Reviewerly/exHarmony huggingface dataset:
Blockchain Using Supply Chain Management for Society 5.0

It is highly challengeable to manage tasks and products in a large supply chain domain. Data fragmentation, low security source, and different terms across many issues and processes act as a blockade in order to detect results. Blockchain provides control, immutability, secure traceability, and trust creation among stakeholders in a low-price IT solution. So, Blockchain has turned up to be a leading technology. In order to handle the quality of supply chain management and use the distributed public general ledger in an effective, we can use Blockchain technology. It is supportive in the supply chain domain such as to avoid product lag, accelerate client/provider trust, remove fraudulent tasks, reduce errors, enhancing management, and so on. In real time, goods and services are tracked and recorded using Blockchain. In case of transparency, it provides information about variety of transactions. In spite of the fact that Blockchain is creating a great impact in many areas, there are many hindrances to its worldwide adoption in supply chain area. Understanding Blockchain and its value in today’s supply chain, key characteristics of Blockchain followed by why Blockchain is introduced to supply chain operations, benefits and challenges in supply chain for Society 5.0 with the help of Blockchain are critically analysed in this chapter.
Data Strings

(Daniel Pearson, Pau Garcia, and Alexandra de Requesens) Our Data Strings installation is an analog collective and living graphic that aims to make people participate and visualize the audience's thoughts around various topics. It is an engaging, fun and relatable way of showing information.
Exploration of Solar Energy in Papua New Guinea through Remote Sensing and GIS

Climatology and meteorology are essentially driven by the incoming solar radiation and the latter’s latitudinal distribution. In the current climate change scenario, every nation is looking forward to taking control of its greenhouse emissions as a mitigation measure. Rapid exploitation of renewable energy and energy competence have culminated in significant energy safety and self-reliance for the fossil fuel importing countries, apart from climate change mitigation and economic benefits. The availability of solar radiation data is essential in order to evaluate the potential of renewable energy options such as photovoltaic power generation capability in a developing economy like Papua New Guinea.&nbsp;Over the past few decades, Papua New Guinea (PNG) has experienced an increase in electrification and the usage of sustainable energy. The bulk of PNG's population (85%) lives in isolated and dispersed villages in rural areas. Most of these isolated and dispersed areas are still yet to be connected to an electricity supply. Installation of rooftop solar panels can easily generate electricity without affecting the environment in the city area or isolated village area situated from the major infrastructure. In a specified latitudinal expanse, the topography is a major factor that determines the spatial distribution of insolation. Spatial variability of topographic elevation, slope, aspect, and shadows influence the amount of insolation received at different point locations. The variation in incoming solar radiation over space and time for power generation using photovoltaic panels also warrants proper feasibility assessment before investment. In this scenario, solar radiation modeling is attempted in this paper to map and analyse the effects of the sun over a geographic area for specific time periods. Different parameters that are mandatory, like atmospheric condition, latitudinal position, elevation, slope and aspect, sun angle, and topographic shadows are taken into account for the modeling purpose. This research is also an initiation to identify potential roof-tops for solar panel installation. Therefore, micro-level solar radiation modeling is attempted using the high-resolution topographic database.
Artificial Intelligence in Veterinary Pathology

The simulation of human intelligence processes by machines, particularly computer systems, is known as artificial intelligence (AI). Learning, reasoning and self-correction are the three intelligence components that AI possesses. Strong AI systems can solve problems on their own when faced with new ones. The purpose of weak AI is to make robots act intelligently. Today, AI is being used in the healthcare industry to maintain digital health records and perform patient checkups with the aid of cutting-edge technologies. This chapter aims to discuss the utilization of Artificial Intelligence in Veterinary Pathology.
Designing the Beachside Area in Lombok Using Sustainable Principles: A Case Study in Gading Beach

