Reviewerly/exHarmony huggingface dataset:
Come si esprimono le emozioni durante il cambio di codice dall’italiano al dialetto nella comunità talamonese

The aim of the article is to show how Italians express their emotions when changing the code from Italian to dialect on the example of the Talamona’s dialect. At the beginning, it was presented what emotions are and in which categories they should be considered, the specificity of the Talamona’s dialect and what the change of code is. The theory is supported by examples of change of code from Italian to dialect. The result of the work is to show that the respondents from Talamona very often change the code from Italian to dialect due to their emotionality. The article introduces the concept of change of code and shows how emotionality affects it.
Implementation of Template Matching on Detection of Stop Line Violations

Road marking is a sign located above the road surface that combines either a line or a symbol and provides information as visual guidance to road users. One example of road marking is the stop line behind the zebra crossing. Many violations of pedestrian rights are committed by motorized vehicles, especially because vehicles do not stop behind the stop line. This paper proposes a system to detect this type of traffic violation using the template matching method. This method is a technique in digital image processing to find small parts of the image that match the image template, so it can detect whether there is a traffic violation or not. This system uses the appropriate template images for each dataset (morning, afternoon, and evening). The system produces accuracy performances for the morning and afternoon dataset of about 100% and 78% respectively, and for the evening dataset is 70%. So, the overall average accuracy rate is 83%. The main factor affecting the overall accuracy performance is the inability to automatically extract the best template for the dataset from the morning and evening datasets.
Research access to in-copyright texts in the humanities

Text analysis in the digital humanities is challenged by legal hurdles, which make it difficult to access and especially to redistribute datasets of modern texts. As large digitisation projects grow, copyright challenges are increasingly acute. We discuss the legal landscape around large bibliographic datasets and explore principles of non-expressive and non-consumptive access as one solution to enabling research access to sensitive texts. Non-consumptive access seeks to make text available in an abstracted but maximally useless form, supporting research use without distributing the original, readable text. The HathiTrust Research Center is presented as a case study of these principles. Devoted to scholarly access to the 17 million works of the Hathirust, the Research Center has been enabling access through feature datasets, high-level visualisation tools, an in-browser analysis suite and a secure virtual machine environment. This assortment of approaches has different strengths and challenges, and we consider how each may be instructive in considering the future of research over sensitive text.
Exploring the use of mobile apps for learning: a case study on final year engineering undergraduates in Malaysia

Mobile devices as a learning tool has shown great possibilities in and out the classroom and has revolutionised how we teach. By providing flexible access, these devices have been instrumental in facilitating the use of online technologies for teaching and learning. The main purposes of this study is to explore undergraduates' use of mobile applications (apps) for leaning. A total number of 59 engineering undergraduates from a higher learning institute (HEI) participated in this study through a survey. The study found that most students used WhatsApp for communication and sharing of learning contents. Their main method of internet access is through their personal internet plan. Concurrently, the most used application for learning are internet browsing and email and the least is cloud storage, educational games and time and schedule manager. It also emerged that mobile apps for learning are mainly used for non-formal learning activities and majority of students view mobile apps influential for teaching and learning in HEIs.
A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

In this paper, we propose a novel model for 3D object watermarking. The proposed method is based on the properties of the discrete cosine transform (DCT) of the 3D object vertices to embed a secret grayscale image three times. The watermarking process takes place by using the vertices coefficients and the encrypted image pixels. Moreover, the extraction process is totally blind based on the reverse steps of the embedding process to recover the secret grayscale image. Various performance aspects of the method are measured and compared between the original 3D object and the watermarked one using Euclidean distance, Manhattan distance, cosine distance, and correlation distance. The obtained results show that the proposed model provides better performances in terms of execution time and invisibility.
Pandemic challenges for educators: transition from physical to digital classroom

The article looks into some challenges that university teaching staff has to face during the modern era of digitalization and especially during the transaction period from “physical classroom” to “digital classroom” provoked by the pandemic of COVID-19. The use of information and communication technologies (hereinafter referred to as ICT), tools for online teaching and learning, computer and digital literacy, digital skills and competencies are becoming important components of professional success. The transition to distance learning (especially due to the pandemic of COVID-19) has changed the modern academic world: now the effective use of digital technologies and educational resources is considered a key factor for improving and increasing the value of the learning process. As a result, students get their knowledge and educators share their theoretical and practical experience through an increasing number of digital technologies and resources. This article aims to describe and analyze the main types of available online tools and services that may provide productive work and interactive learning; to describe the challenges that educators face while transforming the academic environment into “online classroom”. The authors applied a cabinet study approach to the analysis of theoretical database, the method of comparison and generalization of the obtained data. As a result , the article presents theoretical background of the ICT potential and their use in the educational process of modern universities.
Making the Most of Parallel Composition in Differential Privacy

