Reviewerly/exHarmony huggingface dataset:
An empirical study of college students’ reading engagement on academic achievement

With the popularity of Internet technology, reading has developed in the direction of digitalization and mobileization. And entering the metaverse era, both the subject and object of reading may be redefined, presenting a new developmental pattern. This process brings a crisis to reading, such as the fragmentation of reading, the obstruction of reading needs, and the replacement of classical reading. However, reading is still an important way for college students to acquire new knowledge, broaden their horizons and improve their skills. The existence of reading crises inevitably affects the academic achievement of college students. Therefore, from the perspective of university management, this paper conducts regression analysis on 1,155 effective samples of colleges and universities in Anhui Province, extracts the factors that affect college students’ reading engagement, and further explores the relationship between college students’ reading engagement and academic achievement. The study concluded that: (1) in terms of family reading culture, students who grow up in families with good family reading culture perform better in reading engagement. The amount of family books, family reading education and family reading atmosphere all have significant positive effects on reading time and reflective reading strategies of college students. (2) In the cultivation of reading habits in colleges and universities, the course-driven mechanism and the atmosphere stimulating mechanism have a significant positive effect on students’ reading time. The course-driven mechanism, resource supporting mechanism and atmosphere stimulating mechanism have a significant positive effect on the critical reading strategy of college students. (3) In terms of reading time, it is only found that the reading time spent on paper books has a significant positive effect on college students’ academic achievement and professional quality. (4) In terms of reading strategies, the replicative reading strategy only has a significant positive effect on the improvement of college students’ academic achievement and professional quality. The critical reading strategy has a significant positive effect on the professional quality, general ability and career planning ability of college students.
Chinese sentence semantic matching based on multi-level relevance extraction and aggregation for intelligent human–robot interaction

With the development of Internet of Things and cloud computing, intelligent question-answering (QA) has brought great convenience to human's daily activities. As one of the core technologies, sentence semantic matching (SSM) plays a critical role in a variety of intelligent QA systems. However, existing SSM methods usually first encode sentences on either character or word level, and then model semantic interactions on sentence level. Consequently, they fail to capture the rich interactions on multi-levels (i.e., character, word and sentence levels). In this paper, we propose Chinese sentence semantic matching based on Multi-level Relevance Extraction and Aggregation (MREA) for intelligent QA. MREA can comprehensively capture and aggregate various semantic relevance on character, word and sentence levels respectively based on multiple attention mechanisms. Extensive experiments on two real-world datasets demonstrate that MREA outperforms the best-performing baselines by 0.5% and 0.89% w.r.t. ACC. and F1 respectively, and achieves comparable performance with BERT-based methods.
DENSITY RESULTS BY DEEP NEURAL NETWORK OPERATORS WITH INTEGER WEIGHTS

In the present paper, a new family of multi-layers (deep) neural network (NN) operators is introduced. Density results have been established in the space of continuous functions on [−1,1], with respect to the uniform norm. First, the case of the operators with two-layers is considered in detail, then the definition and the corresponding density results have been extended to the general case of multi-layers operators. All the above definitions allow us to prove approximation results by a constructive approach, in the sense that, for any given f all the weights, the thresholds, and the coefficients of the deep NN operators can be explicitly determined. Finally, examples of activation functions have been provided, together with graphical examples. The main motivation of this work resides in the aim to provide the corresponding multi-layers version of the well-known (shallow) NN operators, according to what is done in the applications with the construction of deep neural models.
Understanding the meanings of citations using sentiment, role, and citation function classifications

Abstract Traditional citation analyses use quantitative methods only, even though there is meaning in the sentences containing citations within the text. This article analyzes three citation meanings: sentiment, role, and function. We compare citation meanings patterns between fields of science and propose an appropriate deep learning model to classify the three meanings automatically at once. The data comes from Indonesian journal articles covering five different areas of science: food, energy, health, computer, and social science. The sentences in the article text were classified manually and used as training data for an automatic classification model. Several classic models were compared with the proposed multi-output convolutional neural network model. The manual classification revealed similar patterns in citation meaning across the science fields: (1) not many authors exhibit polarity when citing, (2) citations are still rarely used, and (3) citations are used mostly for introductions and establishing relations instead of for comparisons with and utilizing previous research. The proposed model’s automatic classification metric achieved a macro F1 score of 0.80 for citation sentiment, 0.84 for citation role, and 0.88 for citation function. The model can classify minority classes well concerning the unbalanced dataset. A machine model that can classify several citation meanings automatically is essential for analyzing big data of journal citations.
Producing Open Data

