Reviewerly/exHarmony huggingface dataset:
EVALUATION OF USER EXPERIENCE IN AUGMENTED REALITY MOBILE APPLICATIONS

Globalization, technological development and a dynamic business environment influence the change of customer information demands. It becomes vital for organizations to find out the customer demand change and discover technological solutions to satisfy these demands. One of these technologies is augmented reality, which connects real and digital environments by expanding it with digitally coded information which is decoded by using a specific device. As this type of technology enables the changing information needs of customers to be met faster, organizations are increasingly using these technological solutions to achieve a variety of purposes: to position products innovatively, increase product awareness, create added value for the customer, increase sales. However, organizations often face the challenge of evaluating commercial augmented reality mobile applications in user experience. A two-case study has been selected to evaluate the user experience of augmented reality commercial mobile applications and provide recommendations for their development to address this issue. In this research, such methods as scoping scientific literature review, expert evaluation, and user experience questionnaire method were used. The study has identified the main factors influencing the positive user experience: the explicit purpose of the application, easy to use and learn, smooth operation, imaginative information presentation, and interactivity.
Data mining and XBRL integration in management accounting information based on artificial intelligence

In today’s society, the application of information technology is becoming more and more extensive. At the same time, management accounting, as an important branch of modern accounting, also ushered in new development opportunities, and the research of data mining also pays more attention to the combination of theory and practice. Therefore, data mining can provide some technical support for the implementation of strategic management accounting. Because the most important thing of management accounting informatization is to process, calculate and transmit the business information of the enterprise through the corresponding information processing platform through the use of computer technology, and provide the corresponding data to the management of the major companies in order to better analyze and make decisions and perfect the future development strategy of the enterprise, so the screening of the corresponding technology is more important in the process of management accounting informatization. Based on the development of artificial intelligence technology and the combination of data mining and XBRL technology, this paper discusses the new strategies of contemporary management accounting development.
A Universal Representation Transformer Layer for Few-Shot Image Classification

Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of benchmarks such as Meta-Dataset. A key challenge in this multi-domain setting is to effectively integrate the feature representations from the diverse set of training domains. Here, we propose a Universal Representation Transformer (URT) layer, that meta-learns to leverage universal features for few-shot classification by dynamically re-weighting and composing the most appropriate domain-specific representations. In experiments, we show that URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it achieves top-performance on the highest number of data sources compared to competing methods. We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization.
Using quantum annealers to calculate ground state properties of molecules

Quantum annealers are an alternative approach to quantum computing, which make use of the adiabatic theorem to efficiently find the ground state of a physically realizable Hamiltonian. Such devices are currently commercially available and have been successfully applied to several combinatorial and discrete optimization problems. However, the application of quantum annealers to problems in chemistry remains a relatively sparse area of research due to the difficulty in mapping molecular systems to the Ising model Hamiltonian. In this paper, we review two different methods for finding the ground state of molecular Hamiltonians using Ising model-based quantum annealers. In addition, we compare the relative effectiveness of each method by calculating the binding energies, bond lengths, and bond angles of the H3+ and H2O molecules and mapping their potential energy curves. We also assess the resource requirements of each method by determining the number of qubits and computation time required to simulate each molecule using various parameter values. While each of these methods is capable of accurately predicting the ground state properties of small molecules, we find that they are still outperformed by modern classical algorithms and that the scaling of the resource requirements remains a challenge.
Common Digital Space of Scientific Knowledge: Goals and Tasks

The goals of the formation of the Common Digital Space of Scientific Knowledge (CDSSK) are formulated, which are aimed at providing information support for scientific research, supporting educational processes, popularizing science, and ensuring the preservation of scientific knowledge. Accordingly, the contingent of users of the CDSSK and the tasks that need to be solved in the process of forming the Space are determined.
Blockchain-based secure multi-resource trading model for smart marketplace

Developments in sensors and communication technology lead to the emergence of smart communities where diverse collaborative applications can be enabled. One such application is the Smart Market Place (SMP), where participants of the smart community can trade resources, such as energy, internet bandwidth, water, etc., using a virtual currency (such as ether). However, most of the existing SMP trading models are proposed to trade a single resource and also restrict a participant to perform only a single transaction at a time. Restriction on multiple parallel transactions is imposed to protect the participants against the double-spending attack in the SMP. This work proposes a secure multi-resource trading (SMRT) model that is based on public Ethereum blockchain. SMRT allows participant of a SMP to trade multiple resources and initiate parallel transactions. Moreover, detailed security analysis and adversary model are presented to test the effectiveness and to assess the resilience of the proposed model against the double-spending attack. The adversary model is based on partial progress towards block production which is influenced by time advantage and average computing power. Furthermore, simulation based analysis and comparison of SMRT is also presented in terms of security, performance, cost and latency of transactions. It is observed that SMRT not only provides protection against the double spending attack, but it also reduces the computational overhead of the proposed model up to 50% as compared to existing trading models.
Towards Zero-Shot Learning with Fewer Seen Class Examples

