound image-based models for automatic image analysis, data may also become available over extended time to assist in algorithm refinement. To address this scenario, we propose to use an incremental learning approach to build a hierarchical network model that allows for a parallel inclusion of previously unseen anatomical classes without requiring prior data distributions. Super classes are obtained by coarse classification followed by fine classification to allow the model to self-organize anatomical structures in a sequence of categories through a modular architecture. We show that this approach can be adapted with new variable data distributions without significantly affecting previously learned representations. Two extreme situations of new data addition are considered; (1) when new class data is available over time with volume and distribution similar to prior available classes, and (2) when imbalanced datasets arrive over future time to be learned in a few-shot setting. In either case, availability of data from prior classes is not assumed. Evolution of the learning process is validated using incremental accuracies of fine classification over novel classes and compared to results from an end-to-end transfer learning-derived model fine-tuned on a clinical dataset annotated by experienced sonographers. The modularization of subsequent learning reduces the depreciation in future accuracies over old tasks from 6.75% to 1.10% using balanced increments. The depreciation is reduced from 6.95% to 1.89% with imbalanced data distributions in future increments, while retaining competitive classification accuracies in new additions of fine classes with parameter operations in the same order of magnitude in all stages in both cases.
The Intelligent Medical Platform: A Novel Dialogue-Based Platform for Health-Care Services

The proposed Intelligent Medical Platform is a dialoguebased medical decision-making system that provides medical coaching and recommendation services, based on incremental learning methodology. The prototype demonstrates 90% accuracy for knowledge acquisition, 80% satisfaction level of user interaction with the system, and 95% accuracy for system integration with the legacy system.
Face sketch-to-photo transformation with multi-scale self-attention GAN

In this study, we investigate the sketch-to-photo problem, which currently poses a significant challenge in the field of computer vision. A large number of GAN-based encoder-decoder methods have been proposed for image transformation, inspired by the pix2pix model; however, these methods do not produce satisfactory results for photo generation, due to the fact that (1) they miss detailed information of input images because of a single-scale convolution operator in the shallow encoder layers, and (2) they fail to learn long-range dependencies in the deep encoder layers. To better handle these challenges, we present an approach that follows a “divide and conquer” strategy. Our method combines the advantages of a multi-scale convolutional neural network and an attention mechanism and applies these two modules to different encoder layers. Additionally, by optimizing a well-designed loss function, the complex correlations between the sketch and the photo can be calculated. Experimental results show that our method is able to generate high-quality photos from sketch images, and qualitative and quantitative analysis demonstrates its effectiveness and superiority over state-of-the-art models. This work paves a path to replace the traditional encoder structure with the “divide and conquer” strategy to handle image transformation tasks.
KeySea: Keyword-Based Search With Receiver Anonymity in Attribute-Based Searchable Encryption

In modern digital age, enterprise applications typically outsource user data in pubic cloud storage with the objective of availing flexibility and scalability features of cloud infrastructure, and importantly, making business goal more cost effective. Security and privacy concerns pose a challenging task to handle in cloud setup by both service providers and service consumers. In this landscape, before outsourcing the sensitive data on cloud storage, the data should be protected from unauthorized access and the privacy of the users should be preserved as per application requirement. In this article, we present a scheme, termed as KeySea, keyword-based search over attribute-based encrypted data with receiver anonymity. While searching documents pertaining to the target keyword(s), keeping receiver’s anonymity and ensuring data privacy are important features in applications like healthcare, bureaucracy, social engineering, and so on. The construction of the KeySea scheme uses the hidden access policy in attribute-based searchable encryption. The KeySea scheme provides a secure and practical solution to address the issue of privacy-preserving search over encrypted data in the public cloud storage. We show the security strengths of the KeySea scheme and its practicality with experimental results.
Random Forest based Classification Model for Lung Cancer Prediction on Computer Tomography Images

