Reviewerly/exHarmony huggingface dataset:
Research on Quantum Secure Route Model and Line Model Image Encryption Technology Based on Big Data Technology

At present, the mature application of computer network and digitized information are increasingly popular, people's ability to crack encryption algorithms, and the traditional key system cannot guarantee the absolute security of the information system. This paper, on the data encryption of big data, data integrity and data disaster backup technology and quantum communication security of quantum key distribution, direct communication and quantum secret security sharing technology, proposed a quantum security computing route model based on the management condition of data authentication center. Based on the route model, this paper studies the image encryption technology of the linear model of quantum security computing from different technical perspectives to ensure the security of image information.
Siamese Network with Feature Fusion for Visual Object Tracking

Due to the limitations of learning richness of the model, it is challenging for the trackers to complete tracking precisely under the circumstances of large appearance variance of the objects. Therefore, the improved version of Siamese-RPN are proposed to solve the problem and get a higher accuracy rate. In this paper, multi-level features extraction from layer-wise aggregation are applied to address the limitations of the robustness of Siamese-RPN in some special cases such as large occlusion and illumination variation. By utilizing the feature information in earlier layer and semantic information in latter layer of Siamese-RPN, the performance of CNN can be improved and a complete feature map of the objects can be established. With the improved version of Siamese-RPN, the tracker could have performance gain in its accuracy rate. Experiments show that when we use the original SiamRPN model to test the dataset, the success score is 0.6053. However, we find that the success score of a weighted sum of the features extracted from 0.4 times layer2 and layer5 is 0.6156. The dataset in this paper is based on OTB100K. Experiments show the high efficiency and accuracy rate of our method.
Implementation and Effectiveness Evaluation of Four Common Algorithms of Recommendation Systems - User Collaboration Filter, Item-based Collaborative Filtering, Matrix Factorization and Neural Collaborative Filtering

Recommendation systems are widely used in various industries and are seen as one of the effective methods in reducing information overload. This paper selected four common algorithms for implementation and evaluation among many recommendation algorithms. Two traditional collaborative filtering algorithms - User Collaboration Filter and Item-based Collaborative Filtering; most popular algorithms in the recommendation field - Matrix Factorization; and Neural Collaborative Filtering - the algorithm based on Matrix Factorization and combined with neural networks.
2D Robots Navigation System with Narrow Gap Problems

The path planning and trajectory planning of robots is a hot topic in the field of robot automation planning. This work is intended to find the optimal or near-optimal path for a team of robots with different widths to travel from start point to end point in a given map with path width information. To address this problem, a 2D robot navigation system is introduced to help the robots figure out the optimal path to the destination considering path widths, then guide the team or robots pass through the planned path as fast as possible. The system assumes there are a completely known map and team of robot with width information. In this system, A* algorithm is extended with width limitation to do the path planning, and combine with bin-packing algorithm to do the trajectory plan.
A Pedestrian Detection Method Based on Improved YOLOv5s

Aiming at the lack of accuracy and effect in some scenarios when the YOLOv5s algorithm is applied to pedestrian detection tasks, we designed the InECA_YOLOv5s algorithm to improve the accuracy of the original algorithm and enhance the recognition effect of the algorithm in some special scenarios. First, a small object detection head is added to the original model, and the implementation of some activation functions and convolution units is modified; then, an efficient channel attention module is inserted at the appropriate position in the model; Finally, the calculation strategy of the intersection and union ratio of the network in the training phase is adjusted. Experiments on the self-made multi-scene pedestrian detection dataset show that the improved algorithm effectively improves the accuracy.
The Elaboration and Further Improvements of PageRank

PageRank is a perfect solution that can rank pages in a mathematical based way. This paper introduces some important linear algebra concepts and graph theory, which are the basic of PageRank algorithm, elaborates the PageRank algorithm deeply based on these concepts, and show how PageRank algorithm ranking the web pages according to the hyperlinks pointing at them. Then it uses the method proposed by Taher H. Haveliwala, which focusing on improving the PageRank algorithm by using a set of PageRank vectors, which can prevent the problems of flawed topics in some degrees. However, this method may affect by worthless web pages, so this paper focuses on proposing a method that include two factors of time through users’ view point that could prevent spam web pages. At last, this paper conducts an experiment to show that this new model could add accuracy on searching results, and has practical utilities.
Improving Soil Texture Digital Mapping Using Landsat 8 Satellite Imageries in Calcareous Soils of Southern Iran