Open data offer the opportunity to economically combine data into large-scale datasets, fostering collaboration and re-use in the interest of treating researchers’ resources as well as study participants with care. Whereas advantages of utilising open data might be self-evident, the production of open datasets also challenges individual researchers. This is especially true for open data that include personal data, for which higher requirements have been legislated. Mainly building on our own experience as scholars from different research traditions (life sciences, social sciences and humanities), we describe best-practice approaches for opening up research data. We reflect on common barriers and strategies to overcome them, condensed into a step-by-step guide focused on actionable advice in order to mitigate the costs and promote the benefit of open data on three levels at once: society, the disciplines and individual researchers. Our contribution may prevent researchers and research units from re-inventing the wheel when opening data and enable them to learn from our experience.
Correlational and Configurational Analysis of Factors Influencing Potential Patients’ Attitudes toward Surgical Robots: A Study in the Jordan University Community

The literature on surgical robots (SRs) usually adopts the perspective of healthcare workers. However, research on potential patients’ perceptions and the publics’ points of view on SRs is scarce. This fact motivates our study, which assesses the factors inducing the SRs acceptance in the opinion of potential patients. We consider three variables, based on the unified theory of acceptance and the use of technology (UTAUT): the performance expectancy (PE), the effort expectancy (EE), and the social influence (SI); pleasure (PL), arousal (AR), and the perceived risk (PR). To deal with empirical data, we used the ordered logistic regression (OLR) and the fuzzy set comparative qualitative analysis (fsQCA). The OLR allowed us to check for a significant positive average influence of the UTAUT variables and PL, on the intention to undergo robotic surgery. However, the PR had a significant negative impact, and AR was not found to be significant. The FsQCA allowed the identification of the potential patient profiles, linked to acceptance of and resistance to SRs and confirmed that they are not symmetrical. The proposed input variables are presented as core conditions in at least one prime implicate robotic-assisted surgery acceptance. The exception to this statement is the PR, which is affirmed in some recipes and absent in others. The recipes explaining the resistance to SRs were obtained by combining the absence of PE, EE, SI, and PL (i.e., these variables have a negative impact on rejection) and the presence of the PR (i.e., the perceived risk has a positive impact on a resistance attitude toward SRs). Similarly, arousal played a secondary role in explaining the rejection.
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.
Path Planning of Mobile Robot Based on Improved PRM Based on Cubic Spline

In view of the shortcomings of low search efficiency and many path turning points of Probabilistic Roadmaps (PRM), a bidirectional search PRM global path planning algorithm is proposed. The algorithm improves the search connection rules by using the positive and negative directions to search the path alternately, so that the connection of unnecessary nodes reduces, thereby speeding up the efficiency of path planning. Besides, the algorithm incorporates cubic spline interpolation. That will increase the smoothness of path planning and ensure that the mobile robot can realize the path planning task more smoothly and safely. The simulation results show that the improved algorithm can effectively improve the convergence speed and path smoothness of the algorithm. Finally, the improved algorithm is applied to the actual mobile robot navigation experiment. The experimental results have proven that the path planning strategy was able to a superior advantage over traditional PRM in path quality and computational time.
Metaverse: Opportunities and Challenges for Marketers

Imagine if you are told that the life in which you are living, will going to be changed.How it will make you feel if you get to know that you can meet with your family members, friends, and go to work, not in real life, but rather in a three-dimensional virtual world?A world that is created artificially.Metaverse is a step ahead of imagination.Indeed, it is defined as an imaginary world where we can be a part of it, we can earn money from it, and ultimately, where, we can be a star by living our dream life.
Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL

Coal still occupies a key position in China’s energy consumption structure, and ensuring safe production in coal mines is a key focus for ensuring energy security. Spontaneous combustion fires in coal mines are a serious threat to the sustainability of safe production in coal mines. In order to prevent coal mine fire risk scientifically and effectively and to assess the level of disaster risk effectively and rationally, a study was conducted on the risk of spontaneous combustion fires in underground coal mines. An evaluation cloud model of spontaneous combustion fire risk in coal mines integrating the interval gray number with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was established. Seventeen representative risk evaluation indicators were selected, and a coal mine spontaneous combustion fire risk evaluation index system was constructed based on four aspects: personnel, machinery, environment, and management. The interval gray number theory was introduced to improve the classical DEMATEL analysis method, which fully expresses the expert empirical knowledge and solves the problem of ambiguity and randomness in the semantic expression of expert evaluation. The relative importance of each indicator was determined by analyzing the influence relationships between risk evaluation indicators through the improved DEMATEL. A cloud model capable of transforming quantitative descriptions and qualitative concepts was used for comprehensive evaluation of risk, and based on the results of DEMATEL analysis, a comprehensive evaluation cloud model of coal mine spontaneous combustion fire risk was formed. Finally, the validity and practicality of the model were verified by using a mine in Shenmu City, Shaanxi Province, China as an example. This study provides a powerful tool to prevent spontaneous combustion fires in coal mines and makes a positive contribution to the sustainable development of coal mine safety management.
Using Machine Learning and Neural Networks Technologies, a Bottom-Up Water Process Is Being Used To Reduce All Water Pollution Diseases

Without water, we can’t do anything, and water plays the most important role in our regular life. Actually, the initial stage of water provides excellent water quality; it’s very healthy for the human body. The water continues to flow, but gradually the water becomes polluted and affects human health. Water pollution is one of the major problems on earth nowadays. Some people do some activities; animals, underground water, and industries release wastewater, so gradually the water becomes polluted. We studied a number of papers and searched for water pollution diseases and changes in water quality and the drinking water problem, which causes almost 250 children to die each day, is one of the worldwide risks listed by the World Economic Forum. Every year, almost 4 million people die as a result of drinking contaminated water. Despite technological developments, there aren't enough quality measures available to assess drinking water quality. So, we proposed the bottom-up process of water to reduce all water pollution diseases using hybrid technologies and to decrease all water pollution problems. Machine learning has been widely employed as a potent tool to address issues in the water environment since it can be used to forecast water quality, allocate resources more effectively, handle shortages of resources, etc. We will discuss several methods of machine learning, including classification, regression, and neural network-based techniques. Using data from the last 10 years, it's easy to figure out who is polluting and hurting the water. When implement a use this hybrid technologies gradually reduce all water related issues. In future definitely we get excellent water quality and peoples, animals get healthy water.
UFace: An Unsupervised Deep Learning Face Verification System

Deep convolutional neural networks are often used for image verification but require large amounts of labeled training data, which are not always available. To address this problem, an unsupervised deep learning face verification system, called UFace, is proposed here. It starts by selecting from large unlabeled data the k most similar and k most dissimilar images to a given face image and uses them for training. UFace is implemented using methods of the autoencoder and Siamese network; the latter is used in all comparisons as its performance is better. Unlike in typical deep neural network training, UFace computes the loss function k times for similar images and k times for dissimilar images for each input image. UFace’s performance is evaluated using four benchmark face verification datasets: Labeled Faces in the Wild (LFW), YouTube Faces (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP). UFace with the Siamese network achieved accuracies of 99.40%, 96.04%, 95.12% and 97.89%, respectively, on the four datasets. These results are comparable with the state-of-the-art methods, such as ArcFace, GroupFace and MegaFace. The biggest advantage of UFace is that it uses much less training data and does not require labeled data.
Reliability of MUSE 2 and Tobii Pro Nano at capturing mobile application users' real-time cognitive workload changes

