Reviewerly/exHarmony huggingface dataset:
The Moderating Role of Research and Development (R&amp;D) Support in the Relationship between Entrepreneurship and per Capita Output—A Study on the GCC Countries

The current study examines the moderating role of R&amp;D expenditures by the government on the relationship between entrepreneurship and per capita output in GCC countries. Using secondary quantitative data, panel data analysis was conducted for six GCC countries (Saudi Arabia, Kuwait, Oman, Bahrain, Qatar, and UAE) based on their scores on the Global Entrepreneurship Index, Ease of Doing Business, and R&amp;D expenditure as a percentage of GDP. Descriptive statistics and regression analysis were conducted using Eviews 12. The study found that a supportive business environment and entrepreneurship ecosystem can lead to higher per capita output and that laboratory force and capital are significant positive contributors to per capita output. However, both Ease of Doing Business and the Global Entrepreneurship Index have a significant negative impact on per capita output. The study did not find significant moderation of the relationship between entrepreneurship and per capita output by R&amp;D expenditures. These findings have important implications for policy-makers and academia, emphasizing the significant labour force and capital for per capita output. Future research should explore the relationship between entrepreneurship and growth further and investigate the role of R&amp;D. Policy recommendations include reducing regulatory burdens and providing tax incentives to create a supportive environment for entrepreneurship and increasing R&amp;D funding to promote per capita output. Overall, this study contributes to the state of the art through examining the moderating role of R&amp;D expenditures on the relationship between entrepreneurship and per capita output in the context of GCC countries.
Geochemistry of ore minerals from Chelopech deposit: II. Trace-elements in enargite-luzonite

The concentrations of trace elements in enargite-luzonite from blocks 8, 19-SE, 19-NW, 19-NE, 19-SW, 103-W, 103-E, 144, 146, 147, 149-south, 150, and 151 of copper-gold high sulphidation Chelopech deposit were determined by LA-ICP-MS. It was found that the enargite-luzonites from the individual blocks are enriched in different degrees of “impurity” elements: those from block 19-SW are enriched in Ge, Ga, and W; from block 146 of Bi; from block 147 of Se, Sb, Sn, and In; from block 149-south of Zn, Pb, Tl, and Cd; from block 8 of Fe, Mn, and Ti; from block 103-Е of Au and Hg; from block 103-W of V, Ga, and Mo; from block 150 of Ag and from block 151 of Те. In addition, a preferential distribution of trace elements between pyrite and enargite-luzonite is observed. For example, silver, lead, manganese, nickel, cobalt, thallium, molybdenum, chromium, and titanium are concentrated in the first, while germanium, gallium, indium, tin, zinc, antimony, tellurium, bismuth, mercury, vanadium, tungsten in the second, and in most cases they are structurally related. Both minerals are concentrators of gold, but it reaches its highest values in the latter (309 ppm from block 103-Е and 118 ppm from block 151), and in both cases its presence is mainly due to microinclusions of native gold and/or electrum and tellurides.
Memoryless Quantum Repeaters Based on Cavity‐QED and Coherent States

Abstract A quantum repeater scheme based on cavity‐quantum electrodynamics (QED) and quantum error correction of channel loss via rotation‐symmetric bosonic codes (RSBCs) is proposed to distribute atomic entangled states over long distances without memories and at high clock rates. In this scheme, controlled rotation gates, i.e., phase shifts of the propagating light modes conditioned upon the state of an atom placed in a cavity, provide a mechanism both for the entangled‐state preparations and for the error syndrome identifications. In order to assess the performance of this repeater protocol, an explicit instance of RSBCs—multicomponent cat codes—are studied quantitatively. It is found that the total fidelity and the success probability for quantum communication over a long distance (such as 1000 km) both can almost approach unity provided a small enough elementary distance between stations (smaller than 0.1 or 0.01 km) and rather low local losses (up to 0.1%) are considered. In a quantum key distribution application, secret key rates can become correspondingly high, both per channel use, beating the repeaterless bound, and per second thanks to the relatively high clock rates of the memoryless scheme. Based upon the cavity‐QED setting, this scheme can be realized at room temperature and at optical frequencies.
Unbalanced Web Phishing Classification through Deep Reinforcement Learning

