Reviewerly/exHarmony huggingface dataset:
Analysis of the Argumentation Structure shown in Writing and Earth Science Teachers’ Perception for SSI Teaching through Writing Education

Objectives In this study, the changes in perceptions of SSI teaching among earth science teachers who participated in SSI writing education training and the argumentation structure shown in writing was analyzed.&#x0D; Methods It was targeted at 8 earth science teachers, and during five sessions, the training was conducted on SSI education and writing using the argumentation structure. The perception of SSI teaching was analyzed through a pre-post survey, and the development and characteristics of the argumentation structure shown in the writing process were analyzed.&#x0D; Results After the teachers participating in the training, the element of argumentation in writing increased, and in the interview, they showed an understanding of the effectiveness of writing using the argumentation structure and planning how to apply it to the class. In addition, the values used in decision-making were different depending on the position of SSI, And we could see that the characteristics of SSI influenced the decision-making process, which were reflected in argumentative writing.&#x0D; Conclusions SSI writing education for teachers can be applied directly to the school field in various forms, so it will have a positi
Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review

Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, that allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects.
Deep Learning for Secure Wireless Communications in Cyber Physical Systems

Deep learning has strong potential using data-driven solutions to improve the performance of cyber physical systems (CPS) in utilizing limited spectrum resources in wireless communications. Deep learning is also a powerful method of data exploration to learn about “normal” and “abnormal” behavior according to how CPS components and devices interact with each other. Furthermore, deep learning methods are important in predicting new attacks, which are often mutations of previous attacks because they can intelligently predict future unknown attacks by learning from existing examples. The topic of deep learning for secure wireless communications in CPS has gained much momentum in the industry and the research community very recently. In this issue of IEEE Wireless Communications, we are pleased to present a Feature Topic on “Deep Learning Driven Secure Communication for Cyber Physical Systems” to bring together researchers, industry practitioners, and individuals working on the related areas to address some of the technical challenges faced by the research topics. This Feature Topic includes a collection of 10 articles chosen by Guest Editors W. Wei, C.-H. Hsu, V. Piuri, and A. Rayes. We are also pleased to present a second Feature Topic on “Enhanced Fronthaul for 5G and Beyond” that includes a collection of four articles chosen by Guest Editors Y. Luo, P. Chanclou, K. E. Skouby, H. Zhao, and K. Asaka. Thanks to the two Guest Editor teams who did an excellent job in editing these two Feature Topics for our readers. Please stay tuned for new developments in these research areas, and read the editorials and articles in the two Feature Topics.
Audio and Caption Generation using ML

Using deep learning and computer vision, image caption generation identifies the context of an image and comments on it with the most relevant captions. It entails an image's epithet combined with English keywords, as well as datasets provided during model training. We produce a vectorized representation of an image using a deep Convolutional neural network, which is then fed into a long-short-term memory (LSTM) network, which provides captions. LSTM (Long short-term memory) is also one of the RNN (Recurrent Neural Network) constructs, mainly utilized in the deep learning discipline. The vivid applications of image captioning are natural language processing applications, recommendations in editing applications, helpful for virtual assistants, for indexing the image, for the blind or partially-sighted people, for the internet community, etc.
Storysign App for Speaking and Hearing Impaired Children

Stories are every children’s one way of an entertainment for the time they couldn’t read. Hearing stories from parents, grandparents or from teachers helps children to gradually learn different aspects of language. However there are minority children who are born with hearing impairment and don’t have one of the five human sense, hearing at full extent or can’t hear at all. The proposed system aims to provide an application which will narrate stories to children by taking English text as an input and generates output in the form of images, gifs and speech output. In this paper we are presenting a platform which will translate English text to Indian sign language.
Research on graph classification based on improved metric learning

Abstract Few-shot graph classification is to learn valuable information from limited labelled graph data for graph classification. However, although the traditional graph neural networks (GNNs) have been widely used in graph classification tasks, the problem of insufficient graph sample of many domains is ignored. At present, using meta learning method can greatly solve the problem of low data in many fields. However, when facing the new few-shot graph classification task, there are still fundamental difficulties in using the existing methods. In this paper, a novel loss function compared with prototype networks is proposed which takes both inter-class and intra-class distance into account, is applied to solve the problem of graph classification under the condition of few samples. In addition, combined with the existing idea of considering the structural features of graphs, using this idea can extract a more accurate representation of graph sample features, so as to improve the classification accuracy of the model. Extensive experiments on two data sets Chembl and TRIANGLES verify the effectiveness of our proposed method.
Finnish, Meänkieli, and Kven

