Reviewerly/exHarmony huggingface dataset:
Symbolizing Visual Features for Pre-training with Unlabeled Images

Multi-layer Transformers, which have shown good performance in natural language processing (NLP), have recently started to be used in multi-modal learning tasks that involve both texts and images. In the NLP part of the multi-modal learning, the approach of pre-training the parameters of Transformers from large unlabeled text data has been shown to contribute to an increase in accuracy. On the other hand, for the image part of the Transformer, there are no reports to show the validity of pre-training, even though, intuitively, the prospect of leveraging knowledge obtained from large amounts of unlabeled image data is appealing. This paper aims to construct a single modal pre-training model based on a Transformer in the image domain for multi-modal learning of texts and images. We have found that, unlike the case of discrete values representing word embeddings, current Transformers have trouble handling continuous values like image features. In order to overcome this limitation, we propose a Transformer with the list of features named SymboList which convert the continuous image features of detected objects into discrete ones by referring to a discrete key list. We demonstrate that our proposed method leads to effective image pre-training and is beneficial to the multi-modal down-stream task.
Development of Soft-Skills in Order to Increase the Professional Motivation of Students in the Modern Economy

The purpose of the research is to identify the problem of soft-skills in the educational process. The authors believe that the development of «soft skills» helps to increase the professional motivation of students of higher educational institutions. In order to identify the role of soft-skills in the professional motivation of students, the authors conducted a study of the professional motivation of students of the Institute of Food Technologies and Design—a branch of State University of Engineering and Economics, Nizhny Novgorod. Within the framework of the research, it was revealed that the interviewed students have a fairly high level of educational motivation. The motive of «acquiring knowledge» is ahead of the motives of «mastering a profession» and «obtaining a diploma», and thus is an analogy for the motive of «becoming a highly qualified specialist». The interviewed students strive to realize themselves in professions where they can acquire new knowledge and skills related to this profession. The formation of soft-skills will help to increase the professional motivation of students. In order to increase the professional motivation of students, it is necessary to develop a program of additional education «Soft-skills Academy». Mastering all sections of the program of additional professional education will allow graduates of the IFTD to contribute to their professional development.
A Blockchain-Based Cryptographic Interaction Method of Digital Museum Collections

This article constructs a cryptographic interaction method MAXP (Museum art exchange protocol) for museum digital collections based on blockchain technology. According to this method, we build a digital collection exchange system on Ethereum to realize the digital collectionâ€™s online exchange between two museums. Compared with the traditional centralized collection digital resource database method, MAXP can avoid the security risks caused by subjective factors and force majeure factors in the exchange process of digital collections, such as hackers and network viruses. In the exchange system we have built, the expression of content covered by digital collections is more convenient, and copyright disputes can be quickly resolved. At the same time, in view of the decentralization and anonymity of the blockchain, a regulatory mechanism has been added to MAXP to avoid fraud, illegal fundraising, money laundering and smuggling. The regulatory mechanism we constructed is a dual receiver public key encryption scheme based on the Diffie-Hellman algorithm and the SM2 elliptic curve public key encryption algorithm. The sender encrypts the collection data, and both receivers can decrypt the messages using their respective private keys. One of the receivers is the museum that obtained the collection information, and the other receiver is the regulator. These two receivers can decrypt simultaneously, and the regulator can regulate the information exchange on the blockchain. The Beijing schemeetarium and the Beijing Museum of Natural History have completed the exchange of collections through the system we have built. The analysis results show that the regulation scheme based on the exchange blockchain system of the museum's digital collections proves to be feasible, with security and expansibility. This new encrypted exchange management method of digital collections in museums can effectively promote the exchange of collections between museums, and is of great significance to the promotion of cultural heritage and the dissemination of scientific knowledge.
HMANet: Hyperbolic Manifold Aware Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted significant attentions in recent years. To model the skeleton data, most popular methods utilize graph convolutional networks to fuse nodes located in different parts of the graph to obtain rich geometric information. However, these methods cannot be generalized to different graph structures due to their dependencies on the input of the topological structure. In this article, we design a novel hyperbolic manifold aware network without introducing a dynamic graph. Instead, it leverages Riemannian geometry attributes of a hyperbolic manifold. Specifically, this method utilizes the Poincaré model to embed the tree-like structure of the skeleton into a hyperbolic space to automatically capture hierarchical features, which may explore the underlying manifold of the data. To extract spatio-temporal features in the network, the features in manifold space are projected to a tangent space, and a tangent space features translation method based on the Levi–Civita connection was proposed. In addition, we introduce the geometric knowledge of Riemannian manifolds to further explain how features are transformed in the tangent space. Finally, we conduct experiments on several 3-D skeleton data sets with different structures, successfully verifying the effectiveness and advancement of the proposed method.
Extraction of Entities in Health Domain Documents Using Recurrent Neural Networks

