Reviewerly/exHarmony huggingface dataset:
RETRACTED: Application of Big Data Blockchain Technology in Financial Support of Real Economy

Abstract In the era of big data, the development of Internet technology has caused earth-shaking changes and innovations in various fields of the financial industry. Especially in the financial market, big data has become an important research topic. It can accelerate the deep integration of Blockchain technology with the most advanced information technologies such as artificial intelligence, big data, and the Internet of Things, as well as the in-depth integration with the real economy, and innovatively solve the “trilemma” of difficulties in loan financing for small and medium-sized enterprises, difficulties in bank risk control, and difficulties in departmental supervision. It can provide a way for the application and innovation of Blockchain, and it can also provide assistance for the development of the financial industry. Therefore, how to make good use of big data and better integrate it into the operation of the financial industry is the primary issue at present.
BIALL Covid-19 Industry Survey 2020: an Introduction

Abstract The British and Irish Association of Law Librarians (BIALL) carried out a survey of BIALL members during May 2020 in order to gain a view, at a point in time, of the impact of Covid-19. 1 National lockdowns in both the United Kingdom and Ireland had come into force in March 2020 . The survey was a way of gauging how the legal information community was responding to the challenges and changing circumstances brought about as a result of the pandemic. This brief introduction by Karen Brown, President of BIALL, sets the scene for the results of the survey which appear in the pages that follow. 2
Pengaruh Keaktifan Belajar dan Motivasi Belajar Siswa Terhadap Hasil Belajar Siswa Kelas VII SMP Negeri 2 Sampolawa

The objective of this research was to find out the effect of students’ learning activeness and learning motivation toward students’ learning achievement in the seventh grade of SMPN 2 Sampolawa. The research was an ex-post facto research which using quantitative approach. Population of the research was all students in the seventh grade of SMPN 2 Sampolawa with the total number was 56 students which spread out in two classes. The sample of the research was all population which 56 students. Meanwhile, in the instruments of the researh were questionnaire and documentation. Data was analyzed using multiple linear regression analysis. The result showed that there was effect of students’ learning activeness and learning motivation toward students’ learning achievement in the seventh grade of SMPN 2 Sampolawa. It could be seen from f value was 5.872, and sig. value was 0.005 &lt; 0.05. it meant that H0 was rejected and H1 was accepted. from this result, it could be conluded that there was effect of students’ learning activeness and learning motivation toward students’ learning achievement in the seventh grade of SMPN 2 Sampolawa.
DEVELOPMENT OF SOCIAL PARTNERSHIP IN FLEXIBLE SKILLS FORMATION OF THE YOUNG PEOPLE IN THE REGION

The article examines the role of social partnership in the formation of flexible skills among young people, taking into account the needs of the region. The material reflects the vision of social partnership of an educational organization, its significance and prospects. Described are the problems that modern youth face upon leaving the educational organization and the ways of their solution. The main directions of activities for the organization of effective social partnership in the aspects of the formation of flexible skills of student youth are revealed.
Anomaly Detection Algorithms in Financial Data

The main aim of this project is to understand and apply the separate approach to classify fraudulent transactions in a database using the Isolation forest algorithm and LOF algorithm instead of the generic Random Forest approach. The model will be able to identify transactions with greater accuracy and we will work towards a more optimal solution by comparing both approaches. The problem of detecting credit card fraud involves modelling past credit card purchases with the perception of those that turned out to be fraud. Then, this model is used to determine whether or not a new transaction is fraudulent. The objective of the project here is to identify 100% of the fraudulent transactions while mitigating the incorrect classifications offraud.
Self-organizing Maps for Optimized Robotic Trajectory Planning Applied to Surface Coating

The process of surface coating is widely applied in the manufacturing industry. The accuracy of coating strongly affects the mechanical properties of the coated components. This work suggests the use of Self-Organizing Maps (Kohonen neural networks) for an optimal robotic beam trajectory planning for surface coating applications. The trajectory is defined by the one-dimensional sequence of neurons around a triangulated substrate and the neuron weights are defined as the position, beam vector and node velocity. During the training phase, random triangles are selected according to local curvature and the weights of the neurons whose beam coats the selected triangles are gradually adapted. This is achieved using a complicated coating thickness model as a function of stand-off distance, spray impact angle and beam surface spot speed. Initial results are presented from three objects widely used in manufacturing. The accuracy of this method is validated by comparing the simulated coating resulting from the SOM-planned trajectory to the coating performed for the same objects by an expert.
DCEA: A Reference model for Distributed Ledger Technologies

