Reviewerly/exHarmony huggingface dataset:
Information and communication technologies in teaching Biology in the context of distance learning

Education has always been one of the basic needs of any person. Today, its implementation is provided by many means and methods, among which information technologies occupy a special place. The use of Information and Communication Technologies (ICT) in the organisation of distance learning in the course of teaching the subject «Biology» are discussed in article. This paper shows various instant messengers and platforms for online communication that are actively used in training: Ed App, Zoom, WhatsApp, Google Classroom and others. ICT are affected the development of education, especially in the form of distance learning. The experimentally proven role of Power Point presentation and its impact on the achievements of future biology teachers and their relationship to biology determines the advantage of using it in the classroom. The use of these technologies will make the course of classes more interesting and understandable, develop pupils' self-confidence, increase their level of oral communication and a positive attitude towards learning. The organisation of distance learning makes the learning process such in which the pupils themselves are responsible for the assimilation of the material, which increases their self-regulation. The development of ICT makes it possible to introduce new teaching methods and means of perception of information educational content into the education system. Analysis of educational platforms and experience in the use of ICT in the field of management makes it possible to increase the list of acceptable services for use in the educational process in the subject of «Biology».
Effects of Clutching Mechanism on Remote Object Manipulation Tasks

Remote object manipulation in practice is often an iterative process that needs clutching. However, while many interaction techniques have been designed for manipulating remote objects, clutching mechanism is often an important but overlooked aspect in manipulation tasks. In this paper, we evaluate the effects of clutching mechanism on remote object manipulation tasks, which compares two clutching mechanisms under various tasks settings. The results suggested that an efficient clutching mechanism can effectively improve the usability in remote object manipulation tasks.
ESCA: Effective System Call Aggregation for Event-Driven Servers

The switches between a non-privileged application and the OS kernel running in the CPU&#x2019;s supervisor mode have been inducing performance costs despite manufacturer efforts to provide special instructions for such transition. Software that heavily interacts with the underlying OS (e.g., I/O intensive and event-driven applications) suffers from system call overhead. To deteriorate this situation, security vulnerabilities in modern processors have prompted kernel mitigations that further increase the transition overhead. Particularly system-call-heavy applications have been reported to be slowed down by up to 30% with kernel page-table isolation (KPTI), the widely deployed mitigation for the Meltdown vulnerability. To decouple system calls from mode transitions, we revisit an old idea known as system-call batching or multi-calls: the bundling of system calls into a combined call, which only incurs the mode-transition costs of a single one. And then, we have implemented ESCA scheme to adapt system-call batching to Linux-based servers in the light of Meltdown and Spectre, effectively eliminating the slowdown of KPTI-affected applications. Our evaluation shows that the throughputs of real-world applications, benefiting from ESCA, can be improved with only 2 lines of code changed respectively: Nginx by up to 12%, lighttpd by up to 23%, and Redis by 4%. Meanwhile, using aggregated transitions, our approach allows faster system calls interleaved with full compatibility but without requiring Linux kernel patches.
IMPReSS: Improved Multi-Touch Progressive Refinement Selection Strategy

We developed a progressive refinement technique for VR object selection using a smartphone as a controller. Our technique, IMPReSS, combines conventional progressive refinement selection with the marking menu-based CountMarks. CountMarks uses multi-finger touch gestures to &#x201C;short-circuit&#x201D; multi-item marking menus, allowing users to indicate a specific item in a sub-menu by pressing a specific number of fingers on the screen while swiping in the direction of the desired menu. IMPReSS uses this idea to reduce the number of refinements necessary during progressive refinement selection. We compared our technique with SQUAD and a multi-touch technique in terms of search time, selection time, and accuracy. The results showed that IMPReSS was both the fastest and most accurate of the techniques, likely due to a combination of tactile feedback from the smartphone screen and the advantage of fewer refinement steps.
Designing a Mixed Reality System for Exploring Genetic Mutation Data of Cancer Patients

Increased availability of cancerous genomics mutation data provides researchers the opportunity to discover associations among genetic mutation patterns within the same organ as well as similar mutation patterns among different organs. However, the complexity, variety, and scale of multi-dimensional data involved in analyzing mutations across organs poses challenges for clinicians and researchers to draw such relationships. We present a prototype application that lever-ages multiple-coordinated views in mixed reality (MR) to enable investigations of genetic mutation patterns and the organs affected by cancer. We believe our prototype has the potential to enhance data and association discovery within and across different organs.
Towards Retargetable Animations for Industrial Augmented Reality

