Reviewerly/exHarmony huggingface dataset:
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks

A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of $N$ neurons, $N$ being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps.
Internet of Things Assisted Monitoring Using Ultrasound-Based Gesture Recognition Contactless System

In the last decade, we have seen an increase in the use of digital technologies in our daily lives, including advanced systems such as mobile applications, smart digital kiosks for intelligent retailing, touch screens for ordering food, or simple implementations such as the ticket machines in the butchers. However, from the perspective of interaction design, those sensing systems suffer from several constraints and limitations such as, low usability and poor hygiene. For example, the elderly or the disabled normally have huge difficulties when interacting with these types of digital systems. Moreover, the recent SARS-CoV-2 pandemic has made us rethink the way we interact with the digital devices. Hence, in this paper, we present a novel solution for digital interaction through a contactless model. This system can provide human gesture recognition and therefore it can be integrated into others to achieve contactless control. We have implemented a prototype based on cost efficient sensors to validate the idea's feasibility. Moreover, a sequence of real world experiments has also been conducted to evaluate its performance. This system is composed with 1) a 3d printed grid of ultrasound sensors to capture the distance information from a human body; 2) software to analyse the data for gesture predictions. This analysis results will be transformed into control commands to interact with the attached system. The experimental results have shown that the proposed system is capable of providing a contactless Human Computer Interaction (HCI), and also has a great opportunity to replace existing touching interaction manner with a remote control scheme.
Trends and Prospects of Using Brain-Computer Interfaces in Education

The relevance of the brain-computer interfaces (BCI) implementation in the field of edu-cation is conditioned by the realization of long-life and individualized learning concepts, as well as the requirement of effective and affordable automated learning systems. The article presents the analysis of studies on BCI usage in the educational process, in order to systema-tize the evidence, identify emerging trends and determine the difficulties and prospects of their applications in education. Nowadays, two main directions of BCI application for the purpose of training quality im-provement are revealed. In the first direction, the researchers’ attention is focused on psycho-physiology, meaning the identification of student’s current state characteristics and its timely correction with the teacher’s help (or self-correction). The second one emphasizes the peda-gogical aspect of BCI usage, such as monitoring the student’s cognitive activity in the process of course content perception to determine the most optimal parameters and conditions of its presentation. In the first case, the change of a student’s state or activity is emphasized, in the second one the changes relate to correction of learning content and its delivery. Among the main difficulties of using BCI in education are the following: problems with the equipment of modern BCI systems, the lack of clear classifications of neurophysiological correlates of various mental phenomena, the difficulty of consideration and differentiation of all the factors affecting a user during his interaction with BCI in natural environment. The prospects of BCI usage in learning are proposed: 1. Prediction of learning activity productivity; 2. Development of students’ self-control in the educational process; 3. Real-time identification of cognitive and affective students’ states in learning certain subjects (mathematics, physics, computer science, etc.); 4. Assessment of the impact of electronic learning tools on the process of information acquisition; 5. Monitoring the dynamics of cognitive activity intensity in students while solving dif-ferent learning tasks; 6. Identification of the available amount of information for its successful processing at the neurophysiological level to optimize the delivery of learning materials.
Soft Skills as the Most In-Demand Skills of Future IT Specialists

New requirements to succeed in the future career of IT specialists are influenced by changes in the IT environment and development of web technologies. The importance of soft skills for the IT professionals and the necessity of switching focus on teaching non-technical soft skills at the university are discussed in the article. The author highlights the idea that the focus and more detailed attention paid on technical topics related to the learners, major should be given to soft skills development in the context of higher education. In order to respond to job market's demand IT professional is expected to work upon building soft skills like communication skills, team-building and leadership.
Dataset of Moroccan nursing students’ intention to use and accept information and communication technologies and social media platforms for learning

