Reviewerly/exHarmony huggingface dataset:
Construction of College Chinese Mobile Learning Environment Based on Intelligent Reinforcement Learning Technology in Wireless Network Environment

Technological advancements have resulted in the implementation of technology in various fields. Technologies such as wireless network systems and artificial intelligence have opened new doors in education. The Chinese language reflects the rich tradition of the greatest civilization in the world. Therefore, the university courses in a language with excellent traditional and cultural values have to be updated concerning technological growth. This research article will study the construction of a Chinese university language learning mobile environment based on intelligent reinforcement learning technology in a wireless network environment. Reinforcement learning technology (RLT) is a machine learning training technique that provides rewards and penalties. It is a decision-making approach in which artificial intelligence will face a game-like situation. Artificial intelligence either get rewarded or penalized for its actions. This learning method will significantly increase the transmission level between the student and teacher communication. Still, identifying whether the children are attentive while listening to their online classes is one of the hardest things. Once the energy loss has been rectified, it is possible to improve the learning platform in all universities, colleges, and other educational platforms using cloud computing technologies. With the implementation of RLT, the students and the teachers will be rewarded when there is an increased performance for the Chinese learning system. The results have been recorded at a low packet rate with reduced wireless communication technology. When the learning mechanism is compared to the Q-learning (quality learning) mechanism, a 98.78% accuracy rate can be seen.
System for Recommending Telecommunication Packages Based on the Deep and Cross Network

With the evolution of the 5th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains challenging. Conventional methods for the recommendation of telecommunications packages either rely on complex expert feature engineering or fail to perform end-to-end deep learning (DL) during training. In this study, we propose a recommender system based on the Deep and Cross Network (DCN), deep belief network (DBN), embedding, and Word2Vec using the learning abilities of DL-based approaches. The proposed system fits the recommender system for telecommunication packages in terms of click-through rate prediction to provide a potential solution to the recommendation challenges faced by telecommunication enterprises. The proposed model captures the finite order interactional and deep hidden features. Additionally, the text information in the data is used to improve the model’s recommendation capability. The proposed method also does not require feature engineering. We conducted comprehensive experiments using real-world datasets, the results of which demonstrated that our proposed method outperformed other methods based on DBNs, DCNs, deep factorization machines, and deep neural networks in terms of the area under the ROC curve, cross entropy (log loss), and recall metrics.
Adaptive Anisotropic Morphological Filtering Based on Co-Circularity of Local Orientations

The algorithm presented in this paper is an application of a general framework for morphological processing of signals on weighted graphs.Here we apply it to images by defining what we call a co-circularity graph.In this graph, the vertices are the pixels and the weighted edges depend on a consistency criterion (co-circularity) between local orientations estimated from the structure tensors.This graph induces anisotropic adaptive morphological operators which are related both to anisotropic diffusion in images and path optimality in graphs.We present several applications such as the enhancement of fiber-like structures, completion of interrupted edges and the regularization of grayscale images.We also discuss the parameters setting depending on the application.
Design of indoor mobile robot based on ROS and lidar

In order to improve the speed of indoor express sorting, Based on the development of ROS system, a mobile robot based on ROS and lidar is designed in this paper. The hardware platform and software platform of the mobile robot are designed, Based on the experimental analysis, it is verified that simultaneous positioning and map construction is realized by the proposed indoor mobile robot based on ROS and lidar, and multi-point cruise is realized in the process of autonomous navigation.
Comparative Analysis of AI-powered Approaches for Skeleton-based Child and Adult Action Recognition in Multi-person Environment

