Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Factor Access Control and Ownership Transfer Framework for Future Generation Healthcare Systems

The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.
A Business Intelligence model to analyze consumer opinions on social networks using machine learning techniques

In recent years, social networks have become a rich platform of information and data shared by users in almost all sectors of society. Therefore, the widespread use of the social network generates a huge amount of unstructured social data that must be analyzed and well structured. It is for this purpose that the sentiment analysis has received considerable attention. Sentiment analysis is presented as a text organization used to classify the feelings and opinions expressed by the user in different ways (negative, positive, neutral, favorable, unfavorable, etc.). Due to the massive amount of data available on social networks, sharing opinions, reviews, comments, and suggestions, it is necessary to extract, analyze, and consolidate for improved decision-making. Sentiment analysis presents an effective and efficient opinion of consumers in real-time, which can greatly affect business decision-making. To solve this problem sentiment analysis and machine learning techniques have been united because machine learning models can be beneficial in the field of business intelligence.This article proposes a BI model to observe in real-time the satisfaction and the reviews of consumers on a product or service on social networks, notably Facebook and Twitter, based on machine learning techniques.
Black-box System Identification of CPS Protected by a Watermark-based Detector

The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.
Analysis on Various Approaches of Complexity Reduction for Intra Prediction Modes in High Efficiency Video Coding

Video compression is an image processing technique that removes redundant data while encoding the video content. The purpose of compressing the video is to save storage space and to reduce transmission bandwidth. High Efficiency Video Coding (HEVC) is the latest compression standard available in the market and has better efficiency than its predecessor H.264/AVC. But the complexity arises during the split of the Coding Unit (CU) to form Prediction Unit (PU) and to choose the best prediction modes of the 35 prediction modes available. Hence, there is a need to reduce the computational complexity. This paper presents the different algorithms available in the literature to minimize the number of prediction modes using heuristic, machine learning and deep learning approaches. The performance analysis from the studies are tabulated and the observation made from the study is that the algorithms using deep learning techniques help to get enhanced encoding time reduction with little compromise on Bjontegaard delta bit-rate (BDBR) comparatively. The available reference software HEVC TEST Model (HM) was used as standard to study different video quality metrics such as peak signal-to-noise ratio (PSNR), Bit Rate (BD rate), and encoding time.
Fuzzy ℓ2,p-norm based PCA for Intrusion Detection System

Attacks can be considered the number one nightmare for the cyber security community since it threats the existence of many organizations across the globe. Intrusion detection systems (IDS) are an efficient tool to detect suspicious connections in a network. However, the network traffic data used to build IDS is usually large with meaningless information, which leads to low detection rate. To address that, many techniques were proposed such as PCA, L1-PCA, R1-PCA and ℓ2,p-norm based PCA. Nonetheless, these techniques are more prone to outliers. This paper present the fuzzy variant of ℓ2,p-norm principal component analysis. Namely, the fuzzy ℓ2,p-norm principal component analysis aims to outline the data determined by many variants or various features into little principal components, so that the meaning of the data is damaged as less as possible and that the fuzzy group is taken into consideration during the analysis. Conducted experiments on the popular datasets KDDCup99 and NSLKDD demonstrate that the suggested technique is more effective and robust than the ℓ2,p-norm based PCA and PCA.
Value-Based Adoption Model on E-Wallet in Malaysia: A Conceptual Paper

In the current modern era of accelerated technological progression, e-wallet services are budding rapidly on a global scale and Malaysia swiftly moving towards a cashless society. This conceptual paper aims to shed lights to the body of knowledge and market practitioners by examining the determinants of perceived value and adoption intention, grounded by Value-based Adoption Model. It is believed that the findings of this research will shed lights into important issues related to consumers' adoption intention towards e-wallet in Malaysia that has not been focused by previous studies. From the theoretical point of view, this research will expand the understanding on the factors that affecting new technology adoption from the perspective of consumers. From the managerial implication perspective, e-wallet service providers should create an impression of desirable benefits if a positive impacts of perceived benefits is found. To be precise, higher perceived value indicates higher intention to adopt e-wallet, which indicates that the adoption intention is not only triggered by extrinsic benefits, but also the intrinsic results of using e-wallet. Hence, market practitioners should conduct market research regularly in order to observe consumer needs and wants and improve the products and services to meet consumers' expectations.
Estimations of emission factors for fertilizer-induced direct N2O emissions from agricultural soils in Japan: Summary of available data