We present a meta-learning based generative model for zero-shot learning (ZSL) towards a challenging setting when the number of training examples from each seen class is very few. This setup contrasts with the conventional ZSL approaches, where training typically assumes the availability of a sufficiently large number of training examples from each of the seen classes. The proposed approach leverages meta-learning to train a deep generative model that integrates variational autoencoder and generative adversarial networks. We propose a novel task distribution where meta-train and meta-validation classes are disjoint to simulate the ZSL behaviour in training. Once trained, the model can generate synthetic examples from seen and unseen classes. Synthesize samples can then be used to train the ZSL framework in a supervised manner. The meta-learner enables our model to generates high-fidelity samples using only a small number of training examples from seen classes. We conduct extensive experiments and ablation studies on four benchmark datasets of ZSL and observe that the proposed model outperforms state-of-the-art approaches by a significant margin when the number of examples per seen class is very small.
Configuring the older adult

Later life has become one of the most prominent topics for the design and development of digital technologies, resulting in the creation of a large body of prototypes and products (gerontechnologies). However, the majority of these efforts suffer from a lack of empirical grounding of their imaginaries of ageing and later life while also failing to involve older adults in design processes in meaningful ways. This chapter reviews how age and ageing can be configured across different instances of the development and deployment of digital technologies. We understand such design processes as configuration practices that co-construct older users and later life. By using the concept of re-con-figuration, we critically reflect about conceptual, ethical and pragmatic challenges of involving older people in design processes.
Chaos-Based Image Encryption Using Arnold’s Cat Map Confusion and Henon Map Diffusion

This research designed an image encryption system that focused on securing teledermatology data in the form of skin disease images.The encryption and decryption process of this system is done on the client side using chaos-based encryption with confusion and diffusion techniques.Arnold's cat map is the chaotic map model used for confusion, while the Henon map is used for diffusion.The initial values of both chaotic maps are obtained from a 30-digit secret key that is generated using Diffie-Hellman key exchange.During Arnold's cat map generation, different p and q values are used for every iteration.On the other side, the precision of the Henon map's x and y values is 10-14.From the tests that have been done, histograms of the encrypted images are relatively flat and distributed through all the gray values.Moreover, the encrypted images have average correlation coefficients of 0.003877 (horizontal), -0.00026 (vertical) and -0.00049 (diagonal) and an average entropy of 7.950304.According to the key sensitivity test, a difference of just one number in the secret key causes big differences, as both results have a similarity index of 0.005337 (0.5%).Meanwhile, in the decryption process, that small key difference cannot be used to restore the encrypted image to its original form and generate another chaotic image with average entropies of 7.964909333 (secret key difference) and 7.994861667 (private key difference).
Models for the Design and Operation of Digital Educational Environments

Many educational institutions, assessing their prospects, plan to develop distance educational services using their own electronic and information educational environments (EIEE). During creating and operating EIEE of educational institution (EI), a number of questions arise related to structural, marketing, personnel and other decisions. The purpose of this article is to research the organizational, technical and didactic aspects of the EIEE EI. The methodology of the research is based on methods of system analysis, cybernetics, pedagogical psychology and didacticism. A conceptual model of the EIEE EI as an organizational and technical system has been proposed. It includes a marketing subsystem, a set of technical tools, various types of security and an organizational subsystem. The main functions of these subsystems and their various components have been identified. A didactic model of a typical complex of digital educational resources has been developed. Within this model, the diversity of digital educational resources across each academic discipline is integrated into complexes that provide a complete study at different target levels of assimilation, from initial acquaintance to solving uncharacteristic professionally oriented tasks. From the point of view of pedagogical psychology and cybernetics, the high importance of feedback in EIEE EI is shown. The classification of feedbacks (internal and external) is proposed for the triad “Teacher – Teaching Program – Learner”. The limits of the use of automation in the formation of feedbacks are specified, as opposed to other (non-computer) responses to students’ educational activities. The role and place of feedback in the “digital footprints” of students and teachers is shown and the directions for the use of this data in artificial intelligence systems are outlined.
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs

Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image manifold, it is unclear whether they implicitly capture the underlying 3D object structures. And if so, how could we exploit such knowledge to recover the 3D shapes of objects in the images? To answer these questions, in this work, we present the first attempt to directly mine 3D geometric cues from an off-the-shelf 2D GAN that is trained on RGB images only. Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner. The core of our framework is an iterative strategy that explores and exploits diverse viewpoint and lighting variations in the GAN image manifold. The framework does not require 2D keypoint or 3D annotations, or strong assumptions on object shapes (e.g. shapes are symmetric), yet it successfully recovers 3D shapes with high precision for human faces, cats, cars, and buildings. The recovered 3D shapes immediately allow high-quality image editing like relighting and object rotation. We quantitatively demonstrate the effectiveness of our approach compared to previous methods in both 3D shape reconstruction and face rotation. Our code is available at https://github.com/XingangPan/GAN2Shape.
Blockchain: a brief review of Agri-Food Supply Chain Solutions and Opportunities