Machine learning on graphs is a unique framework structure to handle multiple objects. A lot of studies have gone into the topic of action recognition. Among them, most action recognition systems nowadays are based on skeletons. Now, GNN-based techniques are effective. In GNN-based techniques, the skeleton represents the graph, node as joints, and edges as the bones. As well as temporal relationship between the skeleton points could be obtained frame by frame. Most studies focus on detecting only one action for a single individual rather than several actions performed by multiple persons simultaneously in an untrimmed video in a well-segmented video. Many action recognition systems are focused on adult actions. However, no additional techniques for child action recognition. The action recognition of children is complex since there is no standard dataset for child action recognition. This study aims to determine a child&#x0027;s actions in a multi-person scenario. Initially, this method selects a minor domain action. Identify the child in the video using yolov5&#x0027;s custom object detection. Compare the ANN, 1DCNN, LSTM, and GNN (STGCN) with skeletal data to recognize the actions. The ANN method hasn&#x0027;t successfully determined the temporal relationship between frames. As a result, the GNN-based technique was employed in a multi-person situation to recognize behaviours. Adult skeleton data was taken from standards action datasets KTH and NTU-RGBD-120 using AlpaPose. Cropped YouTube videos are used for child action recognition. In terms of efficiency and accuracy, the GNN-based technique outperforms the others.
Uncovering the course of analogical mapping using eye tracking

Analogical mapping - the core component of analogical reasoning - consists of establishing the relational structure shared by two analogous situations and inferring the missing elements in a less familiar situation from a more familiar one. Several existing models of analogy predicted that the complete relational structure can be considered in parallel. Other models postulated that mapping can be less or more incremental - it can access only a relatively small part of the structure, and needs to move to its other parts in steps in order to construct the final relational correspondence. However, the precise time course of analogical mapping, especially in sufficiently complex analogies, to date was rarely studied empirically. In two studies, eye tracking was used to assess in a rigorous way the extent to which mapping can be incremental. In a newly designed geometric A:B::C:D task, pattern D was generated from C according to the same shape transformations that generated pattern B from A. The six possible response options differed systematically in the number of correct transformations, from no transformation matching, via partial relational match, up to the full match. In Study 1, the relational match of options fixated on by participants was initially low but increased monotonically over the course of analogy. The number of corresponding eye fixations predicted 68% variance in relational match of the final response. The correct option was chosen only if fixated on for a sufficiently long time. Study 2 replicated the findings using a more ecologically valid and less demanding task variant that required to map the changes in people's appearance. The results support these theoretical models of analogy which postulate strictly incremental mapping.
PRIMITIVE ELEMENT PAIRS WITH A PRESCRIBED TRACE IN THE CUBIC EXTENSION OF A FINITE FIELD

Abstract We prove that for any prime power $q\notin \{3,4,5\}$ , the cubic extension $\mathbb {F}_{q^{3}}$ of the finite field $\mathbb {F}_{q}$ contains a primitive element $\xi $ such that $\xi +\xi ^{-1}$ is also primitive, and $\operatorname {\mathrm {Tr}}_{\mathbb {F}_{q^{3}}/\mathbb {F}_{q}}(\xi )=a$ for any prescribed $a\in \mathbb {F}_{q}$ . This completes the proof of a conjecture of Gupta et al. [‘Primitive element pairs with one prescribed trace over a finite field’, Finite Fields Appl. 54 (2018), 1–14] concerning the analogous problem over an extension of arbitrary degree $n\ge 3$ .
Deep Learning Method for Classifying Thyroid Nodules Using Ultrasound Images

The most prevalent endocrine malignant tumour is thyroid cancer, which is the fast growing cancer among all cancers. On palpation, nearly 5% of nodules in thyroid nodular diseases can be discovered, while 10% to 67% can be found on ultrasonography. Thyroid cancer progresses relatively slowly. The cause of thyroid cancer is inexpertly understood, but may involve genetic and environmental factors. If thyroid cancer is diagnosed at an early stage, it can be cured. Convolutional Neural Networks have a higher accuracy than standard Artificial Neural Networks in diagnosing thyroid nodules. For that reason, the proposed method uses the Convolutional Neural Network, a deep learning algorithm, in the classification of thyroid nodules using ultrasound images. The thyroid ultrasound image collection for this study was taken from the open access Thyroid Digital Image Database (TDID). The deep Convolutional Neural Network called Residual Network (ResNet) was employed as a state-of-the-art image classification model in this proposed method. The use of ResNet improves neural network performance. L2 regularization is introduced to prevent overfitting. The experimental results showed that the accuracy of using the ResNet model was 83%.
A Study on Secret Data Sharing through Coverless Steganography

