Reviewerly/exHarmony huggingface dataset:
Hypernetwork Representation Learning with Common Constraints of the Set and Translation

Different from conventional networks with only pairwise relationships among the nodes, there are also complex tuple relationships, namely the hyperedges among the nodes in the hypernetwork. However, most of the existing network representation learning methods cannot effectively capture the complex tuple relationships. Therefore, in order to resolve the above challenge, this paper proposes a hypernetwork representation learning method with common constraints of the set and translation, abbreviated as HRST, which incorporates both the hyperedge set associated with the nodes and the hyperedge regarded as the interaction relation among the nodes through the translation mechanism into the process of hypernetwork representation learning to obtain node representation vectors rich in the hypernetwork topology structure and hyperedge information. Experimental results on four hypernetwork datasets demonstrate that, for the node classification task, our method outperforms the other best baseline methods by about 1%. As for the link prediction task, our method is almost entirely superior to other baseline methods.
Recognition of visual scene elements from a story text in Persian natural language

Abstract Text-to-scene conversion systems map natural language text to formal representations required for visual scenes. The difficulty involved in this mapping is one of the most critical challenges for developing these systems. The current study mapped Persian natural language text as the headmost system to a conceptual scene model. This conceptual scene model is an intermediate semantic representation between natural language and the visual scene and contains descriptions of visual elements of the scene. It will be used to produce meaningful animation based on an input story in this ongoing study. The mapping task was modeled as a sequential labeling problem, and a conditional random field (CRF) model was trained and tested for sequential labeling of scene model elements. To the best of the authors’ knowledge, no dataset for this task exists; thus, the required dataset was collected for this task. The lack of required off-the-shelf natural language processing modules and a significant error rate in the available corpora were important challenges to dataset collection. Some features of the dataset were manually annotated. The results were evaluated using standard text classification metrics, and an average accuracy of 85.7% was obtained, which is satisfactory.
Blockchain and New Digital Technologies: Explaining the Bitcoin Volatility with a Generalized Autoregressive Conditional Heteroskedasticity Model

This study aims to analyze Blockchain and the Bitcoin volatility with a multivariate GARCH statistical model. This paper is the first one to forecast the volatility of the Bitcoin/BRL exchange rate. We applied the time series multivariate statistical method called Generalized Autoregressive Conditional Heteroskedasticity (GARCH), with a daily time series dataset from 13th June 2013 to 18th February 2018, which corresponds to a total of 1057 observations and 12 variables, or 12,684 data entries. The research method involved the selection of explanatory variables, in order to understand the behavior of prices and the volatility of Bitcoin. We built the time series dataset collecting the daily closing prices for the Bitcoin Index and for the other eleven variables. Results show an original reflection concern with regard to the nature of the Bitcoin, which behaves differently than traditional currencies. Results also define the best model in predicting the Bitcoin/BRL exchange rate volatility and allow associating the behavior of the Bitcoin with that of a particular asset class, adding value to the existing literature with regard to its classification. So, this paper is a pioneer in examining and predicting the performance and volatility of a GARCH model in a context of unusually high volatility.
A Floating University: Focusing on Marine Field-Based Education

This paper presents the concept of a &#x201D;floating university&#x201D;: marine science, marine technology and marine robotics can be taught in an innovative yet productive and successful way. This is achieved by blending the various disciplines in field activities and field excursions, focusing on the immersive learning experience where concepts introduced during the study program in lectures are shown to work in the field. Three examples of the &#x201D;floating university&#x201D; are discussed, all related to education in marine science and marine robotics: a field excursion on a Research Vessel in the North Sea, a field research summer school to Vulcano, Italy, and the organisation of a taught class in marine robotics, focusing on hands-on activities. Overall, the Floating University has shown how hands-on and field activities contribute to the successful achievement of intended learning outcomes and provide unique experiences to the students.
Flipping Subscription and Access Fees to Perpetuity

Seeking to maximize budgetary efficiency and safeguard important resources from future budget reductions, the UConn Library collaborated with vendors to buy out its subscription and access fees, flipping the model from leased to perpetual access. Striving for sustainable stewardship of its collections, the Library achieved $125,000 in cost savings—equivalent to approximately 2.5% of its collections budget and 10% of its budget line items. This column explains how we did it.
Reports on Conferences, Institutes, and Seminars

