Reviewerly/exHarmony huggingface dataset:
FOLIO Successfully Goes Alive in the Largest Public Library in China

The official opening of Shanghai Library East on September 28, 2022 marked the full implementation of FOLIO platform in the Shanghai Library. The Shanghai Library has developed and implemented applications for library traditional and smart services based on the FOLIO technical framework. This paper mainly introduces the process, difficulties, and solutions during the FOLIO implementation in the Shanghai Library, and provides a summary of the various activities of a Chinese library network (the Yunhan Alliance) established in 2021.
A novel hybrid multi-verse optimizer with queuing search algorithm

Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train.
Creation of a 3D kinematic model of the Delta manipulator using computer-aided design in NX

The article discusses the design of the Delta robot, which is part of a multirobotic system for aliquoting biological fluid. The purpose of the article is to obtain a dynamic model of the Delta manipulator using 3D modeling, which will allow studying the kinematic and dynamic characteristics of the manipulator for the specified parameters. A computer-aided design (CAD) system is used for modeling. The article presents an analytical calculation of the kinematic and dynamic parameters of the Delta manipulator in the RS structure, a solution to the inverse problem is presented. The process of creating a digital calculation model in the NX Nasrtan system is described. The preliminary calculation of kinematic and dynamic parameters made it possible to set parameters in the NX Nastran system to ensure the rotation of the drive shafts of the engines in accordance with the specified trajectory of the output link. For all parts of the manipulator, the center of mass is determined and the material is assigned. Motion simulation was carried out and dependences of changes in speeds, accelerations and movements of the manipulator links were obtained to implement the required trajectory of the mobile platform. This calculation allows you to build the trajectory of the output link with a given speed, setting the rotation of the drive links of the calculated model taking into account the forces of inertia.
Descriptive Analysis of the Cloud Computing Services and Deployment Models

Cloud have wide-ranging implications in several areas of computing, notably massive knowledge, cloud computing has simply up to the highest of any list of technology subjects. Furthermore, it is one of the most important chop-chop developing technologies due to the availability of a wide range of computer infrastructure and services. The expansion of cloud computing has included mainframe computing, cluster and grid computing, distributed and parallel computing technologies, and so on. Cloud computing has mostly supplanted grid, cluster, and mainframe computing. This refers to the delivery of numerous services over the web differently. Such as databases, infrastructure, run time platforms, knowledge storage, servers, networking, and code area unit samples of these resources. Price savings, bigger productivity, speed, efficiency, high performance, high computing power, and security are simply many of the explanations why consumers and businesses embrace the on demand computing. This article goes through the definition of cloud computing as well as its properties, various cloud services, deployment models, and security concerns.
Comparative Analysis of Quantum Computing Algorithm

The reason of quantum computer systems is to carry out preferred computer systems through jogging the quantum algorithm. Quantum algorithms can be utilized in a ramification of contexts, such as search and optimization, cryptography, the answer of complex linear equations, and simulation of quantum systems. Right here in this look at a quick survey of some familiar quantum algorithms, with the significance of a large assessment of their application instead of their technical details. Also, cover the application of quantum algorithms and a discussion of latest improvement.
Improving the Quality Control and Exploration of Digital Documents Using Visualization and Serendipity

This article contributes to the quality control of digitization workflows, and to the exploitation of historical document collections, by demonstrating the utility of serendipitous exploration (as opposed to targeted searches) through the use of Document Towers, a visual representation of the physical structure of documents as architectural models. The Document Towers are evaluated via a qualitative case study on historical Swiss newspapers, a thought experiment comparing them to alternative solutions, a diagrammatic visual and numeric assessment of exploratory tasks, and several quantitative and empirical usability measurements and psychometric surveys. The experiments confirmed that both serendipitous exploration and the Document Towers visualization are objectively well-suited for the quality control and exploration of digital documents. A significant disparity in subjective usability was observed between librarians and academics, who rated the visualization concept below and above average, respectively. Other findings included the redefinition of quality control as a tool of knowledge and dialogue among stakeholders in information systems; a generic diagrammatic instrument for evaluating the outcomes of explorations; demonstrating how the adoption of novel information technology may benefit from adaptation to individual psychologies and socio-professional contexts; and, unexpectedly, novel historical insights into Swiss map-making, newspaper history, and censorship.
EfficientNets transfer learning strategies for histopathological breast cancer image analysis