Abstract Agricultural fields are significant sources of anthropogenic atmospheric nitrous oxide (N2O). We compiled and analyzed data on N2O emissions from Japanese agricultural fields (246 measurements from 36 sites) reported in peer-reviewed journals and research reports. Agricultural fields were classified into three categories: upland fields, tea fields and rice paddy fields. In this analysis, data measured over a period of more than 90 days for upland fields and 209 days for tea fields were used to estimate annual fertilizer-induced emission factors (EF) because of limitations in the available data. The EF is defined as the emission from fertilized plots minus the background emission (emission from a zero-N control plot), and is expressed as a percentage of the N applied. The mean of N2O emissions from upland fields with well-drained soils was significantly lower than that from poorly drained soils. Mean (± standard deviation) N2O emissions measured over a period of more than 90 days from fertilized upland fields were 1.03 ± 1.14 kg N ha−1 and 4.78 ± 5.36 kg N ha−1 for well-drained and poorly drained soils, respectively. Because the ratio of the total areas of well-drained soils and poorly drained soils was different from the ratio of the number of available EF data for each soil category, we used a weighted mean to estimate EF for all upland fields. The EF was estimated to be 0.62 ± 0.48% for all fertilized upland fields. Mean N2O emissions and the estimated EF for fertilized tea fields measured over a period of more than 209 days were 24.3 ± 16.3 kg N ha−1 and 2.82 ± 1.80%, respectively. The mean N2O emission and estimated EF from Japanese rice paddy fields were 0.36 kg N ha−1 and 0.31 ± 0.31% for the cropping season, respectively. Significant uncertainties remain in these results because of limitations in the available data.
Supporting interaction in augmented reality assisted industrial processes using a CNN-based semantic layer

Even though Augmented Reality (AR) is far from its maturity, we already have solutions and devices that give us an efficient technological frame in different industrial environments. Widely used mobile devices, such as tablets, or more specific ones, such as the current AR glasses available, are enough to offer solutions that improve many industrial processes; repairing, maintenance, plant control, product line reconfiguration are some examples of these tasks. Many industrial applications already utilise AR-based applications to solve those problems. In this work, we aim to go a little bit further beyond current possibilities that only focus on providing visual guidance. Our main goal is to add a semantic layer for existing AR-based applications, that visually validate worker’s actions based on visual interpretation of switches, potentiometers, analog needles or buttons, among others. This semantic layer allows a new level of interaction by adding automatic interpretation of the context that affects the actions of the operator and the display of information of interest in the AR system. We propose and explain the architecture and training of the Convolutional Neural Networks (CNN) used for the semantic layer and its integration in the AR technology.
The Prediction of Buzz in Social Internet of Things

