Reviewerly/exHarmony huggingface dataset:
Implementation of Brute-Force Value Iteration for Mobile Robot Path Planning and Obstacle Bypassing

We applied a brute-force value iteration algorithm to mobile robot navigation. Value iteration is computationally more expensive than search methods used for navigation. However, it can perfectly calculate the expected cost-to-go from any point in a state space. From this cost data, a robot can know not only the optimal behavior at any position and orientation but also the appropriate detour path against suddenly appearing obstacles. This study implemented value iteration and investigated its properties through experiments with simulated and actual robots. Although its computational cost remained high, our implementation could operate a robot in an actual outdoor environment with 3,700 m 2 free space. We also verified that our implementation calculates long detour paths toward closures composed of obstacles.
La insoportable levedad de la cercanía entre dos lenguas

The aim of this paper is to study some translating problems related to the proximity to Spanish and Italian languages. We will be analyzing selected texts from Italian authors Antonio Tabucchi, Alessandro Baricco and Andrea Camilleri in their translation into Spanish identifying interferences in lexicon, syntaxis, rhythm and intertextuality. For Camilleri’s texts, we’ll be working with the filmic text and its translation into Spanish subtitling version.
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More via Filter Pruning

The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication. In this context, facial recognition technologies have emerged as a dependable method for verifying users due to the prevalence of cameras in mobile devices and their integration into everyday applications. The rapid advancement of deep Convolutional Neural Networks (CNNs) has led to numerous face verification architectures. However, these models are often large and impractical for mobile applications, reaching sizes of hundreds of megabytes with millions of parameters. We address this issue by developing SqueezerFaceNet, a light face recognition network which less than 1M parameters. This is achieved by applying a network pruning method based on Taylor scores, where filters with small importance scores are removed iteratively. Starting from an already small network (of 1.24M) based on SqueezeNet, we show that it can be further reduced (up to 40%) without an appreciable loss in performance. To the best of our knowledge, we are the first to evaluate network pruning methods for the task of face recognition.
Mechanisms for concentrating critical metals in granitic complexes: insights from the Mourne Mountains, Northern Ireland

The Tellus stream sediment and deep soil geochemistry data sets for Northern Ireland were used to locate four types of critical metals anomalies in granite bedrocks of the Mourne Mountains. A curvi-linear array of Nb, REE, Th and U soil anomalies across the eastern Mourne Mountains correlated with late-stage and eutectic temperature minerals in the roof zone of the most peralkaline F- and volatile-rich granite body, remobilized on micron to millimetre scales. Li, Be, B, As, Sn, Mn 3+ and Ce 4+ partitioned into pockets of late-stage heterogeneously distributed F-rich silicic residual melts and relatively oxidizing halide-rich magmatic fluids, resulting in drusy mineral and hydrothermal assemblages. Isolated soil anomalies correlated with amorphous Mn 3+ - and Ce 4+ -rich masses infilling drusy cavities, which resulted from short-distance percolation of small volumes of late-stage magmatic fluids. A significant As plume in stream sediments emanated from a greisen that hosted multiple critical and base metals including Sn, from reactions between large volumes of magmatic As + halide-rich fluids and mafic silicate + diverse accessory minerals on the metre- to kilometre-scale along geological structures. Diverse, small-scale REE anomalies in the soil data along structural features in the western Mournes correlate with vein mineralization resulting from episodic migration of hydrous fluids of variable composition, probably with a much smaller magmatic component than elsewhere. The regional geochemical dataset proved useful to develop a multi-stage model for enrichment of critical metals in the Mourne Mountains granites, which is analogous to the petrogenesis of some of the igneous-hosted economic deposits of critical metals. Thematic collection: This article is part of the energy-critical metals for a low carbon transition collection available at: https://www.lyellcollection.org/topic/collections/critical-metals
Social responsibility of scientific institutions as a factor in ensuring competitiveness and national technological sovereignty of Russia

