Reviewerly/exHarmony huggingface dataset:
Perturbed ferromagnetic chain: Tunable test of hardness in the transverse-field Ising model

Quantum annealing in the transverse-field Ising model (TFIM) with open-system dynamics is known to use thermally-assisted tunneling to drive computation. However, it is still subject to debate whether quantum systems in the presence of decoherence are more useful than those using classical dynamics to drive computation. We contribute to this debate by introducing the perturbed ferromagnetic chain (PFC), a chain of frustrated sub-systems where the degree of frustration scales inversely with the perturbation introduced by a tunable parameter. This gives us an easily embeddable gadget whereby problem hardness can be tuned for systems of constant size. We outline the properties of the PFC and compare classical spin-vector Monte Carlo (SVMC) variants with the adiabatic quantum master equation. We demonstrate that SVMC methods get trapped in the exponentially large first-excited-state manifold when solving this frustrated problem, whereas evolution using quantum dynamics remains in the lowest energy eigenstates. This results in significant differences in ground state probability when using either classical or quantum annealing dynamics in the TFIM.
OCLC Local Holdings Records (LHRs): A Means to Improving Lending Fill Rates

The purpose of this project was to update, delete, or create OCLC Local Holding Records (LHRs) for non-electronic periodicals and other serials to accurately reflect the UCF Libraries holdings with the goal of improving the Interlibrary Loan Department’s lending fill rate and turnaround time.
Formation of Professional Self-Determination of Future Teachers of Non-Language Specialties when Learning the English Language

The article reviews the formation of professional self-determination of future teachers of non-language specialties when learning the English language. The aim of the study was the formation of motivation for the study of the English language by students of pedagogical universities of non-linguistic specialties. The hypothesis of the research was that the use of the English language of the professional direction increases the motivation for learning the language, provides an awareness of the practical and theoretical significance of the knowledge gained for future professional activities and is a factor professional self-determination. Based on this goal, the following tasks were solved: studies of the professional orientation of students and the level of motivation for studying the discipline "English language of a professional direction" were carried out, measures were developed to form the professional self-determination of students. At the stage of the formative experiment, the work programs of the discipline are drawn up in such a way that each content module takes into account the urgent problems of the corresponding specialty. The use of modern information and innovative technologies in project activities contributed to the formation of professional motives for learning English, skills and abilities of communication in English in the professional sphere, an increase in the number of special terminology, the intensive development of all language skills: speaking, reading, listening and writing.
Artificial Intelligence-empowered Voice Bots in Hotels

The Encyclopedia of Tourism Management and Marketing is, quite simply, the definitive reference work in the field. Carefully curated by leading tourism scholar Dimitrios Buhalis, this is the largest tourism management and marketing ontology that has ever been put together and offers a holistic examination of this interdisciplinary field This is a 4-volume set. Volume 1 contains entries A–D, Volume 2 contains entries E–I, Volume 3 contains entries J–R and Volume 4 contains entries S–Z. Page numbers start from 1 in each volume.
Task-Based Language Teaching in Korean Classroom Contexts

The goal of this chapter is to review previous research on TBLT and address pedagogical implications for Korean language instruction, particularly in classroom contexts. Within the research domain of TBLT, the chapter focuses on task design and task implementation factors. The first section starts with the review of important terms and concepts such as task, task-based instruction vs. task-supported instruction, task input, and task-based assessment. The next section begins with the review of literature on task design features, which include task complexity and task input. Then the third section focuses on task implementation factors such as task repetition, task phases, the use of technology, and the provision of feedback during task. The final section addresses pedagogical implications of previous task-based research in Korean language teaching contexts and directions for future research.
Embed Wisely: An Ensemble Approach to Predict ICD Coding

International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. The problem of automatically assigning ICD codes has been previously approached as a multilabel classification problem, using neural models and unstructured data. We utilise an approach for efficiently combining multiple sets of pretrained word embeddings to enhance the performance on ICD code prediction. Using post-processing and meta-embeddings techniques, we exploit the geometric properties of word embeddings and combine different sets of word embeddings into a common dimensional space. We empirically show that infusing information from biomedical articles, whilst preserving the local neighbourhood of the embedding, improves the current state-of-the-art deep learning architectures. Furthermore, we demonstrate the efficacy of this approach for a multimodal setting, using unstructured and structured information.
A novel approach to form normal distribution of medical image segmentation based on multiple doctors' annotations