The basic fundamental of steganography is to conceal the confidential data in designated cover media which will carry the secret message in such a way that no one can suspect it. With the increase in multimedia content over the net, the probability of information being theft is increased. To make confidential communication secure, people choose different data hiding techniques. Recently, steganography has gained popularity in information hiding. Information hiding is a subject that manages the concealing of classified data from attackers and hackers. Though the secret messages are not visible to human eyes but can be noticeable under statistical representation of cover media. The coverless hiding technique doesn’t modify the cover object. However, the cover object is used to transfer confidential data. A mapping relationship is created among cover objects and confidential data by following the characteristics of cover media. The term “coverless” is without any modification in the cover image the confidential data can be sent. The prime advantage of using a coverless approach is, it cannot be detected under steganalysis as no embedding is performed in cover media. This technique has drawn more attention in the data hiding field. Currently, it has been observed that most of the research work in coverless hiding approaches selected text and image as cover media. A very few researchers have considered video as cover media in coverless approach which has plentiful contents and provides the opportunity to explore.
<scp>LiDetector</scp> : License Incompatibility Detection for Open Source Software

Open-source software (OSS) licenses dictate the conditions, which should be followed to reuse, distribute, and modify software. Apart from widely-used licenses such as the MIT License, developers are also allowed to customize their own licenses (called custom license), whose descriptions are more flexible. The presence of such various licenses imposes challenges to understand licenses and their compatibility. To avoid financial and legal risks, it is essential to ensure license compatibility when integrating third-party packages or reusing code accompanied with licenses. In this work, we propose LiDetector , an effective tool that extracts and interprets OSS licenses (including both official licenses and custom licenses), and detects license incompatibility among these licenses. Specifically, LiDetector introduces a learning-based method to automatically identify meaningful license terms from an arbitrary license, and employs Probabilistic Context-Free Grammar (PCFG) to infer rights and obligations for incompatibility detection. Experiments demonstrate that LiDetector outperforms existing methods with 93.28% precision for term identification, and 91.09% accuracy for right and obligation inference, and can effectively detect incompatibility with 10.06% FP rate and 2.56% FN rate. Furthermore, with LiDetector , our large-scale empirical study on 1,846 projects reveals that 72.91% of the projects are suffering from license incompatibility, including popular ones such as the MIT License and the Apache License. We highlighted lessons learned from perspectives of different stakeholders and made all related data and the replication package publicly available to facilitate follow-up research.
Citation Oriented AuthorRank for Scientific Publication Ranking

It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p &lt; 0.001) than other baselines for publication ranking.
Block chain Based Framework for Document Verification

Document Verification using Blockchain Technology has a huge scope. With increasing documents generated every year, there is no systematic and simple way to verify the documents. This system could be used to the governments, organizations, employers and basically anybody who wants to verify that the given document is not forged. This could be used to verify all kinds of immutable records ranging from attendance records, birth certificates, graduation and academic credentials. The proposed system could be used by the government to construct a decentralized network to store and maintain record. This is also the best way to ensure that the documents exist in the state of their creation, that they are not tampered with by anyone. Motivated by this, we propose to develop a decentralized blockchain system using Ethereum that will serve as an application to authenticate the documents. An application will be installed to local systems in which the users will verify the documents. These local systems also known as “Nodes” or “Blocks”.Once the documents are added to blocks forming the blockchain, complex calculations are performed to find the unique hash for that particular document.This concept can be implemented through decentralized applications deployed on the blockchain. The blockchain that is intended to be used for the deployment process is the Ropsten Ethereum Network. Thus, the immutability of documents can be maintained, while providing a simple, yet secure way for authenticating/verifying documents.
Regularized CNN for Traffic Sign Recognition