Web phishing is a form of cybercrime aimed at tricking people into visiting malicious URLs to exfiltrate sensitive data. Since the structure of a malicious URL evolves over time, phishing detection mechanisms that can adapt to such variations are paramount. Furthermore, web phishing detection is an unbalanced classification task, as legitimate URLs outnumber malicious ones in real-life cases. Deep learning (DL) has emerged as a promising technique to minimize concept drift to enhance web phishing detection. Deep reinforcement learning (DRL) combines DL with reinforcement learning (RL); that is, a sequential decision-making paradigm in which the problem to be addressed is expressed as a Markov decision process (MDP). Recent studies have proposed an ad hoc MDP formulation to tackle unbalanced classification tasks called the imbalanced classification Markov decision process (ICMDP). In this paper, we exploit the ICMDP to present a double deep Q-Network (DDQN)-based classifier to address the unbalanced web phishing classification problem. The proposed algorithm is evaluated on a Mendeley web phishing dataset, from which three different data imbalance scenarios are generated. Despite a significant training time, it results in better geometric mean, index of balanced accuracy, F1 score, and area under the ROC curve than other DL-based classifiers combined with data-level sampling techniques in all test cases.
It-Sr-NER: CLARIN Compatible NER and Geoparsing Web Services for Italian and Serbian Parallel Text

The paper will showcase the outcomes of the ”It-Sr-NER:Web services for named entities recognition, linking and mapping” project for Serbian and Italian languages. The project was a collaboration between the University of Turin and the Society for Language Resources and Technologies JeRTeh, with the goal of creating the It-Sr-NER web service. This service is designed to annotate named entities such as people, places, organizations, ethnicities, events, and works of art in text and display them on a map.
Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck

At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than Gohr’s work. The details are as follows. Firstly, a new data format (C_r,C_r′,d_l,Cl,Cr,Cl′,Cr′) is proposed for the input data of the differential neural distinguisher, which can help the distinguisher to identify the features of the previous round of ciphertexts in the Speck algorithm. Secondly, this paper modifies the convolution layer of the residual block in the residual network, inspired by the Inception module in GoogLeNet. For Speck32/64, the experiments show that the accuracy of the 7-round differential neural distinguisher is 97.13%, which is better than the accuracy of Gohr’s distinguisher of 9.1% and also higher than the currently known accuracy of 89.63%. The experiments also show that the data format and neural network in this paper can improve the accuracy of the distinguisher by 2.38% and 2.1%, respectively. Finally, to demonstrate the effectiveness of the distinguisher in this paper, a key recovery attack is performed on 8-rounds of Speck32/64. The results show that the success rate of recovering the correct key is 92%, with no more than two incorrect bits. Finally, this paper briefly discussed the effect of the number of ciphertext pairs in a sample on the training results of the differential neural distinguisher. When the total number of ciphertext pairs is kept constant, the accuracy of the distinguisher increases with s, but it also leads to the occurrence of overfitting.
An Enhanced Feature Extraction Network for Medical Image Segmentation

The major challenges for medical image segmentation tasks are complex backgrounds and fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation tasks, we propose an enhanced feature extraction network (EFEN), which is based on U-Net. Our network is designed with the structure of feature re-extraction to strengthen the feature extraction ability. In the process of decoding, we use improved skip-connection, which includes positional encoding and a cross-attention mechanism. By embedding positional information, absolute information and relative information between organs can be captured. Meanwhile, useful information will be strengthened and useless information will be weakened by using the cross-attention mechanism. Our network can finely identify the features of each skip-connection and cause the features in the process of decoding to have less noise in order to reduce the effect of fuzzy object boundaries in medical images. Experiments on the CVC-ClinicDB, the task1 from ISIC-2018, and the 2018 Data Science Bowl challenge dataset demonstrate that EFEN outperforms U-Net and some recent networks. For example, our method obtains 5.23% and 2.46% DSC improvements compared to U-Net on CVC-ClinicDB and ISIC-2018, respectively. Compared with recent works, such as DoubleU-Net, we obtain 0.65% and 0.3% DSC improvements on CVC-ClinicDB and ISIC-2018, respectively.
Cyclostratigraphy at a tipping point

