Reviewerly/exHarmony huggingface dataset:
Compositional peculiarities of the host rocks and ores of the Lazursky ore field, Zmeinogorsk ore region of the Rudny Altai minerogenic zone

The article describes composition of the host rocks and ores of the Lazursky and Maslyansky polymetallic volcanogenic massive sulfide deposits of the Lazursky ore field located within the Zmeinogorsk ore region of the Rudny Altai minerogenic zone. The ore field is composed of various facies of the Devonian (Late Givetian – Frasnian) ore-bearing siliceous-terrigenous basalt-rhyolite formation containing horizons of synvolcanic metasomatites. All rocks of the ore field were subjected to folding and schistosity with zones of tectonic brecciation. Hydrothermal alterations are represented by carbonatization and chloritization. The ore bodies exposed at the Lazursky and Maslyansky ore deposits are represented by copper-pyrite, copper, and zinc-copper-pyrite massive sulfide ores and other varieties. The major ore minerals of the deposits are chalcopyrite, pyrite, sphalerite, marcasite, and pyrrhotite.
Motivation Management of Domestic and International Engineering Students in the Digital Era

Growth sphere of engineering and high technology creates conditions for the rapid economy development, but to realize these opportunities, highly qualified specialists are needed, who are subject to increased requirements both in terms of professional skills and competencies, and in personal qualities terms. This creates new challenges to the educational system, in particular to the engineering education system, which must overcome inertia, reduced student motivation, insufficient funding for the re-equipment of laboratories and increased requirements for lecturers. At the same time, both the system of student motivation, which allows them to reveal their talents, and a change in the paradigm of the educational process organization. Engineering education should combine not only the modern methods use and teaching aids, but also an orientation towards the needs of real production and business for create in student's ability to self-study and subsequently his rapid adaptation to production conditions. The experience gained shows that the use of new teaching methods in engineering education contributes to the development of engineering competencies required for high-tech industries. This requires creating a learning environment that motivates students to pursue engineering professions.
Ranking of Universities in the United Arab Emirates: Exploring a web-based technique

This study investigated university rankings in the UAE within the context of the challenges facing higher education globally in the 21st century, by using the Webometric ranking methodology. Data was collected and analysed on the top 20 UAE universities. It was apparent that the UAE universities do not perform as well as expected in the Webometrics ranking, when compared with peer countries. The ratings are slightly below that which is expected, especially when one considers measures such as research publications. Strategies to improve performance using the Webometrics indicators could have a positive impact on the UAE universities. Improvements in communicating research and their web presence is likely to move the UAE universities up the Webometrics rankings, and their academic reputation in the country.
Emergence of functional information from multivariate correlations

The information content of symbolic sequences (such as nucleic or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence. Here we show that an information score obtained from multivariate (multiple-variable) correlations within sequences of a 'training' ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
Design and Implementation of a Voice Interactive Tool to Facilitate Web Collaboration

This paper proposes a voice interactive tool that is based on a dual voice service model for Web-based collaboration. Instead of restricting team members to rely on the traditional interaction devices, such as keyboards, mice or display screens, this tool aims to enable each of them to "speak to the collaborative tool or the browser" and to obtain multisensory feedback, e.g., audiovisual or voice information. In that way, some specific collaborative tasks could be completed through natural language dialogue. The paper describes and highlights the significance and necessity of multimodal interaction for Web collaboration activities involving more and more smart devices, especially, how integrated voice interaction can provide a friendlier experience for teamwork. For some data-sensitive tasks, the local voice services are very beneficial and flexible because there is no need to upload the given set of voice data to a third party, and the related end-to-end speech models can be adjusted and trained in accordance with certain collaboration requirements. The voice supported collaborative editing and voice supported browsing have been tested as practical examples in a collaborative working environment MCWE2.0. The results show that the tool takes the advantages of voice interaction to expand the potential possible usage of multiple smart devices to a certain extent, thereby facilitating multimodal Web-based collaboration in a dual way.
The World’s First ‘Pop-Up’ Urban Airport: A User-Centred Design Approach to Understand the Customer Journey

