Reviewerly/exHarmony huggingface dataset:
Introduction

Distributed ledger technology (DLT), more commonly called “blockchain,” has captured the imaginations and wallets of the financial services institutions. A blockchain is defined as a decentralized, continuously growing list of records, called “blocks,” across a peer-to-peer network that are linked and secured using cryptography. Each block typically contains a cryptographic hash of the previous block, a timestamp, and transaction data, and contains the information from all previous blocks and transactions to create a network or chain. Once the blockchain processes the information, every computer in the network locks in at the same time, creating a permanent, hard to alter digital record. Each blockchain system determines who can add new blocks to the chain, and how the procedure is done. (Lee Kuo Chuen 2015)
Galois Fields GF(2m)

After the presentation of the basic algebraic circuits, this chapter is specifically dedicated to the circuits based on the finite fields GF(2m). Finite fields or Galois fields have a variety of applications in several areas such as cryptography, coding and digital signal processing.
Survey of State-of-Art in Green Cloud Computing

Cloud computing is witnessing tremendous growth at one time when climate change and reducing emissions from energy use is gaining attention. With the growth of the cloud, however, comes an increase in demand for energy. There is growing global awareness about reducing greenhouse gas emissions and healthy environments. Green computing in general aims to reduce the consumption of energy and carbon emission and also to recycle and reuse the energy usage in a beneficial and efficient way. Energy consumption is a bottleneck in internet computing technology. Green cloud computing related technology arose as an improvement to cloud computing. Cloud data centers consume inordinate amounts of energy and have significant CO2 emissions as they have a huge network of servers. Furthermore, these data centers are tightly linked to provide high performance services, outsourcing and sharing resources to multiple users through the internet. This paper gives an overview about green cloud computing and its evolution, surveys related work, discusses associated integrated green cloud architecture – Green Cloud Framework, innovations, and technologies, and highlights future work and challenges that need to be addressed to sustain an eco-friendly cloud computing environment that is poised for significant growth.
Models, Postulates, and Generalized Nomic Truth Approximation

The qualitative theory of nomic truth approximation, presented in Kuipers (From instrumentalism to constructive realism. On some relations between confirmation, empirical progress, and truth approximation. Synthese library, vol. 287. Kluwer Academic Publishers, Dordrecht, 2000), in which ‘the truth’ concerns the distinction between nomic, e.g. physical, possibilities and impossibilities, rests on a very restrictive assumption viz. that theories always claim to characterize the boundary between nomic possibilities and impossibilities. In Chap. 2 we have already shown that truth approximation is possible by ascribing, in Popperian spirit, theories only an excluding function. By fully recognizing two different functions of theories, viz. excluding and representing (including), this chapter drops the restrictive assumption by conceiving theories in development as tuples of postulates and models, where the postulates claim to exclude nomic impossibilities and the (not-excluded) models claim to represent nomic possibilities. Revising such ‘two-sided’ theories becomes then a matter of adding or revising models and/or postulates in the light of increasing evidence, now captured by a special kind of theories, viz. ‘data-theories’. Under the assumption that the data-theory is true, achieving empirical progress on its basis provides good reasons for the abductive conclusion that truth approximation has been achieved as well. As in Chap. 2 , the notions of truth approximation and empirical progress are formally direct generalizations of the earlier ones. However, truth approximation is now explicitly defined in two-sided terms of increasing truth-content and decreasing falsity-content of theories, whereas empirical progress is defined in terms of lasting increased accepted-content and decreased rejected-content in the light of increasing evidence. As already noted in Chap. 2 , these definitions are strongly inspired by a paper of Gustavo Cevolani, Vincenzo Crupi and Roberto Festa, viz., “Verisimilitude and belief change for conjunctive theories” (Erkenntnis, 2011).
Fourier Transform

