Reviewerly/exHarmony huggingface dataset:
Construction of Smart Campus Under the Background of Big Data

Under the background of the continuous promotion of education informatization and the coming out of the 14th five year plan, the construction focus of university informatization is gradually shifting from building digital campus to building smart campus. The construction of smart campus has become one of the hot topics in campus construction. Therefore, this paper analyzes the connotation of smart campus and the basic structure of smart campus from the perspective of hierarchy concept, then discusses the specific construction of smart campus at different levels. Based on that, some practice of a university campus is discussed and a set of experiences are summed up for the construction of smart campus. Smart campus will be the inevitable trend of future.
Face Counting and Mask Detection using Deep Learning

Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.
Leading by Lecturer of General Education, NIT for Graduate Thesis

Mathematics is recognized as a language for us to understand nature. For example, we could confirm physical theory for the phenomenon by considering a scientific background of a phenomenon with mathematics. In National Institute of Technology, we have some departments and study various academical fields. Hence, students could gain knowledge of not only general education but also each specialties, for example, Mechanics, Electricity and so on. However, faculty members of General Education have not led graduate study in recent years in National Institute of Technology, hence we intend to obtain result which base on cooperative research a lecturer of General Education and a students of Department of Electrical and Computer Engineering.
Detection of Multiscale Center Point Objects Based on Parallel Network

Anchor-based detectors are widely used in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet. Most of which are invalid. Although the anchor-free method can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes a multi-scale center point object detection method based on parallel network to further reduce the number of useless anchor boxes. This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53. Combining feature pyramid and CIOU loss function this algorithm is trained and tested on MSCOCO dataset, increasing the detection rate of target location and the accuracy rate of small object detection. Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy speed.
A Study on the Effective Integration of Mass entrepreneurship and innovation Education and Professional Education

Under the influence of a new round of scientific and technological revolution, the creator movement is advancing and developing in a global scope. Colleges and universities, as an important garden for talent training, are generally not motivated and innovative in classroom teaching. How to stimulate students’ learning motivation and innovative development consciousness is a severe test that every university must face.
Progress overview of the camera-trapping monitoring platform for the Qianjiangyuan National Park, Zhejiang Province

The camera-trapping monitoring platform for the Qianjiangyuan National Park was established by the Qianjiangyuan National Park Administration in cooperation with the
Strategy for the Formation of an Organizational and Technological Model of the Regional Olympiad Environment in the General Education System

The article describes the problem of the current state of the all-Russian school Olympiad: the formation of the available Olympiad environment to support gifted children with maximum coverage of students the school stage of the national Olympiad in all subjects in all schools in the country.&#x0D; The authors present an infrastructure of organizational and technological model Olympiad environment in the region as a network of the Olympics municipal center in each municipality of the region not only to stage the events of the Olympics, but systematic training of students and teachers to the all-Russian school Olympiad.
Evaluating the Performance of a New Text Rhythm Analysis Tool

The paper assesses and evaluates the performance of the ProseRhythmDetector (PRD) Text Rhythm Analysis Tool. The research is a case study of 50 English and 50 Russian fictional texts (approximately 88,000 words each) from the 19th to the 21st century. The paper assesses the PRD tool accuracy in detecting stylistic devices containing repetition in their structure such as diacope, epanalepsis, anaphora, epiphora, symploce, epizeuxis, anadiplosis, and polysyndeton. The article ends by discussing common errors, analysing disputable cases and highlighting the use of the tool for author and idiolect identification.
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation

Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.
New Class Candidate Generation Applied to On-Body Smartphone Localization