Bitcoin's notable success at operating autonomously as a global peer-to-peer network opened the door to a new revolutionary class of distributed systems: blockchain. However, with the rising popularity of this new paradigm, the concept of blockchain shifted greatly to the point where many systems marketed as "blockchain" without sharing the key components introduced by Bitcoin. It has therefore become quite challenging for system designers or decision-makers to achieve a sufficient understanding of different types of existing blockchains or to design and adopt the correct blockchain solution. To overcome this problem, we propose DCEA a holistic reference model for conceptualizing and closely examining blockchains and globally distributed ledger technologies (DLT) using a layer-wise framework that envisions all these systems as constructed of four layers: the data, consensus, execution and application layers.
Belief Rule-Based Adaptive Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm which is successfully applied to enormous applications to solve non-linear and complex high-dimensional optimization problems. The performance of PSO is greatly influenced by the tuning parameters. Due to the presence of uncertainty or noise in the optimization problem of different domains and to maintain a balance between exploration and exploitation in the search space, these parameters need to be adjusted. Therefore, it is crucial to identify the optimal values of the tuning parameters. In this paper, a new Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO) is proposed where the tuning parameters are adjusted dynamically by considering uncertainties, which ensure a balance between exploitation and exploration in the search space. Two Variants of BRBAPSO, namely Conjunctive BRBAPSO and Disjunctive BRBAPSO, are introduced and they are compared with Time-Varying Inertia Weight PSO (TVIW-PSO), Time-Varying Acceleration Coefficient PSO (TVAC-PSO), and Fuzzy Adaptive PSO (FAPSO) using the CEC 2013 real-parameter optimization benchmark functions. The results show that both variants of BRBAPSO outperform other algorithms on the benchmark functions.
Topic Identification via Human Interpretation of Word Clouds: The Case of Instagram Hashtags

Word clouds are a very useful tool for summarizing textual information. They can be used to illustrate the most frequent and important words of text documents or a set of text documents. In that respect they can also be used for topic visualisation. In this paper we present an experiment investigating how the crowd understands topics visualised via word clouds. In the experiment we use the topics mined from Instagram hashtags of a set of Instagram images corresponding to 30 different subjects. By subject we mean the research hashtag we use to gather pairs of Instagram images and hashtags. With the aid of an innovative topic modelling method, developed in a previous work, we constructed word clouds for the visualisation of each topic. Then we used a popular crowdsourcing platform (Appen) to let users identify the topic they believe each word cloud represents. The results show some interesting variations across subjects which are analysed and discussed in detail throughout the paper. Given that the topics were mined from Instagram hashtags, the current study provides useful insights regarding the appropriateness of hashstags as image annotation tags.
StreamBrain

The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other -- less-known -- machine learning algorithms with a mature and solid theoretical foundation whose performance remains unexplored. One such example is the brain-like Bayesian Confidence Propagation Neural Network (BCPNN). In this paper, we introduce StreamBrain -- a framework that allows neural networks based on BCPNN to be practically deployed in High-Performance Computing systems. StreamBrain is a domain-specific language (DSL), similar in concept to existing machine learning (ML) frameworks, and supports backends for CPUs, GPUs, and even FPGAs. We empirically demonstrate that StreamBrain can train the well-known ML benchmark dataset MNIST within seconds, and we are the first to demonstrate BCPNN on STL-10 size networks. We also show how StreamBrain can be used to train with custom floating-point formats and illustrate the impact of using different bfloat variations on BCPNN using FPGAs.
Summarization of video lectures

In any video lecture, there are two important artifacts that need to be taken into consideration, that is, the lecturer’s explanation and the lecturer’s handwritten information (chalkboard representations). Our work mainly focuses on these two aspects and tries to extract the best out of these two artifacts. We have designed the best methods to extract the maximum information out of the explanations and handwritten representations provided by the lecturer and consolidate them into a PDF to act as useful reference material to be used by scholars and working professionals. We are not transforming anything to a general schema; all the information consolidated in the PDF has the characteristics of the respective lecturer. The objective of our solution mainly is to develop a system which can generate a PDF containing text summary and important chalkboard representations of lecture videos which will be helpful for scholars and working professionals taking up online courses or referring to any classroom videos. This would greatly help end users by reducing their time spent on going through complete course before taking up the test for the online course. This study involves developing a component which would help us extract, format and produce output as close to human understanding as possible and make them more efficient in their academic goals.
Designing an incubator for social innovations in libraries: Learnings from the Research through Design approach

