Reviewerly/exHarmony huggingface dataset:
Interview with Courtney Dieckbrader: Seniors’ Program Specialist at QMUNITY

Courtney Dieckbrader is the specialist in Seniors’ Programming for QMUNITY, the British Columbia (BC) queer, trans and Two Spirit Resource Center. As such, she receives many direct inquiries from 55+ elders across the province and is the person who fields these inquiries and attempts to find and offer support and resources to GSM elders across the province. In this interview, Dieckbrader talks about QMUNITY’s goals in partnering with SFU’s research team on the Indigo Project, QMUNITY’s ongoing role in providing therapeutic support to participants, and also, some of the project’s chief challenges and achievements.
Driver behaviour recognition based on eye gaze

Characterisation of the driver’s nondriving activities (NDAs) is of great importance to the design of the take-over control strategy in Level 3 automation. Gaze estimation is a typical approach to monitor the driver’s behaviour since the eye gaze is normally engaged with the human activities. However, current eye-gaze tracking techniques are either costly or intrusive which limits their applicability in vehicles. This chapter proposes a low-cost and nonintrusive dual camera–based gaze mapping system that visualises the driver’s gaze using a heat map. The challenges introduced by complex head movement during NDAs and camera distortion are addressed by proposing a nonlinear polynomial model to establish the relationship between the face features and eye gaze on the simulated driver’s view. The Root Mean Square Error of this system in the in-vehicle experiment for the X and Y directions is 7.80±5.99 and 4.64±3.47 pixels, respectively, with the image resolution of 1440×1080 pixels. This system is successfully demonstrated to evaluate three NDAs with visual attention. This technique, acting as a generic tool to monitor driver’s visual attention, will have wide applications on NDA characterisation for intelligent design of take-over strategy and driving environment awareness for current and future automated vehicles.
Driver behaviour recognition based on the fusion of head movement and hand movement

Accurate recognition of non-driving activity (NDA) is important for the design of intelligent human–machine interface to achieve a smooth and safe control transition in the conditionally automated driving vehicle. However, some characteristics of such activities such as limited-extent movement and similar background pose a challenge to the existing 3D convolutional neural network–based action recognition methods. In this chapter, a dual-stream 3D residual network, named DS3D ResNet, has been proposed to enhance the learning of spatio-temporal representation and improve the performance of activity recognition. Specifically, a parallel 2-stream structure is introduced to focus on the learning of short-time spatial representation and small-region temporal representation. A 2-feed driver behaviour monitoring framework is further built to classify four types of NDAs and two types of driving behaviour based on the driver’s head and hand movement. A novel NDA dataset has been constructed for the evaluation, where the proposed DS3D ResNet achieves 83.35% average accuracy, at least 5% above three selected state-of-the-art methods. Furthermore, this chapter investigates the spatio-temporal features learned in the hidden layer through the saliency map, which explains the superiority of the proposed model on the selected NDAs.
Does Attention to Global Warming Cool Off Cryptocurrencies?

We address this question by investigating how Google users’ search on global warming is associated with the trading activeness of Ethereum, the second largest cryptocurrency in the world. Exploiting Ethereum’s transition to a green blockchain (i.e., “The Merge”), we find that before the transition, Google search on global warming is negatively associated with the transaction volumes of Ethereum, but the relationship turns significantly positive after The Merge, suggesting online users’ green orientation towards energy-efficient cryptocurrencies. Our analysis sheds light upon an emerging research on the environmental issues in crypto markets.
Decentralized Autonomous Organizations (DAOs): Catalysts for Enhanced Market Efficiency

The objective of this study is to determine the factors that contribute to market efficiency in the crypto-asset market. Despite prior evidence from the literature demonstrating variations in efficiency across assets and time, few have investigated driving factors beyond liquidity. By using a dataset of 122 crypto-assets with imbalanced data, our analysis discovers that both market conditions and inherent characteristics of crypto-assets significantly impact the predictability of their returns. Specifically, market efficiency is positively associated with increased liquidity and age. Our findings highlight that DAO projects demonstrate greater efficiency compared to non-DAO projects. This result suggests that transparent decentralized decision-making model can help reduce information asymmetry leading to a more efficient market pricing.
Creation of An Institution of Mentoring at the Language Departments of the University (Создание института наставничества на языковых кафедрах вуза)

