Reviewerly/exHarmony huggingface dataset:
Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.
BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.We have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel.During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved.This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.
Application of the NARX neural network for predicting a one-dimensional time series

Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic
Impact of COVID-19 Pandemic on Disability Care and Persons with Disability in Pakistan

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СОЦІАЛЬНІ МЕДІА: АКАДЕМІЧНА ДОБРОЧЕСНІСТЬ У СИСТЕМІ НАУКОВОЇ КОМУНІКАЦІЇ

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The purpose&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; of the study is to monitor Facebook's information space on the presentation of content on academic integrity and publishing ethics as a tool to prevent the use of dishonest practices in the system of dissemination of scientific content.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Research methodology&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;. The basis of the research methodology are methods of non-continuous statistical observation and content analysis. With the help of non-continuous statistical observation, statistical data on posting was formed within the official Facebook pages of academic communities. Through content analysis of selected official Facebook pages of academic communities for the period, January 2020 - July 2021, a qualitative assessment of the topic of posting was made. The content on academic integrity and features of its presentation were identified. The additional apparatus of the research methodology consists of grouping, stratification, descriptive method, and visualization method.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;. An array of data has been formed to determine the general indicators of posting in official Facebook pages of academic communities. Through stratification, three levels of posting of academic communities were identified by the total number of posts and posting frequency. A qualitative assessment of the topics of posting on academic communities' social platforms was conducted, and content on academic integrity was identified. The general quantitative indicators of posting on academic integrity and the general levels of targeting of posts on the presentation of the content of different types are determined.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The novelty &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;of the obtained results of the research is the presentation of social media of academic communities as a tool for presenting targeted content on academic integrity and prevention of the use of dishonest practices.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Practical meaning&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;. The obtained results can serve as a basis for updating the content of social media of academic communities, developing a posting strategy for diversification of targeted content, and expanding the reach of the target audience.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key words&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: social media, Facebook, academic integrity, academic community, posting.&lt;/em&gt;&lt;/p&gt;
Conversational marketing as a framework for interaction with the customer: Development &amp; validation of the conversational agent's usage scale

Conversational agents are becoming an essential part of a growing number of personal and commercial encounters, bringing the issue of Conversational Marketing to a broader audience. A conversational agent is a developing technology that will be used in various fields throughout life, including e-commerce. The common characteristics of any conversational agent in whatever area are their capacity to engage in one-to-one personalised real-time dialogue with a human user and their availability 24 hours a day. Scale items for conversational agent phenomena have not been created scientifically or managerially in a business environment. The primary goal of this study was to develop and validate a new scale for conversational agents that could be used to quantify individual interactions in conversational marketing. As a result, the creation of a new scale for conversational agents with the objective of measuring individual customer interactions in conversational marketing was separated into two phases: Scale Development and Scale Validation. The Conversational Agent Usage Scale was developed and validated as a consequence of pilot studies. Additionally, this article discusses the practical consequences of conversational marketing, which can now be accomplished through the use of the Conversational Agent Usage Scale, which may be used by Customer Service &amp; Support, Marketing, and Sales departments.
Binary social group optimization algorithm for solving 0-1 knapsack problem

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.
A New Approach for Security in Cloud Data Storage for IOT Applications Using Hybrid Cryptography Technique

The most demanded advanced technology throughout the world is cloud computing. It is one of the most significant topics whose application is being researched in today’s time. Cloud storage is one of the eminent services offered in cloud computing. Data is stored on multiple third-party servers, rather than on the dedicated server used in traditional networked data storage in the cloud storage. All data stored on multiple third-party servers is not bothered by the user and no one knows where exactly data saved. It is minded by the cloud storage provider that claims that they can protect the data but no one believes them. Data stored over the cloud and flowing through the network in the plain text format is a security threat. This paper proposes a method that allows users to store and access the data securely from cloud storage. This method ensures the security and privacy of data stored on the cloud. A further advantage of this method is we will be using encryption techniques to encrypt.
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization

Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process.In this work, we design a heterogeneous semantic slot graph with a slot-level mask cross-attention to enhance the slot features for more correct summarization.We also propose a slot-driven beam search algorithm in the decoding process to give priority to generating salient elements in a limited length by "filling-in-the-blanks". Besides, an adversarial contrastive learning assisting the training process is introduced to alleviate the exposure bias.Experimental performance on different types of factual errors shows the effectiveness of our methods and human evaluation further verifies the results.* Weiran Xu is the corresponding author.Yeah ... It's madness , I'll tell you more this evening .Do you have time ?Yeah ... It's madness , I'll tell you more this evening .
Cyber Bullying Detection for Twitter Using ML Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit
Explore Better Relative Position Embeddings from Encoding Perspective for Transformer Models

Relative position embedding (RPE) is a successful method to explicitly and efficaciously encode position information into Transformer models.In this paper, we investigate the potential problems in Shaw-RPE and XL-RPE, which are the most representative and prevalent RPEs, and propose two novel RPEs called Low-level Fine-grained High-level Coarse-grained (LFHC) RPE and Gaussian Cumulative Distribution Function (GCDF) RPE.LFHC-RPE is an improvement of Shaw-RPE, which enhances the perception ability at medium and long relative positions.GCDF-RPE utilizes the excellent properties of the Gaussian function to amend the prior encoding mechanism in XL-RPE.Experimental results on nine authoritative datasets demonstrate the effectiveness of our methods empirically.Furthermore, GCDF-RPE achieves the best overall performance among five different RPEs.
A novel geometry image to accurately represent a surface by preserving mesh topology

