Reviewerly/exHarmony huggingface dataset:
Adoption of Quantum Computing in Economic Analysis: Potential and Challenges in Distributed Information Systems

INTRODUCTION: Quantum computing technology has become a center of attention in various scientific disciplines, including economic analysis. The adoption of quantum computing in economic analysis offers tremendous potential to improve the processing of complex economic data and provide deep insights. However, the use of quantum technology in the context of distributed information systems also raises several challenges, including data security and the limitations of quantum technology. OBJECTIVE: This research aims to investigate the implications of adopting quantum computing in economic analysis, with a focus on distributed information systems. METHODS: This research was carried out using a descriptive qualitative approach, with data derived from the results of relevant research and previous studies. The collected data will be processed and analyzed to gain a deeper understanding of the adoption of quantum computing in economic analysis in distributed information systems. RESULTS: This research then finds that the adoption of quantum computing in economic analysis has the potential to increase efficiency, accuracy, and depth of economic insight. However, limitations of current quantum technologies, including quantum errors, limited scale of operations, and data security issues, limit their applications. In the long term, research and development will be key to overcoming these obstacles and maximizing the potential of this technology in economic analysis. CONCLUSION: The long-term implications include increased economic competitiveness and significant changes in the way economic decision-making is carried out, assuming that ethical and regulatory issues are also carefully considered.
General phase-shifting algorithm for hybrid errors suppression using variable-frequency fringes

In measurements based on phase-shifting fringe pattern analysis, residual ripple-like artifacts often appear due to the co-influence of several error sources, e.g., phase-shifting errors, temporal intensity fluctuations and high-order fringe harmonics, when existing algorithms are adopted to retrieve phase using limited number of fringe patterns. To overcome this issue, a general phase-shifting algorithm for hybrid errors suppression by variable-frequency fringes is proposed in this paper for what we believe to be the first time. A corresponding fringe model is deduced to represent real patterns more accurately under the co-influence of these error factors. Variable-frequency fringes are introduced to provide a least and sufficient system of equations, while a least-squares iterative technique with a grouped step-by-step strategy is adopted for stable calculating a larger number of desired parameters in the constructed model. For the phase jump problem caused by non-full rank matrices at certain sampling points, a regularization combined with constraints between coefficients of high-order fringe harmonics is further proposed for identification and processing. Simulations and experimental results have shown that compared with the prior techniques, the accuracies of the proposed algorithm have been significantly enhanced at least 2.1 (simulations) and 1.5 (experiments) times respectively using bi-frequency equal three-step as an example in the study.
A Framework of Large-Scale Peer-to-Peer Learning System

Federated learning (FL) is a distributed machine learning paradigm in which numerous clients train a model dispatched by a central server while retaining the training data locally. Nonetheless, the failure of the central server can disrupt the training framework. Peer-to-peer approaches enhance the robustness of system as all clients directly interact with other clients without a server. However, a downside of these peer-to-peer approaches is their low efficiency. Communication among a large number of clients is significantly costly, and the synchronous learning framework becomes unworkable in the presence of stragglers. In this paper, we propose a semi-asynchronous peer-to-peer learning system (P2PLSys) suitable for large-scale clients. This system features a server that manages all clients but does not participate in model aggregation. The server distributes a partial client list to selected clients that have completed local training for local model aggregation. Subsequently, clients adjust their own models based on staleness and communicate through a secure multi-party computation protocol for secure aggregation. Through our experiments, we demonstrate the effectiveness of P2PLSys for image classification problems, achieving a similar performance level to classical FL algorithms and centralized training.
Reimagining China-US Relations Prediction: A Multi-modal, Knowledge-Driven Approach with KDSCINet

