Reviewerly/exHarmony huggingface dataset:
Participating in Cultural Life via Augmented Reality on Cultural Goods: What Role for Copyright?

Abstract Augmented Reality (AR) is a technology enabling the overlap of digital images with those from the real world. This technology can have a wide range of applications, including in the cultural heritage sector. AR initiatives can be implemented by cultural heritage institutes (CHIs) for educational and research purposes, but also by third parties as (complementary) commercial practices. When AR is applied to cultural goods, this can be referred to as the ‘exploitation’ of such goods. These goods often embed works of art which may fall under the protection of an Intellectual Property Right (IPR). This paper focuses on the importance of AR experiences when applied to cultural goods and explains why it is a suitable tool for a comprehensive and consistent achievement of the CHIs’ missions and, more broadly, for economic, cultural and social growth, as well as for a better implementation of the legal right of participation in cultural life. The analysis studies this from an IPR perspective and explains how copyright interferes with the development of AR applications. The work does not deal with the protection of AR outcomes as such. It discusses when and what challenges are raised for both moral rights and economic rights when data and images are communicated to the public via AR applications. Anyone that intends to develop AR needs to own economic rights. When rights are owned, if AR is developed by a public sector body or a CHI, rules on public sector information may also apply and facilitate the initiative. If the IPRs are not owned by the AR developer, the latter should request a license, probably via Collective Management Organizations, unless exceptions and limitations can provide for a solution. Thus, exceptions and limitations are one of the main focuses of this work.
EXAMINATION OF DIGITAL LITERACY LEVELS OF THE SPORTS SCIENCES FACULTY STUDENTS

These days, individuals need more and more skills in order to keep up with the rapidly developing technology and to use the digital world effectively.In this regard, information technologies emerge as an important tool and it is important to provide individuals with digital literacy in order to use these tools effectively.With this study, it is aimed to reveal the digital literacy levels of the students by examining the digital literacy levels of university students studying at the faculty of sports sciences in terms of various variables.The study group of the research consists of a total of 338 university students, 148 female and 190 male, studying at Aksaray University
Sentiment Analysis for Tweets using Patterns and Strategies to Detect the Genuineness of Tweets.

Sentiment analysis related with distinguishing and classifying opinions or sentiments expressed in given text. Social media is completing a colossal quality of wealthy knowledge within the type of tweets, standing updates, diary posts etc. Sentiment analysis of this user generated knowledge is incredibly helpful in knowing the opinion of the gang. Twitter sentiment analysis is troublesome compared to general sentiment analysis thanks to the presence of slang words and misspellings. Knowledge base approach and Machine learning approach square measure the 2 methods used for analyzing sentiments from the text. Public and private opinion a few wide range of subjects’ square measure expressed and unfold frequently via numerous social media. Twitter offers organizations quick and effective thanks to analyze customers' view toward the crucial to success within the market place. Developing a program for sentiment analysis is an approach to be accustomed computationally live customers' perceptions. This project cognitive content together with varied patterns for tweets along a side multiple strategies to discover the sentiment class expressed in a very tweet and if a tweet is real or not. We proposed work to classify sentiments of tweets from people to determine if people are happy, sad, angry, etc. about particular topic. Also the purpose of the work is to check genuineness of tweets so that rumors about any topics can be detected and mitigated. This approach can be used in various fields further as like detecting people sentiment about particular social issues. Also fake tweets and rumors which may further exploit to social issues like riots, religion complexities can be removed. To achieve these goals and fetch tweets we used Twit4j API and various techniques such as NLP, TF-IDF, and Sentiment Classifications are applied to get results accordingly. We had maintained our own database of words for dictionary purpose as well as have been used OpenApache NLP with their predefined dictionaries.
A novel audio steganography technique integrated with a symmetric cryptography: a protection mechanism for secure data outsourcing

Data transfer over the internet is a very common process and security of this data is everyone's responsibility. Many techniques are available to secure such confidential data. Among them, cryptography and steganography are two major sub disciplines of information security. The aim of this paper is to maintain the imperceptibility and provide better security. The goals of this paper are to be acknowledged through: the use of variable sample selection method for audio samples - which enhances the security with better audio quality; and deploying one of the most secure algorithms - blowfish. It is used to secure data from eavesdropping. Performance of proposed work is compared with the traditional LSB algorithm for audio. The proposed system is the best approach for enhancing the security and retaining the good quality of the cover object.
Generative Grammar, Neural Networks, and the Implementational Mapping Problem: Response to Pater

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that does not receive attention in the article: how can we use neural networks to examine whether it is possible to learn some linguistic representation (a tree, for example) when, after learning is finished, we cannot even tell if this is the type of representation that has been learned (all we see is a sequence of numbers)? Drawing a correspondence between an abstract linguistic representational system and an opaque parameter vector that can (or perhaps cannot) be seen as an instance of such a representation is an implementational mapping problem. Rather than relying on existing frameworks that propose partial solutions to this problem, such as harmonic grammar, I suggest that fusional research of the kind proposed needs to directly address how to ‘find’ linguistic representations in neural network representations.
Not All Data are Created Equal - Data Sharing and Privacy