The adoption of Augmented Reality (AR) within manufacturing has surged. However, updating animations due to upstream changes or design variations is expensive. Currently, platform-agnostic stan-dards for computer-aided design files do not adequately specify kine-matic animations. Consequently, animations require manual, indi-vidual updates. We showcase a method for implementing geometry-independent AR assembly animations using skeletal armatures, a technique widely used in the entertainment industry. This technique allows upstream engineering changes to propagate through to the AR assembly visualization, leading to a more automated pipeline for handling animations in Industrial AR systems.
Path Smoothing Using Bézier Curve with Maneuver Constraint of Fixed-Wing UAV

This paper discusses the problem of path planning for fixed-wing unmanned aerial vehicles (UAVs) in a complex static 3D environment. The main idea is to produce a smooth path that conforms to the safety corridor constraints and maneuverability of the fixed-wing UAV. The representation of a static 3D environment is created using the visibility graph method, where this method generates waypoints and all possible paths in the environment. After being given a starting point and a destination point, the Theta* algorithm works to calculate the shortest path on a 3D environment. The shortest path obtained is then smoothed using a Bézier curve-based smoothing path planning method that meets the proposed constraints. The results of this proposed method are compared with previous work by applying the relevant comparative criteria. The simulation results show that the proposed method is safer than the previous method with the smallest distance to the obstacle of 4.73 m.
enhanced anarchic society optimization technique for the classification of ultrasound thyroid images using ILBP

In the recent times, Thyroid Nodules (TNs) is a generic cancer of the thyroid gland, which impacts close to 20% of the population worldwide and nearly 50% of 60- year-old individuals. The conventional diagnostic method, relying on the expertise of doctors, shows a huge drawback that the diagnosis result very much relies on the individual knowledge and experience of the physician. As a result, efficacy of diagnosis is confined, and it varies with the doctor’s experience. To combat this limitation, an efficient double screening technique is employed in few health care centers and hospitals by using one more specialist but, this approach is unaffordable and its time complexity is high. The research classified the thyroid nodules employing different image preprocessing techniques. Utilized histogram equalization for preprocessing in his work. The Gray-Level Co-Occurrence Matrix (GLCM) is deployed for extracting the significant features. The classification is done using ASO, k-Nearest Neighbor (KNN), and Bayesian. It is noticed that the ASO yields improved accuracy compared to KNN and Bayesian techniques.
Effective prediction of soil micronutrients using Additive Gaussian process with RAM augmentation

In soil chemistry, the nutrients exhibit non-linear and complex relationships owing to their stochastic nature but mostly their similarity is a function of the distance between the data points. The similarity assessment using distance metrics is a popular technique employed by the regression, classification and feature selection algorithms. To enhance the precision of distance metric, the kernel trick is performed on the input space and the similarity is ascertained in the new high dimensional feature space. In Kernel Distance Metric Learning (KDML), the relevance of distance metrics is intensified to capture the precise similarity measure. In Hierarchical Kernel Learning (HKL) and Additive Gaussian Process (AGP) models, several orders of interactions among the subsets of predictors are emphasized while learning the kernel. In this paper a novel method, Restricted Additive Model (RAM) embedded in Additive Gaussian Process (AGP), to compute the distance in input space by adding selective weighted distances from the subset of predictors is proposed. RAM focuses on reusing the information content obtained while preprocessing the data and incorporate it while learning with the kernel. This can save a good amount of computational resources for high dimensional datasets. The proposed model is compared with HKL, AGP and a normal Gaussian Process (GP). The adjusted R2 and the Mean Absolute Error values showed that the proposed model showcased good accuracy reducing the computational time and resources. Further, the comparison of RAM with Automatic Relevance Determination of GP testified that the reusability of the information content turned to be effective in building a parsimonious model.
Hardware analysis for motion estimation task

This work introduces hardware metrics to evaluate imaging sensor (camera) ability to cope with temporal change (motion). Shifting from images towards moving elements demands better tools for evaluation than just refresh rate, and this work is here to close that gap. We focus on the sampling frequency, signal to noise ratio, rolling shutter, and modulation transfer function as a set of parameters to define four fundamental conditions to evaluate and compare the quality of motion sensing. We further examine our theory on existing hardware used in modern equipment and report our findings.
Weight-Based Sum Codes with Arbitrary Modulus

