Reviewerly/exHarmony huggingface dataset:
Tackling MeSH Indexing Dataset Shift with Time-Aware Concept Embedding Learning

Medical Subject Headings (MeSH) is a controlled thesaurus developed by the National Library of Medicine (NLM). MeSH covers a wide variety of biomedical topics like diseases and drugs, which are used to classify PubMed articles. Human indexers at NLM have been annotating the PubMed articles with MeSH for decades, and have collected millions of MeSH-labeled articles. Recently, many deep learning algorithms have been developed to automatically annotate the MeSH terms, utilizing this large-scale MeSH indexing dataset. However, most of the models are trained on all articles non-discriminatively, ignoring the temporal structure of the dataset. In this paper, we uncover and thoroughly characterize the problem of MeSH indexing dataset shift (MeSHIFT), meaning that the data distribution changes with time. MeSHIFT includes the shift of input articles, output MeSH labels and annotation rules. We found that machine learning models suffer from performance loss for not tackling the problem of MeSHIFT. Towards this end, we present a novel method, time-aware concept embedding learning (TaCEL), as an attempt to solve it. TaCEL is a plug-in module which can be easily incorporated in other automatic MeSH indexing models. Results show that TaCEL improves current state-of-the-art models with only minimum additional costs. We hope this work can facilitate understanding of the MeSH indexing dataset, especially its temporal structure, and provide a solution that can be used to improve current models.
Mutation operators for Genetic Programming using Monte Carlo Tree Search

Expansion is a novel mutation operator for Genetic Programming (GP). It uses Monte Carlo simulation to repeatedly expand and evaluate programs using unit instructions, which extends the search beyond the immediate – often misleading – horizon of offspring programs. To evaluate expansion, a standard Koza-style tree-based representation is used and a comparison is carried out between expansion and sub-tree crossover as well as point mutation. Using a diverse set of benchmark symbolic regression problems, we prove that expansion provides for better fitness performance than point mutation, when included with crossover. Expansion also provides a significant boost to fitness when compared to GP using crossover only, with similar or lower levels of program bloat. Despite expansion’s success in improving evolutionary performance, it does not eliminate the problem of program bloat. In response, an analogous genetic operator, reduction, is proposed and tested for its ability to keep a check on program size. We conclude that the best fitness can be achieved by including these three operators in GP: crossover, point mutation and expansion.
Application of Artificial Intelligence in Higher Vocational English Teaching in the Information Environment

With the continuous deepening and development of the information environment, artificial intelligence (AI) has been widely popularized and applied in college English teaching, which not only greatly broadens the learning resources of learners, enriches their learning methods and contents, but also provides them with a more suitable and extensive platform for English skills training. In addition, AI can also develop and recommend targeted learning methods and corpus for different learners based on their actual conditions, so as to improve their learning efficiency. Big data, cloud computing, language recognition, deep learning, and other artificial intelligence technologies are widely used in the field of education and teaching, which not only changes students’ traditional learning methods, but also promotes the innovation of teachers’ teaching methods and teaching functions. The application of artificial intelligence technology, especially speech recognition, language translation, language testing, and other technologies in the language service industry, has brought new opportunities and challenges for language teaching in Colleges and universities.
DRecPy: A Python Framework for Developing Deep Learning-Based Recommenders

Frameworks that aid the development of Recommender Systems (RSs) are extremely important, since they reduce their development cost by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models and reproducible evaluations. This work introduces a new framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with these existing challenges. Our evaluation procedure ensures that RSs developed using this new approach are consistent and extensible, by analysing their predictive performance and certain characteristics of their implementation.
A Bidirectional Diffusion Based Image Encryption Scheme for Color Images

The goal of this work is to develop an encryption scheme for secure transmission for colour images over untrusted channel. A bidirectional diffusion based encryption technique for 8-bit RGB colour image is performed. The encryption process is used to protect the image in transit. Also the encryption steps will be conducted using a fixed block size of 8 × 8 which will enhance the robustness against cipher text only attacks. The performance analysis of encryption scheme is conducted using various tests and observed that the proposed algorithm is robust against various cryptanalytic attacks.
Research on dynamic load balancing of data center network based on openflow technology

