Reviewerly/exHarmony huggingface dataset:
Using Reminder Tools to Increase Learning Motivation: A Comparison of Mobile Devices, Email and E-learning Platforms

Most students are not actively concerned about curriculum activity times. However, without reminders, students will miss out on learning opportunities from the course activities. Therefore, due to the use of smartphones — which are central to people's everyday lives —, this study combined apps and the curriculum to develop a course activity notification system — Mobile Secretary of Operating Systems (MSOS) —­, as a curriculum reminder tool. This tool reminds the student of the curriculum activity times anytime and anywhere, like a secretary. This study also investigates students’ preferences between three distinct reminder tools — the reminder app, e-mail notifications and online learning platform announcements — in terms of their effectiveness in accessing curriculum-related matters, and in previewing and reviewing courses. Additionally, the study examines the efficacy of the MSOS app by comparing the questionnaire results of the students who used the app and those who did not use it. From the results of the questionnaire, it is also ascertained whether those students who used MSOS consider the app to be more convenient and immediate than e-mail notifications and the online learning platform, both in giving access to curriculum-related matters and in previewing and reviewing courses.
Repeated-Root Constacyclic Codes With Pair-Metric

Symbol-pair codes are block codes when considering the pair-metric, and are designed to protect against pair-errors occur in the pair-read vector which consisting of overlapping pairs of symbols. A code that achieves the analog of Singleton bound for symbol-pair codes is called a maximum distance separable (MDS) symbol-pair code. The contribution of this letter is twofold. First, we give a lower bound of minimum pair-distance of repeated-root constacyclic codes in terms of minimum Hamming distance, which extends the lower bound given by Chen, Lin and Liu. Second, we give three new classes of MDS symbol-pair codes. As we know, for an MDS symbol-pair code with a fixed minimum pair-distance, the larger the code length of the code has, the higher the code rate is. The first class of MDS symbol-pair codes we show has unbounded code length and the other two classes of MDS symbol-pair codes have larger code lengths than the code lengths of existing MDS symbol-pair codes under the same minimum pair-distance 6.
Introduction to blockchains

A blockchain is a distributed database or ledger that maintains an ever-growing list of data records in opposition to tampering and revision. It provides immutable data storage over a distributed network and supports a large number of encrypted and coded interactions, which improves the reliability of the entire network interaction system and reduces the need for trust. Even if some nodes in the blockchain are hacked and fail, the system can run as usual. (In such a scenario, users are enabled to form a distributed peer -to peer (P2P) network in which they could interact with each other in an efficient manner without a trusted intermediary). In addition to being famous for decentralization, blockchain has shown other significant characteristics during its development, such as reliability, anonymity, transparency, auditability and programming. According to the different degree of openness and coverage, the current blockchain can be classified into three categories: public blockchain, consortium blockchain and private blockchain. In this process, the evolution of blockchain has gone through three processes: blockchain 1.0, 2.0 and 3.0. Blockchain 1.0, known as digital currency stage, is strongly related with the decentralization and payment of cryptocurrencies. Blockchain 2.0, known as digital finance stage, introduces economic, financial and market applications by programming far from simple currency transactions. Among them, the most significant features of blockchain 2.0 are the introduction and application of smart contracts. Blockchain 3.0, known as digital society stage, provides decentralized solutions for a variety of industries beyond just financial scene.
Bridging Ordinary-Label Learning and Complementary-Label Learning

A supervised learning framework has been proposed for the situation where each training data is provided with a complementary label that represents a class to which the pattern does not belong. In the existing literature, complementary-label learning has been studied independently from ordinary-label learning, which assumes that each training data is provided with a label representing the class to which the pattern belongs. However, providing a complementary label should be treated as equivalent to providing the rest of all the labels as the candidates of the one true class. In this paper, we focus on the fact that the loss functions for one-versus-all and pairwise classification corresponding to ordinary-label learning and complementary-label learning satisfy certain additivity and duality, and provide a framework which directly bridge those existing supervised learning frameworks. Further, we derive classification risk and error bound for any loss functions which satisfy additivity and duality.
Covid-19 and doctoral research in Brazil and Portugal: who pays the bill for confinement and remote work in research?

