Reviewerly/exHarmony huggingface dataset:
Serialization in Object-Oriented Programming Languages

This chapter depicts the process of converting object state into a format that can be transmitted or stored in currently used object-oriented programming languages.This process is called serialization (marshaling); the opposite is called deserialization (unmarshalling) processes.It is a low-level technique, and several technical issues should be considered like endianness, size of memory representation, representation of numbers, object references, recursive object connections and others.In this chapter we discuss these issues and give them solutions.We also include a short review of tools currently used, and we showed that meeting all requirements is not possible.Finally, we presented a new C++ library that supports forward compatibility.
New Techniques for Sizing Solar Photovoltaic Panels for Environment Monitoring Sensor Nodes

The development of perpetually powered sensor networks for environment monitoring to avoid periodic battery replacement and to ensure the network never goes offline due to power is one of the primary goals in sensor network design. In many environment-monitoring applications, the sensor network is internet-connected, making the energy budget high because data must be transmitted regularly to a server through an uplink device. Determining the optimal solar panel size that will deliver sufficient energy to the sensor network in a given period is therefore of primary importance. The traditional technique of sizing solar photovoltaic (PV) panels is based on balancing the solar panel power rating and expected hours of radiation in a given area with the load wattage and hours of use. However, factors like the azimuth and tilt angles of alignment, operating temperature, dust accumulation, intermittent sunshine and seasonal effects influencing the duration of maximum radiation in a day all reduce the expected power output and cause this technique to greatly underestimate the required solar panel size. The majority of these factors are outside the scope of human control and must be therefore be budgeted for using an error factor. Determining of the magnitude of the error factor to use is crucial to prevent not only undersizing the panel, but also to prevent oversizing which will increase the cost of operationalizing the sensor network. But modeling error factors when there are many parameters to consider is not trivial. Equally importantly, the concept of microclimate may cause any two nodes of similar specifications to have very different power performance when located in the same climatological zone. There is then a need to change the solar panel sizing philosophy for these systems. This paper proposed the use of actual observed solar radiation and battery state of charge data in a realistic WSN-based automatic weather station in an outdoor uncontrolled environment. We then develop two mathematical models that can be used to determine the required minimum solar PV wattage that will ensure that the battery stays above a given threshold given the weather patterns of the area. The predicted and observed battery state of charge values have correlations of 0.844 and 0.935 and exhibit Root Mean Square Errors of 9.2% and 1.7% for the discrete calculus model and the transfer function estimation (TFE) model respectively. The results show that the models perform very well in state of charge prediction and subsequent determination of ideal solar panel rating for sensor networks used in environment monitoring applications.
Visually Exploring Relations Between Structure and Attributes in Multivariate Graphs

The visual analysis of multivariate graphs is a challenging problem. We address the particular task of studying relations between the structure of a graph and the multivariate attributes associated with it. To facilitate this task, we propose a novel interactive visualization approach. The core idea is to show structure and calculated attribute similarity in an integrated fashion as a matrix. A table can be attached to the matrix on demand to visualize the underlying attribute values in detail. To support the visual comparison of structure and attributes at different levels, several interaction techniques are provided, including matrix reordering, selection and emphasis of subsets, rearrangement of sub-matrices, and column rotation for detailed comparison. To demonstrate the utility of our techniques, we apply them to explore relations between structure and attributes in a network of soccer players.
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs

Bilinear diagonal models for knowledge graph embedding (KGE), such as DistMult and Com-plEx, balance expressiveness and computational efficiency by representing relations as diagonal matrices.Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations.In this paper, we propose a new bilinear KGE model, called BlockHolE, based on block circulant matrices.In BlockHolE, relation matrices can be non-commutative, allowing composite relations to be modeled by matrix product.The model is parameterized in a way that covers a spectrum ranging from diagonal to full relation matrices.A fast computation technique is developed on the basis of the duality of the Fourier transform of circulant matrices.
The Bitcoin’s Network Effects Paradox—A Time Series Analysis