The present study&nbsp; establish the design proposal of new buildings in the Gading Beach area ought to consider a holistic sustainable approach to create buildings that will encourage environmental, social and economic benefits. The West Nusa Tenggara (NTB) island of Lombok, which is known as one of the world's top halal tourism destinations, expects tremendous tourism development. In response, the local government is attempting to hasten the building of the infrastructure needed to facilitate tourism, notably the area around Gading Beach. On contrary, however, buildings are known among the world’s biggest energy consumption. Therefore,. Five key sustainable design principles were utilised for the development of the Gading Beach area. Those principles are including site and ecology, community and culture, health, materials and energy. The implementation of these design strategies in the Gading Beach area is expected to exhibit an effective application of a sustainable approach in beachside buildings.&nbsp; This study is expected to illustrate a comprehensive application of sustainable principles in building, particularly in the beachside area.An evaluation tool needs to be formulated to help to assess the success level of sustainable buildings in the future.
Complex Event Processing Architectures for Smart City Applications

The amount of data that is being generated on a daily basis is huge. Therefore, it has become necessary to separate the data that are useful in order to use them in a meaningful manner. Due to the dynamic nature of data, it is also important that the data are managed in real time. Complex event processing (CEP) is one such technique that has proved to be extremely reliable in dealing with the huge amount of data available. There exists a wide range of tools and methods that make it possible to generate desirable results using CEP. Thus, the primary aim of this chapter is to give an overview of CEP, including the various methods and strategies involved in it. In addition to this, the chapter gives a detailed description of many event processing system architectures and lists the real-world applications of this technology and the challenges that it helps to solve.
Intelligent Distributed Computing Paradigm

Nowadays, very complex computing infrastructures are deployed to deal with the emergence of large amounts of generated data in several domains, with the most cases based on Internet facilities, inexpensive computing units, and advanced communication technologies. Single computing nodes are no longer suitable to respond to the current overwhelming need of the contemporizing systems like social and transport networks. A distributed computing paradigm comes up with an alternative to surpass the former classical architecture constraints in terms of security issues, single points of failure, scalability, and performance. It has emerged as a consequence of the evolution made in the networking and communication technologies, but remains a difficult and complex research area, and a lot of challenges are still present when dealing with the design of such systems, as there are bottlenecks and complexities related to the distributed computing paradigm like synchronization, communication, and performance. Intelligent distributed computing attempts to benefit from intelligence and distributed computing by adapting and synchronizing the concepts from both areas in order to overcome all these shortcomings. Therefore, intelligent distributed computing focuses on the integration of innovative architectures and technologies to develop new generations of intelligent distributed systems. The progress made by intelligent computing in the development of new technologies and strategies has enormously contributed to the creation of advanced architectures composed of collaborating and interacting modules like multiagent systems.
Pathways to digital L2 literacies for text-based telecollaboration at the beginning level

Telecollaboration, a widely used format of virtual language exchange in second language instruction, targets predominantly intermediate and advanced learners; however, there has been little research on beginning learners. To address this gap, this chapter considers the benefits of social media–based telecollaboration for second language beginners in the context of the digital literacies framework. Inspired by the emerging socio-semiotic paradigm in semiotics and communications studies, we conceptualize digital second language literacies as a dynamic interaction between affordances provided by digital environments and learners’ digital mindsets. To understand the benefits of telecollaboration at beginning levels, we explore features of social media to show how beginners capitalize on existing cultures-of-use to engage in meaningful interactions in social media. This conceptual exploration is supported by published and unpublished data from a large empirical study of communication practices during a telecollaboration project conducted in elementary Russian language classes.
Data-driven learning in a low-level language classroom

This chapter briefly reviews theoretical concepts and terminology associated with data-driven learning, a hands-on use of corpora by the students. The advantages and limitations of data-driven learning from both the learner’s and instructor’s perspectives are discussed prior to the description of a low-intermediate English classroom mixed-method study that utilized the Corpus of Contemporary American English. Even though the qualitative portion of this study suggested that most students enjoyed completing corpus work, there were no statistically significant changes in the students’ usage of low-, mid-, and high-frequency words across the analyzed writing samples. The chapter closes with a few corpus tools and pedagogical suggestions that can be immediately put into action by instructors willing to try out data-driven learning in their adult low-level language classrooms.
Combining NSP and NER for public opinion event extraction model

