Reviewerly/exHarmony huggingface dataset:
Can Chirality Flips Occur in a Multi-Fold Universe? What About Conservation Laws II?

In a multi-fold universe, gravity emerges from Entanglement through the multi-fold mechanisms. As a result, gravity-like effects appear in between entangled particles that they be real or virtual. Long range, massless gravity results from entanglement of massless virtual particles. Entanglement of massive virtual particles leads to massive gravity contributions at very smalls scales. Multi-folds mechanisms also result into a spacetime that is discrete, with a random walk fractal structure and non-commutative geometry that is Lorentz invariant and where spacetime nodes and particles can be modeled with microscopic black holes. All these recover General relativity at large scales and semi-classical model remain valid till smaller scale than usually expected. Gravity can therefore be added to the Standard Model. This can contribute to resolving several open issues with the Standard Model (SM) without new Physics other than gravity. These considerations hint at a even stronger relationship between gravity and the Standard Model.In past papers, we have argued that chirality flips can occur in the presence of non-negligible gravitational effects. In multi-fold universes, it led us propose mechanisms to justify the absence of proton decay and the possible existence in flight of right-handed neutrinos and left-handed anti-neutrinos. However, we based these proposals on helicity flips at scales above mass acquisition by the Higgs mechanisms. We did not discuss the obvious implications on conservation of the weak hypercharge or weak isospin. This paper does so.Text added on 8/20/22: the chirality flips of massless particles above the multi-fold gravity electroweak symmetry breaking energy scale can be attributed to spacetime orientation fluctuations, as particle appear.
Detection and Diagnosis of Learning Disabilities in Children of Saudi Arabia with Artificial Intelligence

Abstract Learning Disability (LD) is an umbrella term for various learning difficulties. It is a neurological disorder that affects the brain of children and hinders their capacity to do one or more specified activities. Youngsters with learning disabilities are neither sluggish nor mentally retarded. This paper utilizes Speech, as an acceptable motor activity to address the problem and its solution. In this work, Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) are used to extract speech characteristics. For every speech sample, 205 characteristics are retrieved, consisting of 13 acoustical features from MFCC, 128 features from LPCC, and 64 features from power spectrum density. In addition, linear predictive coding-based parameterization is used to every speech sample to extract Weighted Linear Predictive Cepstral Coefficients (WLPCC) and Linear Predictive Coding features (LPC). The Extracted Features are identified using a Deep Convolutional Neural Network (DCNN), and their performance is analyzed. It is found that the correlation between speech characteristics and various classification models may be assessed to get the highest classification accuracy. These findings demonstrate that the suggested feature extraction approaches and classification models are highly successful in categorizing the speech of intellectually impaired and typically developing youngsters.
Workflow for beta-range forest plots, bootstrap ridgeline plots, and bootstrap violin plots v2

Regression is a widely used statistical method in various research areas, such as educational psychology, and it is common to display regression coefficients in tables. Although tables contain dense information, they can be difficult to read and interpret when they are extensive. To address this issue, we present three innovative visualizations that enable researchers to present a large number of regression models in a single plot. We demonstrate how to transform simulated data and plot the results, which produce visually appealing representations of regression results that are both efficient and intuitive. These visualizations can be applied for screening models in the selection stage or reporting results in research papers. Our method is reproducible using the provided code and can be implemented using free and open-source software routines in R.
Mengidentifikasi Tantangan yang Dihadapi oleh Siswa Kelas 3 SDN 171/1 Bajubang Laut dalam Kemampuan Berhitung melalui Observasi

Learning mathematics has a special purpose in elementary schools so that students can develop numeracy skills as a tool in everyday life, have basic knowledge of mathematics as a provision for further learning in junior high schools, and students can be logical, critical, careful, creative and positive. This research uses a qualitative method with a type of case study research conducted to understand a particular case or situation in depth. The challenge of numeracy ability can have implications for various aspects of a person’s life, be it in daily life, education, and career.
Crossed product codes

Bernal et al. provided a necessary and sufficient condition for a linear code to be realized as an ideal in a finite group algebra and De La Cruz and Willems proved a similar result for ideals in twisted group algebras. In this paper, we extend this characterization to crossed products. Furthermore, we determine conditions for some crossed product codes to be self-dual.
PENERAPAN METODE INQUIRI DALAM MENINGKATKAN KEMAMPUAN MENULIS TEKS EKSPLANASI SDN NURUL JIHAT DESA PEMEPEK