Medical image segmentation annotated by experts provides the labeled data sets for many scientific researches. However, due to the unevenly experienced backgrounds of the experts and limited numbers of patients with certain diseases or illnesses, not only do such labeled data sets have smaller samples but their quality and normality also can range in wide variabilities and be ambiguous. In practice, these segmentations are usually assigned to be the ground truths for the scientific studies, so it may undermine the trustworthiness of the resulting findings. Therefore, it is meaningful to consider how to give a more unified opinion of the annotations among different experts. In this paper, a novel approach to form normal distributions of segmentation is proposed based on multiple doctors’ annotations for the same patient. The proposed approach is developed through the following steps: (1) utilize a framework7 of averaging images to construct an averaged annotation based on different given annotations; (2) determine the image registration deformations from the averaged annotation to the given annotations; (3) build a joint multivariate Gaussian distribution over the logorithm of Jacobian determinants and curls of the registration deformations; lastly, (4) simulate a normal distribution of segmentation by the joint Gaussian distribution of registration deformation. This work translates the problem of forming a normal distribution of the image segmentation into a problem of forming joint Gaussian distribution of image registration deformations, which the latter can be reasoned by Jacobian determinant (models local size of pixel cells) and curl (models local rotation of pixel cells) information. In the following sections, a detailed walk-through of the proposed approach is provided along with its analytical mathematics and numerical examples for its effectiveness. A synthetic example of 3 manually defined label image is made to show how to construct a mean label image, and an example of a real cancer image annotated by 3 doctors demonstrates the formation of the normal distribution and the effectiveness of the propose method
On Huang and Wong’s algorithm for generalized binary split trees

Abstract Huang and Wong (Acta Inform 21(1):113–123, 1984) proposed a polynomial-time dynamic-programming algorithm for computing optimal generalized binary split trees. We show that their algorithm is incorrect. Thus, it remains open whether such trees can be computed in polynomial time. Spuler (Optimal search trees using two-way key comparisons, PhD thesis, 1994) proposed modifying Huang and Wong’s algorithm to obtain an algorithm for a different problem: computing optimal two-way comparison search trees. We show that the dynamic program underlying Spuler’s algorithm is not valid, in that it does not satisfy the necessary optimal-substructure property and its proposed recurrence relation is incorrect. It remains unknown whether the algorithm is guaranteed to compute a correct overall solution.
Capitolo 6. Sull’emergere di a allocutivo nel romanesco dell’Ottocento

In a cursory passage of a recent paper I happened to question, on the basis of new textual acquisitions, the current hypothesis about the chronology of the earliest attestations of the particle a nowadays occurring in Romanesco vocative expressions. Further textual findings now allow us to deepen the discussion, dating the first known instances of vocative a back to the first half of the 19th century, which is surely not too relevant on a purely chronological level, but nevertheless seems to be of some importance in the more interesting field of linguistic history. In fact, this suggests that a constantly critical attitude is needed as to the value of any literary witness of modern Romanesco, including that of Belli’s Sonetti.
Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine

&#x0D; &#x0D; &#x0D; &#x0D; Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keywords:&#x0D; &#x0D; &#x0D; &#x0D;
Mathematical Models of Coordination of Population Employment in the Labor Market

In this work, one of the most pressing issues today is the issue of intellectual modeling of employment in the labor market. The study proposes a classification of methods of labor market regulation, the structure of the conceptual model of the labor market, a stochastic mathematical model of labor market self-organization, the methodology of studying the stability of mathematical models of labor market regulation is considered and experimental results are obtained. The obtained results are compared and explained in graphs and conclusions are given.
A Feature Fusion-based Representation Learning Model for Job Recommendation

Recently, lots of online recruitment sites emerged. It is difficult for people to find their interested jobs only with keywords retrieving. An intelligent job recommendation system is efficient to overcome the information overload of online recruitment. Many existing works on job recommendations focus on the textual data of jobs and resumes, which ignoring the structured features. In this work, a feature fusion-based representation learning model is proposed to predict the matching scores between job postings and resumes. Specifically, two modules are proposed to get the hidden vectors of textual and structural features, respectively. By concatenating the vectors from embedding modules, a comprehensive representation of the job posting (or resume) is composed. Last, a bilinear module is utilized to get the matching degree between the job and resume. Experiments over real-world data validate our proposed model outperforms baseline models.
Knowledge activities applied | towards a holistic knowledge management approach in the software industry

