Reviewerly/exHarmony huggingface dataset:
Epidemic Exposure, Financial Technology, and the Digital Divide

We ask whether epidemic exposure leads to a shift in financial technology usage and who participates in this shift. We exploit a dataset combining Gallup World Polls and Global Findex surveys for some 250,000 individuals in 140 countries, merging them with information on the incidence of epidemics and local 3G internet infrastructure. Epidemic exposure is associated with an increase in remote-access (online/mobile) banking and substitution from bank branch-based to ATM activity. The temporary nature of the effects we identify is more consistent with a demand channel rather than that of supply with high initial fixed costs. Exploring heterogeneity using a machine-learning driven approach, we find that young, high-income earners in full-time employment have the greatest tendency to shift to online/mobile transactions in response to epidemics. Baseline effects are larger for individuals with better ex ante 3G signal coverage, highlighting the role of the digital divide in adaption to new technologies necessitated by adverse external shocks.
LOKI: A Practical Data Poisoning Attack Framework against Next Item Recommendations

Due to the openness of the online platform, recommendation systems are vulnerable to data poisoning attacks, where malicious samples are injected into the training set of the recommendation system to manipulate its recommendation results. Existing attack approaches are either based on heuristic rules or designed against specific recommendation approaches. The former suffers unsatisfactory performance, while the latter requires strong knowledge of the target system. In this paper, we propose a practical poisoning attack approach named LOKI against blackbox recommendation systems. The proposed LOKI utilizes the reinforcement learning algorithm to train the attack agent, which can be used to generate user behavior samples for data poisoning. In real-world recommendation systems, the cost of retraining recommendation models is high, and the interaction frequency between users and a recommendation system is restricted. Thus, we propose to let the agent interact with a recommender simulator instead of the target recommendation system and leverage the transferability of the generated adversarial samples to poison the target system. We also use the influence function to efficiently estimate the influence of injected samples on recommendation results, without re-training the models. Extensive experiments on multiple datasets against four representative recommendation models show that the proposed LOKI outperformances existing method. We also discuss the characteristics of vulnerable users/items, and evaluate whether anomaly detection methods can be used to mitigate the impact of data poisoning attacks.
Recent Developments of an Optimal Control Approach to Nonrigid Image Registration

The Variational Principle (VP) forms diffeomorphisms (non-folding grids) with prescribed Jacobian determinant (JD) and curl under an optimal control set-up, which satisfies the properties of a Lie group. To take advantage of that, it is meaningful to regularize the resulting deformations of the image registration problem into the solution pool of VP. In this research note, (1) we provide an optimal control formulation of the image registration problem under a similar optimal control set-up as is VP; (2) numerical examples demonstrate the confirmation of diffeomorphic solutions as expected.
Research on Application of Artificial Intelligence Technology in Education

With the rapid development of science and technology in China, the continuous progress of artificial intelligence technology has driven the development of all walks of life. The effective application of artificial intelligence system in the field of education and teaching presents a very significant application advantage, and has a profound impact on the whole field of education and teaching. The deep integration of artificial intelligence technology and education has expanded the function of education, improved teaching efficiency and education management services, and promoted the reform of education and teaching. This paper discusses the application of artificial intelligence technology in education and teaching by introducing the meaning of artificial intelligence, its development status in China, and the relationship between artificial intelligence and education and teaching.
Deep Knowledge Tracing on Skills with Small Datasets

Deep Knowledge Tracing (DKT), as well as other machine learning approaches, is biased toward data used during the training step. Thus, for problems where we have few amounts of data for training, the generalization power will be low, and the models will tend to work well on classes containing many samples and poorly on those with few. This situation is quite common in educational data where some skills are very difficult to master while others are very easy. As a result, there will be less data on students who correctly answered questions related to difficult skills, but also on those who provided incorrect answers to questions related to easy skills. In those cases, the DKT is unable to correctly predict the student’s answers to questions associated with these skills. To improve DKT performance under these conditions, we have developed a two-fold approach. Firstly, the loss function is modified so that some skills are masked to force the model’s attention on those that are difficult to generalize. Secondly, to cope with the limited amount of data on some skills, we proposed a hybrid architecture that integrates a priori (expert) knowledge with DKT through an attentional mechanism. The resulting model accurately tracks student Knowledge in the Logic-Muse Intelligent Tutoring System (ITS), compared to the traditional Bayesian Knowledge Tracing (BKT) and the original DKT.
Use of AI for Fault Identification in the E &amp; P Life Cycle

