Reviewerly/exHarmony huggingface dataset:
Validation of Complex Control Systems with Heterogeneous Digital Models in Industry 4.0 Framework

The precise evaluation of the system design and characteristics is a challenge for experts and engineers. This paper considers the problem of the development and application of a digital twin to assess cyberphysical systems. We analyze the details of digital twin applications at different lifecycle stages. The work reviews and summarizes properties of models concerning the digital and physical components of a cyberphysical system (CPS). The other issue of a CPS is increasing cybersecurity threat for objects, so special attention is paid to the heterogeneous digital twin usage scenarios to improve CPS cybersecurity. The article also details the heterogeneous digital twin’s implementation for a real upper-level control system of a nuclear power plant. The presented heterogeneous digital twin combines virtual machines with real equipment, namely hardware-in-the-loop (HiL) components. The achievements and drawbacks of the implemented model, including single timescale maintaining challenges, are discussed.
Salt content in saline-alkali soil detection using visible-near infrared spectroscopy and a 2D deep learning

Quickly measuring the salt content in saline-alkali soil (SAS) is an important task. This study proposes a method for rapid detection of salt content. First, we collected the SAS samples and measured the spectral data of these samples with a visible-near infrared spectrometer. Second, a method of converting one-dimensional into two-dimensional spectral data is proposed. Finally, based on convolutional neural network, gravitational search algorithm and reservoir computing extreme learning machine, a salt detection model is constructed. The experimental results show that our proposed method can effectively detect the salt content of SAS with the coefficient of determination value is 0.9 and the root-mean-square error value is 1.55. This method can achieve online rapid detection of salt content. Compared with chemical analysis method, the proposed method saves time and cost.
On the imperative of thinking through the ethical, health equity, and social justice possibilities and limits of digital technologies in public health

The COVID-19 pandemic has demonstrated both the positive and negative use, usefulness, and impact of digital technologies in public health. Digitalization can help advance and sustain the core functions of public health, including health promotion and prevention, epidemiological surveillance, and response to emergent health issues. Digital technologies are thus—in some areas of public discourse—presented as being both necessary and inevitable requirements to address routine and emergency public health issues. However, the circumstances, ways, and extent to which they apply remain a subject of critical reflection and empirical investigation. In this commentary, we argue that we must think through the use of digital technologies in public health and that their usefulness must be assessed in relation to their short- and long-term ethical, health equity, and social justice implications. Neither a sense of digital technological optimism and determinism nor the demands of addressing pressing public health issues should override critical assessment before development and implementation. The urgency of addressing public health emergencies such as the ongoing COVID-19 pandemic requires prompt and effective action, including action facilitated by digital technologies. Nevertheless, a sense of urgency cannot be an excuse or a substitute for a critical assessment of the tools employed.
TopHUI: Top-k high-utility itemset mining with negative utility

In the field of data science, utility-driven data mining has become an emergent intelligent technique with wide applications. The existing utility mining algorithms usually discover all the patterns satisfying a given minimum utility threshold. However, a huge number of return results is not intuitive, not interpretable, and not easy for users to understand. Besides, it is often difficult and time-consuming for users to set a proper minimum utility threshold that is quite sensitive to the mining results. To address these issues, the problem of top-k high-utility itemset mining has been studied. In this paper, we present an efficient algorithm (named TopHUI) for finding top-k high-utility itemsets from transactional database that contains both positive and negative utility. This algorithm utilizes the positive-and-negative utility-list (PNU-list) to store the compress information, including positive, negative, and remaining utility. Besides, several threshold raising strategies and pruning strategies are proposed to prune the search space. Finally, some extensive experiments were conducted to evaluate the performance of the proposed TopHUI algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency.
A cost-effective computer-vision based breast cancer diagnosis

In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.
DCLNet: Dual Closed-loop Networks for face super-resolution

