Reviewerly/exHarmony huggingface dataset:
Assessment of Digital Competencies of University Faculty and Their Conditioning Factors: Case Study in a Technological Adoption Context

The rise of digital technologies and their educational applications increasingly require the development of digital skills among university faculty. This study focuses on examining the level of digital competencies of university faculty and identifying their conditioning factors. To achieve this objective, an ex post facto methodological design with surveys is used. A sample of 216 university teachers from different regions of Ecuador was used. The non-parametric Chi-square test was used to validate the hypothesis of independence of the variables. The results obtained show that university faculty have a mostly intermediate level of digital skills, which is independent of gender, but dependent on the generational cohort. In particular, it is found that younger teachers (millennials) have a more advanced level of digital skills, although this relationship is not very strong. On the other hand, we observe the positive influence of the university’s strategic leadership in terms of technological adoption on the development of teachers’ digital skills. Universities with better technological resources and with training plans focused on the pedagogical application of technology have teachers with a more advanced level of digital skills. All of this leads to the recommendation that education policies should prioritise actions that promote the development of digital competencies among university faculty
The Effects of Online Learning on Students’ Performance: A Comparison Between UK and Jordan Universities

The global pandemic of Covid-19 has caused lockdowns across the globe, causing education institutions to shut down. As a result, classes have been held online. This study investigates the impact of online learning on student performance by comparing the impact on Jordan and the UK. Both countries have been reported to have high technological competency but are known to have varying sociodemographic structures. Surveys were conducted on undergraduate students from both countries (N = 780) to analyse students’ perception of online learning, self-perception of academic capabilities, and faculty performance during online learning. Semi-structured interviews were conducted on professors from both countries (N = 8). The findings indicate that both Jordan and the UK have been very similarly affected by in terms of student performance, with major challenges being in communication, technological competency, access to hardware for taking online classes, absenteeism, and drop-outs. Some benefits to student performance were identified as having access to recorded lectures, having more access to faculties through e-mail and extended office hours. Ethical implications were not commented on. Privacy concerns were largely voiced by faculties.
Deep-learning-based single-image height reconstruction from very-high-resolution SAR intensity data

Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great interest in the wider image analysis community. Remote sensing is no exception, as the possibility to estimate height maps from single aerial or satellite imagery bears great potential in the context of topographic reconstruction. A few pioneering investigations have demonstrated the general feasibility of single image height prediction from optical remote sensing images and motivate further studies in that direction. With this paper, we present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data, and extensive experimental results for different SAR imaging modes and test sites. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data, even if acquired by different imaging modes and imaging parameters.
Reshaping Perspectives on Flexible Work: The Impact of COVID-19 on Academic Library Management

During COVID-19, academic library employees pivoted to predominantly remote work. Associate deans, associate university librarians, and equivalent managers at the top 50 Association of Research Libraries (ARL) institutions were interviewed about benefits, challenges, pre-pandemic norms, necessary conditions, and the future of flexible work arrangements (FWAs). The findings suggest that successful FWAs require adequate technology and effective managerial communication and depend on the types of positions and individuals involved. Most managers believe FWAs will increase in academic libraries in the future. FWAs provide benefits for both organizations and employees and will likely have a positive impact on library space, recruitment, and retention. At the same time, careful communication and compassionate leadership are needed for successful FWAs.
Data Ownership: A Survey

The importance of data is increasing along its inflation in our world today. In the big data era, data is becoming a main source for innovation, knowledge and insight, as well as a competitive and financial advantage in the race of information procurement. This interest in acquiring and exploiting data, in addition to the existing concerns regarding the privacy and security of information, raises the question of who should own the data and how the ownership of data can be preserved. This paper discusses and analyses the concept of data ownership and provides an overview on the subject from different point of views. It surveys also the state-of-the-art of data ownership in health, transportation, industry, energy and smart cities sectors and outlines lessons learned with an extended definition of data ownership that may pave the way for future research and work in this area.
Benchmarking Ontology-Based Query Rewriting Systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates ``relevant'' test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of ``inferences'', each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.In this work, we propose to transfer the crosstask knowledge from general question answering (QA) corpora for the zero-shot DST task.Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multichoice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST.In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle "none" value slots in the zero-shot DST setting.The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz.Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.
Data Security and Preservation Mechanisms for Industrial Control Network Using IOTA

