Reviewerly/exHarmony huggingface dataset:
Enhancing Security of Relational Databases in Cloud Computing through Blockchain Integration

Using legacy technology and technique is very harmful in IT. Change is only constant in IT for upgrading technology. Running on older architecture is huge risk to IT systems. Current IT is era of cloud edge technology. Cloud is simpler, rapid and low-cost solution to customers. Trending approaches is pay-as-use services, because infra is huge cost to projects. Hence every project is migrating to cloud. Truth is technology itself godship and enemy. So, challenges are security to data. Every application demands security and privacy of customer data. In past seen many incident of security issues into cloud infrastructure. However, organisations move on cloud is smarter choice but also see an increase the huge risk of data security. Today our work focus is about enlighten on issue about security, privacy and authenticity of data into cloud services. Storage to sensible data on cloud to across glob is huge chance of attack, stolen and manipulation. One of the most innovative and trustful solution of cloud security is blockchain. It works for all phase of data security, privacy, encryptions and authenticity. Blockchain as self-proven, life-saver solution for security on cloud computing. Blockchain effectively works for controls on encryption, authentication and validation of data. Blockchain has solve constant issue and prove it by cryptocurrency, smart contacts, smart grid and safe from assaults like brute force. In distributed RDBMS services has many challenges from internal and external attacks. Finally, our work is investigating the blockchain and try for RDBMS services on cloud.
Development of a 3D Modelling Gallery Based on Virtual Reality

Virtual reality (VR) technology has been widely applied in various fields, including entertainment, healthcare and education. In design and engineering education, VR technology shows the potential to improve traditional education practices by enhancing creativity and motivation through immersive learning experiences. With the interactive features and high-quality scenes provided by VR, the students' learning of 3D modelling in design and engineering disciplines may be highly motivating. This paper aims to develop a 3D modelling gallery based on VR to assist design and engineering students in learning 3D modelling. The system offers two main features, a virtual campus scene and a practice showcase, which allow students to explore and understand the modelling process of 3D models in a virtual environment. The integration of the 3D Modelling Gallery into the curriculum can help deepen students' understanding of 3D modelling, break the limitations of traditional teaching, and increase students' learning interest in 3D modelling.
Image Segmentation by Fusing Color and Depth Information for Region Merging

When there are occlusions and shadows in the image, it is difficult for the traditional algorithm to extract the object contour accurately by using only the appearance information. In this paper, an improved Mean-shift algorithm is used to obtain the initial set of oversegmentation subregions. Then the color contrast of the subregions is calculated, the depth contrast is calculated in the depth image, and the fusion contrast is obtained by a dynamic weighted curve fitting method. Finally, the seed regions of the target and background are automatically selected according to the depth information, and MSRM algorithm is used to complete the image segmentation based on the principle of maximum similarity. Experiments on NYU-V2 and Middlebury database show that compared with the current general algorithm, the proposed algorithm can improve the accuracy of segmentation and improve the visual effect of segmentation images.
A Graph-Based Approach for Modelling Quantum Circuits

A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores

Graph databases (GDBs) are crucial in academic and industry applications.The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability.To tackle these challenges, we harness established practices from the HPC landscape to build a system that outperforms all past GDBs presented in the literature by orders of magnitude, for both OLTP and OLAP workloads.For this, we first identify and crystallize performance-critical building blocks in the GDB design, and abstract them into a portable and programmable API specification, called the Graph Database Interface (GDI), inspired by the best practices of MPI.We then use GDI to design a GDB for distributedmemory RDMA architectures.Our implementation harnesses onesided RDMA communication and collective operations, and it offers architecture-independent theoretical performance guarantees.The resulting design achieves extreme scales of more than a hundred thousand cores.Our work will facilitate the development of nextgeneration extreme-scale graph databases.
Should I Stay or Should I Go