The outbreak of COVID-19 has redefined how we carry out our daily activities with emphasis on safety measures like social distancing, the use of face masks and proper hygiene. With nursing personnel at the forefront of combating the menace created by this pandemic, the use of ICT has remained a vital technology that must be embraced by all in order to carry out a safe nursing practice. Therefore, becoming ICT compliant is no more an option to stay relevant and alive during this COVID era. This data article presents the outcome of a survey carried out among Moroccan nursing students. This was done with a view to assess their readiness and willingness to accept and use ICT as well as social media for learning and discharging their duties during and after this COVID era. Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted to structure the questionnaire items used in this survey. This was then administered to Moroccan nursing students via online Google forms. Seven hundred and two (702) respondents completed the questionnaire. After data screening and cleaning, normality assessment of the data was carried out in terms of skewness and kurtosis. Afterwards, Partial Least Square Structural Equation Modeling (PLS-SEM) was employed to establish the statistical significance of the data in terms of the reflective indicator loadings, internal consistency reliability and convergence. This dataset includes the questionnaire used (in English and French), the responses obtained in spreadsheet format, the charts generated from the responses received, the SPSS file and the statistical analysis file. This dataset will help policy makers understand how nursing students use ICT and social media platforms and how these could be adopted as a more secure means of learning and discharging their during the COVID-19 era and beyond.
An efficient and secure protocol for checking remote data integrity in multi-cloud environment

Abstract In cloud environment, huge quantity of data has been generated at each and every second. In order to manage the data, cloud service provider makes use of multi-cloud platform to fulfill the requirement. The service provider cooperatively operates altogether for the accessibility of resources and are improvised by implementing the dynamic operation that can run at a time through the Multi-cloud environment. This paper presents a Dynamic Level Based Integrity Checking Protocol (DA-ICP) for storing data in multicloud environment. The proposed method introduces Provable Data Possession (PDP) approach which enables a user who outsources the data at an untrusted multi-cloud for ensuring that the server possesses the original data without downloading it. This model creates a probabilistic proof of possession by sampling an arbitrary collection of blocks from server that considerably minimizes the cost. The effective and secured outsourced data has been resolved using public key cryptography and undergo encryption using Efficient-PDP (EPDP). During experimentation, the presented DA-ICP shows a maximum accuracy of 96.78%. The proposed method uses Multi-cloud in DA-ICP which produces an efficient output than other existing techniques.
CWCA: Complex-valued encoding water cycle algorithm

<abstract> Since the meta-heuristic water cycle algorithm (WCA) was presented, it has been used extensively in scientific computation and engineering optimization. The aims of this study are to improve the exploration and exploitation capabilities of the WCA algorithm, accelerate its convergence speed, and enhance its calculation accuracy. In this paper, a novel complex-valued encoding WCA (CWCA) is proposed. The positions of rivers and streams are divided into two parts, that is, the real part and imaginary part, and modified formulas for the new positions of rivers and streams are proposed. To evaluate the performance of the CWCA, 12 benchmark functions and four engineering examples were considered. The experimental results indicated that the CWCA had higher precision and convergence speed than the real-valued WCA and other well-known meta-heuristic algorithms. </abstract>
POS-Constrained Parallel Decoding for Non-autoregressive Generation

The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems.A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as "teacher AG").The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model.However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation.To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG.More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding.Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation.This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.
Research on self-adaptive clustering algorithms for large data sparse networks based on information entropy

Abstract With the advent of the era of artificial intelligence and information technology, a large number of data and information pour into all walks of life. These data packages include many online and offline data such as text files, audio and video. However, so many data are unnecessary in real life. The application of data clustering algorithm based on artificial intelligence technology can solve such problems very well. However, the traditional clustering algorithm relies too much on manual operation when choosing clustering centers, which greatly reduces the efficiency of the whole algorithm. At the same time, the traditional clustering algorithm based on sparse network has too many coefficients in its coefficient matrix design, so it can not aggregate the relevant data well. This paper will measure the correlation of related data based on information entropy, and innovatively improve the existing sparse data network model. A model training algorithm based on multi-strategy pattern optimization is proposed to realize the automatic selection of clustering centers and reduce the training time of the algorithm. In terms of data clustering correlation, this paper proposes an optimized adaptive clustering algorithm based on the joint model of sparse subspace clustering algorithm model and the norm of adaptive subspace segmentation. In the experimental part, this paper compares the proposed algorithm with the traditional density peak clustering algorithm. The experimental results show that the proposed algorithm has obvious advantages in text data collection and classification, image data collection and filtering.
Automated Resource Management System based on Kubernetes Technology

