Reviewerly/exHarmony huggingface dataset:
Ideological and legal foundations of translation censorship in Iran

ABSTRACTThe Islamic Republic of Iran exercises one of the world’s most pervasive censorship systems. This system extends far beyond political concerns to include religious, social, and ethical considerations as well. Unsurprisingly, translations as cultural products are not immune from the influence of state censorship. The main objective of the current paper is to reveal the ideological foundations underpinning the censorship system in Iran, with an emphasis on censorship in translated literature. To this end, the justifications made by Iranian high-level decision-makers for censorship are critically analyzed so that readers may have a better understanding of the motives, rationales, and worldviews of perpetrators of censorship in the Islamic Republic. This article also offers supplementary information on the legal foundations and mechanism of implementation of censorship in this country.KEYWORDS: CensorshiptranslationIranideologypolitics of translation AcknowledgmentsThe author would like to express his gratitude to Prof. Luc van Doorslaer for his invaluable help and support in the research project leading to the publication of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 See the Supreme Council of Cultural Revolution’s amended resolution approved in its 660th session on 13/04/2010.2 See the book publication statistics in Iran, available on https://ketab.ir/PublicationStatistics.3 In Persian: هیات بدوی رسیدگی به تخلفات ناشرانAdditional informationFundingThis paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No [grant number 882688].Notes on contributorsBehrouz KaroubiBehrouz Karoubi is an assistant professor of translation studies at Azad University, Arak, Iran, where he teaches various courses on the theory and practice of translation at undergraduate and postgraduate levels. He was a postdoctoral research fellow at the Translation and Intercultural Transfer Research Unit, KU Leuven (2020-2022), where he conducted a Horizon 2020 Marie Sklodowska-Curie Actions research project (Translation Under Constraint: Censorship in the Islamic Republic of Iran (sTUCk)) under the supervision of prof. dr. Luc van Doorslaer. Karoubi's research areas of interest, on which he has published several articles in international academic journals, include translation and gender, translation and ideology, translation quality assessment, translation history, and translation and censorship.
Resource-efficient simulation of noisy quantum circuits and application to network-enabled QRAM optimization

Abstract Giovannetti, Lloyd, and Maccone (2008) proposed a quantum random access memory (QRAM) architecture to retrieve arbitrary superpositions of N (quantum) memory cells via quantum switches and $$O(\log (N))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>log</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> address qubits. Toward physical QRAM implementations, Chen et al. (2021) recently showed that QRAM maps natively onto optically connected quantum networks with $$O(\log (N))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>log</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> overhead and built-in error detection. However, modeling QRAM on large networks has been stymied by exponentially rising classical compute requirements. Here, we address this bottleneck by: (1) introducing a resource-efficient method for simulating large-scale noisy entanglement, allowing us to evaluate hundreds and even thousands of qubits under various noise channels; and (2) analyzing Chen et al.’s network-based QRAM as an application at the scale of quantum data centers or near-term quantum internet; and (3) introducing a modified network-based QRAM architecture to improve quantum fidelity and access rate. We conclude that network-based QRAM could be built with existing or near-term technologies leveraging photonic integrated circuits and atomic or atom-like quantum memories.
A systematic review of VR/AR applications in vocational education: models, affects, and performances

ABSTRACTThis paper presents a systematic review of the application models, affects, and performance outcomes of VR/AR in vocational education. The analysis is based on journal articles retrieved from renowned databases such as Web of Science, Scopus, and EBSCO, spanning from January 2000 to January 2022. It highlights the pedagogical value of VR/AR in teaching, presents the current research status of their implementation in vocational education, and suggests future research directions. VR/AR tools or environments have emerged in the past two decades to enhance vocational education, offering new ways to develop learner’s practical abilities and theoretical concepts, innovate teaching methods, and advance vocational education. Although VR/AR is being integrated into classrooms, it is still in its early stages. Future research should focus on multidimensional investigations, cross-curricular integration, immersive training, and evaluating learning outcomes and learners and teachers’ in-depth interactions with VR/AR technologies. This paper identifies research gaps, expands the scope, and provides directions for researchers and software designers.KEYWORDS: Virtual realityaugmented realityvocational educationsystematic review3T model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by a grant from National Planning Office of Philosophy and Social Sciences of China (Project No. BJA220259) “Research on the construction of evaluation standard system of vocational education textbooks from the perspective of high-quality development.”Notes on contributorsYingjie LiuYingjie Liu, Master student, School of Information Technology, Tianjin University of Technology and Education.Qinglong ZhanQinglong Zhan, Professor, Ph.D., School of Information Technology, Tianjin University of Technology and Education. Reviewers of journals such as Computers & Education, IEEE Transaction series, Journal of Electronics, etc.Wenping ZhaoWenping Zhao, Professor, Ph.D., School of Vocational Education, Tianjin University of Technology and education.
Enhanced Bald Eagle Search Optimizer with Transfer Learning-based Sign Language Recognition for Hearing-impaired Persons