The mentoring in education is one of the central ones in the national projects. The mentoring is a form of ensuring the professional development of teaching staff in the workplace and their readiness for the qualified performance of their duties. The task of the mentor is to influence the young teachers through his involvement in professional activities and interaction with him for a certain time, for example during the semester. There are different forms of mentoring, individual, group and scientific and methodological. There are three stages in the organization of the mentors work. There are organizational, training, productive. The activities of the mentor should be regulated by relevant documents. Encouragement of mentors can be in form of a financial bonus or in accordance with the provisions of an effective contract.
Getting Started with Apache Spark

Now that you have an understanding of what Spark is and how it works, we can get you set up to start using it. In this chapter, I’ll provide download and installation instructions and cover Spark command-line utilities in detail. I’ll also review Spark application concepts, as well as transformations, actions, immutability, and lazy evaluation.
Special Effects and Virtual Reality

This chapter contains selected special topics in computer graphics. Since virtual reality (VR) applications are an important application area of computer graphics, factors are explained that can create a high degree of immersion in such applications so that a user feels truly present in a virtual environment. Simulations of fog, particle systems or dynamic surfaces can create realistic effects in computer graphics scenes. For interactive computer graphics, the selection of objects and the detection and handling of collisions are important. This allows users to explore and manipulate three-dimensional virtual worlds. For these topics, this chapter contains the technical basics, supported by OpenGL examples for most topics. Since the sense of hearing contributes greatly to immersion and thus presence, this chapter presents some important findings and technical principles for auralising acoustic scenes. The last part of this chapter contains a summary of important factors for the creation of a visual impression of depth in a scene. The focus is on seeing with both eyes (binocular vision) and the technical reproduction through stereoscopic output techniques. These techniques are used in 3D television and virtual reality headsets.
Spark Low-Level API

Although the term application programming interface (API) is mostly used regarding web services or resources shared through the Web, when it comes to Apache Spark, it possesses an additional meaning, referring to the way users can interact with the framework.
CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and benchmarked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types.
Natural Language Processing

Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine reports from the healthcare facilities, secondary data like attendance monitoring at workplaces and schools, the web, and the media play a significant informational source with more than 60% of the initial outbreak reporting to the informal sources. Through the application of natural language processing methods and machine learning technologies, a pipeline is developed which extracts the critical entities like country, confirmed case counts, disease, and case dates, which are mandatory entities from the epidemiological article and are saved in the database thereby facilitating the data entry easier. The advantages are the facilitation of relevant score articles shown first, thereby providing the web service results termed EventEpi integrated into the Event Based Surveillance (EBS) workflows.
Promethean Utilization of Resources Using Honeybee Optimization Techniques in Cloud Computing with Reference to Pandemic Health Care

The introduction of COVID-19 put a great strain on the whole health care system in several nations throughout the world owing to the influx of newly infected patients. The pressure was increased by the necessity of maintaining the water, cleanliness, sanitation, and sewage standards, which created new difficulties when managing patients in hospitals and health care facilities. Technologies such as cloud computing have a remarkable impact on the fight against the coronavirus as they significantly reduce its consequences. In terms of contribution, cloud computing and storage are ahead of the game. The vast amount of patient data is stored, tracked, forecasted, and examined via cloud services. This makes the laborious work of information processing a rather simple one and benefits humanity. Cloud computing, a precise clocklike model, characterizes services in which information and assets are rehabilitated by means of the web through online tools and applications. Cloud computing is an assemblage of assets concertinaed together and accustomed to clients on pay based on situation premise. The purpose of adjusting load Virtual Machines (VMs) is to demote energy utilization and afford the most extreme resource wield, consequently diminishing the number of job dismissals. As the cloud swells with millions of users, the load glorifies acclimating and turns into a test for cloud purveyors. The focus of this paper is to examine systematically the idea of load adjusting in cloud encryption and, furthermore, enclose the correlation of different existing loads. This proposed load balancing resource allocation honey Bee optimization algorithm encompasses parameters including fault rate, computation cost, response time, degree of imbalance, and execution time on a cloud datacenter to yield rendition with Quality of Service (QoS) in cloud infrastructure as a service.
Adaptive Sailfish Optimization-Contrast Limited Adaptive Histogram Equalization (ASFO-CLAHE) for Hyperparameter Tuning in Image Enhancement

