Reviewerly/exHarmony huggingface dataset:
The impact of TQM on organizational learning: The moderating role of strategic thinking at Jordanian SMEs

In the workplace of the twenty-first century, there has been a rise in interest in organizational learning. The purpose of this research is to add to the body of knowledge regarding organizational learning, by analyzing how TQM affects organizational learning. Also in this study, we intend to indicate the influence of strategic thinking as moderator on the relationship between TQM and organizational learning. Data were gathered from respondents (N = 306) who were employed in industrial sectors and registered with the Amman stock exchange in Jordan. Furthermore, the study results support the assumption that TQM affects organizational learning. Moreover, the findings confirmed that strategic thinking plays a moderating role in the relationship between organizational learning and TQM, supporting the moderation hypothesis as well. After presenting theoretical and practical implications of the research, limitations and future direction are discussed.
Negativity vs. purity and entropy in witnessing entanglement

Abstract In this paper, we explore the value of measures of mixedness in witnessing entanglement. While all measures of mixedness may be used to witness entanglement, we show that all such entangled states must have a negative partial transpose (NPT). Where the experimental resources needed to determine this negativity scale poorly at high dimension, we compare different measures of mixedness over both Haar-uniform and uniform-purity ensembles of joint quantum states at varying dimension to gauge their relative success at witnessing entanglement. In doing so, we find that comparing joint and marginal purities is overwhelmingly (albeit not exclusively) more successful at identifying entanglement than comparing joint and marginal von Neumann entropies, in spite of requiring fewer resources. We conclude by showing how our results impact the fundamental relationship between correlation and entanglement and related witnesses.
Circuit implementation of discrete-time quantum walks via the shunt decomposition method

Several models have been proposed to build evolution operators to perform quantum walks in a theoretical way, although when wanting to map the resulting evolution operators into quantum circuits to run them in quantum computers, it is often the case that the mapping process is in fact complicated. Nevertheless, when the adjacency matrix of a graph can be decomposed into a sum of permutation matrices, we can always build a shift operator for a quantum walk that has a block diagonal matrix representation. In this paper, we analyze the mapping process of block diagonal operators into quantum circuit form and apply this method to obtain quantum circuits that generate quantum walks on the most common topologies found in the literature: the straight line, the cyclic graph, the hypercube and the complete graph. The obtained circuits are then executed on quantum processors of the type Falcon r5.11 L and Falcon r4T (two of each type) through IBM Quantum Composer platform and on the Qiskit Aer simulator, performing three steps for each topology. The resulting distributions were compared against analytical distributions, using the statistical distance $$\ell _1$$ as a performance metric. Regarding experimental executions, we obtained short $$\ell _1$$ distances in the cases of quantum circuits with a low amount of multi-control gates, being the quantum processors of the type Falcon r4T the ones that provided more accurate results.
The "Elliptic" matrices and a new kind of cryptography

In [17], A. chillali et al introduce a new cryptographic method based on matrices over a finite field Fpn , where p is a prime number. In this paper, we will generate this method in a new group of square block matrices based on an elliptic curve, called "elliptic" matrices.
The Mediating Role of Job Satisfaction on the Relationship Between Human Resources Management Strategies and Employees Performance in Waha Oil &amp; Gas Company in Libya

The structure of planned human resource deployments and activities intended to help an organization accomplish its objectives is known as strategic human resource management. This study aims to investigate the relationship between HRM strategies and employee performance in the Libyan oil and gas sector. Accordingly, 321 survey data were collected from Waha Oil Company employees in Tripoli, Libya. The structural Equation Modelling (SEM) technique was used to analyze the data as well as hypotheses testing. The findings demonstrated that human strategies significantly affect both job satisfaction and employee performance. Job satisfaction influences employee performance. Besides, training and development, recruitment and selection significantly affect job satisfaction, while performance appraisal significantly influences employee performance. The findings are beneficial to HRM department to enhance their emphasis on the crucial factors that boost employee performance through improving job satisfaction.
Decentralized Identity Management System using the amalgamation of Blockchain Technology