Sign language recognition and classification for hearing-impaired people is a vital application of computer vision (CV) and machine learning (ML) approaches. It contains developing structures that take sign language gestures carried out by individuals and transform them into textual or auditory output for transmission aspects. It is critical to realize that establishing a robust and correct sign language recognition and classification method is a difficult task because of several challenges like differences in signing styles, occlusions, lighting conditions, and individual variances in hand movements and shapes. Thus, it needs a group of CV approaches, ML systems, and a varied and representative database for training and testing. In this study, we propose an Enhanced Bald Eagle Search Optimizer with Transfer Learning Sign Language Recognition (EBESO-TLSLR) technique for hearing-impaired persons. The presented EBESO-TLSLR technique aims to offer effective communication among hearing-impaired persons and normal persons using deep learning models. In the EBESO-TLSLR technique, the SqueezeNet model is used for feature map generation. For recognition of sign language classes, the long short-term memory (LSTM) method can be used. Finally, the EBESO approach is exploited for the optimal hyperparameter election of the LSTM method. The simulation results of the EBESO-TLSLR method are validated on the sign language dataset. The simulation outcomes illustrate the superior results of the EBESO-TLSLR technique in terms of different measures.
Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Sign Language Recognition for Hearing- and Speech-impaired Persons

Sign language is commonly used to interact with people who have speech and hearing disorders. Sign language was exploited for interacting with people having developmental impairments who have some or no communication skills. Communication using Sign language has become a fruitful means of interaction for speech- and hearing-impaired people. The hand gesture recognition technique is useful for dumb and deaf people by using convolutional neural networks (CNNs) and human–computer interface for recognizing the static indication of sign language. Therefore, this study presents a new Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Intelligent Sign Language Recognition (SCSO-DWAESLR) technique for hearing- and speech-impaired persons. In the presented SCSO-DWAESLR technique, computer vision and CNN concepts are utilized for identifying sign languages to aid the interaction of hearing- and speech-impaired persons. The SCSO-DWAESLR method makes use of the Inception v3 model for the feature map generation process. In addition, the DWAE classifier is utilized for the recognition and classification of different kinds of signs posed by hearing- and speech-impaired persons. Finally, the hyperparameters related to the DWAE classifier are optimally chosen by using the SCSO algorithm. For exhibiting the effectual recognition outcomes of the SCSO-DWAESLR technique, a detailed experimental analysis was performed. The comparative outcome highlights the superior recognition performance of the SCSO-DWAESLR method over existing techniques under several evaluation metrics.
State Policy of the Russian Federation on attracting foreign students of 2008–2021

The article examines development of the state policy of Russia in the field of higher education in terms of attracting foreign students in the period of 2008-2021. Relevance of the topic is determined by the need to increase competitiveness of the domestic higher school and the realization of its export potential. The purpose of the article is to analyze the state policy in the field of higher education to increase the number of foreigners, as an important component of internationalization and export of education. Special attention is paid to internationalization and export of education and the formation of new approaches to public policy in the field of education under the influence of these trends. The article analyzes a number of state planning documents: the Concept of long-term socio-economic development of the Russian Federation for the period up to 2020, the «May decrees» of the President of Russia of 2012 and 2018. These documents have determined the goals and objectives of improving the competitiveness of the Russian higher education. At the same time, the importance attached to the export of education by the Russian leadership is emphasized by a set of projects and programs aimed at attracting the foreigners. In this regard, project approach in the public administration is being investigated in terms of increasing the export of education. The projects «5-100», «Development of the export potential of the Russian education system» and «Export of education» are considered. Based on the analysis, conclusions on the effectiveness of state policy and its promising directions in terms of attracting foreign students are presented.
Verification and validation of software process simulation models: A systematic mapping study

Abstract Software process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&amp;V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&amp;V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&amp;V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&amp;V, the methods used for V&amp;V, and how to implement V&amp;V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&amp;V activities. Under the guidance of a V&amp;V meta‐model for SPSMs, we study four research questions about V&amp;V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&amp;V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&amp;V methods. Generally, researchers do not pay sufficient attention to V&amp;V, as 65.8% () failed to mention or elaborate on their V&amp;V process. We systematically summarize and map the state‐of‐the‐art V&amp;V research in software process modeling field to support modelers' practice and improve their V&amp;V process.
Multiview feature co‐factorization based dictionary selection for video summarization

Abstract Recently, video summarization (VS) has emerged as one of the most effective tools for rapidly understanding video big data. Dictionary selection based on self‐representation and sparse regularization is consistent with the requirement of VS, which aims to represent the original video with little reconstruction error by a small number of video frames. However, one crucial issue is that the existing methods mainly use a single view feature, which is not sufficient enough for acquiring the full pictorial details and affects the quality of the produced video summary. Although a few methods use more than one features, they only directly concatenate the features, which does not take advantage of the relationship of different features. Considering the complementarity of shallow and deep features, multiview feature co‐factorization based dictionary selection for VS is proposed in this paper to use the common information of both view features for VS. Specifically, two view features are used to fully exploit the full pictorial information of video frames, then the common information of two different views is extracted through coupled matrix factorization to conduct the dictionary selection for VS. Experiments have been carried out on two benchmark datasets, and results have demonstrated the effectiveness and superiority of the proposed method.
RGB depth salient object detection via cross‐modal attention and boundary feature guidance

