Reviewerly/exHarmony huggingface dataset:
Privacy regulation aware service selection for multi-provision cloud service composition

The proliferation of cloud computing is driving the rapid growth of multi-provision cloud service composition (MPCSC), in which each cloud service provider (CSP) can offer multiple services simultaneously. Due to the openness and virtualization of cloud environments, cloud service providers (CSPs) may collect and distribute users’ privacy information without permission or authorization. More seriously, some CSPs have the ability to infer additional privacy information by combining multiple pieces of such data. Therefore, privacy is one of the main concerns of users seeking to take advantage of cloud services (CSs). This paper aims at addressing this problem by constructing a privacy regulation aware cloud service composition under considering the multi-service provision characteristics of CSPs. Firstly, it models user privacy requirements to formulate the problem of privacy regulation aware service selection for MPCSC (PSSM). Then, it proposes a pre-processed Kuhn–Munkres (KM) algorithm-based approach to solving the PSSM problem. Finally, it demonstrates that the proposed approach is effective and efficient for solving the PSSM problem by conducting a series of simulation experiments.
CAS: Correlation Adaptive Sparse Modeling for Image Denoising

Image restoration techniques generally use intrinsic correlations of image contents to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflecting connections between different image data, but such a difference is not taken into consideration in most existing schemes. Typically, sparse representation based works use the same image data to exploit both local and non-local correlation in shared regularization. This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. The proposed scheme adaptively selects different image data to exploit local and non-local correlation respectively. In particular, to exploit local correlation, the image data of interest is extracted from clustered rows of patch groups that consist of similar image contents. Experimental results on image denoising show that the proposed scheme not only outperforms state-of-the-art sparsity and low rank-based methods, but also surpasses recent successful deep learning-based approaches in terms of PSNR, SSIM, and visual quality.
Cybersecurity maturity assessment framework for higher education institutions in Saudi Arabia

The Saudi Arabia government has proposed different frameworks such as the CITC’s Cybersecurity Regulatory Framework (CRF) and the NCA’s Essential Cybersecurity Controls (ECC) to ensure data and infrastructure security in all IT-based systems. However, these frameworks lack a practical, published mechanism that continuously assesses the organizations’ security level, especially in HEI (Higher Education Institutions) systems. This paper proposes a Cybersecurity Maturity Assessment Framework (SCMAF) for HEIs in Saudi Arabia. SCMAF is a comprehensive, customized security maturity assessment framework for Saudi organizations aligned with local and international security standards. The framework can be used as a self-assessment method to establish the security level and highlight the weaknesses and mitigation plans that need to be implemented. SCMAF is a mapping and codification model for all regulations that the Saudi organizations must comply with. The framework uses different levels of maturity against which the security performance of each organization can be measured. SCMAF is implemented as a lightweight assessment tool that could be provided online through a web-based service or offline by downloading the tool to ensure the organizations’ data privacy. Organizations that apply this framework can assess the security level of their systems, conduct a gap analysis and create a mitigation plan. The assessment results are communicated to the organization using visual score charts per security requirement per level attached with an evaluation report.
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems

Estimating a scene’s depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aiming to applications for resource-constrained platforms (including battery-powered aerial, micro-aerial, and ground vehicles) with a monocular camera being the primary perception module. Following the encoder-decoder structure, the proposed framework consists of two branches, one for depth prediction and another for semantic segmentation. Moreover, network structure optimization is employed to improve its forward inference speed. Exhaustive experiments on three self-generated datasets prove our pipeline’s capability to execute in real-time, achieving higher frame rates than contemporary state-of-the-art frameworks (114.6 frames per second on an NVIDIA Jetson Nano GPU with TensorRT) while maintaining comparable accuracy.
Domain Adaptation for Underwater Image Enhancement

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.
Addressing Overfitting Problem in Deep Learning-Based Solutions for Next Generation Data-Driven Networks

Next-generation networks are data-driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning-based solutions designed for data management and processing in next-generation networks.
Library of 3D Visual Teaching Tools for the Chemistry Classroom Accessible via Sketchfab and Viewable in Augmented Reality