On-body device position awareness plays an important role relative to providing smartphone-based services with high levels of usability and quality. Existing on-body device localization methods deal with a fixed number of positions. In contrast, we have proposed a framework to discover new positions that are not initially supported by the system and add them as recognition targets during use. In this paper, we focus on the task of detecting new class candidates in the framework, which consists of anomaly detection, dimension reduction, and clustering. Anomaly detection and dimension reduction are preprocessing to make clustering more effective. A preliminary experiment is carried out to prove the concept and find out that it is appropriate to implement the k-means clustering on the number of clusters estimated by X-means after performing anomaly detection by IForest and dimension reduction by t-distributed stochastic neighbor embedding (t-SNE).
OWEC: Optimised Weighted Ensemble Classifier Using Particle Swarm Optimisation

Machine learning is the process of teaching computers to learn from data and think like humans. Ensemble learning is a process by which multiple machine learning models can be combined strategically to solve a problem. In this subject, a new approach is proposed to find the optimal weights for a weighted ensemble classifier. The optimised weights can be found using an optimiser. Normally, grid search is used to find the optimal weights for the ensemble model. This subject proposes a new approach, Optimised weighted ensemble classifier (OWEC), to find the optimal weights for a weighted ensemble classifier using particle swarm optimisation as an optimiser. This approach is shown to be able to generalise to different datasets with different base learners, reduce the bias and variance of the overall model and is computationally more efficient to train compared to the base learners.
Cross-lingual Semantic Representation for NLP with UCCA

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew.UCCA builds on extensive typological work and supports rapid annotation.The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations.It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing.Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.
Government Document Approval and Management Using Blockchain

Blockchain isAkhilesh, N. S. aAniruddha, M. N. distributedAnirban, Ghosh databaseDakshayini, M. in the form of an immutable decentralized ledger spread across a peer-to-peer network. Blockchain contains several characteristics which are incredibly beneficial to any organization that requires a secure alternative to centralized database solutions. Because Blockchain is distributed, it enables essential data to be easily available to users even when offline, and with the help of cryptography, it can even ensure privacy and security. With all these benefits, one must wonder why Blockchain is not being used commonly among organizations that strongly necessitate such benefits. Organizations like the Government provide excellent use cases for Blockchain yet its implementation in this field is scarce. In this research paper, we hope to explain why this is the case and demonstrate how Blockchain could be implemented within the Government as a practical working database solution, namely, in the area of Civilian Document Management
BUILDING AN INFORMATION SYSTEM TO SUPPORT SCIENTIFIC AND EDUCATIONAL ACTIVITIES BASED ON THE ONTOLOGICAL MODEL OF THE SUBJECT AREA

The paper considers an approach to the creating an information system for supporting scientific and educational activities (ISSEA). Such a system is designed to systematize knowledge and information resources on the required educational and scientific topics, ensure their integration into a single information space and provide meaningful access to them. The conceptual approach to the development process of ISSEA is based on combining the most important components of Semantic Web technology, in particular, the use of ontology to represent the semantics of information educational resources and support their intellectual analysis and knowledge management methods to improve the effectiveness of access to heterogeneous loosely connected information on various aspects of educational and scientific activities. Such information systems (is) represent information objects of knowledge (in the form of a network of knowledge and data), search for them, and ontology-driven navigation. ISSEA is equipped with an ergonomic web user interface and special editors designed to manage the knowledge integrated into it.
Fake Review Detection Using Machine Learning Techniques

Online reviews play a vital role in today's business and commerce. In the world of e-commerce, reviews are the best signs of success and failure. Businesses that have good reviews get a lot of free exposure on websites and pages that have good reviews show up at the top of the search results. Fake reviews are everywhere online. Online fake reviews are the reviews which are written by someone who has not actually used the product or the services. Because of the cut-throat competition, sellers are now willing to resort to unfair means to make their product stand out. This work introduces some supervised machine learning techniques to detect fake online reviews and also be able to block the malicious users who post such reviews.
An Improved Feature Selection Method for Sentiments Analysis in Social Networks