Firstly, this article recommends the principle of DLB algorithm, then describes the DSLB algorithm designed for the improvement of DLB algorithm, and proposes a dynamic scheduling load balancing algorithm on account of data center network fat tree topology. This algorithm mainly improves the DLB load balancing algorithm to reduce the possibility of network congestion caused by DLB algorithm in the real data center network environment. According to the available bandwidth of all possible upload paths and send paths, the DSLB algorithm chooses the best transport path for the traffic, so as to better solve the problem of local network congestion caused by DLB algorithm the traffic load balance of the core network provides a better solution. This paper analyzes and compares the performance of DLB and DSLB, and proves that the load balancing effect of DSLB is better.
Alternative Teaching Classes in Chemistry as a Way to Increase Student Motivation to Learn

Currently, much attention is paid to the development of students ' intellectual abilities. This problem is particularly acute in Universities that provide training in the applied bachelor's degree system. The use of interactive and alternative types of educational activities is proposed, which increases the motivation for learning, stimulates search and research activities. On the example of the chemical block of disciplines, a scheme for building a training course that includes alternative types of classes is proposed. Methods that improve the quality of education are considered; control points that allow you to evaluate the dynamics of learning the discipline.
GAPER: Gender, Age, Pose and Emotion Recognition Using Deep Neural Networks

Facial emotion recognition is identifying human emotion from facial expressions. As the concept is gaining wide popularity with time, researchers are trying to expand the horizon by providing efficient techniques to extract more knowledge from facial features. Hence, this paper presents a multiple face detection system, which is capable of analyzing human facial features for predicting human emotion, pose, gender and age. To the best of our knowledge, no single paper presents a system capable of detecting all these features together in a single frame. Haar cascades and deep convolutional neural networks form the backbone of the entire work. A support of a Web application using flask is also provided, which will enable one to implement the work on another computer, in the same network, via a Web browser. The experimental results of the system provide an accuracy of 68.33%.
An Assessment Model to Establish the Use of Services Resources in a Cloud Computing Scenario

When a system or application is designed to run on the Cloud, the scope on storage, users, infrastructure needs, are set by the use, based on the practical environment. It is necessary to replace practices based on experiences and take into account the measurement practices offered by Quality of Service. The main goal of this chapter is to present an assessment model of the availability and efficiency of the existing applications in Cloud Computing to establish their priority of use through statistical simulation and graph models. This requires an analysis of the concerns of the applications available in the Cloud Services. This model is projected as a guide that provides predictive parameters for its evaluation and availability based on the operation of applications from the point of view of user. To create this model, it is necessary to take into account an analysis of the potential risks during the execution of the application and the data analysis query provided to users, in order to efficiently manage resources in an organization.
Cognitive Fuzzy-Logic Modeling Tools to Develop Innovative Process Management Procedures for Scientific-Industrial Clusters

Making informed decisions at the strategic level is rather complicated and requires a methodological and instrumental base due to the substantial size and heterogeneity of innovative scientific and industrial integration formations. The paper shows existing limitations on the tools used. The authors argue that their improvement with the use of fuzzy logic will enhance the effectiveness of decisions on the development of the region. Thus, the aim of the study is to develop a methodology for managing innovative processes of scientific and industrial structures based on the fuzzy sets theory. This will enable a more complete and clear representation of socio-economic systems. In this regard, it seems most appropriate to integrate fuzzy algorithms with the decision support system in the subject area of this work. To confirm the hypothesis, the authors describe the mechanism developed for managing innovation processes within a regional scientific and industrial cluster, using elements of the neural simulation framework. As an example confirming the hypothesis of the study, a feasibility analysis for the implementation of an innovative project within the cluster in the territory of the Smolensk region is given.
Evaluating Heterogeneous Ensembles with Boosting Meta-Learner