In recent days, lung cancer become a significant reason for the increased mortality rate. But, the identification and medical of the disease can considerably reduce the death rate. The usage of medical imaging especially Computer Tomography (CT) images are applied to diagnose the lung cancer and the physicians find it hard to understand and classify the cancer from the CT scan images. So, computer-based analysis tool has been developed for utilizing image processing approaches. In this paper, a new automated computer aided model has been developed to identify lung cancer on CT images. The presented model operates on four main stages namely pre-processing, segmentation, feature extraction and classification. For simulation processes, a benchmark dataset from Lung Image Database Consortium (LIDC) is applied and the results are investigated under several aspects. The attained experimental outcome clearly depicted that the presented model showed extraordinary classifier results on the applied dataset.
Deep Learning for Inexpensive Image Classification of Wildlife on the Raspberry Pi

Animal conservationists need unobtrusive methods of observing and studying wildlife in remote areas. Many commercial options for wildlife observation are expensive, obtrusive, or sub-optimal in remote environments. In this paper, we explore the viability of a Raspberry Pi-based camera system augmented with a deep learning image recognition model for detecting wildlife of interest. Unlike traditional sensor nodes that would have to transmit every captured image, localized image recognition enables only pictures of desired animals to be transferred to the user. For the purposes of this study, we use TensorFlow and Keras to create a convolutional neural network that runs on a Raspberry Pi 3B+. We trained the model on nearly 3,600 images gathered from publicly available image databases that are split into three classes. Our experiments suggest that our system can detect snow leopards with between 74 percent and 97 percent accuracy. We believe that our results show the viability of employing deep learning image recognition models on the Raspberry Pi to create an inexpensive system to observe wildlife.
End-to-End Residual Neural Network with Data Augmentation for Sign Language Recognition

Sign language is a visual language used by people with hearing impairments. It can help people achieve communication. Due to the lack of popularity of sign language, it is of great social significance to develop a human-computer interaction system that provides a platform for people with hearing impairments to communicate with other people. At present, most of the research on sign language recognition is based on the traditional machine learning non-end-to-end system, which requires a lot of manual design work, and the generalization ability of the model is poor. This paper proposes a system using the Residual Neural Network to implement end-to-end recognition of American Sign language(ASL). The sign language dataset in this work consists of 36 classes static sign language words, including 0-9 numbers and English letters A-Z. We obtained 17640 sign language images as model training data through data enhancement methods. Our Proposed method have got 99. 4% accuracy.
Satisfaction and its correlates in agile software development

In this paper we address the topic of software development team members satisfaction with their development process. We present an in-depth analysis of the results of a nationwide survey about software development in Switzerland. We wanted to find out if satisfaction relates to the applied development method, and to the use of various practices, and impacts on business, team and software issues. We found that higher satisfaction is reported more by those using Agile development than with plan-driven processes. We explored the different perspectives of developers and those with a management role and found a high consistency of satisfaction between Agile developers and Agile management, and differences with those using working plan-driven methods. We found that certain practices and impacts have high correlations to satisfaction, and that collaborative processes are closely related to satisfaction. We then explored the relationship between satisfaction and various other perspectives. Our results in this analysis are principally descriptive, but we think they can be a relevant contribution to understand the challenges for everyone involved in Agile development.
Self-Study in Korea

Teacher educators at Chonnam National University’s College of Education found that there were many opportunities for teachers to improve their classes in Korea, but the opportunities were not effective. Therefore, they decided to collectively study their teaching in their classes from 2013 to 2014 and then also sought ways to apply self-study for classroom teachers in the Korean educational milieu. One of the most distinctive features of this application of self-study to the Korean context is the concept of a critical group of colleagues. With university-based teacher educators at the Research Center for Curriculum and Instruction, teachers of different subject areas played critical roles in the group. These teacher educators collaboratively designed and enacted a self-study approach in a large-scale initiative involving 36 middle and high schools in South Jeolla Province from March until August 2015. They worked closely with the teachers in those schools in self-study research projects. All the while, the teacher educators were also studying the impact of self-study on their own teaching. The teachers who wanted to improve their classes received various suggestions about how to improve their teaching, prepared specific approaches and activities to try out in their classes, enacted these changes with students, and presented the results to the group. This process allowed teachers to have opportunities to improve their classes and gave teacher educators a deeper understanding of the challenges faced by teachers in the field. In documenting the process here, the authors of this paper demonstrate how self-study can be successfully adapted to the Korean educational context and therefore provide inspiration for how other educational researchers and teachers interested in self-study can translate and adapt the approach to their own contexts. This study shows that a collective critical peer group can be effective in educational situations where it is difficult for teachers to reveal their own problems or positively accept personal criticism due to the relationship with their peers, such as Korea.
Overview of Policies, Guidelines, and Standards for Active Assisted Living Data Exchange: Thematic Analysis