Purpose This paper aims to close the gap between the generic concept of knowledge activities (KAs) and implementing them in the context of software engineering organisations concentrating on the non-technical aspects, such as team organisation and practices. Design/methodology/approach This qualitative research used a questionnaire with practitioners such as software developers and team leads who were asked to provide feedback on a set of team practices and measures typically used in software engineering projects and assess their relation to the activities of acquiring, codifying, storing, maintaining, transferring and creating knowledge. The obtained results were analysed using frequency analysis and further descriptive statistics yielding a matrix linking the investigated team practices and measures to KAs. Findings Team practices and measures commonly applied in software engineering can be facilitated to trigger particular KAs. While most of these team practices and measures originate from agile methods, they are not restricted to these. A purposeful composition can help in assembling a balanced set of KAs aimed at fostering given knowledge goals in software engineering organisations. Practical implications By bridging the communication and terminology gap between knowledge management research and software engineering practitioners, this work lays the foundation for assessing software teams’ knowledge profiles more easily and creating prerequisites for implementing knowledge management by facilitating common practices and measures often already part of their daily work. Hence, overhead can be avoided when implementing knowledge management. Originality/value To the best of the authors’ knowledge, this is the first study investigating application and relevance of KAs in the software industry by linking them to practices and measures well-accepted in software engineering, thus providing the necessary vocabulary for the implementation of knowledge management in software development teams.
INTERNAL SABOTEURS AND WORK PERFORMANCE – IMPLICATIONS FOR HRM PRACTICES

This study aims to analyze the relationship between internal saboteurs developed since childhood and job performance, with implications for HRM practices – especially in terms of selection criteria to be used and the whole assessment process. The study was conducted through an online questionnaire which was made available in March-April of 2021. The analyzed instruments are Shirzad Chamine’s inner saboteur questionnaire and Goodman &amp; Syvantek’s Job Performance Scale. The study was performed on a 457-respondent non-probabilistic cohort. Statistical analysis of the data revealed that some internal saboteurs are oriented toward performance, while the others are oriented toward emotion. The performance-oriented cluster seemed to predominate in male respondents, while the emotion-oriented cluster seemed to predominate in female respondents, in line with tradintional gender roles. The main findings are generally in line with previous literature, and they are also suggesting a series of HRM practices aimed at increasing individual and organizational performance.
Employing automatic content recognition for teaching methodology analysis in classroom videos

A teacher plays a pivotal role in grooming a society and paves way for its social and economic developments. Teaching is a dynamic role and demands continuous adaptation. A teacher adopts teaching techniques suitable for a certain discipline and a situation. A thorough, detailed, and impartial observation of a teacher is a desideratum for adaptation of an effective teaching methodology and it is a laborious exercise. An automatic strategy for analyzing a teacher's teaching methodology in a classroom environment is suggested in this work. The proposed strategy recognizes a teacher's actions in videos while he is delivering lectures. In this study, 3D CNN and Conv2DLSTM with time-distributed layers are used for experimentation. A range of actions are recognized for a complete classroom session during experimentation and the reported results are considered effective for analysis of a teacher's teaching technique.
Effects of people’s mental models of cybersecurity on their security behaviour

This research investigates the effects of people’s mental modal of cybersecurity on their security behaviour. Data were collected using survey design and the questions were measured on a Likert five point-scale. The questionnaires were distributed to mainly IT staff, management staff and other staff that uses computers in the discharge of their duties. The data were analysed using percentages, frequencies and statistical methods of regression and ANOVA while Cronbach Alpha reliability coefficient technique was adopted to test the validity of the questions used to collect primary data. The result reveals that people’s prior knowledge of cybersecurity issues has no effect on people’s mental models for improved cybersecurity behaviour, but lack of trained staff (cyber talents), lack of supportive infrastructures, time constraints, exclusion of cybersecurity in non-computing courses, poor knowledge of fundamental computing areas, lack of mentors with hands-on experience, lack of cyber training or challenge programme, and inadequate books on cyber security were admitted by the participants as some of the major factors that inhibit their knowledge and engagement in cybersecurity education. These barriers if not checked could limit the level of cybersecurity awareness among undergraduate students.
Direct detection of quantum non-Gaussian light from a dispersively coupled single atom

