Reviewerly/exHarmony huggingface dataset:
Research on the spatial distribution characteristics of A-level scenic spots and its influencing factors based on geographic big data—Taking Guangxi Province as an example

This study is based on POI data of A-level scenic spots in Guangxi Province. The methods of the nearest neighbor index, kernel density analysis, spatial autocorrelation analysis, and geodetector were used to study the spatial distribution characteristics of A-level scenic spots in Guangxi Province and their influencing factors. The results present the following:1. The spatial distribution of A-level scenic spots in Guangxi province is clustered, and the high-density values are mainly concentrated in Nanning, Guilin, and Liuzhou.2. Through spatial autocorrelation analysis, the spatial distribution of A-level scenic spots in Guangxi Province is positively correlated in space. The spatial differences between the hot and cold areas are obvious, and the provincial level shows a significant “block” distribution feature.3. All the influencing factors passed the significance test. The proportion of the gross domestic product in the tertiary industry is the strongest explanation for the spatial distribution of A-level scenic spots in Guangxi Province. The influence of any two influencing factors after interaction on the spatial distribution of A-level scenic spots is stronger than any influencing factor.
Sales Information System On The Liemo'S Trophy Cup Using Apriori Algorithm

The Liemo'S Trophy trophy shop was pioneered in early 1996 until 2022. Currently, we can see the rapid development of business in the field of trophy sales, with many trophy shops that have been opened. Therefore Liemo'S Trophy trophy shop must take steps forward to achieve high sales strategy by its competitors. The rapid development of information technology and information systems in the era of globalization, makes information can be obtained easily and quickly, one of which is the business field in the era of globalization, there are many information systems that provide convenience and good service for users of information system services, because information systems is the key in the development of an information technology. Like the Liemo's Trophy Cup shop, it sells various kinds of trophies, medals, plaques, and sells spare parts and trophy needs including figures and various other accessories, but this shop still processes data manually so that sellers will find it difficult to manage sales data. Creating a sales information system using a web-based Apriori Algorithm is expected to get a sales strategy and can be widely used in the sales process to increase the marketing of goods widely through online sites.
Cafe Selection Recommendation System in Semarang City Uses Collaborative Filtering Method with Item Based Filtering Algorithm

The cafe selection recommendation system in the city of Semarang aims to provide recommendations for users in finding the desired café according to the type of café expected. This recommendation system serves to predict an item that is of interest to the user. Implementation of recommendation system using Collaborative Filtering and Item Based Filtering algorithms. Collaborative filtering is a recommendation system algorithm where recommendations are given based on consideration of data from other users while the Item Based Filtering algorithm to provide recommendations based on similarities between customer tastes and café characteristics.
An Educational Arabic Sign Language Mobile Application for Children with Hearing Impairment

This research addresses the need for a mobile application to teach Arabic sign language to deaf children in the Arab world. Therefore, the team develops an educational android application to teach deaf children the Arabic sign language using gamification. Also, the research aims to investigate the impact of using a mobile application for Arabic sign language on children’s learning performance. To achieve its goals, the research entails two main stages. The first stage focuses on building the mobile application based on the literature review and searching the Google and Apple stores for similar applications. In the second stage, an experiment is conducted on 10 deaf children—the experimental group adopted the mobile application for the learning process, whereas the control group followed the traditional learning approach. A pre-test and post-test were conducted, and the results show that children who used the game-based mobile application performed better in their quiz.
PENINGKATAN PEMAHAMAN LITERASI ANAK DAN REMAJA DI DESA SUKAREMA KECAMATAN LENEK

The implementation of digital literacy training in the village of Sukarema has a very big influence on changes in people's habits, especially parents in controlling and supervising children when carrying out activities related to digital literacy. The implementation of PKM activities is carried out using the lecture method, aiming to provide understanding to parents that digital literacy is very necessary to help children digitally. We use practical methods to strengthen understanding of digital literacy. After the PKM implementation took place, the community (parents) could feel positively both the usefulness and benefits of digital literacy in finding information related to learning and other things. In addition, about 83.3% of the Sukarema villagers use digital for more than 4 hours per day. This shows that the progress of digital literacy is growing very fast. This proves that the implementation of digital literacy training can increase public or parents' understanding of digital literacy and can control and supervise children in using digital.
Examining the effect of employee commitment on organizational change management: A study of Public Sector Universities in Iraq

