Reviewerly/exHarmony huggingface dataset:
Mobile Tracking in 5G and Beyond Networks: Problems, Challenges, and New Directions

This paper explores mobile tracking as a privacy threat posed by 5G and beyond (5G&B) cellular networks. We reviewed the mobile network operation and protocol design, with a focus on the use of mobile identifiers and localization methods, as they are the key technical enablers of tracking and localization in mobile networks. We note that user location privacy against mobile network operators (MNO) is an extremely challenging problem. While a permanent identifier is the most critical piece of information to pinpoint a user and create linkability among records of a user, using a dynamic identifier presents significant privacy-utility challenges-many mobile applications and national security and emergency response services rely on the knowledge of mobile locations. At the same time, using an obfuscated version of a permanent identifier is only secure against outsider attackers, not the MNOs. We argue that, to protect user privacy against MNOs, a radically new design is necessary and such design must nullify any permanent identifiers to break record linkability and take into consideration of user anonymity, network utility (e.g., incoming call), user accountability, and user privacy awareness and control simultaneously. Lastly, we identify potential new directions as an initial attempt to tackle this challenging problem.
Marketplace Application Analysis And Design Of Paid Software And Web-Based Shareware Services

In this digital era, many people do shopping online. The ease of application facilities offered is a consideration for people doing business transactions, especially during the Covid-19 period. Along with these changing trends, many business actors are also implementing their marketing strategies through various media such as social media, e-commerce, or marketplaces in order to continue to contribute to business income. This electronic marketing strategy makes it possible to market targeted offerings and in turn, build stronger customer loyalty. Like the Marketplace, paid software and shareware products have specific features. This study aims to analyze and design a marketplace application for paid software services and shareware. Applications are developed using the Waterfall method, through analysis, design, coding, testing, and system maintenance. The analysis and design use standard object-oriented tools with the Unified Modeling Language (UML) model. Model Document diagram contains Use case Diagrams, Class Diagrams, and Sequence Diagrams. The results of this study are in the form of an overview of needs analysis and application design as a reference in making applications for transactions in the marketplace.
An Approach For Word Sense Alignment For Romanian Language

The Romanian WordNet (RoWN) database is an impressive collection of Romanian nouns, verbs, adjectives, and adverbs, which can be seen as a network of nodes where, words can be found in a certain context to have the same meaning, and in the mesh of that network are found the other words that in turn can become a network node for another word in a specific context. In this study we propose an approach for the problem of aligning the word senses form RoWordNet with those of the Thesaurus Dictionary of the Romanian Language in electronic format (eDTLR), by exploiting the collections of definitions and examples in the two linguistic thesauri, based on a statistical model, where four heuristics are proposed for solving this issue.
Transfer Learning using BERT &amp; Comparative Analysis of ML Algorithms for Opinion Mining

Sentiment Analysis is an ongoing field of research in text mining that is concerned with the computational treatment of textual views, sentiments, and subjectivity. It's the task of distinguishing between positive and negative viewpoints, emotions, and assessments. Sentiment analysis has been the topic of intensive research since its inception. During COVID-19, there has been a subtle increase in the usage and the time spent on the social networking sites by people as most of the daily operations have moved online. Moreover, in addition to the illness itself, the pandemic has led to dread, anxiety, stress, concern, repugnance, and poignancy in individuals all around the world. Considering these indicators, experts are paying close attention to Twitter data analysis during this pandemic. BERT is used as a transfer learning model and this work analyses the efficacy of fine-tuning it for the task of opinion mining by comparing it to a baseline model that includes a TF-IDF vectorizer and a Naïve Bayes classifier. Its performance is also compared to that of Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and XGBoost classifier. To determine the most effective settings for the BERT model, hyper-parameter tweaking is used. After two epochs of training at a learning rate of le-5 and batch size of 16, the maximum accuracy of 87.6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> is attained. These results outperform all of the machine learning models examined in this study. This work tackles a comprehensive overview of the last update in this field. It can be beneficial to scholars in this domain because it encapsulates the most well-known Sentiment analysis methodologies and their comparison in single research work.
Subjective Perception of Literary Texts to Promote Text Comprehension in the Fifth Grade