Despite the importance of cognitive workload in examining the usability of smartphone applications and the popularity of smartphone usage globally, cognitive workload as one attribute of usability tends to be overlooked in Human-Computer Interaction (HCI) studies. Moreover, limited studies that have examined the cognitive workload aspect often measured some summative workloads using subjective measures (e.g., questionnaires). A significant limitation of subjective measures is that they can only assess the overall, subject-perceived cognitive workload after the procedures/tasks have been completed. Such measurements do not reflect the real-time workload fluctuation during the procedures. The reliability of some devices on a smartphone setting has not been thoroughly evaluated.This study used mixed methods to empirically study the reliability of an eye-tracking device (i.e., Tobii Pro Nano) and a low-cost electroencephalogram (EEG) device (i.e., MUSE 2) for detecting real-time cognitive workload changes during N-back tasks.Results suggest that the EEG measurements collected by MUSE 2 are not very useful as indicators of cognitive workload changes in our setting, eye movement measurements collected by Tobii Pro Nano with mobile testing accessory are useful for monitoring cognitive workload fluctuations and tracking down interface design issues in a smartphone setting, and more specifically, the maximum pupil diameter is the preeminent indicator of cognitive workload surges.In conclusion, the pupil diameter measure combined with other subjective ratings would provide a comprehensive user experience assessment of mobile applications. They can also be used to verify the successfulness of a user interface design solution in improving user experience.
Portable X-ray Fluorescence (pXRF) as a Tool for Environmental Characterisation and Management of Mining Wastes: Benefits and Limits

Portable X-ray fluorescence (pXRF) is one of the main geochemical techniques employed in multi-elemental analysis screening for contaminated sites management. As the confidence of pXRF analyses are matrix-specific, efforts are made to provide studies of pXRF quality on different geochemical datasets, focusing on less investigated elements such as mercury (Hg) and antimony (Sb), to help both new and experienced users. The analysis of environmental solid samples from two decommissioned mining sites in NE Italy, characterised by Pb-Zn and (Hg-rich) Cu-Sb ore deposits, were prepared with two different protocols and compared with traditional destructive analyses. Sample composition was found strictly dependent to the occurrence of false positives and overestimation at low concentrations. In contrast, milling the sample did not produce major variations in the overall quality. Lead (Pb), Sb, and Zn reached the definitive data quality in at least one of the two datasets. Consequently, as far as a thorough QA/QC protocol is followed, pXRF can rapidly produce chemical data that is as accurate as that produced by destructive standard laboratory techniques, thus allowing to identify potential sources of contamination that could be reprocessed for the extraction of valuable elements and mitigating the dispersion of contaminants and ecological or health risks.
Overfitting in quantum machine learning and entangling dropout

The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniques have been developed in the classical machine learning regime, and the dropout is one such effective method. This paper proposes a straightforward analogue of this technique in the quantum machine learning regime, the entangling dropout, meaning that some entangling gates in a given parametrized quantum circuit are randomly removed during the training process to reduce the expressibility of the circuit. Some simple case studies are given to show that this technique actually suppresses the overfitting.
Impredicative Observational Equality

In dependent type theory, impredicativity is a powerful logical principle that allows the definition of propositions that quantify over arbitrarily large types, potentially resulting in self-referential propositions. Impredicativity can provide a system with increased logical strength and flexibility, but in counterpart it comes with multiple incompatibility results. In particular, Abel and Coquand showed that adding definitional uniqueness of identity proofs (UIP) to the main proof assistants that support impredicative propositions (Coq and Lean) breaks the normalization procedure, and thus the type-checking algorithm. However, it was not known whether this stems from a fundamental incompatibility between UIP and impredicativity or if a more suitable algorithm could decide type-checking for a type theory that supports both. In this paper, we design a theory that handles both UIP and impredicativity by extending the recently introduced observational type theory TTobs with an impredicative universe of definitionally proof-irrelevant types, as initially proposed in the seminal work on observational equality of Altenkirch et al. We prove decidability of conversion for the resulting system, that we call CCobs, by harnessing proof-irrelevance to avoid computing with impredicative proof terms. Additionally, we prove normalization for CCobs in plain Martin-Löf type theory, thereby showing that adding proof-irrelevant impredicativity does not increase the computational content of the theory.
High-order Polynomial Comparison and Masking Lattice-based Encryption

The main protection against side-channel attacks consists in computing every function with multiple shares via the masking countermeasure. For IND-CCA secure lattice-based encryption schemes, the masking of the decryption algorithm requires the high-order computation of a polynomial comparison. In this paper, we describe and evaluate a number of different techniques for such high-order comparison, always with a security proof in the ISW probing model. As an application, we describe the full high-order masking of the NIST standard Kyber, with a concrete implementation on ARM Cortex M architecture, and a t-test evaluation.