Reviewerly/exHarmony huggingface dataset:
Enhancing Derivational Information on Latin Lemmas in the LiLa Knowledge Base. A Structural and Diachronic Extension

In this paper 1 we document both the structural and the diachronic extension of the derivational information provided in the LiLa Knowledge Base of interoperable linguistic resources for Latin.Structurally, to the flat information on families (i.e., groups of lemmas that share the same base) and affixes that is already available for the collection of lemmas of the LiLa Lemma Bank, we add hierarchical information on derivation processes provided by the Word Formation Latin (WFL) lexical resource, which in turn is characterised by a step-to-step morphotactic approach, where lexemes that are directly derived from one another are connected through word formation rules of different kinds.This is done by modelling WFL data into an ontology that adheres to the principles of the Linked Data paradigm, and connecting these data to the LiLa Lemma Bank.From a diachronic point of view, while the previous version of WFL only took Classical Latin lemmas into account, in this paper we describe the work conducted to produce a new version of WFL that is enhanced with derivational information on Medieval Latin lemmas.We then show how the data of this new version of WFL were used to extract derivational information in the format required by the LiLa Lemma Bank.
Online Football-Related Antisemitism in the Context of the COVID-19 Pandemic: A Multi-Method Analysis of the Dutch Twittersphere

This paper examines online expressions of rivalry and hate speech in relation to antisemitic discourses in Dutch professional men’s football (soccer), with specific attention devoted to how this has developed within the context of the COVID-19 pandemic. This study analyses football-related antisemitic discourses in the Dutch-speaking Twittersphere between 2018 and 2021. Assuming that during the ongoing COVID-19 pandemic fan activity has moved increasingly toward the online domain, we specifically examine whether and how the past pandemic years have influenced football-related antisemitic discourses on Twitter. Tweets were scraped using the Twitter application programming interface and 4CAT (a capture and analysis Toolkit), producing a dataset of 7,917 unique posts. The authors performed thematic analysis of the Tweets and a selection of the Tweets was analyzed in depth using narrative digital discourse analysis. The findings show how these Tweets, while seemingly targeted exclusively at football opponents, contribute to wider exclusionary discourse in football and society that may have become more aggravated during the COVID-19 pandemic.
Hardware implementation of a strong pseudorandom number generator based block‐cipher system for color image encryption and decryption

Summary This paper proposed a hardware architecture of a strong block‐cipher system dedicated to digital image encryption and decryption. On the one hand, a pseudorandom number generator (PRNG) based on two 3D chaotic systems is created to produce strong keys. On the other hand, a robust algorithm is proposed to ensure high‐level security and low computational complexity of image encryption. The algorithm performs image encryption mainly through three processes: pixel values hiding by applying the XOR operation with a key, pixel positions hiding by operating random permutation, and pixel substitution using the S‐box method. To increase the complexity, R rounds of encryption could be accomplished in a loop. Then as a final step, using the Xilinx Vivado/system generator tool, the hardware cryptosystem is developed, implemented, and evaluated on an FPGA‐Zynq evaluation board. According to the synthesis results, the suggested hardware system performs on a reduced FPGA area and gives a good frequency of 156.813 MHz with a high throughput of 20,072.064 Mbps. Several tools and tests utilizing various images are used to evaluate and analyze the hardware cryptosystem. The experimental results show that the hardware implementation has higher performance compared to other recent works.
Recent Trend of Transform Domain Image Steganography Technique for Secret Sharing

The security of information is one of the most important attributes to be available when the secret information passes between two parties. Many techniques like watermarking, cryptography and steganography used for this purpose. Cryptography changes the position of original information or scramble the original information, but it reveals the existence of secret information. The hiding the data behind any other object is steganography characteristic. Information hiding characteristic make the steganography more popular as compare to cryptography process. In this paper transform domain-based steganography process are discussed. The main focus in transform domain steganography is the wavelet family; paper includes detail information about different wavelet used in steganography process. The procedure is investigated and contended in the provisions of its payload limit i.e., the capacity to conceal data, how much data can be covered up, and its robustness.
The Influence of Accounting Information System Quality and Human Resource Competency on Information Quality