There are key patterns of variation in the (litho‐) stratigraphical record, which, with advances in computing technology in recent decades, have become amenable to objective numerical analysis. This research has chiefly focused on the search for spatially regular cycles in the sedimentary rock record, since there are theoretical grounds for believing that these would be periodic and thus provide a means of time‐calibrating the stratigraphical records in which they occurred. Less popular lines of research consider the scaling relationships of the tangible, measurable sedimentary rock layers, and of the hiatuses, individually of indeterminate length, that punctuate these layers. Objective source data for all these analyses is provided, on limited (usually decametre) scales, by detailed lithological logging, sampling and dating of sections provided by exposures and cores. An objective global lithostratigraphical database, with larger, kilometre scales, is provided by the widespread practice of electric (wire‐line) logging of deep wells. This involves measurement, at 6‐inch (~15 cm) depth intervals of lithology‐related petrophysical properties. Rapid desk top computer analysis of the hundreds or thousands of readings that comprise these two data series, searches for patterns of variation. This review considers the published evidence for ‘ubiquitous’ cyclicity and its use in chronostratigraphy, despite the contrary claims that stratigraphical records are predominantly random in character.
Project-Based Learning as a Methodology to Improve Reading and Comprehension Skills in the English Language

This study aimed to investigate the effectiveness of Project-Based Learning (PBL) in enhancing English language reading skills among ninth-grade students in a school located in Quito. The research employed a mixed-methods approach, combining a descriptive nature with a comprehensive literature review from reputable sources such as ScienceDirect, Scopus, MDPI, Web of Science, and Springer. The analytical-synthetic method was utilized, incorporating an observation guide to diagnose students’ reading preferences and habits, along with questionnaires administered to 10 teachers and two institutional authorities to gather insights on their experiences with PBL. The main objective was increasing interest and competence in reading, reading, and comprehension. Following the implementation of PBL, the 20 participating students exhibited enhanced motivation and demonstrated advancements in their reading skills. They engaged in significant, relevant projects aligned with their social and educational environment. The results highlight the effectiveness of PBL as a teaching strategy for developing English language reading skills. The findings suggest that educators have a fundamental responsibility to stimulate and encourage the development of reading abilities, especially for students at the beginning of their schooling. PBL offers an active and innovative approach to foster student engagement and improve reading and comprehension skills. Therefore, this study demonstrates that implementing PBL can positively impact students’ English language reading skills. These results support the recommendation for the widespread adoption of PBL in English language learning contexts to enhance reading proficiency among students.
Regularization in CNN: A Mathematical Study for $$L_1$$, $$L_2$$ and Dropout Regularizers

Nowadays, the overfitting is still one of the major problems that limits the performance of Convolutional Neural Networks (CNN). To deal effectively with this issue, the regularization technique is the most successively way to improve the performance of models. In this paper, we present a summary of the mathematical study of the most popular regularizers of CNNs including $$L_1$$ , $$L_2$$ and dropout. Indeed, we describe the interpretation of these regularizers thought their behaviors and their impacts on CNNs. Furthermore, we offer the differences between these methods by comparing the numerical results using MNIST data classification.
Retrospective Multi-granularity Fusion Network for Chinese Idiom Cloze-style Reading Comprehension