Promoting social innovations in libraries requires a new approach to designing innovation programs. In order to provide the effective learning program on social innovations for libraries, the Research through Design methodology was selected as a tool for setting the program for the needs of participants as well as for providing scalable approach to designing innovation programs aimed at increasing the innovation competencies of librarians. Design thinking and the design process are a suitable basis for incubating new socially innovative services. However, the design process needs to include the subsequent phases of the social innovation life cycle, such as demonstrating the impact, its successful presentation or scaling. For a higher social impact, it is necessary to strengthen the importance of participatory community network mapping as a key input in solving community problems and the role and future direction of the library. When designing, it is necessary to integrate all these components into one whole, and when facilitating, it is necessary to pay attention to all these parts. Four runs of the incubation program are described as well as their results, outputs, and learnings from the whole designing process. Gasparini’sconcepts of openness, dialogical spaces and temporality have proven to be applicable also for designing the incubation programs for public libraries. Within the incubation programs attention should be paid both to user-oriented and inner organizational social innovations.
Coalescence of Artificial Intelligence with Blockchain: A Survey on Analytics Over Blockchain Data in Different Sectors

The explosion of availability of analyzable data in the recent past has put Artificial Intelligence (AI) at the forefront of innovations in different sectors of society. On the other hand, the emergence of advanced cryptographic tools and distributed technology has made blockchain, a new giant in the secured data solutions. The possibility of alliance of these two diverse fields has triggered an era of novel opportunities for technological explorations. An effective AI solution can be witnessed when AI explores the robust and secure data-storage/sharing platform provided by the blockchain technology. Blockchain having numerous benefits such as decentralization, immutability, and security, has a wide spectrum of applications ranging from cryptocurrency, financial services, medical healthcare, Internet of Things (IoT) to several others. Due to the efforts made toward the synergy of AI and Blockchain, analytics over blockchain data pertaining to these industries have increased significantly. This survey paper focuses on how the data from different sectors securely stored on blockchain can effectively accelerate the development of the data-hungry analytic techniques that subsume machine learning, classical optimization, visual analytics, big data analytics, and others.
Web service recommendation based on event ontology

Abstract Web service recommendation is an important means to help users quickly discover and select services. Service recommendation combined with situational information is one of the development trends of Web services in the future. This paper proposes a Web service recommendation method based on event ontology, which abstracts Web service requirements into service event classes, according to the historical service records of the target users, Web service recommendations combined with service event ontology and content similarity calculation are recommended, and event class elements and hierarchical models in service event ontology are used as reference, calculate the service similarity and generate the fusion recommendation result according to the weight; Then, based on the current situation status of the user, the user’s current situation is located to a node on the event ontology situation model, the predictive recommendation of services is based on the semantic relationship between event classes in the event scenario model. Finally, the service with the highest similarity among the two parts of service recommendation is recommended to users. The experimental results show that this method can better improve the accuracy and diversity of service recommendation.
Large-Scale Contextualised Language Modelling for Norwegian.

We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first large-scale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian. For additional background and access to the data, models, and software, please see this http URL
An Ontology-Based Query System for University Domain

To solve the problem of empty or incongruent results in semantic queries, the paper proposes a flexible query method based on ontology knowledge base. First, it constructs the ontology knowledge base for university domain, then defines transformation mechanism of fuzzy conditions, and provides transformation methods of fuzzy conditions for numeric types and non-numeric types, on the basis of which the conversion function is defined to fuzzy treat inaccurate conditions from query conditions, and finally the search engine of standard SPARQL is called to implement the query operation. According to the proposed flexible query model, a visual intelligent retrieval system for university resource was developed, we constructed some query instances and invited some users to evaluate the availability of the system, and the corresponding experiment results verified the effectiveness of the system proposed.
An Empirical Study of Incremental Learning in Neural Network with Noisy Training Set