research-article Share on The Influence of Accounting Information System Quality and Human Resource Competency on Information Quality Authors: Meiryani Meiryani Accounting, Bina Nusantara University, Indonesia Accounting, Bina Nusantara University, IndonesiaView Profile , Vaeren Teresa Accounting, Bina Nusantara University, Indonesia Accounting, Bina Nusantara University, IndonesiaView Profile , Dezie Leonarda Warganegara Doctoral Program, Bina Nusantara University, Indonesia Doctoral Program, Bina Nusantara University, IndonesiaView Profile , Zaidi Mat Daud Accounting, Universiti Putra Malaysia, Malaysia Accounting, Universiti Putra Malaysia, MalaysiaView Profile , Gazali Salim Management Resources of Waters, University of Borneo Tarakan, Indonesia Management Resources of Waters, University of Borneo Tarakan, IndonesiaView Profile Authors Info & Claims VSIP 2021: 2021 3rd International Conference on Video, Signal and Image ProcessingNovember 2021 Pages 91–97https://doi.org/10.1145/3503961.3503976Published:08 March 2022Publication History 0citation48DownloadsMetricsTotal Citations0Total Downloads48Last 12 Months48Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Statistical image watermark decoder using NSM-HMT in NSCT-FGPCET magnitude domain

The image watermarking technology is an effective technology for protecting image copyright at present. Image watermarking techniques have a constraint relationship between imperceptibility, robustness, and watermark capacity. Balancing imperceptibility, robustness, and watermark capacity becomes a tricky problem. This paper proposes a statistical image watermarking scheme using nonsubsampled Contourlet transform (NSCT) domain fast generic polar complex exponential transform (FGPCET) magnitudes and nonsymmetric mixtures (NSM) based hidden Markov tree (HMT). In the embedding part, to enhance robustness and imperceptibility, we insert the watermark signal into the robust local NSCT domain FGPCET magnitudes through the multiplicative method. In the decoding part, a statistical image watermarking decoder is designed using the NSM-HMT model and maximum likelihood criterion. Here, marginal characteristics and strong correlations of local NSCT domain FGPCET magnitudes are described by NSM-HMT. In addition, the generalized expectation-maximization (GEM)-clusterized method of moments (CMM) approach is used for accurate parameters. Extensive Monte Carlo experiments validate the better performance of the proposed image watermarking method compared to other well-known methods.
Research on Construction Cost Control Technology of Construction Project Based on Big Data Analysis Theory

The current methods proposed in construction cost control of construction projects have problems such as applicability and low accuracy. Therefore, this paper proposes a method for evaluating engineering cost control based on the big data Analytic Hierarchy Process (AHP) method. Based on the big data technology, the construction cost control index of the construction project is selected, and the construction cost control index model of the construction project is constructed according to the selection results, and the evaluation set is constructed. The corresponding weights of the construction cost control indicators of each construction project are obtained through the analytic hierarchy process, and a weight set is constructed. The membership relationship between the index set and the evaluation set is determined, a fuzzy relationship matrix is constructed, and the construction cost control result of the construction project is obtained according to the calculated weight and the constructed fuzzy relationship matrix. The test results of examples show that the risk assessment accuracy of the proposed method is higher than that of ordinary methods, and the quantitative processing of audit risk assessment is realized.
Research on Knowledge Management of Intangible Cultural Heritage Based on Linked Data

At present, the protection of intangible cultural heritage has received more and more attention from all levels of society. Intangible cultural heritage is a treasure of national culture. It is an indispensable part of Chinese civilization, the crystallization of the wisdom of Chinese civilization, and represents the country’s soft power. The effective organization and management of intangible cultural heritage knowledge is the premise and foundation for the protection, dissemination, and inheritance of intangible cultural heritage. Ontology and linked data technology provide a new method and realization path for the organization and management of intangible cultural heritage knowledge. In this paper, the intangible cultural heritage knowledge is organized reasonably semantically based on the method of linked data, and the purpose is to use the structure of linked data to express the resource data of different structures in a structured manner. This paper first introduces the meaning and background of the research and analyzes the relevant research at home and abroad. Second, it introduces the related knowledge of linked data, analyzes and sorts out the elements and semantic relationship of knowledge in the field of intangible cultural heritage, and designs and constructs the ontology model of intangible cultural heritage knowledge, Finally, based on linked data technology, the process of intangible cultural heritage knowledge organization and linked data set construction is studied, including key steps such as entity to RDF, entity association, linked data storage, and publication. The application of linked data technology in the field of intangible cultural heritage knowledge organization and management can promote the standardization and standardization of intangible cultural heritage knowledge management and is of great significance to the protection and inheritance of my country’s intangible cultural heritage culture.
Domain‐specific feature recalibration and alignment for multi‐source unsupervised domain adaptation

Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this problem, multi-source unsupervised domain adaptation (MUDA) is proposed. Multi-source unsupervised domain adaptation aims to adapt the model trained on multi-labelled source domains to the unlabelled target domain. In this paper, a novel MUDA method by domain-specific feature recalibration and alignment (FRA) is proposed. Specifically, to achieve feature recalibration, the authors leverage channel attention to pick out significant channels and spatial attention to focus on important features in different channels. Such integration of channel and spatial attention can lead to effective domain-specific feature recalibration that may be of great importance to MUDA. In addition, to achieve better MUDA, the authors propose domain-specific feature alignment which consists of Maximum Mean Discrepancy and JS-divergence loss. Maximum Mean Discrepancy can reduce the difference between the source domain and target domain. Meanwhile, JS-divergence loss may ensure the prediction consistency of different classifiers in the source domains. Four experiments have proved that FRA can achieve significantly better results in popular benchmarks for MUDA.
RZSR: Reference-Based Zero-Shot Super-Resolution With Depth Guided Self-Exemplars

Recent methods for single image super-resolution (SISR) have demonstrated outstanding performance in generating high-resolution (HR) images from low-resolution (LR) images. However, most of these methods show their superiority using synthetically generated LR images, and their generalizability to real-world images is often not satisfactory. In this paper, we pay attention to two well-known strategies developed for robust super-resolution (SR), i.e., reference-based SR (RefSR) and zero-shot SR (ZSSR), and propose an integrated solution, called reference-based zero-shot SR (RZSR). Following the principle of ZSSR, we train an image-specific SR network at test time using training samples extracted only from the input image itself. To advance ZSSR, we obtain reference image patches with rich textures and high-frequency details which are also extracted only from the input image using cross-scale matching. To this end, we construct an internal reference dataset and retrieve reference image patches from the dataset using depth information. Using LR patches and their corresponding HR reference patches, we train a RefSR network that is embodied with a non-local attention module. Experimental results demonstrate the superiority of the proposed RZSR compared to the previous ZSSR methods and robustness to unseen images compared to other fully supervised SISR methods.
APPLYING THE COMPETENCY APPROACH TO QUALITY MANAGEMENT OF EARLY CHILDHOOD EDUCATION AND CARE

Early childhood education and care is a policy that aims to improve access to education and ensure the well-being of children by creating an environment for learning, nurturing, socialization, support and cooperation. In this process, the formation of key competences in children, not only academic, but also personal and social, has an essential role, in order to prepare them for life in modern society, in which they can realize themselves successfully. The article examines basic competencies of children and those working with them in the context of quality assurance of early childhood education and care. Interrelationships are made between the application of the competence approach and the improvement of quality. A focused study of parents is presented regarding their satisfaction with the provided educational, health and social services, the importance of key competences for the development of children and the support they provide through cooperation with specialists for high quality services. The findings of the study show a good degree of satisfaction, but insufficient participation of families in the general care for the education, upbringing and socialization of children. Parents attach more importance to the personal qualities that children acquire and develop in kindergarten, compared to academic knowledge.
GLFormer: Global and Local Context Aggregation Network for Temporal Action Detection

