Reviewerly/exHarmony huggingface dataset:
Geometry of sequential quantum correlations and robust randomness certification

Quantum correlations between the measurements of two or more separated observers play a fundamental role in many applications, such as randomness generation or key distribution. Recently, it was realized that sequential measurements (i.e., defined with a precise temporal ordering between subsequent measurements on a given system) can enhance the performance of these protocols. However, the theoretical understanding of how to maximize this performance is limited and the relation with the boundary of quantum correlations is unexplored. In the case of one party on one side and two sequential parties on the other, we study the geometry of quantum correlations and its implications for robust device-independent randomness generation. We identify a boundary for the set of these correlations expressed as a trade-off between the amount of nonlocality between different observers and show that this allows to generate the maximum possible device-independent randomness in our setting, namely two bits. We propose a practical protocol based on non-projective measurements that can produce the boundary correlations under ideal conditions, and address its robustness to noise, showing that it is improved compared to previous approaches. Finally, we implement our protocol in a proof-of-concept experiment based on a photonic implementation. With the obtained correlations we could certify more bits per state with respect to the standard CHSH protocol, proving that our protocol is feasible and robust to real-world imperfections. Our work paves the way for a full understanding of sequential quantum correlations and their exploitation for practical and efficient device-independent protocols.
Explicit error bounds for entanglement transformations between sparse multipartite states

The trade-off relation between the rate and the strong converse exponent for probabilistic asymptotic entanglement transformations between pure multipartite states can in principle be characterised in terms of a class of entanglement measures determined implicitly by a set of strong axioms. A nontrivial family of such functionals has recently been constructed, but their previously known characterisations have so far only made it possible to evaluate them in very simple cases. In this paper we derive a new regularised formula for these functionals in terms of a subadditive upper bound, complementing the previously known superadditive lower bound. The upper and lower bounds evaluated on tensor powers differ by a logarithmically bounded term, which provides a bound on the convergence rate. In addition, we find that on states satisfying a certain sparsity constraint, the upper bound is equal to the value of the corresponding additive entanglement measure, therefore the regularisation is not needed for such states, and the evaluation is possible via a single-letter formula. Our results provide explicit bounds on the success probability of transformations by local operations and classical communication and, due to the additivity of the entanglement measures, also on the strong converse exponent for asymptotic transformations.
Core Elements of Framework Design

The framework design is more complicated than imagined. Only completing the function development and ensuring it could be used is not enough. There is still a lot of learning acquired in that. For example, what bundles should our framework provide to users? What is the module format of the product? When users are not using the framework in the expected way, should appropriate warnings be printed to provide a better development experience and allow users to quickly locate problems? What is the difference between building a development version and building a production version? Hot Module Replacement (HMR) requires framework-level support, should we also consider it? In addition, when your framework provides multiple functions, and the user only needs a few of them, can the user choose to turn off other functions to reduce the packaging volume of the final resource? The above questions should be taken into account in the process of creating the framework.
Charting the Path of Financial and Monetary Economics: A Bibliometric Examination of Publication Patterns and Research Productivity

The dynamic landscape of financial and monetary economics has witnessed an evolution marked by scholarly publications that delve into the intricate dynamics of financial systems, monetary policies, and their far-reaching implications. Employing bibliometric analysis, this study maps the trajectory of these fields by scrutinizing publication patterns, research collaborations, influential authors, and emerging research areas. The analysis of comprehensive datasets derived from reputable academic databases provides a comprehensive view of the growth trends, collaborative networks, and research themes within the realm of financial and monetary economics. Utilizing a range of bibliometric indicators and visualization techniques, this research uncovers the historical journey and prevailing patterns that have shaped these vital domains. The findings of this study hold significance for researchers, policymakers, and academic institutions, aiding informed decision-making, innovative research, and effective policy formulation.
Data and Text Interpretation in Social Media: Urban Planning Conflicts

The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts on the example of data and text interpretation in social media. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the Big circle metro line (Southern section) in Moscow (RF). Data collection: 1 October 2020–10 June 2021. Number of tokens: 62 657 289. To analyze the content of social media, a multi-modal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on the user generated content and on digital footprints of users. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts.
BLOCKCHAIN BASED E-VOTING SYSTEM