The increasing growth of user oriented media and users’ preferences in using social networks for communication, causes these virtual communities to become a valuable source of data. These communities provide users with the possibility of being aware of useful and reliable opinions. Many organizations employ efficient classifiers for determining the polarity of users’ opinions in order to make valid decisions in different business domains. However, most of the existing approaches suffer from low accuracy results due to performing classification task in a high-dimensional feature space. To this end, an efficient feature selection method based on a modified version of firefly algorithm is presented in this article. The main contribution of the proposed method is employing a weighted combination of classification performance measures in constructing a fitness function for the firefly algorithm. The proposed model for the fitness function leads to establishing a trade-off between the performance measures while trying to reduce the number of dimensions. The results obtained from experiments conducted on 11000 tweets show that the proposed method outperforms the existing counterparts in terms of polarity classification performance.
Assessing Expression in the Poetry Reading Skills of Elementary Students

This study aimed at describing the expression of the fourth-grade students in reading poetry at SDN Bontokamase Gowa. The study was a quantitative descriptive study. The population was the students in all elementary schools in Gowa district. The sample of the study was all students in grade IV at SDN Bontokamase Gowa that consisted of 20 female and 17 male. The data of the study were collected through observation, documentation, and field note. The three instruments were used to see the studentsâ€™ performance in reading poetry through direct observation, video, literature, and other supported sources. The data were analyzed by using the quantitative descriptive analysis to understand the expression of fourth-grade students in reading poetry. From the direct observation of students' performance in reading poetry, it was found that there are 14 students with a high score, 19 students with medium score and there were 4 students with low score assessed by three assessors. The students with a very good score show very good appreciation. In this case, the very good appreciation or performance is giving appropriate expression in reading title, lines, and rhymes. In contrast, the four students with a low score cannot show good performance or giving flat expression in reading title, lines and rhymes. The average score of students' expression is 3. 13. Thus, it was concluded that the skills to read the poetry of students in terms of expression aspect was in a good category.
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning.

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1,1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restriction for distributions on $\{-1,1\}^n$, and a quantitative analysis of how such a restriction affects the mean vector of the distribution. Along the way, we consider the problem of mean testing with independent samples and provide a nearly-optimal algorithm.
Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting.

This work is supported by SEAT, S.A., and the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat de Catalunya, under the Industrial Doctorate Grant 2017 DI 52. This research is also supported by the grant TIN2017-89244-R from MINECO (Ministerio de Economia, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya).
A Hybrid Deep Learning Model for Long-Term Sentiment Classification

With the omnipresence of user feedbacks in social media, mining of relevant opinion and extracting the underlying sentiment to analyze synthetic emotion towards a specific product, person, topic or event has become a vast domain of research in recent times. A thorough survey of the early unimodal and multimodal sentiment classification approaches reveals that researchers mostly relied on either corpus based techniques or those based on machine learning algorithms. Lately, Deep learning models progressed profoundly in the area of image processing. This success has been efficiently directed towards enhancements in sentiment categorization. A hybrid deep learning model consisting of Convolutional Neural Network (CNN) and stacked bidirectional Long Short Term Memory (BiLSTM) over pre-trained word vectors is proposed in this paper to achieve long-term sentiment analysis. This work experiments with various hyperparameters and optimization techniques to make the model get rid of overfitting and to achieve optimal performance. It has been validated on two standard sentiment datasets, Stanford Large Movie Review (IMDB) and Stanford Sentiment Treebank2 Dataset (SST2). It achieves a competitive advantage over other models like CNN, LSTM and ensemble of CNN-LSTM by attaining better accuracy and also produces high F measure.
Detecting DeepFakes for future robotic systems

