Reviewerly/exHarmony huggingface dataset:
cuConv: CUDA implementation of convolution for CNN inference

Convolutions are the core operation of deep learning applications based on Convolutional Neural Networks (CNNs). Current GPU architectures are highly efficient for training and deploying deep CNNs, and hence, these are largely used in production for this purpose. State-of-the-art implementations, however, present a lack of efficiency for some commonly used network configurations. In this paper we propose a GPU-based implementation of the convolution operation for CNN inference that favors coalesced accesses, without requiring prior data transformations. Our experiments demonstrate that our proposal yields notable performance improvements in a range of common CNN forward propagation convolution configurations, with speedups of up to 2.29x with respect to the best implementation of convolution in cuDNN, hence covering a relevant region in currently existing approaches.
Distance learning in the conditions of Covid-19: problems and prospects of their solution

The problem of ensuring the quality of educational services is extremely important. It is known that the level of education and education as such are of great importance for most areas of life as an individual, and each country and the world as a whole. The authors of the article tried to analyze the current state of education, namely - distance education, in terms of transformational processes. In their study, the authors tried to take into account the total digitalization of education and objective reality in general, the introduction of innovative technologies, and took into account the factor of the pandemic, which in one way or another affected all processes of public life. The result of this study was a reflection on the main trends in the development of distance education and education in general, as well as general recommendations for improving the education system at both local and global levels. This study was conducted on the basis of a number of methods that allowed to make adequate generalizations about the state of distance education: the methods of system analysis, comparative analysis, sociological method, the method of observation, methods of analysis and synthesis, the method of abstraction and the method of generalization of objective phenomena.
COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic

It has been declared by the World Health Organization (WHO) the novel coronavirus a global pandemic due to an exponential spread in COVID-19 in the past months reaching over 100 million cases and resulting in approximately 3 million deaths worldwide. Amid this pandemic, identification of cyberbullying has become a more evolving area of research over posts or comments in social media platforms. In multilingual societies like India, code-switched texts comprise the majority of the Internet. Identifying the online bullying of the code-switched user is bit challenging than monolingual cases. As a first step towards enabling the development of approaches for cyberbullying detection, we developed a new code-switched dataset, collected from Twitter utterances annotated with binary labels. To demonstrate the utility of the proposed dataset, we build different machine learning (Support Vector Machine & Logistic Regression) and deep learning (Multilayer Perceptron, Convolution Neural Network, BiLSTM, BERT) algorithms to detect cyberbullying of English-Hindi (En-Hi) code-switched text. Our proposed model integrates different hand-crafted features and is enriched by sequential and semantic patterns generated by different state-of-the-art deep neural network models. Initial experimental results of the proposed deep ensemble model on our code-switched data reveal that our approach yields state-of-the-art results, i.e., 0.93 in terms of macro-averaged F1 score. The dataset and codes of the present study will be made publicly available on the paper's companion repository [https://github.com/95sayanta/COVID-19-and-Cyberbullying].
SmartBuilder: A Block-based Visual Programming Framework for Smart Contract Development

Recently, smart contracts are increasingly gaining more attention from academia and industries, due to their potential of increasing efficiency in various application domains. However, building syntactically correct smart contracts is very complex and challenging for non-expert programmers. Besides, as smart contracts are still in the early emerging phase, there is a lack of expertise and tools for supporting practitioners to teach or learn smart contract development. In this paper, we introduce SmartBuilder, a block-based visual programming framework for building smart contracts using extended Google Blockly libraries. It allows Hyperledger Fabric smart contract (also known as Chaincode) development learners or non-expert users to build smart contracts using visual blocks without writing a single code. Moreover, it is a useful and efficient educational tool for teaching or learning how to build smart contracts.
How the Library of D.V. Ulyaninsky Entered the State Rumyantsev Museum