Breast cancer (BC) is one of the major principal sources of high mortality among women worldwide. Consequently, early detection is essential to save lives. BC can be diagnosed with different modes of medical images such as mammography, ultrasound, computerized tomography, biopsy, and magnetic resonance imaging. A histopathology study (biopsy) that results in images is often performed to help diagnose and analyze BC. Transfer learning (TL) is a machine-learning (ML) technique that reuses a learning method that is initially built for a task to be applied to a model for a new task. TL aims to enhance the assessment of desired learners by moving the knowledge contained in another but similar source domain. Consequently, the challenge of the small dataset in the desired domain is reduced to build the desired learners. TL plays a major role in medical image analysis because of this immense property. This paper focuses on the use of TL methods for the investigation of BC image classification and detection, preprocessing, pretrained models, and ML models. Through empirical experiments, the EfficientNets pretrained neural network architectures and ML classification models were built. The support vector machine and eXtreme Gradient Boosting (XGBoost) were learned on the BC dataset. The result showed a comparative but good performance of EfficientNetB4 and XGBoost. An outcome based on accuracy, recall, precision, and F1_Score for XGBoost is 84%, 0.80, 0.83, and 0.81, respectively.
Absolute Pose Estimation with a Known Direction by Motion Decoupling

This paper develops an extremely robust solution for absolute pose estimation with known prior gravity direction by motion decoupling. Absolute pose estimation is a fundamental problem in computer vision, and recently the prior known vertical direction is commonly applied to help solve the pose estimation problem. In this paper, we explore the geometrical constraints of the absolute pose estimation with a known direction. We find that the rigid pose can be decoupled with the help of the known direction. Thereby, absolute pose estimation algorithms, which decouple rigid motion, are proposed. Notably, in real applications, there may be imperfect inputs, i.e., outliers, due to incorrect 2D-3D matches. Unfortunately, these outliers may lead to unacceptable results. To suppress the outliers, the decoupled absolute pose estimation problem is solved by branch-and-bound algorithm and globally voting, which can provide the optimal solution with provable guarantees. Moreover, in extreme case, the proposed method can solve absolute pose estimation problem without knowing the 2D-3D correspondences, which is also known as simultaneous camera pose correspondence estimation. To demonstrate the feasibility and the superiority of the proposed methods, comprehensive comparison experiment are conduced. The source code is available at https://github.com/Liu-Yinlong/algorithm-for-PnP-with-known-vertical-direction.
Efficient Approach to Using CNN-Based Pre-trained Models in Bangla Handwritten Digit Recognition

Due to digitalization in everyday life, the need for automatically recognizing handwritten digits is increasing. Handwritten digit recognition is essential for countless applications in various industries. Bengali ranks the fifth largest dialect in the world, with 265 million speakers (native and non-native combined), occupying 4% of the world population. Due to the complexity of Bengali writing in terms of variety in shape, size, and writing style, researchers did not get better accuracy using supervised machine learning algorithms to date. Moreover, fewer studies have been done on Bangla handwritten digit recognition (BHwDR). In this paper, a novel convolutional neural network (CNN)-based pre-trained handwritten digit recognition model has been proposed, which includes ResNet-50, Inceptionv3, and EfficientNetB0 on the NumtaDB dataset of 17 thousand instances with ten classes. The result outperformed the performance of other models to date with 97% accuracy in the 10-digit classes. Furthermore, we have evaluated the result of our model with other research studies while suggesting future studies.
Pengaruh Media Audio Visual terhadap Kemampuan Menulis Cerpen pada Siswa Kelas IV di SD Negeri 195 Palembang