Recently, deep-learning based face super-resolution methods have succeeded in hallucinating high-resolution face images from low-resolution inputs. However, since the given face images have tiny resolution and arbitrary characteristics which need to be reconstructed at high magnification factors, existing methods still suffer from large space of possible mapping functions, resulting in the limited performance in producing sharp textures. In this paper, we propose a novel CNN-based Dual Closed-loop Network (DCLNet) to minimize the possible mapping space. To that end, we design two dual learning networks to form the dual closed-loop structure with a primary face super-resolution network, which can provide the primary branch with additional prior constraint to guide the essential facial features restoring. Our work represents the first attempt to introduce multiple dual learning networks into face super-resolution model to constrain the possible mapping space. In addition, a progressive facial prior estimation framework and a new prior-guided feature enhancement module are presented to integrate the facial prior knowledge and guide the face image super-resolution. By generating multiple facial components maps for the activation of essential facial parts, our enhancement module can address the difficulty in learning and integrating strong priors into a face super-resolution model. In this way, the collaboration between the face super-resolution and alignment processes can be enhanced with effect. Extensive experiments are implemented on CelebA and Helen dataset, showing that our proposed method can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Towards understanding the design of bodily integration

In the field of human-computer interaction, the term “integration” describes an emergent paradigm in which the human and the computer are tightly coupled. Our previous research has contributed to this paradigm through the design of “bodily integrated” systems, where the human body and the computing machinery are coupled in a way that allows bidirectional actuation. In this article, we build on this design research: we identify gaps in knowledge regarding bodily integration design and propose, in response, two key dimensions along which bodily integration systems can be categorized: bodily agency and bodily ownership. Conceiving each dimension from low to high allows us to define a four-quadrant design space that highlights key user experiences of bodily integration: Super-Body, Tele-Body, Chauffeured-Body, and Possessed-Body. We demonstrate how this design space can be used to analyze bodily integration design using three of our own bodily integration systems as illustrative examples. We also identify seven design strategies for interaction designers to design future bodily integration systems: turn-taking, safety, ease-in, movement, sensations & perceptions, personalization, and bystanders. Ultimately, we hope to advance the emergent integration paradigm through a body-centric design perspective.
UAV Forensic Analysis and Software Tools Assessment: DJI Phantom 4 and Matrice 210 as Case Studies

Unmanned Aerial Vehicles (UAVs) also known as drones have created many challenges to the digital forensic field. These challenges are introduced in all processes of the digital forensic investigation (i.e., identification, preservation, examination, documentation, and reporting). From identification of evidence to reporting, there are several challenges caused by the data type, source of evidence, and multiple components that operate UAVs. In this paper, we comprehensively reviewed the current UAV forensic investigative techniques from several perspectives. Moreover, the contributions of this paper are as follows: (1) discovery of personal identifiable information, (2) test and evaluation of currently available forensic software tools, (3) discussion on data storage mechanism and evidence structure in two DJI UAV models (e.g., Phantom 4 and Matrice 210), and (4) exploration of flight trajectories recovered from UAVs using a three-dimensional (3D) visualization software. The aforementioned contributions aim to aid digital investigators to encounter challenges posed by UAVs. In addition, we apply our testing, evaluation, and analysis on the two selected models including DJI Matrice 210, which have not been presented in previous works.
Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors

Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. Lung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. Results show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. As ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.
Multiparty verification in image secret sharing

Multiparties in image secret sharing (ISS) need to verify (detect and recognize) each other, which is seldom considered and realized in traditional methods. In this paper, we introduce the definition of multiparty verification. It includes two stages, i.e., a detection stage and a recognition stage, with evaluation methods that are also discussed. A multiparty verification scheme without pixel expansion is developed, which is suitable for both dealer attendance and nonattendance. The classic hash function, public key cryptography and visual cryptography are technically fused in the developed scheme. In the shadow distribution phase, each participant can verify the received shadow using his private key. In the restoration phase, for the case of dealer attendance, he can verify each shadow received using his secret key; for the case of dealer nonattendance, participants can verify each other before exchanging their shadows. We conduct analyses and illustrations to validate the developed scheme.
Natural Sciences Meet Social Sciences: Census Data Analytics for Detecting Home Language Shifts