Chinese idiom cloze-style reading comprehension task is of great significance for improving the machine’s ability to understand Chinese idioms, which is one of the essential application requirements in advanced artificial intelligence. Existing methods suffer from an insufficient deep semantic understanding of the text. To solve this problem, this paper proposes a novel Retrospective Multi-granularity Fusion Network (RMFNet) for Chinese idiom cloze-style reading comprehension. Our RMFNet is equipped with two novel modules to model deeper contextual information of passage and Chinese idioms, respectively. First, we propose a novel Multi-granularity Passage Fusion (MgPF) module, which enhances the passage representation by integrating different semantic perspectives. Second, we propose a Retrospective Reading (Re \(^2\) ) module that implements a back-and-forth reading mechanism to concentrate on critical Chinese idioms, thereby generating an ultimate memory for the whole text. Notably, the intuition of the MgPF module and the Re \(^2\) module is based on human reading strategies in the real world. The strategies in these modules are similar to how humans perceive the text. Extensive experiments are conducted on Chinese benchmark datasets to evaluate the effectiveness and superiority of the proposed method. Our RMFNet achieves state-of-the-art performance and in-depth analysis verifies its capability for understanding the deep semantics of the text.
BERT‐TriF: An inductive short text classification model for power equipment defect records

Abstract The descriptions of power equipment defect records are often characterized by colloquial short texts. Standardized classification of a large number of colloquial defect descriptions has laid a solid foundation for building a power equipment knowledge graph and improving the level of intelligence in the field of power inspection. Using deep learning and natural language processing technology, this paper proposes a text classification model for power equipment defect records named Bidirectional Encoder Representations from Transformers with Family Feature Fusion (BERT‐TriF). The model firstly leverages BERT to semantically represent the input text. To extract family history as well as text implicit information, we creatively propose a family feature fusion algorithm for training. An improved multi‐head attention mechanism is developed subsequently to enhance text semantic category features and strengthen the learning ability of the model. By comparing BERT‐TriF and baseline models such as TextCNN, TextRNN, and fastText on the specified and generic text dataset, the experimental results demonstrate that it has better performance, robustness, and universality for short text classification.
An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment

Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones.
Eye-tracking control of an adjustable electric bed: construction and validation by immobile patients with multiple sclerosis

In severe conditions of limited motor abilities, frequent position changes for work or passive and active rest are essential bedside activities to prevent further health complications. We aimed to develop a system using eye movements for bed positioning and to verify its functionality in a control group and a group of patients with significant motor limitation caused by multiple sclerosis.The eye-tracking system utilized an innovative digital-to-analog converter module to control the positioning bed via a novel graphical user interface. We verified the ergonomics and usability of the system by performing a fixed sequence of positioning tasks, in which the leg and head support was repeatedly raised and then lowered. Fifteen women and eleven men aged 42.7 ± 15.9 years in the control group and nine women and eight men aged 60.3 ± 9.14 years in the patient group participated in the experiment. The degree of disability, according to the Expanded Disability Status Scale (EDSS), ranged from 7 to 9.5 points in the patients. We assessed the speed and efficiency of the bed control and the improvement during testing. In a questionnaire, we evaluated satisfaction with the system.The control group mastered the task in 40.2 s (median) with an interquartile interval from 34.5 to 45.5 s, and patients mastered the task in in 56.5 (median) with an interquartile interval from 46.5 to 64.9 s. The efficiency of solving the task (100% corresponds to an optimal performance) was 86.3 (81.6; 91.0) % for the control group and 72.1 (63.0; 75.2) % for the patient group. Throughout testing, the patients learned to communicate with the system, and their efficiency and task time improved. A correlation analysis showed a negative relationship (rho = - 0.587) between efficiency improvement and the degree of impairment (EDSS). In the control group, the learning was not significant. On the questionnaire survey, sixteen patients reported gaining confidence in bed control. Seven patients preferred the offered form of bed control, and in six cases, they would choose another form of interface.The proposed system and communication through eye movements are reliable for positioning the bed in people affected by advanced multiple sclerosis. Seven of 17 patients indicated that they would choose this system for bed control and wished to extend it for another application.
Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics

This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master’s students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor’s and master’s students, with master’s students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master’s-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
MuHeQA: Zero-shot question answering over multiple and heterogeneous knowledge bases

There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.