Binary redundant codes are widely used in the synthesis of self-checking and fault-tolerant digital computing systems. Their use allows reducing the added redundancy to ensure self-checking and fault tolerance. The authors present the study results of a comprehensive sum codes class. When constructing such codes, predetermined sequences of weight coefficients and a summation procedure in the residue ring by the specified modulus are used. As an example, codes with two sequences of weight coefficients are considered: 1) natural series; 2) natural series except for powers of number 2. Previously unknown characteristics of error detection by codes in terms of multiplicities and types (unidirectional, symmetrical and asymmetrical) are established. The conditions for constructing noise-immune codes and methods for modifying codes to endow them with noise-immunity properties are given. The results of experiments on analysing the detecting abilities of sum codes when used to test errors at the outputs of combinational benchmarks are provided.
Recyclable Waste Optical Sorting System Based on Neural Networks Ensemble

Special devices can be used to sort waste. They recognize the material of the object and using sorting devices divide the waste into separate fractions. Sorting systems already have optimal solutions. Material recognition systems still have a fairly large number of options. We are developing a project based on the recognition of objects by neural networks from the image. The objectives of this article are the formulation of the main problems of implementing such systems and the proposal of solutions to overcome them. The article considers the method of optimizing the operation of the neural network, the method of creating interconnected neural networks and the method of preliminary image analysis.
New Graphical Ultimate Processor for Mapping Relational Database to Resource Description Framework

The World Wide Web Consortium (W3C) RDB2RDF Work Group (RDB2RDF-WG) recommended two mapping languages, Direct Mapping (DM) and Relational Database to RDF Mapping Language (R2RML) for Mapping Relational Databases (RDBs) to Resource Description Framework (RDF). Direct Mapping directly maps the RDB schema to RDF using a collection of simple transformations, whereas R2RML is a language for manually created mappings from RDB tables to RDF output. The manual creation of mapping is complex, error-prone, and time-consuming, where any single mistake could produce an invalid output document. In this paper, a new Graphical Ultimate Processor (GUP) is proposed for mapping from RDBs to RDF. The proposed mapping processor is called RDB2RDF-GUP, but for simplicity, we shall represent RDB2RDF-GUP by RUP. This processor acts as a standalone tool with a Graphical User Interface (GUI) that facilitates the mapping process and supports a diversity of other features. This new processor is a useful tool for integrating the databases in Semantic Web applications that incorporate all data formats into a combined knowledge model. Through a small set of GUI screens, RUP enables the users to perform the most required tasks by selecting from the available lists most of the time rather than writing. This processor is simple and very useful for domain experts and semi-experts. Our results show that the proposed processor, RUP, outperforms other existing processors in the usability and the number of supported features.
Color restoration of murals based on the latent space in machine learning

Murals are China's precious historical and cultural heritage. They not only record the five thousand years of history of Chinese civilization, but also demonstrate the artistic creativity of the working people in ancient China. They have extremely high artistic and academic value. However, due to long-term exposure to wind and sun and man-made damage, some of the existing color murals have peeled off, color fading and other problems. In order to preserve these cultural treasures for a long time, many scholars have taken the digital restoration and protection of murals as their research direction. I regard the murals as pictures and this article will focus on the remedy of the mural color and the enhancement of the overall color, so that it can restore the original style. In the age of machine learning, I strongly recommend using machine learning for color restoration and restoration of color murals. However, unlike the traditional restoration of image color, the restoration of mural color is very complicated, and many factors need to be considered. Therefore, after referring to the method of the paper [1] of the City University of Hong Kong and Microsoft Research Asia, I optimized it and proposed a dual-domain translation network that uses real and intact color mural data and the data of various damaged color-fading murals. I train two variational autoencoders to convert them into two latent spaces respectively. The difference here with their method [1] is that the conversion between the two latent spaces is learned from real color murals and faded murals. This kind of translation has strong adaptability and integrity when used to restore the color of the mural. At the same time, in order to solve the fading problem of a mural caused by various reasons, I also referred to the methods of City University of Hong Kong and Microsoft Research Asia [1] to design a branch of global information extraction containing non-local modules. This branch is related to the fusion of latent spaces which can further improve the color recovery ability of murals that have faded due to various reasons. Especially from the results obtained later, the overall effect of this method is slightly better than other methods.
Fuzzy Logic in Modifications of Petri Nets in Simulation Modeling of Technological Production Processes

This article discusses the mathematical apparatus of modifications of Petri nets, supplemented by fuzzy calculations. The device is used as a basis for a system of simulation modeling of technological production processes. The article describes the use of fuzzy computation to organize the transition between states.
A dual-path network for source-robust spatial image steganalysis