Introduction. The authors draw attention to the possibilities of ensuring Russia’s technological sovereignty through the development of scientific institutions and their involvement in ensuring national competitiveness. The strategic potential for improvement is seen in the formation and targeted management of the social responsibility of scientific institutions. The novelty of the study lies on the fact that it substantiates the possibilities of effectively using the potential of social responsibility of scientific institutions, contributing to increasing the contribution of the research sector to strengthening the national competitiveness of Russia and, thereby, ensuring national technological sovereignty. Methods. The study is based on the concepts of triple helix of innovative development and social responsibility. A survey was conducted among heads of the social block of 22 research institutions in Russia, representing five federal districts. Research results and its discussion. The following results were obtained: it was argued that the formation and strengthening of national technological sovereignty is a strategic resource for ensuring balanced and sustainable socio-economic development of the Russian Federation, and increasing the competitiveness of scientific institutions in this area can play a decisive role. The shortcomings in the system for ensuring the competitiveness of Russian scientific institutions are shown, among which a prominent role is occupied by ineffective or absent management of social development and social interactions, which sharply contrasts with the social essence of scientific institutions. Attention is drawn to the exceptional potential of social responsibility management in increasing the competitiveness of scientific institutions. Proposals have been developed for establishing systemic management of scientific institutions in Russia, including defining goals and objectives, developing strategy, creating a system for monitoring and evaluating management effectiveness, involving employees of scientific institutions in the management process, activating the potential of the academic community and cooperation with government and public organizations, dealing with issues of social responsibility. In conclusion, it is come to that introduction of systemic management of social responsibility in Russian scientific institutions is an important step to ensure the competitiveness of scientific institutions and the formation of national technological sovereignty.
Harnessing Key Phrases in Constructing a Concept-Based Semantic Representation of Text Using Clustering Techniques

This paper introduces a modified approach for representing text as semantic vectors, building upon the Bag of Weighted Concepts (BoWC) method developed in previous research. The limitations of the BoWC method are addressed, and a proposed solution is presented. Instead of using unigrams, the authors propose extracting key phrases that best represent each document to generate high-quality concepts and reduce concept overlap. These unique key phrases are then used to construct the concept dictionary. Document vectors are created by mapping document key phrases to the concept dictionary using a modified concept weighting function that considers the weight of the key phrase within the document. To evaluate the effectiveness of the resulting vectors, they were employed in a clustering task and compared against robust baselines. Experimental studies demonstrate that the proposed modifications enhance the quality of document vector representation, as evidenced by a minimum 4% increase in clustering accuracy based on the V1 metric.
Echo State Networks for the Prediction of Chaotic Systems

Chaotic systems are complex and challenging to predict due to their sensitive dependence on initial conditions and the complexity of their dynamics. This paper improves the prediction accuracy of chaotic systems using Echo State Networks (ESNs) by considering a complex graph adjacency matrix as the recurrent layer. This helps ESNs better capture the nonlinear dynamics of chaotic systems. ESNs are a type of recurrent neural network with a fixed, randomly generated hidden layer, making them computationally efficient and able to handle large, high-dimensional datasets. This study applies ESNs to the Lorenz, Mackey-Glass, and Kuramoto-Sivashinsky models, which are well-studied examples of chaotic systems. Our results demonstrate that ESNs can effectively capture the dynamics of these systems and outperform existing prediction methods made with ESNs.
Cross-Attention and Seamless Replacement of Latent Prompts for High-Definition Image-Driven Video Editing

Recently, text-driven video editing has received increasing attention due to the surprising success of the text-to-image model in improving video quality. However, video editing based on the text prompt is facing huge challenges in achieving precise and controllable editing. Herein, we propose Latent prompt Image-driven Video Editing (LIVE) with a precise and controllable video editing function. The important innovation of LIVE is to utilize the latent codes from reference images as latent prompts to rapidly enrich visual details. The novel latent prompt mechanism endows two powerful capabilities for LIVE: one is a comprehensively interactive ability between video frame and latent prompt in the spatial and temporal dimensions, achieved by revisiting and enhancing cross-attention, and the other is the efficient expression ability of training continuous input videos and images within the diffusion space by fine-tuning various components such as latent prompts, textual embeddings, and LDM parameters. Therefore, LIVE can efficiently generate various edited videos with visual consistency by seamlessly replacing the objects in each frame with user-specified targets. The high-definition experimental results from real-world videos not only confirmed the effectiveness of LIVE but also demonstrated important potential application prospects of LIVE in image-driven video editing.
Validation of Educational Quality as a Tool to Measure the Degree of Satisfaction of University Students

The aim of this research was the creation and validation of a questionnaire to assess education quality in university contexts and to find factors that can help improve the educational system. The questionnaire was validated via a psychometric study through exploratory (EFA) and confirmatory (CFA) data analyses from a sample of 1302 subjects to find students’ level of satisfaction in the university environment. The results show good reliability and validity, highlighting three factors: access level to computer aspects; different material resources and university students’ degree of satisfaction with the educational community. We concluded that this validated questionnaire provides information regarding students’ opinions about the quality of education in university contexts and can serve as a measurement tool for the educational system’s good functioning in an effective and efficient way.
Earth Fissures and Natural Resources Mining

Earth fissuresEarth fissure are a macroscopicMacroscopic surface phenomenon where surface rock layers and soil formations crack and form fractures of a certain length and width due to natural factors such as tectonicTectonic activity and water action, or human factors such as groundwater extraction, irrigationIrrigation, and excavation.
Is post-editing really faster than human translation?