The Covid-19 pandemic has brought about several changes to doctoral programs due to the prohibition of face-to-face activities. This situation has generated many difficulties but has also facilitated research activities in Sociology in Brazil and Portugal. This essay discusses the changes introduced in sociological research and the main strategy found to overcome the difficulties – remote work – with the aim of raising questions for a research agenda on the subject. The notes and analyses presented here are produced from participant observation and full participation as an academic linked to three universities, where I had access to remote work data and operational notes issued by these universities during the pandemic. In these observations, I have identified that the professors, technicians and researchers pay the bill for remote work in the doctorate programs, and that the pandemic affects researchers unequally, depending on their gender, the stage of the course they are in, whether the academic relationship is national or foreign, whether they receive a scholarship or not, and whether they are at home or on student mobility.
FAMILY FACTORS INFLUENCING THE DEVELOPMENT OF JUVENILE DELINQUENCY AMONG PUPILS IN KABETE REHABILITATION SCHOOL IN NAIROBI COUNTY, KENYA

This paper uses data collected for an MA Thesis on the family factors influencing the development of juvenile delinquency among pupils in Kabete Rehabilitation School in Nairobi County, Kenya. This study has been necessitated by continued concern among policymakers, practitioners, citizens, and researchers about the rising cases of juvenile delinquency in Nairobi County. Although there had been speculations that family factors sit at the core of the trigger factors, there has never been a detailed and systematic inquiry and analysis of this problem. The study was guided by several specific objectives; the first objective examined the range of family factors that influenced the development of juvenile delinquency among pupils in Kabete Rehabilitation School in Nairobi County, Kenya while the second objective examined how family types influenced the development of juvenile delinquency among pupils in Kabete Rehabilitation School in Nairobi County, Kenya. Last but not least, the study assessed the multiplier effects of juvenile delinquency among pupils in Kabete Rehabilitation School in Nairobi County, Kenya. The study adopted a qualitative case study research design and purposive sampling technique. A sample size of 60 respondents who comprised of 30 parents, 24 pupils, 4 teachers and 2 administrators was adopted. The collected data was analyzed thematically and presented in verbatim quotes. The study revealed a relationship between family factors, family types and development of delinquency among juveniles. Family attachment and family conflict are risk factors for delinquency. Further, this study established that not all children follow the same road to delinquency; different life-experience combinations will produce different delinquent activity. For instance, positive parenting behaviours in the early years and later in adolescence tend to serve as barriers mitigating juvenile activity and encouraging teenagers who are already involved in criminal conduct to refrain from more crime. In matters policy, this study recommends interventions with the help of other social institutions such as religion and others that have a direct bearing on children growth and development like the government children departments to deeply engage in altering parenting practices aiming to promote better socialization of the child and also reduce engagement in negative outcomes such as delinquency. The government may consider starting up free family counseling clinics and rehabilitation centers to address or counter any social and behavioural defects that result from inadequate parenting such as delinquency.
A New Lossless Secret Image Sharing Scheme for Grayscale Images with Small Shadow Size

The current paper offers a lossless (k, n)-threshold scheme with reduced shadow size using the algebraic properties of the polynomial ring \(\mathbb {Z}_{251}[x]\) over the field \(\mathbb {Z}_{251}\). Unlike most of the existing secret image sharing schemes, our scheme does not require any preprocessing steps in order to transform the image into a random image to avoid data leakage from the shares of the secret image. Moreover, the efficiency of our proposed scheme is explained through security analysis and demonstrated through simulation results.
FINANCIAL INSTRUMENTS AND THEIR IMPACT ON THE GROWTH OF INVESTMENT – A STUDY ON THE LIBYAN MARKET

Purpose: The aim of this study was to explore the relationship between Financial instruments and growth of investment. Design/Methodology/Approach: The distributed questionnaires include 300 clients from banks and companies represented in Tripoli. The data collected was analyzed using SPSS- SEM. Results: The obtained results showed that indicated that, despite the modest progress made in a very short time regarding all indicators which the paper calculated, however, it can be said that Libyan stock market remain largely underdeveloped, small and relatively ineﬃcient. Its market capitalization to GDP is very low and investors have no access to long-term capital. In addition, the market still has very low liquidity and investors still have a limited choice of ﬁnancial instruments and face liquidity problems. Originality/Value: This study contributes significantly to the importance of diversity in the use of financial instruments that help in the growth of investment. The study added a new discussion, which is the disclosure that financial instruments affect the growth of domestic investment, especially by easing financing restrictions, which allows companies to increase investment in response to increased demand for production. The main finding is that the structure of the financial system does not have an independent effect on investment growth, in the sense that it does not enhance the response of investment to changes in production, whereas financial development makes investment more responsive to output growth. Thus, instead of promoting a specific type of financial instrument, countries should implement policies that reduce transaction costs in financial intermediation and enforce the rights of creditors and investors. This will facilitate the development of banks and stock markets, which will stimulate the growth of domestic investment.&#x0D; &#x0D; JEL: D53; G14; O16
Surveying Composers: Methods of Distribution, Discoverability, and Accessibility of Their Works and the Corresponding Impact on Library Collections