The problem encountered in the study was that the lack of ability in short story writing skills in grade IV students at SD Negeri 195 Palembang properly. This study aims to determine the effect of using audio-visual media on the ability to write short stories in grade IV students at SD Negeri 195 Palembang. The type of research used is experimental and using a one-group pretest-protest design experiment. The population of this research is SD Negeri 195 Palembang. The sample in this study was fourth grade students at SD Negeri 195 Palembang with a total of 23 students. Collection techniques in this study using tests. The instrument used is the short story writing test. The criterion for success in this research is what if the minimum average value is 76. Data analysis techniques are processed by hypothesis testing and t-test. Based on the results of the analysis conclusions show that there is a significant effect of the use of audio-visual media on the ability to write short stories in grade IV students at SD Negeri 195 Palembang, with a tcount of 3.689 &gt; ttable of 2.079. So it can be concluded that there is an influence in the use of audio-visual media in short story writing skills in class IV elementary school students.
A Case Study on the Promotion of Sharing Liberal Arts Curriculum in Regional-Based Universities

This study was conducted as a basic study to review shared subjects and methods necessary for sharing liberal arts education by regional-based universities. It targeted liberal arts education courses at three universities in Chungbuk, which form a consensus on regional-based shared universities. In order to supplement the tribal curriculum with mutual strengths, the liberal arts curriculum of each university was compared and analyzed, and the strong and tribal curriculum were extracted and presented. In addition, the evaluation and method of sharing liberal arts were investigated through a survey on students' perceptions of sharing liberal arts subjects. As a result, students preferred the 'video recording method' as the sharing method of shared curriculum, and the 'Pass/Non-pass' method was selected by the most students as the evaluation method. This study is meaningful in suggesting implications for subject selection, evaluation, and sharing methods when regional-based universities promote cultural sharing in the future.
An Efficient Violence Detection System from Video Clips using ConvLSTM and Keyframe Extraction

Monitoring systems are becoming more crucial for city security, specifically to address any kind of violence. Manual detection of violence through video surveillance requires well-trained human resources. However, accuracy and speed are compromised in the case of manual approach. The lack of human resources and their limited ability to interpret violence from a video is the major concern of this approach. Thus, automatic violence detection is required to take timely actions. Most of the existing work for violence detection have used either traditional Machine Learning (ML) based approach or a combination of Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) network for incorporating Spatio-temporal relations. However, video clips contain an extensive number of frames which contain unnecessary and redundant information. Elimination of such frames increases the efficiency and accuracy of the system. Thus, an Efficient ConvLSTM based Violence Detection System (ECLVDS) model is proposed to address this issue. The proposed model uses a clustering-based keyframe extraction technique to eliminate redundant and useless frames. Moreover, the proposed model uses a ConvLSTM model instead of a simple LSTM because the ConvLSTM network extracts better spatiotemporal correlation than the normal LSTM model. The proposed model is evaluated with the widely used benchmark Hockey Fight Dataset and achieved an accuracy of 98.90%, which is comparatively better than some of the existing models.
Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models