Urban Airport is a transportation infrastructure for electrically powered Vertical Take-Off and Landing (eVTOLs) aircrafts, or so-called flying cars. It is one of the emerging solutions for improving urban environments in terms of sustainability and traffic. This paper explores how adapting a human-centred design approach enhances passengers’ journey through the future Urban Airport. In order to deliver a user-friendly experience, accessibility, comfort and efficiency are vital. Understanding users’ journey throughout the Urban Airport will help us comprehend how the infrastructure should be designed for targeted user groups with different characteristics and expectations. This paper presents three user personas and associated storyboards to provide insights into the spectrum of goals, needs and frustrations of the Urban Airport potential users.
Retrieval Effectiveness of Google on Reverse Image Search

This study investigates the reverse image search performance of Google, in terms of Average Precisions (APs) and Average Normalized Recalls (ANRs) at various cut-off points,on finding out similar images by using fresh Image Queries (IQs) from the five categories “Fashion,” “Computer,” “Home,” “Sports,” and “Toys.” The aim is to have an insight about retrieval effectiveness of Google on reverse image search and then motivate researchers and inform users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine, and for each, the first 100 images retrieved were evaluated with binary relevance judgment. At the cut-off points 20, 40, 60, 80, and 100, both APs and ANRs were calculated for each category and for all 25 IQs. The AP range is from 41.60% (Toys—cut-off point 100) to 71% (Home—cut-off point 20). The ANR range is from 47.21% (Toys—cut-off point 20) to 71.31% (Computer—cut-off point 100). If the categories are ignored; when more images were evaluated, the performance of displaying relevant images in higher ranks increased, whereas the performance of retrieving relevant images decreased. It seems that the information retrieval effectiveness of Google on reverse image search needs to be improved.
Pyramid Residual Convolutional Neural Network based on an end-to-end model

Deep learning(DL) based on image classification, segmentation, detection methods have been providing state-of-the-art performance in recent years. Particularly, these techniques have been successfully applied to medical image to Help doctors have a more convenient and clear understanding of the patient's physical condition, and among them, one deep learning technique, U-Net model has become one of the most popular techniques in the field of image segmentation. In this paper, we propose a Pyramidal Residual Convolutional Neural Network based on U-Net models(PRU-Net). The proposed model takes advantage of U-Net, Residual Network as well as Image Pyramid. There are several advantages of these proposed architectures for segmentation tasks. First of all, the design of residual structure helps us to train deep structure, which often means better effect. Secondly, the pyramid residual convolution layer is used for feature accumulation, which strengthens the relationship between shallow features and deep features, and can learn image information more efficiently. Third, it allows us to design better architecture with small number of network parameters for medical image segmentation. We add pyramid residual structure to the three benchmark models, U-Net, ResU-Net (Residual U-Net) and AttU-Net (Attention U-Net), and test them on two benchmark data sets: skin cancer segmentation and thyroid nodules Segmentation. Experimental results show that these models have better segmentation effect than those without pyramid residual structure. Gaussian pyramid residual structure is better than the max-pooling.
Derin öğrenme tabanlı nesne algılama işlemlerinin farklı uygulama alanları

Automation is spread in all daily life and business activities to facilitate human life and working conditions. Robots, automated cars, unmanned vehicles, robot arms, automated factories etc. are getting place in our lives. For these automated actors, one important task is recognizing objects and obstacles in the target environment. Object detection, determining the objects and their location in the environment, is one of the most important solution for this task. With deep learning techniques like Convolutional Neural Network and GPU processing, object detection has become more accurate and faster, and getting attention of researchers. In recent years, many articles about object detection algorithms and usage of object detection have been published. There are surveys about the object detection algorithms, but they have introduced algorithms and focused on common application areas. With this survey, we aim to show that object detection algorithms have very large and different application area. In this study, we have given a brief introduction to deep learning. We have then focused on standard object detection algorithms based on deep learning and their applications in different research areas in recent years to give an idea for future works. Also, the datasets and evaluation metrics used in the research are listed.
Deep Learning in Content-Based Medical Image Retrieval