The purpose of this study was to improve the quality of the process and learning outcomes of writing explanatory texts at SDN Nurul Jihat Pemepek Village using the inquiry method. The method used is quantitative research that leads to class action. The techniques used in this study through tests, observation and documentation. In this study, the application of the inquiry method can improve the ability to write explanatory texts using the inquiry method in class VI students at SDN Nurul Jihat Pemepek Village, Indonesia in the 2022/2023 academic year. Learning outcomes were marked by an increase in the number of students who reached the mastery limit in the ability to write process explanation texts, namely (a) in the first cycle of 60.39%, (b) in the second cycle the learning mastery results were obtained by 71.42%, and (c ) in cycle III the results of learning completeness were 79.75%.
THE STUDENTS' MEMORY LANGUAGE LEARNING STRATEGY IN MASTERING ENGLISH MATERIALS AT VOCATIONAL SCHOOL LEVEL

This study aims to investigate students' perceptions of the Memory language learning strategy in mastering various English as a Foreign Language (EFL) language skills in SMK N 7 Semarang. A descriptive qualitative method was used with a questionnaire survey to collect data from 34 students of SMK N 7 Semarang who completed the nine items of the Strategy Inventory for Language Learning (SILL) questionnaire and a Background Characteristics survey. Three simultaneous actions in data analysis, according to Miles &amp; Huberman, (1994): data reduction, data display, conclusions, and drawing/verification. The findings showed from the frequently used memory language strategy was to remember new words or phrases by remembering their location on the page on the board. The conclusions of the study's findings indicate that how English is used in practice affects how well it was learned. On the other hand, the findings showed a connection between using memory strategies and effectively learning English the most popular memory language learning strategy was remembering new words or phrases by remembering their location on the page, on the board.
Secure Blockchain-based Framework for Smart City Applications

A city is considered to be smart if it makes use of information and communication technology (ICT) to boost its operational efficacy, enhance the level of government services and citizen welfare, and disseminate information to the general public. The fundamental goal of a smart city is to optimise the efficiency of city operations, stimulate economic progress, and improve the quality of life for its citizens. This is accomplished by exploiting cutting-edge technical advancements and conducting in-depth data analysis. The smart city’s communication happens through various devices, i.e., smart Internet of Things (IoT) devices, servers and smartphone. The data is exchanged through an insecure medium (i.e., the Internet). Therefore, the potential adversary gets a chance to launch some cyber attacks over this. Hence we need to deploy some security mechanisms in the smart city’s applications to provide some security to it. Therefore, in this paper, we propose a blockchain-based framework for the secure data exchange of the smart city’s applications and services. Furthermore, the practical implementation of the proposed framework is also provided to check its usability in the real-time environment.
SPEAKING SKILL IN 21ST-CENTURY: STUDENTSâ€™ PERCEPTIONS AND CHALLENGES IN ENGLISH LANGUAGE TEACHING

It is crucial to learn a new language, especially English, an international language. Nowadays, having the ability to speak English is an essential ability for students to have. Good speaking skills will help them communicate with each other, speak their minds, pronounce words correctly, and construct sentences in grammatically correct terms. This research aims to analyze the students' perception and challenges of speaking skills in the 21st -century. Twenty-six students were taken randomly from three classes enrolled in the Speaking for Informal Interaction of the English Language Education Study Program, Universitas Mahasaraswati Denpasar. The researchers used open-ended questionnaires and semi-structured interviews and analyzed using the qualitative research approach. Recordings and documents supplemented the instruments. The collected data were transcribed, classified, interpreted, and concluded as the findings of this study. The finding reveals students' perception of the need for English textbooks to support their communicative competencies and some challenges affecting their ability to master speaking skills. Lacking in terms of vocabulary, grammar, pronunciation, self-confidence, lack of knowledge, fear of making mistakes, low motivation, fear of criticism, shyness, and nervousness are some challenges the students face. Based on the students' perceptions and challenges, the lecturers are suggested to develop a textbook that can accommodate the student's needs and challenges. Students need a coursebook to build confidence to speak in public.
Psychological and pedagogical significance of communication technologies in personnel training for AIC