In the Internet of Things (IoT), many devices can communicate with each other directly through the Internet, and there are already good applications in transportation systems, smart meters, and smart home appliances. Wireless sensor networks are used in these Internet of things applications. Playing a vital role, the upload of data requires multiple layers of transmission to reach the cloud. In the industrial control field, many IoT devices upload data to the cloud for storage, but the upload process is easily tampered with, and centralized cloud services also have security concerns. In addition, in the local IoT perception layer among them, the security of sensor identity verification is also very important for message transmission. The main reason why the past blockchain technology could not be widely used in industrial control and other application fields is the cost considerations, because the procedures for running blockchain nodes and uploading transactions are too expensive. Therefore, this paper proposes an IoT architecture based on the IOTA Tangle network to solve the centralization problem of IoT storage in the cloud, and uses the method of MAM (Masked Authenticated Message) to trace the information of WSNs, while achieving data security and data preservation. In the edge devices of WSNs, because of the insufficient memory and computing performance of many devices, it is impossible to perform proof of work calculations. Therefore, this paper uses the logical key hierarchy-based method to manage the sensors, and applies symmetric and asymmetric encryption in blockchain. The identity verification of the device can be effectively achieved, and the operating cost can be effectively reduced.
Sampling Moiré method for full-field deformation measurement: A brief review

The sampling Moiré (SM) method is one of the vision-based non-contact deformation measurement methods, which is a powerful tool for structural health monitoring and elucidation of damage mechanisms of materials. In this review, the basic principle of the SM method for measuring the two-dimensional displacement and strain distributions is introduced. When the grid is not a standard orthogonal grating and cracks exist on the specimen surface, the measurement methods are also stated. Two of the most typical application examples are described in detail. One is the dynamic deflection measurement of a large-scale concrete bridge, and the other is the residual thermal strain measurement of small-scale flip chip packages. Several further development points of this method are pointed out. The SM method is expected to be used for deformation measurement of various structures and materials for residual stress evaluation, crack location prediction, and crack growth evaluation on broad scales.
E-Learning Success Model in the Context of COVID-19 Pandemic in Higher Educational Institutions

Nowadays, the extensive use of e-learning in higher educational institutions in many countries leads us to apprehend the reality, precisely the key success/failure factors of the implementation, of e-learning systems in these institutions. This motivation becomes more and more important, inevitable, and urgent, especially for institutions that have heavily adopted e-learning systems under exceptional conditions without any prior planning, such as the COVID-19 pandemic. From this perspective, this research aimed to provide an e-learning success model in the context of the COVID-19 pandemic by assessing e-learning effectiveness and by investigating the key antecedents of e-learning effectiveness. The literature review led to the identification of four main factors influencing e-learning effectiveness: The e-learning system, e-learning readiness, interactivity, and resistance to change. These four variables constituted the antecedents of an effective e-learning system, which was tested in a KSA context. A structured survey, including a sample of 1202 students from Imam Mohammad Ibn Saud Islamic University was used to examine the linkages among our proposed model. The model, with a total of ten direct and six indirect relationships, was tested by using structural equation modeling. The research findings indicate that effective e-learning is supported by the interactions between four factors: the e-learning system, e-learning readiness, interactivity, and resistance to change.
Parameter identifiability analysis: Mitigating the non-uniqueness issue in the inverse identification of an anisotropic yield function

A large number of conventional mechanical tests are required to calibrate advanced anisotropic yield functions. A well-designed non-conventional mechanical test generating information-rich inhomogeneous strain fields enables to reduce the experimental effort via inverse identification methods. Gradient-based inverse identification methods have repeatedly been reported to yield non-unique solutions for advanced anisotropic yield functions. Although the generic design of non-conventional mechanical tests progressively improves by maximizing the information content using indicators for describing the inhomogeneity of the strain field, it remains questionable whether the uniqueness problem can be avoided for advanced anisotropic yield functions. The latter problem is studied in this paper through the concept of parameter identifiability. A general identifiability analysis framework is proposed enabling to efficiently quantify the parameters sensitivity strength as well as parameter interactions. The feasibility of the framework is assessed through the inverse identification of the Yld2000-2d yield function using a virtual experiment, i.e. a complex notched tensile specimen designed via shape optimization to maximize the strain field inhomogeneity. The advanced synthetic experimentation approach is crucial to avoid errors due to the choice of an inappropriate constitutive model, yet allows accounting for the intrinsic Digital Image Correlation (DIC) filter. For the non-conventional mechanical test under investigation, it is shown that the individual parameter sensitivity and parameter interaction jointly determine the inverse identification quality. It is observed that the detrimental effect of low individual parameter sensitivity on the identification quality can be mitigated by proper parameter subset selection. In addition, it is shown that the orientation of the principal material axes enables to significantly improve the identifiability of the sought model parameters of the anisotropic yield function. The virtual experiment shows that the studied notched tensile test with a tensile direction of 45° from the rolling direction enables to reliably identify all anisotropy parameters of the Yld2000-2d yield function. Finally, actual experiments confirm the importance of identifiability analysis and material orientation for the inverse identification of an anisotropic yield function, yet further research is required to arrive at a sufficiently accurate inverse identification quality.
NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.
Exploring generative adversarial networks and adversarial training

Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from the generated replica distribution. The discriminator attempts to distinguish the fake and the real samples and sends feedback to the generator so that the generator can improve the fake samples. Recently, GANs have been competing with the state-of-the-art in various tasks including image processing, missing data imputation, text-to-image translation and adversarial example generation. However, the architecture suffers from training instability, resulting in problems like non-convergence, mode collapse and vanishing gradients. The research community has been studying and devising modified architectures, alternative loss functions and techniques to address these concerns. A section of publications has studied Adversarial Training, alongside GANs. This review covers the existing works on the instability of GANs from square one and a portion of recent publications to illustrate the trend of research. It also gives insight on studies exploring adversarial attacks and research discussing Adversarial Attacks with GANs. To put it more eloquently, this study intends to guide researchers interested in studying improvisations made to GANs for stable training, in the presence of Adversarial Attacks.
Hybridizing cost saving with trust for blockchain technology adoption by financial institutions

Distributed Ledger Technology (DLT) is transforming the financial industry and leading to a rise in the modern banking system. Like in developed nations, disruptive technology is necessary to advance the traditional banking system in emerging economies. The present study aims to investigate the critical factors that influence a user's intention to accept blockchain technology for financial institutions. The proposed model is based on Technology Acceptance Model (TAM) constructs with trust and cost-saving, tested using structural equation modelling. Findings from an online survey of 188 practitioners working in Malaysia's financial sector confirm that all constructs except trust on perceived usefulness were found to have a significant impact during the blockchain implementation. Moreover, cost-saving matters most during the disruptive technology adoption for financial institutions. Based on the findings, the subsequent theoretical and practical implications are assessed, albeit with notable limitations.
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better

Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks.However, PLMs may have risks in overfitting the pretraining tasks and data, which usually have gap with the target downstream tasks.Such gap may be difficult for existing PLM finetuning methods to overcome and lead to suboptimal performance.In this paper, we propose a very simple yet effective method named NoisyTune to help better finetune PLMs on downstream tasks by adding some noise to the parameters of PLMs before finetuning.More specifically, we propose a matrix-wise perturbing method which adds different uniform noises to different parameter matrices based on their standard deviations.In this way, the varied characteristics of different types of parameters in PLMs can be considered.Extensive experiments on both GLUE English benchmark and XTREME multilingual benchmark show NoisyTune can consistently empower the finetuning of different PLMs on different downstream tasks.
4D Human Body Capture from Egocentric Video via 3D Scene Grounding

We introduce a novel task of reconstructing a time series of second-person <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> 3D human body meshes from monocular egocentric videos. The unique viewpoint and rapid embodied camera motion of egocentric videos raise additional technical barriers for human body capture. To address those challenges, we propose a simple yet effective optimization-based approach that leverages 2D observations of the entire video sequence and human-scene interaction constraint to estimate second-person human poses, shapes, and global motion that are grounded on the 3D environment captured from the egocentric view. We conduct detailed ablation studies to validate our design choice. Moreover, we compare our method with the previous state-of-the-art method on human motion capture from monocular video, and show that our method estimates more accurate human-body poses and shapes under the challenging egocentric setting. In addition, we demonstrate that our approach produces more realistic human-scene interaction.
Digital ledger technology-based real estate transaction mechanism and its block size assessment

Distributed ledger technology (DLT) is set to transform the existing architectural models of financial institutions and government machineries. Although real estate transactions are a major source for the governments to earn revenue, these are plagued with the risk of fraudulent practices. The digital documents are vulnerable to the alteration or any other attacks or can be tampered and ownership of the documents can be changed. The centralised storage involves single point of failure as well as network traffic overhead. The proposed distributed and decentralised blockchain-based architecture provides protection against any intrusive activity which is offset by the majority voting achieved in consensus mechanism for each transaction and verification request. The proposed work provides web interface for user queries and analysis of query search time is carried out.