Reviewerly/exHarmony huggingface dataset:
Interconnection of Cultural Literacy in Learning English and its Understanding as a Foreign Language in Higher Education

This research analyzed the model of cultural literacy in learning and understanding English as a foreign language in higher education. Cultural literacy is closely related to applying English as the main foreign language in higher education. The lecturer became a role model and a central point in introducing and guiding students to understand cultural literacy and English as a foreign language. Moroever, this study portrayed a qualitative descriptive method. Five lecturers from private higher education in Malang city were recruited as informants in gathering information. Furthermore, the study unraveled that cultural literacy and learning English as a foreign language had interconnections to explore each other's culture from any aspect, thus, creating adjacent dialogic and informative spaces as intercultural communications.
Socio-Technical Ecosystem Considerations: An Emergent Research Agenda for AI in Cybersecurity

Cybersecurity continues to be a key element of national security. Given that critical infrastructure is powered by digital systems, any disruption to the flow of vital services has the potential effect of causing harm to citizens, organizations, and government entities. The availability of service offerings may be subject to attacks by AI on various aspects of cyber-physical-social systems. And no system is foolproof. Inasmuch as AI can be a threat to cybersecurity, it can also be used to combat attempts at systems penetration through deep learning and other capabilities. However, focusing our attention on AI alone does not allow for a sufficiently accurate understanding of the complex ecosystem that contributes to the conflicting scenarios that are possible in the context of AI in cybersecurity. This Special Issue provides insights into the broader socio-technical ecosystem considerations that are required for the achievement of system robustness, allowing for a more intricate and balanced understanding of the role of AI in cybersecurity, at various levels relevant to individuals, organizations, and communities/ society. Fundamentally, the socio-technical approach calls for an emphasis on people, processes, and technology, and not solely on the shiny gadgetry that we call products. There are four main themes that form the emergent research agenda: 1) the cybersecurity ecosystem that demands an interdisciplinary approach; 2) the state of the art technology development processes and landscape that provide and facilitate cybersecurity mechanisms and competitive advantage through AI; 3) the interconnected and multifaceted nature of the social, technical, and environmental subsystems in the cybersecurity ecosystem; and 4) the emphasis on human requirements and values, inclusive of end-user capacity and awareness, organizational capability and skills development, and societal considerations inclusive of the public interest.
Smartphone app aesthetics influence users' experience and performance

Past research has demonstrated that aesthetics affect users' experiences in various ways. However, there is little research on the impact of interface aesthetics on user performance in a smartphone app context. The present paper addresses this research gap using an online experiment ( N = 281). Two variants of the same web app were created and manipulated in their aesthetics. Participants were randomly assigned to either variant and asked to explore the app before answering questions concerning the app's content. Results showed a significant positive effect of aesthetics on perceived usability and aesthetics. Furthermore, results point toward a positive impact of interface aesthetics on performance (i.e., the number of questions answered correctly). Thus, results indicate that a visually appealing smartphone web app increases users' subjective experience and objective performance compared to an unaesthetic app. This suggests that user interface aesthetics impact users' experiences and provide stakeholders with quantifiable value and competitive advantage.
User trajectory prediction in mobile wireless networks using quantum reservoir computing

Abstract This paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks by using an approach called quantum reservoir computing (QRC). Mobile users' trajectories prediction belongs to the task of temporal information processing, and it is a mobility management problem that is essential for self‐organising and autonomous 6G networks. Our aim is to accurately predict the future positions of mobile users in wireless networks using QRC. To do so, the authors use a real‐world time series dataset to model mobile users' trajectories. The QRC approach has two components: reservoir computing (RC) and quantum computing (QC). In RC, the training is more computational‐efficient than the training of simple recurrent neural networks since, in RC, only the weights of the output layer are trainable. The internal part of RC is what is called the reservoir. For the RC to perform well, the weights of the reservoir should be chosen carefully to create highly complex and non‐linear dynamics. The QC is used to create such dynamical reservoir that maps the input time series into higher dimensional computational space composed of dynamical states. After obtaining the high‐dimensional dynamical states, a simple linear regression is performed to train the output weights and, thus, the prediction of the mobile users' trajectories can be performed efficiently. In this study, we apply a QRC approach based on the Hamiltonian time evolution of a quantum system. The authors simulate the time evolution using IBM gate‐based quantum computers, and they show in the experimental results that the use of QRC to predict the mobile users' trajectories with only a few qubits is efficient and can outperform the classical approaches such as the long short‐term memory approach and the echo‐state networks approach.
Virtualization Maturity in Creating System VM: An Updated Performance Evaluation

Virtualization technologies are indispensable in operating data centers and supporting cloud infrastructures, providing cost reduction (CapEx and OpEx), high availability, and disaster recovery. Hypervisor-assisted virtualization is one of the leading virtualization technologies, with the hypervisor being the software layer responsible for presenting the virtualized view of the hardware to system-level VMs. However, the virtualization overhead it introduces has implications into the computing infrastructure performance. This paper revisits key concepts about virtualization, technologies and techniques, types of VMs and hypervisors, and provides an up-to-date comparison between native and VM environments using workload metrics such as CPU and memory scores, disk speed, and network throughput to determine virtualization overhead. Our results show a clear overall trend toward meritorious performance and the maturity of the technologies used to create system-level VMs.
Local-to-Global Causal Reasoning for Cross-Document Relation Extraction

Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts. Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity. However, similarity-based methods cannot distinguish valid information from highly similar retrieved documents well. How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue. To address this problem, we design a novel local-to-global causal reasoning (LGCR) network for cross-document RE, which enables efficient distinguishing, filtering and global reasoning on complex information from a causal perspective. Specifically, we propose a local causal estimation algorithm to estimate the causal effect, which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE. Furthermore, based on the causal effect, we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning. Experimental results under the closed and the open settings of the large-scale dataset CodRED demonstrate our LGCR network significantly outperforms the state-of-the-art methods and validate the effectiveness of causal reasoning in confusing information processing.
Data quality – The foundation of real-world studies

Real-world data, routinely collected from multiple sources outside typical clinical research settings, are useful in conducting real-world studies. Sub-optimal and inconsistent data quality is a challenging issue which should be addressed whilst planning and conducting real-world studies. This brief review discusses the quality aspects of data needed for RWS.
Confirmatory Factorial Model of Searching for Information on the Internet During COVID-19

The pandemic directly impacted the educational system by moving from the traditional classroom to the electronic blackboard.In this transition process, an educational habitus emerged that consisted of facing the health, economic, social and educational crisis in a self-management of opportunities.In this sense, the search for information was a fundamental tool to promote self-knowledge.The objective of the present was to establish the reliability and validity of an instrument that measures the intention to search for information in digital networks with a non-probabilistic selection of 253 students from a public university in the State of Mexico.The results show the establishment of eight factors among which the intention to select information explained 21% of the total variance.Lines of research are noted regarding the construction of an agenda with central information processing axes and issues to anticipate discussion scenarios and shared agreements.In relation to the state of knowledge in which stigma prevails towards the proximity of people and distancing as well as confinement as central strategies of anti-COVID-19 policies, the establishment of an agenda focused on the self-management of knowledge is recommended.The management, production and transfer of knowledge is recommended from the self-management of resources and opportunities, as well as from the interrupted collaboration between the parties involved in the public education system.
Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.
Dynamic Weighting Network for Person Re-Identification

Recently, hybrid Convolution-Transformer architectures have become popular due to their ability to capture both local and global image features and the advantage of lower computational cost over pure Transformer models. However, directly embedding a Transformer can result in the loss of convolution-based features, particularly fine-grained features. Therefore, using these architectures as the backbone of a re-identification task is not an effective approach. To address this challenge, we propose a feature fusion gate unit that dynamically adjusts the ratio of local and global features. The feature fusion gate unit fuses the convolution and self-attentive branches of the network with dynamic parameters based on the input information. This unit can be integrated into different layers or multiple residual blocks, which will have varying effects on the accuracy of the model. Using feature fusion gate units, we propose a simple and portable model called the dynamic weighting network or DWNet, which supports two backbones, ResNet and OSNet, called DWNet-R and DWNet-O, respectively. DWNet significantly improves re-identification performance over the original baseline, while maintaining reasonable computational consumption and number of parameters. Finally, our DWNet-R achieves an mAP of 87.53%, 79.18%, 50.03%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets. Our DWNet-O achieves an mAP of 86.83%, 78.68%, 55.66%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets.
A Strong Combination of Cryptographic Techniques to Secure Cloud-Hosted Data

In Hybrid Cloud Security, we combine symmetric encryption with key management to ensure anonymity. Our method provides a flexible and scalable deployment solution for cloud-based data security. To store, analyse, and deal with data, cloud computing relies on a core concept: sharing resources. Since cloud services are utilized by many individuals and are dispersed across the Internet, they provide a number of security vulnerabilities. Due to their worldwide accessibility, hybrid clouds open up a wide range of opportunities. The security solution works with a wide variety of PaaS, SaaS, and even IaaS cloud services (Infrastructure as a Service). It also works with most Cloud services out there. Hybrid public key cryptosystems can improve cloud security. Businesses are wary about cloud computing due to security concerns. The primary goal of this research is to strengthen the security of cloud computing by fusing the cryptographic models of Rabin and Rivest-Shamir-Adleman (RSA). Evaluating the efficacy of a hybrid approach to producing secret keys for data encryption and decryption. The purely RSA-based system has higher latency and is less computationally intensive than the hybrid system.
Advanced Intelligent Control in Robots

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges [...]
Experiences, tools, and environments for immersive teaching

Editorial of V. 4 No. 7 (2023) of IUL Research.
Mediating Effect of Green Human Resource Management (GHRM) and Organizational Agility (OA) On Firm Environmental Performance (FEP): By Applying Quantitative Research Approach

