Reviewerly/exHarmony huggingface dataset:
Psycho-pedagogical terms of the valued attitude to yourself

The article attempts to analyze the phenomenon of the value of the relationship to itself, its structure, main forms of expression, moreover, considers the approaches of the scientists to the definition of the essence of the concepts of "personal relationship" and "value attitude to yourself."
Multi Rule-based and Corpus-based for Sundanese Stemmer

The purpose of this study is to develop a stemming method by involved several methods including morphological (with affix and pro-lexeme removal), syllable (canonical) pattern, and corpus data as a comparison of the final results of stemming. The algorithm checks a number of the string first and removes affixes, then check the syllable pattern according to the stripping result, then compares to the corpus data which determines the final stemming process. In this study, the corpus data was taken from Sundanese dictionary consists of a single word used for the root word and the extracted dataset from the online Sundanese magazine. The results showed that the stripping of affix and pro-lexeme can remove the corresponding affixes and pro-lexeme then compares words that have a syllable pattern then executes the basic words quickly and the use of corpus can improve accuracy and reduce the over-stemming problems that occur in the stemming process.
Philomath: Intelligent Tutoring System for Mathematics

Mathematics is all around us and helps us understand real world problems and to figure out their solutions. Mathematics makes life orderly and prevents chaos. Many students find fractions and algebra to be challenging and perplexing, yet as students get older, they will need to be able to understand these concepts. Fractions are significant since they allow one to express how much of an entire, they would like. Algebra is the foundation of other subjects like geometry, trigonometry, calculus, chemistry, etc. Students find algebra easy in the beginning but as equations get more complicated, they start struggling in solving questions. Thus, it is very important to have a strong foundation in these basic mathematical concepts. There are a few commercial tools available to assist students in learning mathematics but most of them are paid or do not provide sufficient practice. Keeping in mind all these aspects, it was decided to develop an intelligent tutor, which will help users in practicing mathematics. If the user requests it, the tutor will also give hints while working on the tasks, as well as feedback when an attempt is unsuccessful.
A Comparative Survey of Multimodal Multilabel Sentiment Analysis and Its Applications Initiated Due to the Impact of COVID-19

This study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis.
Enhanced RSA Cryptosystem: A Secure and Nimble Approach

With the rise in Internet and Network applications in the last decade, there is a need for making communication over the internet to be more secure and strong in order to avoid cyber-attacks. The use of digital signatures has been widely used and there is a need to strengthen the existing public key algorithms. RSA is among the most common public key algorithms but is prone to security risks and attacks due to the advancing computing technology. This paper suggests a new algorithm based on RSA which increases the randomness and diffusion of the RSA algorithm making it more secure from cyber-attacks than RSA. The suggested approach employs four prime integers to generate two sets of public and private keys and involves double encryption and decryption. This modified form of the RSA method adds to the complexity of the encryption process while simultaneously decreasing the decryption time by leveraging the Chinese Remainder theorem.
Deep Learning Approach for Mammographic Breast Density Classification and Cancer Risk Prediction

Breast cancer is one among the various vulnerable types of cancer, after skin cancer and lung cancer. Although deaths from breast cancer have decreased over the years, it is still the major leading causes of women deaths of all races. Many research efforts have been taken to prevent breast cancer by using different breast cancer biomarkers in the last few decades. “Mammographic Breast Density” is amongst various significant markers utilized for the prevention of breast cancer. As there is an increase in the “Mammographic Breast Density”, the mammograms sensitivity also decreases causing wrong prediction of breast cancer. The primal motive behind this article is to study all the research innovations for Mammographic Breast Density classification. This survey article covered all Deep Learning methods proposed for mammographic breast density classification. From 2010-2017 there is an inclination towards the Machine Learning approach, and from 2017 onwards, there is more research inclination towards the Deep Learning approach. Statistics of classification accuracy of Deep Learning is in between 86%-98.87%. Due to the variations, no methods were found so precise and accurate. Hence, current mammographic breast density assessment is subjective, thus raising the need to develop an accurate and accurate mammographic Breast Density classification tool suitable for clinical practice. Implementing a successful CAD system for breast density classification is a social need and can act as a supporting mechanism for the precise classification of mammograms. More research efforts are required in this area to reduce faulty predictions.
Privacy Preserving Document Classification using Convolution Neural Network- A Deep Learning Approach

