Reviewerly/exHarmony huggingface dataset:
GPNet: Key Point Generation Auxiliary Network for Object Detection

Abstract For existing object detectors, anchor‐based detectors lack global information, while anchor‐free detectors based on key points lack prior position information. The above issues may lead to the imbalance of detection accuracy and shape robustness. In order to alleviate the above contradictions, a new auxiliary network key point generation network (GPNet) is proposed to improve the performance of existing object detectors. Specifically, a series of key points are generated through ground truth (GT) supervision to obtain more global information. These key points generate pseudo boxes (Pbox) with learnable parameters. Pbox has more specific prior information than the manually designed candidate boxes. The scale information of the Pbox is embed into the classification branch to obtain a more appropriate receptive field. In addition, a novel improved strategy for label assignment by combining Pbox and GT to enhance the ability to classify positive and negative samples is proposed. Extensive experiments on multiple dense prediction methods validate the effectiveness of GPNet, with a performance improvement of 1.5 AP over baseline. In particular, with ResNext‐101‐64× 4d‐DCN as the backbone, this method achieves 49.5 AP with single‐scale testing.
Automatic Detection of Indian Currency Denominations using Deep Learning

Identification of the denomination of the currency note to pay physically without UPI is the first step of paying to the seller by the consumer. In this project, we have proposed an approach to detect denominations of Indian currency using Convolutional Neural Networks. Computer Vision and object detection is an area of great interest for research in today’s world. It has several applications like detection of defects in machinery, intruder detection, computer vision for code and character recognition among many others. Through the work we have done, we explored something that could be of great help to people in day-to-day life. In this project we have tried to investigate the approaches to detect currency denominations using Convolutional Neural Networks. The objective is to build a model that would be able to detect Indian currency denominations efficiently. Typically the model will be useful for people with vision impairment. The experimental results show that the use of Convolutional Neural Networks is a good way and the model can further be improved if it is trained in such a way that it could also identify the regions of interest.
Novel Study on Localization in Scene Text Detection

Scene text detection has been one of the most important topics for research in computer vision. With constant development and rise in deep learning, computer vision technology has undergone an impactful transformation. In the era before deep learning, there existed algorithms and technologies for scene text detection, but the performance was mediocre. In recent years, deep learning technology has remarkably transformed scene text detection. Researchers have witnessed notable advancements in the approach, methodology, and overall performance of the newly discovered techniques. In this paper, the predominant focus is on summarizing and analysing the significant progress in scene text detection through deep learning. This paper covers an introduction to scene text detection, steps to perform scene text recognition and detection, technique before deep-learning, recent techniques and their insights, some results, and an overview by comparing the algorithms. We will also emphasize the criteria that make a search algorithm a good choice for performing scene text detection and recognition, the notable differences incorporated by deep learning, and analyse the drawbacks of the techniques used before deep learning. This paper would be helpful to understand the key differences that have changed this field and also some remaining challenges.
Online Article Recommender System

Recommender System recommends relevant items to users, based on user habits or preference. Preference does not have quantitative measure. It is subjective matter. Generally it indirectly measure by items that consumed by users in past. There is a plethora of text available on the web and there are many online platforms that provide text (article) for reading. This is an attempt to develop a Recommender System (RecSys) for the article suggestion for the online article reading to the end user by the online article service provider. RecSys will use collaborative learning, content-based learning and combination of both, i,e, hybrid learning for the recommendation process. The proposed RecSys is tested and trained on is one article sharing platform service and it has been found that the hybrid learning model performed better than other.
Sign Language Gesture to Speech Conversion Using Convolutional Neural Network

A genuine disability prevents a person from speaking. There are numerous ways for people with this condition to communicate with others, including sign language, which is one of the more widely used forms of communication. Human body language can be used to communicate with one another using sign language, where each word is represented by a specific sequence of gestures. The goal of the paper is to translate human sign language into speech that can interpret human gestures. Through a deep convolution neural network, we first construct the data-set, save the hand gestures in the database, and then use an appropriate model on these hand gesture visuals to test and train the system. When a user launches the application, it then detects the gestures that are saved inthe database and displays the corresponding results. By employing this system, it is possible to assist those who are hard of hearing while simultaneously making communication with them simpler for everyone else.
Analytics of Epidemiological Data using Machine Learning Models