Mammography is the major breast cancer imaging method. Mammogram contrast is weak, especially in thick and glandular areas. The radiologist may miss diagnostically important microcalcifications. Image enhancement (IE) helps radiologists make accurate cancer diagnoses. Previously, contrast enhancement (CE) techniques were created. Most CE methods require global or local histogram modifications. CLAHE uses local CE to combat global scheme restrictions. This method depends on number of tiles (NT) and clip limit (CL). Existing CLAHE and brightness-preserving methods limit contrast amplification by clipping the histogram at a fixed CL. This CL is accurate and mammogram-free. All pixels in the mammogram's window region are changed. This work aims to improve mammography image quality for early breast cancer detection. ASFO-CLAHE is an optimization and histogram-based method to boost mammography contrast. ASFO optimizes mammography contrast by automating CL and NT. ASFO produces new ensemble members from mini-batches of photographs as more photos become available, making CL flexible and NT variable without human intervention. CBIS-DDSM evaluates recommended enhancement and current various histogram equalization (HE) techniques. Image quality measuring techniques including PSNR, SSIM, MSE, and CII are utilized to evaluate these methods.
No-Reference Metrics for Images Quality Estimation in a Face Recognition Task

No-reference metrics (BRISQUE, NIQE, PIQE) and full-reference metrics (PSNR, SSIM) for face recognition quality estimation are presented in this paper. Different noise types are considered: gaussian and salt &pepper noise with SNR in range 0...40 dB. Regression models between full-reference and no-reference metrics are considered. The quality of the regression models is estimated via $$R^2$$ and RMSE.
Application of the SIFT Algorithm in the Architecture of a Convolutional Neural Network for Human Face Recognition

Solving the problem of pattern recognition is one of the areas of research in the field of digital video signal processing. Recognition of a person’s face in a real-time video data stream requires the use of advanced algorithms. Traditional recognition methods include neural network architectures for pattern recognition. To solve the problem of identifying singular points that characterize a person’s face, this paper proposes a neural network architecture that includes the method of scale-invariant feature transformation. Experimental modeling showed an increase in recognition accuracy and a decrease in the time required for training in comparison with the known neural network architecture. Software simulation showed reliable recognition of a person’s face at various angles of head rotation and overlapping of a person’s face. The results obtained can be effectively applied in various video surveillance, control and other systems that require recognition of a person’s face.
Chicken Swarm-Based Feature Selection with Optimal Deep Belief Network for Thyroid Cancer Detection and Classification

The combination of machine-learning (ML) approaches in health care is a massive advantage designed to cure the illness of millions of people. Several efforts are used by researchers to detect and provide primary phase insights into cancer analysis. Thyroid cancer is one of the worst forms of cancer, and the chances of survival for those who are diagnosed with it is bleak even today. Early stage detection of cancer is not an easy task; however, if it can be detected, it can be curable. This study develops a Chicken Swarm Optimization Feature Selection (CSOFS) with Optimal Deep Belief Network (ODBN) model named CSOFS-ODBN for Thyroid Cancer Detection and Classification. The proposed CSOFS-ODBN model aims to identify and classify the presence of thyroid cancer. Initially, the CSOFS-ODBN model undergoes a min-max data normalization approach to pre-process the input data. Besides, the CSOFS algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs thyroid cancer classification. Finally, the Adam optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the CSOFS-ODBN model, a wide-ranging experimental analysis is performed, and the results report the supremacy of the CSOFS-ODBN model, and the tool used for evaluation is named python.
Guaranteed Safe States in Speed Space for Ships Collision Avoidance Problem

The article has considered approach to ships collision avoidance problem for infinite time horizon in unpredictable navigation environment on the basis of guaranteed safe states in speed space. The possible collisions areas are determined on the basis of the reachable sets of target vessels in the speed space. By avoiding getting into the possible collisions area of each target vessel, the controlled vessel prevents any possible collision, while not having explicit information about the expected trajectories of the target vessels. The safety of navigation is achieved by a combination of reachable sets as functions of time for target vessels, taking into account their dynamic capabilities and the representation of sets in speed space. The control option, which is outside the reachable set of the target vessel in the speed space, is guaranteed to prevent a collision even under conditions of unpredictable motion of the target vessel. On the basis of the target vessels reachable sets in speed space the guaranteed safe states of controlled vessel are defined. Recommendations for practical use have been given. Performed researches contribute to the improvement of the ship’s handling methods.
Contrast Enhancement of Digital Mammograms Based on High Contrast-Limited Adaptive Histogram Equalisation