The purpose of this article is to study the importance of communication skills among teachers and IT specialists. Due to the fact that currently information and communication technologies are rapidly developing, and also actively affect all spheres of human activity, there is a huge demand on the labor market for IT specialists and highly qualified teachers, especially when training personnel for the agro-industrial complex. Employers' requirements for such specialists are expanding and becoming tougher. Often, applicants for such positions are aimed at developing their "hard" skills, underestimating the importance of "soft" skills. Having strong communication skills is an important component of success in the IT industry of the agro-industrial complex. This study aims at confirming the most relevant scientific approaches under modern conditions. Such approaches should identify effective mechanisms and modern organisational tools for the professional guidance activities of teachers in the agricultural education system. In modern universities, various scientific and educational technologies are used, and an important feature lies in their continuous improvement. Priority in educational innovations should be methods with individual moral attitudes that are based on the ethics of professionals, on their critical thinking, the ability to present and defend their own opinion, become a multifunctional mentor, be able to make timely, adequate and quick decisions and respond to all changes in the educational process. Today, teachers and students should be equally interested in the desire to receive high-quality innovative and technological education. They should be aimed at achieving success, which is possible only with the competent interaction of team members with each other.
Integration of a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles

We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multiprocess software architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the achievement of intentions. Since driving on roads implies huge dynamic considerations, we tackle both reactivity and context awareness considerations on the execution loop of the vehicle. While the proposed architecture gradually offers 4 levels of reactivity, from arch-reflex to the deep modification of the previously built execution plan, the observation module concurrently exploits noise filtering and introduces frequency control to allow symbolic feature extraction while both fuzzy and first order logic management are used to enforce consistency and certainty over the context information properties. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels.
Blockchain-Empowered Federated Learning Through Model and Feature Calibration

With the proliferation of computationally powerful edge devices, edge computing has been widely adopted for wide-ranging computational tasks. Among these, edge artificial intelligence (AI) has become a new trend, allowing local devices to work cooperatively and build deep learning models. Federated learning is one of the representative frameworks in distributed machine learning paradigms. However, there are several major concerns with existing federated learning paradigms. Existing distributed frameworks rely on a central server to coordinate the computing process, where such a central node may raise security concerns. Federated learning also relies on several assumptions/requirements, e.g., independent and identically distributed (i.i.d.) data and model homogeneity. Since more and more edge devices are able to train lightweight models with local data, such models are normally heterogeneous. To tackle these challenges, in this paper, we develop a blockchain-empowered federated learning framework that enables learning in a fully decentralized manner while taking model heterogeneity and data heterogeneity into account. In particular, a federated learning framework with a heterogeneous calibration process, i.e., Model and Feature Calibration (FL-MFC), is developed to enable collaboration among heterogeneous models. We further design a two-level mining process using blockchain to enable the secure decentralized learning process. Experimental results show that our proposed system achieves effective learning performance under a fully heterogeneous environment.
Augmented Reality in Orthognathic Surgery: A Multi-Modality Tracking Approach to Assess the Temporomandibular Joint Motion

Augmented Reality (AR) is a rapidly emerging technology finding growing applications in various surgery domains. In this study, we develop and test the feasibility of a novel AR application for Microsoft HoloLens2 Head Mounted Display (HMD) to support surgeons in the clinical evaluation of temporomandibular joint (TMJ) alterations that may require surgery. The application implements a multi-modality tracking based on the combination of a marker-less and a marker-based approach to simultaneously track the fixed part of the joint and the moving mandible. The AR application was tested on a volunteer performing the TMJ task, i.e. the opening and closing of the mouth. During the task, video recordings were taken from the HoloLens cameras to derive the trajectories as well as the horizontal and vertical excursions of the jaw movements. The AR-derived TMJ movements were then compared with standard kinesiographic acquisitions. The results demonstrated the feasibility of the proposed AR application in superimposing the 3D visualization of the joint to the patient’s head, thus facilitating the diagnostic evaluation for the surgeon. The AR-derived trajectories were consistent with the kinesiography curves. Future improvements are needed to reduce the encumbrance of the tracker and to provide additional visual cues for the surgeon. The presented methodology can be easily transferred to other surgical applications which require simultaneous tracking of two anatomical parts, such as the case of bone repositioning to a pre-planned target location in maxillofacial and orthopedic surgery.
DiffDefense: Defending Against Adversarial Attacks via Diffusion Models