Future robotic systems are likely to include AI-based professional to provide expert advice. For example, AI-based legal experts or judges try to explain the laws, and AI-based medical experts provide medical diagnosis and corresponding prescriptions. DeepFake is a collective of algorithms that aim to replace the face in one source video with the face of another person. The advancements in deep learning has made video manipulation and synthesis much more accessible than before. Thus,what if an AI-based DeepFake synthetic judge tries to explain the law differently and makes offensive decisions, or if a DeepFake synthetic doctor tries to give a wrong diagnosis and prescribes harmful medicines? Effective detection methods are important to combat malicious spread and use of damaging fake media as well as harmful fake robots in future robotic expert systems. This chapter first provides the general background on future robot-based expert systems,and on DeepFake,such as facial manipulation,forged video datasets,and detection techniques. These include 3D modeling and deep-learning-based manipulation techniques,forged video datasets from different sources,and current detection methods. This chapter covers a proposed detection method that utilizes image segmentation as part of the solution.
Adventure Game “Detective Adventure” Using Unity Virtual Reality

Abstract “Detective Adventure” is a Virtual Reality game for Android using cardboard. Where the detective must seek clues to reveal a serial killer by investigating crime scene. This game use three-dimensional graphics and virtual reality headset. The game uses a remote Virtual reality to pick up item and move. This game created by Unity game engine with C# as programming language and photoshop to use to make object, and edit image. Testing was done by the the blackbox testing, alpha testing by lecturer, and beta testing by surveying 30 respondens. Respondens show that the “Detective Adventure” is an interesting game to play.
Dynamic Secret-Key Assignment in QKD Access Networks (QAN)

We propose an architecture of QKD access networks (QAN) and a dynamical secret-key assignment (DSA) scheme for multiple edge users. Simulation results show that the DSA can effectively provide secret keys with less queuing time.
Recensione: Italia, Paola. 2020. Editing Duemila. Per una filologia dei testi digitali. Roma: Salerno

This contribution offers a review of the last Paola Italia's volume, Editing Duemila, which focuses on the impact of the digital revolution on writing, publishing, and reading. The author also offers proposals concerning a model of an accessible and sustainable Digital Philology.
RETRACTED: Real-Time monitoring of Athletes training data based on wireless sensors

The appearance of complex and biomedical and competitive-based information applications, sports, and other fields with zero-can remote sensors. These areas' junctions were reflected and shown in the methods and applications used for assembly, measurement, and identification of specific central parts determined by the human body. These policies have been proven in various biomedical and sports applications, including diagnostics, recovery, real-time viewing, and implementation of competitors' progress domains. But the two uses have only been described in two areas of study (biomedicine and sports), with the comparative application being sufficient in both, with minor discretion or modification. To speak with the contemporaneity of jobs, have been looking at specific documents that have been scattered over the last six years. In this exciting situation, this review's central criterion is to demonstrate the bulk of the sensor composites and the excellent sensor that focuses on their use and reference, to the disruption of others, without significantly looking at an express system or strategy.
Fostering Participation in Digital Public Health Interventions: The Case of Digital Contact Tracing

The SARS-CoV-2 pandemic has made all the more clear that public health is a public good. Public health interventions try to encourage contributions to this public good. Digital public health interventions are increasingly being used to complement traditional public health interventions. The rollout of digital contact tracing apps during the SARS-CoV-2 pandemic is a prominent example. We review the literature on how participation in such digital public health interventions could be fostered, provide an overview of digital contact tracing as one such intervention, provide policy recommendations on how to increase its adoption and usage as well as recommendations for further research.
A Comparative Study of Image Deblurring Techniques

The method of recovering a true image from degraded one, to analyze that digital image and characteristics with no artifact errors is known as Image Restoration. These techniques are of two types: direct methods and indirect methods. Direct methods are those in which the results of image restoration are produced in one single step. Indirect methods are those in which the results of image restoration are produced after various steps. This method is termed as blind image deconvolution, when the known info is just the blurred digital image and no info about the (Point Spread Function) (PSF) or the degrading model. The target of the procedure is to recover both the latent (un-blurred) image and the blur kernel, simultaneously. In this paper, we presented a comprehensive research of image noise model,de-blurring methods, blur types, and a comparative study of various deblurring methods. We have implemented number experiments to study these methods according to their performance, (Peak Signal to Noise Ratio) PSNR, (structural similarity) SSIM, blur type, and (Minimum Mean Square Error) MMSE.