Reviewerly/exHarmony huggingface dataset:
Digital Transformation of Organizational Culture of Higher Educational Institutions and Types of Organizational Culture that Academicians and Administrative Personnel Perceive at Their Institutions (Case of Georgia)

This study aims revealing culture types that the academicians as well as administrative personnel of Georgian private and public higher educational institutions (HEIs) perceive at their organizations on the basis of the four types of cultures (Clan, Adhocracy, Hierarchy, and Market) given in the Competing Values Framework-CVF by Kim Cameron and Robert Quinn and to identify current problems, barriers, features, trends, and directions of digital transformation of organizational culture of HEIs. The study encompasses totally185 academicians of different faculties and positions (Professor, Associate Professor, Assistant Professor, Assistant) and administrative personnel (from Rectors to technical staff) as participants. The data was obtained through the Quantitative research, mainly—Self-Assessment Questionnaire of organizational culture. Data results was calculated in the Ms. Excel file based on the calculation method. According to the findings obtained by research, dominant type of organizational culture in Georgian Private HEIs is Market culture and as for public universities’ preferable culture is Adhocracy culture.
Emoji

Abstract Online users devise different strategies and techniques to make up for the absence of physical bodies in online communications, one of which is using emojis. Emojis are a diverse set of small images, symbols, or icons, standardized by the Unicode Consortium for utilization in electronic communication platforms. Their primary role is to effectively convey the emotional attitude of the writer, succinctly provide information, and playfully communicate messages. This article posits that bodily emojis (emojis portraying a body gesture or facial expression) are a form of digital embodiment. Their usage, thus, creates a form of digital performance. Emojis appear as a screen body in a space that lacks the physical one. Furthermore, I suggest that this body could be described as aesthetically Baroque. My proposition is that emojis exhibit Baroque characteristics such as dynamic and exaggerated forms and decorate texts. Emojis share similarities with the appearance and function of the Baroque body both in Baroque visual art and Baroque dance.
Implementasi Literasi Membaca melalui Program Asesmen Nasional Berbasis Komputer di SDN 1 Karangklesem

This research aims to evaluate the implementation of the reading literacy program through the ANBK Program at SDN 1 Karangklesem. The research method used in this research is descriptive qualitative. The results of this research show that the reading literacy program through ANBK has had a positive impact on student preparation, increasing reading comprehension. There are several stages used by SSDN 1 Klarangklesem in implementing learning literacy through ANBK, namely: In the planning stage, the school principal forms an ANBK implementation committee consisting of the committee chairman, secretary, technician, and proctor. Next, students were given an ANBK simulation link to prepare them for answering numeracy questions, and study groups were formed before answering the questions to improve their understanding. During the implementation phase, students answer practice questions in the form of ANBK simulations and take part in dress rehearsal sessions. The teacher provides full assistance to help students understand the contents of the sentences and questions they read, as well as providing an understanding of the quick answer method in answering ANBK questions. The evaluation stage involves printing out the simulation results which the teacher evaluates. Questions that are difficult for students to answer become important evaluation points for teachers' and students' future development. Keywords: Assessment, Computers, Literacy.
Cost-Sensitive Decision Tree Induction on Dirty Data

As the rapid growth of data in our society, dirty data are increasingly common. In the process of cost-sensitive decision tree induction, dirty data in training data sets have negative impacts on the selection of splitting attributes and division of decision tree nodes. Hence, dirty data cleaning is necessary before classification tasks. However, many users give an acceptable threshold of data cleaning costs since time costs and expenses of data cleaning are expensive. Therefore, in addition to misclassification cost and test cost, data cleaning cost is an important factor in cost-sensitive decision tree induction. However, existing researches have not considered data quality issues in the problem. To fill this gap, this chapter aims to focus on cost-sensitive decision tree induction on dirty data and presents three decision tree induction methods integrated with data cleaning algorithms. Evaluation results demonstrate the effective of the proposed approaches. Section 7.1 gives the research background. Section 7.2 introduces the problem definitions of this chapter. Section 7.3 discusses three proposed cost-sensitive decision tree building methods. Section 7.4 analyzes the experiments and evaluation results. Section 7.5 concludes the chapter and provides some future research directions.
Impacts of Dirty Data on Classification and Clustering Models