In the recent past, with the advancement of tech-nologies, many changes have been made in Identity Management Systems across the globe. Identity cards have become an inextri-cable part of our lives. With time, authentication of a person's identity and data breaching has led to privacy concerns. The rapid growth of the population raises the challenge of storing large data securely. A new approach to data encryption and hashing can resolve concerns of privacy, security, data handling, and more. Blockchain Technology and the Internet Of Things will improve the current ecosystem. The devised model will be using the AES algorithm for the encryption of biometrics and demographic data. Additionally, the symmetrically distributed storage system, the InterPlanetary File System(IPFS) will be used and data will be hashed by SHA-256. Furthermore, key pairs will be assigned to IoT devices to store the data on a distributed ledger system. This paper will also introduce the merging of Blockchain and IoT, thus ensuring better security and connectivity in a decentralized manner.
Validation of cytopathology specimens for digital pathology

Digital cytopathology is being progressively implemented in centres worldwide, but impediments such as the three-dimensionality of specimens and the size of scanned images have prevented its use from becoming widespread. This study aimed to validate the use of digital whole slide image scanning of cytopathology samples for routine sign-out.Specimens were scanned using the Leica Aperio GT 450 System. The following sample types were used: liquid-based cytology, direct conventional smears from fine needle aspirates and cytospins. Cases were validated by the same pathologist who originally rendered the conventional diagnosis, with a washout of at least 3 months. Final digital diagnoses were compared to the original analogical diagnoses, and cases were considered concordant up to a one-degree difference between the original and digital diagnoses. Reasons for the unsuccessful scanning of slides were also noted. The technical procedures followed the College of American Pathologists' guidelines for digital pathology validation.A total of 730 slides from 383 cases (337 female, 51 male; median age 42) were successfully scanned. These cases consisted of the following sample types: 81 (21.1%) conventional smears, 240 (62.7%) liquid-based cytology samples and 62 (16.2%) cytospins. There were only five discordant cases, with a 98.7% agreement between original and digital diagnoses using the difference rate of up to one degree. Seventy-seven slides (10.5%) had to be rescanned due to technical problems. The main reasons for unsuccessful scanning were paucicellular samples (44; 57.1%), the thickness of the smears (18; 23.4%) and issues with the coverslip (15; 19.5%).Cytological specimens can be successfully scanned and used for digital pathology, with excellent agreement with the original diagnoses.
Metaverse and Cultural Marketing in Hospitality and Tourism

Metaverse is a thirty-year-old term, but a new formation. Metaverse is termed the 4th era of networking and computing – thereby succeeding mainframes (Ball, 2022). The Chapter will analyse the metaverse from two perspectives; it will look at the metaverse as a marketing tool and, or promote and sell goods and services in the hospitality industry; to use the metaverse as a marketing strategy in cultural marketing, and the future implications of metaverse on the hospitality industry as a whole. The global tourism and hospitality industry, currently, is experiencing some significant transformations due to certain disruptive innovations in the digital spheres across the globe – this includes the ‘metaverse'. Similarly, the notions of the ‘metaverse' as well as its virtual reality experiences have altered people's perspectives radically on the future of technology and its potential impacts on the tourism and hospitality industries.
Scalable cognitive modelling: Putting Simon’s (1969) ant back on the beach.

A classic goal in cognitive modelling is the integration of process and representation to form complete theories of human cognition (Estes, 1955). This goal is best encapsulated by the seminal work of Simon (1969) who proposed the parable of the ant to describe the importance of understanding the environment that a person is embedded within when constructing theories of cognition. However, typical assumptions in accounting for the role of representation in computational cognitive models do not accurately represent the contents of memory (Johns & Jones, 2010). Recent developments in machine learning and big data approaches to cognition, referred to as scaled cognitive modelling here, offer a potential solution to the integration of process and representation. This article will review standard practices and assumptions that take place in cognitive modelling, how new big data and machine learning approaches modify these practices, and the directions that future research should take. The goal of the article is to ground big data and machine learning approaches that are emerging in the cognitive sciences within classic cognitive theoretical principles to provide a constructive pathway towards the integration of cognitive theory with advanced computational methodology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Criteria for maximal nonlinearity of a function over a finite field

