Reviewerly/exHarmony huggingface dataset:
Primary Key Encryption Using Hill Cipher Chain (Case Study: STIE Mandala PMB Site)

The condition of the world experiencing the COVID-19 pandemic has resulted in some daily activities limited by health protocols.The Indonesian government's policy in the academic field has forced STIE Mandala Jember, as one of the private universities, to implement online-based new student admissions.The registrant's identity must be kept confidential during the online-based new student registration process, so the running system needs an identity coding (encryption) process.Hill Cipher is a cryptographic algorithm that utilizes the multiplication of matrix inverse operations.In ordinary Hill Cipher, to encrypt the entire plaintext, a key is needed.In this paper, we will modify the Hill Cipher named Hill Cipher Chain.The process of chained Hill Cipher will encrypt and decrypt the primary key based on the previous character.Firstly, we will encrypt the primary key with the key from the next column of the database.In this case, we use the name column.The encryption result will be used as a key for the subsequent character encryption.Implementation of this method makes the difficulty level of decryption more complex than the ordinary Hill Cipher, which only uses one key for all so that the security of the data obtained is also getting better.
Research on detection methods of high beam in night driving vehicles

In order to solve the problem that it is difficult to control the abuse of high beam at night, a high beam detection algorithm YOLO-CAU based on YOLOv4 is designed and implemented. Then, aiming at the problem that it is difficult deployed on devices with limited computing power with a complex neural network, we apply the model compression algorithm of knowledge distillation to the image object detection task, and conducts distillation training on the YOLOCAU model with the high beam data set, transferring the knowledge learned by YOLO-CAU to a simple network (YOLO-CAU-S) with less computation and faster detection speed. Compared with the existing knowledge distillation algorithm, the algorithm designed in this paper not only restricts the output of the student network to be close to the output of the teacher, but also proposes a multi-scale pixel attention module to construct the attention feature maps of the teacher model and student model, which further transfers the strong attention of the teacher network to the student. The experimental results show that the knowledge distillation algorithm designed in this paper can greatly improve the detection efficiency of the model on the premise of ensuring the detection accuracy, and the compressed model parameters are only 6% of the teacher network.
Amplified: A Design History of the Electric Guitar

"Amplified: A Design History of the Electric Guitar." Music Reference Services Quarterly, ahead-of-print(ahead-of-print), pp. 1–2
The factors affecting reading habits of primary school students in ethnic minority

This article aims to understand the factors that promote and hinder the formation of reading habits of primary school students in ethnic minority in Yen Bai and Quang Nam provinces. The results of this study will be used to make the necessary changes to encourage children's reading in order to achieve the goal of supporting children in primary school. The study collected detailed data on reading habits, and the factors affecting the formation and maintenance of habits of primary school students. Methods of Focus Group Discussion, In-depth Interview, and Personal Interview (using a questionnaire) were used with the study's beneficiaries (students, teachers, parents). This article presents findings from a study on factors affecting reading habits of.
Object detection based on SURF feature matching and Hough transform

During the inspection process on the electric power underground pipe gallery, the intelligent inspection robot has the problems of low positioning ac-curacy and poor real-time performance, due to the influence of the surrounding complex environment and network signal shielding in the underground pipe gallery. Based on the prior information of the environment in the pipe gallery, the accurate positioning of the inspection robot can be achieved by identifying and locating the related targets in the pipe gallery and geometric constraints. Among them, the target recognition and positioning in the pipe gallery is a key technology to complete the accurate positioning of the intelligent inspection robot. In this paper, a method based on SURF feature points matching and Hough Transform is proposed. By extracting and matching the feature points of the densitometer on the pipeline, the accurate positioning of the densitometer in the camera coordinate system of the intelligent inspection robot is completed. The experimental results show that the algorithm proposed in this paper has high positioning accuracy and fast speed, which can provide important information for the accurate positioning of the intelligent inspection robot based on the geometric features of the densitometer on pipeline.
Which Pattern for a Low Pattern-Induced Bias?