With the increasing urban population, city expan- sions come along with the problem of traffic management. In today’s era of the computational world, where there is a smart AI-based solution for almost every problem, AI-based Traffic Sign Recognition (TSR) comes to our rescue. This work develops a customized convolutional neural network for TSR. This work explores the impact of different regularization techniques namely L1, L2, Dropout, and batch normalization on the performance of a developed TSR system. To conduct the experiments, this work utilizes the benchmark GTSRB dataset (German Traffic Sign Recognition Benchmark). Experimental results show that our proposed classifier outperforms the existing approaches for TSR. This paper tries to cover one such aspect and tries to train the system to respond in accordance with a rich dataset containing 39,209 color images spanned over 43 different categories of traffic signs, whereas the system is tested with 12,630 examples, achieving an accuracy of 99.56%.
Creating concept maps with augmented reality: a case of eclipse of the lunar and solar topic

Abstract Concept maps are the tools used to facilitate meaningful conceptual learning. In this study, an augmented reality (AR)-based concept map (AR-ConMAP) application was developed to facilitate the concept map creation process to overcome the challenges that students face when creating concept maps. The study was carried out as a case study. Observations via using video records and worksheets were used to examine the effect of this application on students’ concept map creation skills and their evaluations about their experiences. The results suggested that using AR for creating concept maps provided more accurate results in associating the concept map components and supported students for meaningful conceptual learning. Students evaluated their experiences in using AR also as increasing their sense of enjoyment and curiosity which positively contributed to their motivation to create concept maps. Recommendations for future research and practices were also included.
Pose error analysis method based on a single circular feature

The measurement accuracy of pose parameters based on a single circular feature depends not only on the accuracy of camera calibration and feature extraction but also on the relative pose of the feature and camera—different poses correspond to different error transmission coefficients. To obtain the relationship between measurement errors and pose parameters, we propose an error analysis method based on geometric interpretation. The method characterises measurement error by the sensitivity the imaging feature has to the variation of pose parameters. In addition, the method can be extended to the error analysis work of other coplanar features' pose measurement algorithms. We conducted simulations on measurement errors of pose parameters under different poses, and the results show that the error distribution of pose parameters is in good agreement with the theoretical analysis. Moreover, we propose a method for judging and optimising outliers, and experimental results show the feasibility of this method.
Automated Facemask Detection and Monitoring of Body Temperature using IoT Enabled Smart Door

Living with the novel Coronavirus is becoming the new normal as nations around the globe resume. However, in order to stop the virus from spreading, we must isolate Covid-infected persons from the rest of the population.Fever is the most common symptom of coronavirus infection, according to the CDC [1], with up to 83 percent of symptomatic patients presenting indications of fever. Early symptom detection and good hygiene standards are therefore critical, particularly in situations where people come into random contact with one another. As a result, temperature checks and masks are now required in schools, colleges, offices, and other public spaces. However, manually monitoring each individual and measuring their respective body temperatures is a cumbersome task. Currently, most of the temperature checkups are done manually which can be inefficient, impractical, and riskybecause sometimes people checking manually may be reluctant to check every person’s temperature or sometimes allow people even if they violate the guidelines. Moreover, the person assigned to manually check will be at high risk as he is exposed to a lot of people. To solve these issues, we propose a project that reduces the growth of COVID-19 by monitoring the presence of a facial mask and measuring their temperature. The Face Mask Detection can be done using the TensorFlow software library, Mobilenet V2 architecture and OpenCV.A non-contact IR temperature sensor is used to monitor the individual’s body temperature. To avoid false positives, the system will be strengthened by training it with a variety of cases. Once the system detects a mask, it measures the body temperature of the person. If the temperature is within the normal range, sanitization is done,and the person is permitted entry through an IOT enabled smart door. However, if the system fails to detect a mask or the person’s temperature falls out of the predefined range, a buzzer rings and the door remains closed. Our model is intended to be effective in preventing the spread of this infectious disease.
HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning

Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report “Anna stole Ada’s car”, imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.