Abstract We show that the ‘optimal’ use of the parallel composition theorem corresponds to finding the size of the largest subset of queries that ‘overlap’ on the data domain, a quantity we call the maximum overlap of the queries. It has previously been shown that a certain instance of this problem, formulated in terms of determining the sensitivity of the queries, is NP-hard, but also that it is possible to use graph-theoretic algorithms, such as finding the maximum clique, to approximate query sensitivity. In this paper, we consider a significant generalization of the aforementioned instance which encompasses both a wider range of differentially private mechanisms and a broader class of queries. We show that for a particular class of predicate queries, determining if they are disjoint can be done in time polynomial in the number of attributes. For this class, we show that the maximum overlap problem remains NP-hard as a function of the number of queries. However, we show that efficient approximate solutions exist by relating maximum overlap to the clique and chromatic numbers of a certain graph determined by the queries. The link to chromatic number allows us to use more efficient approximate algorithms, which cannot be done for the clique number as it may underestimate the privacy budget. Our approach is defined in the general setting of f -differential privacy, which subsumes standard pure differential privacy and Gaussian differential privacy. We prove the parallel composition theorem for f -differential privacy. We evaluate our approach on synthetic and real-world data sets of queries. We show that the approach can scale to large domain sizes (up to 10 20000 ), and that its application can reduce the noise added to query answers by up to 60%.
Parenting Pattern, Socioeconomic Status and Parent’s Communication on Early Childhood Language Ability

This study aims to describe the relationship parenting pattern and socioeconomic status through parent’scommunication on children's language ability in Kindergarten of Anjir Muara District. This study uses descriptive quantitative methods. The population were 300 children and sample were 173 people using the proportionate random sampling technique. Data colection uses quesioners and observation. Hypothesis testing uses Path Analysis. The results (1) there is significant relationship between parenting pattern and parent communication (2) there is significant relationship between the socioeconomic status and parent communication (3) there is significant relationship between parents parenting pattern and children's language ability (4) there is significant relationship between family socioeconomic status and children's language ability (5) there is significant relationship between parent communication and children's language ability (6) there is significant indirect relationship parenting pattern, parent’s communication on children's language ability (7) there is significant indirect relationship between socioeconomic status through parental communication on children's language ability.
A trajectory planning method for the redundant manipulator based on configuration plane

This study focuses on the method of trajectory planning of spatial obstacle avoidance for redundant manipulators based on configuration plane method. Firstly, according to the summary of the work configuration for redundant manipulator, kinematics analysis method based on configuration plane is proposed, which helps to establish a basic kinematics model of configuration plane. Secondly, the analysis of velocity is conducted and velocity iterative formula is derived. Then, the process of the trajectory planning for redundant manipulator based on the velocity distribution of configuration plane is given, during which some key procedures such as the determination of work configuration, achieving spatial obstacle avoidance, and analysis of velocity distribution are deduced. Finally, the simulation of spatial circle trajectory planning for the 7-degree-of-freedom redundant manipulator is done. The experimental results show that the proposed trajectory planning method for redundant manipulator can satisfy the requirements of complex spatial obstacle avoidance and increase the controllability of the trajectory between spatial interpolation points of the manipulator’s end effector.
Multiperson Target Dynamic Tracking Method for Athlete Training Based on Wireless Body Area Network

Aiming at the problems of large tracking error and long tracking time in traditional multiperson target dynamic tracking methods, a new method based on wireless body area network for athlete training multiperson target dynamic tracking is proposed. First, the microinertial sensor in the wireless body area network is used to collect the multiperson image data of the athlete training, and the sparse representation is performed after processing, which improves the reliability of the data and reduces the tracking error. Secondly, the multiperson target dynamic tracking method based on the adaptive search box is used, combined with target isolation and occlusion detection, to judge the athlete’s training target. Finally, the nearest neighbor algorithm is used to construct an adaptive search box to achieve dynamic tracking of multiple targets. Experimental results show that this method can accurately measure the similarity of target features, with small tracking error and short tracking time. The minimum tracking error is only 0.11 frame.
Analisi cognitiva degli eventi di parola sul coronavirus SARS-COV 2 e sul morbo COVID-19

The purpose of this paper is to perform a brief cognitive analysis of speech events containing information about the coronavirus SARS-COV 2 and the disease its causes, COVID-19. As the author acknowledges primacy of cognitive linguistics research tools towards explanation of how language is used and how the extralinguistic reality is perceived, the object of research comprises M Johnson and G. Lakoff’s conceptual metaphors, which are to be extracted among seven chosen articles derived from the digital version of the Italian journal La Stampa. The results of such performed research should deliver a list of structural, ontological and orientative metaphors, which, in this context, are not just pure eristic speech figures but mental constructs which indicate people’s way of reasoning and of conceptualizing the surrounding extralinguistic world, in this case the pandemic situation affecting us all.
Empirical Bayes Control of the False Discovery Exceedance

In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR).We develop an empirical Bayes approach to controlling the FDX.We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to FDX constraint.We propose a data-driven FDX procedure that emulates the oracle via carefully designed computational shortcuts.We investigate the empirical performance of the proposed method using simulations and illustrate the merits of FDX control through an application for identifying abnormal stock trading strategies.
Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation

It is well-established that the preferred mode of communication of the deaf and hard of hearing (DHH) community are Sign Languages (SLs), but they are considered low resource languages where natural language processing technologies are of concern.In this paper we study the problem of text to SL gloss Machine Translation (MT) using Transformer-based architectures.Despite the significant advances of MT for spoken languages in the recent couple of decades, MT is in its infancy when it comes to SLs.We enrich a Transformer-based architecture aggregating syntactic information extracted from a dependency parser to wordembeddings.We test our model on a wellknown dataset showing that the syntax-aware model obtains performance gains in terms of MT evaluation metrics.
Joint Linear Discrimination and Graph Regularization for Task-Oriented Cross- Modal Retrieval

Aiming at the problem of insufficient consideration of the differences between different retrieval tasks and semantic consistency of retrieval-modal data in the current common subspace based cross-modal retrieval algorithms, a task-oriented cross-modal retrieval based on jointing linear discrimination and graph regularization is proposed.The approach constructed different mapping mechanisms for retrieval tasks in a joint learning framework, and mapped multi-modal data into common subspaces for similarity measuring.During the learning process, correlation analysis and single-modal semantic regression were combined to preserve the correlation between paired data and enhance the semantic accuracy of query-modal data.Simultaneously, linear discrimination analysis was utilized to ensure semantic consistency of retrieval-modal samples.The approach also constructed local neighbor graphs for multi-modal data to preserve structural information, which can improve the retrieval performance.Experiments results on two cross-modal datasets, namely Wikipedia and Pascal Sentence showed that the average mAP value on different retrieval tasks of the proposed method had respectively increased by 1.0%-16.0%and 1.2%-14.0%compared with the twelve existing methods.
On a family of two–piece circular distributions

A new way of constructing flexible and unimodal circular models, focusing on the modal direction, is proposed. Starting from a base symmetric density and a weight function, a two–piece four parameters density is introduced. The proposed density provides an extension of the base density to allow for sharply peaked and flat–topped unimodal distributions as well as a wide range of skewness. In particular, it generalizes some well–known peakedness–free models such as the Batschelet and Papakonstantinou densities. The four parameters of the model have a clear interpretation: modal direction, concentration, peakedness at the left and at the right of the modal direction. Symmetric submodels are obtained when the peakedness parameters are equal. The main properties related to the shape of the new density are presented and asymptotic results for maximum likelihood estimators are derived. An illustrative application concerning the flight orientation of migrating raptors is investigated.
Implementation three-step algorithm based on signed digit number system by using neural network

The need for a simple and effective system that works with high efficiency features such as high processing speed, the ability to solve problems by learning method and accomplish the largest amount of data processing accurately and in little time produces that system, which attracted the efforts of the researcher to employ neural networks in computing away from the complexities that burden traditional computers. We presented a model for the design of the arithmetic circuit for the process of addition the sign digit numbers in a new way to deal with the arithmetic operations, which employment of the use of neural networks, this model includes a theoretical and practical simulation of them. The model relied on the implementation of the addition process based on a three-step algorithm adopted by the signed systems. Which is characterized by the possibility of execution in a parallel way, and therefore it provides the advantage of completion of arithmetic operation regardless of the length of their operands, or in other words, whatever the number of bits in the operands. The simulation of the model is done by entering operands for 6 addition operations (each one has operands are 15-bit length) to be executed simultaneously.
Integrated Architecture Model of Tourism Information Service in Smart Scenic Spots Based on Hybrid Cloud

Tourism industry can promote the prosperity of the local economy. In addition, the booming tourism business in recent years has also led to the development of related industries and services. According to high-tech technologies such as cloud computing, hybrid cloud, and panoramic image, we will plan to design a comprehensive and integrated architecture model of scenic spots that integrates all tourism information and various services, strive to create smart scenic spots, bring the best play experience to tourists, and strive to attract more attention and attention. The results show that (1) the overall average satisfactory approval of UI test is as high as 93%, which can meet the requirements. (2) After the improved panoramic image mosaic method, the correct rate is as high as 93.90%, the effect is 4.90% better than that before the improvement, and the algorithm is more efficient. (3) The scenic spot configuration management effect is excellent, and various situations in the scenic spot are effectively monitored. (4) The average response time per request of the original system is about 161 ms. Compared with the original system, the response time of the new system is reduced by about 57%, and the access success rate is 100%. The system in the experiment runs well and satisfies the integration of tourism information resources and services in scenic spots.
Flexible Bayesian Nonlinear Model Configuration

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.&#x0D;
Percepción de la educación virtual universitaria y su impacto social en tiempos de la epidemia Covid-19

The crisis generated by the coronavirus epidemic (Covid-19) affects all aspects of the world (economic, health, social and educational). The virtual education that has been implemented in an emergency to continue classes and guarantee quality academic training, shows us the weaknesses and shortcomings that occur in the Peruvian educational environment. The present study attempts to describe the perception of virtual university education as well as its social impact on students today. It is important to have measures that improve educational quality and implement training and methodologies to optimize digital skills in teachers so that the teaching-learning process is guaranteed.&nbsp
Implementation of Markov Chain in Detecting Opportunities for Natural Disasters in Klaten (Case Study: Number of Floods, Landslides, and Hurricanes 2019-2020)

Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021