Reviewerly/exHarmony huggingface dataset:
STATISTICAL ANALYSIS OF INTERNAL DYNAMICS OF ARC4 STREAMCIPHER

The aim of this paper is to show, how the approach of nonlinear dynamics and statisticalphysics help to analyze pseudorandom number generators (PRNG) used incryptography. In fact, each PRNG is a highly nonlinear dynamical system, with itsinternal states, and may be analyzed as such.It is shown using PRNG of ARC4 stream cipher as example. A nonlinear phenomenon:1/fα dynamics was found in the new measure of complexity of the cipher: female count,a combinatorial quantity of direct cryptological bearing. We show that this measure canbe helpful in such tasks as measuring stationarity of internal dynamics of the PRNG andmeasuring of quality of initial condition (cryptographic key), which is statisticallyindependent from its entropy.
Create a dynamic dashboard with R, flexdashboard, and Shiny

Now that I’m on the tenure track, I’ve been looking for a way to keep track of my different research projects so I can get them all finished and published. Matt Lebo’s “Managing Your Research Pipeline” presents a neat way of quantifying and tracking the progress of your research, and I recently adopted it for my own stuff.
Digital solutions for early breast cancer detection

By 2040, more than 3 million new cases of breast cancer and 1 million deaths are estimated to occur each year worldwide. Identifying those at greatest risk of developing life-threatening breast cancers is a public-health priority. In this issue of The Lancet Digital Health, Ash Kieran Clift and colleagues developed and evaluated four prognostic models to predict 10-year risk of breast cancer mortality in women aged 20–90 years. The best performing model could be used to identify women at high risk of mortality, including those too young for breast cancer screening.
Experimental Corroboration

In this chapter we discuss the results of the experimental component to the study, which is designed to examine the degree to which the probabilistic contrasts uncovered in the corpus analysis are also discernible in language users’ introspective preferences. These experiments were designed to corroborate and interrogate the scope of the patterns observed in our corpus models.
Parameters of variation in the syntax of expressive suffixes: Case studies of Russian, German, Spanish and Greek

This work compares the morphosyntactic properties of expressive suffixes in four European languages: Russian, German, Spanish and Greek. It shows that although these suffixes share the same expressive meaning, they differ significantly in their syntactic structure, namely in the manner and place of attachment in the syntactic tree. Thus, the Russian and Spanish expressive suffixes that refer to the size of a referent (or size suffixes) are syntactic modifiers, while the German size suffixes are syntactic heads. And in Greek, the two most productive expressive suffixes -ak and -ul have homophonous counterparts that possess contrasting syntactic properties: syntactic heads vs. syntactic modifiers. This shows that across languages as well as within single languages, such as Greek, there is no 1:1 correspondence between the meaning and the structure of expressive forms. These findings are further supported by two novel case studies of the homophonous suffixes -its (in Greek) and -ic (in Russian).
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator

This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection.
Identification of fake currency using soft computing

Advancements in color printing techniques have led to a surge in the creation and duplication of counterfeit currency notes, which has become prevalent in India. Although it was previously difficult to carry out this type of operation, anyone with a laser printer now has the capability to do it. This issue is considered a major problem due to the country's various problems, such as frauds, corruption and black money. It is needed to develop the system for the detection of fake currency. A system that can quickly identify counterfeit money is being developed. The proposed solution will utilize image processing to authenticate Indian currency notes. The proposed system will involve extracting different features from the notes, such as the security thread, Bleed Line, edges, shapes, textures, colors and Fluorescence. It is being developed using the software known as MATLAB. The supervised learning model of support vector machine for the extraction of the notes' features is also being used. This is utilized for analyzing the data related to classification and regression. Once the statistical features have been extracted, they were matched with the features stored in MAT file using SVM (Support Vector Machine) classifier. We have also conducted a test of other methods, such as the black box algorithm. The proposed solution is very fast and simple to implement, allowing it to identify if the notes are fake or real. The outcome of proposed system is the remarkable working of proposed system for the detection of fake currency and accuracy has been measured with the help of confusion matrix. The accuracy, precision &amp; F- score are best by using SVM as compare to SVC (support Vector Classifier) &amp; GBC (Genetic Bee Colony) algorithm.
Pengenalan Budaya Tiongkok Melalui Kerajinan Tangan Simpul Cina di Sekolah SMA PL Don Bosko