Improving Soil Texture Digital Mapping Using Landsat 8 Satellite Imageries in Calcareous Soils of Southern Iran
Variations in Alternative Pattern of Light Verb Construction Between Taiwan and Mainland Mandarin

In this study, we examine the variations in the alternative pattern of light verb construction between Taiwan and Mainland Mandarin, based on a large-scale comparable corpora statistical approach. The results show that these two variants display significant differences in preference for introducing the theme of the taken complement of the light verb, and Taiwan light verb significantly favors two specific alternations: e.g., 进行对可行性研究 jinxing dui kexingxing yanjiu, 进行研究可行性 jinxing yanjiu kexingxing ‘to conduct research on practicability’. Moreover, we have also shown that some linguistic factors influence the alternations’ choices: e.g., the complexity of the theme. Based on the observations, we further argue that the specific alternations in Taiwan indicate the different syntactic types of light verb complements in the two language variations.
The Semantic Roles of Chinese Verbs of Saluting

The human body is the basis of a human’s perception of the world. Verbs describing body actions are ubiquitous in human languages. Among different kinds of verbs involving the body, verbs of saluting express respect to others through saluting actions. This study has screened out single sentences from Chinese corpora and carried out semantic role annotation based on dependency grammar. The study found that more than half of the sentences are used with agent-like semantic roles; the most frequently used agent-like semantic role of these verbs is the Agent, which shows the originator of the action; the primary patient-like semantic role is the Dative, which shows the receiver of saluting actions in these sentences. The most frequently used situational role is the Manner, which indicates how the movement occurs. This study has investigated the semantic roles of verbs of saluting, which provides reference for the study of verbal semantics and the compilation of dictionaries.
A Study on the Recovery of Omitted Constituents in Chinese Elliptical Sentences

Rule-based processing of an elliptical sentence corpus and the construction of a dataset is the basis for various elliptical recovery tasks. We clarify the scope of Chinese elliptical sentences from a linguistic point of view, manually annotate the existing elliptical corpus, and construct a dataset for elliptical recovery. Moreover, we address the problems of different criteria for judging ellipsis and different degrees of fineness in recovering elliptical components in the manual annotation process and study the criteria for recovering elliptical components of Chinese elliptical sentences based on the needs of elliptical recovery tasks and the operability of computers. The linguistic explanation provides a reference for the dataset’s expansion in the subsequent tasks of omission position detection and omission-referent disambiguation.
Speckle Interferometry Applied to the Detection of Defects in Stiffened Panel

In accordance with the Damage Tolerance (DT) approach, the structural components of the aircraft must be periodically maintained to improve the durability of airframes. This approach is applied to anticipated loads in the presence of fatigue, corrosion, or accidental damage until detection via inspections. To prevent the catastrophic growth of the damage, it is necessary to proceed with the replacement or repair of the piece itself. Due to the complex damage growth in composites, the DT approach is not as well-established as on metals. Furthermore, in the presence of a crack, its propagation is rapid and the scheduled inspections do not always cover the time from the crack creation to a critical damage size that is dangerous for the structural safety of the aircraft. In this work, it is proposed a non-destructive interferometric investigation technique to identify artificial defects in load-bearing structural parts in non-contact mode. Results have shown that the speckle technique was able to identify the Kapton sheet and other geometrical discontinuities in the structure.
Are Girls More Anxious Than Boys? The Analysis of Writing Anxiety on Writing Achievement

It has been said that writing is a difficult skill to acquire because it involves not just language but also personality traits (age) and emotional aspects (writing anxiety).Therefore, the purpose of this study is to compare writing performance and writing anxiety across gender.This study also examined the correlation patterns between age, writing anxiety, and writing achievement.Factors that affect most on writing achievement most are also highlighted.As many as 83 EFL students participated in the current study.They were enrolled in an essay writing course.An independent sample t-test, Pearson-product moment, and ANCOVA analysis were employed to answer the research questions.The results demonstrated a considerable gap between males' and females' writing abilities.Despite the two groups' high anxiety levels, there is no significant difference in their writing anxiety.The results also revealed that writing performance among students is substantially connected with gender but not writing anxiety.The third finding showed that gender significantly impacts students' writing achievement more than anxiety.The implications of this study lay in the need for teachers to find suitable instructions for both male and female students and create non-threatening classrooms to optimize the students' writing performance.
Extracting Concepts and Semantic Associates for Teaching Tang 300 Poems to L2 Learners

