Reviewerly/exHarmony huggingface dataset:
Research on Image Denoising Based on Wavelet Threshold

Images are the main source of information for people. In the information age, digital image processing has become an important means for people to obtain, process, analyze and share information. It has penetrated into all aspects of life and achieved huge social and economic benefits. Image denoising is a large category of digital image processing technology, and it is a key step of the underlying processing. The quality of its processing results directly affects the performance of subsequent segmentation, recognition, and analysis. In traditional denoising methods, effective denoising and preservation of image detail information are very contradictory, and the denoising effect is not very satisfactory. Wavelet transform can successfully preserve the edge information of the image while denoising the image. Therefore, it is very necessary and important to study image denoising based on wavelet threshold and optimize and improve algorithm performance.
Experience of organization of the course " the Basics programming for schoolchildren " by the center of pre-university training in a pandemic

The article discusses the processes of designing, developing and launching an online course "The basics of programming for schoolchildren" for the center of pre-university training in a pandemic. The choice of the top-level design model SAM1 for courses that need to be developed and launched in a short time without complete data on the target audience is substantiated. The advantages of choosing a backward design model for developing the content of the target course are shown. Using the method of backward design, the course "The basics of programming for schoolchildren" was designed and developed. The program of the course is presented in the form of a sequence of modules and individual topics. The use of gamification elements based on the "VKontakte" social network has been substantiated. Examples of educational activities are given. A link is given to the developed course posted in LMS Moodle Donetsk Academy of Management and Public Administration.
Pose Estimation and Video Annotation Approaches for Understanding Individual and Team Interaction During Augmented Reality-Enabled Mission Planning

Two video analysis approaches (pose estimation and manual annotation) were applied to video recordings of two-person teams performing a mission planning task in a shared augmented reality (AR) environment. The analysis approaches calculated the distance relations between team members and annotated observed behaviors during the collaborative task. The 2D pose estimation algorithm lacked scene depth processing; therefore, we found some inconsistencies with the manual annotation. Although integration of the two analysis approaches was not possible, each approach by itself produced several insights on team behavior. The manual annotation analysis found four common team behaviors as well as behavior variations unique to particular teams and temporal situations. Comparing a behavior-based time on task percentage indicated behavior-type connections and some possible exclusions. The pose estimation analysis found the majority of the teams moved around the 3D scene at a similar distance apart on average with similar variation in fluctuation around a common distance range between team members. Outlying team behavior was detected by both analysis approaches and included: periods of very low distance relations, infrequent but very high distance relation spikes, significant task time spent adjusting the HoloLens device during wearing, and exceptionally long task time with gaps in pose estimation data processing.
Image-To-Image Translation Using Deep Convolutional GANs

The paper proposes a framework to build a deep neural network (DNN), established by heaping layers of GANs. The GAN architecture comprises both generator and discriminator layers which are locally trained to generate fake images which are of similar resolution as the original input image. The generated inputs are reconstructed exactly as the original input. The algorithm is a candid variation by stacking the ordinary GAN algorithm. The discriminator network is basically a classification problem of machine learning yielding less classification error. Hence, spanning the performance gap with DNNs and in many of the cases surpassing it results in the reconstruction of the inputs depending upon the training parameters such as the upsurge of the epoch and batch size. It will increase the training period, thus increasing the accuracy of synthesized images. A novel mode of training the GANs is discussed in the proposed model. The key point is to grow generator and discriminator progressively, with a low resolution, adding new layers model increasingly and finding details in the model as training progresses. It speeds up training process and also stabilizes results, allowing the results to be of high quality. Additionally, a non-random noise is added and the noise is fixed for the latent space size which yields stable results. A novel metric for evaluating GANs in terms of image quality and variation is discussed.
Applied Aspects of Countering Information and Ideological Threats in the Digital Space and Peculiarities of their Prevention

The paper examines topical issues of countering the most dangerous information and ideological threats both for all mankind and for the Russian Federation, namely extremism and terrorism. The authors present the experience of integrating the fundamental scientific developments of representatives of the school of forensic situational science into the practical activities of the Educational and Methodological Center for the Prevention of Terrorism of Immanuel Kant Baltic Federal University. The authors identify the most typical problems associated with the organization of an effective system of anti-terrorist prevention in the educational environment, and suggest ways to solve them. It is concluded that early detection, prevention and suppression of extremist (terrorist) crimes committed on the Internet contributes to the timely prevention of the spread of the ideology of extremism (terrorism), as well as the involvement of citizens in informal extremist communities. The most important task is to create, on the basis of educational and methodological centers, which are found in all federal universities of the country, the so-called cyber squads from among law students. Combining the efforts of volunteers with certain knowledge in the field of criminal law and forensics will allow us to timely and, most importantly, systematically identify suspicious content in the digital space and signal this to the competent law enforcement agencies.
Effective Parametric Image Sequencing Technology with Aggregate Space Profound Training