Abstract Finnish, a language descending from diverse Northern Finnic dialects of the area which until 1809 belonged to Sweden, is a national language in Finland and also spoken by migrant diasporas in other countries, especially in Sweden. Two varieties which linguistically belong to the continuum of the Finnish Far North dialects, Meänkieli in Sweden and Kven in Norway, are now officially recognized and codified as minority languages. This chapter will briefly describe the development of Finnish, with special respect to its heterogeneous dialectal basis. The major part of the chapter will focus on the structure and characteristics of Finnish, Meänkieli, and Kven, especially those features which distinguish them from other Finnic languages. The chapter ends with a glossed text example.
Real Time Face Mask Detection using Yolov5 and OpenCv

Abstract: COVID-19 cases have been a big threat to human mankind with different variants found worldwide infecting many people alike. The whole world struggled through different waves of coronavirus and is struggling to reduce the spread effectively. The use of mitigation and suppression was practised throughout and it was concluded use of suppression couldn’t come without economic downfall for long term. With results fromChina and South Korea it was clear that use of masks helped reduce spread within social gatherings ,to ensure everyone wears a mask is one thing and to wear a mask properly covering their nose and mouth fully leaving no part exposed was other problem to tackle. This paper attempts to develop effective yet simple approach to monitor real time data of people wearing masks. The main aim of this project is to help identify and monitor gatheringin public places where wearing mask is essential as it takes a week to show symptoms and anyone can get infected, results show if two individuals are wearing a mask the risk of virus spreading is at its lowest. This model successfully recognizes ifany individual is wearing face mask or not and also determinesif the face mask covers nose and mouth only then it recognizesit as an masked individual.
The Rights of Reproduction, Adaptation, and Distribution (Articles 8, 9, 12, 14, 14bis(1), 14ter, and 16)

This chapter explores the development of the reproduction, adaptation, and distribution rights in the Berne Convention through to the 1971 Paris Revision. The reproduction right is one of the most basic, and earliest-recognized, rights in copyright. The author’s reproduction rights under Berne are not limited, however, to the exclusive right to copy the work in its original form. They also extend to complete or partial reproductions of the work into other forms, and thus encompass the broad realm of adaptations. Although reproduction and adaptation rights concern the incorporeal work of authorship, Berne also provides for certain rights pertaining to the physical copies. These tend to be specific to certain categories of works: distribution of cinematographic works (articles 14(1), 14<italic>bis</italic>(1)), resale right (<italic>droit de suite</italic>) in works of art and manuscripts (article 14ter), but article 16 provides for a general right to seize infringing copies imported into Union countries. The chapter then provides an analysis of the impact of later treaties, the Agreement on Trade-Related Aspects of Intellectual Property Rights 1994 (TRIPs Agreement), and the World Intellectual Property Organization (WIPO) Copyright Treaty (WCT), on the reproduction and distribution rights.
Images indexing and matched assessment of semantics and visuals similarities applied to a medical learning X-ray image base

Medical diagnostic support systems are important tools in the field of radiology. However, the precision obtained, during the exploitation of high homogeneity image datasets, needs to be improved.To develop a new learning system dedicated to public health practitioners. This study presents an upgraded version dedicated to radiology experts for better clinical decision-making when diagnosing and treating the patient (CAD approach).Our system is a hybrid approach based on a matching of semantic and visual attributes of images. It is a combination of two complementary subsystems to form the intermodal system. The first one named α based on semantic attributes. Indexing and image retrieval based on specific keywords. The second system named β based on low-level attributes. Vectors characterizing the digital content of the image (color, texture and shape) represent images. Our image database consists of 930 X-ray images including 320 mammograms acquired from the mini-MIAS database of mammograms and 610 X-rays acquired from the Public Hospital Establishment (EPH-Rouiba Algeria). The combination of two subsystems gives rise to the intermodal system: α-subsystem offers an overall result (based on visual descriptors), then β-subsystem (low level descriptors) refines the result and increases relevance.Our system can perform a specific image search (in a database of images with very high homogeneity) with an accuracy of around 90% for a recall of 25%. The average (overall) accuracy of the system exceeds 70%.The results obtained are very encouraging, and demonstrate efficiency of our approach, particularly for the intermodal system.
An Improved Calibration Method for the Structured Light System Based on Self-correction of Reprojection Error