This chapter presents an evolutional journey from Fourier seriesFourier series to Fourier transformFourier transform and to discrete Fourier transformFourier transform . It aims to prepare readers with mathematical fundamentals and build a foundation for understanding wavelet theories. It begins with sinusoidsSinusoids that are the building blocks of Fourier transformFourier transform . After the fundamental properties of sinusoidsSinusoids are understood, Fourier seriesFourier series and Fourier transformFourier transform are formally introduced. It is followed by describing DFTDiscrete Fourier Transform (DFT) and samplingSampling theory. The connection between DFTDiscrete Fourier Transform (DFT) and its application on signal processing is systematically revealed to the readers. By finishing the Fourier transformFourier transform chapter, readers will be able to understand the power and limitations of Fourier transformFourier transform , this naturally leads to windowed FT in the following chapter.
School in a Box in Low Resource Primary School in Mozambique

The new Sustainable Development Goals call for quality education for all. This study aims to examine the role of mobile educational technology, through the School in a Box intervention, particularly in supporting teachers skills in achieving quality education, in the context of a public primary school in Mozambique. Quality education is examined specifically in relation to participative pedagogical practices designed to contextualise learning in locally relevant meaning, and providing children with the skills of learning how to learn. Additionally it is examined in the context of teacher professional development and creating peer support structures within the teaching community enabling teachers to be at the forefront of initiatives introducing mobile educational technology in low resource learning environments in Sub Saharan Africa. This paper sets the context for the use of the School in a Box tool in the classroom and in teacher professional development against the theoretical framework of Vygotsky's zone of proximal development. The study aims to furthermore examine how teachers can be enabled to appropriate use of the technology to the local environment and curriculum needs.
Sparse matrix–vector multiplication

Abstract This chapter introduces irregular algorithms and presents the example of parallel sparse matrix-vector multiplication (SpMV), which is the central operation in iterative linear system solvers. The irregular sparsity pattern of the matrix does not change during the multiplication, which may be repeated many times. This justifies putting a lot of effort into finding a good data distribution. The Mondriaan distribution of a sparse matrix is a useful non-Cartesian distribution that can be found by hypergraph-based partitioning. The Mondriaan package implements such a partitioning and also the newer medium-grain partitioning method. The chapter analyses the special cases of random sparse matrices and Laplacian matrices. It uses performance profiles and geometric means to compare different partitioning methods. Furthermore, it presents the hybrid-BSP model and a hybrid-BSP SpMV, which are aimed at hybrid distributed/shared-memory architectures. The parallel SpMV can be incorporated in applications, ranging from PageRank computation to artificial neural networks.
Psychosemiotic analysis of virtual self-representation of Russian teachers: Evidence from social networks

А. Б. Углова, Н
Encouraging Digital Literacy and ICT Competency in the Information Age

This chapter determines the overview of digital literacy and ICT competency, the encouragement of digital literacy in the information age, and the encouragement of ICT competency in the information age. The encouragement of digital literacy and ICT competency is essential for modern organizations that seek to serve suppliers and customers, increase business performance, strengthen competitiveness, and achieve regular prosperity in the information age. Thus, it is required for modern organizations to encourage their digital literacy and ICT competency and develop a strategic plan to regularly investigate their practical improvements toward satisfying customer requirements. The chapter argues that encouraging digital literacy and ICT competency has the potential to enhance organizational performance and achieve strategic goals in the information age.
A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.
Cybersecurity and Human Capital in Community Banks

The topic is cybersecurity and human capital. The research question explored is, “Do United States (US) community businesses have the right human capital and technology resources to effectively counter the rising threat of cybercrime in the US?” Research findings conclude that US businesses need to increase their efforts and invest in technologies, staff, technical training, and processes and programs aimed at improving the use of risk-based assessments, defenses, intrusion and anomaly detection, and the business's ability to recover should a cybercrime take place.
Student Perceptions on the Effectiveness of Collaborative Problem-based Learning Using Online Pair Programming Tools

his paper describes a qualitative study of how undergraduate students majoring in Information Technology perceive the effectiveness and evaluate the learning experience of pair-programming. The phenomenographic research approach was used to analyze student interviews and revealed 4 categories of descriptions: Effective Problem Solving, Participation, Enjoyment and Coding. Pair-programming as a teaching methodology was commonly perceived as a positive experience. The resulting outcome space maps a logical hierarchy of students’ conceptions of reality (categories of description). Findings of this research identify the factors that affect student engagement in a problem-solving process and can be used as a guiding principle on how to improve students’ learning experience of computer programming.
CyberIR@MIT: Exploration &amp;amp; Innovation in International Relations