This research aims to explore the possibility of extracting language concepts and semantic associates through the diachronic construction of cross-linguistic knowledge resources based on the framework of an existing fine-grained ontological system developed by the Sinica BOW Tang 300 poems project. The authors attempt to provide language educators with a different perspective of instructional design by understanding and meeting L2 learners’ needs through the in-depth analysis of massive bilingual language data. It is hoped that synergized linguistic knowledge net(s) could better facilitate L2 Chinese language teaching and learning.
A pedagógusok önbevalláson alapuló digitális kompetenciaszintjének vizsgálata egy székelyföldi megyében a Covid-19 idején

"In our study, we are interested in the role of digital technologies in education and its inequality-forming/reducing effect, with a special focus on the social gap dimension of the phenomenon. In our study, we present the partial results of an empirical research among teachers in one of the counties of Szeklerland (Covasna), in which we compared the level of digital competence of teachers based on self-reflected data with social background variables such as gender, age, number of years in the profession, professional background, etc. The sample included teachers who had attended teacher training courses (N=312). The survey was conducted during COVID-19. Our results show that there are significant differences between users (teachers). Demographic indicators of teachers such as gender, age, type of workplace, location, etc. can be used to differentiate digital inequalities at the societal level. Keywords: teacher, COVID-19, social divide, digital divide, DigComp 2.1, DigCompEdu"
Medical Image Security Using RIW for Grayscale and Color Images

In recent years, the application of reversible image watermarking (RIW) in the field of medical science has significantly increased. In order to ensure medical image security, reversible or lossless watermarking technique is very useful, as it enables the user to embed secret data (or payload), imperceptible to the naked eye, into the host or cover image while the host information is kept intact. As the term “reversible” suggests, it is possible to extract the embedded payload from the image with the watermark to retrieve host image. This becomes crucial for several applications including medical ones. This paper presents a new efficient reversible watermarking scheme that utilizes prediction-error expansion algorithm with the use of location map that has been implemented and tested on grayscale standard as well as special medical images. Further, a novel prediction-error expansion-based reversible watermarking technique with location map has been proposed for RGB color images. Experimental results have been shown on standard and special medical test images. Relevant plots are shown for grayscale as well as RGB color images to assess how well the algorithm performs which demonstrate promising results.
Continuous Speech Recognition in Hindi for Health Care Using Deep Learning

Speech is the most natural, convenient, and effective way of communication among human beings. An automatic speech recognition (ASR) system is an effective way for converting speech signals into text. This paper proposes a continuous speech recognition system for the healthcare domain in the Hindi language. Although ample effort has been taken to develop general-domain ASR systems in Hindi, we could not find any ASR system in the healthcare domain. The system is built using convolutional neural network (CNN), bidirectional gated recurrent units (Bi-GRU) using connectionist temporal classification (CTC). We prepare a healthcare domain speech corpus for the task. However, as the training corpus was insufficient, we employed another openly available general-domain corpus and the domain data. A combined corpus has been used to train and evaluate the system. The system is then evaluated using character error rate (CER) and word error rate (WER), and the performance is promising. We achieved a CER of 0.1285 in the developed system.
Attribute-Based Personal Health Record Protection Algorithm for Cloud-Based Healthcare Services

Today, personalized health records (PHR) have been created as a forum for the processing of patient data. The practical implementation of PHR in cloud-based technologies raises the security and privacy problems that must be addressed. Even then, such as personal health records, there has been large security implications. This could be created for remote server and considered as cloud storage that is not authorized. The huge percentage of PHR customers and data owners could have a high computation and administrative workload on the resources throughout the methodology; this will limit the functionality and availability of PHR records. The attribute-based patient records security (AHRP) method is used to provide security, integrity, and privacy of data access control in order to create a better resolution to above problems. It offers user access to ensure data security and a privileged feature to authenticate a document before discovering the person’s medical details. The encode and decode times are used to assess the system’s performance. The result clearly shows that the proposed approach requires less encode and decode time for the appropriate medical data.
Deep SqueezeNet-Based Diagnosis of the Breast Cancer Using Ultrasound (US) Images