Elections are a critical component of democracy, and their integrity is essential to ensuring a fair and just society. Traditional voting systems have a number of flaws, including a lack of transparency, vulnerability to tampering, and the possibility of human error. Blockchain technology has gained attention in recent years for its potential to address these issues while also improving election security and transparency. This research paper describes the design and implementation of a blockchain-based e-voting system. The proposed system uses a distributed ledger to record votes securely and transparently, making it difficult for anyone to manipulate or alter the results. The system safeguards voter anonymityand prevents double voting. The system uses Ethereum, a popular blockchain platform, and smart contracts to automate the voting process. Smart contracts handle vote counting and tallying, which eliminates the need for centralised authority. By requiring a majority of network nodes to agree on the validity of each transaction, a consensus algorithm ensures thesystem's security. The proposed blockchain-based electronic voting system outperforms traditional voting systems in terms of transparency, security, and efficiency. Because of the system's design, it is easy to integrate with existing election processes, making it a viable option for future adoption. Both Simply Voting and Election Buddy provide secure and customizable online voting services for businesses, blockchain-based e-voting systems offer distinct advantages in terms of security, transparency, and decentralisation.[21][22]
APRCOIE: A Open Information Extraction System for Chinese

Open information extraction (OIE) is a key task in natural language processing. Compared to the flourishing development of OIE systems in English, there are very few high-quality Chinese OIE systems that are publicly available. APRCOIE is a system to conduct open information extraction for Chinese text data. Differing from both traditional rule-based approaches and learning-based methods, the new system innovatively employs automated methods to explore the nature of complex Chinse literal characteristics, and thus generate a large number of extraction rules and subsequently establish the extraction model. The feature of low resource demanding makes APRCOIE is easy to be deployed in various real applications.
PENGEMBANGAN SCRAPBOOK MATERI KALIMAT PERSUASIF DALAM PELAJARAN BAHASA INDONESIA DI KELAS IV SD NEGERI 56 GRESIK

Writing activity is a way for someone to express an idea or a way to communicate, indirectly conveying messages in written form. In Indonesian language lessons, it is closely related to material that requires students to be able to write good and correct Indonesian. As is the case with persuasive sentence material in grade IV elementary school, in learning about persuasive sentences students still have difficulty understanding the subject matter presented. The main difficulty factors experienced by students are that they are not used to writing and making persuasive sentences, limited vocabulary so it is still difficult to arrange words into persuasive sentences. So from the existing problems, researchers tried to use scrapbook media assistance in the learning process in the classroom, with the help of scrapbooks being able to attract students' attention and increase learning interest in following the learning process because previously in the learning process they had never used media assistance.
A Study on the Influence of Ecological Translation Theory on University English Translation Teaching in the Context of Big Data Analysis

Abstract This paper aims to investigate the influence of ecological translation theory on university English translation teaching, constructs a regression prediction model prediction result evaluation index on the basis of a regression model, uses the regression prediction model to analyze the data of classroom percentage in the process of university English teaching, and combines the prediction result evaluation index to study the influence of ecological translation theory on university English translation teaching, by analyzing the English translation content and translation By analyzing the two factors of English translation content and translation standards, suggestions are made for the subsequent development of university English translation teaching. The result data showed that in the university English translation classroom, the teacher-led time accounted for 60 minutes, the students’ independent discussion time was only 20 minutes, and there were only 10 minutes to test everyone’s classroom results. In terms of classroom content, the highest percentages of vocabulary content and grammar content were 30%, 40%, and 35%, respectively, and the lowest percentages of translation content were 10%, 5%, and 5%, respectively. The research data in this paper provide an effective reference for university English translation teaching and promote the development and progress of university English translation teaching.
varTestnlme: an R package for Variance Components Testing in Linear and Nonlinear Mixed-effects Models

The issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random. While tests for fixed effects are available in R for models fitted with lme4, tools are missing when it comes to random effects. The varTestnlme package for R aims at filling this gap. It allows to test whether any subset of the variances and covariances corresponding to any subset of the random effects, are equal to zero using asymptotic property of the likelihood ratio test statistic. It also offers the possibility to test simultaneously for fixed effects and variance components. It can be used for linear, generalized linear or nonlinear mixed-effects models fitted via lme4, nlme or saemix. Theoretical properties of the used likelihood ratio test are recalled, numerical methods used to implement the test procedure are detailed and examples based on different real datasets using different mixed models are provided.
Advancements in Robotic Integration: Navigating Everyday Environments and Enhancing Interaction Through Neural Networks