The first objective of this presentation is to show that it is possible to explain the cause of the pattern-induced bias (PIB) observed in displacement fields obtained by local DIC. A model is presented for this purpose. It gathers the different errors made when retrieving this displacement by minimizing the optical residual over subsets. It is shown that PIB predicted with this model and its counterpart observed with displacement fields obtained with DIC are in good agreement. When DIC is applied on periodic patterns like checkerboards instead of random speckles, it is observed that PIB becomes negligible. Such regular patterns are however not well suited for DIC. Hence it is recalled how to process such images by minimizing the optical residual in the Fourier domain instead of the spatial one. PIB is assessed in this case, and it is also observed that PIB is negligible in displacement maps obtained with such regular patterns processed by minimizing the optical residual in the Fourier domain.
Low Complexity and High Speed Montgomery Multiplication Based on FFT

The particular augmentation activity is the most tedious activity for number-hypothetical cryptographic calculations in RSA and Diffie-Hellman. There are quick multiplier structures to limit deferral and increment the throughput utilizing parallelism and pipelining. Such structures are gigantic with limited efficiency. In this endeavor, we incorporate the improved fast Fourier transform (FFT) technique into the McLaughlin’s system and Montgomery calculation to accomplish high zone time efficiency. Contrasted with the past FFT-based structures, we hinder the zero-cushioning activity by processing the particular increase steps straightforwardly utilizing cyclic and nega-cyclic convolutions. Moreover, upheld by the number-hypothetical weighted change, the FFT calculation is utilized to give quick convolution calculation. The outcomes illustrate that our work has a superior efficiency with best in class FFT-based MMM representations from or more 1024-piece operand dimensions.
A Corpus Based Quantitative Analysis of Gurmukhi Script

The present study deals with the syntactic aspects of the Gurmukhi script by applying several standard statistical measures. The analysis is performed on the text written in seven distinct genres, which amount to &gt;6 million words and &gt;440 thousand sentences. The assessment of the textual data is performed at two syntactic levels—words and sentences. Revelations are made using statistical techniques on parameters such as word length, character frequency, vowel usage, word frequency, word length frequency, type token ratio (TTR), characters usage in a sentence, words usage in a sentence, words usage after the removal of stop-words in the sentence, characters usage after the removal of stop-words in the sentence, and correlation. This manuscript reveals the hidden facts of the Gurmukhi script and lays the groundwork for future research in the quantitative linguistics research.
Unmasking the Masked Face Using Zero-Shot Learning

Nowadays, people are required to wear masks due to the COVID-19 pandemic. The COVID-19 is an ongoing crisis that has resulted in a large number of fatalities and safety concerns. People also carry masks to cover themselves to effectively prevent the transmission of this virus. In this situation recognizing a face is very challenging. In certain cases, like facial attendance, face access control, facial security, this makes traditional facial recognition technology ineffective, for that urgent requirement to improve this recognition performance and use the technology on the masked face. During the current pandemic, the main objective of researchers is to deal with these problems through quick and accurate approaches. Throughout this report, suggest a clear way centred on removing masked areas and deep learning related techniques to resolve the issues of mask detection. Another way of finding the masked face is to go through TensorFlow, YOLOv5, SSDMNV2, SVM, OpenCV, Keras. Deep Learning of artificial intelligence (AI) is an exciting future technology with explosive growth. Masked face recognition is a mesmerizing topic that contains several AI technologies including classifications, SSD object detection, MTCNN, FaceNet, data preparation, data cleaning, data augmentation, training skills, etc. Takes two datasets, CASIA-Web face datasets used for training purpose and LFW is used for testing purpose. Here, face alignment has been done by SSD and MTCNN. After face alignment, deleting the misleading image. Then wear a face mask in the image by using Dlib. From Dlib get the facial landmark. So take the mouth part for the face mask. Then detect the face mask. In green box shows that the person is wearing the mask. In red it shows that person is not wearing the mask. After detecting the face, also have to recognize the person in the mask. In training, it gives 99% of accuracy and in testing, it gives 96% of accuracy.
Digitalization in Pharmaceutical Industry: A Recent Perspectives

The digitalization of any manufacturing industry is a critical step in the advancement of the manufacturing process. The digitalization process includes increased use of robotics, automation solutions, and computerization, allowing for cost savings, increased efficiency and productivity, and greater adaptability to change. Pharmaceutical Industry (PI) has however been resistant to digitalization, mainly due to fair experience and complexity of the entailed development and manufacture processes. Nevertheless, there is a clear need to digitalize PI as the de- mand in both traditional and new drugs is constantly growing. Contract Development Manufacture Organizations (CDMOs) have a special digitalizing challenge. Digitalization of PI, and CDMO precisely, should be tightly related to the main aspects of Good Manufacture Practice (GMP), and, to succeed in PI digitalizing requires constant focus on GMP. Close collaboration with constantly changing stakeholders is another important factor which should be in focus during digitalization of CDMO. This paper represents an overview over the main aspects of CDMO digitalization and discusses both the opportunities and challenges of the process, focusing on the practical solutions for successive digital implementation.
Measuring Developer Productivity and the New Future of Work