As the core component of video analysis, Temporal Action Localization (TAL) has experienced remarkable success. However, some issues are not well addressed. First, most of the existing methods process the local context individually, without explicitly exploiting the relations between features in an action instance as a whole. Second, the duration of different actions varies widely; thus, it is difficult to choose the proper temporal receptive field. To address these issues, this paper proposes a novel network, GLFormer, which can aggregate short, medium, and long temporal contexts. Our method consists of three independent branches with different ranges of attention, and these features are then concatenated along the temporal dimension to obtain richer features. One is multi-scale local convolution (MLC), which consists of multiple 1D convolutions with varying kernel sizes to capture the multi-scale context information. Another is window self-attention (WSA), which tries to explore the relationship between features within the window range. The last is global attention (GA), which is used to establish long-range dependencies across the full sequence. Moreover, we design a feature pyramid structure to be compatible with action instances of various durations. GLFormer achieves state-of-the-art performance on two challenging video benchmarks, THUMOS14 and ActivityNet 1.3. Our performance is 67.2% and 54.5% AP@0.5 on the datasets THUMOS14 and ActivityNet 1.3, respectively.
Cloud Storage Data Verification Using Signcryption Scheme

Cloud computing brings convenience to the users by providing computational resources and services. However, it comes with security challenges, such as unreliable cloud service providers that could threaten users’ data integrity. Therefore, we need a data verification protocol to ensure users’ data remains intact in the cloud storage. The data verification protocol has three important properties: public verifiability, privacy preservation, and blockless verification. Unfortunately, various existing signcryption schemes do not fully provide those important properties. As a result, we propose an improved version of a signcryption technique based on the short signature ZSS that can fulfill the aforementioned data verification important properties. Our computational cost and time complexity assessment demonstrates that our suggested scheme can offer more characteristics at the same computational cost as another ZSS signcryption scheme.
Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection.
Continuous Prompt Tuning for Russian: How to Learn Prompts Efficiently with RuGPT3?

Adaptation to downstream tasks is a crucial part of the pre-trained language model (PLM) life cycle. Fine-tuning, traditionally used for this purpose, is an expensive procedure in terms of computation and memory. Dramatic growth of PLM capacities has led to the emergence of zero- and few-shot methods, which use natural language to describe tasks. Although these methods do not modify the parameters of the model, they rely on manual prompt design, which may be suboptimal. To address this issue, a range of techniques for automatic prompt search have been proposed recently. In this paper, we present a framework for continuous prompt tuning (CPT) in Russian. We evaluated our framework by adapting RuGPT3 to tasks in the Russian benchmark SuperGLUE. We obtained metrics better or comparable to fine-tuning, while training only an auxiliary model that provides prompt embeddings, so the total number of trained parameters accounts for less than 0.4% of that of RuGPT3. In addition, we conducted experiments comparing different configurations of the framework and explored the lower bound to which we can reduce the number of parameters. Our source code is publicly available at https://github.com/sberbank-ai/ru-prompts .
On the Pareto-Optimal Solutions in the Multimodal Clustering Problem

The paper considers the application of multi-objective optimization methods in the problem of multimodal clustering. An overview of the algorithms that deliver Pareto-optimal solutions is made. The use of multi-objective optimization makes it possible to interpret clusters in terms of several criteria. An evolutionary multi-objective algorithm for multimodal clustering is proposed. The algorithm was tested on two and three-dimensional formal contexts that simulate data on myocardial infarction complications in patients of various ages and medical records. Clustering is used to extract facts as specific combinations of data from three sets. The simulation results were compared with the known methods of Formal Concept Analysis.
Reconfigurable Architecture for DNA Diffusion Technique-Based Medical Image Encryption

The aim of this paper is to develop and test the features of reconfigurable architecture for its suitability for a secured medical image encryption. The image security has been ensured using three-level zigzag confusion, bit-level confusion, self-invertible matrix and DNA diffusion. The zigzag pixel-level and circular shift-based bit-level confusion offer a good scrambling to overcome the correlation among the pixels whereas the self-invertible matrix is used to have a better substitution effect. The DNA operations are performed to get new random-valued sequence for diffusion thereby the encryption process has been designed to get good protection with simple and effective coding. The concept of secured medical image encryption has been designed using verilog HDL and implemented using field programmable gate array (FPGA) with an intention to create a standalone embedded hardware for encryption. The analysis of the encryption includes correlation coefficient, entropy, Chi-square analysis, NPCR and UACI and so on, which gives a better result and conveysto us that it can resist the statistical and differential attacks. The synthesis report of the embedded hardware has been presented to understand the utilization of the FPGA.