This paper reviews the subtask “A” of eHealth-KD challenge 2021, using a strategy oriented to the use of recurrent neural networks. In subtask “A” the entities are identified by document and their types (Concepts, actions, predicates, and references). This paper mainly compares various word embedding models, annotation styles in BIO, BILOU, and an own annotation style to distinguish entities composed of more than one word from entities of a single word. Is also proposed a solution based on POS tagging to improve the results of systems that use recurrent neural networks by changing the pre-processing. An evaluation process was performed with the following well-known metrics: precision, recall, and F $$_1$$ .
Blockchain in a Smart City: Its applications and a selection framework

In this chapter, one of the emerging technologies, namely blockchain, is discussed to create the foundation of technology and information management in the peer-to-peer network to enhance the functionalities and reliability of connected systems in smart cities. Three areas are involved in the blockchain discussion, namely (i) definition and overview of blockchain, (ii) features of blockchain, (iii) applications of blockchain in daily life. A case study in blockchain technology selection was also included.
Personalized Information Automatic Recommendation Algorithm E-commerce Application of Big Data Technology

Because the current personalized recommendation algorithm application does not build an incremental update recommendation model to process the big data of the e-commerce platform, the recommendation results cannot be updated in time, which seriously affects the user experience. Therefore, an automatic recommendation algorithm for personalized information of e-commerce based on big data technology is proposed. Firstly, the dissimilarity between users and projects in the platform is calculated, and the calculated dissimilarity matrix is used to construct the data incremental update model; Then, the model is used as a personalized recommendation model to improve the prediction accuracy. Finally, taking e-commerce logistics as the application object, the proposed method is introduced into the application for exploration.
Immersive virtual interactive environment based on hologram technology for youth art education

Arts education for young people (mainly aged 16-18) varies greatly geographically due to differences in economic development between regions. The current situation in arts learning is that high schools' teaching models and thinking need to be more exciting and interactive. With the rise of the 'meta-universe', the use of hologram technology and immersive virtual interactive environments such as AR and VR can make up for the current waste of educational resources due to 'egalitarianism' in the arts education sector. This is also a sign of increasing economic investment in education.Based on several years of experience in high school art education, the author has been able to adapt the teaching equipment to the teaching behaviour, through literature research, field studies and experience summaries, to sort out the most basic functions that teaching should have when designing, and to conduct product research based on existing products on the market, to give certain product function deficiencies and aspects that can be improved. The study also provides some insights into hologram technology and immersive virtual interactive environments for art education.
Find the Unseen Actions: Abnormal Action Recognition

Deep neural networks are gaining increasing traction in action recognition. Existing literature are based on a basic assumption that the test sample belongs to a certain category in the training set. However, this assumption is severely limited in real world where unseen (socalled “abnormal”) events may occur. Most existing methods usually fail to identify abnormal actions, which are usually classified as a normal category. To address these issues, we propose abnormal action recognition that effectively detect unpredictable abnormal actions. Specifically, we treat multiple categories of the training set as normal categories, and the test set contains normal and abnormal categories (not belonging to any of the categories in the training set). We study abnormal action recognition from three aspects: 1) The softmax distribution of the classifier; 2) Mahalanobis distance-based confidence score; 3) Classification feature. Finally, we analyze the experiment in detail and discuss the idea of improving the recognition of abnormal actions. Our codes and results can be verified at https://github.com/zhaijianyang/abnormal-action-recognition .
Classification of URLs Using N-gram Machine Learning Approach