Since dirty data have negative influence on the accuracy of machine learning models, the relation between data quality and model results could be used in the selection of the proper model and data cleaning strategies. However, rare work has focused on this topic. Motivated by this, this chapter compares the impacts of missing, inconsistent, and conflicting data on basic classification and clustering models. Based on the evaluation observations, we suggest users how to select appropriate classification and clustering models and clean dirty data in the database and machine learning communities. Section 2.1 gives the research motivation of this chapter. Section 2.2 describes our assessment methodology. Section 2.3 presents our experimental results and analyses. We discuss the lessons learned from the evaluation in Sect. 2.4 and provide strategies of model selection and data cleansing, followed by a brief summary in Sect. 2.5.
Incomplete Data Classification with View-Based Decision Tree

Missing values bring negative influence in data analyses and decrease the accuracy of machine learning models. Since traditional classification methods are only able to be adopted on complete data sets, this chapter presents a generalized classification model for incomplete data in which existing classification models are easily embedded. We first generate complete views for the incomplete data based on the selection of proper attribute subsets. With the selected views, we obtain multiple base classifiers and a final classifier combined by base classifiers. Experimental results on real data sets show the effectiveness of the proposed classifier. We give the research motivation in Sect. 4.1. The sketch of tree-like structure is presented in Sect. 4.2. In Sect. 4.3, we discuss how to generate a view for each node. We report the experimental results and analysis in Sect. 4.4. Finally, in Sect. 4.5, we summarize the work of this chapter.
Dirty Data Impacts on Regression Models

Due to the negative influence of dirty data on the accuracy of regression models, the relation between the data quality and model results is able to be used in the selection of proper regression models and dirty data repairing strategies. Motivated by this, we develop an evaluation framework to measure the dirty data impacts on regression models. Based on the framework, we compare the impacts of missing, inconsistent, and conflicting data comprehensively. According to the evaluation observations, we suggest users how to select appropriate regression models and clean dirty data. Section 3.1 provides the research background of this chapter. Section 3.2 presents the methodology to measure the dirty data impacts on regression models and designs a generalized evaluation framework. Section 3.3 discusses the experimental observations of dirty data impacts on regression model results and gives guidelines of regression model selection and dirty data cleaning. Finally, we conclude this chapter in Sect. 3.4.
Introduction

In recent years, with the development of the information age, the amount of data has grown dramatically. At the same time, dirty data have already existed in various types of databases. Due to the negative impacts of dirty data on data mining and machine learning results, data quality issues have attracted widespread attention. Motivated by this, this book aims to analyze the impacts of dirty data on machine learning models and explore the proper methods for dirty data processing. This chapter discusses the background of dirty data processing for machine learning. In Sect. 1.1, we analyze three basic dimensions of data quality to motivate the necessity of processing dirty data in the database and machine learning communities. In Sect. 1.2, we summarize the existing studies and explain the differences of our research and current work. We conclude the chapter with an overview of the structure of this book in Sect. 1.3.
A Smart Contract Vulnerability Detection Method Based on Multimodal Feature Fusion and Deep Learning

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy.
On the additive differential probability of ARX construction

Aim The additive differential cryptanalysis is a significant technique used in the analysis of ARX ciphers. In this paper, we will focus on accurately and efficiently calculating the additive differential probability of $$ x \lll d \oplus y \lll e $$ . Methods Inspired by the work of Niu et al . at Crypto 2022, we use a delicate partition of $$ \mathbf{F}_2^m \times \mathbf{F}_2^m $$ into subsets. Result We derive an algorithm that can calculate it with linear time complexity. Compared with our algorithm, the one proposed by Velichkov et al . is only suitable when $$ e=0 $$ . Conclusion For the ARX construction: $$ (x \boxplus y) \lll d \oplus y \lll e $$ , which appears in Alzette, Speck, etc. , our algorithm can find more accurate additive differential characteristics for such ARX constructions. It is essential to evaluate the resistance of such ARX primitives against Additive differential cryptanalysis.
A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.
Exceptional points in coupled vortex-based spin-torque oscillators

We theoretically study the linear and nonlinear dynamics of a system of mutually coupled magnetic vortices. We consider the excitation of vortex dynamics by both an external harmonic magnetic field and a spin-polarized current. The results based on the Thiele equations for the motion of coupled vortices are compared with the numerical integrations. Theoretical analysis shows that the appearance of the exceptional point corresponding to the coincidence of the normal modes of the system allows one to observe such effects as the death of amplitudes and parametric resonance. We propose a method for observing the effect of amplitude death in coupled magnetic vortices. In the investigated system, the emergence of exceptional points is controlled by the spin-polarized current and the strength of the coupling between vortices. Thus, possibilities are uncovered for the development of tunable spintronic devices.
Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs

We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the loop-tree duality or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover's based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.
Future-Proofing Supply Chain Education

Abstract This special issue aims to support future-proofing supply chain education—ensuring that the education we offer meets the needs of our students today as well as their future employers tomorrow. Specifically, this issue offers approaches to instructional methods and design that can help educators better respond to the growing need to develop future supply chain leaders. In this editorial, we consider the critical need to focus on this in research and teaching, and we offer a reflection upon today’s focus on education and introduce key facets of a more future-proof educational approach.
The possibility and necessity of reconstructing ancient Greek buildings to study the depicted architecture in Greek vase painting of the Archaic era.