As the existing literatures show, deep learning based steganalysis has achieved much better detection performance compared to traditional hand-crafted feature based methods. However, most methods based on deep learning are quite vulnerable to keep great performance under the image-source mismatch scenario, which is the inconsistency of the source of the training set and testing set. To improve this drawback, we specially design the architecture of the proposed network. Moreover, we first introduce the bilinear pooling into steganalysis to reduce the data source dependence of the model for further improving the performance. Experimental results show that the proposed work can outperform other networks in the image-source mismatch scenario.
Visual search during dynamic displays: Effects of velocity and motion direction

Abstract The visual search performance during dynamic environment has been demonstrated that could be affected by the motion of target and distractors. In present study, two experiments were conducted to investigate the effect of motion direction and velocity on visual search on screen. In Experiment 1, participants were required to complete the search task at three velocities(2, 8, and 16 deg/s) and four directions of motion(up, down, left, right). In Experiment 2, participants finished the more difficult visual search tasks within two directions (right, left) at higher velocities (16, 24, and 32 deg/s). The experiments revealed that the increase of velocity on the screen has a negative effect on the visual search performance, and when the velocity is higher than 8 deg/s, search times for horizontally moving images were shorter than those for vertically moving images. Furthermore, the difference in search performance could also be found in left‐moving and right‐moving images with the increase of velocity. Right‐moving images were proved to correspond to higher visual search performance. In addition, eye movement data indicated that saccade amplitude and saccade velocity might be responsible for this difference. The results of this study can be applied to industrial inspection, safety inspection, driving, and other fields.
Ethereum Smart Contract Templating for Parallel Computing

In this article we describe an application used for Ethereum smart contract templating. The idea for using this application is that anyone who needs to compute a well-decomposed and well-parallelized task, which requires huge computing power, would be able to use the application presented in the article for smart-contract generation and automatically install them into the Ethereum network. Thus, such a user does not need to have own huge computing powers or depend on cloud provider because blockchain networks are fully decentralized.
Intelligent Action Performed Load Balancing Decision Made in Cloud Datacenter Based on Improved DQN Algorithm

Due to dynamic changes of cloud state and increases user demand, load of datacenter fluctuated at regularly that shows load balancing problem. It is a challenging issue to take an appropriate action by datacenter controller to reduce the processing time of all incoming task with allocating the best resources in minimum time period. Therefore, an effective task scheduling is required to balance the load in datacenter. This paper proposed an Improved Deep Q-Network (I-DQN) task scheduling algorithm to balance the load. In this algorithm agent take a suitable action that minimize the makespan time. Simulation is done by using Google Colab with Tensorflow show the effectiveness of proposed scheduling algorithm. From the experiment we show our proposed algorithm is better success rate with reduce makespan time, waiting time and throughput as compare to existing DQN algorithm.
[DC] Dynamic facial expressions on virtual humans for virtual reality (VR) mental health therapy

This PhD research aims to implement dynamic facial expressions on virtual humans and explore their potential to enhance the efficacy of virtual reality (VR) mental health therapy. A systematic review of virtual humans in mental health VR indicated that only around 10&#x0025; of applications used dynamic facial expressions. The potential of virtual humans&#x0027; emotion richness is understudied and it is unclear how the facial expressions affect the individuals in VR. Therefore, we will focus on understanding people&#x0027;s behavioural, physiological, and psychological reactions toward facial-animated humans in VR experimental studies. The first study examines whether particular non-verbal behaviours can enhance people&#x0027;s therapy engagement, by applying warm facial expressions and head nods on a virtual coach. Future experiments will further look at individuals&#x0027; interpretations of facial expressions on virtual crowds and the virtual infant. This research will explore how best to use facial expressions to facilitate VR therapy through the practice of psychiatric research, VR programming and 3D animation.
Effect of Finite Sampling

The objective of this chapter is to discuss a very important issue of the effect of finite sampling with respect to either the finite length of the record or the finite sampling intervals. A few sampling theorems are discussed.
Shared Wind: Creating Awareness by Nature-related Ambient Design for Emotional Communication

In this paper, we discussed a Long-distance relationship (LDR) design system for subtle emotional communication. In-the-wild user studies with qualitative interviews were conducted. The results suggest that slowness, content-less, limitation, and ambiguity that are elements often less considered in interaction design, actually are valuable for creating the sense of awareness and companionship between LDR groups.