Nowadays, the internet is growing so rapidly in a lightning way that changes our daily behaviors, from online shopping, online learning to online banking and more activities that make our lives easier. However, using such of ways imposed sharing personal informations such as email, password, credit card information etc. Cybercriminals try to find their victims in the cyberspace by tricking the user using the anonymous structure of the internet. Cybercriminals set out new techniques such as phishing, to deceive victims with the use of false websites, in order to collect their sensitive informations. Understanding whether a web page is legitimate or phishing is a very challenging problem that requires our attention. In this work, we propose a new model that classify whether a web page is legitimate or phishing, based on URLs natural language processing and by applying the n-gram model. We analyze the model with different machine learning algorithms and our system achieves an accuracy of 96.41 $$\%$$ with 97 $$\%$$ precision.
Two New Algorithms for Effective Information Hiding in Chest X-Ray Images

Several medical consultations and examinations have been undertaken online due to the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique for concealing information inside a cover medium such as images. This paper presents two new algorithms (kN-LSB and HDCT) to enhance steganography's performance in medical images. The first algorithm introduces a new approach for systematically dispersing a concealed number of bits across multiple separate locations, while the second algorithm enhances the first by incorporating the capacity to process a full-size hidden image. Each algorithm includes three different settings. The experimental results demonstrated a significant improvement over the standard LSB method, since the error of the first algorithm is nearly zero, resulting in a hidden image reconstruction of extremely high quality. In addition, the suggested algorithm has a payload capacity of 100% and a very low cover changing rate. For applications in which a full-size hidden image must be concealed, we recommend the second algorithm, which has a high payload capacity of 98.96%. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed inÂ chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. Another advantage of our proposed methods is that no training datasets are required.
Cross-Project Software Refactoring Prediction Using Optimized Deep Learning Neural Network With the Aid of Attribute Selection

Cross-project refactoring prediction is prominent research that comprises model training from one project database and testing it for a database under a separate project. While performing the refactoring process on the cross project, software programs want to be restructured by modifying or adding the source code. However, recognizing a piece of code for predicting refactoring purposes is remained to be actual challenge for software designers. To date the entire refactoring procedure is highly dependent on the skills and software inventers. In this manuscript, a deep learning model is utilized to introduce a predictive model for refactoring to highlight classes that need to be refactored. Specifically, the deep learning technique is utilized along with the proposed attribute selection phases to predict refactoring at the class level. The planned optimized deep learning-based method for cross-project refactoring prediction is experimentally conducted on open- source project and accuracy found as 0.9648 as comparison to other mentioned state of the art.
A Methodology to Manage Structured and Semi-structured Data in Knowledge Oriented Graph

Data has become fundamental to every business process and research area like never before. To date, one of the main open points of research activities is to manage the data acquired in the field by sensors, logs etc. by modeling the data structures according to the analyzes that will be carried out. In fact, with the advent of Big Data, the need to have a single reference data structure has been reduced, but with modern architectures there is a tendency to generate specific and optimized data structures for the analyzes that will be carried out. In this work we propose an agile data modeling methodology guided by analytics focused on the management of structured and semi-structured data sources.
Interactive Nearest Lattice Point Search in a Distributed Setting: Two Dimensions

The nearest lattice point problem in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> is formulated in a distributed network with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> nodes. The objective is to minimize the probability that an incorrect lattice point is found, subject to a constraint on inter-node communication. Algorithms with a single as well as an unbounded number of rounds of communication are considered for the case <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=2$ </tex-math></inline-formula> . For the algorithm with a single round, expressions are derived for the error probability as a function of the total number of communicated bits. We observe that the error exponent depends on the lattice structure and that zero error requires an infinite number of communicated bits. In contrast, with an infinite number of allowed communication rounds, the nearest lattice point can be determined without error with a finite average number of communicated bits and a finite average number of rounds of communication. In two dimensions, the hexagonal lattice, which is most efficient for communication and compression, is found to be the most expensive in terms of communication cost.
Password Security: Best Practices and Management Strategies