Previous research articulates the need for further investigation in this area.Several authors discuss the issues of collecting and storing materials and the need to build partnerships with vendors, but without providing concrete solutions.They look at purchasing from the perspectives of librarians as consumers, rather than from the perspective of the creator of the work, composers.Another concern is the difficulty of purchasing scores when they are available through independent Web sites only, as evidenced by Kent Underwood's 2016 study "which concluded, as evidenced by library holdings in WorldCat, that commercial publication and distribution is still the principal conduit for new scores going into libraries, and that selfpublished music has a relatively slim chance of making it into our collections" 1 .In his 2019
Augmented Reality sebagai Media Edukasi Sejarah Bangunan Peninggalan Kesultanan Utsmaniyah menggunakan Metode Marker Based Tracking dan Algoritma Fast Corner Detection

At this time, information and communication technology is growing rapidly. One example of technological developments is Augmented Reality (AR). Augmented Reality in theory is to present virtual effects in the real world. This AR technology has been applied in several fields. One of them is in the field of education. In this study, an AR application was made as an educational medium for the history of the heritage buildings of the Ottoman Empire, especially for Madrasah students in the subject of Islamic Cultural History (SKI). This research will discuss the history of heritage buildings of the Ottoman Empire such as; Hagia Sophia Monument, Yeni Valide Mosque, and Blue Mosque. The Ottoman Empire was one of the world's Islamic empires that played a major role in the spread of Islamic teachings. This AR application uses the Marker Based Tracking method and the Fast Corner Detection algorithm. The tools used are Android Studio, Unity 3D, Sketchup, Figma, and Vuforia. The results of this study resulted in the application having succeeded in displaying a 3D Object from the Ottoman Sultanate heritage building, along with a description next to the 3D Object. In addition, this Monument AR application also works well on the Android operating system.Keywords:Augmented Reality, Marker Based Tracking, Fast Corner Detection, 3D Object, Android.
MONITORING THE DYNAMICS OF A PROJECT-ORIENTED ORGANIZATION ENERGY ENTROPY

The subject of the research is the approaches to studying the dynamics of information and energy entropy of a project-oriented organization. The purpose of this study is to develop an approach for monitoring the dynamics of energy entropy when adding a new project to the portfolio of a project-oriented organization. To achieve this goal, it is necessary to perform the following tasks: determine the expression of the criterion for the success of the project in the framework of the energy-entropic concept of the organization, formulate entropic barriers and ways to overcome them. The following methods are used: systems analysis, probability theory, energy-entropy theory. Results. The main parameters that determine the dynamics of the organization's energy entropy are information entropy and relative energy efficiency. When a new project is added to an organization's structure, it takes time to improve its energy efficiency. Therefore, to form conclusions on the analysis of changes in entropy, it is important to review the period and take into account its relationship with new projects of the organization. The dynamics of the information entropy of development projects and their influence on the information entropy of the organization are described. It was found that negative leaps of information entropy are possible as a result of the implementation of projects aimed at changing business processes, quality of resources, etc. The work proposes a criterion for the success of the project, taking into account the time period that is "provided" to the project for the manifestation of its result in terms of energy entropy. Also, the work formulated barriers for information and energy entropies as levels, upon reaching which the organization is destroyed. Conclusions. This article presents a tool for analyzing the dynamics of information entropy and energy entropy of a project-oriented organization when a new project is added to the portfolio. Also, a criterion for the success of the project is proposed, taking into account the time period that is "provided" to the project for the manifestation of its result in terms of energy entropy. Entropic barriers of a project-oriented organization are formulated and ways to overcome them are proposed to reduce entropy, increase energy efficiency and ensure its development.
Two Structures in One Object

Abstract In this article the author analyzes the problem stated by Ingarden in his ontology: under what conditions can the subject−properties structure and the whole−parts structure coexist in one object? After the presentation of Ingarden’s doctrines concerning both structures, the author argues that for Ingarden a whole is nothing over and above a plurality of objects linked by relations. However, Ingarden was convinced that a compound object is not identical with a whole which is associated with it. Then the author analyzes the difference between the two types of compound objects: higher-order objects and compound, primarily individual objects. The former are founded on parts while the latter are such that their parts are founded on them. Finally, the author considers Ingarden’s theory as an answer to the one–many problem. The author argues that Ingarden’s conception of a higher-order object cannot solve this problem, and he also points to some difficulties concerning primarily individual compound objects.
Medical Image Data Classification Using Deep Learning Based Hybrid Model with CNN and Encoder

