Reviewerly/exHarmony huggingface dataset:
Formation of Project and Research Competence of Students at Pedagogical Universities in the Sphere of Information Technologies

This paper focuses on the formation of project and research competence of students at pedagogical universities in the sphere of information technology. Some specific experience in working with undergraduates at different levels is presented through particular examples.
AN RL APPROACH FOR ABSA USING TRANSFORMERS

Sentiment evaluation is an incredibly powerful tool for an enterprise to effectively test the general emblem notion, however also evaluate feelings in the direction of a specific product line. When an organization launches their product it is critical for them to have the information like what people consider their product, their offerings, about their manufacturers. How the agency knows about it. Manifestly through Sentiment analysis in order to analyze all of the reviews. The level of emotional appearance is intended to predict the emotional polarity of a particular element in a sentence. However, Most of the available methods are based on the knowledge that every sentence has a part that describes the feature, making it difficult to identify the map in the middle of the feature and half. To reduce this problem, we suggest a novel method that models certain aspect-level components senses. We intend to build a real time interactive Aspect Based sentiment analyzer model using Transformers and to implement the functionality of Reinforcement Learning in the version. The proposed model works to extract maximum aspects by the use of available NER(Name Entity Recognition) module. To construct an component based totally Sentiment Analyzer model using Transformer (Distilled BERT) to exceptional tune the sentiment analysis and to implement the capability of incremental gaining knowledge of in the version in order that the version capable of get the feedback from the customers and study from the remarks to boom the accuracy of the model.
Current Issues of Legal Transformation of Educational Systems in Russia in the Context of Digitalization

The article describes the relevance of the application of various models in the educational process in higher education in Russia today and in the near future. The problems are presented in the dynamics of the development of the situation in the world, law and economics. The stages of the transition of Russian education to the Bologna system are studied in detail. Positive and negative aspects of the practice of its application. What are the advantages of returning to the traditional Russian specialty. The results of the work are shown. Conclusions are drawn.
Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning

Unexploded Ordnance (UXO) classification is a challenging task which is currently tackled using electromagnetic induction devices that are expensive and may require physical presence in potentially hazardous environments. The limited availability of open UXO data has, until now, impeded the progress of image-based UXO classification, which may offer a safe alternative at a reduced cost. In addition, the existing sporadic efforts focus mainly on small scale experiments using only a subset of common UXO categories. Our work aims to stimulate research interest in image-based UXO classification, with the curation of a novel dataset that consists of over 10000 annotated images from eight major UXO categories. Through extensive experimentation with supervised deep learning we uncover key insights into the challenging aspects of this task. Finally, we set the baseline on our novel benchmark by training state-of-the-art Convolutional Neural Networks and a Vision Transformer that are able to discriminate between highly overlapping UXO categories with 84.33% accuracy.
Collusion-resistant Fingerprinting of Parallel Content Channels

The fingerprinting game is analysed when the coalition size k is known to the tracer, but the colluders can distribute themselves across L TV channels. The collusion channel is introduced and the extra degrees of freedom for the coalition are made manifest in our formulation. We introduce a payoff functional that is analogous to the single TV channel case, and is conjectured to be closely related to the fingerprinting capacity. For the binary alphabet case under the marking assumption, and the restriction of access to one TV channel per person per segment, we derive the asymptotic behavior of the payoff functional. We find that the value of the maximin game for our payoff is asymptotically equal to L2/k2 2 ln 2, with optimal strategy for the tracer being the arcsine distribution, and for the coalition being the interleaving attack across all TV channels, as well as assigning an equal number of colluders across the L TV channels.
Temporal-Consistent Visual Clue Attentive Network for Video-Based Person Re-Identification

Video-based person re-identification (ReID) aims to match video trajectories of pedestrians across multi-view cameras and has important applications in criminal investigation and intelligent surveillance. Compared with single image re-identification, the abundant temporal information contained in video sequences makes it describe pedestrian instances more precisely and effectively. Recently, most existing video-based person ReID algorithms have made use of temporal information by fusing diverse visual contents captured in independent frames. However, these algorithms only measure the salience of visual clues in each single frame, inevitably introducing momentary interference caused by factors like occlusion. Therefore, in this work, we introduce a Temporal-consistent Visual Clue Attentive Network (TVCAN), which is designed to capture temporal-consistently salient pedestrian contents among frames. Our TVCAN consists of two major modules, the TCSA module, and the TCCA module, which are responsible for capturing and emphasizing consistently salient visual contents from the spatial dimension and channel dimension, respectively. Through extensive experiments, the effectiveness of our designed modules has been verified. Additionally, our TVCAN outperforms all compared state-of-the-art methods on three mainstream benchmarks.
A live context model for semantic reasoning in IoT applications