The COVID-19 pandemic has increased our online presence and unleashed a new discussion on sharing sensitive personal data. Upcoming European legislation will facilitate data sharing in several areas, following the lead of the revised payments directive (PSD2), which enables payments data sharing with third parties. However, little is known about what drives consumers’ preferences with different types of data, as preferences may differ according to the type of data, type of usage or type of firm using the data. Using a discrete-choice survey approach among a representative group of Dutch consumers, we find that next to health data, people are hesitant to share their financial data on payments, wealth and pensions, compared to other types of consumer data. Second, consumers are especially cautious about sharing their data when they are not used anonymously. Third, consumers are more hesitant to share their data with BigTechs, webshops and insurers than they are with banks. Fourth, a financial reward can trigger data sharing by consumers. Last, we show that attitudes towards data usage depend on personal characteristics, consumers’ digital skills, online behaviour and their trust in the firms using the data.
Convolutional Neural Network Based Approach to Detect Pedestrians in Real-Time videos

Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn’t have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in pre-recorded videos.
Linear Regression

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Pixel-based hybrid copy move image forgery detection using Zernike moments and auto colour correlogram

All of us rely on the images for the memories of our life and loved ones. The images are useful in proving an evidence of the event. Image forgery has become much prominent nowadays and is being done either for fun or intentionally. In this paper, we detect copy move forgery by combining the two features namely, Zernike moments and auto colour correlogram. The first checks the shape of the objects in the block, the latter for distance of each colour pixel taking into account the 64 colours. Both combine together to identify the regions for which copy-move forgery exists. The first completes the number of comparisons in the order of n2, where n is the number of blocks. The ACC verifies the colours used in the block. If both matches are confirmed, the suspicious blocks are returned. The method out-performs the existing methods based on the probability approach.
Data Trusts

Recent years have seen the burgeoning of a literature on data trusts, and the unwary might therefore be led to believe that it is an idea whose time has come.Unfortunately, the ideas of the various authors who have contributed to this literature, who include the present author 1 , haven't always coincided, and have been aimed at different problems at different levels of detail and hand-waving.We might therefore say more accurately that 'data trust' is a brand whose time has come, which in itself is a not uninteresting phenomenon, worthy of consideration.The general background to this flourishing body of work is, of course, the big data revolution, the benefits it promises and the problems it is causing, as technologists, lawyers, individuals (either as citizens or consumers), governments, businesspeople, scientists, social scientists, medical scientists, engineers, designers of games, propagandists, advertisers, secret policemen and Russian troll farmers adjust to and get to grips with it.The combination of opportunity and threat is important -data trusts are pitched by their champions as means both of combating a threat and enabling more effective exploitations of opportunities.The term 'data trust' trades upon the image of a medieval legal instrument, the trust, in which property is managed on fiduciary lines, so that the trustee runs it in the interests of the beneficiary.The idea was present in Roman Law, but reached fruition in common law jurisdictions, most obviously England.Yet it is not an instrument of common law.Its roots are in equity, the body of judgment of the Court of Chancery specifically designed to address injustice arising from strict application of the law.Trusts nowadays may be encountered in the administration of property inherited by minors, or in the management of commonly-held assets.The point of the fiduciary arrangement is the recognition that different and even opposing interests are in play and need to be balanced. 2 The idea of a data trust speaks to a general notion of unease in the rich democracies that the data-driven world is out of our control, not being run for our benefit, and exploiting us in new and increasingly devious ways, the sort of unease articulated by such commentators as Zuboff 3 , who excoriates surveillance capitalism, and Ekbia and
Videos in Chemistry Education: Applications of Interactive Tools

Practical Approaches for Engaging Chemistry Students. The application of new methods for engaging students with content is complicated by the lack of real‐life models. This work provides the reader with rich examples of ways that faculty have implemented videos to help students prepare for classwork. Describing the practical use of tools, such as SMART boards and iPad apps, chapters serve as how‐to manuals to support faculty looking to put these tools into practice in their chemistry courses, from high school and general chemistry to analytical chemistry and special topics.
A Novel Method on Enhanced Video Security using Steganography

Security is imperative to individuals' key opportunity. In the course of recent decades with the headway of correspondence innovation the utilization of web has developed amazingly to trade data with no separation bearer. The most recent drama covers the threats against our technology infrastructures. The most challenging issue in the military network is that secure retrieval of confidential data. Information installing is the way toward implanting data in an information source without changing its perceptual quality. Stenography is the method of thrashing a document, message, picture or video surrounded by another record, message, picture or video. In previous methods, usage of image and text as key produces low security and reversible data hiding as algorithm produces less compression ratio. In our paper we proposed a novel method for increasing the video security by using One Time Password (OTP) and voice key as password and improving compression rate by using Blind detection algorithm. The uses of blind detection algorithm includes greater computational accuracy, high flexibility and deliver faster execution by an estimated high PSNR value .In addition, this paper also includes compressing image, audio and video files and embedding into a single video.
Improved Frequent Item-Sets Mining in Pharmacovigilance