We present the Emi reasoner, based on a new interpretation of the tableau algorithm for reasoning with Description Logics with unique performance characteristics and specialized advantages. Emi turns the tableau inside out, solving the satisfiability problem by adding elements to expressions rather than adding expressions to element node labels. This strategy is inspired by decidable reasoning algorithms for Horn Logics and $$\mathcal{E}\mathcal{L}^{++}$$ that run on a loop rather than recursive graph-based strategies used in a tableau reasoner. Because Emi solves the same problem there will be a simple correspondence with tableaux, yet it will feel very different during execution, since the problem is inverted. This inversion makes possible many unique and straightforward optimizations, such as paralellization of many parts of the reasoning task, concurrent ABox expansion, and localized blocking techniques. Each of these optimizations contains a design trade-off that allows Emi to perform extremely well in certain cases, such as instance retrieval, and not as well in others. Our initial evaluations show that even a naive and largely un-optimized implementation of Emi is performant with popular reasoners running on the JVM such as Hermit, Pellet, and jFact.
CTA: Confidential Transactions Protocol with State Accumulator

Considering the increasingly large storage of post-quantum RingCT-like protocols, we construct a blockchain-based confidential transactions protocol with state accumulator (CTA), where each user only needs to store a concise state of the blockchain. More precisely, CTA can compress the historical data of all transactions into a short deterministic state, while preserving privacy and post-quantum security. The key component of our CTA is an efficient zero-knowledge lattice-based accumulator, which is based on Peikert et al.’s vector commitment scheme proposed in TCC 2021. We have modified their construction to ensure that the length of the underlying M-SIS parameters is kept short for the Merkle-tree structure. At a 128-bit security level, the membership proof size for our accumulator with $$2^{20}$$ members is only 225 KB under the Module-SIS and Extended-MLWE assumptions. Compared with previous lattice-based works where the time and storage complexity of each user is linear with the number of coins, our CTA is capable of achieving logarithmic storage space and computational time. Specifically, the concrete transaction size of spending a coin in CTA is around 236 KB, when the size of anonymity set is $$2^{20}$$ .
PMG—Pyramidal Multi-Granular Matching for Text-Based Person Re-Identification

Given a textual query, text-based person re-identification is supposed to search for the targeted pedestrian images from a large-scale visual database. Due to the inherent heterogeneity between different modalities, it is challenging to measure the cross-modal affinity between visual and textual data. Existing works typically employ single-granular methods to extract local features and align image regions with relevant words/phrases. Nevertheless, the limited robustness of single-granular methods cannot adapt to the imprecision and variances of visual and textual features, which are usually influenced by the background clutter, position transformation, posture diversity, and occlusion in surveillance videos, thereby leading to the deterioration of cross-modal matching accuracy. In this paper, we propose a Pyramidal Multi-Granular matching network (PMG) that incorporates a gradual transition process between the coarsest global information and the finest local information by a coarse-to-fine pyramidal method for multi-granular cross-modal features extraction and affinities learning. For each body part of a pedestrian, PMG is adequate in ensuring the integrity of local information while minimizing the surrounding interference signals at a certain scale and can adapt to capture discriminative signals of different body parts and achieve semantically alignment between image strips with relevant textual descriptions, thus suppressing the variances of feature extraction and improving the robustness of feature matching. Comprehensive experiments are conducted on the CUHK-PEDES and RSTPReid datasets to validate the effectiveness of the proposed method and results show that PMG outperforms state-of-the-art (SOTA) methods significantly and yields competitive accuracy of cross-modal retrieval.
Night vision self-supervised Reflectance-Aware Depth Estimation based on reflectance

The depth estimation of nighttime images is a challenging problem due to the lack of accurate ground-truth depth labels. Although various self-supervised methods leveraging texture information have been proposed to solve the problem, the performance is still not satisfactory due to the imaging limitations of visible cameras. To this end, we propose a self-supervised Reflectance-Aware Depth Estimation approach based on reflectance for nighttime images. Two major factors strengthen the proposed approach: a Reflectance Extraction Network and a feature consistency loss. We introduce the Reflectance Extraction Network to extract texture information based on the finding that the texture is beneficial for depth estimation. Then, we utilize the feature consistency loss to help the baseline network to learn the intrinsic feature rather than the images’ light. Experiment results on the challenging Oxford RobotCar dataset confirm the robustness and effectiveness of our approach.
Analyzing University Students' Awareness of Cybersecurity