Structured light 3D reconstruction is widely applied in various fields. During the calibration process of structured light 3D reconstruction, it is very important to raise the calibration accuracy on the parameters of the structured light system. In this paper, we propose a method based on re-projection error self-correction to obtain more accurate corner positions by screening the re-projection error values of DMD images. Experimental results show that this method can improve the calibration accuracy by 64.17%. We also propose an effective standard for the placement of calibration plate, which is of great significance to reduce the number of iterations of the program. According to a series of experiments based on the above standard, the number of iterations of the proposed re-projection error self-correction method is no more than 5 times. It proves that the proposed self-correction method and placement standard are feasible, the calibration process of structured light 3D reconstruction is optimized, and its calibration efficiency is improved.
Impact of Blockchain Technology on E-Commerce Website

Blockchain-based technologies are predicted as major disruptors for numerous business applications and pro Blockchain cesses, which bears huge implications for e-commerce. Given the ability of blockchain and related technologies Distributed Ledger Technology to create so-called “trustless systems” with idiosyncratic properties, various business models and established processes that have emerged over the years to ensure trust, reliability and enforceability in business-to-consumer (B2C), business-to-business (B2B), business-to-government (B2G) and consumer-to-consumer (C2C) relations need to be questioned and potentially adjusted. Blockchain has the potential to shake the foundation of e- commerce by enabling exchange relations that are trustless and operate without dedicated intermediaries or even central authorities in the case of permissionless blockchains. Furthermore, the exchange of information and value between companies and consumers might change considerably by enabling unified access to immutable data along the entire supply chain. In this paper, a framework and 19 high-level research questions are developed to inspire researchers to closely investigate the potential impact of blockchain on e-commerce. The main categories include (a) technological, (b) legal and (c) organizational and quality issues as well as (d) consumer issues. This paper illustrates how blockchain potentially impacts different elements of e-commerce in these respective areas
Data Storage and Security in Cloud Computing: A Survey

Cloud computing is envisioned as the next generation of IT enterprise design. Cloud computing distributes application software and data bases to massive data centres, where data and service management may not be completely reliable. This creates a slew of new security issues that have yet to be properly addressed. In this article, we primarily focus on features for ensuring data storage security in the cloud, as well as architecture for data storage that is applied by other cloud service providers vendors, and essential points for establishing data storage security.
Python-based film review data acquisition and visualization design

Abstract With the rapid development of the Internet and artificial intelligence era, how to obtain effective information is particularly important in the complex network world. In order to obtain a real movie viewing experience and provide some reference for other users, this article is based on the Python language, taking the popular movie “Shuimen bridge of Changjin Lake” as an example on Douban website, and using web crawler technology to comment and rate users, etc. Relevant data is crawled, the crawled data is the object of analysis, and data visualization processing is carried out to more intuitively show the true feelings of moviegoers. The results show that the film meets the expectations of the public and is worthy of recommendation.
Face Match Detection using AI and ML

Artificial intelligence and machine learning are the part of computer science that are correlated with each other. Application of AI &amp; ML is increasing rapidly. Face detection has over time proven to be the least intrusive and fastest form of biometric verification. Facial Detection is a category of biometric software that maps an individual’s facial features and stores the data as a face print. The software uses deep learning algorithms to compare a live captured image to the stored face print to verify one’s identity.
Authorship and Ownership (Articles 7 and 15)

This chapter focuses on issues of signal importance to the international protection of works of authorship, but concerning which the Berne Convention for the Protection of Literary and Artistic Works is at best enigmatic. The Convention declares that its ‘protection shall operate for the benefit of the author and his successors in title’, but the Convention neither designates who is an ‘author’ nor establishes general rules concerning the transfer of title. As a result, it generally leaves the determination of authorship, ownership, and transfer of rights to national law. The chapter first explores the lacunae in the Conventional treatment of individual authors and joint works. It then addresses the special treatment the Convention reserves for the ‘makers’ of cinematographic works. The chapter considers the extent to which, despite the lack of explicit provisions on authorship, some definition of authorship may be inferred from the provisions on cinematographic works and from the treaty as a whole. Finally, it reviews questions of ownership and transfer of copyright, with particular attention to the elaborate rules set out for cinematographic works.
A Smart System using Hand Gestures and Voice

