Reviewerly/exHarmony huggingface dataset:
Natural language processing with deep learning enabled hybrid content retrieval model for digital library management

Abstract In recent times, natural language processing (NLP) technique has received significant attention in content retrieval (CR) domain. The emergence of digital libraries, in recent years, enables people from across the globe to access and store books, documents, and literature of multiple kinds. The development of NLP models has considerably improved the performance in terms of digital library management. In this scenario, artificial intelligence‐based expert systems are required to handle massive quantities of data that exist in digital libraries and achieve effective CR performance. In this background, the current study designs NLP with deep learning enabled hybrid content retrieval (NLPDL‐HCR) model for digital library management. The aim of the presented NLPDL‐HCR is to effectually retrieve the images as well as textual data from digital libraries based on a user's query. The proposed NLPDL‐HCR model encompasses two major stages namely, text retrieval and image retrieval (IR). During text retrieval process, the proposed NLPDL‐HCR model includes term frequency inverse document frequency vectorizer with optimal gated recurrent unit (GRU) model. The hyperparameters of the GRU model are optimally adjusted with the help of RMSProp approach. Besides, the IR process involves three sub‐processes namely, densely connected networks‐based feature extraction, butterfly optimization algorithm‐based hyperparameter tuning, and Euclidean distance‐based similarity measurement. The experimental analysis results, accomplished by the proposed NLPDL‐HCR model using benchmark datasets, highlighted its superior performance over recent state‐of‐the‐art approaches.
Computational Intelligence in Medicine

Two paradigms have historically formed in artificial intelligence: neurocybernetics and black box cybernetics. The cybernetics of the “black box” is based on a logical approach. The rapid development of modern medicine is due to the use of technical diagnostic tools, and the use of new information technologies. Technical means of diagnosing allow you to visualize the processes of diagnosing. Intelligent information technologies use the methods and tools of artificial intelligence and can speed up the diagnosis and improve its accuracy. The authors of the work have been working on biomedical images for many years, building CAD systems with elements of artificial intelligence. Biomedical (cytological, histological, and immunohistochemical) images are used for diagnosis in oncology. The problems of classification, generation, segmentation, and clustering of biomedical images are solved in the work. For these purposes, the following means of computational intelligence were used: CNN, GAN, and U-net. CNN is used in the paper to classify images. Several models for the classification of biomedical images are proposed and a comparative analysis with existing analogs is given. The accuracy of classification for cytological images was 86%, for histological was 84%. The authors analyzed the architectures of convolutional neural networks of the U-net type for automatic segmentation of immunohistochemical images. A modified neural network architecture for the segmentation of immunohistochemical images has been developed. Computer experiments were performed on different numbers of stages and iterations. ROC curves are built to assess the quality of segmentation of known and modified network architectures such as U-net.
Classification Methods of Heterogeneous Data in Intellectual Systems of Medical and Social Monitoring

The research is devoted to the solution of the scientific and practical problem of creation of intelligent systems (IS) of medical and sociological monitoring (MSM) on the basis of the improved models of representation of heterogeneous MSM’s data and the developed methods of their classification by means of Kohonen network training. In the research improve the models of representation of weakly structured and weakly labeled heterogeneous MSM’s data in the spaces of properties and features, taking into account values, types, formats, sources, quality assessments and procedures of aggregation/transformation of properties of detailed data. Based on these models, a method of matching classes and clusters markers in learning of Kohonen network with partial teacher involvement was developed, which is based on constructing a two-dimensional histogram of pairwise matches of classes and clusters markers values with its subsequent intersecting by rows and by columns to the developed rule. The method allows to obtain additional class markers in the unlabeled part of the training sample. Based on the method of matching classes and clusters markers to assess the suitability of chromosomes in the population of the genetic algorithm for each example from the training sample, a method of heuristic weight adjustment in the learning process of the Kohonen neural network is proposed. The use of such an adjustment of the weights allowed to reduce the training time of the Kohonen net-work without losing the level of reliability of the classification. The method of classification of weakly structured and weakly labeled heterogeneous MSM’s data has been improved due to the use of developed methods of matching class and cluster markers and heuristic adjustment in the process of learning the Kohonen network
Software Based on Ontological Tasks Models