Abstract RGB depth (RGB‐D) salient object detection (SOD) is a meaningful and challenging task, which has achieved good detection performance in dealing with simple scenes using convolutional neural networks, however, it cannot effectively handle scenes with complex contours of salient objects or similarly coloured salient objects and background. A novel end‐to‐end framework is proposed for RGB‐D SOD, which comprises of four main components: the cross‐modal attention feature enhancement (CMAFE) module, the multi‐level contextual feature interaction (MLCFI) module, the boundary feature extraction (BFE) module, and the multi‐level boundary attention guidance (MLBAG) module. The CMAFE module retains the more effective salient features by employing a dual‐attention mechanism to filter noise from two modalities. In the MLCFI module, a shuffle operation is used for high‐level and low‐level channels to promote cross‐channel information communication, and rich semantic information is extracted. The BFE module converts salient features into boundary features to generate boundary maps. The MLBAG module produces saliency maps by aggregating multi‐level boundary saliency maps to guide cross‐modal features in the decode stage. Extensive experiments are conducted on six public benchmark datasets, with the results demonstrating that the proposed model significantly outperforms 23 state‐of‐the‐art RGB‐D SOD models with regards to multiple evaluation metrics.
Fast neural network for TV super resolution scaling‐up system

Abstract In this paper, we propose a modified architecture aimed at reducing the computational demands of the generative adversarial network for super‐resolution image generation. To achieve this, we embedded depth‐wise and point‐wise convolution into the convolution layer, effectively decreasing operational complexity and improving the overall network structure. For training and validation, we utilized a dataset consisting of 900 image pairs with resolutions of 480 × 270 and 1920 × 1080. Our experimental results demonstrated that the proposed method can reduce computational operators by 63% compared to the original network, while still maintaining the quality of super‐resolution images. To enable real‐time implementation, the architecture with light model subsequently deployed it on a GPU processor, allowing for efficient scaling of TV signals for 16× resolution expansion. Our experiments showed that the peak signal‐to‐noise ratio (PSNR) reached approximately 28 dB, and the processing rate ranged from 6 to 14 frames per second. The network effectively produced output with 16 times greater resolution without introducing any blurring and obvious artifact.
Height reverse perspective transformation for crowd counting

Introduction Crowd counting plays a critical role in the intelligent video surveillance of public areas. A significant challenge to this task is the perspective effect on human heads, which causes serious scale variations. Height reverse perspective transformation (HRPT) alleviates this problem by narrowing the height gap among human heads. Methods It employs depth maps to calculate the rescaling factors of image rows, and then it performs image transformation accordingly. HRPT enlarges small human heads in far areas to make them more noticeable and shrinks large human heads in closer areas to reduce redundant information. Then, convolutional neural networks can be used for crowd counting. Previous crowd-counting methods mainly solve the scale variation problem by designing specific networks, such as multi-scale or perspective-aware networks. These networks cannot be conveniently employed by other methods. In contrast, HRPT solves the scale variation problem through image transformation. It can be used as a preprocessing step and easily employed by other crowd-counting methods without changing their original structures. Results and discussion Experimental results show that HRPT successfully narrows the height gap among human heads and achieves state-of-the-art performance on a large crowd-counting RGB-D dataset.
Effects of NDRT interface display and control modalities on motion sickness during autonomous driving

This study comparatively evaluated four different non-driving-related task (NDRT) interface designs varying in the interface display and control modalities in terms of their impacts on the severity of autonomous vehicle (AV) motion sickness (MS). In an on-road experiment, participants conducted a simulated AV NDRT (a letter 2-back task) using each NDRT interface design. The independent variables were NDRT interface display modality and control modality. Display modality had two levels: visual (V) and auditory (A). Control modality also had two levels: manual (M) and speech (S). Thus, the four NDRT interface designs were: VM, VS, AM and AS. The dependent measures were the pre-post differences in the motion sickness assessment questionnaire (MSAQ) scores quantifying the increases in MS due to the NDRT. Display modality significantly affected all the dependent measures. The mean pre-post differences were larger for visual than for auditory. Control modality did not affect any of the dependent measures
Computing the Sound–Sense Harmony: A Case Study of William Shakespeare’s Sonnets and Francis Webb’s Most Popular Poems

Poetic devices implicitly work towards inducing the reader to associate intended and expressed meaning to the sounds of the poem. In turn, sounds may be organized a priori into categories and assigned presumed meaning as suggested by traditional literary studies. To compute the degree of harmony and disharmony, I have automatically extracted the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment, sounds have been associated with lexically and semantically based sentiment analysis, obtaining an 80% of agreement. In a second experiment, sentiment analysis has been substituted by Appraisal Theory, thus obtaining a more fine-grained interpretation that combines dis-harmony with irony. The computation for Francis Webb is based on his most popular 100 poems and combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony, where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them. Shakespeare and Webb have been chosen to prove the applicability of the method proposed in general contexts of poetry, exhibiting the widest possible gap at all linguistic and poetic levels.
Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.