Breast cancer affects millions of women. It is the most common cause of death in the world. Therefore, the detection and diagnosis of breast cancer is very necessary. The conventional method for diagnosis of breast cancer relies on manual analysis and interpretation by radiologists. However, this process is slow and the success of the diagnosis is proportional to the radiologist’s capabilities and experiences. Thus there is a need for a system that can perform a successful automatic diagnosis. Over the last few years, extensive research has been done on deep learning (DL)-based diagnostic processes of breast cancer. In this work, we fine tune a pre-trained deep convolutional neural network (SqueezeNet) for breast cancer detection from ultrasound (US) images. The experimental results reveal that the proposed work achieves more than 98% accuracy.
Development of a Program Code Review System Using Machine Learning Methods

The paper presents a description of the developed approach and service for analyzing source code in Python. The service reduces the time for code review due to partial automation. The FastText algorithm is used to obtain vector representations of source code texts. A pre-trained neural network language model based on the transformer architecture was used to derive a possible natural language function assignment. A classifier based on the gradient boosting algorithm was used to detect duplicate PR. The developed service checks the changeset and publishes error and duplicate reports in changeset comment format after the changeset is published to a remote Git repository. The conducted testing did not reveal any errors that affect the operation. All the main functions of the system are performed correctly.
Russian Sign Language Recognition Using MediaPipe

Sign Language Recognition is a wonderful research area with promising outcomes, and the availability of vast resources related to this field. For researchers, on the one hand, it gives an opportunity to help hearing-impaired people. On the other hand, it allows the scholars to explore the vast area of machine learning. This article focuses on the Russian sign language recognition system using MediaPipe and the dynamic time warping algorithm. The process includes understanding the basics of Russian sign language (RSL), development of a video dataset for RSL, MediaPipe Detection, implementation of sign models, sign prediction using dynamic time warping as a similarity metric between signs, analyzing the results, and so on. The video datasets of Russian Sign Language that have been used for implementing two MediaPipe models are original, performed by professionals, and reviewed by Russian Sign Language experts.
Development of Software for Managing Treadmills Based on Computer Vision

The article discusses the development of software for managing treadmills. As one of the ways for solving this problem, it is proposed to use computer vision technologies and an external camera that analyzes the entire workspace and the position of a person on a treadmill. This allows researchers to determine its position and adjust its speed in accordance with a given control system. To solve this problem, an analysis of approaches to determining the position of a person using computer vision technologies has been carried out. A research task is set and an algorithm for its solution is proposed. Based on the theoretical results obtained, an experiment was conducted using the developed software for managing treadmills, functioning on the basis of computer vision technologies. The results obtained can be used in the implementation of adaptive treadmills, the analysis of the mechanics of human movement, as well as during musculoskeletal rehabilitation to study the correctness of the trajectories of the patient’s movements.
An Enhanced Item Recommendation Approach Using the Sigmoid Function and Jaccard Similarity Coefficient

Based on prior interactions between users and service items, recommendation systems have developed into practical tools for filtering and obtaining vital data. These systems are often used in a range of commercial industries, including e-commerce, tourism, social networking, and academic research. Collaborative filtering, content-based filtering, and hybrid recommender systems are the three main categories of recommender systems. Collaborative filtering recommender systems, which presume that users would be interested in products that users similar to them have highly rated, take into consideration users' tastes (in terms of item preferences). Content-based filtering recommender systems base their recommendations on the textual content of a product, using the assumption that customers would prefer items that are similar to those they have previously enjoyed. In a hybrid recommender system, two techniques are combined. These systems struggle with scalability, data sparsity, and cold starts, which leads to low-accuracy prediction and coverage. In this study, we proposed a unique recommendation method and applied the sigmoid function to the Jaccard similarity index. In our proposed method, which included the rating significance of items, we used the sigmoid function on the Jaccard similarity index to evaluate the asymmetry relationship between users. The similarity between the target user and his or her neighbours is then assessed using the asymmetric association and rating significance. Several cuttingedge similarity metrics are evaluated using experiments on three real-world data sets. The results show that the new similarity model performs better than the baseline models in terms of diversity and prediction accuracy.