Abstract An n -place function over a field with q elements is called maximally nonlinear if it has the greatest nonlinearity among all such functions. Criteria and necessary conditions for maximal nonlinearity are obtained, which imply that, for even n , the maximally nonlinear functions are bent functions, but, for q &gt; 2, the known families of bent functions are not maximally nonlinear. For an arbitrary finite field, a relationship between the Hamming distances from a function to all affine mappings and the Fourier spectra of the nontrivial characters of the function are found.
Blind Quality Evaluator of Light Field Images by Group-Based Representations and Multiple Plane-Oriented Perceptual Characteristics

Due to the emergency of multi-view cameras and commercial Light Field (LF) cameras, the demand of high-performance LF quality evaluator is of great significance for guiding LF acquisition, processing and application and further promoting the visual perceived quality of LF visualizations. However, LF Images (LFIs), as high-dimensional data, suffer from various quality degradations not only in the spatial domain but also in the angular domain. Therefore, it is of great challenge to predict LF quality accurately. An effective LF evaluator should be able to represent these heterogeneous artifacts. In this paper, we provide a novel No-Reference LF Quality Assessment Evaluator (NR LF-QAE) to tackle this problem. Firstly, to measure angular consistency among viewports, we utilize group-based representations to character information similarity of aligned view stacks. Secondly, to better describe the texture information of LFIs, unifying spatial-angular texture statistic measurement is performed via Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Thirdly, we design 3D Log-Gabor filters to extract LF global structure information in Sub-Aperture Images (SAIs) as spatial feature characterizations and 2D Log-Gabor filters are adopted to characterize ray direction/depth information in Epipolar Plane Images (EPIs) as angular feature characterizations. By comprehensive LF information analyses in angular consistency and spatial-angular feature extraction with texture and structure descriptors, experimental results demonstrate the superiority of the proposed NR LF-QAE over the state-of-the-art comparative models in predicting the quality of LFIs on three available benchmark databases. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zerosola/NR-LF-QAE</uri> .
Computational capability of ecological dynamics

Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem?
Feature Necessity &amp; Relevancy in ML Classifier Explanations

Abstract Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction. In some applications, and besides asking for an explanation, it is also critical to understand whether sensitive features can occur in some explanation, or whether a non-interesting feature must occur in all explanations. This paper starts by relating such queries respectively with the problems of relevancy and necessity in logic-based abduction. The paper then proves membership and hardness results for several families of ML classifiers. Afterwards the paper proposes concrete algorithms for two classes of classifiers. The experimental results confirm the scalability of the proposed algorithms.
Deep polyp image enhancement using region of interest with paired supervision

Endoscopic medical imaging in complex curved intestinal structures are prone to uneven illumination, low contrast and lack of texture information. These problems may lead to diagnostic challenges. This paper described the first supervised deep learning based image fusion framework to enable the polyp region highlight through a global image enhancement and a local region of interest (ROI) with paired supervision. Firstly, we conducted a dual attention based network in global image enhancement. The Detail Attention Maps was used to preserve more image details and the Luminance Attention Maps was used to adjust the global illumination of the image. Secondly, we adopted the advanced polyp segmentation network ACSNet to obtain the accurate mask image of lesion region in local ROI acquisition. Finally, a new image fusion strategy was proposed to realize the local enhancement effect of polyp image. Experimental results show that our method can highlight the local details of the lesion area better and reach the optimal comprehensive performance with comparing with 16 traditional and state-of-the-art enhancement algorithms. And 8 doctors and 12 medical students were asked to evaluate our method for assisting clinical diagnosis and treatment effectively. Furthermore, the first paired image dataset LHI was constructed, which will be made available as an open source to research communities.
Testing collapse models with Bose-Einstein-condensate interferometry