The purpose of this research is intended to study and analyze the Kubernetes technology using the processes of performance evaluation by inspecting how many requests and responses could the server handle. According to the ability of Kubernetes technology, there is no systematic measure of performance despite the Kubernetes is currently in use broadly. Therefore, this paper proposed to test out the system by measuring the effectiveness according to a structured process by studying such measurement variables including the number of requests per second, number of responses to requests, and resource extension period with Kubernetes technology. Which from the testing and analysis of all three variables as mentioned, it is possible to know the efficiency of the Kubernetes technology in such a similar environment. Moreover, the testing experiment could display a piece of information on the dashboard for visualization and analytic purposes.
Analyzing the Interplay Between Regulatory Compliance and Cybersecurity (Revised)

Today, regulatory compliance is a critical component of any cybersecurity program. However, although compliance is often the driver for developing or improving cybersecurity, it may be incomplete as a cybersecurity measure itself. The result is that even a compliant organization may have gaps in its security posture. Through an in-depth literature review, this paper investigates the complexity surrounding compliance and the factors that have an impact on the interplay between compliance and cybersecurity.
Proof of Concept Example for Use of Simulation to Allow Data Pooling Despite Privacy Restrictions

Background: Integrating results from multiple samples is often desirable, but privacy restrictions may preclude full data pooling, and most datasets do not include fully harmonized variable sets. We propose a simulation-based method leveraging partial information across datasets to guide creation of synthetic data based on explicit assumptions about the underlying causal structure that permits pooled analyses that adjust for all desired confounders in the context of privacy restrictions. Methods: This proof-of-concept project uses data from the Health and Retirement Study (HRS) and Atherosclerosis Risk in Communities (ARIC) study. We specified an estimand of interest and a directed acyclic graph (DAG) summarizing the presumed causal structure for the effect of glycated hemoglobin (HbA1c) on cognitive change. We derived publicly reportable statistics to describe the joint distribution of each variable in our DAG. These summary estimates were used as data-generating rules to create synthetic datasets. After pooling, we imputed missing covariates in the synthetic datasets and used the synthetic data to estimate the pooled effect of HbA1c on cognitive change, adjusting for all desired covariates. Results: Distributions of covariates and model coefficients and associated standard errors for our model estimating the effect of HbA1c on cognitive change were similar across cohort-specific original and preimputation synthetic data. The estimate from the pooled synthetic incorporates control for confounders measured in either original dataset. Discussion: Our approach has advantages over meta-analysis or individual-level pooling/data harmonization when privacy concerns preclude data sharing and key confounders are not uniformly measured across datasets.
The Role of Suspended Accounts in Political Discussion on Social Media: Analysis of the 2017 French, UK and German Elections

Content moderation on social media is at the center of public and academic debate. In this study, we advance our understanding on which type of election-related content gets suspended by social media platforms. For this, we assess the behavior and content shared by suspended accounts during the most important elections in Europe in 2017 (in France, the United Kingdom, and Germany). We identify significant differences when we compare the behavior and content shared by Twitter suspended accounts with all other active accounts, including a focus on amplifying divisive issues like immigration and religion and systematic activities increasing the visibility of specific political figures (often but not always on the right). Our analysis suggests that suspended accounts were overwhelmingly human operated and no more likely than other accounts to share “fake news.” This study sheds light on the moderation policies of social media platforms, which have increasingly raised contentious debates, and equally importantly on the integrity and dynamics of political discussion on social media during major political events.
Transdisciplinary consolidation of information invironments