Through the implementation of the free website/smartphone application Sketchfab, we have found a simple means to introduce 3D visual tools into the chemistry classroom. Sketchfab stores 3D models and animations with free cross-platform accessibility. Blender is a free 3D modeling tool that has been used to prepare models and animations for dissemination via Sketchfab. By combining these two tools, we have created and are currently maintaining an ever-growing free library of interactive 3D models on various chemistry topics. Through the Sketchfab smartphone application, these models are viewable in augmented reality on most devices. Topics include molecular motion, isomerism, molecular orbital theory, chirality, and polymerization.
TCIC_FS: Total correlation information coefficient-based feature selection method for high-dimensional data

High-dimensional data have been a challenging problem in classification. Feature selection works as a filter to remove irrelevant or redundant features and has made comparative progress. However, this problem is still challenging because current methods consider only the correlation between two variables while leaving the correlation among multiple variables largely unsolved, and multivariate interactions can contain joint information that cannot be obtained pairwise. Furthermore, many feature selection methods require hyperparameter settings, which require prior knowledge and lack interpretability. Focusing on the above problems, this paper proposes the total correlation information coefficient-based feature selection (TCIC_FS) method to select the optimal solution, which can avoid setting hyperparameters and fully consider the correlations among multiple variables. First, based on a Gaussian copula, the total correlation information coefficient (TCIC) is proposed to evaluate the correlations among multiple variables. Compared with the existing multivariate correlation methods, TCIC can measure a wider range of multivariate correlations, including linear, nonlinear, functional, and nonfunctional correlations. Second, a novel evaluation mechanism based on TCIC is proposed to measure the relevance between features and classes and the redundancy between a single feature and a selected feature subset. Finally, the TCIC_FS method is constructed based on the TCIC and the evaluation mechanism. Compared with the baseline values, the TCIC_FS method has the lowest time complexity and the smallest optimal feature subset obtained by single selection. Therefore, TCIC_FS is more suitable for processing high-dimensional data.
Decentralized Attribute-Based Server-Aid Signature in the Internet of Things

Devices of Internet of Things (IoT) play a significant role in people’s daily life. A large scale of data is generated, collected, and analyzed in these devices, which inevitably faces secure authentication and access control problem. Attribute-based signature (ABS), where a signer signs a message over a set of attributes, plays an elegant tool for privacy-preserving access control and data authentication. In multiauthority ABS scheme, multiple authorities distribute users’ private keys over their different attributes and these attribute authorities are managed by a central authority. Nevertheless, the whole ABS system can be broken if the central authority is compromised. Besides, the multiauthority ABS scheme needs a lot of pairing and exponentiation operations in the verification and signature algorithms. Therefore, it is very expensive for resource-limited devices (e.g., sensors in IoT) to utilize the ABS scheme. In order to solve above problems, we present a decentralized attribute-based server-aid signature (DABSAS) scheme. In the DABSAS scheme, a server can help users execute heavy computation in the signature and verification algorithms. The proposed scheme provides anonymity and unforgeability. In addition, our scheme mitigates the burden of the signature and verification phase. The proposed scheme is proved secure under the well-known computational co-Diffie–Hellman (co-CDH) assumption. Compared with the existing schemes, the presented DABSAS scheme is efficient.
Encrypted Network Traffic Analysis of Secure Instant Messaging Application: A Case Study of Signal Messenger App

Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential artifacts from the encrypted network traffic of the prominent social messenger app Signal (on Android version 9). The analysis of the installed app was conducted over fully encrypted network traffic. By adopting the proposed strategy, the forensic investigator can easily detect encrypted traffic activities such as chatting, media messages, audio, and video calls by looking at the payload patterns. Furthermore, a detailed analysis of the trace files can help to create a list of chat servers and IP addresses of involved parties in the events. As a result, the proposed strategy significantly facilitates extraction of the app’s behavior from encrypted network traffic which can then be used as supportive evidence for forensic investigation.
Data-Driven Symbol Detection Via Model-Based Machine Learning

We present a data-driven framework to symbol detection design that combines machine learning (ML) and model-based algorithms. The resulting data-driven receivers are most suitable for systems where the underlying channel models are poorly understood, highly complex, or do not well-capture the underlying physics. Our approach is unique in that it only replaces the channel-model-based computations with dedicated neural networks that can be trained from a small amount of data, while keeping the general algorithm intact. Our results demonstrate that these techniques can yield performance close to that of model-based algorithms with perfect model knowledge without knowing the exact channel model or state.
Examining the Link Between Stress Level and Cybersecurity Practices of Hospital Staff in Indonesia