Based on the unpublished documents, the author reconstructs the history of acquisition of the famous book collection of D.V. Ulyaninsky (1861—1918) by the State Rumyantsev Museum. The well-selected library, significant part of which consisted of publications on bibliography, book studies and book business, became widely known thanks to the three-volume printed catalogue prepared by the owner. After the suicide of bibliophile in February 1918, which occurred due to the threat of deprivation of the apartment where the library was kept, the collection was bought by the State Rumyantsev Museum. Archival documents allow examining this process in detail. During his life, D.V. Ulyaninsky had extensive connections with the oldest museum in Moscow, but he clearly had no plans to transfer or sell his library to the institution. After his tragic death, the State Rumyantsev Museum first moved his library to the museum’s territory. Then, despite the presence of third-party buyers, the museum actually forced the widow of the collector T.F. Ulyaninskaya to sell the library, taking into account the circumstance that the library located in her apartment. The organizer of the actions on acquisition of the D.V. Ulyaninsky book collection was the librarian of the State Rumyantsev Museum Y.V. Gauthier; and the Head of the reading room Y.G. Kvaskov assessed the collection. The article introduces into scientific circulation the materials on assessment of the library and the contract of the State Rumyantsev Museum with the widow of bibliophile, which legally secured the transfer of ownership of the collection of D.V. Ulyaninsky to the State Rumyantsev Museum.
Application of Massive Open Online Course to Grammar Teaching for English Majors Based on Deep Learning

The study aims to explore the roles of Massive Open Online Courses (MOOCs) based on deep learning in college students' English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and does not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of "teaching students according to their personalities" to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers' deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students.
SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city

In today's era, Sustainable city is evaluated using the services provided to the society. The designing of the integral part of the society should be focused towards the benefits of people. Internet is extensively utilized by the society using Search Engines. The accuracy and time it takes for different search engines to retrieve information from a cloud computing repository around the world, varies. However, it has been discovered in the literature that webpage ranking reduces the amount of time a user spends surfing, which saves a significant amount of energy during computation and transmission across the network. The hyperlink structure of the web graph is used in most of the earlier solutions documented in the literature, which consumes a lot of energy during calculation. It may exacerbate the link leakage problem by increasing the frequency of spam pages. In light of the energy consumption of various smart gadgets, hyperlink structure alone is no longer sufficient for predicting webpage relevance. Its true importance is revealed by user surfing activity. To improve search engine accuracy and speed, it is critical to demote spam pages, lowering energy consumption. Among all the existing ranking algorithms in the literature, one of the important components is the PageRank algorithm used by Google's ranking module. Keeping focus on these points, in this paper, various page ranking algorithms based upon supervised learning are surveyed and summarized with respect to different selected parameters and experiments performed. Using this information, a detailed taxonomy of search engine results is presented in the text. Moreover, PageRank algorithms are explored by using different supervised learning techniques applied in the existing proposals for getting and processing the results. In the nutshell, the PageRank methodology is surveyed with respect to web spam detection which is the demand of cognitive systems in smart cities.
Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method

Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images.A Resnet101-9 ensemble model was developed for classifying ALL in microscopic images. The proposed Resnet101-9 ensemble model combined the use of the nine trained Resnet-101 models with a majority voting strategy. Each trained Resnet-101 model integrated the well-known pre-trained Resnet-101 model and its algorithm hyperparameters by using transfer learning method to classify ALL in microscopic images. The best combination of algorithm hyperparameters for the pre-trained Resnet-101 model was determined by Taguchi experimental method. The microscopic images used for training of the pre-trained Resnet-101 model and for performance tests of the trained Resnet-101 model were obtained from the C-NMC dataset. In experimental tests of performance, the Resnet101-9 ensemble model achieved an accuracy of 85.11% and an F1-score of 88.94 in classifying ALL in microscopic images. The accuracy of the Resnet101-9 ensemble model was superior to that of the nine trained Resnet-101 individual models. All other performance measures (i.e., precision, recall, and specificity) for the Resnet101-9 ensemble model exceeded those for the nine trained Resnet-101 individual models.Compared to the nine trained Resnet-101 individual models, the Resnet101-9 ensemble model had superior accuracy in classifying ALL in microscopic images obtained from the C-NMC dataset.
Single photon randomness originating from the symmetric dipole emission pattern of quantum emitters

Quantum random number generation is a key ingredient for quantum cryptography and fundamental quantum optics and could advance Monte-Carlo simulations and machine learning. An established generation scheme is based on single photons impinging on a beam splitter. Here, we experimentally demonstrate quantum random number generation solely based on the spontaneous emission process in combination with the symmetric emission profile of a dipole aligned orthogonal to the laboratory frame. The demonstration builds on defect centers in hexagonal boron nitride and benefits from the ability to manipulate and align the emission directionality. We prove the randomness in the correlated photon detection events making use of the NIST randomness test suite and show that the randomness remains for two independently emitting defect centers. The scheme can be extended to random number generation by coherent single photons with potential applications in solid-state based quantum communication at room temperature.
Relaxation of stationary states on a quantum computer yields a unique spectroscopic fingerprint of the computer’s noise