Developer productivity is about more than an individual’s activity levels or the efficiency of the engineering systems, and it cannot be measured by a single metric or dimension. In this talk I will discuss how to use the SPACE framework to measuring developer productivity across multiple dimensions to better understand productivity in practice. I will also discuss common myths around developer productivity, and propose a collection of sample metrics to navigate around those pitfalls. Measuring developer productivity at Microsoft has allowed us to build new insights about what challenges the shift to remote work has introduced for software engineers, and how to overcome many of those challenges moving forward into a new future of work.
Generation of Facial Drawings Using Generative Adversarial Networks

Facial sketches are widely used in judicial and legal proceedings. Law enforcers use facial sketches to help them with the visual aspects of the case, using witness descriptions and video footage. However, drawing forensic sketches by hand is a time-consuming procedure and a situation may arise where the authorities have less time in hand to solve a case. The present research work aims to create a basic model which can generate facial images from a given set of input features; similar to what a forensic artist does, thus, enabling a faster and efficient sketching procedure. In this work, a category of generative algorithms, called Generative Adversarial Networks has been used to build this model. To train this model, a dataset of anime girls has been used and thus it can only generate the same, making sure that the generated image contains the input features.
An Adaptive Algorithm for Emotion Quotient Extraction of Viral Information Over Twitter Data

In social media platforms, a viral information or trending term draws attention, as it asserts the impact of user content towards topic/terms. In real-time sentiment analysis, these viral terms could deliver potential insights for the analysis and decision support. A traditional sentiment analysis tool generates the level of predefined sentiments over social media content for the defined duration and lacks in the extraction of emotional impact created by the same. In these settings, it is a multifaceted task to estimate precisely the emotional quotient viral information creates. A novel algorithm is proposed, to (i) extract the sentiment and emotions quotient of current viral information over twitter, (ii) compare co-occurring trending/viral information, (iii) in-depth analysis of potential Twitter text data. The generated emotion quotients and micro-sentiment reveals several valuable insight of a viral/trending topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also.
The Effects of Academic Self-Efficacy on College Life Satisfaction of Taekwondo University Students in Untact COVID-19

This study was conducted to investigate the relationship between academic self-efficacy and college life satisfaction of University students in Taekwondo department in the era of untact COVID-19. For this purpose, 362 students in department of Taekwondo and As a result of analysis using SPSS ver .25.0, first. As a result of examining the difference in the academic self-efficacy of students in Taekwondo department, it was found that there was a significant difference only in preference for low task difficulty for both gender and grade level. Second, there was a significant difference only in overall college life and learning satisfaction. Third, it was found that self-regulation and confidence factors had a positive effect on college education and administrative service factors, and low task difficulty factors had a negative effect. On the overall college life satisfaction factors, only the low task difficulty factors had a negative effect It was found that only the self-regulation factor had a significant effect on learning satisfaction and school satisfaction.
Application for the Management of Sports Performance in CrossFit Supported by an Artificial Intelligence Cognitive Service

Nowadays, there is a massive concern with resource management and monitoring the status and evolution of performance over time. The same is true in the domain of health, namely in the sporting aspect, once it is important to have tools that enable the analysis and measurement of the evolution of the physical condition over time. Considering specifically the market of management and performance monitoring platforms in CrossFit, their contents do not reveal unique and innovative features that improve the sporting experience of those who use them. Thus, the main goal of this project was the development of a digital platform (application) that would allow the control, analysis, and recording of sports progress over time. To this end, OutSystems’s low-code development platform was used to build an application that met the expectations of gym owners, coaches, members, and athletes. This new solution stands out from the rest of the competition, as a new way of registering daily workouts was implemented, using artificial intelligence to extract handwritten text. In user acceptance tests, it was possible to verify that the app proved to be a solid and helpful option in improving CrossFit's sporting experience and analysing and monitoring the performance over time.
Tracking transients in steelpan strikes using surveillance technology

This paper presents advancements in tracking features in high-speed videos of Caribbean steelpans illuminated by electronic speckle pattern interferometry, made possible by incorporating robust computer vision libraries for object detection and image segmentation, and cleaning of the training dataset. Besides increasing the accuracy of fringe counts by 10% or more compared to previous work, this paper introduces a segmentation-regression map for the entire drum surface yielding interference fringe counts comparable to those obtained via object detection. Once trained, this model can count fringes for musical instruments not part of the training set, including those with non-elliptical antinode shapes.
Designing Hybrid Recommender Systems