Social Internet of Things is a critical digital tool for companies of different sizes. Good governance of the Social Internet of Things allows successful strategy to emphasise brands, making products no longer discretionary. Buzz marketing is a powerful advertising strategy that focuses on exploiting the value of a promotion or item, through conversations between family members and friends, or through wider conversations on the Social Internet of Things platforms. Buzz's forecast allows for an overview of the ranking of retailers by posts shared by future consumers or members on social media platforms. Buzz estimation in the Social Internet of Things Platforms, like Twitter, is a daunting challenge over real-time data by defining multiple aspects for buzz exploration. The bulk of buzz predictor analysis studies are modelled on machine learning methods like Radial Base Feature, Neural Network Models, Ant Colony Optimization, and Support Vector Devices. In this study, Random Forest is being used to make buzz predictions as it creates multiple decision trees and mixes them together again to make predictions more precise. The findings of comparative studies with Support Vector Machine (SVM) in three separate kernels and Radial Basis Function (RBF), it suggest that Random Forest is better at forecasting buzz and has also established a rating list of important features in order to achieve reasonable prediction performance. The features suggest that the main important characteristics to define the subject being generated are either “Buzz” or “Not Buzz”. Random Forest showed an average improvement in accuracy with a rate of 99% and the highest training time. However, although the Radial Based Function method has proved to be quite efficient, the Support Vector Machine solution is of mediocre accuracy.
Business Analytics of E-Commerce Policy and Practice: An Ethical Perspective

E-commerce has become an important technological business phenomenon all over the world. This paper aims at investigating e-commerce policy perspectives and business analytics of e-commerce practice statistics. In doing so, it highlights the importance of promoting ethical values in preventing illegal and unauthorized e-commerce practices. The study shows that e-commerce is more than just the purchase and sale of products and services over the internet, but rather a much broader concept of transformations in the way of doing business and communicating with stakeholders. A sound e-commerce strategy should manage the integration between business strategy and information technology strategy. Business analytics of e-commerce worldwide practices statistics and trends reveal that global e-commerce is no longer a choice but rather an inevitable business activity adopting online strategies. Innovative e-commerce infrastructure should incorporate all e-commerce aspects including physical, online, and ethical. Hence, the study has important implications for both businesses and stakeholders promoting the ideology of integrating ethical infrastructure within the mainstream business infrastructure.
A Novel Radiometric Cross-Calibration of GF-6/WFV With MODIS at the Dunhuang Radiometric Calibration Site

To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.
Influence of growth conditions on hydrophobicity of Lactobacillus acidophilus and Bifidobacterium lactis cells and characteristics by FT-IR spectra

In this study we have found, that the values of cell surface hydrophobicity (CSH) of L. acidophilus LA5 and B. lactis Bb12 cells change in response to varied growth conditions – phase of growth, concentration or type of carbon source, presence of oxygen. An evaluation of FT-IR spectra using cluster and quantitative analyses revealed substantial changes of the chemical composition depending on the CSH level of L. acidophilus LA5 and B. lactis Bb12 cells. Decrease of the carbohydrate level was observed in proportion to the increased CSH values alongside with the elevated protein content of more hydrophobic cells of both cultures. The results of present study could help to specify the appropriate physiological state and environment for L. acidophilus LA5 and B. lactis Bb12 to ensure their probiotic properties.
Preordering Encoding on Transformer for Translation

The difference in word orders between source and target languages is a serious hurdle for machine translation. Preordering methods, which reorder the words in a source sentence before translation to obtain a similar word ordering with a target language, significantly improve the quality in statistical machine translation. While the information on the preordering position improved the translation quality in recurrent neural network-based models, questions such as how to use preordering information and whether it is helpful for the Transformer model remain unaddressed. In this article, we successfully employed preordering techniques in the Transformer-based neural machine translation. Specifically, we proposed a novel <italic>preordering encoding</italic> that exploits the reordering information of the source and target sentences as positional encoding in the Transformer model. Experimental results on ASPEC Japanese–English and WMT 2015 English–German, English–Czech, and English–Russian translation tasks confirmed that the proposed method significantly improved the translation quality evaluated by the BLEU scores of the Transformer model by <inline-formula><tex-math notation="LaTeX">${\text{1.34}}$</tex-math></inline-formula> points in the Japanese–to–English task, <inline-formula><tex-math notation="LaTeX">${\text{2.19}}$</tex-math></inline-formula> points in the English–to–German task, <inline-formula><tex-math notation="LaTeX">${\text{0.15}}$</tex-math></inline-formula> points in the Czech–to–English task, and <inline-formula><tex-math notation="LaTeX">${\text {1.48}}$</tex-math></inline-formula> points in the English–to–Russian task.
Involving Stakeholders in the Implementation of Microservice-Based Systems: A Case Study in an Ambient-Assisted Living System