The ability to read and perceive a text is one of the foundations necessary for a person to work with diverse texts in different life situations. Thus, the proficiency of reading competence in the last decade is a topical study object. Observations show that at the end of the first stage of primary education (at the end of the third grade), students demonstrate technically good reading skills, that is, they can clearly read written texts, follow the boundaries of sentences and read in appropriate intonation. To promote reading competence successively in the second stage of primary education, reading skills must be continuously improved by using various texts, in addition to fiction. As an art of word, literature creates, encourages and develops different experiences of each future reader. The subjective perception of a literary work is related to the awareness of the reader’s experience, synthesis of images, visualisation and awareness of fiction and reality, which is a prerequisite for understanding the text. This research aims to explore the methodology used in literature acquisition to promote text comprehension by fifth-grade students through activating the subjective perception of literary work. The objectives of this study are (1) to analyse research on the promotion of text comprehension and the subjective perception of literary work and (2) identify and analyse the text and subjective perceptions of fifth-grade students. Results of the text comprehension test and survey are examined in the study. The study involved 96 fifth graders.
Image Super-resolution Algorithm Based on Dual Regression Networks with Cross-scale Non-local Attention Mechanisms

Image super-resolution reconstruction is an ill-posed problem in nature because there are infinitely many high-resolution images that can be reconstructed from a low-resolution image. To limit the solution space and make good use of the widely existing cross-scale feature similarities in natural images, we propose a dual regression network based on the cross-scale non-local attention mechanism, which can not only restrict the solution space of the image super-resolution but also make the network better embrace the abundant external information. The method has been tested on five benchmark datasets and both achieve good results.
Obj-SA-GAN: Object-Driven Text-to-Image Synthesis with Self-Attention Based Full Semantic Information Mining

In recent years, text-to-image synthesis techniques have made considerable breakthroughs, but the progress is restricted to simple scenes. Such techniques turn out to be ineffective if the text appears complex and contains multiple objects. To address this challenging issue, we propose a novel text-to-image synthesis model called Object-driven Self-Attention Generative Adversarial Network (Obj-SA-GAN), where self-attention mechanisms are utilised to analyse the information with different granularities at different stages, achieving full exploitation of text semantic information from coarse to fine. Complex datasets are used to evaluate the performance of the proposed model. The experimental results explicitly show that our model outperforms the state-of-the-art methods. This is because the proposed Obj-SA-GAN model utilises textual information, which provides a better understanding of complex scenarios.
Research and Application of Mathematical Knowledge Graph Based on Ontology Learning

The knowledge graph has the potential to provide strong data support for systems such as those that automatically make recommendations and answer intelligent questions. An ontology learning (OL) approach is provided for use in the building of a junior high school mathematics knowledge map in an effort to address the challenge posed by the use of a single data source in the construction of an existing domestic subject knowledge map. Learners receive learning support services that are both timely and intelligent through the realization of an intelligent question answering and automatic recommendation system based on the mathematics knowledge map of junior high school. This not only offers a potential solution to the issue of personalization in junior high school mathematics online instruction, but also offers an idea for how the issue might be resolved. In comparison to a number of traditional benchmark systems, the methodology presented in this paper demonstrates marked improvements in terms of accuracy, precision, recall, and F value in the tests conducted on the dataset that was self-constructed to measure junior high school mathematics knowledge recommendation. The suggested method can recommend knowledge points to learners with more accuracy and help them develop a knowledge system, both of which are expected to provide support for the adaptive and personalized learning of learners.
Curriculum Evaluation Model Based on Improved Gaussian Mixture Clustering

To address the problem of uneven development of online course volume and course evaluation system in China, this paper proposes an intelligent course evaluation model based on improved Gaussian mixture clustering using adaptive adjustment of Gaussian distribution mixing coefficients and soft optimization of the relationship between data points and clustering centers. It can be used for course construction and course implementation evaluation, including the initial screening of national first-class online courses, and greatly reduces the investment in labor costs. The experimental results demonstrate the effectiveness of the model.
Exploiting Static and Dynamic Human Joint Relations for 3D Pose Estimation via Cascade Transformers

Transformer has become the dominant model in natural language processing (NLP). Researchers have recently attempted to exploit transformer architecture for various computer vision tasks and achieved competitive results. However, few works have been done to explore transformer architecture for 3D human pose estimation (HPE). In this work, we propose cascade transformers, a novel transformer-based method for 3D HPE from a single image. Specifically, our cascade transformers consist of two transformer encoders exploiting static and dynamic human joint relations respectively. Leveraging the self-attention module and the cascade structure, our method comprehensively models the static and dynamic human joint relations. We evaluate our method on Human3.6M. Extensive experiments show that our method achieves excellent performance without explicitly using human skeleton priors. Notably, our single-image method achieves approximately the same performance as the current best transformer-based method PoseFormer even when PoseFormer uses 9 frames to predict pose.
BERT Tokenization and Hybrid-Optimized Deep Recurrent Neural Network for Hindi Document Summarization

