Reviewerly/exHarmony huggingface dataset:
Research on WBS-RBS Oriented Security Risk Assessment Index System for Space Domain Private Network

Abstract To solve the problems such as unclear overall situations, incomplete risk analysis, and lack of targeted evaluation indicators of the security of the special network in the aerospace field, this paper takes the special network in the aerospace field as the research object, uses the WBS-RBS method to analyze the risk of network security, and uses the OSM model to establish an evaluation indicator system for the identified risks, to provide an indicator model for the subsequent security detection and evaluation of the special network in the aerospace field.
Generalized Two-Stage Particle Filter for High Dimensions

The curse of dimensionality has been a long-standing problem in the field of particle filters (PFs), and prevents their use in real complex systems characterized by large number of unknowns. Recently a two-stage PF (TPF) for high dimensions was proposed, showing promising results with modest number of particles. The TPF modifies the proposal distribution by tempering each state dimension towards the most likely particle proposed and carrying out regular filtering using the constructed proposal. However, it is limited to cases where the measurement equations are completely separable. We propose a new filter inspired by the TPF principle as well as multiple PF (MPF), that can be applied to any setup and that provides a posterior distribution of the tempering coefficient that is updated recursively. Simulations show comparable, and in some cases, even better results than those of a recently proposed improved MPF for high dimensions.
Identification of Counterfeit Notes using Pattern Recognition

Counterfeit notes are a significant problem faced by economies worldwide, as they not only disrupt the monetary system but also undermine trust in the financial system. In recent years, advances in technology have led to the development of sophisticated methods for detecting counterfeit currency. One such method is pattern recognition, which involves the use of software algorithms to analyse the visual characteristics of banknotes and identify patterns that indicate the authenticity of the note. This paper discusses the identification of counterfeit notes using pattern recognition and explores the effectiveness of this method in detecting counterfeit currency.
Learn Basic Statistical Analysis with Panda using Chatbots: ChatGPT in Programming Education

Background: Pandas is a powerful data analysis library for Python that provides easy-to-use tools for data manipulation, cleaning, and comprehensive data analysis. In this manuscript, we will learn the basics of statistical analysis using Pandas in Python. We will cover the critical concepts of descriptive statistics, such as mean, median, mode, and how to calculate these statistics on a Pandas dataframe. We will also explore how to solve errors.In this manuscript, we have used chat ChatGPT first version (2023 version). The manuscript shows that Chatbots can help to write codes. It can be used in programming education.
A Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization

The problem of removing mixed noise from images is a challenging problem due to their ill-posed nature. In this paper, we propose a Bayesian technique for the removal of mixed Gaussian-Impulse noise from images. The proposed optimization problem is derived from the maximum a posteriori (MAP) estimates of the noise statistics and makes use of a total variation (TV) and a nuclear norm of the Hessian as its two regularization terms. While TV ensures smoothness to the solution, the use of Hessian takes into account detail preservation in the final optimized output. The proposed problem is then solved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.
Quality evaluation method of agricultural talents training based on improved random forest algorithm

At present, the evaluation mechanism of higher education is still relatively old, with too much emphasis on theoretical assessment and insufficient assessment on practical application. Because the assessment mechanism is too single, it limits academic freedom and innovation. The inflexible educational mechanism will also lead to the shift of the focus of personnel training. Therefore, we must improve the evaluation mechanism of higher education. In this paper, the posterior probability is selected as an important part of the cost function. The cost function is combined with the Gini coefficient, which is the feature division index of random forests It is to achieve the purpose of considering the sample misclassification cost when the random forests weak classifier selects features. Squaring can increase the cost of misclassifying the actual class as a sample. It can also realize the evaluation of educational resources and verify the experimental results by comparing the performance of a single model. The results prove that this model is more suitable for evaluating the education and training of agricultural talents. It can also measure the application value of resources to maximize the use effect of educational resources.
Developing English learning materials for grade eight students at IOs International Education Yogyakarta

This research aims to develop English learning materials for grade eight students at IOs International Education. This research adopted the material development procedure proposed by Graves (2000: 105) and Dick, Carey and Carey (2005:1). The data were collected by using observation sheets and questionnaires. The data were quantitative and qualitative data. The quantitative data were analyzed using descriptive statistics, while the qualitative data were analyzed qualitatively. The results of this study are four units of learning materials. Each unit consists of 18 to 20 tasks. Each unit is organized into six components namely introductory page, warm-up, main tasks (spoken cycle and written cycle), summary, evaluation, and glossary. The research results show that the developed course materials of English for grade eight obtained a score of 4.414 (very good). Then, the students’ try-out results obtained 4.47 (very good). These results show that the developed English course materials of English for eighth grade students in IONs International Yogyakarta are appropriate.
Guided reading strategy and its implementation on grade 2 students in Sekolah Pilar Indonesia