Summary The use of Artificial Intelligence (AI) for seismic interpretation has been steadily growing over the past couple of years. AI is ideally suited to fault identification since fault picking is time consuming, often considered boring and the results can be controversial with different human interpreters having quite different views based on the same data. We will show examples from the use of a commercially available Deep Learning approach for fault identification based on pre-trained Convolutional Neural Networks on datasets from exploration, development and production projects and attempt to illustrate the value brought to the interpretation by the AI and the limitations of its use in this context.
Representing Vietnamese Traditional Dances and Handling Inconsistent Information

The first part of this paper proposes an application based upon a Vietnamese traditional Dance (VTD) ontology used for storing, searching, and reasoning with VTD knowledge and multimedia data available on the Web. We use lightweight ontologies, with prioritized assertional bases, to represent expert knowledge. We focus on the detection of movements in the ethnic VTD, where prioritized assertional bases contain motion present in the data. In general, the automated classification of the movements leads to inconsistency problems. In the second part of this paper, we propose a practical inconsistency-tolerant method for totally ordered information that goes beyond the so-called non-defeated inference.
Methodology for Linguistic Education at Digital Age: Case Study for Regional Universities

The aim of the article is the review for the experience of regional universities in the RF for teaching Master and postgraduate students and integrating digital humanities in the students’ methodological skills and culture development in the linguistic area. The example is represented on two levels of education in Russia including master’s and postgraduate studies. A set of methods used to achieve the goal and foster the IT in philology and linguistic is represented. The designed model includes the study of scientific literature; the analysis of curricula and programs; the modeling and simulation of linguistic and linguo-methodological tasks based on the collaborative work for the project to create and update the author’s university site “Virtual Museum of the Samara Linguistic School”. The conducted pedagogical experiment has shown that the integration of digital tools in the field of linguistics increases the efficiency both at the master’s level and postgraduate levels at the Russian Federation and its education system and represents the necessity of digital humanities for society.
A New Fuzzy Convolutional Neural Network for Face Recognition

The human sense of sight is surprisingly precise and complex. Therefore, various researchers have always tried to simulate the human visual system by computer systems, to be able to design systems in the field of machine vision that like humans see, distinguish and classify images. Despite all the efforts that have been made in this field over the decades, and of course the very remarkable successes that have been achieved, it seems that there is still a long way to go in this field. Recently, methods called deep learning have revolutionized machine vision and come much closer to human vision. Deep learning methods use neural networks that try to discover patterns within the image without human intervention and to learn that. This is in stark contrast to machine learning systems. In machine learning systems, patterns are selected and extracted by humans, and since human labor may be erroneous for any reason, selecting inappropriate patterns can lead to incorrect results. Deep learning methods have taken an effective step towards image recognition by eliminating this problem. One of the most popular algorithms in this field is the convolutional neural network algorithm or model. This model consists of several layers. Therefore, it receives an input image and after passing it through several layers and discovering the pattern in the image, it can recognize the content of the image and determine the classification of the image. This study aims to provide a model of neural-fuzzy, based on convolutional neural network algorithm. In this research, we use the positive advantages of deep learning methods and fuzzy inference systems and present a new model of their application to Classify authorized and unauthorized Persons. We also compared our proposed method results to an existing novel method FaceNet. The results of the implementation of the above model show the efficiency and success of this system .
Prediction Model of Optimized Grey Neural Network Based on Mind Evolutionary Algorithm

