Reviewerly/exHarmony huggingface dataset:
Classification Study of the Order of Coleoptera for High School Biology E-bookletslet Development Materials

E-bookletss are needed to provide references for students who have limited access to books. The use of e-bookletss as learning media will make it easier for students to understand the subject matter compared to delivering information orally. Through the e-bookletslet, students can directly observe the pictures and explanations in the media. The e-bookletslet learning media on the classification of the order Coleoptera that was developed can be used as an enrichment learning medium. This study aims to examine the classification of the order Coleoptera for materials for developing e-bookletss for biology learning in senior high schools. The initial step of the research was to examine the material for the classification of the order Coleoptera. Furthermore, the results of the study were used as substances that were developed into e-bookletslet learning media. The e-bookletslet's development uses the ADDIE model with five stages: analysis, design, development, implementation, and evaluation. Products developed go through validation stages overseen by a team of material experts and media experts. The validation by material experts and media experts obtained very feasible results with a percentage of 91% and 90%, respectively. Small group and large group trials on high school students obtained very good results with a percentage of 90% and 89%, and the teacher's assessment percentage was 95% with a very good category. Based on the results of the data analysis carried out, the e-bookletslet learning media on the classification material of the order Coleoptera is suitable for use in learning activities.
Learning Applications about Standard Service Operational Procedures in Restaurants using Augmented Reality (AR)

The purpose of this study is to enrich the teaching materials that have been given in lectures regarding operational service standards in restaurants for Table Set-Ups so that they are more exciting and innovative by developing learning media using Augmented Reality. This application is designed using the UML (Unified Modelling Language) model built using the Unity application and Easy AR as the database API. The development of this learning system uses the ADDIE model. Evaluation of this application was obtained for measurements using N-gain in the "moderate" and "fairly effective" categories with values of 0.57 and 57.16, while for measurements using usability testing, the learnability is 2.86, the memory is 2,84, the efficiency value is 2.87, the error value is 2.82, and the satisfaction value is 1.29.
Implementation of Laravel Framework on Online Presence App Design for Internship Employees (Case Study: PT. XYZ)

Numerous companies use presence as a component of employee presence. PT. XYZ uses presence for all its employees. PT. XYZ has permanent employees, contract employees, and internship employees. Currently, presence data recording for internship employees at PT. XYZ still uses the manual method so it takes a long time and is vulnerable to data manipulation. This study aims to facilitate the management of the presence of internship employees so that they can validate the work location of internship employees and facilitate internship employees in making daily activity reports. This online presence system is designed using the PHP programming language and MySQL as the database. In this study, the author uses the Waterfall Software Development Life Cycle (SDLC) method. Then the system testing was carried out using User Acceptance Testing (UAT) by distributing questionnaires to respondents. Based on the results of the black box testing and user acceptance test (UAT) that have been carried out, it was found that the system developed was in accordance with its function and was very well accepted by internship employees and employees of PT. XYZ.
Meaning and Use of Haiku in the Game “Bungou to Alchemist”

Haiku is a Japanese poem that is unique and uses a minimum of words, but it is still beautiful and has deep meaning. In this modern era, there are educational games that use haiku. One of them is the Japanese literary-themed game 'Bungou to Alchemist'. This game is about poets who are brought to life by alchemists to protect endangered Japanese literary works and eradicate the enemy, namely shinshokusha. Alchemist plays the role of librarian (shisho), who directs the bungou (poet) to eradicate the shinshokusha. One of them is Kawahigashi Hekigotou, a famous Japanese haiku poet. The aim of this study is to find the meaning contained in Hekigotou's four haiku that appear in the game and to identify the cause of the four haiku being used, using Roland Barthes' semiotic theory. This study used qualitative research methods. The result of the analysis is to show that Hekigotou's haiku used in this game has meaning related to life and is used to strengthen the conveying of Bungou to Alchemist ideas contained in the haiku in the form of a representation of Kawahigashi Hekigotou's feelings. At the same time, this game is a place for young people to get to know and learn literature, especially haiku.
MRIFlow: Magnetic resonance image super-resolution based on normalizing flow and frequency prior