The research is aimed to investigate teachers’ implementation of guided reading strategy on grade 2 students in Sekolah Pilar Indonesia. The objectives of the research are (1) to understand how teachers view guided reading as a teaching strategy to develop students’ reading comprehension; (2) to describe guided reading strategy implementation which is intended to support grade 2 students’ reading comprehension; and (3) to evaluate the learning outcome of guided reading strategy on grade 2 students’ reading comprehension. To gain the objectives, the research applied the qualitative case study method. The research subjects comprised four grade-2 teachers and twenty-seven grade-2 students in Sekolah Pilar Indonesia. The data were collected through teacher surveys, in-depth interviews, class observations, and document collection of students’ reading assessment records. This research reveals five findings. First, all grade-2 teachers view guided reading as a strategy that can answer inquiries about how to deal with students’ different levels of reading and as a context in which students are able to achieve comprehensive reading behaviours. Second, teachers are prepared to implement a guided reading strategy because the essential elements are administered well. Third, the seven essential elements of the guided reading strategy are effective to teach students reading. Fourth, the characteristics often indicators of teacher-self assessment in implementing guided reading strategy are necessarily managed to create an effective guided reading class. Fifth, the learning outcome related to students’ reading comprehension shows a significant result.
Inherit With Distillation and Evolve With Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory

As a front-burner problem in incremental learning, class incremental semantic segmentation (CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods have utilized knowledge distillation to transfer knowledge from the old model, they are still unable to avoid pixel confusion, which results in severe misclassification after incremental steps due to the lack of annotations for past and future classes. Meanwhile data-replay-based approaches suffer from storage burdens and privacy concerns. In this paper, we propose to address CISS without exemplar memory and resolve catastrophic forgetting as well as semantic drift synchronously. We present Inherit with Distillation and Evolve with Contrast (IDEC), which consists of a Dense Knowledge Distillation on all Aspects (DADA) manner and an Asymmetric Region-wise Contrastive Learning (ARCL) module. Driven by the devised dynamic class-specific pseudo-labelling strategy, DADA distils intermediate-layer features and output-logits collaboratively with more emphasis on semantic-invariant knowledge inheritance. ARCL implements region-wise contrastive learning in the latent space to resolve semantic drift among known classes, current classes, and unknown classes. We demonstrate the effectiveness of our method on multiple CISS tasks by state-of-the-art performance, including Pascal VOC 2012, ADE20K and ISPRS datasets. Our method also shows superior anti-forgetting ability, particularly in multi-step CISS tasks.
New Approach for an Affective Computing- Driven Quality of Experience (QoE) Prediction

In human interactions, emotion recognition is crucial. For this reason, the topic of computer-vision approaches for automatic emotion recognition is currently being extensively researched. Processing multi-channel electroencephalogram (EEG) information is one of the most researched methods for automatic emotion recognition. This article presents a new model for an affective computing-driven Quality of Experience (QoE) prediction. In order to validate the proposed model, a publicly available dataset is used. The dataset contains EEG, ECG, and respiratory data and is focused on a multimedia QoE assessment context. The EEG data are retained on which the differential entropy and the power spectral density are calculated with an observation window of three seconds. These two features were extracted to train several deep-learning models to investigate the possibility of predicting QoE with five different factors. The performance of these models is compared, and the best model is optimized to improve the results. The best results were obtained with an LSTM-based model, presenting an F1-score from 68% to 78%. An analysis of the model and its features shows that the Delta frequency band is the least necessary, that two electrodes have a higher importance, and that two other electrodes have a very low impact on the model's performances.
Web Application Using HTML, CSS, Java script and Java

This paper presents the development of a web application for online clothing sales using Java, Postgre SQL, and AJAX. The web application includes an admin dashboard, product page, cart page, login and sign-up page, landing page, and orders page. The project aimed to provide a user-friendly interface and a secure, efficient system for buying and selling clothes online. The web application was developed using servlets, JSP, HTML, CSS, JavaScript, and Postgre SQL database management system. AJAX was also employed to enhance user experience by enabling dynamic updates of web pages. The study discusses the methodology used for the development of the web application, including system analysis and design, database design, user interface design, and implementation. The results demonstrate the successful development of a reliable, efficient, and secure web application for online clothing sales. The application can help clothing businesses improve their online presence and reach a broader audience. Key Word: E-Commerce Web Application; HTML,CSS,JS,JAVA
Random Aggregation: Differential Privacy in k-Anonymity