Quantum computing has the potential to revolutionize computing for certain classes of problems with exponential scaling, and yet this potential is accompanied by significant sensitivity to noise, requiring sophisticated error correction and mitigation strategies. Here we simulate the relaxations of stationary states at different frequencies on several quantum computers to obtain unique spectroscopic fingerprints of their noise. Response functions generated from the data reveal a clear signature of non-Markovian dynamics, demonstrating that each of the quantum computers acts as a non-Markovian bath with a unique colored noise profile. The study suggest that noisy intermediate-scale quantum computers (NISQ) provide a built-in noisy bath that can be analyzed from their simulation of closed quantum systems with the results potentially being harnessed for error mitigation or open-system simulation.
An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic; instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer” algorithm, providing an infection risk assessment for individuals in potential contact with patients.
Competencia Digital Docente en el País Vasco durante la pandemia del COVID-19

The objective of this research is to measure the perception that teachers had about their own performance when they were forced to carry out an Emergency Remote Teaching due to the COVID-19 pandemic. A questionnaire was provided to teachers of every educational stage in the Basque Country (Child, Primary and Secondary Education, Professional Training and Higher Education) obtaining a total of 4,586 responses. The statistical analysis of the data shows that the greatest difficulties reported by educators are shortcomings in the training of teachers in digital skills, as well as a lack of resilience of the system and / or lack of a 'plan B' in extraordinary situations. The existing digital divide between teachers based on their gender, age and type of school should be highlighted. Another worrying result is the lower technological competence in lower educational levels, which are the most vulnerable in remote teaching. These results invite us to reflect on the measures to be taken to improve equity, social justice and resilience of the educational system.
A general method for evaluating the overhead when consolidating servers: performance degradation in virtual machines and containers

Abstract Server consolidation is one of the most commonly used techniques for reducing energy consumption in datacenters; however, this results in inherent performance degradation due to the coallocation of virtual servers, i.e., virtual machines (VMs) and containers, in physical ones. Given the widespread use of containers and their combination with VMs, it is necessary to quantify the performance degradation in these new consolidation scenarios, as this information will help system administrators make decisions based on server performance management. In this paper, a general method for quantifying performance degradation, that is, server overhead, is proposed for arbitrary consolidation scenarios. To demonstrate the applicability of the method, we develop a set of experiments with varying combinations of VMs, containers, and workload demands. From the results, we can obtain a suitable method for quantifying performance degradation that can be implemented as a recursive algorithm. From the set of experiments addressing the hypothetical consolidation scenarios, we show that the overhead depends not only on the type of hypervisor and the workload distribution but also on the combination of VMs and containers and their nesting, if feasible.
Constrained Deep Reinforcement Learning for Smart Load Balancing

In this paper, we explore the use of an actor-critic architecture for Deep Reinforcement Learning (DRL) to improve load balancing beyond traditional algorithms. Some centralized Reinforcement Learning (RL) algorithms have targeted in the reward function expression the Quality of Experience (QoE) for video flows, but this requires access to clients, or the Maximum Link Utilization (MLU) for other types of flows. In our approach, we tune the actor-critic algorithm to only leverage on QoS parameters in order to load balance traffic in the network and maximize the QoE experienced by the users. This avoids having to collect observations and performance measurements from client applications, as it only focuses on network metrics that can be easily measured. We explore both centralized and distributed solutions to assess the feasibility of the proposed smart load balancing solutions. We compare them to ECMP, QoE-based reward methods, and RILNET that uses an underlying DDPG optimization approach. The proposed algorithms are shown to outperform previous approaches.
Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.
SOFT-COMPETENCIES AS A RESULT OF VOCATIONAL TEACHERS TRAINING

The article analyzes the concepts of “competence”, “soft skills” and presents the comparative analysis of these concepts in the psychological and pedagogical literature and regulatory documents. An expanded interpretation of the concept of softcompetence – as a universal competence of the Federal State Educational Standard of Higher Education (3++) is proposed. The possibility of using this approach to evaluate the training of vocational education teachers is considered.
Examination of blood samples using deep learning and mobile microscopy

Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected.Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care.