As we are living in a global environment, it is not unusual to have more than one languages or dialects used in a country. Examples include Canada in the Americas, Singapore in Asia, and Switzerland in Europe. With the initiatives of globalization, many people immigrate or live in a country other than their birthplace. As a result, different people in the same country may have different home language (i.e., first language). For instance, as a nation composed of a highly diverse language population, Canada provides a unique opportunity to study the factors causing certain languages (or families of language) to be lost over subsequent generations among allophones (i.e., people whose mother tongue is neither English or French). In this paper, we focus on census data analytics. Specifically, we analyze census microdata by exploring machine learning and data mining techniques-such as decision tree induction, random forest, and categorical naive Bayes-to study the influence of various social and economic factors on the probability that allophones adopt official languages as their language spoken at home. This study is a showcase where natural sciences and engineering (NSE) meet social sciences, in which NSE solutions (e.g., census data analytics) are applicable for the study of social science related phenomena (e.g., successful detection of shifts in home languages).
Infrared image denoising based on the variance-stabilizing transform and the dual-domain filter

In the past decades, image denoising has been widely studied as a basic technology for image processing. However, most denoising methods are designed for Gaussian noise, while few researches focus on the suppression of mixed Poisson-Gaussian noise, which usually found in infrared images. In order to remove the mixed noise in infrared images, this paper proposes a denoising method based on the variance-stabilizing transform (VST) and the dual-domain filter (DDF). We transform the mixed noise data into an approximate Gaussian distribution with uniform variance through the VST and then adopt the improved DDF to denoise the transformed data. Finally, we apply the closed approximation inverse of the VST to the denoised data for the final denoising estimate. The denoising results of infrared images with different intensity all effectively suppress the mixed noise and retain abundant details. The quality and quantity comparisons with nine other methods reveal that our method can achieve a superior performance.
A color image encryption scheme reducing the correlations between R, G, B components

In this paper, a new color image encryption method based on confusion and diffusion processes has been introduced. A chaotic system based sub-block image swapping has been proposed in order to permutate the positions of the pixels as a confusion method. Moreover, the pixels in sub-block have been spirally shifted to increase the confusion level. Modulo and XOR operations which are based on a chaotic system have been suggested in order to transform the values of the pixels as a diffusion method. To do that, each pixel value is XORed with the next pixel and then modulo operation has been performed for the current and next pixels throughout the image. Double Random Phase Encoding (DRPE) method is also utilized as an optical setup in this study. In addition, an algorithm is suggested in order to reduce the correlations between R, G, B components of the color image. The security analyses performing histogram analysis, correlation coefficient analysis of two adjacent pixels, information entropy analysis, chi-squared test and differential attack have been carried out in order to show that the proposed algorithm scheme is robust against differential, statistical and exhaustive attacks. Moreover, secret key sensitivity, initial condition sensitivity, cropping attack, noise test and plaintext attacks have been done to evaluate the performance of the introduced algorithm. All analysis results have been carried out in MATLAB environment.
Privacy-Aware Federated Learning for Page Recommendation

Traditional page recommendation models are endangered by stricter privacy regulations, such as the General Data Protection Regulation (GDPR). The performance of these models suffer when only a part of the users share their personal data, such as cookies, with web servers, while the rest of the users choose to opt-out from sharing these data. Furthermore, these models are not designed to provide recommendations for users who do not share their data. This paper addresses the question of how to provide good page recommendations to all users, independent of their privacy attitudes. We propose Fed4Rec, a privacy-preserving framework for page recommendation based on federated learning (FL) and model-agnostic meta-learning (MAML), which allows machine learning models to train on data collected from both public users, who share data with the server, and private users, who do not share data with the server. Fed4Rec enables recommendations for both public users, computed at the server, and private users, computed at their local devices. Private users' data are stored only on user devices and never shared with the server. FL is used to train on local data, and Fed4Rec shares with the server only partial model parameters, computed on local devices. MAML is used to jointly train on the public data and the model parameters from the private users. We compare Fed4Rec against several baseline frameworks, using a publicly available dataset from a large news portal. The results show that Fed4Rec outperforms the baselines in terms of recommendation accuracy. We also conduct one ablation study to examine the impact of varying the ratio between the number of public and private users. Fed4Rec performs better than the baselines for all ratios, but it is especially beneficial w hen t he p ercentage of public users is low.
CNN and multi-feature extraction based denoising of CT images