Epidemiological data is the data obtained based on disease, injury or environmental hazard occurrence using the previous data on the epidemic situation. We can use it for analysis and find the trends and patterns. We can use different machine learning models to create a platform that can be used for different time series data. We can rely on the properties of time series data like trends and seasonality and use this for future prediction. Acquiring the dataset is the first step in data preprocessing in machine learning. We have collected the dataset from ourWorldIndia website which is a real-life dataset of covid-19. This paper presents the idea of a dedicated machine learning model to forecast the future using epidemiological data. We have taken a data-set of covid-19 for the prediction of the number of daily cases infected by the coronavirus. Our machine learning model can be applied on the dataset of any country in the world. We have applied it on the dataset of India in the experimentation. Our goal behind this research paper is to give the ML model which can be easily used on any epidemiological data for prediction by analysing the seasonality.
Classification of Handwritten Digits on the web using Deep Learning

Development of a handwriting classifier using deep learning approach that can classify handwritten numbers and digits on the web. It is a deep-learning based system that uses modern algorithms and focuses on creating a portable web application that aims to classify handwritten numbers powered by the MNIST dataset
Transformer based image caption generation for news articles ·

We address the task of news-image captioning, which generates a description of an image given the image and its article body as input. The motive is to automatically generate captions for news images which if needed can then be used as reference captions for manually creating news image captions This task is more challenging than conventional image captioning because it requires a joint understanding of image and text. We present an N-Gram model that integrates text and image modalities and attends to textual features from visual features in generating a caption. Experiments based on automatic evaluation metrics and human evaluation show that an article text provides primary information to reproduce news-image captions written by journalists. The results also demonstrate that the proposed model outperforms the state-of-the-art model. In addition, we also confirm that visual features contribute to improving the quality of news-image captions. Also, we present a website that takes an image and its associated article as input and generates a one-liner caption for the same.
Text-based Language Identifier using Multinomial Naïve Bayes Algorithm

Language Identification is among the crucial steps in any NLP based application. Text - based documents and webpages are rapidly increasing in the modern Internet. It is simple to locate documents written in different languages from all across the world that are available with just one click. Therefore, a language identifier is absolutely necessary in order to help the user interpret the content. Language identification has so far tended to be more concentrated on European languages and is still rather limited for Indian Traditional Languages. Many researchers have become more interested in the study of language identification for similar languages from popular languages. In this paper, Multinomial Na¨ıve Bayes Algorithm is used for detecting languages in Devanagari like Marathi, Sanskrit and Hindi, and three European languages French, Italian and English. An experiment done ondatasets of each language has produced satisfactorily accurate results after training and testing the model.
Towards an Efficient Scheme for User Authentication based on Decentralized Blockchain

Peer-to-peer network principles are the foundation of Blockchain Cybersecurity. Blockchain creates a reliable verification method that protects against online dangers. Cryptocurrency on the Blockchain is supported by three pillars: network availability, secrecy, and integrity. A third route toward stronger security, one that is less traveled and not nearly as inviting to attackers, is provided by Blockchain. This method lessens risks, offers robust encryption, and more successfully confirms the ownership and integrity of data. Some passwords frequently referred to as the weakest link in Cybersecurity, may even be unnecessary without them. So we aim to build a secure user authentication system using blockchain and also learn about how SCADA systems work in healthcare.
A study on exploring the learning domains and content elements of education for sustainable development (ESD) in Elementary and Secondary Schools in Korea

This study aims to develop the goals and learning contents of education for sustainable development (ESD) required in Korean school education. To this end, we first examined the origins of sustainable development and the three areas of ESD, analyzed the current status of ESD and the changes in the scope and learning contents of ESD in curriculum-related documents, and then derived the framework for preparing the goals, learning domains and content elements of ESD suitable for school education in Korea. Afterwards, Delphi survey was developed to evaluate the appropriateness of the goals, learning domains and content elements of ESD developed in this study by gathering expert opinions. A panel of 24 experts was formed, and two rounds of Delphi survey were conducted. As a result of the Delphi survey, we set a goal that focuses on fostering civic capabilities to realize a sustainable present and future. And then, we derived four learning domains, including ‘Ⅰ. Sustainable present and future’, ‘Ⅱ. Sustainable environment and civic life’, ‘Ⅲ. Sustainable society and civic life’, and ‘Ⅳ. Sustainable economy and civic life
Analysis of Scale Stability of NAEA in the Presence of Test Length Change

