Computers, Volume 10, Issue 3 (March 2021) – 14 articles

In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method’s effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality. Full article

The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, relaxed), happy (excited, happy, pleased), sad (sleepy, bored, sad). A total 120 emotion signals were collected by using Emotive 14 channel EEG headset. Emotions are elicited by using three types of stimulus thoughts, audio and video. The system is trained by using captured database of emotion signals which include 30 signals of each emotion class. A total of 24 features were extracted by performing Chirplet transform. Band power is ranked as the prominent feature. The multimodel approach of classifier is used to classify emotions. Classification accuracy is tested for K-nearest neighbor (KNN), convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN) classifiers. The system is tested to detect emotions of intellectually disable people. Meditation music therapy is used to stable mental state. It is found that it changed emotions of both intellectually disabled and normal participants from the annoying state to the relaxed state. A 75% positive transformation of mental state is obtained in the participants by using music therapy. This research study presents a novel approach for detailed analysis of brain EEG signals for emotion detection and stabilize mental state. Full article

Recently, feature extraction from user reviews has been used for requirements reuse to improve the software development process. However, research has yet to use sentiment analysis in the extraction for it to be well understood. The aim of this study is to improve software feature extraction results by using sentiment analysis. Our study’s novelty focuses on the correlation between feature extraction from user reviews and results of sentiment analysis for requirement reuse. This study can inform system analysis in the requirements elicitation process. Our proposal uses user reviews for the software feature extraction and incorporates sentiment analysis and similarity measures in the process. Experimental results show that the extracted features used to expand existing requirements may come from positive and negative sentiments. However, extracted features with positive sentiment overall have better values than negative sentiments, namely 90% compared to 63% for the relevance value, 74–47% for prompting new features, and 55–26% for verbatim reuse as new requirements. Full article

The impact of the strategies that researchers follow to publish or produce scientific content can have a long-term impact. Identifying which strategies are most influential in the future has been attracting increasing attention in the literature. In this study, we present a systematic review of recommendations of long-term strategies in research analytics and their implementation methodologies. The objective is to present an overview from 2002 to 2018 on the development of this topic, including trends, and addressed contexts. The central objective is to identify data-oriented approaches to learn long-term research strategies, especially in process mining. We followed a protocol for systematic reviews for the engineering area in a structured and respectful manner. The results show the need for studies that generate more specific recommendations based on data mining. This outcome leaves open research opportunities from two particular perspectives—applying methodologies involving process mining for the context of research analytics and the feasibility study on long-term strategies using data science techniques. Full article

Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance. Full article

Metric Multidimensional Scaling is commonly used to solve multi-sensor location problems in 2D or 3D spaces. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, because the number of anchors is small and the ranging error asymmetrically distributed. We then propose a two-step iterative approach based on geometric resolution of angle inaccuracies. The first step reduces the effect of poor ranging exchanges. The second step reconstructs the anchor positions, starting from the distances of highest likely-accuracy. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Metric techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known location, can allow a set of fixed sensors to auto-determine their position on a floor plan. Full article

Monitoring the development of infectious diseases is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as early as possible by monitoring data sources which allow to capture the occurrences of a certain disease. Recent research mainly concentrates on the surveillance of specific, known diseases, putting the focus on the definition of the disease pattern under surveillance. Until now, only little effort has been devoted to what we call non-specific syndromic surveillance, i.e., the use of all available data for detecting any kind of infectious disease outbreaks. In this work, we give an overview of non-specific syndromic surveillance from the perspective of machine learning and propose a unified framework based on global and local modeling techniques. We also present a set of statistical modeling techniques which have not been used in a local modeling context before and can serve as benchmarks for the more elaborate machine learning approaches. In an experimental comparison of different approaches to non-specific syndromic surveillance we found that these simple statistical techniques already achieve competitive results and sometimes even outperform more elaborate approaches. In particular, applying common syndromic surveillance methods in a non-specific setting seems to be promising. Full article