The growing complexity of software reflects the trend of mobility and changeability of business processes in our increasingly interconnected world. The change in the business process entails the difference in the supporting software, which should updated quickly and efficiently. Existing software architectures and approaches to design cannot provide a quick software adaptation to the change in requirements. This article explores using task ontologies as building blocks for software. Such software is modeled as a network of executable task models. Each model can initiate actions such as executing operating system commands, querying a database or sending requests to external services. While the task model can perform a small task, the network of interacting models can perform complex tasks. The article describes the application of the proposed approach in automated software testing. The advantages of using task models in software are analyzed for different types of change requirements compared to the traditional software design approach. The prototype of the modeling tool, allowing the creation of onto-logical task models and their execution, is described.
Comparative Study of Physical Expression Contents and Teaching/Learning Methods in “Pleasant Life” before/after the Integrated Curriculum

This study aims to conduct a comparative analysis of the physical expression area contents in “Pleasant Life” in the 2007 revised curriculum before the development of an integrated curriculum textbook for elementary students in lower grades and the 2015 revised curriculum and discuss problems based on this to present improvement directions. For this purpose, literature research was conducted with guidebooks, textbooks, related studies, and reports on the 2007 revised curriculum and the 2015 revised curriculum as the subjects of analysis. First, this study examined the characteristics of “Pleasant Life” in the 2007 revised curriculum and the 2015 revised curriculum and conducted a comparative analysis of the 2007 revised curriculum prior to the development of the integrated curriculum textbook and physical expression education contents and teaching/learning methods in “Pleasant Life” in the integrated curriculum of the 2015 revised curriculum used presently. Through this, problems were found, including the unbalance of the music, art, and physical education areas in the integrated curriculum textbook, the decrease in the weight of physical expression activity education, and the problem concerning the physical expression competence of creative convergence, and improvement directions for them were presented.
Self-supervision &amp; meta-learning for one-shot unsupervised cross-domain detection

Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models are used off-the-shelf on unseen domains. Most of the existing works on domain adaptation simplify the setting and access jointly both a large source dataset and a sizable amount of target samples. However this scenario is unrealistic in many practical cases as when monitoring image feeds from social media: only a pretrained source model is available and every target image uploaded by the users belongs to a different domain not foreseen during training. We address this challenging setting by presenting an object detection algorithm able to exploit a pre-trained source model and perform unsupervised adaptation by using only one target sample seen at test time. Our multi-task architecture includes a self-supervised branch that we exploit to meta-train the whole model with single-sample cross-domain episodes, and prepare to the test condition. At deployment time the self-supervised task is iteratively solved on any incoming sample to one-shot adapt on it. We introduce a new dataset of social media image feeds and present a thorough benchmark with the most recent cross-domain detection methods showing the advantages of our approach. • This work focuses on the task of one-shot unsupervised cross-domain detection. • We propose FULL-OSHOT that combines meta-learning and self-supervision. • We present a new dataset to study the setting of social media image monitoring. • The experimental analysis shows how FULL-OSHOT improves over its basic OSHOT version. • FULL-OSHOT also outperforms current SOTA cross-domain detection models.
A Study on the Development and Effectiveness of a Psychology Liberal Arts Class Incorporating Self-Directed Learning