The 2019 NAEA(National Assessment of Educational Achievement) for high school is planed to implement a new type of item to measure student subject-specific competency to apply the 2015 Revised National Curriculum. Since the new type of items are implemented to measure complex abilities rather than simple knowledge, the issue of reducing the test length was considered to solve higher-level items. Namely, the major change in the 2019 NAEA is the reduction in the number of items caused by the introduction of the new type of items. This study inspected how the test length change of the 2019 NAEA for high school had an effect on the scale stability. In the NAEA, the media attention is focused on the year trend of the proportion of below basic students, so the scale stability of the NAEA which categorizes students into basic and below basic, is main point. According to the results of this simulation study, in order to precisely categorize basic level students from below basic level students with less than 0.05 estimated RMSE at the cut-off score: at least three items should be included for each subject test with the ‘low’ difficulty level (item difficulty parameter b &lt; -1.5) and the ‘moderate’ discrimination level (item discrimination parameter a ≥ 0.5).
CHANGES IN HIGHER EDUCATION INSTITUTIONS AS A RESULT OF TODAY'S CHALLENGES

The article examines the causes of changes in higher educational institutions. Modern concepts used in the academic life of higher education institutions are singled out, and their essence is explained. Possibilities of conducting distance learning on various platforms are noted. The main principles of ensuring the quality of functioning of universities and possible obstacles to their implementation are analyzed. The need to optimize the educational process for all its participants is emphasized. Recommendations are given to teachers for students to get an effective result from their studies.
Combining variable neighborhood with gradient ascent for learning to rank problem

Abstract Metaheuristic applications for information retrieval research are limited in spite of the importance of this problem domain. Ranking the retrieved documents based on their importance is a vital issue for the scientific and industrial communities. This paper proposes a novel variable neighborhood search (VNS) algorithm with adaptation based on an objective function for the learning to rank (LTR) problem. VNS is a global optimum metaheuristic algorithm that has been engaged to evolve the optimal solutions for heuristic problems based on exploring better neighbor solutions from the current one. The changes from the current to the next optimal solution are made during the perturbation stage to identify the global optimal solutions. The exploration procedure has been made through various mutation step sizes, whereas the exploitation process has been done by checking the quality of the evolved solutions using the fitness function. This research proposes a novel version of VNS based on four random probability distributions with gradient ascent. In addition to using the traditional random generator with gradient ascent for modifying the mutated genes of the neighborhood candidate solution in the following evolving iteration. This novel method in LTR is called gradient variable neighborhood (GVN). In the experiments, we utilized Microsoft Bing search (MSLR-WEB30K), Yahoo, and TREC Million Queries Competitions in 2008 and 2007 (LETOR 4) datasets. From the findings of the results, we can deduce that the GVN method outperformed recent studies on LTR methods.
Phoenician–Punic amphorae in northern coastal Etruria: New evidence from Pisa (Italy)

Abstract This study outlines the circulation of Phoenician–Punic amphorae in northern coastal Etruria, with a particular focus on Pisa (Italy), where their presence has been attested since the mid‐eighth century BCE. A set of specimens from Piazza del Duomo was analysed by minero‐petrographic and geochemical techniques. The results were compared with literature data from Mediterranean production areas. The research allowed a better definition of the role of Etruscan Pisa in the frame of commercial and cultural routes in the Mediterranean, specifically in the Tyrrhenian area, also providing the opportunity to review the attestations of Phoenician–Punic amphora on a regional scale.
LCP of rank metric codes and its an application

Linear complementary pair (LCP) of codes and rank metric codes over finite fields have been intensively investigated recently due to their applications in cryptography. In this paper, we first give the definition of LCP of rank-metric codes combined by the LCP of codes and rank-metric codes, and we establish the relationship between LCP of Delsarte codes and LCP of Gabidulin codes. We then present a criterion for Gabidulin codes (C,D) to be LCP. Using this criterion, we obtain two classes of LCP MRD of Gabidulin codes. Finally, we exhibit an interesting application of LCP MRD of Gabidulin codes in coding for the two-user binary adder channel.
Domain 8: Software Development Security

This chapter introduces Domain 8 of the CISSP®, Software Development Security. The most important aspects of this domain are related to managing the development of software and applications. Approaches to software development that attempt to reduce the likelihood of defects or flaws are a key topic in this domain. In particular, the Waterfall, Spiral, and Rapid Application Development (RAD) models of software development are considered. Another significant portion of this chapter is dedicated to understanding the principles of Object-Oriented programming and design. A basic discussion of several types of software vulnerabilities and the issues surrounding disclosure of the vulnerabilities are also a topic for this domain. Finally, databases, being a key component of many applications, are considered.
Domain 1: Security and Risk Management