Abstract Time efficiency is paramount for the localisation industry, which demands ever-faster turnaround times. However, translation speed is largely underresearched, and there is a lack of clarity about how language service providers (LSPs) can evaluate the performance of their post-editing (PE) and human translation (HT) services. This study constitutes the first large-scale investigation of translation and revision speed in HT and in the PE of neural machine translation, based on real-world data from an LSP. It uses an exploratory data analysis approach to investigate data for 90 million words translated by 879 linguists across 11 language pairs, over 2.5 years. The results of this research indicate that (a) PE is usually but not always faster than HT; (b) average speed values may be misleading; (c) translation speed is highly variable; and (d) edit distance cannot be used as a proxy for post-editing productivity, because it does not correlate strongly with speed.
DIGITALIZATION OF TEACHER TRAINING IN UKRAINE IN THE CONTEXT OF THE EXPERIENCE OF HUNGARY AND THE REPUBLIC OF POLAND

The article reveals some recommendations for the digitalization of teacher training in Ukraine based on the experience of Hungary and the Republic of Poland. To solve the tasks set and achieve the purpose of the article, the method of theoretical analysis was used to highlight the peculiarities of the development of the system of digitalization of education in Ukraine; empirical methods were used to study the works of domestic and foreign scholars on the digitalization of teacher training; and generalization was used to present recommendations for improving the digitalization of teacher training in Ukraine, based on the experience of the Republic of Poland and Hungary. It has been determined that the digitalization of education in Ukraine is proceeding at an active pace. The content of the educational levels of the program «Google Digital Tools for Education» is considered. It is found that a significant indicator of achieving digitalization of the national education system is the introduction and active development of the Unified State Electronic Database on Education, whose main purpose is to provide individuals and legal entities with information in the field of education. It has been established that the Unified State Electronic Database on Education has been modernized to ensure the possibility of admission to higher education institutions in times of war. Furthermore, it has been found that in the context of developing recommendations for Ukraine on the digitalization of teacher training, taking into account the experience of Hungary and Poland, it is advisable to focus on the need for intensive development of the following technologies: artificial intelligence; augmented and virtual reality; robotization; the Internet of Things; media education; blockchain; cloud-based environments; gamification; interdisciplinary, etc. It has been determined that digital competence is an important component of the professionalism of a modern teacher or lecturer, and at the same time, indicators of its effective formation ensure the success of human functioning in modern society. The positive aspects of the gamification environment are highlighted. Useful online services for teachers are presented. Recommendations for the digitalization of teacher training in Ukraine are proposed, taking into account the experience of Hungary and the Republic of Poland.
The moderating effect of income inequality on the relationship between economic growth and political economy, human capital, innovation, and saving channels in Ethiopia

Abstract Background This research focuses on examining the relationship between income inequality and economic growth in Ethiopia over the period from 1981 to 2020. The study aims to understand the various channels through which income inequality affects economic growth and explores the role of different factors such as political economy, innovation, human capital, and saving channels. Method To analyze the relationship between income inequality and economic growth in Ethiopia, the researchers employ the Autoregressive Distributed Lag (ARDL) model. They also conduct the Bayer Hanck (BH) Cointegration Test using four different methods: Engle-Granger, Johansen, Banerjee, and Boswijk. These techniques help determine whether there is a long-term connection, or cointegration, between the variables under investigation. Results The findings of this study indicate strong evidence of cointegration between income inequality and economic growth in Ethiopia, except in the Engle-Granger test. This suggests a long-term relationship between these variables. The researchers analyze various channels through which income inequality affects economic growth. They discover that income inequality significantly hampers economic growth in Ethiopia. On the other hand, the development of human capital, as measured by school enrollment, has a positive and significant impact on economic growth. However, other channel variables such as gross domestic saving, gross fixed capital formation, high-technology exports, general government revenue, and general government total expenditure do not exert a significant influence on economic growth. Conclusions Based on the research findings, the study emphasizes the role of government expenditure as a moderating factor that can mitigate the adverse effects of income inequality on economic growth in Ethiopia. It also highlights the importance of savings channels, particularly gross fixed capital formation, in fostering economic growth. Moreover, the study suggests that innovation, represented by high-technology exports, may indirectly influence economic growth by promoting the development of human capital. Overall, this research provides valuable insights into the intricate relationships between income inequality and economic growth in Ethiopia, exploring both direct and moderating effects through various transmission channels.
Loop Closure Detection Based on Compressed ConvNet Features in Dynamic Environments

