Reviewerly/exHarmony huggingface dataset:
Parallel Tempering With a Variational Reference

Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating between the posterior distribution and a fixed reference distribution, which is typically chosen to be the prior. However, in the typical case where the prior and posterior are nearly mutually singular, PT methods are computationally prohibitive. In this work we address this challenge by constructing a generalized annealing path connecting the posterior to an adaptively tuned variational reference. The reference distribution is tuned to minimize the forward (inclusive) KL divergence to the posterior distribution using a simple, gradient-free moment-matching procedure. We show that our adaptive procedure converges to the forward KL minimizer, and that the forward KL divergence serves as a good proxy to a previously developed measure of PT performance. We also show that in the large-data limit in typical Bayesian models, the proposed method improves in performance, while traditional PT deteriorates arbitrarily. Finally, we introduce PT with two references -- one fixed, one variational -- with a novel split annealing path that ensures stable variational reference adaptation. The paper concludes with experiments that demonstrate the large empirical gains achieved by our method in a wide range of realistic Bayesian inference scenarios.
Context-Aware Video Reconstruction for Rolling Shutter Cameras

With the ubiquity of rolling shutter (RS) cameras, it is becoming increasingly attractive to recover the latent global shutter (GS) video from two consecutive RS frames, which also places a higher demand on realism. Existing solutions, using deep neural networks or optimization, achieve promising performance. However, these methods generate intermediate GS frames through image warping based on the RS model, which inevitably result in black holes and noticeable motion artifacts. In this paper, we alleviate these issues by proposing a context-aware GS video reconstruction architecture. It facilitates the advantages such as occlusion reasoning, motion compensation, and temporal abstraction. Specifically, we first estimate the bilateral motion field so that the pixels of the two RS frames are warped to a common GS frame accordingly. Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames at arbitrary times. Furthermore, we derive an approximated bilateral motion field model, which can serve as an alternative to provide a simple but effective GS frame initialization for related tasks. Experiments on synthetic and real data show that our approach achieves superior performance over state-of-the-art methods in terms of objective metrics and subjective visual quality. Code is available at \url{https://github.com/GitCVfb/CVR}.
ON THE MATHEMATİCAL MODEL OF STEGANOGRAPHY AND SOME OF İTS APPLİCATİONS

One of the most important areas of information technology in modern times is information security. Steganography is one of the most important tools in protecting the security of electronic information. In this regard, research in the field of steganography is one of the most pressing issues of our time. Therefore, the publication of this article is relevant.
Sistem Informasi Penerimaan Siswa Baru SMP Negeri Kabupaten Nias Utara Berbasis Web

Registration of new students is an annual cycle experienced by all educational institutions, both public and private institutions. The education office of North Nias Regency has noted that the number of junior high schools, especially public junior high schools in North Nias Regency is 49 units. Of all the schools, the admission of new students is still manual. Judging from the distance, time, and opportunities for parents who want to register their children to school are limited, which is caused by work and other activities, and especially at the time of registration of new students, schools are always filled with prospective students and parents of students who want to register. So that the assigned officer or teacher becomes overwhelmed and sometimes the file that has been received by the officer or teacher assigned is scattered or does not match the file. So that it makes teachers who want to compile files overwhelmed or confused in compiling them. North Nias district education office even from the school also needs a web-based information system that is able to process data for prospective new students. This system is also expected to be able to minimize data collection errors and also file preparation by officers or teachers who have been assigned
The Efforts to improve Children's Expressive Language Ability through Picture Book Media (Big Book)

The golden age or period for early childhood (0-6 years) is a period where children experience very rapid growth and development, which then at the age of 4-6 years is a period where they take formal education such as Kindergarten. This period is a good time to develop language skills, physical, emotional, social, discipline and all the abilities of the child. In addition, family also plays an important role in the development of the children’s potential and growth. One of the important aspects in the growth and development of children is language skills, as the language is a means of communication with parents, teachers, and peers who are their present social environment. Children at this age cannot be separated from their parents or family as the children initially learn language from adult examples. At an early age, problems often occur because sometimes children cannot speak clearly and firmly which is caused by the slow development of children in language due to the lack of stimulus provided. Therefore, this study aims to improve expressive language skills of early childhood through the media of the Picture Book (Big Book).
REINFORCEMENT LEARNING FOR AUTONOMOUS 3D DATA RETRIEVAL USING A MOBILE ROBOT