Security and Risk Management, the topic of this chapter and Domain 1 of the CISSP®, presents numerous critically important terms and concepts that permeate several domains. This chapter introduces the CIA triad of Confidentiality, Integrity, and Availability, which are touched upon in virtually every domain and chapter. In addition to CIA, concepts such as the Principle of Least Privilege and Need to Know are presented. Key terms, concepts, and formulas related to risk management are presented within this chapter. Risk, threat, vulnerability are basic terms that must be understood to prove successful with this domain. Understanding how to perform calculations using Annualized Loss Expectancy (ALE), Single Loss Expectancy (SLE), Annualized Rate of Occurrence (ARO), and Exposure Factor (EF) are highlighted as part of quantitative risk analysis. Important concepts related to information security governance such as privacy, due care, due diligence, certification and accreditation are also a focus of this chapter.
Domain 6: Security Assessment and Testing

Domain 6 discusses security assessment and testing, which are critical components of any information security program. Organizations must accurately assess their real-world security, focus on the most critical components, and make necessary changes to improve. This domain describes two major components of assessment and testing: overall security assessments (including vulnerability scanning, penetration testing, security assessments, and security audits) and testing software via static and dynamic methods. Static testing tests the code passively: the code is not running. This includes walkthroughs, syntax checking, and code reviews. Dynamic methods include fuzzing, a type of black box testing that submits random, malformed data as inputs into software programs to determine if they will crash.
Physical inventory

Physical inventory in a library is the act of reconciling assets with holdings reports. A shelf list, generated by an integrated library system (ILS) documents all of the items that are supposedly held by a library in its collection. Taking a physical inventory verifies that those items listed on the shelf list actually exist on the library’s shelf. It is also an opportunity to gauge collection quality in terms of physical condition and accuracy of catalog records. Further, a clean collection, verified by taking inventory, increases customer satisfaction and staff efficiency, as well as the overall health of the collection. It limits redundancy and waste and can even help identify processes and workflows that could be leaned in an organization. Having a plan to tackle inventory a bit at a time makes the process less overwhelming and more attainable. Incorporating mobile apps into the inventory procedure make the process even more realistic and practical.
Introduction and advances to neutrosophic probability and statistics and plithogenic probability and statistics and their applications in bioinformatics and other fields (review chapter)

This chapter is a review study of over two decades (1998–2021) of the introduction and development of the neutrosophic probability and neutrosophic statistics and their extensions to plithogenic probability and plithogenic statistics, together with their applications in medical and other fields.
Design of a novel privacy preservation based cyber security system framework for secure medical data transactions in cloud storage

Individuals and corporations are increasingly finding it difficult to keep sensitive data safe in the cloud. As such, there is a need to establish and strengthen cyber security preservation methods through intelligent techniques that have the potential to resolve this tough predicament. Technological challenges are necessitating the deployment of several techniques to address privacy and accuracy issues while transporting confidential data stored in the cloud. This chapter proposes a novel cyber security technique called privacy preserved cyber security approach (PPCSA) for storing and transporting medical data on the cloud while preserving privacy. The suggested framework consists of a sanitization and restoration process. In the sanitization phase, the sensitive data was buried throughout the process, preventing secret information from escaping into the cloud. For the sanitization phase a key had to be developed as a fresh addition to the effort, as it was based on a recent metaheuristic method. In the restoration phase, a similar key must be used to retrieve the actual data effectively. The key generation was accomplished by combining objective restrictions, including the degree of change, rate of information preservation, and hiding rate, all of which considerably improve cloud cyber security efficiency. The convergence analysis, statistical analysis, and chosen-plaintext attack (CPA) and known-plaintext attack (KPA) analysis were tested using three datasets: WHO, CDC, data.gov. As a result, the proposed cyber security strategy had a better cost feature for the 76th iteration of the WHO dataset, which was 12.3% higher than the Lion Algorithm (LA), 28.9% higher than Whale Optimization Algorithm (WOA), and 34.6% higher than Gray Wolf Optimizer (GWO). Finally, the PPCSA was successful in developing the key in the sanitization and restoration technique, which aids in data preservation while preventing data leaking to the cloud. The security of medical data was well managed, and numerous phases specifying their features were created. For future researchers’ better comprehension and clarity, the optimum key generation technique is highlighted in the chapter.