Super-resolution (SR) is a computer vision task that involves recovering high-resolution (HR) images from low-resolution (LR) ones. While SR is applied to various disciplines, it is particularly important in the medical field which requires accurate diagnosis. L1 and L2 loss-based SR methods produce high values for the peak signal-to-noise ratio and structural similarity index measure but do not have high perceptual quality because SR methods are trained with the average of plausible HR predictions. In addition, SR is an ill-posed problem because only one LR image can be mapped to various HR images. This is crucial because poorly generated HR images can lead to misdiagnosis. In this paper, we propose MRIFlow, a novel method based on normalizing flow that transforms LR magnetic resonance (MR) images into HR MR images. MRIFlow contains frequency affine injectors to reflect frequency information. The frequency affine injector receives the output of a pre-trained LR encoder as the input and obtains frequency information from a wavelet transform based on ScatterNet. Using this method, its inverse operation is possible. MRIFlow has two versions based on the type of ScatterNet employed. In this paper, MRIFlow is compared with normalizing flow-based SR methods by using various MR image datasets such as IXI dataset, NYU fastMRI dataset, and LGG dataset and is demonstrated to produce better quantitative and qualitative results.
Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease

Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment.
Identifying Hate Speech Trends and Prevention in Indonesia: a Cross-Case Comparison

Abstract This article provides an analysis on Indonesia’s hate speech phenomenon within the past two decades. It identifies shared characteristics across hate speech campaigns and how state and non-state actors have addressed them. In doing so, the article compares three campaigns targeting: the Ahmadiyya (2005–2011), Shi’a (2006–2012) and Jakarta’s Chinese Christian Governor Basuki Tjahaja Purnama (2016–2017). It finds that despite their unique dynamics, each campaign shared several commonalities. They were enabled by rising religious conservatism, sustained support from politicians, and competition for socio-economic capital; their framing strategy similarly used a threat-to-survival narrative, leveraged state regulations, and involved amplifying their voices through online spaces; and they had similar long-term impact which, despite an absence of mass violence, normalised the deprivation of vulnerable minorities’ fundamental rights.
Запутанные состояния поля по квадратурным амплитудам в процессе невырожденного параметрического распада в оптическом резонаторе

For the process of nondegenerate parametric decay in an optical cavity, where a photon with energy ħω3 decays into two photons with energies ħω2 and ħω1 , where ħω3 = ħω2 +ħω1, the possibility of forming entangled field states of the interacting modes of the optical system based on the variable of quadrature amplitudes is studied. It is shown that strongly entangled states are obtained between the modes with the frequencies ω2 and ω1 in the case of a very strong coupling of the interacting modes. Weakly entangled states are formed in the case of strong coupling between modes, and no entanglement of these modes occurs in the case of weak coupling. Entangled states by the variable of quadrature amplitude do not form for the modes with frequencies ω3 and ω1 , in the optical system.
Bridging the Gap between Handwriting and Machine: AI-based Handwritten Text Recognition

Due to the variety and complexity of handwriting styles, handwritten text recognition (HTR) is a difficult job.In HTR, artificial intelligence (AI) has demonstrated enormous promise, opening the door to the creation of effective and precise identification systems.An overview of the HTR process using AI methods, including preprocessing of the data, feature extraction, classification, and post-processing, is provided in this paper.To get the input data ready for feature extraction, data preprocessing includes image enhancement and segmentation.To feed the classification model with useful features, such as character shapes and patterns, pertinent features are extracted from the input picture.A neural network is frequently used in the classification model to map the extracted features to the associated characters.Spell checkers and language models are two post-processing tools that can be used to improve recognition outcomes.We discuss the challenges and opportunities for HTR using Neural Networks, including the importance of training data, model selection, and performance evaluation.The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and the testing and results of the system development.The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.
Recommender systems based on matrix factorization and the properties of inferred social networks