This paper presents a brief introduction to Cyber-IR@MIT—a dynamic, interactive knowledge and networking system focused on the evolving, diverse, and complex interconnections of cyberspace and international relations. The goal is to highlight key theoretical, substantive, empirical and networking issues.Cyber-IR@MIT is anchored in a multidimensional ontology. It was initially framed as an experiment during the MIT-Harvard collaboration on Explorations in Cyber International Relations (MIT, 2009-2014) to serve as a forum for quality-controlled content and materials generated throughout the research project.The vision for Cyber-IR@MIT is shaped by the research for Cyberpolitics in International Relations, a book written by Nazli Choucri and published by MIT Press in 2012. The operational approach to the knowledge system is influenced by the Global System for Sustainable Development (GSSD), developed earlier and focused on challenges of system sustainability. Cyber-IR@MIT gradually evolved into a knowledge-based system of human interactions in cyberspace and international relations, all embedded in the overarching natural system.The method consists of differentiating among the various facets of human activity in (i) cyberspace, (ii) international relations, and (iii) the intersection of the cyber and “real.” It includes problems created by humans and solution strategies, as well as enabling functions and capabilities, on the one hand, and impediments to behavior and associated barriers, on the other. See https://cyberir.mit.edu for functions. The value of this initiative lies in its conceptual foundations and method of knowledge representation – embedded in an interactive system for knowledge submission, with f search and retrieval functions.
A Novel Framework for Efficient Extraction of Meaningful Key Frames From Surveillance Video

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.
zucchero caffettino

Abstract Various hypotheses have been put forward to explain the etymology of Old It. (zucchero) caffettino ( Jaffa &lt; Yāfā , cafis &lt; qafīz , Caffa = Feodosia , caffa &lt; qafaṣ , Haifa &lt; Ḥayfā , Kafá &lt; ?, coffa &lt; quffa(h) ). All these proposals, however, are unsustainable for reasons of cultural-historical and/or semantic, phonetic and morphological incompatibilities. In reality the etymon is the Arabic nisba adjective of the name of the Egyptian city of Qifṭ ( qiftī ), which in Medieval times was highly renowned for its sugar production. The Arabic equivalent of zucchero caffettino is as-sukkar al-qiftī . The phonomorphological adaptation of caffettino is absolutely regular (anaptyctical e , gemination f &gt; ff , initial ca - &lt; qi -, final – ī &gt; - ino ); the original type qiftī is still reflected by the ancient Italian and Romance variants chafethi , cafeti and cafati .
Evolution of neural networks

tutorial Public AccessEvolution of neural networks Share on Author: Risto Miikkulainen The University of Texas at Austin and Sentient Technologies, Inc. The University of Texas at Austin and Sentient Technologies, Inc.View Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 495–525https://doi.org/10.1145/3377929.3389858Online:08 July 2020Publication History 0citation111DownloadsMetricsTotal Citations0Total Downloads111Last 12 Months51Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Introduction

Navigation generally refers to the determination of the orbit (position and velocity) and attitude parameters of a vehicle (or a moving body) relative to a coordinate system at a given time. In general, spacecraft navigation refers only to the determination of orbit parameters. In general, the navigation relying on only the onboard measuring and computing devices rather than ground support is referred to as spacecraft autonomous navigation, while the spacecraft autonomous navigation realized by the fusion processing of multiple information sources (multiple observed objects, multi-sensor measurements, priori knowledge, etc.) is called multi-source information fusion-based autonomous navigation. Equation Chapter (Next) Section 1.
Artificial Neural Network What-If Theory