The purpose of this study is to develop a psychology liberal arts class incorporating self-directed learning and to analyze the effects of its implementation. In this study, a psychology liberal arts class connected with the self-directed learning factors was developed for self-directed learning. In order to analyze the effects of liberal arts class, data were collected and analyzed using reflection journal writing and in-depth interviews with 17 university students. The results are as follows. First, at the beginning of the liberal arts class, the process of students attempting to learn was found to be of significant importance regarding the learning process itself, as well as the trial and error process based on past experiences. From the perspective of the Galaxy model, this process is related to cognitive self-directed learning. From the perspective of the education subject, the intention of the learner is founded in education. Second, at the mid point of the liberal arts class, the process of realization among the students in regards to their learning was found to lie in an understanding of the learning phenomenon based on psychology, solving problems related to learning through the connection between psychology and learning science, and the use of teaching media for effective learning. From the perspective of the Galaxy model, this process is related to educational self-directed learning. From the perspective of the education subject, the intentions of learners and instructors are implemented in education. Third, at the end of the liberal arts class, the students’ growth process through learning was found to rest in the areas of their pursuit of value and their pursuit of change. From the perspective of the Galaxy model, this process is related to existential self-directed learning. From the perspective of the education subject, the intention of the learner becomes central in education.&lt;/br&gt;The conclusions are as follows. First, it is necessary for us to develop a liberal arts class with self-directed capabilities that will be required in the era of the 4th Industrial Revolution. Second, such classes need to reflect the learner’s intentions that take into consideration the self-directed learning elements of time, space, exchange, and body elements in the design process of the liberal arts class for self-directed learning capabilities. Third, we need to discuss the appropriate role of the instructor in the operational process of the liberal arts class that possess self-directed learning capabilities.
Exploring the Metaverse Reuse Intention of University Liberal Arts Computer Class for Students through the Technology Acceptance Model

In this study, we analyzed the metaverse reuse intention of college students. We introduced the “Zepeto” metaverse platform in liberal arts computer classes at a four-year university. In this study, the technology acceptance model was used as a theoretical background, and self-efficacy, perceived pleasure, and information quality were used as research variables. This study's independent variables are self-efficacy, perceived pleasure, information quality, perceived usefulness, and perceived ease of use. The study identified the intention of college students to use the metaverse as a survey method. As a result of the study, we found that self-efficacy had a significant effect on university liberal arts computer ease of instruction. Also, metaverse reuse intention along with perceived ease of use, and the relationship between perceived usefulness and perceived ease of use, had a positive effect. We found that perceived play had a positive effect on perceived usefulness, perceived ease of use, and metaverse intention. Information quality had a significant effect on perceived usefulness, perceived ease of use, and intention to reuse the metaverse. When self-efficacy, perceived pleasure, and information quality were viewed as independent variables, and metaverse reuse intention was viewed as a dependent variable, there was a mediating effect between perceived usefulness and perceived ease of use.
Queueing-Theoretic Performance Analysis of a Low-Entropy Labeled Network Stack

Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>9</mml:mn> <mml:msup> <mml:mrow> <mml:mn>9</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>t</mml:mi> <mml:mi>h</mml:mi> </mml:mrow> </mml:msup> </mml:math> percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>×</mml:mo> </mml:math> -3.5 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>×</mml:mo> </mml:math> in concurrency.
Developing Class Model of Personality Education through Case Study of Liberal Education －Focusing on the Case of D University

The purpose of this writing is to uncover the planning intention, composition method, and implementation process of ‘DU EYE(Early Year Experience)’ planned as common personality education of D university. This article scrutinized the two-track personality education system of D university and proposed the model of personality education by analysing subjects and reflecting improvement plan. This study examines the case example of the operation of the personality subjects of D University from the planning stage to its execution. The class model that is designed based on the analysis of fruits and limitations of the actual personality subjects can be a practical plan for the education field.&lt;/br&gt;D University systematizes the structure of the personality subject largely with two tracks. One pertains to the subjects such as &lt;DU Vision Design&gt; and &lt;DU Career Design&gt;, and the other one deals with the community-based activity subjects such as &lt;DU Community-based Project Learning&gt; and &lt;DU Sharing and Devotion&gt;. The renewal through continuous implementation and reflection of improvements in personality education at D university will be a good example of personality education.
A Fast Hardware Pseudorandom Number Generator Based on xoroshiro128