The virtual Electronic Resources & Libraries Conference 2021 provided attendees with the option to choose sessions from a list of eight tracks focusing on a particular subject. Many sessions focused on the ways libraries adapted to the changes brought on by the COVID-19 pandemic, examining the changes in workflows and reducing spending and reevaluating collection development methods as well as cancelation methods due to budget cuts while still meeting the needs of communities during a transition of remote working and learning. Other sessions examine implementing different authentication methods.
Proposal of a computer security scheme for a Comprehensive Planning System

At present, Comprehensive Planning Systems are a fundamental part of any organization, since they function as a tool in the scheduling of activities as well as their Budgeting, Control, and Monitoring. The improvement and continuous updating of said systems are necessary to provide reliability in any context, that is why this research proposes to design and implement a computer security scheme focused on the entire administrative system, benefiting users who interact operating in the various related processes. In addition to this, the methodology used will be divided into two phases constituted by activities and tasks that will allow to satisfactorily find critical, severe, and moderate vulnerabilities in the server. Consequently, by obtaining a comprehensive diagnosis of the server, a security scheme is implemented to solve the problems detected and improve the organization.
Virtual school

The article deals with the issue of virtual school as a kind of alternative to the traditional classroom system of teaching. The introduction attempts to clarify the meaning of several terms that are essential for this subject field, including the concept of virtual reality and, of course, virtual school. First of all, the essence and types of virtual schools are discussed, with an articulation of the advantages of virtual education. The professional competencies of a virtual school teacher have also been touched upon briefly.
Motivation for Studying Mathematics of the Students in Post-secondary Technical Vocational Education – Future Primary School Teachers in the Republic of Moldova

Mathematics is the universal compartment through which both the study and the discovery of the surrounding environment take place. At the same time, mathematics is the academic discipline through which the formation of logical and practical thinking is pursued, so for the students of pedagogical specialties this discipline is an abstract science about which students know only what they have learned in gymnasium. Therefore, the motivation and professional orientation for studying mathematics are necessary, allowing in this context the application of mathematical knowledge and skills acquired in practical activities. This article thoroughly analyses the concept of motivation, but also addresses the motivation issue of the students in post-secondary technical vocational education – future primary school teachers in the Republic of Moldova. At the same time, the problematic aspects of the professional training process of future primary school teachers are determined.
APPLICATION OF THE PICTURE AND PICTURE MODEL IN THE TEACHING OF EXPLANATION TEXT

This study was based on students' difficulties with vocabulary when writing Explanation Text. Thus, the study is aimed to improve students’ vocabulary in writing explanation text using ‘Picture and Picture’ learning model in XI Accounting 1 SMK Negeri 3 Banjarbaru Academic year 2019/2020.Classroom Action Research is used in this study. The validity of the study is gained by having methodological triangulation that took data from class observation, students’ questionnaire, and vocabulary test. ‘Picture and Picture’ learning model is the learning model which uses picture series to help students’ understanding in elaborating their writing of the explanation text. This study proved an improvement in the learning process and the students’ vocabulary in writing explanation text by using ‘Picture and Picture’ learning model in two cycles. The teacher experienced an improvement in the skill of using media and teaching English skills. The students' average vocabulary score increased from 61.42 in cycle 1 to 69.76 in cycle 2. The classical success also improved from 66.67% students reached KKM of 70 in Cycle 1 to 80.95% in cycle 2.
The Ticket Sales Problem

This chapter covers the principles behind dealing with huge spikes in traffic to a service or site. We investigate the use of message brokers to solve that problem, along with how to implement a request / response pattern in a message broker. We also look at what a target architecture is, and introduce a technique for analysing a system that we will re-use in later chapters. Along the way, we look at the concepts of a Minimum Viable Product, CQRS, the Proxy Pattern, and the concept of using a cloud Vendor.
Exploring Country's Preference Over News Mentions to Academic Papers

News mention to academic papers has provided an important channel for research to yield impact on broad audience in the society, and is particularly useful in scholarly communication, scientific outreach and altmetrics. Unlike academic paper, news articles are often influenced by social factors such as culture, ideology and geography. It is therefore uncertain whether news mention to academic paper is valid for evaluative purpose as expected by altmetrics studies. In response, this study has conducted large scale statistical analysis to explore country's preference over news mentions, based on the assumption that if news mention to academic paper is not (or weakly) influenced by social factors, no obvious country's preference would be observed, and the vice versa. The major findings are: (1) From the macro perspective, overall distribution of news mentions is highly imbalanced, with several developed countries taking the dominant position. However, no obvious preference towards domestic papers was observed. (2) From the micro perspective, based on the ZINB test results, news from all countries have more positively mentioned domestic papers and simultaneously shown positive or negative preference over papers from some specific countries. (3) In terms of disciplinary comparison, disciplinary differences were observed in the impact strength and specific preference. These results suggest that in micro level country's preference exist for news mentions to academic papers, but in macro level, country's preference is insignificant. Therefore, it is suggested that news mention can be used for assessing purpose in the macro level.
Multiple-Valued Neural Networks and Branching Neural Networks