This paper presents a novel reconstruction method that leverages Diffusion Models to protect machine learning classifiers against adversarial attacks, all without requiring any modifications to the classifiers themselves. The susceptibility of machine learning models to minor input perturbations renders them vulnerable to adversarial attacks. While diffusion-based methods are typically disregarded for adversarial defense due to their slow reverse process, this paper demonstrates that our proposed method offers robustness against adversarial threats while preserving clean accuracy, speed, and plug-and-play compatibility. Code at: https://github.com/HondamunigePrasannaSilva/DiffDefence .
Error analysis of Students’ Recount Writing Text

In this research, the researcher intended to find the significant error that occur in class X MIPA 1 SMAN 11 Kendari. By this research, the aim was to describe the main problem, the most common error which students made in their recount text writing at X MIPA 1 SMA Negeri 11 Kendari. Data is collected using Students writing draft. Students were asked to write a recount text at least 100 words in 45 minutes. The results of data analysis revealed that omission of auxiliary was the common error done by students X MIPA 1 SMA Negeri 11 Kendari. In students’ writing there are several errors that researcher have found that namely; omission of articles, omission of preposition, omission of apostrophe, omission of auxiliary, omission of countable and uncountable nouns. Based on the results, analysis students’ error is giving many the descriptions of students’ writing ability.
Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments

Neural Radiance Fields, or NeRFs, have drastically improved novel view synthesis and 3D reconstruction for rendering. NeRFs achieve impressive results on object-centric reconstructions, but the quality of novel view synthesis with free-viewpoint navigation in complex environments (rooms, houses, etc) is often problematic. While algorithmic improvements play an important role in the resulting quality of novel view synthesis, in this work, we show that because optimizing a NeRF is inherently a data-driven process, good quality data play a fundamental role in the final quality of the reconstruction. As a consequence, it is critical to choose the data samples -- in this case the cameras -- in a way that will eventually allow the optimization to converge to a solution that allows free-viewpoint navigation with good quality. Our main contribution is an algorithm that efficiently proposes new camera placements that improve visual quality with minimal assumptions. Our solution can be used with any NeRF model and outperforms baselines and similar work.
STint: Self-supervised Temporal Interpolation for Geospatial Data

Supervised and unsupervised techniques have demonstrated the potential for temporal interpolation of video data. Nevertheless, most prevailing temporal interpolation techniques hinge on optical flow, which encodes the motion of pixels between video frames. On the other hand, geospatial data exhibits lower temporal resolution while encompassing a spectrum of movements and deformations that challenge several assumptions inherent to optical flow. In this work, we propose an unsupervised temporal interpolation technique, which does not rely on ground truth data or require any motion information like optical flow, thus offering a promising alternative for better generalization across geospatial domains. Specifically, we introduce a self-supervised technique of dual cycle consistency. Our proposed technique incorporates multiple cycle consistency losses, which result from interpolating two frames between consecutive input frames through a series of stages. This dual cycle consistent constraint causes the model to produce intermediate frames in a self-supervised manner. To the best of our knowledge, this is the first attempt at unsupervised temporal interpolation without the explicit use of optical flow. Our experimental evaluations across diverse geospatial datasets show that STint significantly outperforms existing state-of-the-art methods for unsupervised temporal interpolation.
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability

Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them susceptible to spurious variations -- changes in task-irrelevant components such as background distractors or lighting conditions. In this paper, we propose a visual model-based RL method that learns a latent representation resilient to such spurious variations. Our training objective encourages the representation to be maximally predictive of dynamics and reward, while constraining the information flow from the observation to the latent representation. We demonstrate that this objective significantly bolsters the resilience of visual model-based RL methods to visual distractors, allowing them to operate in dynamic environments. We then show that while the learned encoder is resilient to spirious variations, it is not invariant under significant distribution shift. To address this, we propose a simple reward-free alignment procedure that enables test time adaptation of the encoder. This allows for quick adaptation to widely differing environments without having to relearn the dynamics and policy. Our effort is a step towards making model-based RL a practical and useful tool for dynamic, diverse domains. We show its effectiveness in simulation benchmarks with significant spurious variations as well as a real-world egocentric navigation task with noisy TVs in the background. Videos and code at https://zchuning.github.io/repo-website/.