Higher Education is obliged to carry out the Tridarma, including Education and Teaching, Research, and Community Service. This is stated in Law Number 12 of 2012. In the Law, Paragraph 11 states that community service is an activity of the academic community that utilizes Science and Technology to advance public welfare and educate the nation's life. The Faculty of Language and Arts Universitas Negeri Jakarta (FBS UNJ) has knit sustainable partnerships with several leading schools around Jabodetabek and want to expand partnerships with several other important related institutions, other museums, one of which is PL Don Bosko High School as part of FBS UNJ, The Chinese Language Education Study Program (PSPBM) this year collaborates with several excellent schools and plans to expand public knowledge about Mandarin language and Chinese culture. In accordance with the study program knowledge family, this activity is expected to contribute to the development of MSMEs, especially the provision of skills for students to start online entrepreneurship with the production of handicrafts 'Chinese knots' as one of the symbols of Chinese culture. This activity is one of PSPBM's efforts to contribute to introducing Chinese culture to ordinary people and also participate in developing Small and Medium Community Enterprises.
Progress in techniques and methods for uncontaminated and highly efficient separation of zircon from geological samples

锆石是应用最广泛的定年矿物,无污染地从地质样品中高效分选出锆石是获得代表地质样品准确信息的前提条件。传统锆石分选方法基于锆石的高密度和无磁性,主要通过机械破碎和不同水动力条件分选,存在破碎过程易污染和分选流程效率低的问题。针对这两个问题,本研究通过回顾和总结前人地质样品粉碎技术和高效矿物分选的方法,将高压脉冲电动力破碎技术、高温淬火和酸溶法结合,开发了一种能够实现锆石无污染、高回收的操作流程:高压脉冲电动力破碎→(淬火)→HF+HNO<sub>3</sub>溶解→HCl+H<sub>3</sub>BO<sub>3</sub>溶解。本次研究以锆石含量较低的基性样品为对象进行了不同方法的对比,对于含有≥50μm锆石的地质样品,新方法的锆石回收率是传统方法的6倍以上;对于只含有<50μm锆石的地质样品,传统方法无法从几千克的样品中分选出锆石,而新方法可以从几十克样品中分选出几十颗锆石。拉曼光谱分析显示,酸溶数小时并不会显著影响中等结晶度锆石的结晶度,而高温淬火可以明显提高锆石的结晶度。离子探针分析表明,酸溶分选和淬火后酸溶分选的锆石,在测试精度范围内均没有明显Pb丢失。采用新分选方法,并结合离子探针U-Pb定年,本文首次报道了洛阳牡丹石和雅鲁藏布江中段玄武岩的年龄(分别为~1831Ma和~164Ma),以证明改进后的锆石分选方法实用有效。新锆石分选流程的推广将显著扩展锆石在基性岩研究中的应用空间。
Design Paradigms of 3D User Interfaces for VR Exhibitions

Virtual reality (VR) technology capably complements exhibitions. Despite the considerable attention to the development of VR exhibitions, corresponding 3D user interface (UIs) design is in strong need. In this paper, we focus on the design of 3D UIs for VR exhibitions based on head-mounted displays (HMDs). The paper presents two workshops: one explores UI design paradigms that improve the user’s perceived usability and sense of immersion in the VR space, while the other examines ergonomic arrangements of the UI to address spatial layout issues. We aim to assist designers and developers by suggesting design choices, thus saving them time and avoiding the need to start from scratch.
ACTIVATION OF STUDENTS’ CRITICAL THINKING BY MEANS OF COGNITIVE-VISUAL TECHNOLOGIES IN MATHEMATICS LESSONS

The article examines the role and place of cognitive visualization in activating students' critical thinking in mathematics lessons.The development of the modern information society requires a competitive specialist in almost all fields, capable of critically working with a large amount of information to obtain and structure one's own knowledge system.The article states that the use of cognitive visualization methods in mathematics lessons would stimulate the development of personality, its creative and critical thinking.The authors highlight the components of critical thinking for which the use of a cognitive-visual approach is most appropriate: information analysis, recognition of patterns and connections, generation and evaluation of ideas, resolution of contradictions.Some methods, technologies and tools that can help to apply cognitive visualization for the formation of critical thinking of students in the process of learning mathematics are proposed.Several specific examples of such applications are given.Conclusions were made about the advantages of using the cognitive-visual approach in the educational process, including: highlighting the relationships and relationships contained in the information; keeping the student's attention; reduction of verbal and informational overload; disclosure of various aspects of concepts; analysis and structuring of a large amount of information; aesthetic appeal.It is emphasized that the combination of a cognitive-visual approach with technologies for the development of students' critical thinking in the process of teaching mathematics should be systematic, implemented using informational computer tools.The research used general scientific methods, as well as methods of comparison and generalization on the problem of critical thinking and the use of a cognitive-visual approach.
Pull-based Operations in a Hostel Mess: A Case Study