Event extraction in the field of public opinion aims to extract important event arguments and their corresponding roles from the moment-to-moment generated opinion reports. Most of the existing research methods divide the task into three subtasks: event trigger extraction, event type detection, and event argument extraction. Despite the remarkable achievements of the event argument extraction paradigm combining part-of-speech (POS) and event trigger features, the performance of POS features in combinatorial event argument extraction tasks is struggling due to its inherent semantic diversity in Chinese. In addition, previous research work ignored the deep semantic interaction between event trigger and text. To address the aforementioned problems, this paper proposes an opinion event extraction model (NN-EE) combining NSP and NER, which alleviates the lack of performance of combinatorial event argument extraction by introducing NER technology. Meanwhile, the event trigger features are incorporated into the NSP mechanism of the pre-trained language model BERT to prompt the model to learn the deep semantic interaction between the event trigger and original text. The results of the self-constructed food opinion report dataset (FD-OR) in this paper show that the NN-EE model achieves optimal performance.
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction

The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study networks with a single type of connection between vertices, while in many applications interactions between objects vary, yielding multiplex networks. We propose ANOMULY, a general, unsupervised edge anomaly detection framework for multiplex dynamic networks. In each relation type, ANOMULY sees node embeddings at different GNN layers as hierarchical node states and employs a GRU cell to capture temporal properties of the network and update node embeddings over time. We then add an attention mechanism that incorporates information across different types of relations. Our case study on brain networks shows how this approach could be employed as a new tool to understand abnormal brain activity that might reveal a brain disease or disorder. Extensive experiments on nine real-world datasets demonstrate that ANOMULY achieves state-of-the-art performance.
Romantic Science, a Light-speed Trip, SF: Focusing on Kim Bo-young"s People Going to the Future

21세기 한국의 문학과 문화를 설명하는데 있어 ‘장르적 상상력’이라는 새로운 창작의 원천을 제외하는 일은 이젠 가능하지 않다. 그중 과학소설(SF, Science Fiction)은 판타지와 더불어 새로운 세대의 상상력이 발아되는 양질의 지반이 되고 있다. 한국의 SF를 논할 때 빠질 수 없는 대표적 작가 중 한 명은 김보영이다. 김보영이 자신의 작품에서 빈번하게 다루는 소재 중 하나는 광속에 가까운 속도로 우주를 여행할 때 만들어지는 시공간의 변화, 이를 통해 필연적으로 발생하는 인간의 놀라운 지적, 감각적 경험이다. 시간과 공간을 아무런 관계도 없는 독립된 기준으로 계산했던 뉴턴과 달리 아인슈타인은 여기에 시간이라는 네 번째 차원을 도입하였다. 인간이 빛의 속도에 가깝게 이동할 수 있다면 그들의 시간은 멈춰있는 사람이 체감하는 시간보다 아주 느리게 간다는 것이 증명된 것이다. 2004년 발표한 김보영의 중편인 ｢미래로 가는 사람들｣은 이러한 빛의 속도가 발생시키는 시간의 왜곡을 소재로 사용한 초기 대표작 중 하나이다. 김보영이란 작가의 관심이 집중되는 곳은 광속여행이라는 새로운 기술이 만들어지는 과정이나 이러한 새로운 테크놀로지의 등장과 함께 변해가는 사회가 아니다. 김보영에게 광속여행은 비교적 잘 알려진 양자물리학의 이론적 논의와 SF의 다양한 고전적 서사 속에서 가져온 일종의 장치이며, 서사를 전개시킬 수 있는 안정적 지평이다. 중요한 것은 그 지평 위에서 전개되는 문학적 플롯과 서사가 담아내고 있는 인간의 이야기이다. 김보영의 우주 서사가 담아내고 있는 것은 낭만주의 과학관에서 발아한 끝없는 지적 탐구의 여정을 삶의 지향으로 하는 인간에 대한 찬미이다. It is no longer possible to exclude a new source of creation called genre imagination in explaining Korean literature and culture in the 21st century. Science Fiction (SF) is becoming a high-quality ground for a new generation of imagination along with fantasy. One of the most important writers in discussing Korean science fiction is Kim Bo-young. One of the materials Kim Bo-young frequently deals with in her work is the change in time and space made when traveling in space at a speed close to the speed of light. And it is an amazing intellectual and sensory experience of humans that inevitably occurs through light-speed travel. Unlike Newton, who calculated time and space on an independent basis, Einstein introduced a fourth dimension, time. If humans can move close to the speed of light, their time has proved to be much slower than the time felt by a stopped person. Kim Bo-young"s novel People Going to the Future, released in 2004, is one of the earliest representative works that used the distortion of time caused by the speed of light. What Kim Bo-young looks at with interest is not the process of creating new technologies or a society that is changing with the advent of these new technologies. For Kim Bo-young, light speed travel is a kind of device brought from the theoretical discussion of quantum physics, which is relatively well-known, and various classical narratives of SF. It is only a stable horizon that can develop the narrative of the novel. What is important is the human story of literary plots and narratives that unfold on the horizon. Kim Bo-young"s space narrative contains praise for humans who aim for life on the journey of endless intellectual exploration and Faustic subjects discovered in romantic science.
Analysis of the Use of Technologies in &lt;Artificial Intelligence Mathematics&gt; Textbooks Focused on Achievement Standards of the 2015 Revised Curriculum