This chapter proposes a deep learning system for medical image retrieval based on content by training a profound convolutionary neural network for the purpose of classification. The first step is to get a prediction of the query class of the query picture by the qualified network and then find related pictures in that particular class. The second approach is to search for all applicable images in the entire database without adding details about the query image type. Deep learning-based technique Deep Neural Network (DNN) classifier is used to classify medical images for categorizing the images. For tuning the parameters of deep learning models, Spotted Hyena Optimizer (SHO) is used. By learning discriminatory characteristics from pictures directly, the proposed solution reduces semantic distance. The network has succeeded in training medical images with 99.77 per cent average rating accuracy. In order to remove the recovery functionality, the last three fully connected network layers were used. Widespread measurements such as accuracy and reminders have been used to assess the efficiency of the proposed system for recovery of medical images, which helps the doctors to analyse the disease better.
Terminology-aware sentence mining for NMT domain adaptation: ADAPT’s submission to the Adap-MT 2020 English-to-Hindi AI translation shared task

This paper describes the ADAPT Centre’s submission to the Adap-MT 2020 AI Translation Shared Task for English-to-Hindi. The neural machine translation (NMT) systems that we built to translate AI domain texts are state-of- the-art Transformer models. In order to improve the translation quality of our NMT systems, we made use of both in-domain and out-of-domain data for training and employed different fine-tuning techniques for adapting our NMT systems to this task, e.g. mixed fine-tuning and on-the-fly self-training. For this, we mined parallel sentence pairs and monolingual sentences from large out-of-domain data, and the mining process was facilitated through automatic extraction of terminology from the in-domain data. This paper outlines the experiments we carried out for this task and reports the performance of our NMT systems on the evaluation test set.
Improving the purity of heralded single-photon sources through spontaneous parametric down-conversion process*

The high-purity single-photon source plays an important role in the field of quantum information. Usually, it is generated through spontaneous parametric down-conversion process. In this paper, we investigate and summarize a few approaches on obtaining single-photon sources with a high purity using either PPKTP or PPLN nonlinear crystals. Moreover, we present improved schemes to increase the purity based on existing work, corresponding applicable conditions and procedures are discussed and analyzed. Besides, we carry out numerical simulations and show that nearly perfect purity can be reached even without using any filters. Therefore, this work might provide valuable references for the generation and application of high purity single-photon sources.
Prospects of Digital Currency in India - A Way Forward

The era of information and communication technologies has created many opportunities in every aspect. The field that has been benefitted the most from these recent developments in the financial and business sector. The scope and speed of evolution in technology, customer perception, and wide acceptance indicate that these technologies and disruptions will transform the finance and banking sector as we know it, providing both opportunities and challenges to financial institutions. Systems that have traditionally been used for savings, investments are not well suited given the scale and agility required for the digital age. Keeping in mind this array, financial institutions are responding with new digitization and innovation initiatives, using analytics, cloud technologies, and other new alternatives to respond to customer expectations. In a country like India, with a large economy and a despotic regime, the country shows decent economic growth even after having a complex system of arbitrary rules and regulations. Amidst all the growth, there is a huge scope of transforming the investment and lending patterns, with the help of technologies becoming more customer-centric and efficient. So, the technologies like blockchain are looking to drive innovation across various industries in India. In the era of blockchain, cryptocurrencies are the main part of it. They are said to be the most disruptive technologies in this area. This paper is an attempt to find the future of digital currency in India from the international experience.
A System for Information Extraction from Scientific Texts in Russian

In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.
Secured Information Infrastructure for Exchanging Information for Digital Governance