Abstract That containing the full workflow of evaluation, patient stratification, treatment preparation, operation and follow-up is assisted by robot and quick solutions for target identification and segmentation. Present state-of-the-art describing approaches are generally established on computer teaching approaches that use supervised learning image databases. The reliability in processing large volumetric images and the need for powerful, representative image characteristics reflect two major challenges to be tackled. In high-dimensional spaces where the object of interest is parametric, traditional volume scanning methods do not scale to the vast spectrum of possible possibilities. The representativeness of the picture is subject to considerable manual engineering efforts. We suggest a process in the sense of volumetric image parsing for object identification and segmentation to solve a two-step study problem: structural position approximation and boundaries differentiation. To this end, we implement a new paradigm, MSDL, which uses the advantages of efficient object approximation in marginalised centralised environments and the automatic functional nature of the network architecture of Deep Learning (DL). Intelligent systems immediately define and detach explicative properties immediately from close to zero image data, but just the approximation’s high difficulty restricts their use in the volume context.
Numeric Keypads or Character Keyboards for Numeric Entries on Surveys and Forms: Surprising Results from Older Adults Using Mobile Devices

These days a growing number of adults are using smartphones to fill out online forms or surveys (For a review of recent empirical studies on older adults using smartphone when answering forms see: [2–4]). The touchscreen keyboard that pops open on a smartphone when users must type their answers into the form can be challenging to use. This is due to its small size and the fact that for some smartphones, the initial keyboard that opens has only characters, not numbers. If the form requires a number to be entered, a user must press a small button, located in the far-left corner to change the keyboard so that it displays numbers. Using the small touchscreen is challenging for older adults, whose fine motor skills may have deteriorated with age (Loos and Romano Bergstrom [12]). Survey and form designers face the challenge of creating an interface that is both convenient to use while also leading to accurate data entry. More recently, some survey designers have been using a numeric keypad design on mobile phones when the expected entry is a number. This is based on the idea that the numeric keypad, with its bigger touch areas offering only numbers, would lead to an improved user experience. This paper reports the results of an experiment with older adults, comparing performance when using a numeric keypad to that when using a touchscreen character keyboard for number entries on a smartphone. When entering a number, results indicate that the numeric keypad design did not lead to more accurate data entry over the character keyboard design. While overall efficiency was also no different between the two designs, there was some evidence that the keypad design takes users less time to initially enter the number. While participants across both conditions were equally satisfied with their experience completing the survey, they overwhelmingly preferred to use the numeric keypad to enter numbers. For designers creating interfaces for smartphones, the recommendation is to use a numeric keypad for input fields that require a number as the answer.
Better Performance Through Mindfulness: Mobile Application Design for Mindfulness Training to Improve Performance in College Athletes

Collegiate student athletes commit almost all of their time and energy to extremely demanding and competing pressures as both a full-time student and athlete. With very limited time for self-care or mindfulness practices, athletes are restricted to their exhausting schedules that are set for them each and every day. In this paper, we introduce an application design named Muses which facilitates the coordination between coaches/trainers and student-athletes with their mindfulness training. Through a complementing two-faced interface design approach, we began our process by researching current competing mindfulness applications along with the NCAA guidelines to required participation hours each week as well as current programs’ mindfulness practices. With two questionnaire surveys completed and a second round of research, we were able to obtain key insights from both general and collegiate athlete mindfulness surveys. This enabled us to begin and determine the final design direction and methods. The personal progress tracking and customization strategies were chosen as the primary features of the application motivating collegiate athletes and programs to practice mindfulness more often.
Towards a Map for Incremental Learning in the Limit from Positive and Negative Information

In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data. Prior research investigating the impact of additional requirements on iterative learners left many questions open, especially in learning from informant, where the input is binary labeled. We first compare learning from positive information (text) with learning from informant. We provide different concept classes learnable from text but not by an iterative learner from informant. Further, we show that totality restricts iterative learning from informant. Towards a map of iterative learning from informant, we prove that strongly non-U-shaped learning is restrictive and that iterative learners from informant can be assumed canny for a wide range of learning criteria. Finally, we compare two syntactic learning requirements.
Identification of Potentials for the Personalization of Products

Individualization is considered a megatrend in postmodern societies. The markets react with the individualization of products. However, many approaches fail after considerable sums have already been invested. Therefore, this paper presents a benefit-oriented method to identify potentials for the personalization of products. It first accompanies the product through its life cycle (product journey) in order to identify various users and then these users on their user journey in order to observe the product experience. From these observations, a set of ideas for personalization is derived, which together with the associated benefits represent the personalization potential. The method has successfully been tested by individuals and teams in face-to-face workshops and online events using scenarios from the mobility, construction and service sectors. The learning effort is low. The personnel input per analysis amounts to approx. 150 h. The method is to be further evaluated using concrete use cases in industrial practice.
Color Me Good: Branding in the Coloring Style of Movie Posters

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.
Development and Evaluation of a Gaze Information Collection System in e-Testing for Examinee Authentication

With the development of communication technology, distance learning has improved to a point where it is comparable to in-person lectures and the demand for e-testing has increased. However, there are many challenges to e-testing, especially in preventing cheating. In this study, we develop and evaluate four systems that use different methods to collect gaze information during e-testing to authenticate examinees without interfering with natural responses. The results of the evaluation experiments show that the "gaze drawing system" is the most accurate and the load on the user is within an acceptable range, indicating that the system is the most appropriate method for collecting gaze information during e-testing.
Image Denoising Using Sparse Representation and Principal Component Analysis