Nowadays, the rapid increase of IoT devices and the corresponding high volume of produced data leads to a higher complexity of data analysis and context reasoning tasks. In the IoT, monitoring the context of an application, i.e., the involved devices sensors and actuators as well as their surroundings, is of vital importance. Ontologies can be used to model such context, nevertheless, they usually do not consider live information like sensor values and monitoring data. Yet, this is necessary to capture the full context of an IoT application. In this paper, we propose a context model that includes the current state of devices, composing their static information, such as device names, and live information gathered from monitoring systems and sensors. We further propose an extendable architecture to enrich the ontology with static and live data. Our approach can be extended by integrating different data sources and extending the context model.
Handwritten Character Recognition using Convolutional Neural Network

The proposed work aims on the task of identifying handwritten alphabets using a predictive designed CNN model. The visual image of alphabet character can be stored digitally for future use. The collection of handwritten english alphabet data-set is available at Kaggle website. The data-set file includes twenty-six folders containing twenty-six english alphabet images of 28×28 pixel size. Every image is stored as a default color as greyscale. The data-set of images is classified into 20% of test data and the remaining 80% as training data for our neural network. This model is constructed with the help of TensorFlow as it's back-end. It contains Convolution filters with 3 layers of fully integrated visualization. The intake of this Convolutional Neural Network model are categorical values. So, greyscale images is converted to categorical values as vector form. Filter layers in CNN extracts the image elements from all the image data. After image integration for feature extraction gives out the image, It's size is further reduced by applying max-pooling technique. The image is now forwarded to fully integrated CNN layers. Character prediction based on 97.83% of the professional accuracy obtained by this model. The model is compiled by Adam's optimizer which is an extension of the stochastic gradient descent learning algorithm. This proposed model has found better accuracy and performance than older models.
Development of a Forensic Toolkit for Small-Medium Size Business (SMB)

With the increasing rate of digitalization around the world, more and more small-medium size businesses (SMBs) now depend on technology. Sources claim that more than half of SMBs experience cyberattacks, which leave certain artefacts that can be used as information to reveal the cause of the attack, if processed and analyzed correctly. This work proposes a portable toolkit that aims to assist users in the extraction and identification of forensic data for SMBs. Through its graphical interface, users will be able to select which type of information they would like to extract, by selecting the appropriate built-in tool.
Intelligent ubiquitous technology as a precision agri-food framework: a proposed framework

Abstract Agri-Food is another term for the agriculture-based food sector, which is currently one of Indonesia’s main pillars of the national economy. One of the well-known plants for agricultural-based food processing called Agri-Food in Indonesia is Cocoa. Cocoa is one of the plantation commodities that has an essential role in economic activities in Indonesia. Given the critical role of cocoa as a source of the country’s economy, it must guarantee productivity and quality of cocoa and sustainability. To ensure cocoa productivity and quality sustainability, agricultural activities in today’s era can integrate intelligent ubiquitous technology as a framework for obtaining precise production results. What can measure precision production results from three aspects: increasing agricultural land productivity, reducing production costs, and minimizing the environmental impact. This paper discusses the proposed framework with intelligent ubiquitous technology to support precision agri-food that focuses on context-aware systems. The context-aware system in the proposed framework is expected to become a control center in collecting and processing data in real-time to help farmers make the best decisions regarding weather conditions and diseases caused by plants.
Towards QoS-Aware Function Composition Scheduling in Apache OpenWhisk

Function-as-a-Service (FaaS) is increasingly popular thanks to the benefits provided to application developers and operators. Besides commercial Cloud-based offerings, open-source solutions have emerged enabling FaaS deployment on private infrastructures and possibly at the edge of the network. When moving from the Cloud to Fog/Edge environments, optimizing resource allocation for function execution becomes a critical challenge. Unfortunately, existing FaaS platforms have little or no support for fine-grained scheduling and resource allocation, nor allow users to enforce Quality-of-Service (QoS) requirements. We take a first step towards the development of a QoS-aware FaaS platform. We design and implement new mechanisms to support differentiated classes of services within Apache OpenWhisk, a popular open-source FaaS framework. Our experiments show that our prototype efficiently supports state-of-the-art scheduling policies and provides throughput improvements when dealing with function compositions under high load scenarios.
Methods for Speech Signal Structuring and Extracting Features