Wastage is reduced in the manufacturing process by employing a lean manufacturing technique known as the pull production system.In pull production, Kanbans are used to manage production activities.They are a basis for: when an item is to be manufactured, how many are to be produced, etc.In the current operation, food and groceries are carried from one station to another and enhancing it using lean manufacturing principles is addressed in this article.The primary aim is to reduce inventory, eliminate shortages, and reduce system work-in-progress of raw materials, all of which will be accomplished through lean methodologies.A schematic diagram of the pull process is constructed considering the current workstations in the mess kitchen.The number of distinct kanban's necessary is computed.Implementation of lean production system with kanban's dramatically improves the mess's performance.Compared to the existing system, the proposed system would use fewer raw materials (work-in-progress) to meet the same demand.The proposed system will greatly improve the system's efficiency and station cycle times.
FtlSPG: A Federated Transfer Learning Framework for Personalized Safety Protective Gear Detection in Electric Power Industry

Safety protective gear (SPG) detection based on the machine learning model plays an important role in improving outdoor personnel safety in the electric power industry. However, the detection method of transmitting video to the cloud faces a series of challenges such as privacy disclosure and high latency. To solve this problem, we present FtlSPG, a federated transfer learning framework for SPG detection. In particular, under the three-layer pyramid architecture of “site-companyCloud-globalServer”, we propose a federated personalized model based on local batch normalization and dynamical weighting for the source domain with labeled video. Moreover, a federated domain adaptation model based on a federated deep adversarial network and model self-training is presented for the target domain with unlabeled video. Finally, we verify the effectiveness of FtlSPG in real-world power companies. Extensive experiments demonstrate that FtlSPG can significantly outperform existing schemes, in terms of privacy protection, detection precision, and response latency.
A Big Data Framework for Criminal Investigation using Call Detail Records

Call Detail Records (CDRs) can be considered as big data source as it has a huge volume, variety of data and high data rate, as well.The analysis of CDRs can produce big value and offer opportunities to maximize revenue and improve the community's standard of living.However, the analysis of such data with those characteristics calls for using big data technology.Big data analytics is a rapidly growing field that has the potential to revolutionize the way we handle various aspects of our lives.One area where this technology is particularly relevant is in criminal investigations, where the analysis of call detail records (CDRs) can provide valuable insights into the activities and movements of individuals associated with criminal activity.This paper proposes a framework that leverages the massive amounts of data generated by telecommunication networks to uncover valuable insights that can help criminal investigators.Furthermore, the proposed framework is optimized to reduce the underlying computing resources needed to analyze such huge amount of data and to improve the overall performance of the proposed framework, as well.
FQN Inference in Partial Code by Prompt-tuned Language Model of Code

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects (referred to as type inference) is the prerequisite to effective search and reuse of partial code. Existing dictionary-lookup based methods build a symbolic knowledge base of API names and code contexts, which involve significant compilation overhead and are sensitive to unseen API names and code context variations. In this article, we propose using a p rompt-tuned c o de m asked language mod e l (MLM) as a neural knowledge base for type inference, called POME, which is lightweight and has minimal requirements on code compilation. Unlike the existing symbol name and context matching for type inference, POME infers the FQNs syntax and usage knowledge encapsulated in prompt-tuned code MLM through a colze-style fill-in-blank strategy. POME is integrated as a plug-in into web and integrated development environments (IDE) to assist developers in inferring FQNs in the real world. We systematically evaluate POME on a large amount of source code from GitHub and Stack Overflow, and explore its generalization and hybrid capability. The results validate the effectiveness of the POME design and its applicability for partial code type inference, and they can be easily extended to different programming languages (PL). POME can also be used to generate a PL-hybrid type inference model for providing a one-for-all solution. As the first of its kind, our neural type inference method opens the door to many innovative ways of using partial code.
DRAMATIC PLAY AS A DEVELOPMENTAL MEANS OF PRESCHOOL CHILDREN'S MOTOR CREATIVITY