Statistical models and data driven models have achieved remarkable results in international relation forecasting. However, most of these models have several common drawbacks, including (i) rely on large amounts of expert knowledge, limiting the objectivity, applicability, usability, interpretability and sustainability of models, (ii) can only use structured unimodal data or cannot make full use of multimodal data. To address these two problems, we proposed a Knowledge-Driven neural network architecture that conducts Sample Convolution and Interaction, named KDSCINet, for China-US relation forecasting. Firstly, we filter events pertaining to China-US relations from the GDELT database. Then, we extract text descriptions and images from news articles and utilize the fine-tuned pre-trained model MKGformer to obtain embeddings. Finally we connect textual and image embeddings of the event with the structured event value in GDELT database through multi-head attention mechanism to generate time series data, which is then feed into KDSCINet for China-US relation forecasting. Our approach enhances prediction accuracy by establishing a knowledge-driven temporal forecasting model that combines structured data, textual data and image data. Experiments demonstrate that KDSCINet can (i) outperform state-of-the-art methods on time series forecasting problem in the area of international relation forecasting, (ii) improving forecasting performance through the use of multimodal knowledge.
A search tool based on language modelling developed for The Index of Middle English Prose

<ns3:p>Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the <ns3:italic>Index of Middle English Prose</ns3:italic> (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the <ns3:italic> Stanford Research Institute Language Modelling </ns3:italic> (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the ‘correct’ one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.</ns3:p>
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos

Classifying player actions from soccer videos is a challenging problem, which has become increasingly important in sports analytics over the years. Most state-of-the-art methods employ highly complex offline networks, which makes it difficult to deploy such models in resource constrained scenarios. Here, in this paper we propose a novel end-to-end knowledge distillation based transfer learning network pre-trained on the Kinetics400 dataset and then perform extensive analysis on the learned framework by introducing a unique loss parameterization. We also introduce a new dataset named “SoccerDB1” containing 448 videos and consisting of 4 diverse classes each of players playing soccer. Furthermore, we introduce an unique loss parameter that help us linearly weigh the extent to which the predictions of each network are utilized. Finally, we also perform a thorough performance study using various changed hyperparameters. We also benchmark the first classification results on the new SoccerDB1 dataset obtaining 67.20% validation accuracy. The dataset has been made publicly available at: https://bit.ly/soccerdb1 .
Graph-Based Diagnostic Prediction Model Based on Global Visit Contexts

In the healthcare field, predicting future diagnoses of patients based on their medical history is a critical task. Electronic health records (EHR) have facilitated the development of many deep-learning models for prediction in this field. However, two issues have not been addressed simultaneously, which can impact the accuracy of the prediction model. The first issue is that rare diseases have little opportunity to be learned during the training process, while the second issue is that short medical visit record sequences pose a challenge for sequential models. To address these challenges, we propose using a graph neural network to encode medical ontology and co-occurrence information into diagnosis representation. We also use a pre-trained vanilla prediction model to obtain similarities between visit contexts and extend visit records by referencing similar visit contexts in the training dataset. Our experiments show that the proposed model performs better than the current state-of-the-art in diagnostic prediction.
The role of the concept of knowledge management at the turn of systemic transformations in higher education

Based on the analysis of planned and already adopted, but not effective legislative and regulatory acts, September 1, 2025 is projected as the milestone of systemic changes in higher education. The authors substantiate the role of the knowledge management concept in the planned transformations. The directions of advanced scientific research on scientific justification and development of practical proposals for solving innovative problems are formulated
Quantum nonlocality evolution for two entangled mesoscopic fields under decoherence

Abstract Investigation of the nonlocality evolution of entangled mesoscopic fields under decoherence not only is important for understanding the quantum-classical transition, but also has relevance to quantum communication and quantum computation protocols based on continuous variables. According to previous formulations of Bell inequalities, the system loses nonlocal features far before&amp;#xD;the disappearance of entanglement. We here constructed a new version of Bell signal based on rotated and displaced on-off correlations, with which the Bell inequality is violated as long as there remains entanglement and the field state components keeps quasiorthogonal. Consequently, the nonlocal character revealed by our formulation decays much slower compared with those based on previous ones. More importantly, there exists a wide regime where the Bell inequality is restored with previous formulations but remains to be violated based on our correlations operators.
Sequential Transformer for End-to-End Person Search