The preliminary stage of the biometric identification is speech signal structuring and extracting features. For calculation of the fundamental tone are considered and in number investigated the following methods – autocorrelation function (ACF) method, average magnitude difference function (AMDF) method, simplified inverse filter transformation (SIFT) method, method on a basis a wavelet analysis, method based on the cepstral analysis, harmonic product spectrum (HPS) method. For speech signal extracting features are considered and in number investigated the following methods – the digital bandpass filters bank; spectral analysis; homomorphic processing; linear predictive coding. This methods make it possible to extract linear prediction coefficients (LPC), reflection coefficients (RC), linear prediction cepstral coefficients (LPCC), log area ratio (LAR) coefficients, mel-frequency cepstral coefficients (MFCC), barkfrequency cepstral coefficients (BFCC), perceptual linear prediction coefficients (PLPC), perceptual reflection coefficients (PRC), perceptual linear prediction cepstral coefficients (PLPCC), perceptual log area ratio (PLAR) coefficients, reconsidered perceptual linear prediction coefficients (RPLPC), reconsidered perceptual reflection coefficients (RPRC), reconsidered perceptual linear prediction cepstral coefficients (RPLPCC), reconsidered perceptual log area ratio (RPLAR) coefficients. The largest probability of identification (equal 0.98) and the smallest number of coefficients (4 coefficients) are provided by coding of a vocal of the speech sound from the TIMIT based on PRC.
High-resolution MR imaging using self-supervised parallel network

One general practice for radiotherapy in acquiring the magnetic resonance (MR) images is to acquire longitudinally coarse slices while keeping the in-plane spatial resolution high in order to shorten the scan time as well as to ensure enough body coverage. The purpose of this work is to develop a deep learningbased method for synthesizing longitudinal high-resolution (HR) MR images using parallel trained cycleconsistent generative adversarial networks (CycleGANs) with self-supervision. The parallel CycleGANs independently predict HR MR images in two planes along the directions that is orthogonal to the longitudinal MR scan direction. These predicted images are fused to generate the final synthetic HR MR images. MR images in the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to investigate the performance of the proposed workflow with qualitative, by visual inspections on the image appearance, and quantitative, by calculations of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), evaluations. Preliminary results show that the proposed method can generate HR MR images visually indistinguishable from the ground truth MR images. Quantitative evaluations show that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2 and FLAIR images. The feasibility of the proposed method to synthesize HR MR images using self-supervised parallel CycleGANs is demonstrated and its potential usage in common clinical practices of radiotherapy is expected.
Three-Tier Database Forensic Model

The evolution of information and communication technology has transformed our way of processing and storing information. Currently, a plethora of crucial data is held and processed in databases. Thus, a significant number of cyber-attacks target databases, thereby prompting the need for database forensics. Existing research publications on Database Forensic focus on two areas; (i) technology and (ii) Database forensic processes. Accordingly, current solutions are well suited to specific scenarios or technology. Hence, they cannot be relied upon in different technology scenarios. In addition, there are no clear procedures that should be adhered to during database forensics to make the best use of existing database forensic tools. We propose the Three-Tier Database Forensic (TT-DF) Model, a practical generic model that the examiner can use to examine incidents involving cyber-attacks against databases. The TT-DF model we present is a two-dimensional approach that considers stages and processes of investigation. It considers three investigation stages: application, database, and server investigation. Moreover, there are four processes at each stage, namely preparation, collection, analysis, and presentation. Our solution ensures that an investigator can efficiently discover and collect artefacts in the aftermath of cyber-attacks irrespective of compromised database audit trails, metadata and other potential artefacts within a reasonable time.
Over de drempels van geletterdheid: laaggeletterde ouders helpen hun jonge kind te ondersteunen