Objectives The purpose of this study is to analyze the use of technologies revealed in the new subject &lt;Artificial Intelligence Mathematics&gt; textbook. Methods Five textbooks were analyzed based on the ‘related learning contents’ of the ‘area/core concept’, the type of technologies, and the use of technologies was analyzed from the perspective of achievement standards. Results First, textbooks used technologies only in some related learning contents such as matrices and trend lines. Second, textbooks used a total of 16 technologies and websites. Third, only some of the teaching and learning methods and precautions of &lt;Artificial Intelligence Mathematics&gt; related to technologies were reflected in the textbook. Conclusions The &lt;Artificial Intelligence Mathematics&gt; textbook should be configured to use technologies in more various related learning elements. Furthermore, it is necessary to consider in-depth the teaching and learning method of &lt;Artificial Intelligence Mathematics&gt;, which recommends the use of technologies.
Expert- Independent Initial Weights Assignment Technique for Learning Fuzzy Cognitive Map With Hebbian Learning Algorithms

Abstract Fuzzy Cognitive Maps (FCMs) are single layer neural network-like Supervised Machine Learning Algorithm which can be used as a tool for modelling dynamic systems in a graphical cause-effect relationships form. Though a powerful tool, FCM do not always converge to a desired state but relies on other learning algorithms to find connection matrix that will lead the system to a stable state. Of the various algorithms for learning FCM connection matrix, Hebbian variants are the earliest and simplest. However, they depend on human experts for initial weight matrix before they can be applied or learning commences. So without human experts, these algorithms are limited and even with availability of human expert, the weights could be a result of experts’ subjective opinion or limited knowledge of the system. This paper present scientific method and algorithm for finding initial weight from node activation values and guide in the choice of map density. This allow application of Hebbian learning algorithms without human experts and a multi-map and multi-density Hebbian Learning solution of FCM which may provide optimal solution than single map expert initial map
Student Family as an Object of Scientific Research (Review of Scientific Research on the Problems of Student Families)