The Graphcore Intelligent Processing Unit contains an original pseudorandom number generator (PRNG) called xoroshiro128aox, based on the <inline-formula><tex-math notation="LaTeX">${ \boldsymbol{F}}_{2}$</tex-math></inline-formula> -linear generator xoroshiro128. It is designed to be cheap to implement in hardware and provide high-quality statistical randomness. In this paper, we present a rigorous assessment of the generator's quality using standard statistical test suites and compare the results with the fast contemporary PRNGs xoroshiro128+, pcg64 and philox4x32-10. We show that xoroshiro128aox mitigates the known weakness in the lower order bits of xoroshiro128+ with a new ’AOX’ output function by passing the BigCrush and PractRand suites, but we note that the function has some minor non uniformities. We focus our testing with specific tests for linear artefacts to highlight the weaknesses of both xoroshiro128 PRNGs, but conclude that they are hard to detect, and xoroshiro128aox otherwise provides a good trade off between statistical quality and hardware implementation cost.
A Lyapunov-based Shaking Function for a Class of Non-bilinear Quantum Systems

This paper introduces a Lyapunov-based feedback law for quantum systems that are controlled by shifting the phase of an optical beam. The objective is to drive the system to a target eigenstate of the nominal Hamiltonian (i.e. phase equal zero) by designing a shaking function such that the Hilbert-Schmidt distance is monotonically decreasing. After identifying the control law that maximises the descent rate, two possible tuning strategies are proposed to address bounded input requirements. Convergence of the proposed controller is shown using the Krasovskii-LaSalle principle under the same conditions commonly found for bilinear quantum systems. Numerical simulations showcase the effectiveness of the proposed shaking function.
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control

Reinforcement learning (RL) is a promising approach for deriving control policies for complex systems. As we show in two control problems, the derived policies from using the Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms may lack robustness guarantees. Motivated by these issues, we propose a new hybrid algorithm, which we call Hysteresis-Based RL (HyRL), augmenting an existing RL algorithm with hysteresis switching and two stages of learning. We illustrate its properties in two examples for which PPO and DQN fail.
DC-MPQ: Distributional Clipping-based Mixed-Precision Quantization for Convolutional Neural Networks

Quantization is a representative network compression technique that reduces the number of computational operations and memory accesses in the computation process of convolutional neural networks (CNNs). The existing naïve quantization method has a problem in that the quantization point corresponding to the near-zero value decreases as the precision decreases; as a result, the quantization error increases. Recent quantization-related studies have suggested various solutions to this problem. Nevertheless, studies that suggest a method to solve this problem by considering the characteristics of hardware accelerator implementation have not been actively conducted. To address this problem, this study proposes a method of using standard deviation values, which are simple statistical values of distribution for each layer, as clipping points and setting a scale factor with a clipping point as the base to quantize the weights into a mixed-precision integer format of 4-bit/8-bit. The proposed technique can be applied to any network without additional training, and only biasing and mapping are performed based on the pre-stored standard deviation values; thus, the computational complexity is low, rendering it hardware-friendly. Experimental results indicate that the proposed mixed-precision quantization of the weights of ResNet-18 on ImageNet achieved an effect of reducing the weight capacity by 84% with a 0.34% Top-1 accuracy drop compared to full precision. In YOLACT, an instance segmentation model using a ResNet-50 backbone, on MS COCO, a weight capacity reduction of 81.7% was achieved with only 0.27% and 0.19% drops in box mean average precision (mAP) and mask mAP, respectively.
Perceived Usefulness and Satisfaction with Location Adaptive Smart Learning

Technology has been increasingly important for current and future societies, becoming even more so as the decades pass. This research work presents an educational application with location adaptive smart mobile learning. The educational tool enhances the knowledge of the society about the monuments and landmarks of the country, enriching the experience of living. An inhabitant needs to enhance their knowledge about the magnificent landmarks available in their vicinity to establish a deep connection with people from that region. Using geofencing technology, we develop a framework that shows pertinent educational data based on the user’s current location. The app enables data retrieval and integration using Google scrapers to incorporate the most relevant information from existing web pages. Moreover, we distribute a survey to determine whether the mobile application is beneficial, has pedagogical features, and is satisfactory to the UAE society based on the Technology Acceptance Model factors. After gathering the results, we analyze the alpha coefficient, the outcome of the reliability test. The proposed application achieved a Cronbach’s alpha coefficient of 0.874; therefore, the internal consistency is good. The Department of Culture and Tourism can also adopt the smart application to integrate information that educates visitors and tourists.