The integration of robotics into various aspects of human life has prompted anurgent need for robots to possess essential skills such as object recognition andnavigational proficiency. Recent strides in robotics technology have usheredrobots into domains like surveillance, quality control, and assisted living. Thisstudy delves into the optimization of neural networks for enabling robotnavigation tasks. The investigation employs a support vector machine (SWM)structure and compares its performance against other neural networkarchitectures using the same dataset derived from ultrasound sensormeasurements. The SWM exhibits superior accuracy, achieving a remarkableaverage classification accuracy of 99.93%, outperforming other architectures.The findings underscore the potential of neural networks, particularly the PNN,in advancing robot navigation and fostering their integration into variousapplications. The study contributes to the growing field of robotics by offeringinsights into the effectiveness of different neural network models for enhancingmobile robot navigation capabilities.
A Study of Forward-Forward Algorithm for Self-Supervised Learning

Self-supervised representation learning has seen remarkable progress in the last few years, with some of the recent methods being able to learn useful image representations without labels. These methods are trained using backpropagation, the de facto standard. Recently, Geoffrey Hinton proposed the forward-forward algorithm as an alternative training method. It utilizes two forward passes and a separate loss function for each layer to train the network without backpropagation. In this study, for the first time, we study the performance of forward-forward vs. backpropagation for self-supervised representation learning and provide insights into the learned representation spaces. Our benchmark employs four standard datasets, namely MNIST, F-MNIST, SVHN and CIFAR-10, and three commonly used self-supervised representation learning techniques, namely rotation, flip and jigsaw. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-)supervised training, the transfer performance is significantly lagging behind in all the studied settings. This may be caused by a combination of factors, including having a loss function for each layer and the way the supervised training is realized in the forward-forward paradigm. In comparison to backpropagation, the forward-forward algorithm focuses more on the boundaries and drops part of the information unnecessary for making decisions which harms the representation learning goal. Further investigation and research are necessary to stabilize the forward-forward strategy for self-supervised learning, to work beyond the datasets and configurations demonstrated by Geoffrey Hinton.
DIDACTIC CULTURE OF MEDICAL UNIVERSITY TEACHERS AND STUDENTS IN THE CONDITIONS OF WAR

The aim: To analyse the essence of the concept of “didactic culture”, to outline its structure, to describe the peculiarities of the development of the didactic culture of teachers and students in the conditions of war, to characterize the role of information technologies in its formation. Materials and methods: A number of scientific methods were used in the research: a comparative-historical method to consider retrospectively the concept of didactic culture; bibliographic method to identify the existing source base; content analysis for analytical processing of literature; chronological method to analyse the study of didactic culture in a time sequence; structural and systemic analysis to clarify its specifics at different levels; comparative analysis to establish common and specific features in the system of didactic culture development in different countries; a questionnaire method for studying students’ opinions about the structure of speech culture as a component of didactic culture; a method of generalization and specification that contributes to the formulation of conclusions and recommendations on the didactic culture development. Conclusions: The didactic culture is considered as a socio-pedagogical phenomenon and an important component of professional and pedagogical culture. The didactic culture development in wartime conditions should be based on the principles of humanism, praxeology and acmeology, with the account of the challenges and features of the information society and the globalized world. In this context, systematic comparative studies will contribute to the use of the experience of other countries, and the development of psychodidactics will allow to approach the understanding of the deep patterns of the process of didactic culture development.
太さの異なる二本の棒が生み出すGestaltにより惹起されるベルベット錯触

In tactile virtual reality (VR), there is a limit to realizing the real objects’ textures only by hardware. We focus on Velvet Hand Illusion (VHI), in which a person rubs his/her hands together on either side of two wires stretched in a parallel to produce a sensation of rubbing a smooth virtual surface like velvet on the palm. We conducted a psychophysical experiment by changing the wire diameter to investigate the relationship between the smoothness of virtual surface and wire diameter, and one between the thickness of the virtual surface and wire diameter. Based on the experimental results, we newly proposed the closure factor defined by the two hands as palms together factor. Since a virtual surface doesn’t occur with only one hand in VHI, this factor is essential for the induction of VHI in tactile Gestalt, which cannot be observed in sight or hearing sensations.
Combining Mindwave, MPU6050, Ultrasonic, and Internet of Things for a reliable, safe, and monitored wheelchair control system