Microaggregation is used to solve the problem of privacy protection.A new method of microaggregation is proposed in this paper to satisfy differential privacy.It adds random noises to the centroids of equivalence classes, which reduces the risks of differential attacks and link attacks.Since the process of k-partition is very timeconsuming, a top-down method is used to divide the original dataset into equivalence classes.Firstly, a matrix of distances is built and the original dataset is divided into several smaller datasets.And then the equivalence classes are formed by splitting datasets repeatedly.The experiments are conducted on two different datasets.Comparing with the existing methods, the results show that the proposed methods have the advantage of high efficiency and less information loss.
Examining the validity of eye tracking during the computerized Wisconsin card sorting test in a sample of stroke patients and healthy controls

Introduction Each year, approximately 50,000 Canadians, one million Americans, and millions of people worldwide are hospitalized for stroke. Cognitive impairment is common after experiencing a stroke and is known to affect functioning on daily tasks. While neuropsychological assessments are often employed to assess cognitive abilities and make inferences about functional capabilities, there is growing interest in integrating contemporary technologies to augment assessment. Eye tracking allows previously overlooked information, such as overt visual attention based on fixations and saccades, to be quantified to help elucidate how responses are made during testing.Method The current study investigated the validity of eye tracking during completion of the computerized Wisconsin Card Sorting Test (cWCST), a common test of higher level cognition, in a sample of inpatients recovering from stroke (n= 41) and a control group of healthy individuals (n = 46).Results Results provided supporting evidence for the construction, criterion, and ecological validity of eye tracking on the cWCST with inpatients recovering from a stroke. Specifically, eye tracking metrics differentiated between inpatients and controls; fixations on cWCST areas of interest differed between type of response (conceptual versus non-conceptual); and average time per fixation predicted functional status early after a stroke as well as recovery during inpatient rehabilitation, above-and-beyond cWCST scores. Time spent on testing negated the effects of fixation and saccade counts for predicting cWCST performance, due to the substantial overlap in variance.Conclusion Current findings of this preliminary study provided support for the validity of eye tracking, integrated with the cWCST, for inpatients recovering from a stroke. Implications and areas for future research are discussed.
Improving reading comprehension through lesson study among tenth-grade students at a public school in the city of Loja during 2021 – 2022 school year

The present research had as main objective to improve the reading comprehension through lesson study among tenth-grade students at a public school in the city of Loja during 2021-2022 school year. This skill is considered the most important because learners can obtain lexical knowledge and general information from written texts. Furthermore, this research used a quasi-experimental design with a mixed method approach in order to gather quantitative (scores) and qualitative (perceptions) data through the use of a pretest and posttest, as well as a questionnaire and field notes. The data analyzed demonstrated that students significantly improved (p ≤ 0.05) their reading comprehension after the application of the lesson study model and helped pre-service teachers develop professionally in their field. It was concluded that the lesson study model impacted students’ reading comprehension, mostly in the aspects of skimming and scanning, and that students found the strategies applied as relevant for their learning process. In addition, this model helped pre-service teachers to improve the creation of relevant lesson plans and to successfully conduct a class.
Visually meaningful image encryption combining with reversible data hiding in encrypted images

Abstract At present, most image encryption schemes directly change plaintext images into ciphertext images without visual significance, such ciphertext images can be detected and therefore subject to various attacks. To protect the content security and visual safety of images, a visually meaningful image encryption (VMIE) algorithm combining with Reversible Data Hiding in Encrypted Images (RDHEI) is proposed. First, the secret image is encrypted and additional information is embedded by Chen hyperchaotic system and prediction error expansion (PEE) method. Then the source image is encrypted by a Lorentz hyperchaotic system. Finally, bit-xor operation is performed on the resulting encrypted image and the result is saved in a cloud database to generate an indexing key using SHA-256,which enables the receiver to recover the secret image with the resource image and the key alone. The scheme combines with the RDHEI, improving the transmission efficiency and has no image quality problems.The experimental results and security analysis show that the image encryption scheme has sufficient key space, strong key sensitivity and wide applicability.
Analysis and optimization of validation procedure in blockchain-enhanced wireless resource sharing and transactions

To ensure the security of resource and intelligence sharing in 6G, blockchain has been widely adopted in wireless communications and applications. Although blockchain can ensure the traceability and non-tamperability of data in the concatenated blocks, it cannot guarantee the honest behaviors of users in the application before the generation of transactions. Thus, additional technologies are required to ensure that the source of blockchain data is reliable. In this paper, the detailed procedure is designed for the application-oriented task validation in the blockchain-enhanced computing resource sharing and transactions in ultra dense networks (UDN). The corresponding queuing model is built and analyzed with the consideration of the wireless re-transmission and the probability of malicious deception by users. Based on the analysis results, the UDN deployment is optimized to save network cost while ensuring latency performance. Numerical results verify our analysis, and the optimized system deployment including the number and service capacities of both base stations and mobile edge computing (MEC) servers are also given with various system settings.
Special Issue: Contemporary Policing Issues Addressed Through Collaborative Partnerships