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.
Image features for vision-based robot manipulation based on deep reinforcement learning

Deep Reinforcement Learning (DRL) provides a potential toolset that enables industrial robots to autonomously learn manipulation skills, but the learning efficiency (success rate within certain learning episodes) is the bottleneck. In this work, we ascertained well-designed environmental observations to be vital for improving efficiency. To determine the impacts of different observations, we conducted simulation experiments of robots grasping, and evaluated three popular categories of environment observations -positions of the Tool-Center-Point, raw images from a fixed viewpoint camera, and image features (Sobel, Laplacian, HOG, LBP). The results indicate "image features" proved to be superior to the others, they contribute to higher success rate and learning speed.
Need of Hadoop and Map Reduce for Processing and Managing Big Data

The amount of data around us in three sixty degrees getting increased second on second and the world is exploding as a result the size of the database used in today's enterprises, which is growing at an exponential rate day by day. At the same time, the need to process and analyze the bulky data for business decision making has also increased. Several business and scientific applications generate terabytes of data which have to be processed in efficient manner on daily bases. Data gets collected and stored at unprecedented rates. Moreover the challenge here is not only to store and manage the huge amount of data, but even to analyze and extract meaningful values from it. This has contributed to the problem of big data faced by the industry due to the inability of usual software tools and database systems to manage and process the big data sets within reasonable time limits. The main focus of the chapter is on unstructured data analysis.
Context-Responsiveness

Context-responsiveness refers to the quality of a system, artefact or agent of dynamic feedback or adaptation in response to contextual conditions as they evolve in an interactional environment. For the field of communication for social change, the concept of context-responsiveness opens up, in first instance, a new way of looking at change: when something changes, this reverberates in waves of change that expand in interconnected systems. This chapter proposes an analysis of these systems as fluid contexts generated by interactional situations and inclusive of objects, people, activities and psychological processes. This perspective is used to examine critical issues in communication for social change, such as cultural change, human and technical agency, and the design of development initiatives that align dynamically to evolving conditions.
Term Rewriting-Based Programming

One of the directions of algebraic programming is the use of rewriting rules technique. This direction formalizes the transformational aspects of programming, which allows us to describe transformations of some formal objects and research properties of such transformations. The rewriting rules technique is both a powerful formal tool for transformation of formal systems and a practical tool for programming that allows implementing transformations of complex objects. In this chapter, the main definitions associated with algebraic programming based on rewriting rules are given, term rewriting systems are overviewed, and applications of these systems for processing and transformation of programs are considered.
The Aesthetics of Virtual Reality

Journal Article The Aesthetics of Virtual Reality Get access The Aesthetics of Virtual Reality GRANT TAVINOR Routledge, 2021. PP. 178. £104.00 (HBK). Nicholas Whittaker Nicholas Whittaker The City University of New York, USA nwhittaker@gradcenter.cuny.edu Search for other works by this author on: Oxford Academic Google Scholar The British Journal of Aesthetics, Volume 63, Issue 2, April 2023, Pages 293–297, https://doi.org/10.1093/aesthj/ayab070 Published: 02 March 2023
Summary and Outlook

In this chapter we summarize the main theoretical and experimental results presented in this book.
Using External Attention in Vision-based Autonomous Driving

Imitation learning (IL) method provides a concise framework for autonomous driving, and it learns the policy from human demonstration via mapping from sensor data to vehicle controls. However, how to achieve effective learning is an ongoing challenge in driving policy learning. A feasible solution is to introduce attention mechanism, which enables the deep model focus on the features related to the driving task. In this paper, we propose to train a driving policy model with latest external attention joining the policy network as blocks. The experimental results on CARLA driving benchmark demonstrate our external attention guided driving policy model has better performance and costs less training time than the model without attention mechanism. Moreover, our method outperforms the state-of-thearts self-attention driving policy methods.