The purpose of the research is to study approaches to the concretization of the student family as an object of scientific research and policy pursued by the state, as well as to analyze available scientific research (on the problems of student families) and present them in chronological order, in retrospect. The methodological basis of the research consists of general logical methods (analysis, synthesis, induction), theoretical methods (the axiomatic method used by the authors in the analysis of historical sources; the historical method involving the study of the emergence and development of students and student families in chronological sequence), methods of sociological analysis of the functioning of the institute of student family. The results of the research. The review of scientific views on the evolution of the student family category, its functions and roles in society is presented. The prospects of the research are to use the materials obtained for a more in-depth study of the place and role of the student family in society at a certain stage of historical development.
Machine learning with nonlinear state space models

Abstract In this dissertation, a novel class of model structures and associated training algorithms for building data-driven nonlinear state space models is developed. The new identification procedure with the resulting model is called local model state space network (LMSSN). Furthermore, recurrent neural networks (RNNs) and their similarities to nonlinear state space models are elaborated on. The overall outstanding performance of the LMSSN is demonstrated on various applications.
Identification and Classification of Risk Factors in Distributed Agile Software Development

Distributed Agile Software Development (DASD) is an amalgamation of Agile Software Development (ASD) with Distributed Software Development (DSD). Although DASD integrates the speed benefits of ASD with the cost benefits of DSD, however, it brings along various risk factors that arise due to the fact that both ASD and DSD works on a different set of principles. These associated risks must be addressed and managed well in time for the successful completion of the project. This paper reviews the current literature and presents the current challenges of Risk Management in the DASD environment. This paper also determines 71 risk factors associated with DASD and analyses them based on their causes and sources. Further, these risk factors are segregated into 11 different categories. Timely management of these risks may reduce the uncertainty of project failure in the DASD environment.
A Memory-Driven Neural Attention Model for Aspect-Based Sentiment Classification

Sentiment analysis techniques are becoming more and more important as the number of reviews on the World Wide Web keeps increasing. Aspect-based sentiment analysis (ABSA) entails the automatic analysis of sentiments at the highly fine-grained aspect level. One of the challenges of ABSA is to identify the correct sentiment expressed towards every aspect in a sentence. In this paper, a neural attention model is discussed and three extensions are proposed to this model. First, the strengths and weaknesses of the highly successful CABASC model are discussed, and three shortcomings are identified: the aspect-representation is poor, the current attention mechanism can be extended for dealing with polysemy in natural language, and the design of the aspect-specific sentence representation is upheld by a weak construction. We propose the Extended CABASC (E-CABASC) model, which aims to solve all three of these problems. The model incorporates a context-aware aspect representation, a multi-dimensional attention mechanism, and an aspect-specific sentence representation. The main contribution of this work is that it is shown that attention models can be improved upon using some relatively simple extensions, such as fusion gates and multi-dimensional attention, which can be implemented in many state-of-the-art models. Additionally, an analysis of the parameters and attention weights is provided.
A Proposed Anti-Fraud Authentication Approach for Mobile Banking Apps

This paper proposes a novel user-transparent unavoidable authentication approach for mobile banking apps. It is called Mobile Device Fingerprinting-Based Identifier and Authenticator (MDFIA). The objective is to detect and prevent mobile banking fraud despite the spoofer knowing the legitimate bank customer user's credentials. MDFIA exploits mobile device fingerprinting to generate a unique 256-bit mobile device credentials hash for each user. MDFIA merges the user's credentials with the user's unique 256-bit mobile device credentials hash to distinguish a legitimate bank account owner from a spoofer. In this context, verification of a user's unique 256-bit mobile device credentials hash acts as a second authentication factor that is transparent to the user, and cannot be circumvented. Thus, through MDFIA and by exploiting a mobile device's unique 15-digit code International Mobile Equipment Identity of the Subscriber Identity Module card slot of the registered bank's customer mobile number, it is completely insufficient for a fraudulent to illegally know a victim user's credentials, and use a similar mobile device as that of the victim. However, it is essential for the fraudulent to illegally know a victim user's credentials, and additionally use the mobile device owned by the victim itself, which is practically very difficult to achieve.