The current study seeks to investigate the influence of Environmentally Specific Transformational Leadership (ESTL) with the mediating effect of Green Human Resource Management (GHRM) and Organizational Agility (OA) On Firm Environmental Performance (FEP) by applying a quantitative research approach to collect and analyze the data and its results. A quantitative research design will be used, and a survey questionnaire comprised of 40 items from which 11 items for demographic questions and 29 items measuring the impact of Environmentally Specific Transformational Leadership, Organizational Agility Firm hypothesized Environmentally Specific Transformational Leadership has an impact on Firm Environmental Performance via Green Human Resource Management and Organizational Agility. The research framework is seen under the light of Resource Based View theory (RBV) and Transformational Leadership theory. The finding may be able to help organizations better understand the role of leaders in making efforts for environmental performance and better-equipping organizations with agility.
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression

Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and can be helpful in explaining the system’s behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that evolves populations of candidate solutions that are subsequently modified by genetic operators crossover and mutation. However, the approach suffers from several deficiencies: it does not scale well with the number of variables and samples in the training data – models tend to grow in size and complexity without an adequate accuracy gain, and it is hard to fine-tune the inner model coefficients using just genetic operators. Recently, neural networks have been applied to learn the whole analytic model, i.e., its structure as well as the coefficients, by means of gradient-based optimization algorithms. In this paper, we propose a novel neural network-based symbolic regression method that constructs physically plausible models based on very small training data sets and prior knowledge about the system. The method employs an adaptive weighting scheme to effectively deal with multiple loss function terms and an epoch-wise learning process to reduce the chance of getting stuck in poor local optima. Furthermore, we propose a parameter-free method for choosing the model with the best interpolation and extrapolation performance out of all models generated throughout the whole learning process. We experimentally evaluate the approach on four test systems: the TurtleBot 2 mobile robot, the magnetic manipulation system, the equivalent resistance of two resistors in parallel, and the longitudinal force of the anti-lock braking system. The results clearly show the potential of the method to find parsimonious models that comply with the prior knowledge provided.
The Water Myth in Cinema

This study aims to explore the portrayal of the Fountain of Youth and the myth of the Flood in movies. Firstly, the meaning of the water myth is explained. Then, the focus shifts to how the myth of the Fountain of Youth and the Flood are depicted in different mythologies. The study utilizes qualitative method and thematic content analysis, selecting six films through purposive sampling. The analysis incorporates Joseph Campbell’s Monomyth theory and Klaus Koch’s work on the eight features of the apocalypse. The study reveals how these myths are reproduced through sub-themes identified within the main themes of the Fountain of Youth and the Flood. The results indicate that films present the myth of the Fountain of Youth in various forms. Heroes have different motivations for seeking the Fountain of Youth and encounter various obstacles. Characters who reach the Fountain of Youth undergo significant positive transformations. In flood narratives depicted in movies, the cause of the flood and the awareness of it differ from film to film. Furthermore, other movie findings illustrate who boarded the ship or vehicle to escape the flood and who faced punishment.
Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation

Hypergraphs are an omnipresent data structure used to represent high-order interactions among entities. Given a hypergraph H wherein nodes are associated with attributes, attributed hypergraph clustering (AHC) aims to partition the nodes in H into k disjoint clusters, such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes on large attributed hypergraphs for accurate clustering. Existing AHC solutions suffer from issues of prohibitive computational costs, sub-par clustering quality, or both. In this paper, we present AHCKA, an efficient approach to AHC, which achieves state-of-the-art result quality via several algorithmic designs. Under the hood, AHCKA includes three key components: (i) a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, (ii) a joint hypergraph random walk model to devise an effective optimization objective towards AHC, and (iii) a highly efficient solver with speedup techniques for the problem optimization. Extensive experiments, comparing AHCKA against 15 baselines over 8 real attributed hypergraphs, reveal that AHCKA is superior to existing competitors in terms of clustering quality, while often being up to orders of magnitude faster.
Optimally Secure Tweakable Block Ciphers with a Large Tweak from n-bit Block Ciphers

We consider the design of a tweakable block cipher from a block cipher whose inputs and outputs are of size n bits. The main goal is to achieve 2n security with a large tweak (i.e., more than n bits). Previously, Mennink at FSE’15 and Wang et al. at Asiacrypt’16 proposed constructions that can achieve 2n security. Yet, these constructions can have a tweak size up to n-bit only. As evident from recent research, a tweakable block cipher with a large tweak is generally helpful as a building block for modes of operation, typical applications including MACs, authenticated encryption, leakage-resistant cryptography and full-disk encryption.We begin with how to design a tweakable block cipher with 2n-bit tweak and n-bit security from two block cipher calls. For this purpose, we do an exhaustive search for tweakable block ciphers with 2n-bit tweaks from two block cipher calls, and show that all of them suffer from birthday-bound attacks. Next, we investigate the possibility to design a tweakable block cipher with 2n-bit tweak and n-bit security from three block cipher calls. We start with some conditions to build such a tweakable block cipher and propose a natural construction, called G̃1, that likely meets them. After inspection, we find a weakness in G̃1 which leads to a birthday-bound attack. Based on G̃1, we then propose another construction, called G̃2, that can avoid this weakness. We finally prove that G̃2 can achieve n-bit security with 2n-bit tweak.