Increased digitization in nearly every sector demands huge data storage requirements. Every person upload tons of information related to themselves on Internet through some mobile or web application, knowingly or sometimes unknowingly. Such increasing personal data storage requirement has created data privacy issues. There is no law which prohibits someone from using personal information of an individual. India is still in the process of preparing personal data protection law, whereas European Union's data protection regulation has already took place in the year 2018. Some organizations are in the process of developing applications which can check whether a document is personal or non-personal. Such applications can be developed with the help of deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Term Short Memory (LSTM), etc. This research focuses on different text representation techniques required to represent text in text classification problems such as private data classification, sentiment analysis, language detection, online abuse detection, recommendations systems, to name a few. Having represented text in different formats, helps in increasing accuracy of classification algorithms.
SIGN-UNITE ASL Detection using ML for Deaf-Mute People Communication

There have been several advancements in technology and a significant amount of research has been done to help the people who are deaf and dumb. Aiding the cause, Deep learning, and computer vision has been used for detection of ASL to make an impact on this cause. We used linear SVM Algorithm and MediaPipe Libraries in our system that translates gestures like alphabets which is available for total 16 datasets A-K and U, V, W, Y with 99.6 to 99 percent accuracy then numbers from 1–10 with 99.83 percent accuracy and then 5 sentences with 99.6 percent accuracy. We attained overall 99.6 percent accuracy for detection of alphabets, numbers and words.
Generating Datasets with Glyph-level Annotations for Devanagari Text Recognition

Most of the work done in text recognition focuses on English, more specifically, Latin script languages. Unlike Latin, Indic scripts like Devanagari, Gujarati, Malayalam, Tamil etc. are a family of abugida writing systems. In these scripts each unit is made up of a consonant and an optional vowel notion. This makes the separation of different characters a non-trivial task as each visible letter may represent more than one character. Letters derived from the same base consonant can be visually similar to each other and make distinguishing between them quite difficult. This paper focuses on Devanagari script and a technique to generate synthetic text recognition datasets with rich glyph-level annotations.
Donor Analytics and Data Platform for Indian NGOs: A Digitization Approach

There are several non-profits and non-governmental organizations (NGOs) in India that support a wide range of social causes. Some NGOs are large in size and outreach, whereas the majority are small in size and outreach. However, one thing that all NGOs have in common is the inflow of financial donations from well-wishers and donors. This project offers a simple open-source data platform for grass-roots NGOs with little resources to gather donations information, allowing them to move towards digitalization. This platform will assist them in correctly tracking their donations as well as categorizing them, allowing them to take better informed decisions in the foreseeable future utilizing the underlying data. On top of this data platform, the project also provides a donor analytics solution to help donors get started with analytics. It includes a MIS data dump as well as a thorough donor / donations exploratory data analysis dashboard. It then offers a more targeted segmentation and clustering solution, assisting the NGO in accurately categorizing their donations into distinct buckets. The research employs the well-known RFM Model for segmentation and the K-Means technique for grouping donors. This study divides the donor population into five categories based on the data utilized in this project: Loyal Donors, Big Donors, about to Lose Donors, Best Donors, and Lost Donors.
New Four-dimensional Chaos and Its Application in Color Image Encryption

For the sake of better removing dependence of color image pixel values, this paper designs a color image encryption algorithm based on a new four-dimensional chaos. Firstly, a new four-dimension chaos is built through feedback control, which has three equilibrium points. In comparison with the original, this system is of more complex topology and better randomness. Secondly, the image channel R, G, B are combined into one matrix. Finally, the encrypted image is the result of scrambling the matrix on the bit. The experiment shows that the algorithm applies to color image encryption because of its large key space, good key sensitivity, high encryption and decryption efficiency, and was suitable for color image encryption.
Research on Level Assessment of English Teaching Model Based on K-Means and Information Fusion