This paper examines the problem of depicting and interpreting architectural structures on the two-dimensional surface of vases in the archaic era. And accordingly, during the period of formation of the necessary iconographies and methods of interaction between the depicted space and plane. The purpose of this study is to study the architecture depicted in vase paintings during the Archaic period. As part of such research, there is a need to analyze the remaining relief or structural parts of ancient architecture, scan them and create at least approximate virtual models. What becomes possible with the development of the latest information technologies. This will help a deeper study of the origins of the architectural fantasies of ancient Greek vase painters. In the meantime, within the framework of the initial stages of such a study, examples have been selected for comparisons of individual elements of architectural buildings and types in general. Some origins and share of the vase painter’s fantasies are determined. This study proves that the depicted architecture can play many functions, solve a large number of problems in vase painting scenes; often, masters use it as a kind of language to convey important non-verbal, semantic points.
Student Privacy and Data Literacy: An Educational Opportunity

In ShortStudent privacy is a critical area of higher education that deserves greater focus, particularly as student data digitalization increases.Many colleges and universities use data literacy as a way to prepare students, sometimes from different disciplines, to work with others’ data postgraduation.Data literacy can be an avenue for helping all students understand, in plain language, the privacy implications for how colleges, universities, applications, and university partners (education companies, educational technology companies, etc.) collect and use their data. Disclosure StatementNo potential conflict of interest was reported by the authors. Additional informationNotes on contributorsStephanie J. BlackmonDr. Stephanie J. Blackmon (sjblackmon@wm.edu) is an Associate Professor of Higher Education. Dr. Blackmon’s research explores the qualitative experiences people have with technology integration in higher education and professional development settings; experiential learning in technology-related settings; and trust, privacy, and security with the use of analytics, apps, artificial intelligence, and other technologies.
Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments.
Markov chain composite likelihood and its application in genetic recombination model

AbstractPhylogenetic Trees are critical in human genome research for investigating human evolution and identifying disease-associated genetic markers. New high-throughput genome sequencing technologies raise an urgent need to develop statistical methods that can construct phylogenetic trees from long genome sequences with quick computation speeds, while considering various biological complexities. Though an ancestral mixture model has been proposed [Chen SC, Lindsay BG. Building mixture trees from binary sequence data. Biometrika. 2006;93(4):843–860. doi: 10.1093/biomet/93.4.843] to this end by allowing genetic mutations over generations, another essential evolution factor, genetic recombination, is missed. Therefore, in this paper, we develop a novel genetic recombination model for tree construction and propose to use Markov chain composite likelihood (MCCL) to make model estimation computationally feasible. To further reduce computation complexity, a hierarchical estimator is constructed to estimate unknown ancestral distributions through MCCL. Simulation studies and real data example show that our proposed methods perform well and fast, so have the potential for implementation in long sequence genome data.Keywords: Phylogenetic treegenetic recombinationcomposite likelihoodMarkov chainhierarchical estimator AcknowledgmentsThe authors express their sincere gratitude for the reviewer's insightful comments and valuable suggestions, which have significantly contributed to the enhancement of this manuscript. During the preparation of this work, Dr. Bruce G. Lindsay passed away due to an illness. We miss this brilliant statistician, wise and excellent advisor, and warm friend dearly. The first author is particularly grateful to Professor Lindsay's invaluable mentor, support, and encouragement to her early stage research, which the author will treasure forever.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the NSF [grant number 1950549].
Homorphic Encryption in Smart Grid System for Secure Information Aggregation

In our digital world, the conventional power system has to meet the demands of a digital society. Thus, upgradation was required. It included smart meters. In this paper, we emphasized the security of smart meters forwarded data. Data over a network is highly likely to be hacked and modified by attackers. At each node, data is encrypted using Pure Paillier Homomorphic Encryption. Data aggregation is supported while user privacy is protected. In smart grids, repetitive data aggregation is useful.