Breast cancer is one of the leading causes of death in women around the world. Mammography is the most common screening method for an early finding of breast cancer. The major goal of mammography is to identify tiny lumps as soon as possible because they might lead to cancer. However, standard mammography makes it difficult to detect very small tumours. Image processing technologies in digital mammography methods permit for modification of small dissimilarities in image contrast. Enhancement is used to bring out specific mammographic aspects such as mass and micro-calcification, as well as to highlight certain characteristics for early identification and diagnosis of breast cancer. Increasing the contrast of a medical image is a crucial first step in the analysing process. The contrast and brightness preservation of digital mammograms with the high contrast-limited adaptive histogram equalisation (HCLAHE) method is improved. This research checks the proposed strategy against well-established methods like HE (histogram equalisation), AHE (adaptive histogram equalisation), and CLAHE (contrast-limited adaptive histogram equalisation). It has a significant impact on the characteristic of minimising the absolute mean brightness error between input and output images, and it displays increased entropy to extract possible information from variables to reduce visualisation difficulties. The results reveal that using the proposed methodology can outperform in terms of contrast enhancement and brightness preservation in a digital mammogram.
Implementation of a ‘Useful’ Information Measure for Healthcare Decision Making

Cryptography, molecular biology, natural language processing, and statistical inference are a few fields where information theory is used. It’s also utilized in medical science. In this article, we show how principles from information theory have been applied to improve medical decision-making in various ways. We begin with an overview of information theory and the notions of available data and entropy. In this study, we show how ‘useful’ relative entropy may be utilized to determine which diagnostic seems to be the utmost useful at a given stage of analysis. Shannon information may be utilized to determine the range of standards of medical reports across which the test offers relevant information about the patient’s condition when the result is binary. Of course, this isn’t the only approach available, but it may create a visually appealing representation. Next, the article introduces the more complex ideas of ‘useful’ conditional entropy and ‘useful’ mutual information, demonstrating how they may be used to prioritize clinical testing and uncover redundancies. Finally, we evaluate our findings thus far and suggest that providing a well-informed framework for the extensive use of theory of information to clinical managerial problems is worthwhile.
Fast Calculation of Parameters of Parallelepipedal Nets for Integration and Interpolation

This work is devoted to the construction and analysis of search algorithms for two-dimensional parallelepipedal nets with the best parameters. The paper presents algorithms for computing the hyperbolic parameter, Bykovskii sums, and the H-function. Sequences of the best lattices are compared for each parameter. The connection between the lattices parameters (N, a) and the value of the hyperbolic parameter is investigated. In particular, the relationship between the value of a hyperbolic parameter and the representation of the number a/N as a continued fraction is shown. For lattices with an increasing hyperbolic parameter, a conjecture is put forward about the structure of the continued fraction representation of the number a/N.
RNS Reverse Conversion Algorithm and Parity Detection for the Wide Arbitrary Moduli Set

Due to absence of long carry propagation logic in its modular adders and multipliers Residue Number System (RNS) provides advantages for performing arithmetic computations in the hardware accelerators targeting multiplications and additions only. However, a conversion from RNS to Binary Number System (BNS) is a well-known complex operation requiring larger hardware area and consuming more power than modular multipliers which significantly limits applicability of RNS. Many non-modular operation (e.g. comparison, division) implementations are also based on reverse conversion. In this article we demonstrate a variation of Chinese Reminder Theorem (CRT) based algorithm for RNS to BNS reverse conversion for an arbitrary moduli set. We propose an approximate method to overcome the heaviest $$mod\;M$$ operation via the rank of number calculation and demonstrate its correctness on the covered number range. We show that algorithm parameters selection has no restrictions on moduli set and needed parameters can always be found assuming that covered maximum value is less than $$M - 1$$ . Hardware implementation of the new RNS reverse conversion algorithm based on CRT is more than 30% faster and consumes up to 80% less power than implementations of CRT based reverse conversion algorithms using other known ways to compute the final reduction operation.