Since healthcare information systems have many important data that can attract many adversaries, it is important to take the right steps to prevent data breaches. Recent studies suggested that 85% of breaches involved a human element and the frequent patterns used are social engineerings. Therefore, many studies focus on making a better understanding of human behavior in cybersecurity and the factors that affect cybersecurity practices. However, there are only a few peer-reviewed studies that focus on the link between stress level and cybersecurity practices. In this study, we examined the link between stress level and cybersecurity practices among hospital employees in Indonesia by surveying 99 hospital workers. Perceived Stress Scale (PSS) was used to measure the employees' stress level and a new scale to measure hospital staff's risky cybersecurity practices was proposed. This study showed that both PSS and proposed cybersecurity practices scales are reliable with Cronbach's α value of more than 0.7. The survey results also revealed that hospital worker's higher stress levels correlate significantly with riskier cybersecurity practices (rs = 0.305, p < 0.01). Besides, a higher stress level is also significantly linked to certain cybersecurity practices, such as clicking on a link in an email from an unknown sender, not preventing colleagues from viewing patients' information for a non-therapeutic purpose, posting patient information on social media, ignoring colleagues who engage in negative information security practices, and failing to create strong passwords.
Competitive Teacher for Higher Education: Risk-Based Models of its Development

The significance of the research is determined by its scientific and practical significance. The modern global transformation of the world, in particular, automation, greening, the growing complexity of global risks and challenges, a network-centric society, a digital environment and many other aspects have led to the fact that the society in which the individual lives his life can rightfully be called a VUCA world. The VUCA world is a world of volatility, uncertainty, complexity and ambiguity. This is the world of “turbulent time” or “perfect storm”, when all the factors complicating the situation converge at one time and at one point. Changes in the social, economic and geopolitical spheres associated with digitalization and a change in the technological structure start having a wider impact on many spheres including the education system. “Skills of the future” of both a specialist and a teacher are becoming an increasingly topical problem, while the issue related to the formation of a resource educational environment and the architectonics development of the required competences of the teacher’s personality in the face of the VUCA world come to the fore in the tasks of academic science. The article is aimed at solving the problem of determining the structure, content and conditions for the implementation of pedagogical support to enhance the competitiveness of a higher education teacher for a VUCA world, as well as ensuring the effective formation of teachers’ noxological competences for a VUCA world in the information and educational environment of a higher education institution. The designed pedagogical support will contribute to the effective organization of career development and professional growth of a higher education teacher for a VUCA world. The results of the study will make it possible to contribute to the development of the theory and methodology of higher education in terms of scientific substantiation and selection of the content to form a teacher’s noxological competence in the information and educational environment of a higher education institution for a VUCA world. Giving a more detailed content and precise structure of the teacher’s noxological competence and the proposed classification of modern risks of the educational environment will complement the theoretical foundations of pedagogical risk studies. A model of pedagogical support for the formation of noxological competences of future teachers which is to be theoretically substantiated will give an opportunity to determine the capabilities and technologies of using risk factors in pedagogical education. The expected results are believed to contribute to the solution to the problem of VUCA undertaken by the world social and humanitarian scientific community, which the modern education system in general faces and all subjects of the educational process, in particular.
Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.
UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input

Density-based clustering has the ability to detect arbitrary shaped clusters in any dataset. In recent years, several density peak clustering methods have been reported. Among these, a few need user input(s), but majority use cluster validity indices to provide the best results. In this paper, we propose a density-based user-input-free clustering method named UIFDBC, which is capable of detecting clusters of arbitrary shapes, without depending on any specific cluster validity index. The method is evaluated on 16 synthetic and 7 real-world datasets and compared with 8 recent density-based clustering methods. The results show our method is superior, in general, to its counterparts in terms of discovering arbitrary shaped clusters on tested datasets. The approach also has the ability to handle low-density instances in a special manner to minimize error propagation. Our method is available as an R package and can be downloaded by clicking the link https://sites.google.com/view/hussinchowdhury/software.