Human-Computer Interaction (HCI) grew enormously over the years. Hand gesture recognition technology brought a brand-new era to the artificial intelligence branch of human-computer interaction. Hand Gesture-based communication is one of the most effortless and natural methods. Identifying and interpreting hand gestures from a continuous sequential stream of input data is called gesture recognition. Amateur or aged people find it hard to identify and press the exact alphabet that they need. Overcoming this difficulty stands as the ultimate goal for our proposed system. Typing has taken many forms, first using a keyboard, gradually changing to touch screens, and now too much easier finger motion tracking systems. The gesture is made by the user is detected by the machine via the image processing techniques and the operation unique to the machine is carried out, thereby minimizing the requirement of any hardware input device. This paper proposes a virtually controlled system that uses hand gestures and voice to perform operations. The system will allow the user to manage cursor, keyboard, and writing functions using hand gestures, and also provides the user with an additional feature to convert the user’s voice to text. The proposed system utilizes a webcam as an input device. This application will add a brand-new era of HCI implementation for educational purposes.
Perceptual Memory II: Visual Perceptual Long-Term Memory

Chapter 17 lays out primary features and functions of visual long-term memory. Recollection and familiarity are distinguished. Different levels of abstractness/concretion in long-term perceptual retention are highlighted. Ability-general, or schematic long-term memory, often misleadingly called ‘semantic memory’, is distinguished from <italic>de re</italic> long-term memory, which retains perceptual reference to particulars. Episodic memory is a species of long-term <italic>de re</italic> memory. The representational forms, including the iconicity, of these different types of long-term memory are set out. There is some discussion of whether the different types of memory are different systems or rather different aspects or faces of a single memory system. The discussion stresses functions to retain perceptual contents in long-term visual memory. Relations between perceptual and conceptual attributives in long-term memory are outlined. The chapter concludes with a summary of relations among the various types of perceptual memory, short- and long-term.
Precision Testing of a Single Depth Camera System for Skeletal Joint Detection and Tracking

Implementing a marker-less, camera-based system, to capture skeletal joint motion in a timely manner that is robust to variables such as lighting conditions, occlusion and changes in the subject’s distance from the camera remains a challenge. This paper aims to test the feasibility of such a system using the Intel RealSense D435i depth camera to capture joint coordinates in the camera reference frame. The subject was placed in various lighting conditions to analyze the effect of glare on joint coordinate precision, a decimated filter and a spatial filter were applied to test which of these filters reduced noise in the image signal most effectively. To examine which setup is the most effective in measuring coordinates of the neck, elbows, wrists, knees and ankles precisely, variations were made in the subject’s distance from the camera, position in the field of view and ambient lighting conditions. The empirical cumulative distribution function (ECDF) plots of the true distance obtained from the data collected were used to systematically eliminate undesirable recording conditions. The coordinates measured will be used to calculate gait parameters.
Improved LBP Face Recognition Using Image Processing Techniques

The face recognition process is used to distinguish individual’s faces based on their unique facial traits. In face recognition, the detection of faces in real-time videos under varying illumination conditions is one of the challenging tasks. In this study, we are detecting faces using Haar classifiers because of their high detection accuracy and local binary pattern (LBP) classifiers due to their invariant nature under varying illumination conditions. Image processing techniques such as contrast adjustment, bilateral filtering, histogram equalization, image blending, and quantization are applied to improve the detected faces. Also, we have applied quantization on raw face images at various levels to evaluate the feasibility of the proposed method in effectively recognizing the faces in low-quality images. Using local binary pattern histogram (LBPH) recognizer, a face recognition rate of 100% has been achieved when resized raw images and preprocessed images are blended. Also, an equal performance has been achieved when the quality of the images is reduced by applying quantization of 16 levels. Hence, the proposed method has proven its effectiveness in recognizing the faces in low-quality images. The results show that using the preprocessed image, the proposed face recognition method is invariant to varying illumination conditions.
Digital Platforms and Techniques for Marketing in the Era of Information Technology

Digital marketing is the promotion of a product or service through at least one form of electronic media. This form of marketing is distinct from traditional marketing, but it uses some of the ideologies of traditional marketing. This research article examines the various technologies and platforms used in digital marketing that allow any organization or business to do this form of marketing and study what works for them and what does not. The article also explores the recent advancements in digital marketing due to the increase in users and the vast amount of data collected from these users. The two main advancements analyzed and discussed in this paper are machine learning (ML) and artificial intelligence (AI) tools.