Bitcoin faces a network effects problem: although its widespread adoption is related to an increase in the number of users, its price volatility lowers consumption.Given the low consumption, a low number of merchants accepting bitcoins would also be expected.However, an increasing number of venues (i.e.merchants) are observed.This paper aims to investigate that paradox.An econometric procedure is followed examining the linkages among Bitcoin's price volatility, market capitalization, and the number of venues, by using weekly time series data for a five-year time period (February 2013-February 2018).The results indicate that the number of merchants is unaffected by a shock of the market capitalization, while it is only initially affected by a shock of the price and then stabilizes.Our study contributes to a better understanding of the network effects' phenomena appearing in the Bitcoin's market and has significant implications for e-commerce practitioners concerning their decision-making process about Bitcoin adoption.
Learning Outcomes and Their Relatedness in a Medical Curriculum

A typical medical curriculum is organized in a hierarchy of instructional objectives called Learning Outcomes (LOs); a few thousand LOs span five years of study.Gaining a thorough understanding of the curriculum requires learners to recognize and apply related LOs across years, and across different parts of the curriculum.However, given the large scope of the curriculum, manually labeling related LOs is tedious, and almost impossible to scale.In this paper, we build a system that learns relationships between LOs, and we achieve up to human-level performance in the LO relationship extraction task.We then present an application where the proposed system is employed to build a map of related LOs and Learning Resources (LRs) pertaining to a virtual patient case.We believe that our system enables building educational tools to help medical students grasp the curriculum better, within classroom and Intelligent Tutoring Systems (ITS) settings.
Methodological foundations for the interdisciplinary studies of modern book culture

The processes of globalization and the challenges of post-industrial civilization expand the boundaries of research in the social and human sciences significantly and dictate the need for close interdisciplinary cooperation in exploring the ways for adapting traditional sociocultural phenomena to the new conditions of the developing information society. The shift in emphasis from studying traditional booklore from the historical aspects to new book culture is increasingly attracting the attention of researcher in the humanities who are trying to comprehend the current state and predict the vectors for the book culture development in the dynamically changing information environment. Over the past quarter of the century, changes in research approaches are obvious: from offering definitions for the “book culture” concept to understanding the multidimensional nature of this phenomenon and the need for interdisciplinary cooperation in the studies. Intensive discussions in academic periodicals demonstrate the diversity of ideas, concepts and research approaches [1]. The author analyzes the findings of recent book culture studies from the standpoint of various scientific schools and approaches, provides examples of precise interpretations of philosophical, cultural, documentary aspects of the books’ functioning within the system of culture. The necessity for identifying mutually acceptable guidelines for interdisciplinary cooperation and updating the methodology for studying the books, booklore and book culture is substantiated.
Zero-shot Text-to-SQL Learning with Auxiliary Task

Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realistic unseen data, which naturally raises a question: does this impressive performance signify a perfect generalization model, or are there still some limitations? In this paper, we first diagnose the bottleneck of text-to-SQL task by providing a new testbed, in which we observe that existing models present poor generalization ability on rarely-seen data. The above analysis encourages us to design a simple but effective auxiliary task, which serves as a supportive model as well as a regularization term to the generation task to increase the models generalization. Experimentally, We evaluate our models on a large text-to-SQL dataset WikiSQL. Compared to a strong baseline coarse-to-fine model, our models improve over the baseline by more than 3% absolute in accuracy on the whole dataset. More interestingly, on a zero-shot subset test of WikiSQL, our models achieve 5% absolute accuracy gain over the baseline, clearly demonstrating its superior generalizability.
Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Sequential Recommendation (SRs) that capture users' dynamic intents by modeling user sequential behaviors can recommend closely accurate products to users. Previous work on SRs is mostly focused on optimizing the recommendation accuracy, often ignoring the recommendation diversity, even though it is an important criterion for evaluating the recommendation performance. Most existing methods for improving the diversity of recommendations are not ideally applicable for SRs because they assume that user intents are static and rely on post-processing the list of recommendations to promote diversity. We consider both recommendation accuracy and diversity for SRs by proposing an end-to-end neural model, called Intent-aware Diversified Sequential Recommendation (IDSR). Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to capture different user intents reflected in user behavior sequences. Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training. Extensive experiments on two benchmark datasets show that IDSR significantly outperforms state-of-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy.
Modeling a Flow of Errors for Digital Radiotracts