Mining frequent item-sets is an important concept that deals with fundamental and initial task of data mining. Apriori is the most popular and frequently used algorithm for finding frequent item-sets which is preferred over other algorithms like FP-growth due to its simplicity. For improving the time efficiency of Apriori algorithms, Jiemin Zheng introduced Bit-Apriori algorithm with the enhancement of support count and special equal support pruning with respect to Apriori algorithm. In this paper, a novel Bit-Apriori algorithm, that deletes infrequent items during trie2 and subsequent tries are proposed which can be used in pharmacovigilance to identify the adverse event
The post-verbal pronoun KEOI in child Cantonese: A corpus-based study

This study investigates the acquisition of the third person pronoun keoi5 [inline-graphic 02i] with inanimate referents in post-verbal position (henceforth, keoi) in Hong Kong Cantonese. Following a linguistic analysis of keoi vis-à-vis its equivalents it in English and tā [inline-graphic 03i] in Mandarin Chinese, we conducted a corpus-based study on the use of keoi in 9 Cantonese-English bilingual children (1;03–4;06) and 3 Cantonese monolingual children (1;10–2;09) in naturalistic settings. Results show that Cantonese-speaking children mainly used keoi as a canonical object of verbal predicates expressing irrealis bounded disposal events, indicating their early sensitivity to the aspectual properties of keoi-clauses. While monolingual children were consistently adult-like in using keoi, bilingual children produced unbounded keoi-clauses unattested in their monolingual peers and the adults. They also used higher rates of realis keoi-clauses and demonstrated interchangeable use between keoi and it in code-mixed utterances. Our findings lend support to the proposal that keoi marks bounded disposals with irrealis results or states. Input and language experience are shown to influence the acquisition of keoi-clauses, with cross-linguistic influence of English likely induced by the interplay among ambiguity of input in Cantonese, extensive exposure to English and regular processing of the English pronouns.
A brain-like cognitive process with shared methods

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.
Similarity Measurement Technique for Measuring the Performance of Page Rank Algorithm Based On Hadoop

In this century big data manipulation is a challenging task in the field of web mining because content of web data is massively increasing day by day. Using search engine retrieving efficient, relevant and meaningful information from massive amount of Web Data is quite impossible. Different search engine uses different ranking algorithm to retrieve relevant information easily. A new page ranking algorithm is presented based on synonymous word count using Hadoop MapReduce framework named as Similarity Measurement Technique (SMT). Hadoop MapReduce framework is used to partition Big Data and provides a scalable, economical and easier way to process these data. It stores intermediate result for running iterative jobs in the local disk. In this algorithm, SMT takes a query from user and parse it using Hadoop and calculate rank of web pages. For experimental purpose wiki data file have been used and applied page rank algorithm (PR), improvised page rank algorithm (IPR) and proposed SMT method to calculate page rank of all web pages and compare among these methods. Proposed method provides better scoring accuracy than other approaches and reduces theme drift problem.
Conditions for Teaching Professional Competence of Future Electronics Engineers' in Digital Technology

The article reveals the urgency of the future electronics engineers' professional competence in digital technology. The article presents its historiography and points out the scientists' points of views. The following key terms are analyzed: information, information-oriented society, professional competence, competence of digital technology, information digital competence, information technology, computer technology. The article gives the definition of professional competence in digital technology and its content and also characterizes the system of principles underlying the informational and cybernetic approaches. The most effective technologies, forms and methods of training are highlighted. Particular attention is paid to the reflexive -evaluative activities; tasks for practical and independent work, that are of applied nature. It is stressed the necessity for the development of a health culture in connection with the informatization of the educational process.
Motion planning of autonomous mobile robot based on ELANFIS

Aiming at the problem of target reaching and avoiding obstacles in the nonlinear motion planning of autonomous mobile robots, a new motion planning method is proposed. Due to the uncertain factors brought by the unknown environment and modeling errors to the control system, the performance of the precise controller is poor and the design requirements are high. Designed an adaptive fuzzy neural network planning method based on extreme learning machine (Extreme-ANFIS), which combines the easy-to-express characteristics of fuzzy control and the self-learning of neural network, and introduces the theoretical idea of extreme learning machine, which reduces the computational complexity of the traditional adaptive fuzzy neural network. The simulation experiment results show that the designed controller based on the fuzzy neural network can achieve the expected effect through the learning of training samples.
Multiparameter Real-World System Identification Using Iterative Residual Tuning

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation — where we outperform two baselines — and on a real system, where we achieve marble tracking error of 4.02% after just 5 optimization iterations.
RDF Schema

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.