In order to improve the prediction mode accuracy of grey neural network, a prediction model of optimized grey neural network based on mind evolutionary algorithm (MEA) is proposed. Firstly, using MEA with extremely strong global search ability to train and optimize the grey parameters of grey neural network. Secondly, calculate the weights and thresholds of grey neural network using the optimized grey parameters. After comparative study of the prediction mode of MEA optimized grey neural network(MEA-GNNM), genetic algorithm optimized grey neural network (GA-GNNM) and not optimized grey neural network (GNNM) on the prediction for silicon epitaxial layer thickness, the results show that MEA-GNNM has higher prediction accuracy than other two models.
A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines

Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification.However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters.To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM).The framework consists of a feature extraction module and a classification module.The feature extraction module first maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through cross-connections; then, it uses depthwise separable convolution to replace part of the original convolution to reduce the number of parameters in the module; The classification module uses a soft margin support vector machine for classification.The results on 6 different visual datasets show that LC-CKMSVM obtains better classification accuracies on most cases than the other five models.
Effect of Environment, Safety, and Health on Employee Performance

This study aims to determine whether there is an influence on the work environment, occupational safety, occupational health, and employee performance. Occupational health and safety on employee performance at the Bekasi City Regional General Hospital (RSUD). The method used in this research is a quantitative approach. The population in this study were all employees as many as 55 respondents with sampling using random sampling technique by calculating the PLS formula on a sample of 55 respondents. The results showed that there was a significant influence between the independent variables, namely the work environment, occupational safety and health, on the dependent variable, namely employee performance.
Classical Visual Question Answering

VQA has received considerable attention from both the computer vision and the natural language processing research communities in recent years. Given an image and the corresponding question in natural language, a VQA system is required to comprehend the question and find the essential visual elements in the image to predict the correct answer. In this chapter, we first introduce the prevalent datasets for VQA tasks, such as the COCO-QA, VQA v1 and VQA v2 datasets. Subsequently, we present a detailed description of several classical VQA methods classified as joint embedding methods, attention-based methods, memory networksMemory networks and compositional methods.
A Privacy-Preserving Framework for Conducting Genome-Wide Association Studies Over Outsourced Patient Data

Due to the sheer volume of data, data owners (e.g., hospitals or other data collectors) tend to outsource their data to cloud service providers (CSPs) for the purpose of storage and analytics. However, privacy concerns about genomic and phenotype data significantly limit the data owners&#x2019; choice. In this work, we propose the first solution, to the best of our knowledge, that allows a CSP to perform efficient and privacy-preserving search and analysis over encrypted genomic and phenotype data that is multi-tenant, i.e. owned by multiple hospitals. We first propose an encryption mechanism for phenotype data, where each data owner is allowed to encrypt its data with a unique secret key. Moreover, the ciphertext supports privacy-preserving search and, consequently, enables the identification of the case and control groups for a genome-wide association study (GWAS) without any privacy violations. Furthermore, we provide a per-query based authorization mechanism for a client to access and operate on the data stored at the CSP. Additionally, we apply multi-key fully homomorphic encryption to encrypt genomic data and show how to compute GWAS statistics (e.g., chi-square distribution test) over the ciphertext of individuals in the identified case and control groups. Thus, for the first time, the proposed scheme provides privacy-preserving computation for the entire GWAS pipeline. Finally, we implement the proposed scheme and run experiments over a real-life genomic dataset to show its effectiveness. The result shows that the proposed solution is capable to efficiently identify the case/control groups and subsequently conduct GWAS on the identified case/control groups.
A Triangular Inverse Element Coupling Mixed Interpolation of Tensorial Components Technique for Shape Sensing of Plate Structure

This novel inverse element, named as iMITC3+, is proposed by coupling the inverse finite element method (iFEM) and mixed interpolation of tensorial components (MITC) technology for real-time reconstructing the structural deformation based on discrete strain measurement. This reconstructed method is established through combining the kinematics of degenerate solid element and enhanced 3-node triangular MITC element. Furthermore, the approach containing the cubic bubble function inside the element is less sensitive to deformation under bending-dominated and thus alleviates the shear locking for shape sensing of thin plate structure. In addition, the method is suitable for geometrically arbitrarily plate structures, due to that the rotation degree of freedom of the element is represented by the nodal director vector. The high accuracy and practicability of iMITC3+ has been verified by numerical and experimental results through analyses of plate model subjected to different loading.Funding: MISSINGDeclaration of Interest: MISSING
Serious Video Game Interaction for Collaborative Problem Solving in Elementary School