The model of continuous spontaneous localization (CSL) is the most prominent consistent modification of quantum mechanics predicting an objective quantum-to-classical transition. Here we show that precision interferometry with Bose-Einstein-condensed atoms can serve to lower the current empirical bound on the localization rate parameter by several orders of magnitude. This works by focusing on the atom count distributions rather than just mean population imbalances in the interferometric signal of squeezed Bose-Einstein condendates, without the need for highly entangled Greenberger-Horne-Zeilinger-like states. In fact, the interplay between CSL-induced diffusion and dispersive atom-atom interactions results in an amplified sensitivity of the condensate to CSL. We discuss experimentally realistic measurement schemes utilizing state-of-the-art experimental techniques to test new regions of parameter space and, pushed to the limit, to probe and potentially rule out large relevant parameter regimes of CSL.
Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach

To expand the use of cognitive diagnosis models (CDMs) to longitudinal assessments, this study proposes a bias-corrected three-step estimation approach for latent transition CDMs with covariates by integrating a general CDM and a latent transition model. The proposed method can be used to assess changes in attribute mastery status and attribute profiles and to evaluate the covariate effects on both the initial state and transition probabilities over time using latent (multinomial) logistic regression. Because stepwise approaches generally yield biased estimates, correction for classification error probabilities is considered in this study. The results of the simulation study showed that the proposed method yielded more accurate parameter estimates than the uncorrected approach. The use of the proposed method is also illustrated using a set of real data.
A group recommender system for books based on fine-grained classification of comments

Purpose The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web. Design/methodology/approach The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations. Findings Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments. Originality/value Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.
SELECTION OF DIGITAL TOOLS FOR ORGANIZING STUDENTS' GROUP WORK IN DISTANCE EDUCATION

The transformation of classical education into distance education has become increasingly relevant, especially given the challenging conditions posed by the COVID-19 pandemic and military operations in our country. In these circumstances, distance learning requires practical educational materials that are easily accessible to students. This paper proposes criteria and indicators for selecting digital tools that can facilitate group work with educational content in distance learning. We analyzed the tools available on the Microsoft 365, Google Workspace, and Cisco Webex platforms and evaluated their suitability for managing students’ group work in distance learning in institutions of higher education at various stages of the learning process including setting tasks, designing the learning environment, facilitating group interaction and communication, managing educational content and files, promoting teamwork and productivity, and presenting results. To obtain expert assessment of the defined criteria and indicators, we engaged seventeen researchers and educators who have practical experience using digital tools in distance learning. Our study identified three important criteria for selecting digital tools for group work with educational content, namely: design, functional-technological, education-communication ones. We determined the weight of the indicators for these criteria and assessed digital tools for group work with educational content in distance learning. Digital tools such as Microsoft 365 and Google Workspace was found to be more suitable for providing group work of students in a distance learning, while Cisco Webex was found to be the most suitable tool for organizing real-time group work. Other digital tools for group work of students in distance learning can be evaluated according to defined criteria and indicators.
Translating Akkadian to English with neural machine translation

Abstract Cuneiform is one of the earliest writing systems in recorded human history (ca. 3,400 BCE–75 CE). Hundreds of thousands of such texts were found over the last two centuries, most of which are written in Sumerian and Akkadian. We show the high potential in assisting scholars and interested laypeople alike, by using natural language processing (NLP) methods such as convolutional neural networks (CNN), to automatically translate Akkadian from cuneiform Unicode glyphs directly to English (C2E) and from transliteration to English (T2E). We show that high-quality translations can be obtained when translating directly from cuneiform to English, as we get 36.52 and 37.47 Best Bilingual Evaluation Understudy 4 (BLEU4) scores for C2E and T2E, respectively. For C2E, our model is better than the translation memory baseline in 9.43, and for T2E, the difference is even higher and stands at 13.96. The model achieves best results in short- and medium-length sentences (c. 118 or less characters). As the number of digitized texts grows, the model can be improved by further training as part of a human-in-the-loop system which corrects the results.