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.
Interacting Safely with an Unsafe Environment

We give a presentation of Pure type systems where contexts need not be well-formed and show that this presentation is equivalent to the usual one.The main motivation for this presentation is that, when we extend Pure type systems with computation rules, like in the logical framework DEDUKTI, we want to declare the constants before the computation rules that are needed to check the welltypedness of their type.
Pokerepo Join: Construction of a Virtual Companion Experience System

The purpose of this study is the Construction and implementation of a Virtual Companion Experience System, “Pokerepo Join”, that considers user relationships. Virtual Companion Experience refers to the experience when a client in a remote location is assisted by a reporter at a certain site, in such a way that the client feels as though they are actually out with the reporter. The client is able to act and experience based on his or her own will. In particular, in this study, we consider that the information required for the realization of the Virtual Companion Experience differs depending on the human relationship between the client and the reporter, and propose an interface that considers the relationship between users. In addition, the proposed system has a mechanism that allows two or more people to communicate, in consideration of the intervention of a third party who is on-site. Requirements for constructing the system were defined through two experiments. After that, we constructed and actually operated the proposed system.
Entropic Alternatives to Initialization

Local entropic loss functions provide a versatile framework to define architecture-aware regularization procedures. Besides the possibility of being anisotropic in the synaptic space, the local entropic smoothening of the loss function can vary along the training, thus yielding an evolving, possibly adaptive, model complexity. A schedule where the regularization is strong in the early stage of the training and progressively lighter at later stages constitutes an alternative to standard initialization procedures for deep convolutional neural networks. We analyze anisotropic, local entropic regularizations in the language of statistical physics and information theory, providing insight into both their interpretation and workings. We comment some aspects related to the physics of renormalization and the spacetime structure of convolutional networks.
QoS and Business Association aware based selection of excellent Process Granular Services in Enterprise

With the development of network economy, many manufacturers provide various kinds of cloud manufacturing services for users to use on demand. However, facing the increasingly diverse needs of users, traditional atomic services and their combined services can no longer meet the needs of the market. Cloud service providers urgently need to provide abundant cloud services with different granularity. Nevertheless, there are few studies on coarse-grained service construction. In this paper, quality of service (QoS) and business association aware based enterprise process granular service superiority selection evaluation model is provided. On the basis of this, the preferred framework is put forward, including QoS-aware computing, process service node selection and assemblying, process granular service merging, process granular service inspection. And the related algorithms are designed. Experiments show that the proposed superiority selection evaluation model and related algorithms are practical, which can effectively extract the superior granular services needed by enterprises.
A (Bit)slice of Rainbow

We explore the performance impact of bitslicing on implementations of the NIST PQC Round 3 Finalist Digital Signature Algorithms: Rainbow. We show that for the Ia parameters set, when not using special or vector instructions, our bitsliced implementation signs 40% faster and verifies 50% faster than the submission implementation while remaining constant time. We argue that using this technique, when combined with vector instructions might lead to substantial speed-up as well.
Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network

Graph neural networks (GNNs) are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction. Comparatively lesser work has been done to design GNNs which can operate directly for community detection on graphs. Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities. However, such decoupled approaches are inherently sub-optimal. Designing an unsupervised loss function to train a GNN and extract communities in an integrated manner is a fundamental challenge. To tackle this problem, we combine the principle of self-expressiveness with the framework of self-supervised graph neural network for unsupervised community detection for the first time in literature. Our solution is trained in an end-to-end fashion and achieves state-of-the-art community detection performance on multiple publicly available datasets.
Segmentation Based Biomedical Image Retrieval with Low-Level Feature Extraction

Abstract Content based image retrieval (CBIR) is an imperative and testing errand in numerous fields, for example, military, common, restorative and even in web applications. Here, we implemented medical image retrieval focused on novel material with decimated bi-orthogonal spline wavelet filter banks. Proposed algorithm is an extension for the existing low-level feature extraction method done by using spline wavelet filters, also utilized iterative partitioning (IP) for extracting the similar patterns of Pictures of query &amp; database. In terms of consistency &amp; memory, simulation results indicate that the approach suggested was better than the current scheme.
Path Planning Algorithms for Different Scenarios

Searching of the target(s) in unknown Scenarios is a very complex task in the field of Robotics. This paper is an introduction to the works that are seminal in the field of Swarm Robotics. Swarm Robotics is the application of the principles used in swarm Intelligence. Different types of algorithms are used to decide the path in different types of situations or environments. Due to the robustness, scalable, fault-tolerant, and many other properties of the swarm of robots, the SRS is used in many search and tracking applications. This is the review that focuses on the different problems in SRS. Firstly an algorithm that deals with the local minima problem is explained, further, there are two more algorithms that are used to search the target and guide the robots to the target are explained. These algorithms perform differently for different scenarios. Further, seven different types of algorithms are compared with each other based on some parameters. Most of these algorithms perform well in some situations but may fail to perform well in some other situations.