Background A primary concern for governments and health care systems is the rapid growth of the aging population. To provide a better quality of life for the elderly, researchers have explored the use of wearables, sensors, actuators, and mobile health technologies. The term AAL can be referred to as active assisted living or ambient assisted living, with both sometimes used interchangeably. AAL technologies describes systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for those who need assistance at any stage of their lives. Objective The aim of this study was to understand the standards and policy guidelines that companies use in the creation of AAL technologies and to highlight the gap between available technologies, standards, and policies and what should be available for use. Methods A literature review was conducted to identify critical standards and frameworks related to AAL. Interviews with 15 different stakeholders across Canada were carried out to complement this review. The results from interviews were coded using a thematic analysis and then presented in two workshops about standards, policies, and governance to identify future steps and opportunities regarding AAL. Results Our study showed that the base technology, standards, and policies necessary for the creation of AAL technology are not the primary problem causing disparity between existing and accessible technologies; instead nontechnical issues and integration between existing technologies present the most significant issue. A total of five themes have been identified for further analysis: (1) end user and purpose; (2) accessibility; (3) interoperability; (4) data sharing; and (5) privacy and security. Conclusions Interoperability is currently the biggest challenge for the future of data sharing related to AAL technology. Additionally, the majority of stakeholders consider privacy and security to be the main concerns related to data sharing in the AAL scope. Further research is necessary to explore each identified gap in detail.
Probabilistic Analysis of Targeted Attacks Using Transform-Domain Adversarial Examples

In the past decade, Deep Neural Networks (DNNs) have achieved breakthrough collaborations in developing smart intelligent systems within the field of computer vision, natural language processing, autonomous systems, etc. Recent research has revealed that stability of such smart systems is at greater risk when they come across to adversarial perturbations. Although, these perturbations may not be perceivable in nature when seen from naked eye, yet, they are capable enough to fool state-of-the-art DNN classifiers. Till now, much of the previous work related to fool such classifiers focuses on generating adversaries that directly change pixel values of an image in spatial-domain. In this paper, we propose a novel transform-domain imperceptible attack methodology “TDIAM” to generate adversaries based on image steganography-approach using a “single carefully selected targeted watermark”. We use three different frequency-domain approaches, i.e., Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) to craft perturbations in selective frequency component which makes it robust and it requires less computational time as it is a non-gradient approach. We present our case study on MNIST handwritten digits dataset. Our results demonstrate that the generated perturbation vector successfully fool simple Convolutional Neural Network (CNN), LeNet-5 and AlexNet architectures by increasing probability of adversarial examples for the targeted class (to which the targeted watermark belongs) in both “black-box” and “white-box” adversarial attacks. The results have shown that among these three perturbation approaches, DWT based perturbation shown promising results by effectively fooling DNNs while ensuring the high imperceptibility as well.
A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling

In this work, a novel approach to model the dynamic behavior of the flash separation process (as a main building block of non-reacting stage-wise operations) based on Mamdani Fuzzy Inference Systems is proposed. This model surmounts the need to solve various types of mathematical equations governing the system and does not require thermodynamic properties which are either not available or computationally demanding. Hence it can be easily used in dynamic simulation of multi-phase flow in distributed systems. In the proposed approach the overall model is broken into several simple sub-models based on intuitive analysis of an expert. Moreover, a new fuzzy concept, named "Linguistic Composition Variable" is introduced to represent components mole fractions of each phase as a fuzzy variable. Accordingly, large number of rules which is the main shortcoming of the Mamdani Fuzzy system is significantly reduced. The performance of the proposed dynamic model is evaluated through comparing its results against their corresponding counterparts for a flash separator of crude oil. Overall MAPE (Mean Absolute Percentage Error) values of 7.17% for the gas molar fractions, 3.06% for liquid molar fractions, 10.16% for the temperature, 0.63% for the pressure and 16.44% for the liquid level of the separator have been achieved showing that the proposed fuzzy model can effectively capture the general trends of process data of the dynamic process.
Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.