Recommender Systems (RS) are a vital part of companies with an active participation on the web. These companies require strategies that allow them to take advantage of product ratings from users in order to provide future recommendations to other users. In the last decade, several algorithms have been developed for movie recommendation, with Matrix Factorization algorithm being one of the most popular algorithms. Our approach is to evaluate the performance of this recommendation algorithm in scenarios where underlying social networks, which characterize certain types of interactions between users, can be inferred. In particular, the MovieLens dataset is used, which consists of approximately 100,000 ratings by 671 users on 9066 movies, during the period from 29 March 1996 to 24 September 2018.
Discrete analytical objects in the body-centered cubic grid

We propose a characterization of discrete analytical spheres, planes and lines in the body-centered cubic (BCC) grid, both in the Cartesian and in the recently proposed alternative compact coordinate system, in which each integer triplet addresses some voxel in the grid. We define spheres and planes through double Diophantine inequalities and investigate their relevant topological features, such as functionality or the interrelation between the thickness of the objects and their connectivity and separation properties. We define lines as the intersection of planes. The number of the planes (up to six) is equal to the number of the pairs of faces of a BCC voxel that are parallel to the line.
Variational inference accelerates accurate DNA mixture deconvolution

We investigate a class of DNA mixture deconvolution algorithms based on variational inference, and we show that this can significantly reduce computational runtimes with little or no effect on the accuracy and precision of the result. In particular, we consider Stein Variational Gradient Descent (SVGD) and Variational Inference (VI) with an evidence lower-bound objective. Both provide alternatives to the commonly used Markov-Chain Monte-Carlo methods for estimating the model posterior in Bayesian probabilistic genotyping. We demonstrate that both SVGD and VI significantly reduce computational costs over the current state of the art. Importantly, VI does so without sacrificing precision or accuracy, presenting an overall improvement over previously published methods.
Efficient Implementation of Tanh: A Comparative Study of New Results

Hyperbolic tangent (Tanh) activation function is used in multilayered artificial neural networks (ANN). This activation function contains exponential and division terms in its expressions which makes its accurate digital implementation difficult. In this paper we present two different approximation techniques for digital implementation of Tanh function using power of two and coordinate rotation digital computer (CORDIC) methods. A comparative study of both techniques in terms of accuracy of their approximations in hardware costs as well as their speed when implemented on FPGA is also explained
An Exploration of Prediction Accuracy of Developmental Degrees of Music Expression in Early Childhood Based on A Simultaneous Analysis of Both Eye Movement and Full-Body Movement

This article explores validity to introduce eye movement of children in association with body movement as feature quantities of machine learning, in particular, to assess children’s maturity level of musical expression. There is a growing awareness of applying machine learning technique to yield practical benefit on predicting developmental degrees of music education by observing body movements with motion capture technology. From professional teaching perspective, it is believed only experienced teachers can evaluate children’s music achievement because coordinated functioning of body movements need carefully be observed in connection with various music factor such as rhythm, beat strength, tones, etc. If new teachers can take advantage of having objective results of evaluation of children’s expression level by machines, it will enhance educational efficiency to certain levels which experts could attain. The author aims to improve classification accuracy of machine learning by focusing on eye movements data simultaneously recorded with body movements. In this study, children (n=43) at two child facilities participated in the data capture of both eye movement and body movement during musical expression. Feature quantities were extracted from the results of a three-way of ANOVA, and applied to machine learning to improve the classification accuracy of developmental degree in musical expression. Specifically, when using several classifiers such as NN (Neural Network model), the classification accuracy of developmental degree of musical development was more precisely in the case of both body movement data and eye movement data included in feature quantities than in the case of only body movement data as feature quantities.
U-statistics of local sample moments under weak dependence

In this paper, we study the asymptotic distribution of some U-statistics whose entries are functions of empirical moments computed from non-overlapping consecutive blocks of an underlying weakly dependent process. The length of these blocks converges to infinity, and thus we consider U-statistics of triangular arrays. We establish asymptotic normality of such U-statistics. The results can be used to construct tests for changes of higher order moments.
Faster optimal ate pairings for cyclotomic sparse families of pairing-friendly elliptic curves with embedding degrees k = 5,7