Healthcare sector is one of the prime and different from other trade. Society expects high priority and highest level of services and care irrespective of money. Presently medical field suffers from accurate diagnosis of diseases and it create huge loss to society. The prime factor for this is due to the nature of medical data, it is a combination of all varieties of data. Medical image analysis is a key method of Computer-Aided Diagnosis (CAD) frameworks. Customary strategies depend predominantly on the shape, shading, and additionally surface highlights just as their mixes, a large portion of which are issue explicit and have demonstrated to be integral in medical images, which prompts a framework that does not have the capacity to make portrayals of significant level issue area ideas and that has poor model speculation capacity. In this paper we are attempting a medical image data classification technique using hybrid deep learning technique based on Convolutional Neural Network (CNN) and encodes. What's more, we assess the proposed approach on two benchmark clinical picture datasets: HIS2828 and ISIC2017. The proposed algorithm is applied on the considered 2 datasets for performing data classification using deep learning based CNN and encoders. The proposed model is compared with the traditional methods and the results show that proposed model classification accuracy is better than the existing models.
Quote-Based Manipulation of Illiquid Benchmarks

We document a form of marking the close committed without transacting. Instead, manipulators utilize periods of order-book illiquidity to inflate the benchmark price. We find instances of closing price manipulation are associated with increases in end of day returns and, contrary to findings in the previous literature, observe no subsequent price reversals. Instead, we show these price effects continue in the manipulated direction over extended periods, with average excess returns of over 80% in the six-months following the manipulation. Our results demonstrate that both regulators and exchanges need to be mindful of the pressures of external incentives on benchmark prices.
A Multi-Classifier Method based Deep Learning Approach for Breast Cancer

Breast cancer is one of the diseases that haunt every woman around the world.It is one of the main killers of women not only in Malaysia, even around the world.Medical diagnosis such as breast cancer is considered a significant but complicated task that needs to be carried out correctly and effectively.In improving the prediction accuracy of breast cancer dataset, this study evaluates the performance of multi-classifier based deep learning approach on datasets.There are five classifiers that are involved like Sequential Minimal Optimization (SMO) , decision tree (J48) , random forests (RFs), Naïve Bayes (NB) and Instance Based for K-Nearest neighbor (IBk).These classifiers will be combined and analyzed using deep learning approach.This strategy utilizes models of deep neural network that is a variant of Neural Network but with big approximation to human brain using an advance system compared to a straightforward neural network.The results of combination different classifiers using deep learning approach indicate the highest accuracy than single classification with 96.63% as a combination SMO+RF+IBK+NB.
Privacy-Preserving Correlated Data Publication with a Noise Adding Mechanism

The privacy issue in data publication is critical and has been extensively studied. However, most of the existing works assume the data to be published is independent, i.e., the correlation among data is neglected. The correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, behavioral, and genetic relationships. In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively. Specifically, (ε,δ)-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy. It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy. Then, we explore the effects of deterministic correlations on privacy disclosure. For deterministic correlations, it is shown that the successful disclosure rate with correlations increases compared to the one without knowing the correlation. Meanwhile, a closed-form solution of the optimal disclosure probability and the strict bound of privacy disclosure gain are derived. Extensive simulations on a real dataset verify our analytical results.
Selecting Content Analysis Approach in Social Networking Sites: What’s the Best for Cyberbullying Studies?

Insight into students’ behaviour and interaction is essential when it comes to improving the development and implementation of educational policies. Content analysis is a staple method to study online communication. However, there seems to be no clear reference for researchers to decide which approach is suitable to address a particular purpose. So, this study aims to recommend the most suitable content analysis approach for SNS research. This study extracted relevant studies from open-access databases and analysed the full-texts with NVivo 12. From a raw data collection of 120 texts, this study identified 19 approaches. This study recommends four approaches which are ideal to study cyberbullying among students in the higher education context, specifically on the types, styles, motives, and solutions of cyberbullying. First is Neuendorf’s descriptive content analysis approach to study about the types. Second is Hijmans’ rhetorical content analysis to study the styles. Third is Hsieh and Shannon’s directed content analysis approach to study the motives. Fourth is Miles and Huberman’s collaborative social content analysis to study the solutions. These four approaches have different focuses that seem to be the perfect match to study different aspects of online interaction on SNS, particularly in the context of cyberbullying.