The paper discusses an ontological approach to solving the problem of integrated use of Big Data through transdisciplinary semantic consolidation of information resources. The category of taxonomy is considered as a constructive for the formation of consolidated information, taking into account its semantics. The formation of a hyperset of taxonomies — taxonomic diversity — implements the consolidation of information resources, defined as a verbal-active function of interpreting a set of binary relations between all contexts reflecting the meanings of concepts that form the content of subject areas, whose information resources are involved in network interaction. The category of information consolidation, as a taxonomic diversity of information resources, is characterized by the existence of verbally active reflection and recursion. The concept of discourse is defined as the inter-contextual connectivity of network information resources, which is represented by verbal-active reflection, on the basis of which taxonomic diversity is realized. For consolidated network information, the format of narrative discourse is determined. The concept of an arbitrary taxonomy is defined as a term, which makes it possible to implement the process of forming taxonomies based on the use of sets of λ-terms. The topology of interaction of taxonomy concepts sets is represented as a set of marked Böhm trees. An example of the formation of the information resources consolidation in the study of historical and cultural heritage with the display of museum expositions in the format of 3D panoramas is given. An algorithm for the consolidation of 3D models of heritage conservation objects with network GIS services and network information resources is given.
A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo)

Persistent identifiers (PID) to identify digital representations of physical specimens in natural science collections (i.e., digital specimens) unambiguously and uniquely on the Internet are one of the mechanisms for digitally transforming collections-based science. Digital Specimen PIDs contribute to building and maintaining long-term community trust in the accuracy and authenticity of the scientific data to be managed and presented by the Distributed System of Scientific Collections (DiSSCo) research infrastructure planned in Europe to commence implementation in 2024. Not only are such PIDs valid over the very long timescales common in the heritage sector but they can also transcend changes in underlying technologies of their implementation. They are part of the mechanism for widening access to natural science collections. DiSSCo technical experts previously selected the Handle System as the choice to meet core PID requirements. Using a two-step approach, this options appraisal captures, characterises and analyses different alternative Handle-based PID schemes and the possible operational modes of use. In a first step a weighting and ranking the options has been applied followed by a structured qualitative assessment of social and technical compliance across several assessment dimensions: levels of scalability, community trust, persistence, governance, appropriateness of the scheme and suitability for future global adoption. The results are discussed in relation to branding, community perceptions and global context to determine a preferred PID scheme for DiSSCo that also has potential for adoption and acceptance globally. DiSSCo will adopt a ‘driven-by DOI’ persistent identifier (PID) scheme customised with natural sciences community characteristics. Establishing a new Registration Agency in collaboration with the International DOI Foundation is a practical way forward to support the FAIR (findable, accessible interoperable, reusable) data architecture of DiSSCo research infrastructure. This approach is compatible with the policies of the European Open Science Cloud (EOSC) and is aligned to existing practices across the global community of natural science collections.
Hyperspectral Image Stitching via Optimal Seamline Detection

Hyperspectral images (HSIs) with both spatial and spectral information have found broad applications. Since most cameras have narrow viewing angle, generating panoramic images is essential to show a large-range view of the environment. So far, there are few studies on HSI stitching, and the stitching result still suffers from some problems, such as blurring and ghosting, geometric misalignment, visible seam, and spectral distortion. Hence, to address the above disadvantages, we propose a novel HSI stitching strategy using optimal seamline detection approach in this letter. First, we use a fast and robust seam estimation method to determine the seamline in each single band of HSI. This method works in RGB images, and we have modified it to be used in a single-band gray-scale image of HSI. Then, to guarantee the integrity of spatial and spectral information of hundreds of bands of HSI, we propose to apply the structural similarity (SSIM) index to select the optimal one among all band candidate seamlines and use the selected optimal seamline to stitch all the remaining bands. The experimental results demonstrate that our proposed approach outperforms traditional HSI stitching approach in both spatial and spectral performances.