The notion of incremental learning is to train an ANN algorithm in stages, as and when newer training data arrives. Incremental learning is becoming widespread in recent times with the advent of deep learning. Noise in the training data reduces the accuracy of the algorithm. In this paper, we make an empirical study of the effect of noise in the training phase. We numerically show that the accuracy of the algorithm is dependent more on the location of the error than the percentage of error. Using perceptron, feedforward neural network and radial basis function neural network, we show that for the same percentage of error, the accuracy of the algorithm significantly varies with the location of error. Furthermore, our results show that the dependence of the accuracy with the location of error is independent of the algorithm. However, the slope of the degradation curve decreases with more sophisticated algorithms.
Developing Ideas and Methods for Supporting Whole Body Interaction in Remote Co-Design with Children

Drawing from a present need as well as a growing interest towards developing methods to engage participants in research remotely [1,2], this workshop focuses on identifying opportunities and challenges around designing with children from an embodiment perspective [3,4,5,6,10,11,12,13,15]. Themed around whole body interaction and digital technologies [3,7,8,9,10,11,14,15,16,17,18,20,21] the aim of this workshop is to gain methodological insights for the remote participation of children in design, and arrive at design insights addressing the question how we can design remote, inclusive whole body interactions for and with children while attending to child participants’ multimodal communication and bodily engagement [19,21] in a physically dislocated setting [1,2]. The workshop will involve discussions around data gathering from different angles, including relational, ethical, safety and practical perspectives, to create a comprehensive picture of what it entails to orchestrate embodied co-design workshops with children virtually. Outputs from the workshop will contribute to the IDC community by offering methodological insights from attendees’ own experience and presented case studies from researchers and practitioners from both non-academic and academic settings to support children's embodied engagement in remote workshop situations and beyond.
TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER, by Tucker S.McElroy and Dimitris N.Politis. Published by CRC Press, 2020. 586 pp. ISBN: 9781439876510

Journal of Time Series AnalysisEarly View Book Review TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER, by McElroy, Tucker S. and Politis, Dimitris N.. Published by CRC Press, 2020. 586 pp. ISBN: 9781439876510 Alexander Aue, Corresponding Author Alexander Aue aaue@ucdavis.edu orcid.org/0000-0003-1553-0509 Search for more papers by this author Alexander Aue, Corresponding Author Alexander Aue aaue@ucdavis.edu orcid.org/0000-0003-1553-0509 Search for more papers by this author First published: 24 June 2021 https://doi.org/10.1111/jtsa.12606Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
EVOLUTION OF KARAKORAM SEQUENCE, HUNZA VALLEY NE PAKISTAN: EVIDENCES FROM FIELD MAPPING AND MICRO-ANALYTICAL WORK

The Karakoram metamorphic Complex (KMC) in the southern Karakoram block is one of the best examples of Barrovian type metamorphism that comprises numerous exhumed metapelite units where a series of low to high grade (green schist to sillimanite facie) rocks are exposed. This sequence shows a complex polyphase history of metamorphism and deformation which offer deeper understanding of collision orogeny. Karakoram metamorphic Complex contains metapelites, meta-carbonates, meta-igneous and amphibolite layers, cross-cut by granite sheets in the northern part. This complex is bounded to the north by the Hunza plutonic unit which is the central portion of the massive east-west trending Karakoram axial batholith and to the southwest by the Main Karakoram thrust (MKT). In this contribution, we provide detail geological mapping, petrography, geochemistry and micro-analytical work using Electron Prob-micro analysis in the central Hunza Valley.
Adaptation of Students to the Principles and Norms of Healthy Lifestyle in the Face of Uncertainty in the Situation of the COVID-19 Pandemic

The authors focus on the increased interest of students and society as a whole in healthy lifestyle, which is associated with public concern about the health of future specialists, both during their studies in higher school and in the course of their future professional activities, since the increase in morbidity in the course of professional training will be a factor in reducing the performance of young specialists. The article examines how the change in the way of life of students in connection with the COVID-19 pandemic and the introduction of a self-isolation regime affected students ' compliance with the principles and norms of healthy lifestyle, how they were able to adapt to the conditions of uncertainty created by the COVID-19 pandemic. The authors collected and analyzed information about the lifestyle of 85 university students, using expert assessment methods and sociological methods. The results of the analysis allowed us to draw a conclusion about the destructive nature of the lifestyle of our students, non-compliance with a significant part of the requirements of healthy lifestyle and the need to change approaches in promoting healthy lifestyle, using innovative methods and modern technologies.