According to the large amount of data existing in the level assessment of the English teaching model, the models cannot be classified correctly. Thus, a model combining the K-means algorithm and data fusion is proposed to estimate the level of English lectures. First, the indicators under the constraints are modeled; Second, the regression model is applied for the information modeling, and the information parameter is used for the limited information; finally, information is fused that the technology is related to the algorithm technology to cluster and integrate various indicators of the level of English lectures. Based on the parameter, a lecture resource allocation plan suitable for English teaching is suggested. The test result shows that the model clusters the information fusion, makes teachers’ teaching ability assessment more accurate, and effectively utilizes teaching resources.
Design Scheme of Artificial Intelligent Training System for College English Writing

In college English writing teaching, using the intelligent college English writing training system as an auxiliary teaching tool can effectively support the teaching plan design of English teachers. Thus, the design scheme of the intelligent training system for college English writing is explored. In this context, this study first analyzed the construction objectives and requirements of the intelligent training system for college English writing and then articulated the design scheme of this system, including the overall architecture design, the knowledge point library design, the detailed design of the business function module, and the database design. The study result can enrich and extend classroom teaching, improve the work efficiency of the teachers, and enhance the student’s interest in English writing. Also, it effectively helps to integrate the traditional university English writing teaching method, the new modern teaching mode, and the intelligent technology, which is beneficial to the modern English teaching mode.
The Research on Intelligent Catering Distribution for High-speed Rail

Intelligence is an important direction for the future development of high-speed rail. As a part of high-speed rail system, catering distribution is still distributed by crew, which is time-consuming and laborious. This paper focuses on an intelligent catering distribution system based on intelligent vehicle. The system is based on raspberry pie ROS. In terms of hardware, binocular camera and lidar are used for tracking and obstacle avoidance, and mcnamu wheel is used for omni-directional movement. In terms of software, the navigation framework of global path planning and local real-time planning is adopted for path planning, Hector slam composition algorithm is used for environment map construction, and the closed-loop cascade control algorithm is used to control the car body. The system is committed to creating a more convenient and comfortable high-speed rail travel in the era of Internet of things. Moreover, its feasibility and application prospects are analyzed.
Research on PIR Technology Program

The PIR technology refers to the fact that the querying party can only get the characteristics of a query user ID, and the data-checked party does not know the user ID queried by the querying party. When the query target is the user feature, this paper is to improve the existing PIR implementation scheme and propose a high-performance PIR technical scheme. This scheme can also be called CFB-PIR. The protocol mainly adopts the consistent hash algorithm, the symmetric encryption of the national secret type, and the counting Bloom filter. Experiments show that when the query ID may be in the database, the running performance of the solution is only closely related to the preset user discrimination. It has little to do with the number of users in the database. Under the same experimental conditions set in the article, the article compares the performance of three PIR schemes and the CFB-PIR scheme. When the server database exceeds 100,000, the performance superiority of the CFB-PIR scheme is more obvious.
Application of Intelligent Sports Goods Based on Human-Computer Interaction Concept in Training Management

We researched the relevant literature on intelligent wearable technology in the Internet of Things era to comb out the development path of intelligent wearable technology. The types of intelligent wearable products were collected and sorted out in sports goods to analyze the important performance of related products and innovative technical means. As a result, the development focus of intelligent wearable technology is proposed for sensing accuracy, real-time and standardized information, and electricity acquisition, storage, and maintenance. Three dimensions of the application of intelligent wearable technology in sports are also proposed in the context of the Internet of Things. Finally, the development status of wearable technology is summarized to provide strategies for the development and application of intelligent wearable disciplines in the future.
Decentralized Data Protection System For Public Institutions

In companies internal communication, most staff will use penetration of high communication tools such as QQ, Wechat. Most of this kind of communication tools are based on AES, RSA, encryption process handled by the backend server, Such software can view the messages and files sent and transferred by users, but within the confidential file cannot allow the situation to exist. In view of the appeal, the data protection system, namely fine granularity permission management, is proposed. On this basis, an application tool integrating encryption decentralized real-time communication and data security storage is realized. This system is based on the Django framework to complete the construction of communication, storage function, this system is mainly divided into two modules, the first module is decentralized encryption communication module, the proposed system provides identity authentication function, user decentralized encryption real-time communication, to ensure the communication information is not outside the attacker or system developers to obtain; The second module is cloud storage service module, cloud storage technology as the core, for file transfer of decentralized communication and data storage services, and cloud storage in the database files will be combined with fine granularity rights management function, the user can undertake the level set to other users and one-way level set of file, In this way, you can control the range of files in the cloud storage database. Based on blockchain technology, blockchain will replace traditional third-party servers, so as to complete decentralized integrity authentication. In this system, encryption-related content will not be generated by the back-end server, and key information and other important information will not enter the database based on the idea of decentralization, while ciphertext transmission and encrypted ciphertext information storage are provided by the cloud storage server. Based on the idea of decentralization, this system can be completed under the circumstance that the data is not leaked when the cloud database is invaded. All the encryption algorithms of the system use the national secret algorithm to achieve decentralized secure real-time communication, integrity authentication and fine granularity permission management.
Functional Design and Model Development on Mobile Learning System of College English