In dynamic environments, convolutional neural networks (CNNs) often produce image feature maps with significant redundancy due to external factors such as moving objects and occlusions. These feature maps are inadequate as precise image descriptors for similarity measurement, hindering loop closure detection. Addressing this issue, this paper proposes feature compression of convolutional neural network output. The approach is detailed as follows: (1) employing ResNet152 as the backbone feature-extraction network, a Siamese neural network is constructed to enhance the efficiency of feature extraction; (2) utilizing KL transformation to extract principal components from the backbone network’s output, thereby eliminating redundant information; (3) employing the compressed features as input for NetVLAD to construct a spatially informed feature descriptor for similarity measurement. Experimental results demonstrate that, on the New College dataset, the proposed improved method exhibits an approximately 9.98% enhancement in average accuracy compared to the original network. On the City Center dataset, there is an improvement of approximately 2.64%, with an overall increase of about 23.51% in time performance. These findings indicate that the enhanced ResNet152 performs better than the original network in environments with more moving objects and occlusions.
Fidelity-estimation method for graph states with depolarizing noise

Graph states are entangled states that are useful for several quantum information processing tasks, such as measurement-based quantum computation and quantum metrology. As the size of graph states realized in experiments increases, it becomes more essential to devise efficient methods for estimating the fidelity between the ideal graph state and an experimentally realized actual state. Any efficient fidelity-estimation method, in general, must use multiple experimental settings, i.e., needs to switch between at least two measurements. Recently, it has been shown that a single measurement is sufficient if the noise can be modeled as the phase-flip error. Since the bit-flip error should also occur in several experiments, it is desired to extend this simplest method to noise models that include phase and bit-flip errors. However, it seems to be nontrivial because their result strongly depends on properties of the phase-flip error. In this paper, by analyzing effects of the bit-flip error on stabilizer operators of graph states, we achieve the extension to the depolarizing noise, which is a major noise model including phase and bit-flip errors. We also numerically evaluate our simplest method for noise models interpolating between the phase-flip and depolarizing noises.
Retos de la traducción para la primera adolescencia Una aproximación a la traducción al italiano de <i>Alas de fuego</i> de Laura Gallego García (2004)

The article presents an analysis of the translation into Italian of a fantasy novel for young adults: Alas de fuego (2004) by Laura Gallego. Particular attention is paid to the definition of young adult literature by framing the work within the age group to which it is addressed, i.e. early adolescence. The representation of the linguistic variety intended for this sector of readers is analysed from the point of view of the register used and through the analysis of the translations of interjections and offensive epithets into the target languages (Italian in particular, but also French). Secondly, some of the aspects of the imaginary environment created by the author are brought to light, as they are reflected in a specific and evocative lexicon, which is an aspect that the translator cannot fail to grasp and bring to life through the translated text. Finally, a review is presented of the main challenges and strategies involved in translating texts for young adults from the point of view of reception according to the lexical competence of this category of readers.
MFSNet: Enhancing Semantic Segmentation of Urban Scenes with a Multi-Scale Feature Shuffle Network

The complexity of urban scenes presents a challenge for semantic segmentation models. Existing models are constrained by factors such as the scale, color, and shape of urban objects, which limit their ability to achieve more accurate segmentation results. To address these limitations, this paper proposes a novel Multi-Scale Feature Shuffle NetWork (MFSNet), which is an improvement upon the existing Deeplabv3+ model. Specifically, MFSNet integrates a novel Pyramid Shuffle Module (PSM) to extract discriminative features and feature correlations, with the objective of improving the accuracy of classifying insignificant objects. Additionally, we propose an efficient feature aggregation module (EFAM) to effectively expand the receptive field and aggregate contextual information, which is integrated as a branch within the network architecture to mitigate the information loss resulting from downsampling operations. Moreover, in order to augment the precision of segmentation boundary delineation and object localization, we employ a progressive upsampling strategy for reinstating spatial information in the feature maps. The experimental results show that the proposed model achieves competitive performance, achieving 80.4% MIoU on the Pascal VOC 2012 dataset, 79.4% MIoU on the Cityscapes dataset, and 40.1% MIoU on the Coco-Stuff dataset.