Microservice-based systems promote agility and rapid business development. Some features, such as fast time-to-market, scalability and optimal response times, have encouraged stakeholders to get more involved in the development and implementation of microservices architectures in order to translate their business vision into the implementation of the architecture. Although some techniques allow the inclusion of the stakeholders’ perspective in the design of microservice architectures, few proposals consider such perspectives in the selection and evaluation of technologies that implement microservice architectures. Indeed, the qualities that characterize microservice-based systems strongly depend on the suitable selection of technologies, such as application frameworks and platforms. This article proposes a collaborative technique that includes stakeholders and software architects in the selection and evaluation of application frameworks and platforms to implement microservice-based systems. We evaluated the technique in an industrial case of design and implementation of an Ambient-Assisted Living (AAL) system, which combines microservice architecture and Internet-of-Medical-Things (IoMT) sensors. The case results indicate that the proposed technique supported stakeholders in the pragmatic evaluation of alternative technological solutions. Additionally, it allowed the implementation of an AAL system that satisfies the quality specifications of stakeholders and end-users. This initial study suggests that actively including stakeholders in the implementation of microservice-based systems allows architects to make design decisions that better consider stakeholders viewpoints as well as managing their expectations.
Congestion Control Using Diffusion Method in Named Data Networking

Named Data Networking (NDN) is a new networking mechanism to amend problems in the current Internet. There are many features to improve the efficiency of network in NDN, and one of them is that routers can cache data conveyed by packets. This results in network node to client communication unlike the client to client principle in TCP/IP architecture. Therefore, end-to-end flow control cannot be applied to NDN unlike the Internet. Inspired from thermal diffusion, researchers proposed hop-by-hop flow control to solve this problem. However, the congestion control in hop-by-hop method is still under research. In this paper, a diffusion-based flow control method for NDN is proposed. We conducted simulations to verify its performance.
Systematic analysis and control of low-temperature GaN buffer layers on sapphire substrates

The growth of low-temperature (LT) GaN buffer layers on sapphire substrates was systematically studied using x-ray photoelectron spectroscopy with regards to processes such as substrate treatment and deposition conditions, along with annealing treatments of the GaN buffer layer during two-step metalorganic chemical vapor deposition. Variations observed in the LT-buffer layer depended strongly on both the chemical state of the sapphire surface as a result of the substrate treatment and the subsequent annealing conditions. A 20 nm buffer layer on non-nitrided sapphire evaporated after the formation of islands during the conventional annealing process (N2, H2, and NH3 gas mixture). Adding H2 gas to the annealing ambient enhanced the evaporation and reduced the surface coverage. It was found that AlxGa1−xN was formed at the interface, which has a low evaporation coefficient. In contrast, a buffer layer deposited onto a nitrided sapphire substrate evaporated completely in a layer-by-layer mode. The buffer laye.
A Machine Learning Approach to Human Activity Recognition

Human Activity Recognition (HAR) is the problem of classifying an individual's activity into well-defined moments, utilizing responsive sensors that are influenced by human movement. Sensor-enabled smartphones make Human Activity Recognition progressively significant and well known. The physical sensors, gyroscope and accelerometer combinedly allow the devices to provide motion measuring capabilities in a more accurate manner. The present research work adopts a machine learning based approach for recognizing activity on the basis of data collected through the smartphone sensors (accelerometer and gyroscope). Various state-of-the-art machine learning based techniques have been employed and compared on the basis of the performance metrics, accuracy, recall, precision, and the F1-score. Of all the selected different machine learning classifiers, the best result is given by the Support Vector Machine (SVM) with ‘RBF’ kernel, which achieved an accuracy of 96.61 % in classifying the activities into the six different classes.