Teamy is a serious video game in a mobile application format, which was tested and evaluated in terms of interface usability, as well as its impact on collaborative work for problem solving at elementary level. To achieve this, research and development methodology was used in this project, pursuing the construction, evaluation, and implementation of educational products. The first tests and evaluations made by experts regarding usability and functionality allowed improvements to be made during the development of the serious video game, which allowed a correct implementation with end users. These users, after using Teamy, were able to carry out a usability test to also provide their feedback. Subsequently, evaluations pertinent to collaborative work in relation to the use of Teamy were made, such as questionnaires to end users, class observation and a focus group. The results of the various evaluations were favorable since end users validated the video game both in its interface usability and its use as a tool that facilitates collaborative work.
Public Auditing for Multi-Version Cloud Data with Storage Efficiency and Version Dynamics

Multi-version data storage has been an increasingly popular service in cloud computing, which is shown to support flexible rollback recovery of cloud data. However, how to verify the integrity of this kind of data efficiently still remains open. As an attempt at addressing this issue, this paper presents a novel public auditing scheme for multi-version data in the cloud to achieve secure and efficient verification for all the versions of cloud data, which employs a three-pronged approach. First, we propose an incremental storage table (IST) to store all data versions in an efficient and low-cost manner; second, we develop a dynamic version control table (DVCT) to support efficient update for the multi-version data; finally, we design a custom-built tag-generation algorithm that can achieve authentication for all versions with only one tag. We formally prove the security of the presented scheme and evaluate its performance through comprehensive experiments and comparisons with state-of-the-art ones. The experimental results demonstrate that our scheme can effectively achieve public auditing for multi-version data while providing excellent security and outperforming state-of-the-art schemes in terms of computation and communication costs.
Spatial Image Steganography Incorporating Adjacent Dependencies

Most of the existing model-based image steganographic schemes in spatial domain assume the independence among adjacent pixels, thus ignores the embedding interactions among neighbouring pixels. In this paper, we propose a new image steganographic scheme by taking advantages of the correlations between each pixel and its eight-neighbourhood and determine the embedding cost of the pixel through the minimization of the KL-divergence between the cover and stego objects. Experimental results demonstrate that our scheme show superior or comparable performance to two state-of-the-art algorithms: HILL and MiPOD in resisting steganalysis detectors.
A novel secured ledger platform for real-time transactions

The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided.
Adaptive Gaussian Mixture Method for Uncertainty Propagation in Space Surveillance

The orbital uncertainty propagation problem is treated, where the uncertainty concerning an object position and velocity is propagated over a long time interval because observations are scarce. This paper focuses on the Gaussian mixture description of the uncertainty and proposes a method that adaptively changes the number of mixture components to represent the uncertainty efficiently. The proposed method uses the mean square error-based measure of nonlinearity to generate a decision on whether the mixture components should be split in order to preserve the fidelity of the uncertainty description. Further, the paper analyzes the performance of four local propagators, which propagate individual mixture components in time. The performance analysis is accomplished using a low-earth orbit scenario.
Image Forgery Detection Using CNN and Local Binary Pattern-Based Patch Descriptor

This paper aims to propose a novel method to detect multiple types of image forgery. The method uses Local Binary Pattern (LBP) as a descriptive feature of the image patches. A uniquely designed convolutional neural network (LBPNet) is proposed where four VGG style blocks are used followed by a support vector machine (SVM) classifier. It uses ‘Swish’ activation function, ‘Adam’ optimizing function, a combination of ‘Binary Cross-Entropy’ and ‘Squared Hinge’ as the loss functions. The proposed method is trained and tested on 111,350 image patches generated from phase-I of IEEE IFS-TC Image Forensics Challenge dataset. Once trained, the results reveal that training such network with computed LBP patches of real and forged image can produce 98.96% validation and 98.84% testing accuracy with area under the curve (AUC) score of 0.988. The experimental result proves the efficacy of the proposed method with respect to the most state-of-the-art techniques.