The generalizations of common single-valued artificial neural networks are proposed. The main proposition is to consider multiple-valued networks. Such neural networks can have multiple values of elements and multivalued connections between elements at given time moment. Modified Hopfield neural networks with strong anticipation property are considered as the examples. New aspects of learning processes considered for the case of multiple-validness. Branching networks are described. Presumable applications of multi-valued neural networks are proposed. Also some new research problems are described.
Ship Navigation in Narrowness Passes and Channels in Uncertain Conditions: Intelligent Decision Support

The intelligent approach and methods for increasing the safety of ships’ navigation in channels, fairways, rivers, straits, etc. based on automation of the processes of decision making in uncertain conditions are considered in the chapter. The structure of the decision-making units based on fuzzy-logic devices, namely fuzzification, aggregation, accumulation and solving devices, is presented to highlight their complementing role to accomplish desired target functions. Novel fast-acting working algorithms for a qualitative coordinates fuzzification device for a case of the first Gaussian form of membership function are proposed. The results of extensive computer simulations confirm the efficiency of the proposed algorithms as well as fuzzy models of ships passing afloat narrow places and channels. Thus some new insight into the underlying complexities of physical constraints and limitations has been achieved while confirming the validity of the proposed novel results. The tasks of the computerized control and decision support system design including hierarchy, reconfiguration, structure-parametrical optimisation, reliability and survivability are discussed.
A Synthetic Indicator of the Quality of Support for Businesses in Burkina-Faso, Cameroon, and Ghana

This paper proposes a synthetic indicator of the quality of support for companies as well as identifies the factors that can contribute towards improving the quality of such support in three countries (i.e., Burkina-Faso, Cameroon, and Ghana). The approach used to construct this synthetic indicator builds on the works of Benzécri (1973) and Asselin (2002), who use static mechanics and apply techniques of factor analysis. A principal component analysis is performed on the data collected from 80 business support structures in the sampled countries. After constructing the indicators, correlates are provided on how the constructed indicators are linked to the objectives of sustainable development. Our results are robust after controlling for variables relating to the general characteristics of the support structure. The findings are consistent with the thesis that taking sustainable development objectives into account in business support practices would significantly improve business performance in sampled countries and, by extension, in sub-Saharan Africa. The originality of the study stems from the fact that it considers specific SDGs (SDG4, SDG5, SDG8, and SDG9) and assesses their contribution to improving the quality of support for companies, a research area that has not been investigated hitherto by the extant literature. Implications for all stakeholders in the entrepreneurial ecosystem are discussed.
Novel Calibration Method for Large-Scale Measuring Camera Based on Distance-Related Distortion Mode

When a camera with large field-of-view performs large-space precision measurement, the variation of camera distortion with measuring distance can be substantial. However, in traditional camera models, camera distortion is usually described using constant parameters. Meanwhile, large-field measuring cameras lack efficient calibration methods when used in the field. This is because, on the one hand, it is impossible to make a huge calibration target consistent with the measurement space and, on the other hand, it is inefficient to construct a large number of 3D calibration points by using precision instruments such as total stations. To resolve these problems, this study proposes a camera calibration method that uses a distance-related camera distortion model. The method integrates a small checkerboard target and a small number of 3D calibration points. The checkerboard target is used as a close-range target to provide a large number of calibration points, and the 3D calibration points arranged in the measurement space are used as long-range targets. Physical experiments confirm that the proposed method can improve calibration efficiency and ensure calibration accuracy.
Blockchain Enabled Smart Community for Bilateral Energy Transaction

The blockchain enabled near-real-time, peer-to-peer energy trading market is one of the promising ways to enhance energy system flexibility, achieve peer privacy preserving and transaction trackability. However, providing blockchain-based solutions compatible with energy trading is not an easy task, where real-world feasibility for business model, blockchain configuration, and physic limitations is rather to be investigated. In this work, a permissioned Hyperledger Fabric (HF) blockchain is leveraged to implement real-world applications for energy trading in a smart community. A near-real-time bilateral trading scheme, directly negotiated on a peer-to-peer basis without any intermediary, is designed for achieving supply-demand balance within power network limits. Thenceforward, the specific process of the HF blockchain establishment and the smart contract creation for system initialization, registration, negotiation, trading, balancing and settlement, are detailed demonstrated. In addition, the benchmark is presented to test the performance metrics of execution time, latency, throughput for feasibility and scalability analyses in real-world application. The results illustrate the proposed HF-assisted energy trading platform corroborates the feasibility for the future scalable P2P trading.
A Service Composition Evolution Method that Combines Deep Clustering and a Service Requirement Context Model