Mobile learning is an emerging approach that can transcend geographical limitations by making full use of portable technology. With the development of information technology, media technology, and communication technology, various intelligent terminals have been applied in English mobile learning. However, how to combine mobile learning and college English organically has become one of the core issues of college English teaching. In this context, the functional design and model development of a college English mobile learning system are discussed. First, the basic concept of mobile learning is analyzed, and then presented the functional design technology and implementation scheme of the college English mobile learning system. Focusing on learners and intelligent terminal research, this study is carried out to investigate the relation between mobile learning and college English and establish a new type of college English learning mode, which is expected to enrich the research perspective of college English.
Application of Iterative CART Decision Tree Algorithm in Studying Influence of Early Education Curriculum on Children’s Attention Improvement

Early education curriculum plays an important role in improving children's attention. Different early education courses have different effects on improving children’s attention. We conducted a teaching experiment in an early childhood education institution in Beijing, China, asking children to choose different early childhood education curriculum packages and carry out learning. After the experiment, the children’s attention improvement level before and after learning the curriculum package was measured. The label of the significance of the attention improvement was given as the dependent variable of the study. Secondly, several features were extracted from the curriculum package as independent variables. Then, an iterative CART decision tree algorithm was proposed to construct a dichotomous model of significant attention improvement. The classification model was tested by 10-fold cross test. The test results showed that the accuracy was 84.5%, the recall rate was 90.0%, and the F1-score was 87.2%. The accuracy of the model was acceptable.
A Natural Speech Synthesis Method Based on Vocal Action Sequence Analysis

A natural speech synthesis method based on vocal action sequence is proposed to make the audiences to ignore the sense of machine synthesized speech and focus on the conveyed information. By signal modulation operations on source waveform according to vocal action sequence, breathing sound, muscle contraction and release and so on, are added to the synthesized speech. As a result, the naturalness is improved. In terms of legitimacy, incidence matrix between words and pronunciations are established to avoid the synthesis of sensitive text and homo-phonic voice. Finally, the effectiveness is verified by the speech synthesis of a special application case of course teaching. The tests result indicate that the proposed method of speech synthesis is effective.
Analysis of Chinese Calligraphy Fonts Using Content-based Image Retrieval

Chinese have a very special category due to individual skills and font variations and present rich and varied visual forms, “calligraphy”. Traditionally, optical character recognition (OCR) for Chinese characters has achieved good results. However, calligraphic characters have more diverse fonts and backgrounds, which are also needed to be further recognized to learn and copy. Therefore, we establish an image retrieval system for calligraphic characters to present different styles and fonts, allowing users to learn from retrieved similar images. The user inputs the query calligraphy image to retrieve from the system. Then, the system automatically finds the calligraphic images with similar styles and the closest characteristics from the database for the user to copy. We use a calligraphic database provided by United Digital Publications, which contains each calligraphic style such as seal script, clerical script, cursive script, running script, regular script, and a total of about2,500 scanned images of calligraphic characters. It also contains metadata indicating the dynasty, age, and author of each image. In the experiment, a convolution neural network is used to build a classification model and extract the features of each image in the target image database. While querying, the system automatically calculates the cosine similarity between the features of all images in the target image database and the features of the query image. The top ten images with the highest similarity are used as the retrieval results. As a result, mean average precision (mAP) is used to evaluate the retrieval performance of the system. The experimental results show that the mAP of this system is 0.91, which indicates that the system can retrieve the calligraphic images of a specific calligraphic style effectively.