The purpose of the study is to analyze the existing methods of building hybrid recommender systems and develop the most accurate and adequate movie recommender system of that type. The paper addresses basic algorithms for designing recommender systems (content-based, collaborative filtering, hybrids), and corresponding similarity metrics (cosine distance, Pearson correlation coefficient). The interest in hybrid systems is due to the fact that combining different modelling methods can improve not only the accuracy of rating predictions but also other characteristics of a recommender system, such as novelty, serendipity, diversity. In the course of the study, three approaches were used: content-based, collaborative and hybrid ones. As a result, two best models were chosen based on the first two approaches, with reference to which a hybrid model was built, whose quality was evaluated through a survey of potential users of the system. This made it possible to cover a whole range of evaluation criteria for recommender systems and understand corresponding user requirements. Trust to the system turned out to be the most important one. In other words, it means the extent to which the user is confident that he or she will actually like the recommendations suggested. Recommender systems were tested using open source data of the MovieLens dataset. This makes it possible to verify the results and improve the models suggested.
Management: Using intuition as a brain skill

According to a number of futurists, we are approaching an era of upheaval in which the economic and political environment will be marked by fast change, crises, and significant structural dislocations. Technological progress is likewise anticipated to be enormous. If these predictions are close to being accurate, future managers are likely to be confronted with the reality of having to make important choices under very challenging conditions on a regular basis. To begin with, tomorrow's top executives will have to deal with very complicated management issues. Second, they will often be forced to deal with these issues in situations where full data bases required for “left brain” (analytical, deductive) processing are either unavailable or too expensive to collect in a timely manner. Third, in order to meet their employees’ growing expectations for a larger involvement in the decision-making process, top managers will need to make choices in a “high touch” way under these circumstances.
Research on Railway Geological Hazard Detection Method Based on Few-Shot Deep Learning

The frequent occurrence of geological disasters has brought great challenges to the operation safety of railways. Therefore, it is necessary to detect and respond to geological disasters in a real-time method to improve the safety and stability of the railway system. In railway disasters, fewer samples can be obtained, which affects the efficiency of various deep learning algorithms. Therefore, in order to improve the accuracy of corresponding deep learning algorithms in disaster detection, this paper studies to apply few-shot learning method to detect railway hazard. We first expand the railway geological disaster data set based on a variety of image transformation methods. Based on the lightweight mobilenet-yolov4-lite model, we train the model in the augmented dataset through transfer learning to achieve autonomously and quickly multiple types of geological hazards detection. The experiments on the original data set and the augmented data set show that the trained algorithm can improve the detection accuracy by 4% on average under the same number of training iterations after data augmentation, which verifies that the method proposed in this paper can solve the problem of autonomous detection of railway geological disasters.
Machine learning algorithm for classification of breast ultrasound images

Breast cancer is the most common type of cancer globally. Early detection is important for reducing the morbidity and mortality of breast cancer. The aim of this study was to evaluate the performance of different machine learning models to classify malignant or benign lesions on breast ultrasound images. Three different convolutional neural network approaches were implemented: (a) Simple convolutional neural network, (b) transfer learning using pre-trained InceptionV3, ResNet50V2, VGG19 and Xception and (c) deep feature networks based on combinations of the four transfer networks in (b). The data consisted of two breast ultrasound image data sets: (1) an open, single-vendor, data set collected by Cairo University at Baheya Hospital, Egypt, consisting of 437 benign lesions and 210 malignant lesions, where 10% was set to be a test set and the rest was used for training and validation (development) and (2) An in-house, multi-vendor data set collected at Unilabs Mammography Unit, Skåne University Hospital, Sweden, consisting of 13 benign lesions and 265 malignant lesions, was used as an external test set. Both test sets were used for evaluating the networks. The performance measures used were area under the receiver operating characteristic curve (AUC), sensitivity, specificity and weighted accuracy. Holdout, i.e. the splitting of the development data into training and validation data sets just once, was used to find a model with as good performance as possible. 10-fold cross-validation was also performed to provide uncertainty estimates. For the transfer networks which were obtained with holdout, Gradient-weighted Class Activation Mapping was used to generate heat maps indicating which part of the image contributed to the network’s decision. For 10-fold cross-validation it was possible to achieve a mean AUC of 92% and mean sensitivity of 95% for the transfer network based on Xception when testing on the first data set. When testing on the second data set it was possible to obtain a mean AUC of 75% and mean sensitivity of 86% for the combination of ResNet50V2 and Xception.