Precisely assessing the severity of persons with COVID-19 at an early stage is an effective way to increase the survival rate of patients. Based on the initial screening, to identify and triage the people at highest risk of complications that can result in mortality risk in patients is a challenging problem, especially in developing nations around the world. This problem is further aggravated due to the shortage of specialists. Using machine learning (ML) techniques to predict the severity of persons with COVID-19 in the initial screening process can be an effective method which would enable patients to be sorted and treated and accordingly receive appropriate clinical management with optimum use of medical facilities. In this study, we applied and evaluated the effectiveness of three types of Artificial Neural Network (ANN), Support Vector Machine and Random forest regression using a variety of learning methods, for early prediction of severity using patient history and laboratory findings. The performance of different machine learning techniques to predict severity with clinical features shows that it can be successfully applied to precisely and quickly assess the severity of the patient and the risk of death by using patient history and laboratory findings that can be an effective method for patients to be triaged and treated accordingly. Full article

Edge detection is one of the main steps in the image processing field, especially in biomedical imaging, to diagnose a disease or trace its progress. The transfer of medical images makes them more susceptible to quality degradation due to any imposed noise. Hence, the protection of this data against noise is a persistent need. The efficiency of fractional-order filters to detect fine details and their high noise robustness, unlike the integer-order filters, it renders them an attractive solution for biomedical edge detection. In this work, two novel central fractional-order masks are proposed with their detailed mathematical proofs. The fractional-order parameter gives an extra degree of freedom in designing different masks. The noise performance of the proposed masks is evaluated upon applying Salt and Pepper noise and Gaussian noise. Numerical results proved that the proposed masks outperform the integer-order masks regarding both types of noise, achieving higher Peak Signal to Noise Ratio. As a practical application, the proposed fractional-order edge detection masks are employed to enhance the Diabetic Retinopathy disease diagnosis. Full article

This paper presents the structure of an encyclopedia-based framework (EbF) in which to develop computer vision systems that incorporate the principles of agile development with focussed knowledge-enhancing information. The novelty of the EbF is that it specifies both the use of drop-in modules, to enable the speedy implementation and modification of systems by the operator, and it incorporates knowledge of the input image-capture devices and presentation preferences. This means that the system includes automated parameter selection and operator advice and guidance. Central to this knowledge-enhanced framework is an encyclopedia that is used to store all information pertaining to the current system operation and can be used by all of the imaging modules and computational runtime components. This ensures that they can adapt to changes within the system or its environment. We demonstrate the implementation of this system over three use cases in computer vision for unmanned aerial vehicles (UAV) showing how it is easy to control and set up by novice operators utilising simple computational wrapper scripts. Full article

There is always an increasing demand for data storage and transfer; therefore, data compression will always be a fundamental need. In this article, we propose a lossless data compression method focused on a particular kind of data, namely, chat messages, which are typically non-formal, short-length strings. This method can be considered a hybrid because it combines two different algorithmic approaches: greedy algorithms, specifically Huffman coding, on the one hand and dynamic programming on the other (HCDP = Huffman Coding + Dynamic Programming). The experimental results demonstrated that our method provided lower compression ratios when compared with six reference algorithms, with reductions between 23.7% and 39.7%, whilst the average remained below the average value reported in several related works found in the literature. Such performance carries a sacrifice in speed, however, which does not presume major practical implications in the context of short-length strings. Full article

Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study. Full article

Water quality is the most critical factor affecting fish health and performance in aquaculture production systems. Fish life is mostly dependent on the water fishes live in for all their needs. Therefore, it is essential to have a clear understanding of the water quality requirements of the fish. This research discusses the critical water parameters (temperature, pH, nitrate, phosphate, calcium, magnesium, and dissolved oxygen (DO)) for fisheries and reviews the existing sensors to detect those parameters. Moreover, this paper proposes a prospective solution for smart fisheries that will help to monitor water quality factors, make decisions based on the collected data, and adapt more quickly to changing conditions. Full article

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR. Full article