Service composition can quickly build new value-added composite services by combining existing Web services. However, with the complex and changeable Internet environment, composite services need to effectively understand and manage requirements in a flexible and adaptive manner, and also be able to quickly and proactively provide high-quality services through a series of dynamic evolution models. In response to these challenges, in this study, we propose a service composition evolution method that can adaptively select the appropriate service to improve user satisfaction and service quality. First, we introduce a deep clustering method based on the topic model and auto-encoder, which considers the function description documents and parameter information of the services to reduce the search space of the candidate services. Furthermore, we describe a requirement-oriented context-sensitive task model to integrate functional and non-functional user requirements by connecting the context of Web subtasks. Then, we present a dynamic service matching method with QoS threshold judgement to filter and rank the services. We applied the proposed evolution method to the datasets of real-world Web services and measured the performance using standard measurement metrics. The prototype implementation and results of simulation experiments verified the effectiveness of each part of our method.
When Less is More: Bbr Versus Ledbat++

We present a study of the interaction between two recent congestion control algorithms, LEDBAT++ and BBR. LEDBAT++ is a less-than-best-effort congestion control mechanism and BBR is model-based congestion control algorithm that aims to implement a best-effort traffic class. Naturally, the expectation is that LEDBAT++ yields in front of BBR. While we observe that this is the case in networks with moderate size buffer and when the base RTT is larger than 60 ms, we find that in other common conditions (base RTT smaller than 60 ms and moderate size network buffers), the interaction between LEDBAT++ and BBR is not as expected, as LEDBAT++ fails to yield in front of BBR. The observed behaviour is different for the two existent versions of BBR. Specifically, LEDBAT++ competes in equal grounds against BBRv1, and BBRv2 yields in front of LEDBAT++. The BBRv2/LEDBAT++ interaction is exactly the opposite to the desired outcome. We present a number of experiments in diverse conditions that support our conclusions.
The Development of Constructivist Web-based Learning Environments Model to Enhance Critical Reading and Reading Literacy

Critical reading and Reading literacy It is important and necessary to read in today's technology far and wide. If the reader has a critical reading and reading literacy will make the reader read more efficiently. This research applied the principles of critical reading and reading literacy to create a networked learning environment to promote critical reading and reading literacy. Therefore, this research aims to synthesize the theoretical framework of a networked learning environment that promotes critical thinking and reading literacy. Model Research [9]: The design and development process was used in the study in Phase I. The statistical method was document analysis and survey. The steps are as follows: 1) Synthesize a theoretical framework that consists of 5 components: context base, psychology base, technology base, critical reading base, and reading knowledge base. And learning environment a model of basic teaching, knowledge; 2) a synthesis of the constructivist theory framework; critical reading and reading literacy.
OS-DS Tracker: Orientation-Variant Siamese 3D Tracking with Detection Based Sampling 1

LIDAR point clouds are less affected by the weather and possess more depth of field than 2D images. However, sparsity and disorder of point clouds bring challenges for 3D object tracking due to the difficulty in detecting and encoding. In addition, ineffective candidate targets increase the phenomenon of tracking wrong candidate regions. In this paper, we propose an Orientation-variant Siamese 3D object tracking network that utilizes a Detection Based Sampling module to generate candidate regions (OS-DS tracker). The detection based sampling module is used to cut the excessive and useless proposals. And the multivariate Gaussian sampling generates the candidate regions when the objects are not detected. Concretely, we first pre-train PointRCNN Net to globally detect objects from LIDAR point clouds. Then the 3D detected objects are refined by candidate regions sampling to generate candidate regions. Meanwhile, in order to make the feature vectors more discriminative, we design an Orientation-variant Siamese Auto-encoder, $i.e.$, tracking loss regress to the intersection over union(IOU) of the template box and the candidate region boxes. Our method is tested on KITTI dataset. Compared with the baseline, the proposed method outperforms the previous in vehicle tracking with 17.4%/16.3% improvement in Success and Precision respectively.