In the past decades, Computed Tomography (CT) images have been widely used and played a critically role in medical diagnosis. In low-dose CT images, reducing the radiation dose can reduce damage to patients, but at the same time, the projected image is contaminated with noise, resulting in a lot of noise in the reconstructed medical CT image, which can affect the clinical diagnosis. Based on the network structure of GoogleNet and Inception series, and combined with deep residual learning and convolutional neural networks, a novel denoising method with multi-feature extraction is proposed in this paper for medical CT images. The extraction of shallow multi-features in medical CT images is achieved by combining convolution filters of different sizes. This is useful for obtaining more detailed feature information in the image. Through the fusion of image features, the noise learning in medical CT images is realized. The developed neural network is more targeted to the removal of noise in the medical CT image. Experimental results show the denoised medical CT images can better retain edge and texture area details with the proposed method. Compared with the existing methods, the method proposed in this paper improves the denoising effect of medical CT images.
Project-based learning in a computer modelling course

Abstract The paper reports authors’ experience of implementing educational projects in a computer modelling course offered to the students majoring in “Secondary Education (Computer Science)” at Ternopil Volodymyr Hnatiuk National Pedagogical University. We analyze approaches to teaching mathematical and computer modelling such as: integration of modelling tasks, naturalistic case study, using of role-playing games, possibilities of STEM-education, motivation and positive attitude to modelling training, etc. Then we illustrate the implementation of the project to study the population dynamics of the grape snail Helix pomatia. The implementation of the project splits into several stages: formulation of the problem, presentation of project tasks, brainstorming, development, testing, presentation of results. The study was conducted at Ternopil Volodymyr Hnatiuk National Pedagogical University within the Norwegian-Ukrainian Project “Development of students' mathematical competencies through Digital Mathematical Modelling” (DeDiMaMo) in partnership with the University of Agder (Norway) and Borys Grinchenko Kyiv University.
SplitSR

Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning models with significant memory requirements, so they have yet to be deployed on mobile devices and instead operate in the cloud to achieve feasible inference time. This shortcoming prevents existing SR methods from being used in applications that require near real-time latency. In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock. The SplitSRBlock supports channel-splitting, allowing the residual blocks to retain spatial information while reducing the computation in the channel dimension. SplitSR has a hybrid design consisting of standard convolutional blocks and lightweight residual blocks, allowing people to tune SplitSR for their computational budget. We evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy and up to 5× faster inference than previous approaches. We then deploy our model onto a smartphone in an app called ZoomSR to demonstrate the first-ever instance of on-device, deep learning-based SR. We conducted a user study with 15 participants to have them assess the perceived quality of images that were post-processed by SplitSR. Relative to bilinear interpolation --- the existing standard for on-device SR --- participants showed a statistically significant preference when looking at both images (Z=-9.270, p&lt;0.01) and text (Z=-6.486, p&lt;0.01).
Algorithmic and human decision making: for a double standard of transparency

Should decision-making algorithms be held to higher standards of transparency than human beings? The way we answer this question directly impacts what we demand from explainable algorithms, how we govern them via regulatory proposals, and how explainable algorithms may help resolve the social problems associated with decision making supported by artificial intelligence. Some argue that algorithms and humans should be held to the same standards of transparency and that a double standard of transparency is hardly justified. We give two arguments to the contrary and specify two kinds of situations for which higher standards of transparency are required from algorithmic decisions as compared to humans. Our arguments have direct implications on the demands from explainable algorithms in decision-making contexts such as automated transportation.