Person Search aims to simultaneously localize and recognize a target person from realistic and uncropped gallery images. One major challenge of person search comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons so as to distinguish persons from the background, while person re-identification (re-ID) focuses on the differences among different persons. In this paper, we propose a novel Sequential Transformer (SeqTR) for end-to-end person search to deal with this challenge. Our SeqTR contains a detection transformer and a novel re-ID transformer that sequentially addresses detection and re-ID tasks. The re-ID transformer comprises the self-attention layer that utilizes contextual information and the cross-attention layer that learns local fine-grained discriminative features of the human body. Moreover, the re-ID transformer is shared and supervised by multi-scale features to improve the robustness of learned person representations. Extensive experiments on two widely-used person search benchmarks, CUHK-SYSU and PRW, show that our proposed SeqTR not only outperforms all existing person search methods with a 59.3 $$\%$$ mAP on PRW but also achieves comparable performance to the state-of-the-art results with an mAP of 94.8 $$\%$$ on CUHK-SYSU.
Features of Combining Traditional and Modern Pedagogical Technologies at a University

Modern and traditional pedagogical technologies are various methods and tools used in education for the learning and development of students. They can be combined effectively to provide a comprehensive and quality educational experience. Modern educational technologies are often aimed at increasing interactivity, accessibility, and individualization of learning. They can effectively complement traditional methods, enriching the educational experience of students. It is important to select and adapt those technologies that best suit specific learning goals and needs. Pedagogical technologies are of great relevance in education, as they contribute to the development of students’ competencies and prepare them for the modern world and the future labor market.
CPSR-HQKDN: A Hybrid Trusted Relay Quantum Key Distribution Network Routing Scheme Based on Classification of Packet Security Requirements

To ensure the security of information exchange in software-defined optical networks, quantum key distribution (QKD) based on quantum mechanics is introduced. However, the slow and valuable process of generating quantum key resources contradicts the high-speed data transmission requirements of optical networks. To address this issue, this paper proposes the CPSR-HQKDN scheme, which takes into account factors such as security requests, key demand, key residual, and key update rates for trusted and untrusted links. This approach improves resource utilization and service efficiency by optimizing the processing order of key requests. Moreover, the routing strategy dynamically adjusts based on the network resource environment, thereby increasing the success rate of key requests. Through simulation experiments comparing the performance of the CPSR-HQKDN routing scheme with existing schemes, it is observed that in high-concurrent scenarios, the CPSR-HQKDN routing scheme can improve the success rate of key requests by at least 5%.
Examination of Cybersecurity Technologies, Practices, Challenges, and Wish List in K-12 School Districts

With the growth in digital teaching and learning, there has been a sharp rise in the number of cybersecurity attacks on K-12 school networks. This has demonstrated a need for security technologies and cybersecurity education. This study examined security technologies used, effective security practices, challenges, concerns, and wish list of technology leaders in K-12 settings. Data collected from 23 district websites and from interviews with 12 district technology leaders were analyzed. Top security practices included cloud-based technologies, segregated network/V-LAN, two-factor authentication, limiting access, and use of Clever or Class Link. Top challenges included keeping users informed, lack of buy-in from staff and decision-makers, lack of expertise to implement modern best practices, and cost of resources. Top concerns included possible cyberattacks, leaked student data, and lack of user awareness. Finally, their wish list included technology personnel, access to Clever of Class Link, external system diagnostic checks, professional development for staff, and replacing aging infrastructure. The findings have implications for K-12 administrators, technology leaders, and teachers.
Data-Driven Sequential Change Detection in Privacy-Sensitive Networks

We study privacy-preserving sequential change detection in a setting where the data are distributed over a network and locally sensitive to each node, and probabilistic data model is unknown. We design and analyze a data-driven solution scheme where each node observes a high-dimensional data stream for which it computes a local outlierness score, perturbs, encrypts, and sends it to a network operator. The network operator then decrypts an aggregate statistic over the network and performs online network change detection via the proposed generalized cumulative sum (CUSUM) algorithm. We derive an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Additionally, we derive an asymptotic upper bound and asymptotic approximation for the average detection delay of the proposed algorithm under a certain anomaly. We show the analytical tradeoff between the change detection performance and the differential privacy level, controlled via the local perturbation noise. Experiments illustrate that the proposed algorithm offers a good tradeoff between privacy and quick change detection against the UDP flooding and spam attacks in a real Internet of Things (IoT) network.
Job Crafting Antecedents and Consequences: Evidence from Jordanian Universities