According to current research evidence, security awareness is an issue of great importance in cyber security. Although various investigations have been carried out on numerous areas of information and cybersecurity awareness, all are contradictory and depend on the circumstances. The purpose of this study is to determine young knowledge of online security risks and knowledge of countermeasures to protect them from online threats. A questionnaire technique is used for this, and an online survey through Google forms is used to collect the data from university students. The supplemental dataset in this study is utilized to assess the level of cybersecurity knowledge among university students, which is reported in detail. To recruit subjects, two different methods are used: the individual has to be of Indian descent and their age should be over 17. Participants are gathered using a combination of selective and snowball methods via institutional emails from universities and WhatsApp messaging to persons who met the criteria.
An overview of video recommender systems: state-of-the-art and research issues

Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.
The CELI corpus: Design and linguistic annotation of a new online learner corpus

This article introduces the CELI corpus, a new learner corpus of written Italian consisting of ca. 600,000 tokens, evenly distributed among CEFR (Common European Framework of Reference for Languages) proficiency levels B1, B2, C1 and C2. The collected texts derive from the language certification exams administered by the University for Foreigners of Perugia all around the world. The corpus contains rich metadata pertaining to text-related and learner-related variables. It expands the domain of learner corpora by being, among other things, both freely available online to the research community, and by focusing on a target language other than English. The article also presents and evaluates the POS-tagging procedure, thus contributing to best practices in learner corpus annotation.
Foundations of Language Modeling

In this chapter, we first introduce the required background knowledge about computational language models. We describe the fundamental methodological concepts and modeling decisions behind these models.
A Prediction-to-Prediction Remote Sensing Image Super-Resolution Network under a Multi-Level Supervision Paradigm

Super-resolution enhances the spatial resolution of remote sensing images, yielding clearer data for diverse satellite applications. However, existing methods often lose true detail and produce pseudo-detail in reconstructed images due to an insufficient number of ground truth images for supervision. To address this issue, a prediction-to-prediction super-resolution (P2P-SR) network under a multi-level supervision paradigm was proposed. First, a multi-level supervision network structure was proposed to increase the number of supervisions by introducing more ground truth images, which made the network always predict the next level based on the super-resolution reconstruction results of the previous level. Second, a super-resolution component combining a convolutional neural network and Transformer was designed with a flexible super-resolution scale factor to facilitate the construction of multi-level supervision networks. Finally, a method of dividing the super-resolution overall scale factor was proposed, enabling an investigation into the impact of diverse numbers of components and different scale factors of components on the performance of the multi-level supervision network. Additionally, a new remote sensing dataset containing worldwide scenes was also constructed for the super-resolution task in this paper. The experiment results on three datasets demonstrated that our P2P-SR network outperformed the state-of-the-art (SOTA) methods.
The Status of the Saudi Construction Industry during the COVID-19 Pandemic