The information infrastructure should be secure to trade in the design of visual management. A secure blockchain management device can protect integrated statistics, simpler methods and save you from fraud, garbage, and harassment to improve compliance and accountability for real exchanges. Individuals, groups, and governments are collaborating on the distribution of digital-enabled virtual reality using cryptography on a government-based blockchain that removes the sin point of failure and apparently protects the realities of the citizen concerned and the authorities. Desire to reduce calls needs to be eliminated by making available adoptions to control gadget use of the public blockchain to facilitate cross-border or record sharing and reduce environmental waste by eliminating virtual garage space demand with virtual management and protecting a different visibility and number system. The keys to the measurable areas considered in this consideration are considered real and reliable in the control of facts. Seeking different facts through people to study development into automated security solutions to transform digital governance data into a wide range of financial areas includes system performance, content testing and Internet of Thing (IoT), and online-based ownership. As a result, blockchain technology can improve the credibility of public institutions that use fraudulent systems to store information. The blockchain technology allows for the exclusion of social and fashion contexts of dialogue, as it can create realistic balance in the ecosystem of corporations and actors across borders.
Blockchain Concept and its Area of Applications

Block chain is the chain of blocks where the digital information is stored as a block located and organized by a chain in the public database. The blocks contain mainly three important pieces of information, like information about transactions, information about who is participating in the transactions and information about block to distinguish it from the other blocks. This paper explains the concept of Blockchain, its working and the application areas of blockchain.
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments

Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a challenging task, also due to the inherent difficulty in creating conditions for designing continuously evolving dynamics that are typical of the real-world. Many existing research works usually involve training and testing of virtual agents on datasets of static images or short videos, considering sequences of distinct learning tasks. However, in order to devise continual learning algorithms that operate in more realistic conditions, it is fundamental to gain access to rich, fully-customizable and controlled experimental playgrounds. Focussing on the specific case of vision, we thus propose to leverage recent advances in 3D virtual environments in order to approach the automatic generation of potentially life-long dynamic scenes with photo-realistic appearance. Scenes are composed of objects that move along variable routes with different and fully customizable timings, and randomness can also be included in their evolution. A novel element of this paper is that scenes are described in a parametric way, thus allowing the user to fully control the visual complexity of the input stream the agent perceives. These general principles are concretely implemented exploiting a recently published 3D virtual environment. The user can generate scenes without the need of having strong skills in computer graphics, since all the generation facilities are exposed through a simple high-level Python interface. We publicly share the proposed generator.
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks

Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in specific cases by assuming that the network has a certain activation function or topology. It is difficult to derive such weight initialization strategies, and modern architectures therefore often use these same initialization schemes even though their assumptions do not hold. This paper introduces AutoInit, a weight initialization algorithm that automatically adapts to different neural network architectures. By analytically tracking the mean and variance of signals as they propagate through the network, AutoInit appropriately scales the weights at each layer to avoid exploding or vanishing signals. Experiments demonstrate that AutoInit improves performance of convolutional, residual, and transformer networks across a range of activation function, dropout, weight decay, learning rate, and normalizer settings, and does so more reliably than data-dependent initialization methods. This flexibility allows AutoInit to initialize models for everything from small tabular tasks to large datasets such as ImageNet. Such generality turns out particularly useful in neural architecture search and in activation function discovery. In these settings, AutoInit initializes each candidate appropriately, making performance evaluations more accurate. AutoInit thus serves as an automatic configuration tool that makes design of new neural network architectures more robust. The AutoInit package provides a wrapper around TensorFlow models and is available at https://github.com/cognizant-ai-labs/autoinit.
Cyberneticisation as a Theory and Practice of Matter 

The ecological implementation of cybernetic ideas in architecture requires a material theory and practice that enables their propositions to be tested. The need for approaches that move from simulation to cybernetic reality is a documented limitation of cybernetics recognised by Stafford Beer with his pond ecology experiments and Gordon Pask through electrochemical devices. While both experimented with adaptive material platforms as embodiments of designed cybernetic systems, their approaches were limited by the available toolsets. This article considers an ecological trajectory of cybernetisation by revisiting notions of biological computation as a generative material practice. In particular, the growing fields of biodesign and living architecture go beyond notions of biological analogues that inform modern architecture by directly incorporating living systems into the very fabric of buildings as designed expressions of ecology.