In the past few decades, the public sector organizations started implementing the reforms and changed their contents, contexts, structures, policies, and strategies due to internal and external reasons (Ahmad and Cheng, 2018).However, not all the initiatives taken to adapt and implement the changes in the organizations were successful ( Van der Voet et al., 2016).Hassan (2018) considered the change management process as an important aspect of the modern organizations.It is important for the institutions to evolve irrespective of its nature such as the public sector or private sector, companies, or factories.Hence, the educational institutions must adapt to the changes in
MFVT: Multilevel Feature Fusion Vision Transformer and RAMix Data Augmentation for Fine-Grained Visual Categorization

The introduction and application of the Vision Transformer (ViT) has promoted the development of fine-grained visual categorization (FGVC). However, there are some problems when directly applying ViT to FGVC tasks. ViT only classifies using the class token in the last layer, ignoring the local and low-level features necessary for FGVC. We propose a ViT-based multilevel feature fusion transformer (MFVT) for FGVC tasks. In this framework, with reference to ViT, the backbone network adopts 12 layers of Transformer blocks, divides it into four stages, and adds multilevel feature fusion (MFF) between Transformer layers. We also design RAMix, a CutMix-based data augmentation strategy that uses the resize strategy for crop-paste images and label assignment based on attention. Experiments on the CUB-200-2011, Stanford Dogs, and iNaturalist 2017 datasets gave competitive results, especially on the challenging iNaturalist 2017, with an accuracy rate of 72.6%.
Modular and Lightweight Networks for Bi-Scale Style Transfer

With the emergence of deep perceptual image features, style transfer has become a popular application that repaints a picture while preserving the geometric patterns and textures from a sample image. Our work is devoted to the combination of perceptual features from multiple style images, taken at different scales, e.g. to mix large-scale structures of a style image with fine-scale textures. Surprisingly, this turns out to be difficult, as most deep neural representations are learned to be robust to scale modifications, so that large structures tend to be tangled with smaller scales. Here a multi-scale convolutional architecture is proposed for bi-scale style transfer. Our solution is based on a modular auto-encoder composed of two lightweight modules that are trained independently to transfer style at specific scales, with control over styles and colors.
Towards Handling Unconstrained User Preferences in Dialogue

A user input to a schema-driven dialogue information navigation system, such as venue search, is typically constrained by the underlying database which restricts the user to specify a predefined set of preferences, or slots, corresponding to the database fields. We envision a more natural information navigation dialogue interface where a user has flexibility to specify unconstrained preferences that may not match a predefined schema. We propose to use information retrieval from unstructured knowledge to identify entities relevant to a user request. We update the Cambridge restaurants database with unstructured knowledge snippets (reviews and information from the web) for each of the restaurants and annotate a set of query-snippet pairs with a relevance label. We use the annotated dataset to train and evaluate snippet relevance classifiers, as a proxy to evaluating recommendation accuracy. We show that with a pretrained transformer model as an encoder, an unsupervised/supervised classifier achieves a weighted F1 of .661/.856.
A Survey on AR Newspapers

Newspapers provide coverage of a country's economy, sports, games, entertainment, trade, and commerce. Reading the newspaper would be an excellent ritual to develop, It has already become a part of everyday life. This habit will enhance your knowledge and broaden your horizons. We can learn about the current condition of any incident by reading the newspaper, in order to learn more about it and pique the readers' curiosity, In order to assist enthusiastic readers an augmented reality application is the better solution. By incorporating a digital aspect into the printed material, it helps to bring the content of the magazine to life. It allows users to view a video, animation, or other unexpected content that appears on a page of their newspaper. When an augmented reality program and the camera software on a Smartphone or tablet work together, the illusion is generated. In order for the illusion to operate, the newspaper must be viewed through the mobile device's camera. So, a typical physical newspaper is taken and augmented reality objects can be used to increase the variety of information it gives, such as buttons to movies, links, YouTube, 3D models, and so on. The augmented reality is combined with the newspaper here. Using a marker, write: When you display markers in augmented reality, a computer identifies them and projects objects over them. You need a newspaper with a marker and a smartphone with a non-standalone application that recognizes the marker. It could be a newspaper image or a QR code. Integrating the traditional method AR experience is one of the best methods for print media to stand out from the crowd. In this work, the Vuforia Engine tool and Augmented Reality Tools are studied to design and develop a newspaper with Augmented Reality to improve user Usability..
In Memorium: Vance Stevens

This is the first time for TESL-EJ to publish a remembrance. Vance Stevens’ great energy and significant influence on the use of technology for language learning, particularly through informal networks, behooves us to honor him in this way.
Converting Real Human Avatar to Cartoon Avatar using CycleGAN

Cartoons are an important art style, which not only has a unique drawing effect but also reflects the character itself, which is gradually loved by people. With the development of image processing technology, people's research on image research is no longer limited to image recognition, target detection, and tracking, but also images In this paper, we use deep learning based image processing to generate cartoon caricatures of human faces. Therefore, this paper investigates the use of deep learning-based methods to learn face features and convert image styles while preserving the original content features, to automatically generate natural cartoon avatars. In this paper, we study a face cartoon generation method based on content invariance. In the task of image style conversion, the content is fused with different style features based on the invariance of content information, to achieve the style conversion.
Anomaly Detection based on Alarms Data

Alarms data is a very important source of information for network operation center (NOC) teams to aggregate and display alarming events occurring within a network element. However, on a large network, a long list of alarms is generated almost continuously. Intelligent analytical reporting of these alarms is needed to help the NOC team to eliminate noise and focus on primary events. Hence, there is a need for an anomaly detection model to learn from and use historical alarms data to achieve this. It is also important to indicate the root cause of anomalies so that immediate corrective action can be taken. In this paper, we aim to design an anomaly detection model in the context of alarms data (categorical data) in the field of telecommunication and that can be used as a first step for further root cause analysis. To do this, we introduce a new algorithm to derive four features based on historical data and aggregate them to generate a final score that is optimized through supervised labels for greater accuracy. These four features reflect the likelihood of occurrence of events, the sequence of events and the importance of relatively new events not seen in the historical data. Certain assumptions are tested on the data using the relevant statistical tests. After validating these assumptions, we measure the accuracy on labelled data, revealing that the proposed algorithm performs with a high anomaly detection accuracy.
Word Predictability is Based on Context - and/or Frequency

In this paper we present an experiment carried out with BERT on a small number of Italian sentences taken from two domains: newspapers and poetry domain. They represent two levels of increasing difficulty in the possibility to predict the masked word that we intended to test. The experiment is organized on the hypothesis of increasing difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we alternate canonical and non-canonical versions of the same sentence before processing them with the same DL model. The result shows that DL models are highly sensitive to presence of non-canonical structures and to local non-literal meaning compositional effect. However, DL are also very sensitive to word frequency by predicting preferentially function vs content words, collocates vs infrequent word phrases. To measure differences in performance we created a linguistically based “predictability parameter” which is highly correlated with a cosine based classification but produces better distinctions between classes.
A Single Level Detection Model for Traffic Sign Detection using Channel Shuffle Residual Structure

Traffic sign recognition (TSR) is a challenging task for unmanned systems, especially because the traffic signs are small in the road view image. In order to ensure the real-time and robustness of traffic sign detection in automated driving systems, we present a single level detection model for TSR which consists of three core components. The first is we use channel shuffle residual network structure to ensure the real-time performance of the system, which mainly uses low-level features to enhance the representation of small target feature information. Secondly, we use dilated convolution residual block to enhance the receptive field to detect multi-scale targets. Thirdly, we propose a dynamic and adaptive matching method for the anchor frame selection problem of small traffic signs. The experimental surface on TsinghuaTencent 100k Dataset and Chinese Traffic Sign Dataset benchmark has better accuracy and robustness compared with existing detection networks. With an image size of 800 × 800, the proposed model achieves 92.9 running at 120 FPS on 2080Ti.
Image Segmentation in Shape Synthesis, Shape Optimization, And Reverse Engineering

Image segmentation and segmentation of geometry are one of the basic requirements for reverse engineering, shape synthesis, and shape optimization. In terms of shape optimization and shape synthesis where the original geometry should be faithfully replaced with some mathematical parametric model (NURBS, hierarchical NURBS, T-Spline, …) segmentation of geometry may be done directly on 3D geometry and its corresponding parametric values in the 2D parametric domain. In our approach, we are focused on segmentation of 2D parametric domain as an image instead of 3D geometry. The reason for this lies in our dynamic hierarchical parametric model, which controls the results of various operators from image processing applied to the parametric domain.
Trustworthy Artificial Intelligence for Blockchain-based Cryptocurrency

Blockchain-based cryptocurrency has attracted the immersive attention of individuals and businesses. With distributed ledger technology (DLT) consisting of growing list of record blocks and securely linked together using cryptography, each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. The timestamp proves that the transaction data existed when the block was created. Since each block contains information about the block previous to it, they effectively form a chain, with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks. The blockchain-based technologies have been emerging with a fleet speed. In this paper, the trustworthy Artificial Intelligence will be explored for blockchain-based cryptocurrency where the prohibitive price leap creates a challenge for financial analysis and prediction.
On the Landscape of One-hidden-layer Sparse Networks and Beyond

Sparse neural networks have received increasing interest due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and the understanding of sparse networks is very limited. In this paper, we study the loss landscape of one-hidden-layer sparse networks. First, we consider sparse networks with a dense final layer. We show that linear networks can have no spurious valleys under special sparse structures, and non-linear networks could also admit no spurious valleys under a wide final layer. Second, we discover that spurious valleys and spurious minima can exist for wide sparse networks with a sparse final layer. This is different from wide dense networks which do not have spurious valleys under mild assumptions.
Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture

Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.
Shortened universal cycles for permutations

Kitaev et al. (2019) described how to shorten universal words for permutations, to length n!+n−1−i(n−1) for any i∈[(n−2)!], by introducing incomparable elements. They conjectured that it is also possible to use incomparable elements to shorten universal cycles for permutations to length n!−i(n−1) for any i∈[(n−2)!]. In this note we prove their conjecture. The proof is constructive, and, on the way, we also show a new method for constructing universal cycles for permutations.
Applications of multivariate quasi-random sampling with neural networks

Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes. The stochastic processes considered are geometric Brownian motions and ARMA-GARCH models. Geometric Brownian motions lead to an application of pricing American basket call options under dependence and ARMA-GARCH models lead to an application of simulating predictive distributions. In both types of applications the benefit of using GMMNs in comparison to parametric dependence models is highlighted and the fact that GMMNs can produce dependent quasi-random samples with no additional effort is exploited to obtain variance reduction.
Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

In the field of online learning, the development of learning objects (LOs) has increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, the lack of a conceptual model for sharing suitable LOs between learners makes multiple challenges. In this regard, multi-label classification plays a significant role to make high-quality LOs, which can be accessible and reusable. This article highlights a new way of using learning objects based on Multi-Label Classification (MLC) and clustering algorithms with feature selection techniques. It suggests a new system that makes the most suitable choice among many alternative sources based on the Sharable Content Object Reference Model (SCORM). The proposed algorithm has been tested on a real-world application dataset related to the data analysis service for the learning science community. The experimental results show that our algorithm outperforms the traditional approach and produces good results.
Ivanka’s Rhetoric in Defense of Donald Trump’s Political Standing

This study was to find out about the rhetorical appeals and the prominent forms of persuasions in Ivanka Trump's political speech. The source of the data of this study are texts (both written and spoken) related to Ivanka Trump’s 2020 RNC speech. The data of this study are the verbal speech, gestures, vocal delivery, and facial expression (from the video) as well as words, phrases, and sentences (from the written transcript). In this study, the writer used a qualitative content analysis approach. The theories applied consist of rhetorical appeals and persuasion theory as main theory, supported by the theories of rhetorical styles, rhetorical delivery, and context. The findings reveal that Ivanka used all forms of rhetorical appeals (ethos, pathos, logos) in her 2020 Republican National Convention speech. In addition, 20 forms of prominent persuasions were present in her speech.