The security of credit card fraud detection (CCFD) models based on machine learning is important but rarely considered in the existing research. To this end, we propose a black-box attack-based security evaluation framework for CCFD models. Under this framework, the semisupervised learning technique and transfer-based black-box attack are combined to construct two versions of a semisupervised transfer black-box attack algorithm. Moreover, we introduce a new nonlinear optimization model to generate the adversarial examples against CCFD models and a security evaluation index to quantitatively evaluate the security of them. Computing experiments on two real data sets demonstrate that, facing the adversarial examples generated by the proposed attack algorithms, all six supervised models considered largely lose their ability to identify the fraudulent transactions, whereas the two unsupervised models are less affected. This indicates that the CCFD models based on supervised machine learning may possess substantial security risks. In addition, the evaluation results for the security of the models generate important managerial implications that help banks reasonably evaluate and enhance the model security. History: Accepted by Ram Ramesh, Area Editor for Data Science &amp; Machine Learning. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 72171160 and 71988101], Key Program of National Natural Science Foundation of China and Quebec Research Foundation (NSFC-FRQ) Joint Project [Grant 7191101304], Key Program of NSFC-FRQSC Joint Project [Grant 72061127002], Excellent Youth Foundation of Sichuan Province [Grant 2020JDJQ0021], and National Leading Talent Cultivation Project of Sichuan University [Grant SKSYL2021-03]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1297 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0076 ) at ( http://dx.doi.org/10.5281/zenodo.7631457 ).
Cold &amp; Warm Net: Addressing Cold-Start Users in Recommender Systems

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.
Sublinear-Communication Secure Multiparty Computation Does Not Require FHE

Secure computation enables mutually distrusting parties to jointly compute a function on their secret inputs, while revealing nothing beyond the function output. A long-running challenge is understanding the required communication complexity of such protocols—in particular, when communication can be sublinear in the circuit representation size of the desired function. Significant advances have been made affirmatively answering this question within the two-party setting, based on a variety of structures and hardness assumptions. In contrast, in the multi-party setting, only one general approach is known: using Fully Homomorphic Encryption (FHE). This remains the state of affairs even for just three parties, with two corruptions. We present a framework for achieving secure sublinear-communication $$(N+1)$$ -party computation, building from a particular form of Function Secret Sharing for only N parties. In turn, we demonstrate implications to sublinear secure computation for various function classes in the 3-party and 5-party settings based on an assortment of assumptions not known to imply FHE.
Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes

When a robot executes a task, it is necessary to model the relationships among its body, target objects, tools, and environment and to control its body to realize the target state. However, it is difficult to model a relationship using classical methods if it is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes of the model. In this study, we have developed the Deep Predictive Model with Parametric Bias (DPMPB) as a more human-like adaptive intelligence to deal with these modeling difficulties and temporal model changes. We categorize and summarize the theory of the DPMPB and various task experiments on the actual robots and discuss the effectiveness of the DPMPB.
The Effects of Preprocessing on Turkish and English News Data

In a standard text classification (TC) study, preprocessing is one of the key components to improve performance. This study aims to look at how preprocessing effects TC according to news text, text language, and feature selection. All potential combinations of commonly used preprocessing techniques are compared on one domain, namely news data, and in two different news datasets for this aim. Preprocessing technique contributions to classification performance at multiple feature sizes, possible interconnections among these techniques, and technique dependency on corresponding languages are all evaluated in this way. Using best combinations of preprocessing techniques rather than using or not using them all, experimental studies on public datasets reveals that, choosing best combinations of preprocessing techniques can improve classification accuracy significantly.
A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data

This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models, has shown interesting properties when compared to other popular approaches such as those based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors or on multiple imputations by chained equations. However, Gaussian mixture models are known to be non-robust to heterogeneous data, which can lead to poor estimation performance when the data is contaminated by outliers or have non-Gaussian distributions. To overcome this issue, a new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data. This paper shows that this problem reduces to the estimation of a mixture of angular Gaussian distributions under generic assumptions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , each sample is drawn from a mixture of elliptical distributions, which is possibly different for one sample to another). In that case, the complete-data likelihood associated with mixtures of elliptical distributions is well adapted to the EM framework with missing data thanks to its conditional distribution, which is shown to be a multivariate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -distribution. Experimental results on synthetic data demonstrate that the proposed algorithm is robust to outliers and can be used with non-Gaussian data. Furthermore, experiments conducted on real-world datasets show that this algorithm is very competitive when compared to other classical imputation methods.