In this paper, heterogeneous ensemble of classifiers is evaluated and the outputs are integrated by a boosting meta-learner. Both ADABOOST and XGBOOST are tried for the meta-learning stage, and XGBOOST performed best. The heterogeneous ensemble consists of a diverse set of base classifiers—k-nearest neighbors, logistic regression, Support Vector Machines (linear, Gaussian kernels), Random forest of decision trees, and Naïve Bayes classifier. Smaller ensembles are also hierarchically formed by removing the weak learner in every stage. The Entropy of base classifier predictions is computed to identify the presence of weak learners. The predictions of the base classifiers are learnt by the boosting meta-learner using a 9:1 split of the training data, where 9 parts are used for training the base classifiers and 1 part for obtaining the ensemble predictions and training the meta-learner. A 10-fold cross-validation is introduced to avoid bias. Experimental results show higher scores on evaluating the Human Action Recognition (HAR) smartphone dataset using our ensemble model as compared to the other state-of-the-art models.
Characterizing outdateness with technical lag

Background: Nowadays, many applications are built reusing a large number of components, retrieved from software collections such as npm (JavaScript) or PyPi (Python). Those components are built in their corresponding upstream repositories, where they are being developed. This architecture of reusing causes some constraints on how much outdated is an application when it is deployed in production environments.
Research on Face Image Morphing Based on Automatic Feature Detection

Image morphing refers to the technology that can realize the transformation of the source digital image to the target digital image in a smooth transition way. Among them, the most representative is to deform the human face. This paper implements a morphing method based on the Dlib library for automatic detection of facial feature points, including automatic feature detection, warp image and generalized morphing. This method can automatically calibrate the key points of the face more accurately, and use the Delaunay triangulation mesh deformation algorithm to achieve face image morphing. Experiments on multiple pairs of images prove the effectiveness and feasibility of the method.
An Improved Design Scheme for Perceptual Hashing Based on CNN for Digital Watermarking

Digital watermarking technology is used extensively in the field of digital rights management. However, there are a few problems when it comes to making effective use of digital watermarking. First, for conventional digital watermarking, a digital image is used only as a carrier for embedded watermarking information, and as this information may be diverted to other images, the watermark information needs to be generated based on the original image. Second, after the original image is modified/edited, the watermark information needs to prove that it is from the original image. Third, multiple digital watermarks need to be stored and managed without depending on trusted third parties. In an earlier work, we proposed a digital rights management system based on digital watermarking, blockchain, and perceptual hashing to resolve these issues. However, because we used conventional perceptual hashing, we could not draw sufficient conclusions about the first and second problems. In order to obtain a stable digest message of an image for digital watermarking, we here propose a new construction method for perceptual hashing using a convolutional neural network (CNN). In the proposed method, we first construct a machine-learned CNN for accepting an image that we want to take the perceptual hash value. The perceptual hash value is the cryptographic hash value of the weights that make up the CNN. We then verify that the reconstructed CNN can guarantee the hash value used when obtaining the hash value, and confirm that the image to be verified is accepted and is the perceptual hash value of this image.
Weighted Non-negative Matrix Factorization for Image Recovery and Representation

Non-negative matrix factorization and its variants cannot learn an effective subspace from the dataset corrupted by outliers. In this article, we propose a robust non-negative matrix factorization approach, called weighted non-negative matrix factorization, which can both recover the corrupted data space and learn a more effective subspace from the corrupted data space. In the proposed method, introducing a weighted graph, which uses Boolean values to mark noise points, while a clean data space and subspace can be achieved by the unlabel data points. The proposed problem can be formulated as a nonconvex optimization problem, which can be optimized by the multiplicative update methods. Taking the face data polluted by Salt and Pepper noise as an example, the effectiveness of the proposed method in image recovery and low-dimensional representations learning is verified.
Generalized Net Model for Creating and Evaluating of Educational Content

The research expounded in this paper is a continuation of previous investigations into the modelling of information flow with a typical university. A generalized net model which describes the process of creating and evaluation of educational content using a set of criteria is constructed. The example for education of medical students in diabetes mellitus and possibilities for venous thrombotic disease is given.
The Steganographic Approach to Data Protection Using Arnold Algorithm and the Pixel-Value Differencing Method

Steganographic data transformation is an effective means of ensuring the confidentiality and integrity of information resources, so developing the methods for improving the reliability and authenticity of steganographic systems is a promising research area. In the article we propose two approaches for embedding secret information into a BMP image by changing the difference between its pixel values. Both approaches involve the prior use (before embedding additional information) of Arnold's transformation to rearrange the pixels of the image. In the first approach, the Arnold transform is applied to the entire color matrix of the image, in the second - the image matrix is broken into blocks, and then twice do the Arnold transformation: to change the order of the blocks themselves and the sequence of pixels inside blocks. As a result, the pixels with the embedded information will be located in the image chaotically and evenly. The application of developed algorithms does not change the visual image quality, but complicates the fact of hidden information detection.
Long- and Short-Term Preference Model Based on Graph Embedding for Sequential Recommendation

As sequential recommendation mainly obtains the user preference by analyzing their transactional behavior patterns to recommend the next item, how to mine real preference from user's sequential behavior is crucial in sequential recommendation, and how to find the user long-term and short-term preference accurately is the key to solve this problem. Existing models mainly consider either the user short-term preference or long-term preference, or the relationship between items in one session, ignoring the complex item relationships between different sessions. As a result, they may not adequately reflect the user preference. To this end, in this paper, a Long- and Short-Term Preference Network (LSPN) based on graph embedding for sequential recommendation is proposed. Specifically, item embedding with a complex relationship of items between different sessions is obtained based on graph embedding. Then this paper constructs the network to obtain the user long- and short-term preferences separately, combing them through the fuzzy gate mechanism to provide the user final preference. Furthermore, the results of experiments on two datasets demonstrate the efficiency of our model in Recall@N and MRR@N.
The scenographic potential of immersive technologies: virtual and augmented reality at the Prague Quadrennial 2019

A wide range of works made with virtual and augmented reality technologies was on display at the 2019 Prague Quadrennial. This article discusses these works in the context of a renewed wave of research interest in these technologies. I argue that virtual and augmented reality provides a new paradigm for scenographic practice that differs from other digital media. Furthermore, I contend that a scenographic perspective can provide insights into how immersive technologies might be utilised to create affective audience experiences. Through a description of my own experiences of the works at PQ, I consider the scenographic environments that immersive technologies make possible, the specific affordances that they offer scenographic practice, and the attendant materialities of virtual environments. Rather than focus on presence or immersion in virtual environments as primary goals of immersive technologies, I suggest that a scenographic approach might instead foreground the porous boundaries between different orders of reality or ‘worlds’, and between bodies and worlds.
Design and Practice of Machine Learning Course Based on CDIO and Student Behavior Data

Machine learning has a wide range of applications in many fields, and a good application prospect makes it more urgent to carry out machine learning teaching. In the process of building machine learning courses, this paper not only follows the CDIO teaching concept, but also uses the student behavior data as the real data source of the practice link. Through refined theoretical analysis, real experimental cases and complete development process, students will master the application skills of machine learning.
Research on the Data Management and Decision-Making Method of the Intelligent Substation System Based on SCD

With the construction of the intelligent substation, it is more important to realize data management power transformation in intelligent substations. In this paper, a data management and decision-making method of the intelligent substation system is designed. Such method manages the system configuration documents (SCD) in the intelligent substation. Through the SCD content change of the online monitoring intelligent substations, an assistant decision-making method is provided.
Neural Network-based Inherently Fault-tolerant Hardware Cryptographic Primitives without Explicit Redundancy Checks

Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is fault tolerant by its construction and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show “some degree” of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to “high degree” (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8×8 AES SBox and 4×4 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.
Automating Continuous Planning in SAFe

The Scaled Agile Framework (SAFe) is a popular realisation of the agile methodology for large organisations. It is widely adopted but challenging to implement. We describe a new tool which automates aspects of the SAFe PI Planning process to enable continuous planning and facilitate collaboration between remote teams.
Study of Performance Measures and Energy Consumption for Cloud Computing Centers Based on Queueing Theory

Abstract Cloud computing, as an effective method to provide computing resource and service to users on demand, is attracting more and more attention. This paper establishes a GI/G/m queue to characterize the cloud computing centers with general arrival process and service times. We analyze the main performance measures such as the mean waiting queue length and average waiting time of service requests in the cloud computing system. Besides, the energy consumption model of the cloud computing center is established. The results show that increasing the service rate can reduce the average waiting time, the quantity of running servers needs to be varied with the arrival rates of service requests to meet the satisfaction of users. The total energy consumption is connected with the arrival rate, service rate and the quantity of servers in the cloud computing center, but it has nothing to do with the coefficient of variation squared of the interarrival times and service times.