Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning.

The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.

Restoration and content analysis of ancient manuscripts via color space based segmentation.

Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums.

Community Question Answering (CQA) web service provides a platform for people to share knowledge. Quora, Stack Overflow, and Yahoo! Answers are few sites where questioners post their queries and answerers respond to their respective queries. Due to the ease of use and quick responsiveness of the CQA platform, these sites are being widely adopted by the community. For better usability, there is a dire need to route the question toward the relevant answerers. To fulfil this gap, recommender systems play an important role in identifying the relevant answerers. To map the user interests more effectively, this research work proposed a dynamic feature representation of the latent user attributes for user profiling. The latent features are mapped by leveraging the Latent Dirichlet Allocation (LDA) for topic modelling of user data. The proposed recommendation model segments the user profile based on these latent user profiles incorporating the incremental learning of the users' interests to produce the relevant recommendations in near real time. The experimental setup generated recommendation lists of variable sizes and evaluated using multiple evaluation metrics, such as mean average precision, recall, throughput, and different quality metrics, such as discounted cumulative gain and mean reciprocal rank. The results showed that the proposed model provided a better quality of recommendations in CQA forums, which is promising for future research in this domain.

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR).

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.

m6A-Finder: Detecting m6A methylation sites from RNA transcriptomes using physical and statistical properties based features.

N6-methyladenosine (m6A) is one of the abundant post-transcription modification in cellular RNA. It regulates different biological processes, such as, protein synthesis, X-chromosome inactivation, cell stability, cell-reprogramming and miRNA regulation etc. Most recently, various studies claimed that mutations in m6A sites are linked with various diseases, such as, brain-tumor, heart attack, obesity and cancer. The correct identification of m6A sites is essential to overcome these diseases. However, the state-of-the-art predictors face many challenges for precise detection of m6A sites. Even for model organisms, such as Saccharomyces cerevisiae, the detection of m6A sites is difficult due to complex patterns surrounding the m6A sites. These patterns are not widely understood and lead to non-discriminative features for detecting m6A sites. To overcome this problem, we propose a novel predictor called m6A-Finder that creates features based on global and local sequence order. The global sequence order is captured by physical properties based features, while the local sequence order is captured by the statistical features. The fusion of these features results in high dimensional vector which lead to over-fitting, to solve this problem, we use mRMR algorithm to remove redundant features. The proposed technique is evaluated on the most widely used Saccharomyces cerevisiae species dataset. Overall, the m6A-Finder achieved an accuracy of 82.02%, the sensitivity of 82.10%, specificity of 81.94% and a Matthew correlation coefficient value of +0.64.

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM.

In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time-consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best-selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

Correction: High voltage DC power supply with power factor correction based on LLC resonant converter.

[This corrects the article DOI: 10.1371/journal.pone.0239008.].

DSP-Assisted Nonlinear Impairments Tolerant 100 Gbps Optical Backhaul Network for Long-Haul Transmission.

High capacity long haul communication and cost-effective solutions for low loss transmission are the major advantages of optical fibers, which makes them a promising solution to be used for backhaul network transportation. A distortion-tolerant 100 Gbps framework that consists of long haul and high capacity transport based wavelength division multiplexed (WDM) system is investigated in this paper, with an analysis on different design parameters to mitigate the amplified spontaneous emission (ASE) noise and nonlinear effects due to the fiber transmission. The performance degradation in the presence of non-linear effects is evaluated and a digital signal processing (DSP) assisted receiver is proposed in order to achieve bit error rate (BER) of 1.56 × 10-6 and quality factor (Q-factor) of 5, using 25 and 50 GHz channel spacing with 90 µm2 effective area of the optical fiber. Analytical calculations of the proposed WDM system are presented and the simulation results verify the effectiveness of the proposed approach in order to mitigate non-linear effects for up to 300 km length of optical fiber transmission.

RF-chirp phase dither for MPI mitigation in RoF-based 5G mobile fronthaul networks.

The multipath interference (MPI) noise is one of the most important limiting factors on the performance of the mobile fronthaul network (MFN) based on the radio-over-fiber (RoF) technology. Recently, it has been proposed to suppress this MPI noise by using the Gaussian phase dither. However, it broadens the optical spectrum significantly and, as a result, increases its vulnerability to the chromatic dispersion. To overcome this problem, we propose to suppress the MPI noise by using the RF-chirp dither instead of the Gaussian dither. The results show that, due to the narrow optical spectrum achieved by the RF-chirp dither, we can increase the transmission distance of the RoF-based MFN operating in the 1.5-µm region by three times.

High voltage DC power supply with power factor correction based on LLC resonant converter.

This paper presents analysis, design and experimentation of a high voltage DC power supply (HVDCPS) with power factor correction based on LLC resonant converter. For power factor correction improvement, the proposed topology has an input rectifier with two filter capacitors, two inductors with a bus capacitor (Cbus) and a resonant tank. To prevent the reverse current flow towards the source diodes (D9 & D10) are employed. A couple of power switches are inserted in a single leg that makes a half-bridge network. To form an LLC resonance circuit, a capacitor and two inductors are connected to the primary winding of the high voltage transformer (HVT). To rectify the high frequency and high voltage, a full-bridge rectifier is inserted to secondary side of high voltage transformer (HVT). The secondary diodes always get on and off under zero current switching (ZCS) due to discontinuous conduction mode of proposed topology. It is found that due to power factor correction, less cost, lower losses and smaller size, the proposed topology achieves several major improvements over the conventional high voltage power supply. To obtain zero voltage switching (ZVS) the converter operate in a narrow frequency range. The output voltage can be varied or regulate through pulse width modulation of power switches. Due to ZVS and ZCS, the proposed topology has minimum switching losses and therefore higher efficiency. To verify the feasibility of the proposed topology a prototype is being implemented and verified by simulation & experimental results for 1.5KV prototype of the proposed topology. The results make sure the achievement, good efficiency and successful operation of the proposed topology.