This special issue of Policing: A Journal of Policy & Practice is the second in a series of editions created through the partnership between the American Society of Criminology’s Division of Policing (DoP) and Oxford University Press. Building on the inaugural edition, we aimed to maintain and enhance the focus on collaborative research by featuring the high-quality work accomplished by practitioners and academics working together to create knowledge. The development of this special issue was guided by key principles to ensure the final product would offer benefit to police leaders, researchers, analysts, and policy makers. All submitted manuscripts needed to include at least one DoP member and a co-author who is either a current police practitioner, a current criminal justice practitioner who collaborates with police agencies, or a member of a community organization that partners with police agencies. This requirement ensured that all contributions reflected police practitioner partnerships that exemplified how using scientific principles to address challenging issues can produce policy-relevant guidance. In this vein, the special issue was also crafted with a specific focus on four themes/challenges currently facing policing agencies and the field of policing:
Testing Hypotheses About the Value of a Parameter

An introduction to formal hypothesis testing in Statistics. Two approaches to hypothesis testing: one using critical points and one using P-values. The five steps to each approach with a particular focus on the example of coin flips and the example of the effectiveness of a new drug. An overview of the concepts of deduction and induction in Statistics. The definition of power and relevant formulas.
Continuous Random Variables

The definition of continuous random variables and their means and variances. An introduction to the normal distribution and the standardization procedure. A discussion of sums, averages, and differences of independent normal random variables. An exposition of the Central Limit Theorem and its use in Statistics. A discussion of the use of a continuity correction when using the normal distribution to approximate probabilities associated with discrete distributions.
Probability: One Discrete Random Variable

The definition of discrete random variables in probability theory. The definition of the probability distribution of a random variable and of parameters. An introduction to the binomial and hypergeometric distributions. Definitions and formulas for the mean and the variance of discrete random variables.
Non-parametric Tests

An introduction to non-parametric tests. The specific cases of the Wilcoxon signed-rank test and the Wilcoxon rank-sum test. An introduction to non-parametric permutation methods.
Paleozoic sedimentary processes and magmatism in Zamenwude area, Mongolia, and their implications for tectonic evolution of the Xingmeng orogenic belt

兴蒙造山带属于中亚造山带的东段,关于其演化过程存在两种主要观点:一种观点认为它是由古亚洲洋经历整个古生代的连续俯冲-碰撞过程后在早三叠世形成;另一种观点则认为古亚洲洋在晚泥盆世之前就通过俯冲-碰撞过程闭合,形成早-中古生代造山带,随后在石炭-二叠纪又经历了从陆内伸展到再次闭合的过程,并形成陆内造山带。蒙古国东南部扎门乌德地区出露各类古生代沉积岩和岩浆岩,可以为解决上述争议提供典型研究实例。本文通过沉积学、年代学和地球化学等多种手段综合研究,取得以下研究成果:(1)根据年代学和岩性特征,在该地区识别出三类古生代岩石组合,第一类是以黑云母二长花岗岩为代表的中志留世侵入岩,第二类是中泥盆世大套的粗碎屑岩-火山岩沉积旋回,第三类是不整合地沉积于早期造山带之上的二叠纪巨厚火山-沉积岩系。这三类岩石组合分别属于俯冲阶段的大陆边缘岛弧带岩浆岩、碰撞造山后期的上叠盆地以及叠加在早期造山带岩石圈之上的晚古生代陆内伸展时期的裂谷盆地的沉积。(2)利用研究区所有古生代碎屑锆石和岩浆岩全岩资料,揭示了该地区古生代时期地壳厚度变化趋势如下:500~425Ma的俯冲-碰撞过程造成地壳加厚;425~375Ma的碰撞造山后伸展过程使地壳变薄;375~350Ma地壳再次加厚,可能与造山带物质堆叠有关;350~275Ma地壳再次减薄,对应于广泛而强烈的晚石炭世-早二叠世火山岩,证明此时期岩浆活动的构造背景是区域伸展而不是挤压作用。(3)根据研究区出现的三类岩石组合特点,结合研究区以南的艾力格庙地区已有的研究成果,可以划分出五个早-中古生代造山带构造单元和两个叠加其上的晚古生代陆内造山带构造单元,揭示蒙古国扎门乌德地区经历了早-中古生代加积造山带和晚古生代陆内造山带等两个构造演化过程。本文研究为认识兴蒙造山带的两阶段构造发展提供了新资料。
A Semantic Network Encoder for Associated Fact Prediction

Abstract: Semantic network is a network of concepts connected by semantic relations. It contains two forms of binary semantic network and multiplex semantic network. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes a Semantic Network Encoder (SemNE), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing