Reviewerly/exHarmony huggingface dataset:
Formation and Development of Library Education in the Republic of Kazakhstan in the First Half of the Twentieth Century

The article considers the initial stages of development of library and information education in the Republic of Kazakhstan in the early years of Soviet power, as well as in the pre-war period and during the Great Patriotic War. The present work reveals the issues of formation of the library education system in Kazakhstan, the evolution of forms of training organization, the development of personnel potential of the library sector. The study of the history of library education is of undoubted interest, since it allows better understanding of the processes of socio-economic and cultural development of the country. This is due to the increased attention of society to the problems of culture and, in particular, libraries as necessary socio-cultural institutions, to the efficiency of their work and the quality of training of library specialists. In the course of research, the author studied published and unpublished sources, namely: archival documents, research studies (dissertations, monographs), publications in periodicals and continuing publications. The analysis of sources, to one degree or another touching upon the issues of training the library specialists, showed that from the earliest stages of development of library education, the society set serious tasks for it. The article focuses on the organization of library education, various forms of training and advanced training of library specialists – groups, seminars, courses and conferences. The author characterizes the work of associations of library workers on the increase of personnel potential of the sector and methodological support of libraries. The article reveals the main problems and achievements associated with the organization and work of library technical schools. The author considers the evolution of library education in the context of cultural, historical, socio-economic and political conditions for the development of library sector in the Republic of Kazakhstan.
Leveraging Real-Time Simulation and Collaboration Platform for Project-Based Learning: Case Study of Astana IT University

Project-based learning is a teaching approach designed for active learning processes where students produce solutions to real-life problems. In this paper, we describe an experience in organizing complex project-based student learning during quarantine using a real-time simulation and collaboration platform. During 10 weeks of training, students managed to create a virtual three-dimensional model of the university on the basis of a real building. The use of new technology from Nvidia enabled all participants of the training to work together with a complex three-dimensional project in real-time. This approach of teaching made it possible to effectively monitor the progress of students and correct their mistakes in time. Data gathered during the project-based learning can be used for applying appropriate analytical techniques and detecting insights of collaborative learning.
Face Identification Under Disguise and Makeup Based on Hybrid Deep Learning

Facial recognition has been broadly used in advanced intelligent systems (i.e: smart video surveillance, intelligent access control system, and online payment). The performance of existing algorithms for automatic facial recognition is hampered by various covariates like pose variations, face aging, disguises, and makeup. Disguises and makeup are especially used to intentional or unintentional changes facial appearance to either hide one's personal identity or impersonate someone's different identity. While new algorithms continue to improve performance, most face recognition systems are liable to failure when disguised or makeup altered, which is one of the most challenging factors to overcome. With enormous capability and promising results, deep learning technology becomes attracted to the greatest attention to the research in a diversity of computer vision tasks. In order to overcome this problem, the database of disguised and makeup faces (DMFD) is used. In this paper, face features are extracted by Linear Discriminant Analysis (LDA). Facial recognition is done by using proposed hybrid-deep learning Classifier for more precise feature learning. Also, we compared the proposed method with two pre-trained models (AlexNet and VGG16). The Experimental results taking after implementation and testing showed the effectiveness of the proposed system provided better precision by (94%)
Recognition of power quality disturbance based on artificial bee colony algorithm to optimize kernel extreme learning machine

Aiming at the problem of classification and recognition of power quality disturbance signals, this paper proposes an artificial bee colony algorithm to optimize the classifier model of the kernel extreme learning machine. First, the wavelet transform is used to extract the features of the power quality disturbance signal simulated by Matlab, and then the traditional back-propagation (BP) neural network feedforward neural network is used for classification and recognition. Due to the inherent limitations of the algorithm, the network training calculation is large, the calculation is complicated, and the time is long. Disadvantages, and the proposal of the extreme learning machine just solves this problem, but the accuracy of the classification cannot meet the requirements, and the network needs to be optimized. Therefore, this paper proposes to use the artificial bee colony algorithm to optimize the nuclear extreme learning machine, and classify and recognize the disturbance signal under the optimal parameters of the nuclear extreme learning machine. The simulation results show that after the artificial bee colony algorithm is optimized, the correct rate of classification and recognition has increased by nearly 20%, and the misjudgment rate has dropped to about 3%.
A Blockchain-Enabled Federated Learning System with Edge Computing for Vehicular Networks

Machine learning (ML) algorithms are essential components in autonomous driving. In most existing connected and autonomous vehicles (CAVs), a large amount of driving data collected from multiple vehicles are sent to a central server for unified training. However, data privacy and security are not well protected during the data sharing process. Moreover, the centralized architecture has some inherent issues, such as single point of failure, overload requests, intolerable delay, etc. In this article, we propose Bift: a fully decentralized machine learning system combined with federated learning and blockchain to provide a privacy-preserving ML process for CAVs. Bift enables distributed CAVs to train machine learning models locally using their own driving data and then upload the local models to the nearest mobile edge computing node (MECN) to get a better global model. More importantly, Bift provides a consensus algorithm named PoFL to resist possible adversaries. We evaluate the performance of Bift and demonstrate that Bift is scalable and robust, and can defend against malicious attacks.
Composite Pattern for Autonomic Switching of Service Back-Ends between the Fog and the Cloud

Given that cloud machines are usually remotely located from the devices of the end-users of the front-end of mobile apps, the end-users can face delays. The Fog has been introduced to augment mobile apps with machines for data analytics that are close/at the network edge. However, edge machines are resource constrained and hence, the execution of heavy data-analytics on edge machines is not always feasible. Thus, light versions of data-analytics algorithms should be deployed on edge machines. But, how can software engineers develop mobile apps that autonomically switch between the Fog and the Cloud? To answer this, we found the composite pattern of the Autonomic Integrator that extends the back-end of mobile apps to use alternative data-analytics algorithms. The pattern first includes the definition of the conceptual model of an extensible back-end that integrates back-end instances deployed on the Fog and the Cloud. Secondly, the pattern includes the conceptual model of an autonomic component. The autonomic component decides at runtime the switching of the front-end to a back-end instance that has the lowest response-time. Finally, the pattern covers the integration between the extensible back-end and the autonomic component.
On the Approaches of Solving Arithmetic Problems

It becomes a hotspot research problem again in the past decade to develop algorithms for solving arithmetic problems after several decades. Currently, one challenge prob-lem is to establish a theory that can not only explain the existing algorithms but also can guide the further research. Facing this challenge, this paper conducts a preliminary study on establishing the theory of solving problems. This study is going to propose the approaches of solving arithmetic problems based on the existing algorithms and then to assess the proposed approaches. To reach the above-stated research goal, this paper implements multiple divided tasks. First, it proposes a state-action mining method to establish an algorithm framework that abstractly captures the main states and main actions of connecting these states. Second, it proposes approaches of solving arithmetic problems by finding sharing states and sharing actions of the relevant algorithms. Third, it proposes a confusion scheme to assess the performance of approaches against different types of problems. The types of problems are defined according to the description indicator and the knowledge indicator of problems. This paper forms a new paradigm of defining and assessing the approaches and intends, which will be used in evaluating developing new algorithms and in identifying new research problems.
Arabic Handwriting Recognition System Based on Genetic Algorithm and Deep CNN Architectures

It is vital to understand Arabic handwritten characters because of their numerous advantages and applications. This research paper proposes a non-segmented Arabic Handwritten Recognition (AHR) system. For the best results, the genetic algorithm was used to optimize the VGG-16 and ResNet-50 Deep Convolutional Neural Networks (CNN) architectures to extract features followed by the classification. Our investigation was based on the HACDB database. Finally., a comparison of the proposed method to other approaches demonstrates its efficacy and robustness. The optimized ResNet-50 architecture was able to achieve a 100% success rate.
OP-K-Means: Optimized Algorithm for Recommendation System Based on User Preferences

Abstract For a recommender system (RS), it is difficult to capture all the user’s interest lists simultaneously, which leads to the problem of insufficient performance of the existing joint RS based on the K-Means clustering algorithm. In this paper (1), we introduce a cluster optimization method OP -K-means for user preference data. This method starts with propagation from the center of the user preference data. By selecting relatively distant positions between each initial center, the distance between them is increased as much as possible. (2) Finally, we validate the effectiveness of our algorithm on a dataset from Facebook and compare our algorithm with original K-means. Our experimental results justify the validity of our OP -K-means algorithm.
Caption Generator for images in isolation

Generating a textual description for an image was an inconceivable problem in computer vision earlier, but with recent development in Deep Learning this problem can be solved very easily. The purpose of this paper is to present our idea of generating textual descriptions using an RNN. Our model was trained on Flickr 8k dataset which was passed through ResNet- 152 to generate feature vectors. Other concepts of pre-processing, generator function, word embeddings, and evaluation are also discussed in this paper. We have provided a novel approach for solving image captioning problem starting from data collection to the evaluation of the model. Our model performed better in many cases at corpus level and the performance is expected to be better with bigger dataset with large corpus.
Performance Analysis of a Hyperledger Iroha Blockchain Framework Used in the UK Livestock Industry

This investigation focuses on the performance analysis of one of the most nascent Blockchain frameworks, Hyperledger Iroha. This paper evaluates the performance of the Hyperledger Iroha framework based on three parameters: (a) total requests per seconds (RPS) over time; (b) response times in milliseconds over time; and (c) number of users in the network over time. The results indicate that the integration of Hyperledger Iroha with a major livestock management and trading platform in the UK, can support at least 200 participants with no errors in the network. Additionally, the total requests per second can reach as high as 40.6, and the response times are in the order of a fraction of a second. This research and its results can assist scholars and practitioners regarding selection of an ideal Blockchain framework for their problem setting.
BioHanBERT: A Hanzi-aware Pre-trained Language Model for Chinese Biomedical Text Mining

Unsupervised pre-trained language models (PLMs) have boosted the development of effective biomedical text mining models. But the biomedical texts contain a huge number of long-tail concepts and terminologies, which makes further pre-training on biomedical corpora relatively expensive (more biomedical corpora and more pre-training steps are needed). Nonetheless, this problem receives less attention in recent studies. In Chinese biomedical text, concepts and terminologies consist of Chinese characters, and Chinese characters are often composed of sub-character components which are also semantically informative; thus in order to enhance the semantics of biomedical concepts and terminologies, the use of a Chinese character’s component-level internal semantic information also appears to be reasonable.In this paper, we propose a novel hanzi-aware pre-trained language model for Chinese biomedical text mining, referred to as BioHanBERT (hanzi-aware BERT for Chinese biomedical text mining), utilizing the component-level internal semantic information of Chinese characters to enhance the semantics of Chinese biomedical concepts and terminologies, and thereby to reduce further pre-training costs. BioHanBERT first employs a Chinese character encoder to extract the component-level internal semantic feature of each Chinese character, and then fuse the character’s internal semantic feature and its contextual embedding extracted by BERT to enrich the representations of the concepts or terminologies containing the character. The results of extensive experiments show that our model is able to consistently outperform current state-of-the-art (SOTA) models in a wide range of Chinese biomedical natural language processing (NLP) tasks.
Scaling Silicon Photonic Switches and Digitalization of The Optical Layer

The state-of-the-art silicon photonic switches will be introduced as scalable as a bi- section bandwidth of 125 Pbps with a radix of more than 130,000 through 9-stage Clos topology. Then, to handle such large-scale photonic systems generally and automatically in the future converged network and compute, the functional block-based disaggregation model will be addressed as a key.
Virtual adversarial training for semi-supervised breast mass classification

The study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740&plusmn;0.015 and 0.760&plusmn;0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.
Surface Type Classification for Autonomous Robots Using Temporal, Statistical and Spectral Feature Extraction and Selection

Real-time surface recognition has become a crucial component in assuring the safe walking of intelligent autonomous robots in a complex human-living interior environment. Numerous studies have been done addressing the problem recently. Still, there is a scope of improvements for accurate classification and inference time. In this paper, we have extracted features from accelerometer and gyroscope data in the temporal, statistical and spectral domain and classified them using a tree-based ensembling classification algorithm. We have achieved 80.81% mean accuracy, classifying 9 different surfaces with 1.0% standard deviation in 10-fold cross-validation and 97.25% average AUC score. Our method acquired state-of-the-art accuracy ensuring minimal inference time which is essential for real-time recognition for the autonomous robots.
Error Reduction and Diagnostic Concordance in Breast Pathology

Errors in anatomic pathology can result in patients receiving inappropriate treatment and poor patient outcomes. Policies and procedures are necessary to decrease error and improve diagnostic concordance. Breast pathology may be more prone to diagnostic errors than other surgical pathology subspecialties due to inherit borderline diagnostic categories such as atypical ductal hyperplasia and low-grade ductal carcinoma in situ. Mandatory secondary review of internal and outside referral cases before treatment is effective in reducing diagnostic errors and improving concordance. Assessment of error through amendment/addendum tracking, implementing an incident reporting system, and multidisciplinary tumor boards can establish procedures to prevent future error.
Unknown Aware Feature Learning for Face Forgery Detection

The face forgery detection problem has attracted wide attention in recent years. Although the vanilla convolutional neural network achieves promising results under the intra-domain testing scenario, it always fails to generalize to unseen scenarios. To address this problem, we propose Generalized Feature Space Learning (GFSL) with unknown forgery awareness, which leverages domain generalization to utilize face images forged with various methods. Considering that the true distribution of fake samples is harder to predict than the real samples, we regularize the model with an asymmetric triplet loss, aggregating only the real samples to learn an accurate real-image distribution, which forms an classification boundary that surrounds the real samples and generalizes well to unknown fake samples. Moreover, we apply Representation Self-Challenging (RSC) to perform selective dropout on features, which forces the model to learn more completed features rather than one or a few of the most prominent features, leading to better generalization ability. Extensive experiments show that our method consistently outperforms baseline models under various cross-manipulation-method tests and achieves comparable performance to the state-of-the-art methods on both intra- and cross-dataset evaluations.
Interpretability of Machine Learning Models

Machine Learning (ML) or Artificial Intelligence (AI) models have become a common tool in business and research over the past years. Models, for example, assist our decisions on which hotel to book or set the optimal price for a flight ticket based on real-time demand, and recommendation apps advise us where to eat or which sights are worth seeing. However, not all decisions taken by automated systems are easily understandable by human beings. How can one make sure that the model does not encompass unwanted bias or discriminate clients? How can one explain to the CEO that the price should be less than what the concurrence proposes? Fortunately, ML models that are explainable do indeed exist, and there is also active research on how to make unexplainable models interpretable. The most popular methods involve Lime and Shapley values, allowing researchers to identify the importance of the input variables and, in turn, to add an interpretation to the model prediction. In this regard, this chapter will describe how to use interpretability tools with ML models and will also provide intuition on the usefulness and limitations of these tools.
Quantum hashing on the high-dimensional states

In the paper, we consider using high-dimensional quantum states (so-called <i>qudits</i>) for the implementation of the quantum hashing technique. The importance of qudits for quantum procedures that implement quantum hashing and protocols based on it follows from two considerations. First of all, <i>d</i>-dimensional qudit describes a <i>d</i>-level quantum system, which can be in a superposition of its <i>d</i> basis states without the need of using highly entangled qubit states. The second consideration is that the <i>d</i>-dimensional qudit can be represented by a single particle. These two properties of qudits are considered in this paper to generalize the notion of quantum hashing. The latter is used to present a variant of a quantum protocol for secret key verification.
The Impact of Artificial Intelligence Voice Assistants on Consumer Experience Based on the Cognitive-Affective-Conative Model

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Efficient Facial Landmark Detector by Knowledge Distillation

Facial landmark detection aims to locate some predefined points on human face images, which is the basis of many facial analysis tasks and applications. Compared with traditional methods, facial landmark detector based on deep learning has made significant progress in accuracy and efficiency. However, the model parameters and computational cost have not received enough attention, leading to the algorithm facing many challenges in practical applications. In this paper, we propose a novel method named efficient facial landmark detector (EFLD), which significantly compresses the model size and computational cost while achieving superior performance compared with state-of-the-art frameworks. We introduce a knowledge distillation framework suitable for facial landmark detection, which can transfer geometric information from the pre-trained teacher model to the lightweight student model. In addition, we integrate adversarial learning into the knowledge distillation framework to guide the student model to imitate the heatmap distribution generated by the teacher model. Extensive experiments demonstrate the superior performance of our approach against state-of-the-art methods.