This paper proposes the development of a wheelchair control system by combining a Mindwave EEG (electroencephalogram) sensor, an MPU6050 inertial sensor, an ultrasonic sensor, and IoT technology. Stroke survivors often face challenges in controlling their wheelchairs due to cognitive and physical impairments, requiring specific solutions to ensure their safety and comfort. The integration of the Mindwave EEG sensor captures the user's cognitive state, such as level of attention and focus, while the MPU6050's inertial sensor monitors physical movement and changes in head orientation. Additionally, the ultrasonic sensor is incorporated to detect obstacles and provide proximity information for safe navigation. This wheelchair control system is also linked with IoT technology to enable monitoring of the user's cognitive, physical, and environmental condition and remote assistance. In this paper, to implement this wheelchair control system, a T-BEAM with an ESP32 controller is used, which is equipped with GPS to provide wheelchair location data and LORA radio communication for remote data transmission, and a TTGO LORA32 to forward the data to Arduino IoT Cloud service over Wi-Fi. The results of the development show that the wheelchair control system is safer and more monitored, and users are easier and more flexible in operating wheelchairs.
Arabic cyberbullying detecting using ensemble deep learning technique

&lt;span lang="EN-US"&gt;There has been a huge growth in recent years interest in studies on abusive language and cyberbullying detection due to its effects on both individual victims and societies. Hate speech, bullying, racism, aggressive content, harassment and other forms of abuse have all significantly increased as a result of Facebook, Instagram, and other social media platforms (SMPs). Since there is a significant need to detect, control, and prohibit the circulation of offensive content on social networking sites, we undertook this study to automate the identification of abusive language or cyberbullying. Arabic data set is balanced and will be used in the offensive detection process. Recently, ensemble machine learning has been used to increase the effectiveness of categorization models. Arabic detection is more precise given that each spatial feature text can make references to every other contextual piece of information. The authors utilized a model that merged convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) and inverse document frequency gated recurrent unit (GRU) in a hybrid fashion without any post-processing. Our work outperformed every other publicly released cutting-edge ensemble model in the specifications of the official deep learning challenge. The findings indicate that the three-layer inverse document frequency long short-term memory (LSTM) classifier surpassed other classifiers in accuracy with a score of 92.75% compared to different algorithms.&lt;/span&gt;
A survey on deepfake video detection datasets

Deepfake video has usefulness in entertainment and multimedia technology, however, the danger of deepfake is significant to the social, economical, and political sectors so far. Specifically, to diverge any public opinion by generating fake news and spreading misleading information, national security may be under risk due to misrepresenting statements given by political leaders. The creation of such manipulated videos are getting easier day by day and at the same time it is necessary to detect and prevent them. In order to do that, researchers are creating challenging fake video databases for artificial intelligence (AI) based detection models to contribute to the research. This paper reviewed the existing deepfake video detection datasets available online and used in the previous research articles. We analyzed the literature from two different perspectives, datasets and detection models. The goal of this study is to introduce all publicly available datasets in this field including the discussion of techniques used to generate the data. In addition to our contribution, we showed a result comparison among different deepfake datasets and discussed the findings.
A Dual Blind Watermarking Method for 3D Models Based on Normal Features

Digital watermarking technology is an important means to effectively protect three-dimensional (3D) model data. Among them, "blind detection" and "robustness" are key and difficult points in the current research of digital watermarking technology based on 3D models. In order to realize the blind detection of a watermark and improve its robustness against various common attacks at the same time, this paper proposes a dual blind watermarking method based on the normal feature of the centroid of first-ring neighboring points. The local spherical coordinate system is constructed by calculating two different normal vectors, and the first pattern watermark and the second random binary sequence watermark are embedded, respectively. The experimental results show that this method can not only realize the blind detection of dual watermarks, but also have the ability to resist common attacks such as translation, rotation, scaling, cropping, simplification, smoothing, noise, and vertex reordering to a certain extent.