Purpose - This article explores academic job crafting's significant conditions (antecedents and consequences). The impact of encouraging learning environments and the leadership that fortifies learning was studied. Knowledge management and job crafting are closely related in their ability to empower employees and enhance organizational effectiveness. Design/methodology/approach –217 academics serving in a Jordanian University, in Jordan, took part in this study. Statistical parameters endorsed the significant impact of two learning organization building blocks on job crafting. Findings - It was shown that supportive learning environments and leadership reinforces learning; and positively impacts job crafting, which consequently reflects upon job satisfaction levels among academics. The results imply that strategies conducive to learning provide the most rewarding approach to enhancing academic job crafting and job satisfaction. Originality/value – To the authors’ knowledge, no prior study has examined job crafting and linked it to the study dimensions within the Arab context generally and particularly in the academic field. We propose this model as a system crafting theory and end our research with suggestions for further research on these issues. Keywords: Job crafting, job satisfaction, learning organization, academic organization, supportive learning environment, leadership support, Jordan.
Detecting Phishing Emails Using Random Forest and AdaBoost Classifier Model

Phishing attack occurs when a phishing email which is a legitimate-looking email, designed to lure the recipient into believing that it is a genuine email to open and click malicious links embedded into the email. This leads to user reveal sensitive information such as credit card number, usernames or passwords to the attacker thereby gaining entry into the compromised account. Online surveys have put phishing attack as the leading attack for web content mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attack is about $1.5 billion per year. This is a global threat to information security and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these loses and reputation injury. This research paper explores and reports the use of a combination of machine learning algorithms; Random Forest and AdaBoost and use of more phishing email features in improving the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier which can detect phishing and prevent phishing emails and test the model with existing data. A dataset consisting of both benign and phishing emails will be used to conduct a supervised learning by the model. Expected accuracy is 99.9%, False Negative (FN) and False Positive (FP) rates of 0.1% and below.
EVALUATING THE EFFECTIVENESS OF ANIMAKER AS A PEDAGOGICAL TOOL IN TEACHING RECOUNT TEXTS: STUDENTS’ INSIGHTS

This qualitative research delves into the perspectives of English as a Foreign Language (EFL) students regarding the utilization of Animaker as an educational tool for recount text materials. The study involved interviews with three EFL students hailing from an Indonesian private junior high school. The findings of this investigation unveiled six significant advantages associated with using Animaker in the learning process. Firstly, it simplified subject matter comprehension for students. Secondly, it facilitated the retention of lesson details. Thirdly, it contributed to improved classroom performance, ultimately resulting in higher grades. Fourthly, it rendered the learning process enjoyable, fostering a sense of motivation among students. Lastly, it streamlined vocabulary acquisition, making it more accessible. However, despite these advantages, students encountered challenges, particularly in dealing with complex vocabulary found within the recount text materials. To overcome these hurdles, they devised four strategies. These strategies encompassed seeking clarifications from teachers and peers, consulting bilingual and monolingual dictionaries, and deducing word meanings from contextual clues. Furthermore, the research revealed that Animaker effectively enhanced 21st-century literacy skills by combining digital literacy, global literacy, technological literacy, visual literacy, and information literacy. This, in turn, equipped students with valuable skills for thriving in today's technologically advanced society. In conclusion, the study highlighted the overwhelmingly positive perception of EFL students towards the use of Animaker as an educational medium for recount text materials. While students did encounter vocabulary challenges, they were proactive in employing various strategies to overcome these obstacles. This research holds implications for the development of technological resources in English language learning, benefiting both EFL students and educators.