Many applications in quantum communication, sensing and computation need provably quantum non-Gaussian light. Recently such light, witnessed by a negative Wigner function, has been estimated using homodyne tomography from a single atom dispersively coupled to a high-finesse cavity. This opens an investigation of quantum non-Gaussian light for many experiments with atoms and solid-state emitters. However, at their early stage, an atom or emitter in a cavity system with different channels to the environment and additional noise are insufficient to produce negative Wigner functions. Moreover, homodyne detection is frequently challenging for such experiments. We analyse these issues and prove that such cavities can be used to emit quantum non-Gaussian light employing single-photon detection in the Hanbury Brown and Twiss configuration and quantum non-Gaussianity criteria suitable for this measurement. We investigate in detail cases of considerable cavity leakage when the negativity of the Wigner function disappears completely. Advantageously, quantum non-Gaussian light can be still conclusively proven for a large set of the cavity parameters at the cost of overall measurement time, even if noise is present.
Universal Convolutional Neural Network for Recognition of Traffic Lights and Road Signs in Video Frames

The paper proposes a universal architecture of a convolutional neural network designed to recognize road signs and traffic lights on a video frame. The main aim was to simultaneously recognize objects of different classes, such as road signs and traffic lights of different types, by one neural network at the same processing step simultaneously. The neural network implementation was based on Keras and TensorFlow for classification and OpenCV to highlight the contours of possible road signs and traffic lights on video frames. By processing video recordings, datasets were prepared containing images of road signs and traffic lights that are used in Russia. On the prepared datasets, a study was carried out and the accuracy of object recognition by video frame was calculated: the recognition accuracy of road signs was 94.4% and of traffic lights was 95.2%.
CNF-FSS and Its Applications

Function Secret Sharing (FSS), introduced by Boyle, Gilboa and Ishai [BGI15], extends the classical notion of secret-sharing a value to secret sharing a function. Namely, for a secret function f (from a class $$\mathcal F$$ ), FSS provides a sharing of f whereby succinct shares (“keys”) are distributed to a set of parties, so that later the parties can non-interactively compute an additive sharing of f(x), for any input x in the domain of f. Previous work on FSS concentrated mostly on the two-party case, where highly efficient schemes are obtained for some simple, yet extremely useful, classes $$\mathcal F$$ (in particular, FSS for the class of point functions, a task referred to as DPF – Distributed Point Functions [GI14, BGI15]). In this paper, we concentrate on the multi-party case, with $$p\ge 3$$ parties and t-security ( $$1\le t<p$$ ). First, we introduce the notion of CNF-DPF (or, more generally, CNF-FSS), where the scheme uses the CNF version of secret sharing (rather than additive sharing) to share each value f(x). We then demonstrate the utility of CNF-DPF by providing several applications. Our main result shows how CNF-DPF can be used to achieve substantial asymptotic improvement in communication complexity when using it as a building block for constructing standard (t, p)-DPF protocols that tolerate $$t>1$$ (semi-honest) corruptions (of the p parties). For example, we build a 2-out-of-5 secure (standard) DPF scheme of communication complexity $$O(N^{1/4})$$ , where N is the domain size of f (compared with the current best-known of $$O(N^{1/2})$$ for (2, 5)-DPF). More generally, with $$p>dt$$ parties, we give a (t, p)-DPF whose communication grows as $$O(N^{1/2d})$$ (rather than $$O(\sqrt{N})$$ that follows from the $$(p-1,p)$$ -DPF scheme of [BGI15]). (We ignore here terms that depend on the number of parties, p, the security parameter, etc. See precise statements in the main body of the paper below) We also present a 1-out-of-3 secure CNF-DPF scheme, in which each party holds two of the three keys, with poly-logarithmic communication complexity. These results have immediate implications to scenarios where (multi-server) DPF was shown to be applicable. For example, we show how to use such a scheme to obtain asymptotic improvement ( $$O(\log ^2N)$$ versus $$O(\sqrt{N})$$ ) in communication complexity over the 3-party protocol of [BKKO20].
Learning Preference Recommendation with Heterogeneous Graph Neural Networks in MOOC

In order to solve the problem of low utilization of MOOC resources and information overload, in this paper we propose a MOOC recommendation model based on heterogeneous graph neural networks combining with attention mechanism. First, we design multiple meta-paths containing semantic relations to guide the propagation of users’ preferences in the graph neural network based on residual connection. And then combine with the attention mechanism to strengthen the factors that effectively affect users’ preferences. Finally, we use regularized matrix decomposition to predict the rating. In this study, we evaluate the proposed model by collecting and organizing the MOOC dataset. Through repeating experiments, we prove the evaluation metrics of HGMCRec is better than other baseline methods. And we also prove the usability of this model on two public datasets.