Text summarization generates a concise summary of the available information by determining the most relevant and important sentences in the document. In this paper, an effective approach of document summarization is developed for generating summary of Hindi documents. The developed deep learning-based Hindi document summarization system comprises of a number of phases, such as input data acquisition, tokenization, feature extraction, score generation, and sentence extraction. Here, a deep recurrent neural network (Deep RNN) is employed for generating the scores of the sentences based on the significant features, wherein the weights and learning parameters of the deep RNN are updated by using the devised coot remora optimization (CRO) algorithm. Moreover, the developed CRO-Deep RNN is examined for its efficacy considering metrics, like recall-oriented understudy for gisting evaluation (ROUGE), recall, precision, and f-measure, and is found to have attained values of 80.896%, 95.700%, 95.051%, and 95.374%, respectively.
A Fault Diagnosis Method for Satellite Reaction Wheel Based on PSO-ELM

In this paper, the high-precision mathematical model of satellite reaction wheel is built, and the common fault types of reaction wheel are analyzed and simulated. In order to improve the efficiency of the computation in satellite fault diagnosis, a particle swarm optimization extreme learning machine (PSO-ELM) algorithm is introduced in this paper. Compared with the classical feedback neural network, ELM uses random weights and thresholds to reduce the amount of calculation by at least half. Due to the existence of the controller, the change of satellite observation signal is very small when some faults occur. Compared with the simple ELM algorithm, this new algorithm uses PSO to select the optimal output weight and threshold. So that when the training set has relatively close data, it still maintains high regression accuracy. In this paper, a reaction wheel fault diagnosis model based on PSO-ELM is established, and the data obtained from the fault simulation model are trained and tested. Compared with ELM, the regression accuracy has been significantly improved. This new fault diagnosis model provides a new research direction for the fault diagnosis of satellite reaction wheel.
Ultrasound Image Segmentation Algorithm of Ocular Muscle of Breeding Pig Based on Continuous Minimum Cut

In the measurement of ocular muscles in live pigs, segmentation methods based on ultrasound images are commonly used. However, due to various interferences in the ocular muscle region and ultrasound images, the segmentation accuracy is low. We proposed a segmentation algorithm for porcine ocular muscle ultrasound images based on continuous minimum cuts. Firstly, the ultrasound image of the ocular muscle is preprocessed to reduce common interference; then, the initial prediction edge is extracted, and the foreground and background templates are roughly constructed; finally, a segmentation algorithm is constructed according to the two templates to obtain the contour edge of the ocular muscle of the breeding pig, so as to achieve accurate measurement. The experimental results show that the segmentation accuracy of the proposed algorithm is significantly better than that of the mainstream ultrasound image segmentation algorithms, and the segmentation results are closer to the manual measurement results.
A Pyramidal CNN-Based Gleason Grading System Using Digitized Prostate Biopsy Specimens

Prostate cancer (PC) is the most common cancer, a significant cause of morbidity, and is the second vital cancer that causes death in the US. Early PC detection is one of the major factors in decreasing mortality. We introduce a deep learning (DL) system for automated Gleason system grading (Gleason pattern (GP) and Gleason score (GS)) and grade groups (GG) using whole slide images (WSIs) of the digitized prostate biopsy specimens (PBSs). The DL is a pyramidal convolution neural network (CNN) approach consisting of progressively larger patch-sized shallow CNN to provide hierarchical information features. We used three patches sizes 100×100 (small), 150×150 (median), and 200×200 (large) pixels, so the pyramidal CNN affords us varying contextual features. The small patches give more local information, while the large patches provide global features. The patch-wise classification yields five probabilities representing the GP types from 1 to 5 at each pyramidal level. Then, we get the average for those three levels. We used three metrics to evaluate the GP classification diagnostic: recall, accuracy, and precision. The classification accuracy for the CNN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> (large patches) is 0.77, the best among the three CNNs. The GG results are between 50% to 75% for recall. GG’s results are highlighted in our DL systems by comparing them with the current work.
VampNet: Unsupervised Vampirizing of Convolutional Networks

Numerous applications need to concurrently solve multiple tasks. We present an unsupervised method enabling to create from two pre-trained neural networks A and B, a network B’ approximating B while feeding on a part of A’s layers. This “Vampire” Network allows to significantly reduce the combined weight of the two networks. We propose the following contributions: (1) we show that two networks of the same structure but trained on different tasks display quite strong linear properties between their layers; (2) an unsupervised algorithm replacing part of the vampire network’s features by linear projections of features from the first network; (3) we show that the vampire network thereby created significantly reduces the number of additional parameters needed to accomplish the second task, and thus the computational load of the full system.
Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity

Abstract This paper uses nonlinear differential equations to establish a relationship model between enrollment, education, and employment in vocational education. In this paper, the local stability of the vocational education system is analyzed utilizing nonlinear differential theory and numerical simulation. At the same time, we carried out a numerical simulation of the system’s dynamic behavior. Through numerical simulation research, it is found that the model has peculiar nonlinear behavior and dynamic characteristics when its parameters take some specific values. Finally, we get the critical condition of the stability of the vocational education system according to the model established in the article.
Cave map: condition, potential, and danger information of cave passages in Malang Regency, Indonesia

Abstract The cave maps provide information of the cave passage condition. Cave maps are generally used in various studies, such as archaeology, geomorphology, hydrology, and others. In this study, the map used is the basic cave map which is generally used in field surveys. The method used in this research is a map literature study which is then followed by a field survey to observe the real conditions. After doing the observation, the next step is interpreting the cave map, digitizing on screen, and doing descriptive analysis. The results of this study show that there is a variety of information presented in the cave map, including the condition of the cave related to the cave passage segment, the overall pattern of the cave passage, and quantitative aspects in the form of passage length, passage depth, cave passage area, and the cave area itself. Potentials that can be seen with the cave map are subterranean rivers and the presence of cave ornaments. And by looking up the map, the information about the dangers in cave exploration related to the dangers of collapse, flooding, landslides/erosion, and low oxygen will be found out.
Frustratingly Easy Knowledge Distillation via Attentive Similarity Matching

Knowledge distillation is an effective approach to transferring knowledge from the large teacher network to its small proxy student one, thereby letting the proxy student work on those resource-limited mobile devices. Most previous arts manually select the paired intermediate layers of teacher and student networks to align their pertinent features by dimension reduction. This sort of approach may confront information loss and insufficient layer-wise alignment that limit knowledge transferability. In this paper, we propose a simple and effective knowledge distillation method named attentive similarity matching (ASM). ASM at first concatenates the teacher’s intermediate features and the student’s ones together to enhance similarity representation of all the student’s layers, without involving dimension reduction, then align all cross-layer advanced similarities in an attentively weighted manner for semantic calibration. Experiments of image classification on three popular datasets show the effectiveness of the proposed method as compared to its previous cousins.
APPLYING INTELLIGENT TECHNIQUES FOR TALENT RECRUITMENT

The objective of this research is to describe a system to aligned the hard and soft skills of the applicant to the current labor market. For this, a system was implemented which uses Web Scraping to get a general profile of an area, meanwhile for the evaluation of the applicant soft skills is used a Test Cleaver and for the hard skills fuzzy inference system is implemented. Therefore, the data is entered into an Analytic Hierarchy Process, with this, the applier is able to see which area is better to improve according to the hard and soft skills.
Building an adaptive digital library interface based on data ontology

The work is devoted to the problem of customizing the user interfaces of an information system that integrates data. An adaptive interface serves as one of the means of organizing the presentation of subject domain data. The issue of using the semantic relations of ontology to select data corresponding to the objectives of the study is investigated. A model of an adaptive interface is considered, which allows the most accurate reflection of the needs of a researcher within a particular subject domain. It is shown how the adaptive interface is formed by means of the semantic library model.
Deep Sentiment Analysis of the Feelings Expressed by Tourists Based on BERT Model

In response to the call to consolidate and expand the success of poverty alleviation and promote the overall revitalization of the countryside, rural tourism is taken as the research direction in this paper. In the existing research work on rural areas of tourism to poverty alleviation, it is mainly to put forward feasible suggestions for the overall level or to carry out one-way developing analysis for individual areas. A personalized analysis of rural areas of tourism to poverty alleviation from the perspective of users with real travel experience is conducted innovatively in this paper, which can enrich the research content of travel behavior. Through the research, training, and application of the Bidirectional Encoder Representation from Transformer (BERT) model, a deep sentiment analysis of the feelings expressed by tourists in demonstrative areas of tourism to poverty alleviation in China via Trip.com is conducted in this paper. The experiment shows that the accuracy of the BERT model on the test set is 86.9%. Based on completing the sentiment classification, this experiment completed the drawing of the word cloud by counting the frequency of sentiment words in the comments of different tendencies and completed the related results display on the WeChat Mini Program. Tourists can access the platform to learn about the scenic features, geographical location, cultural background, advantages, disadvantages, and characteristics of the scenic spot in advance.