Family literacy programs aim at supporting parents in stimulating their young children’s emergent literacy development. Reviews of effect studies in general show positive outcomes, while the effects on children from low socioeconomic families are mixed. The literacy level of parents, however, is mostly not taken into account in effect studies and therefore it is unclear what types of activities might work in families where parents have limited reading skills. For non-literate or low-literate parents traditional family literacy programs might create several barriers, due to the fact that these programs often assume a certain level of reading skill in parents and require them to initiate cognitively challenging interactions around sometimes difficult texts. In this contribution, we analyse the main barriers such programs might create for these parents and we offer suggestions how to overcome these barriers. Promising approaches can be found in empowering parents by personalizing and tailoring the program activities, by offering interesting materials that are not dependent on advanced reading skills and trigger children and parents to interact, and by making use of the technological innovations in new media, such as digital, animated picture books.
SiSP: Japanese Situation-dependent Sentiment Polarity Dictionary

In order to deal with the variety of meanings and contexts of words, we created a Japanese Situation-dependent Sentiment Polarity Dictionary (SiSP) of sentiment values labeled for 20 different situations. This dictionary was annotated by crowdworkers with 25,520 Japanese words, and consists of 10 responses for each situation of each word. Using our SiSP, we predicted the polarity of each word in the dictionary and that of dictionary words in sentences considering the context. In both experiments, situation-dependent prediction showed superior results in determining emotional polarity.
Using Mobile Devices to Realize Blended Learning at the University

Currently, blended learning is becoming a promising form of organization of higher education. Use of smartphones and other mobile devices for the realization of blended learning makes it possible to provide students with equal access to educational and methodological content, that is, the conditions for reducing the information inequality of students. In this regard, the article reveals the structure of the activities of a teacher and a student in the process of mobile learning in the context of the realization of blended learning. The novelty of the research is determined by the fact that the structure and main content of the advanced refresher course “Blended learning at the university as a means of improving the quality of education in the context of the spread of coronavirus infection” are formulated. The practical significance of the study is determined by the fact that it shows the experience of organizing teacher training in an online format based on the platform ocs.wkau.kz.
Management of Higher Education Development: Conceptual Foundations and Analytical Management Models

In recent decades, there have been significant changes in management in general, and in particular in the management of education, which is associated with such processes as globalization, commercialization, public sector reforms and the emergence of the knowledge economy and society. The article discusses the main theoretical approaches developed by researchers in recent years, including the main models of management of the development of higher education. It is concluded that the applicability of a particular management model in countries is determined by the role and place of the education system in them, the historically established interaction of the education system and society. None of the considered models makes it possible to identify its advantages over other models, therefore, the development of education management models needs further development.
Perceptual Relations in Digital Environments

Abstract The aim of the paper is to develop the concept of perceptual relation and to apply it to digital environments. First, the meaning of perceptual relation is phenomenologically analyzed and defined as the interaction between the whole and its parts, which is theorized by the founders of Gestalt psychology. However, this relation is not considered as an intrinsic, but as an extended one, implying also the relation with the surrounding world (Umwelt). Subsequently, this concept of extended relation is applied to a chosen object (a ball) as it is perceived in four different kinds of digital dimensions (on-screen, virtual, augmented, and hybrid). Through a phenomenological analysis, I argue that, whereas the whole-part configuration remains the same, some modes of appearance of the object (multisensoriality, figure-ground interaction, affordances, and persistence) are different. In order to define this dynamic, I have coined the concept of transdimensional analogy.
Geology of National Parks, Seventh Edition

Book Review| June 24, 2022 Geology of National Parks, Seventh Edition Greg M. Stock Greg M. Stock National Park Service, Yosemite National Park, 5083 Forests Road Box 700, El Portal, CA 95318 Search for other works by this author on: GSW Google Scholar Environmental & Engineering Geoscience (2022) 28 (3): 329–330. https://doi.org/10.2113/eegs-28-03-07 Article history first online: 24 Jun 2022 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Greg M. Stock; Geology of National Parks, Seventh Edition. Environmental & Engineering Geoscience 2022;; 28 (3): 329–330. doi: https://doi.org/10.2113/eegs-28-03-07 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Encompassing 423 national park sites, the U.S. National Park System preserves some of Earth's most spectacular geologic features and landforms. From glaciers to hot springs, volcanoes to coral reefs, or canyons to caves, national parks and preserves curate our geologic heritage while sparking the imaginations of tens of millions of park visitors each year. The fascinating and compelling geologic stories of many of these parks are told in the newest edition of the book Geology of National Parks by David Hacker and David Foster (2019), with contributions from Esther Tuttle and Sherwood D. Tuttle and senior editing by Ann... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.