Password security is one of the most troubling issues administrators and users can face as not choosing the right password can lead to accounts being hijacked and identity theft. It has been well established that passwords that include elements of personality to them can be easily cracked using specific methods such as a dictionary attack breaching user account. In addition to this the general public tend to use the same password across various accounts compromising them all and risking attackers hijacking personal details leading to more malicious activities. Typical users will generally follow a standard policy when setting passwords for all accounts by using basic words with no regard to how they can satisfy the basic principles of setting a complex password that cannot be brute forced using basic hacking methods. So how does one strategise a password that can be easily memorable and complex enough so it can’t be hacked? I will present guidelines for the most optimal password security practices to be used to mitigate the risk of accounts being hijacked. An analysis and comparison of studies will be shown that can show strong passwords can be remembered and how to manage them. Different security frameworks will be discussed and password policies will be analysed including the benefits of using multiple forms of passwords where it can bolster the overall security of an account.
An Improved Hunger Games Search Algorithm for Global Optimization

This paper proposes an improved version of the hunger games search algorithm (HGS) based on the opposition-based learning (OBL) and evolutionary population dynamics (EPD) strategies called OEHGS for solving global optimization tasks. The proposed OEHGS algorithm consists of three stages: the first stage generates an initial population and its opposite using OBL strategy; the second stage uses the EPD to prevent premature convergence and stagnation; and the third stage uses the OBL as an additional phase to update the HGS population at each iteration. The opposition-based learning approach is incorporated into HGS with a selecting rate, which can jump out of the local optimum without increasing the computational complexity. The performance of our proposed algorithm was tested through a set of experimental series. The experiments revealed that the proposed algorithm is superior to those of state-of-the-art algorithms in this domain for solving optimization problems.
A Weighted Method Using Normal-View Angle of Three Subsystems in Surface Structured Light 3d Profile Measurement

In surface structured light 3D profile measurement, to achieve higher measurement accuracy for surfaces on which the normal vector varies greatly, we explored a weighted method based on Normal-View angle in the three subsystems that were formed by a binocular structured light measurement system. Here, these subsystems were used to accomplish multiple measurement from different angles and provide initial values. And then every initial value was endowed with weight by the Normal-View angle that was defined by the angle of surface normal vector and view vector of each subsystem. The proposed method has reduced the large error caused by different subsystems and has been indicated the effectiveness by experiment results.
Meerkat: An Algorithm to Reliably Identify Structural Variations and Predict Their Forming Mechanisms

Next-generation sequencing technologies have been widely used to query genetic variants in normal individuals as well as in those with diseases. Large-scale structural variations are a common source of genetic diversity in human population, and some of them have significant contributions to the etiology of diseases. However, the detection of large-scale structural variations from sequencing data remains challenging. Here, we describe Meerkat—an algorithm which can reliably detect structural variations from Illumina short-read sequencing data at basepair resolution. A unique feature of Meerkat is that it can infer the variant forming mechanisms based on the DNA content and features at the breakpoints.
Wavelet-Based Aerial Image Enhancement Tools and Filtering Techniques

Image enhancement procedures have a broad range of options for manipulating images to produce appealing graphical outcomes. The methodology to enhance is determined by the task at hand, image quality, observer characteristics, and viewing conditions. This survey paper provides an overview of resolution enhancement strategies and analyzes their performance. The work also discusses a wide variety of filtering techniques and georectification applications currently on the market. The review shows that image enhancement techniques that do not use wavelets have the downside of missing high-frequency information, which causes blurring. Also, the CWT method almost shifts invariant, resulting in improved efficiency. Combining wavelet transform with other enhancement and filtering methods will yield fewer artifacts related to other methods for hyperspectral satellite images. Similarly, improve the accuracy of a satellite image in terms of MSE and PSNR.
Tracking a Moving Target in 2D Using a Particle Filter Based on Innovative Adaptive Estimation

In this work, a particle filter based on innovative adaptive estimation for tracking a moving target in 2D is proposed. Aiming to mitigate the uncertainty or lack of knowledge of the process and measurement noise covariance matrices, the particle filter is allied to an innovative adaptive estimation. For such purpose, the difference between the theoretical and measured innovation covariances is defined as an approximation that uses the average of a moving estimation window for the innovation sequence calculus. This difference is computed continuously, using innovative adaptive estimation based on the maximum likelihood theory to dynamically adjust the covariances of the particle filter. To illustrate the efficiency and applicability of the proposed filter, simulations are carried out for estimating the state of a moving target considering different scenarios. The simulation results show that the proposed filter performs well in terms of robustness compared to extended Kalman filter and classic particle filter. It is shown that, although the simultaneous adaptation of noise covariance matrices can generate instability in some cases, more accurate estimates can be obtained in others.