Geometry images parameterise a mesh with a square domain and store the information in a single chart. A one-to-one correspondence between the 2D plane and the 3D model is convenient for processing 3D models. However, the parameterised vertices are not all located at the intersection of the gridlines the existing geometry images. Thus, errors are unavoidable when a 3D mesh is reconstructed from the chart. In this paper, we propose parameterise surface onto a novel geometry image that preserves the constraint of topological neighbourhood information at integer coordinate points on a 2D grid and ensures that the shape of the reconstructed 3D mesh does not change from supplemented image data. We find a collection of edges that opens the mesh into simply connected surface with a single boundary. The point distribution with approximate blue noise spectral characteristics is computed by capacity-constrained delaunay triangulation without retriangulation. We move the vertices to the constrained mesh intersection, adjust the degenerate triangles on a regular grid, and fill the blank part by performing a local affine transformation between each triangle in the mesh and image. Unlike other geometry images, the proposed method results in no error in the reconstructed surface model when floating-point data are stored in the image. High reconstruction accuracy is achieved when the xyz positions are in a 16-bit data format in each image channel because only rounding errors exist in the topology-preserving geometry images, there are no sampling errors. This method performs one-to-one mapping between the 3D surface mesh and the points in the 2D image, while foldovers do not appear in the 2D triangular mesh, maintaining the topological structure. This also shows the potential of using a 2D image processing algorithm to process 3D models.
Introducing Variational Autoencoders to High School Students

Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly focus on Generative Adversarial Networks (GANs) while paying less attention to Autoregressive Models, Variational Autoencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs' latent-space structure and interpolation ability could effectively ground the interdisciplinary learning of AI, creative arts, and philosophy. Thus, we designed a lesson to teach high school students about VAEs. We developed a web-based game and used Plato's cave, a philosophical metaphor, to introduce how VAEs work. We used a Google Colab notebook for students to re-train VAEs with their hand-written digits to consolidate their understandings. Finally, we guided the exploration of creative VAE tools such as SketchRNN and MusicVAE to draw the connection between what they learned and real-world applications. This paper describes the lesson design and shares insights from the pilot studies with 22 students. We found that our approach was effective in teaching students about a novel AI concept.
Enhanced Secure ATM authentication using NFC Technology and Iris Verification

In today's world technology has advanced to such an extent that it is interchangeable with connection and convenience. ATM was one of the major breakthroughs, and over the time it has provided better convenience in fulfilling one’s banking needs. Although, there are certain predicaments that such ATM transactions are susceptible too. The conventional PIN based authentication that is presently accustomed in all ATM apparatus is liable to shoulder surfing, hassle in remembering the multiple PIN and the rest. The physical card brings along setbacks in particular, wearing out of the magnetic strip attributable to frequent usage, losing or getting it stolen. Aside from these there are other unlawful activities that are carried upon. The objective of this paper is to present a solution to the above stated problems. In contrast to standard architecture, the proposed solution incorporates NFC enabled smartphones as a substitute for physical card and iris based authentication for PIN.
How Does BERT Rerank Passages? An Attribution Analysis with Information Bottlenecks

Fine-tuned pre-trained transformers achieve the state of the art in passage reranking.Unfortunately, how they make their predictions remains vastly unexplained, especially at the endto-end, input-to-output level.Little known is how tokens, layers, and passages precisely contribute to the final prediction.In this paper, we address this gap by leveraging the recently developed information bottlenecks for attribution (IBA) framework.On BERT-based models for passage reranking, we quantitatively demonstrate the framework's veracity in extracting attribution maps, from which we perform detailed, token-wise analysis about how predictions are made.Overall, we find that BERT still cares about exact token matching for reranking; the [CLS] token mainly gathers information for predictions at the last layer; top-ranked passages are robust to token removal; and BERT fine-tuned on MSMARCO has positional bias towards the start of the passage.
The Ability of Digital Literacy for Elementary School Teachers

This study aims to analyze the digital literacy skills of elementary school teachers. This study used quantitative research. The research subjects were 76 elementary school teachers. The research instruments used were observation sheets, interview guides, and questionnaires. The data analysis technique used in this research is a descriptive statistical analysis which is carried out by providing an overview of the data that has been collected and then making conclusions or descriptions related to the data. Based on the results of this study, it can be seen that the digital literacy skills of elementary school teachers need to be improved. This is done so that elementary school teachers can have good literacy skills in using and utilizing existing technology tools and digital media to design or design learning processes that are creative, innovative, and fun and can make students think critically. Also, good digital literacy skills can help teachers respond to the challenges of the times related to the application of mixed learning models in the world of education. A good teacher's digital literacy skills can help teachers transform learning so that it is more creative, innovative. Also, teachers can answer the challenges of the times related to the application of blended learning. A teacher who has good digital literacy skills shows that the teacher has a desire to continue to learn and develop his professional teaching.
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution

In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers.We show that bilingual transfer learning improves the strong transformer-based neural coreference models by 2-4 F1.We also systemically investigate the effectiveness of several pre-trained transformer models that differ in training corpora, languages covered, and model capacity.Our best model achieves a new stateof-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset.Our code is publicly available at https://github.com/bnmin/arabic_coref.