Nowadays, pairing-based cryptography researchers are looking for new parameters for standard security levels against the new number field sieve tower number field sieve algorithm. Recently, they have suggested new parameters for well-studied pairing-friendly curves with odd embedding degrees five and seven resistant to this attack. In this paper, we define optimal ate pairing on curves using sparse families with embedding degrees five and seven. We also provide details to perform the miller loop and the final exponentiation using addition chain process. Our theoretical results costs indicate that these families of curves offer the best performance in the computation of the optimal ate pairing at the 128-bit security level compared to Cocks–Pinch curves of embedding degrees five and seven. The improvement is about [Formula: see text] and [Formula: see text] faster than the optimal ate pairing previously computed on Cocks–Pinch curves of embedding degrees five and seven, respectively.
DetAC: Approach to Detect Access Control Vulnerability in Web Application Based on Sitemap Model with Global Information Representation

Access control vulnerabilities that lead to elevated privileges are among the most dangerous vulnerabilities in Web applications. Most of the existing detection methods use dynamic or static analysis techniques alone, which suffer from high manual involvement, low automation, high leakage rate, low page coverage, and other deficiencies. To this end, this paper proposes a novel access control vulnerability detection method (DetAC) based on a sitemap model with global information representation. This method first constructs a static site-wide sitemap model based on the page link addresses in the Web application source code through static analysis techniques. After that, the application is logged in and executed dynamically with different role users. During this process, execution traces and request parameters are collected and converted into annotations to fill the corresponding edges of the static site-wide sitemap model. Then, the sitemap model with global information representation is obtained. This model can represent both the global control flow and data flow of the application. Then DetAC analyzes the role-based and user-based access control policies of the Web application based on the node reachability and annotated data features of the model. And according to the information such as role, user, and access resources, it generates attack vectors to achieve different roles and the same role of different users to access each other’s resources. Finally, access control vulnerabilities are detected based on the equivalence of the results obtained using attack vector access and normal access to the Web application server. DetAC was validated on five real open-source Web applications, and the results showed that DetAC successfully detected up to 12 access control vulnerabilities, which are more than those of the traditional seven tools. The dynamic analysis page coverage rate was significantly improved during the detection process, reaching an average of 91.37%.
Intraoperative zoom lens calibration for high magnification surgical microscope

An augmented reality (AR)-based surgical guidance system is often used with high-magnification zoom lens systems such as a surgical microscope, particularly in neurology or otolaryngology. To superimpose the internal structures of relevant organs on the microscopy image, an accurate calibration process to obtain the camera intrinsic and hand–eye parameters of the microscope is essential. However, conventional calibration methods are unsuitable for surgical microscopes because of their narrow depth of focus at high magnifications. To realize AR-based surgical guidance with a high-magnification surgical microscope, we herein propose a new calibration method that is applicable to the highest magnification levels as well as low magnifications. The key idea of the proposed method is to find the relationship between the focal length and the hand–eye parameters, which remains constant regardless of the magnification level. Based on this, even if the magnification changes arbitrarily during surgery, the intrinsic and hand–eye parameters are recalculated quickly and accurately with one or two pictures of the pattern. We also developed a dedicated calibration tool with a prism to take focused pattern images without interfering with the surgery. The proposed calibration method ensured an AR error of < 1 mm for all magnification levels. In addition, the variation of focal length was within 1% regardless of the magnification level, and the corresponding variation with the conventional calibration method exceeded 20% at high magnification levels. The comparative study showed that the proposed method has outstanding accuracy and reproducibility for a high-magnification surgical microscope. The proposed calibration method is applicable to various endoscope or microscope systems with zoom lens.
Modeling of Serials

This work examines the modeling of serial publications in Anglo-American cataloging practice from the cataloging codes of the nineteenth century to the IFLA Library Reference Model, focusing on the challenges and implications of the various models. Its coverage moves from the models implicit in earlier cataloging codes, through the explicit models extracted from catalog records in the late twentieth century, to current models that attempt to align with models in neighboring domains. Some complementary and competing models from outside of library cataloging are also included. Then, it addresses the evolving impact of online serials and their displacement of their print analogs as a "canonical" version. It, then, finishes with a modest proposal for realigning the modeling of serials with that of other bibliographic resources.