This article aims at presenting Blockchain and Distributed Ledger Technologies from business perspective (although providing adequate technology context) and, especially, highlighting concrete implementations in Agri-Food Supply Chain, bringing security, transparency and robustness to solutions, and enabling the creation of added value through the provisioning of information to consumers which allow them to understand the origin, the transformation and the transportation of agri-food goods. It also brings some examples of European Programmes and Projects that are supporting innovative solutions to reach the market.
BOIL: Towards Representation Change for Few-shot Learning

Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. It has recently been hypothesized that representation reuse, which makes little change in efficient representations, is the dominant factor in the performance of the meta-initialized model through MAML in contrast to representation change, which causes a significant change in representations. In this study, we investigate the necessity of representation change for the ultimate goal of few-shot learning, which is solving domain-agnostic tasks. To this aim, we propose a novel meta-learning algorithm, called BOIL (Body Only update in Inner Loop), which updates only the body (extractor) of the model and freezes the head (classifier) during inner loop updates. BOIL leverages representation change rather than representation reuse. A frozen head cannot achieve better results than even a random guessing classifier at the initial point of new tasks, and feature vectors (representations) have to move quickly to their corresponding frozen head vectors. We visualize this property using cosine similarity, CKA, and empirical results without the head. Although the inner loop updates purely hinge on representation change, BOIL empirically shows significant performance improvement over MAML, particularly on cross-domain tasks. The results imply that representation change in gradient-based meta-learning approaches is a critical component.
BLOCKCHAIN FUTURES IN CRYPTOCURRENCIES, TRADE AND FINANCE: A PRELIMINARY ASSESSMENT

The study explores whether blockchain technology can change the paradigm of the current financial structure and the balance of power in the international financial system. Accordingly, this study reviews the development of blockchain technology by analyzing China and Venezuela, both of which struggle to harness their technological advancement and to enhance their power in the international realm. We found that Venezuela invests in blockchain technology to create an alternative payment structure for survivability, while China’s desire is to become a global leader in global blockchain technology.
TRAVART: An Approach for Transforming Variability Models

A large number of variability modeling approaches have been developed including feature modeling, decision modeling, and Orthogonal Variability Modeling (OVM). Multiple variants of each approach have been developed, i.e., there are many different types of feature models. The usefulness of variability modeling approaches has been demonstrated for both research and practice, e.g., to support domain analysis or configuration of products. However, industry still develops their own custom solutions to manage variability and academia frequently proposes new approaches. The growing plethora of modeling approaches, often only described in academic papers, makes it very difficult to find, understand, and eventually pick an existing approach for a specific context or (set of) system(s). In this paper, we present and evaluate TRAVART, an approach for transforming artifacts describing variability, such as variability models. TRAVART is based on generic transformation operations and implemented in specific transformation algorithms. Using TRAVART, researchers and practitioners can experiment with and compare different variability models and switch from one modeling approach to another. We present a feasibility study conducted with several feature models, decision models, and OVMs. Based on this study, we conclude that despite some limitations, our approach can correctly transform one model into the other and also supports a full roundtrip. TRAVART is a good basis to develop further transformations for other types of variability models and an important step towards unifying variability modeling.
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. In this paper, we introduce an adaptive temporal fusion network, called AdaFuse, that dynamically fuses channels from current and past feature maps for strong temporal modelling. Specifically, the necessary information from the historical convolution feature maps is fused with current pruned feature maps with the goal of improving both recognition accuracy and efficiency. In addition, we use a skipping operation to further reduce the computation cost of action recognition. Extensive experiments on Something V1 & V2, Jester and Mini-Kinetics show that our approach can achieve about 40% computation savings with comparable accuracy to state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AdaFuse/
Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.
Entanglement of graph states of spin system with Ising interaction and its quantifying on IBM's quantum computer

We consider graph states generated by operator of evolution with Ising Hamiltonian. The geometric measure of entanglement of a spin with other spins in the graph state is obtained analytically and quantified on IBM's quantum computer, IBM Q Valencia. The results of quantum calculations are in good agreement with the theoretical ones. We conclude that the geometric measure of entanglement of a spin with other spins in the graph state is related with degree of vertex representing the spin in the corresponding graph.
Research on online teaching of college teachers under the background of education informatization

With the rapid development of information technology, educational informatization is the popularization and penetration of information technology. At present, online teaching has been implemented all over the world, which is a practical reform of educational informatization. This paper discusses the teaching concept, platform selection, study situation analysis, resource preparation, curriculum implementation, summary and reflection and other aspects of the curriculum under the background of educational informatization, so as to do a good job in the organization and management of online teaching under the background of educational informatization, and lay a foundation for the innovative practice of information-based leading educational concept and education mode.