The outbreak of COVID-19 has had a profound impact on the Saudi construction industry as well as the country’s economy. The pandemic jeopardized the positive perspectives and growth in megaproject numbers, as it has amplified the constraints that prevent the construction sector from realizing growth. This research work is intended to evaluate the status of the Saudi construction industry during the COVID-19 pandemic. In order to achieve this objective, a three-phased methodology was developed. In the first part of the research, semi-structured interviews with forty industry experts were conducted. The gathered data from both, the literature review and interviews were synthesized. This process resulted in four domains to be explored: project performance measurement, workforce, supply chain, and financial management. The outcomes from phase one were then utilized to develop a questionnaire survey that was communicated to construction firms all over Saudi Arabia, for which 124 responses were received. Data analysis was carried out, and the obtained results were clarified and triangulated through a focus group discussion in the third phase of the research. The outcomes from the mixed-methods research methodology were aggregated to enrich and interpret findings and draw conclusions and recommendations. The findings indicate that the pandemic has had a total of ten core impacts. The highly impacted areas in the industry were the technical performance of projects, reduction in productivity, risk management practices, downsizing the scope of ongoing projects, reduction in new projects or contracts, material shortage, recruitment of manpower in the construction firms and affiliated projects, and the financial performance of the organization. In the meantime, it was found that the government implemented mitigation measures from which the sector benefited, where 27% of contractors obtained Saned system assistance, 51% received cash compensation and 22% received tax postponement. The scope of this study is limited to exploring the status of the Saudi construction industry (SCI) during the COVID-19 pandemic. The study findings are of added value and represent a significant contribution to the body of knowledge in the field. However, further research on the exit strategies bringing the industry to the new normal, including the use of cutting-edge technologies in the age of multi-faceted disruption would be of great importance.
Using Virtual Outcrop Models as a Geological Learning Media

The Covid-19 pandemic has impacted various sectors due to a new wave of technology rising shortly, specifically geological learning models. Using research and development methods, the research aims to develop more scientific observations as a provision for pre-field work activities at Geophysical Engineering Department Institut Teknologi Sepuluh Nopember. Furthermore, we need a media that can provide learning that can emphasize process skills and students' abilities to determine and understand the objective concepts of material. Virtual outcrop models integrated with video learning platform can fulfil the learning process by providing experience in observations outcrops directly in the class. A three-dimensional (3D) Virtual Outcrop Model (VOMs) was created using the photogrammetry software Agisoft PhotoScan based on photos taken in the field using smartphones and drones. The existence of this media is expected to add direct experience to visualize how geological phenomena occur in nature. The learning activities are divided into four sequential stages: observation, data collection, interpretation, and hypothesis giving. 87% of respondents stated that learning models using VOMs and videos can boost their knowledge of scientific observation. This learning method can help improve academic achievement because it applies technology implementation in theory and practice, providing geological information to us
Perception of Sports Science Students in Higher Education on Basic Digital Competences: Spanish Case

In order to improve the teaching–learning process at the university level, it is essential to consolidate students’ digital competences (DCs) during their initial training. This development is analysed in the area of sports management as part of the physical activity and sports science (CAFyD) bachelor’s degree. Students (n = 236) from private (n = 120) and public (n = 116) universities participated by completing the COBADI questionnaire (registered trademark: 2970648®), structured into three dimensions: (I) Competences in knowledge and use of ICTs in social communication and collaborative learning; (II) competences in the use of ICT for information search and processing; and (III) virtual and social communication tools of the university. Likert scale responses ranged from 1 to 4 points. The results show significant differences in terms of the type of university. In terms of gender, females have a better digital perception, with a significant difference (… I know how to use programs…). This pioneering research is of relevance for higher education professors in the field of sports, as it helps to detect areas where students lack DCs and engages them in the enhancement of their learning.
Federated Meta-Learning for Few-Shot Fault Diagnosis With Representation Encoding

Deep-learning (DL)-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federated learning (FL) enables multiple clients to collaboratively train a shared model with data privacy guaranteed. However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model. To tackle these issues, we propose a novel framework called representation-encoding-based federated meta-learning (REFML) for few-shot FD. First, a novel training strategy based on representation encoding and meta-learning is developed. It harnesses the inherent heterogeneity among training clients, effectively transforming it into an advantage for out-of-distribution (OOD) generalization on unseen working conditions or equipment types. In addition, an adaptive interpolation method that calculates the optimal combination of local and global models as the initialization of local training is proposed. This helps further use local information to mitigate the negative effects of domain discrepancy. As a result, high diagnostic accuracy can be achieved on unseen working conditions or equipment types with limited training data. Compared with the state-of-the-art methods, such as FedProx, the proposed REFML framework achieves an increase in accuracy by 2.17%–6.50% when tested on unseen working conditions of the same equipment type and 13.44%–18.33% when tested on totally unseen equipment types, respectively.