The purpose of the research was to design, implement and evaluate an experimental program based on dramatic play which aims to develop the motor creativity of preschool children.In particular, we designed a mixed-methods experimental study using the integration technique.The following school year, follow-up research was conducted to check the preservation of the results.Statistical analysis results and qualitative analysis of the data revealed that the experimental program based on dramatic play had a statistically significant improvement in the children's fluency, originality, and imagination of their motor creativity.In addition, the results of the experimental intervention were preserved through time for the factor of imagination, but not for the factor of fluency and originality.The results of our research confirm that activities based on dramatic play are suitable for the development of preschool children's motor creativity, while creativity, movement, and dramatic play seem to be interconnected.
RECOMPOSITION AND PHOTOMONTAGE AS A STIMULUS FOR THE DEVELOPMENT OF CREATIVITY IN TEACHING VISUAL ARTS

Recomposition and photomontage are an integral part of contemporary art and should be implemented in the contemporary teaching of Visual Arts.This paper aimed to determine whether the use of recomposition and photomontage in Visual Arts classes stimulates student creativity.The research was carried out from 2019 to 2022 among students at the Department of Teacher Education and the Department of Preschool Education at the Faculty of Humanities and Social Sciences in Split, who applied the methods of recomposition and photomontage in Visual Arts classes.The research was qualitative, with the use of observation method and descriptive method, and a total of 133 students participated in the research.The resulting artworks were divided into groups according to the degree of creativity that the students expressed in those works.After the research, it was concluded that the use of recomposition and photomontage stimulated student creativity.However, a significantly greater degree of freedom could have been reached in terms of combining, using imagination and creating new ideas.
TEACHERS’ STRESS AS A MODERATOR OF THE IMPACT OF POMPEDASENS ON PRESCHOOL CHILDREN’S SOCIAL-EMOTIONAL LEARNING

This study examines the extent to which the impact of a universal intervention program, i.e., POMPedaSens, on children's early social-emotional learning (SEL) is different depending on early childhood education (ECE) teachers' stress at work.The POMPedaSens program aims to promote children's (5-6-year-olds) SEL by supporting ECE teachers' engagement and emotional availability.The intervention effectiveness has been monitored using an 8-month randomized controlled trial design with an intervention (IG; 26 teachers and 195 children) and a waiting control group (CG; 36 teachers and 198 children) that provided the data before and after the program implementation.The ECE teachers in the IG are trained to implement the intervention program in their early childhood education and care groups.Latent change score analysis suggests that the program increases children's prosocial behavior in the IG when teachers show a low level of stress.No significant results were found for the IG regarding a change in antisocial behavior.Unexpectedly, when teachers showed a high level of stress, an increase in prosocial behavior and a decrease in antisocial behavior were only found for children in the CG.The results suggest a promising application of the POMPedaSens program for promoting prosocial behavior in early childhood when teachers have low stress.The intervention will likely need a longer time to display the moderating effect of ECE teachers' well-being on children's antisocial behavior change.The stress in CG might mean that the teachers were doing their best at the cost of their own well-being.
DYNAMIC ACCESS CONTROL IN CLOUD STORAGE THROUGH MULTI LEVEL ENCRYPTION

Cloud storage is an operation pattern of pall computing to store massive data, so more and more individualities and associations shift their data from original computers to pall.This new paradigm poses serious trouble to the sequestration of their possessors.To break this problem, people have figured out a variety of approaches.One common way is to resort to the traditional public key encryption technology to encrypt data, but the data possessors fail to have fine-granulated access to their data flexibly.To deal with this problem numerous experimenters turn to Multi-Authority Attribute-Grounded Encryption( Mama-ABE).It can't only satisfy the need for the fine granulated access control and confidentiality of data, but also the secret keys of attributes are issued to data consumers with the corresponding boons for different trait authorities, independently
On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.
Machine learning in computational literary studies

Abstract In this article, we provide an overview of machine learning as it is applied in computational literary studies, the field of computational analysis of literary texts and literature related phenomena. We survey a number of scientific publications for the machine learning methodology the scholars used and explain concepts of machine learning and natural language processing while discussing our findings. We establish that besides transformer-based language models, researchers still make frequent use of more traditional, feature-based machine learning approaches; possible reasons for this are to be found in the challenging application of modern methods to the literature domain and in the more transparent nature of traditional approaches. We shed light on how machine learning-based approaches are integrated into a research process, which often proceeds primarily from the non-quantitative, interpretative approaches of non-digital literary studies. Finally, we conclude that the application of large language models in the computational literary studies domain may simplify the application of machine learning methodology going forward, if adequate approaches for the analysis of literary texts are found.