In theoretical studies of coding, transmission and decoding of digital information, the concept of a discrete communication channel is often used. In this case, to reflect the influence of the channel on the transmission path, it is sufficient to simulate the sequence of errors. To simulate the sequence of errors, a mathematical model was proposed, which is based on the representation of the envelope of a fading signal in the form of a probabilistic discrete automaton, for which a simple Markov chain is used. The parameters of the model are determined, taking into account the characteristics of the radio path.
Pairing Hope and Job Satisfaction with MENA Business Leaders

Hope has the potential to reengineer organizational structure and generate a new vibrant culture committed to positive behavior.Whether brick and mortar business or virtual, business leaders tend to work industriously to attain company goals and may play a dynamic role in shaping perceptions of hope through positive organizational behavior.This study is an exploratory one focusing on the role of hope and job satisfaction when paired with leadership in the business workplace.Contemporary business-related research has found that hope, an observable and measurable phenomenon in the workplace, plays a vital role in the business context.This case focuses on perceptions of hope, job satisfaction, and leadership inside business units in the Middle East and North African Region (MENA).The aim of this research paper is twofold: (1) to determine whether business leaders are perceived as hopeful and (2) to correlate perception of hope to perception of job satisfaction.Data was gathered using a survey; analysis was conducted; recommendations were made based on the results.It was concluded that in the MENA business context, leadership, hope, and job satisfaction play a relevant role.
A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision

Why do some continue to wonder about the success and dominance of deep learning methods in computer vision and AI? Is it not enough that these methods provide practical solutions to many problems? Well no, it is not enough, at least for those who feel there should be a science that underpins all of this and that we should have a clear understanding of how this success was achieved. Here, this paper proposes that the dominance we are witnessing would not have been possible by the methods of deep learning alone: the tacit change has been the evolution of empirical practice in computer vision and AI over the past decades. We demonstrate this by examining the distribution of sensor settings in vision datasets and performance of both classic and deep learning algorithms under various camera settings. This reveals a strong mismatch between optimal performance ranges of classical theory-driven algorithms and sensor setting distributions in the common vision datasets, while data-driven models were trained for those datasets. The head-to-head comparisons between data-driven and theory-driven models were therefore unknowingly biased against the theory-driven models.
3D Colored Mesh Structure-Preserving Filtering with Adaptive P-Laplacian on Directed Graphs

Editing of 3D colored meshes represents a fundamental component of nowadays computer vision and computer graphics applications. In this paper, we propose a framework based on the p-laplacian on directed graphs for structure-preserving filtering. This relies on a novel objective function composed of a fitting term, a smoothness term with a spatially-variant pTV norm, and a structure-preserving term. The last two terms can be related to formulations of the p-Laplacian on directed graphs. This enables to impose different forms of processing onto different graph areas for better smoothing quality.
Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile and Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment. Common approaches address service composition from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints. Our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.
The World Building Framework for Immersive Storytelling Projects

This article explores issues associated with immersive storytelling in order to examine how the field of World Building can constitute a theoretical framework for practice in the context of VR-based and Full Dome artistic projects. With respect to immersion, the intent will be to interpret the concept of storytelling in relation with the recent formulation of the concept of extended reality (XR). The very concept of World Building is transauthor and transmedia by nature. The transauthor dimension of World Building resides in the idea of subcreation, i.e., designing environments and interaction rules that help create a storytelling basis for generating multiple stories. Once the universe has been conceived, stories written by different authors take shape through transmedia processes across multiple distribution media (film, video games, web, etc.). The question then arises: How can the World Building approach shape the construction of immersive experiences? The article sets out to answer this question, and in doing so, to contribute to the research on environmental storytelling.
APS: Audience Presentation System Using Mobile Devices

It is not easy for a student to present a question or comment to the lecturer and other students in large classes. This paper introduces a new audience presentation system (APS), which creates slide presentations of students' mobile responses in the classroom. Experimental surveys demonstrate the utility of this APS for classroom interactivity.
Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed.
İŞLETMELERDE HİLELERİN ÖNLENMESİNE YÖNELİK İÇ KONTROLLER VE BİST İMALAT SEKTÖRÜNDE BİR ARAŞTIRMA

In every fraud act, fundamentally, there are three main factors.These are pressure, opportunity, and rationalization.The fact that all these three factors are a part of human life show that fraud acts could frequently occur.Financial statements that are a product of frauds cause information users who make decisions based on these statements to go wrong.Frauds do not only cause financial loss, but also damages their reliability and image which consequently prevents sustainability.In order to avoid negative effects caused by results of frauds, an internal control system should be established and actively operated.In our study, a survey on how internal control systems affects preventing frauds has been conducted.In this survey, 179 organizations from BIST (Istanbul Stock Exchange) manufacturing sector, which forms the main mass of this study, were asked 30 questions.Data has been evaluated using, a descriptive statistical method, frequency analysis.The results of this study leads to the conclusion that actively applied internal control systems in organizations play a significant role in preventing potential frauds.
3DMM for Accurate Reconstruction of Depth Data

In this paper, we propose a framework to derive accurate reconstructions of the 3D face surface from low resolution depth frames by means of a 3D Morphable Model (3DMM). By using a 3DMM specifically designed to support local and expression-related deformations of the face, we propose a two-steps 3DMM fitting solution: initially the model is warped based on landmarks correspondences; subsequently, it is iteratively refined by means of a mean-square optimization on the nearest-neighboring vertices. Preliminary results show that the proposed solution is able to derive faithful 3D models of the face, both for low-and high-resolution scans; quantitative results also evidence the higher accuracy of our approach with respect to methods that use one step fitting based on landmarks. In addition, we employed the 3DMM fitting to learn expressions specific coefficients, that can be further applied to the deformed models so as to generate subject-specific expressive scans, while the fitting procedure allows maintaining unaltered the general surface topology of the original scans.
Exposing Standardization and Consistency Issues in Repository Metadata Requirements for Data Deposition

We examine common and unique metadata requirements and their levels of description, determined by the data deposit forms of 20 repositories in three disciplines—archaeology, quantitative social science, and zoology. The results reveal that requirements relating to Creator, Description, Contributor, Date, Relation, and Location are common, whereas those regarding Publisher and Language are rarely listed across the disciplines. Data-level descriptions are more common than study- and file-level descriptions. The results suggest that repositories should require detailed study-level descriptions and information about data usage licenses and access rights. Moreover, repositories should determine metadata requirements in a standardized and consistent manner.
Proofs and Cross-Validations: <i>Three Lessons for Financial Data Science</i>

Like any new research program, financial data science must successfully demonstrate its utility to researchers who are accustomed to working with more established analytical frameworks and tools. This is especially important in the early stages of financial data science, where much of the methodological groundwork of the field will be laid. Given this, in this article the author draws on the history of mathematics, an exemplar of a successful scientific endeavor, to provide three lessons for researchers in financial data science that the author hopes will assist them in aligning their research priorities more closely with those of mainstream finance. The author closes the article with some additional guidance on the related topic of effectively writing about and presenting financial data science research. <b>TOPICS:</b>Statistical methods, quantitative methods, big data/machine learning <b>Key Findings</b> • Financial Data Science must be in epistemic dialogue with traditional finance. • Financial Data Science must aim for epistemic transparency. • Financial Data Science must aim for epistemic connectivity.