Abstract. 3D data retrieval is required in various fields such as an industrial monitoring, agriculture, and robotics. Recent advances in photogrammetry and computer vision allowed to perform 3D reconstruction using a set of images captured with uncalibrated camera. Such technique is commonly known as Structure-from-Motion. In this paper, we propose a reinforcement learning framework RL3D for online strong camera configuration planning onboard of a mobile robot. The mobile robot consists of a skid-steered wheeled platform, a single-board computer and an industrial camera. Our aim is developing a model that plans a set of robot location that provide a strong camera configuration. We developed an environment simulator to train our RL3D framework. The simulator was implemented using a 3D model of the indoor scene and includes a model of robot’s dynamics. We trained our framework using the simulator and evaluated it using a virtual and real environments. The results of the evaluation are encouraging and demonstrate that the controller model successfully learns simple camera configurations such as a circle around an object.
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting

While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may share dissimilar attributes and distinct labels. Therefore, GNNs smoothing node proximity holistically may aggregate both task-relevant and irrelevant (even harmful) information, limiting their ability to generalize to heterophilic graphs and potentially causing non-robustness. In this work, we propose a novel edge splitting GNN (ES-GNN) framework to adaptively distinguish between graph edges either relevant or irrelevant to learning tasks. This essentially transfers the original graph into two subgraphs with the same node set but exclusive edge sets dynamically. Given that, information propagation separately on these subgraphs and edge splitting are alternatively conducted, thus disentangling the task-relevant and irrelevant features. Theoretically, we show that our ES-GNN can be regarded as a solution to a disentangled graph denoising problem, which further illustrates our motivations and interprets the improved generalization beyond homophily. Extensive experiments over 11 benchmark and 1 synthetic datasets demonstrate that ES-GNN not only outperforms the state-of-the-arts, but also can be more robust to adversarial graphs and alleviate the over-smoothing problem.
ANALYSIS OF INFLATION CONVERGENCE IN THE ECONOMIC COMMUNITY OF WEST AFRICAN STATES (ECOWAS) ZONE (1987-2021)

This study examines the convergence of inflation (country annual average inflation rate) through it determinants vis-à-vis the set convergence criteria in the Economic Community of West African States during the period 1987-2020.The study adopted ex-post factor design in order to answer the research questions.The study undertook unit root test employing Levin, Lin, and Chu (LLC) panel unit root method to check the stationarity of the variables, Auto Regressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to determine whether or not they are co-integrated and a dynamic fixed effect panel data model covering 15 countries over the period under review.The study found that while West African Economic Monetary Union (WAEMU) countries inflation rates fall below the threshold, West African Monetary Zone (WAMZ) inflation rates are beyond the ECOWAS inflation convergence rate of less than or equal to 5 percent, that cross-country differences in inflation rates are attributed to the variation of the output gap, the increase in liquidity, the variation of exchange and interest rates, and the persistent increase in the price level.In the view of targeting the five percent (5 %) percent convergence criterion, the study recommended that Changes of traditional country monetary approaches to a supranational monetary authority that compels compliance to a regional monetary and inflation targets, stimulation of economic growth, lowering of interest rates as well as adoption of a 378 www.ijrp.org
The adoption factors of two-factors authentication in blockchain technology for banking and financial institutions

Malaysian &lt;span&gt;banks and financial organisations urgently require a secure authentication mechanism. However, there is a lack of research on the factors that drive blockchain authentication technology adoption, notably in Malaysian banks. This study identified the factors impacting adopting blockchain authentication technology in Malaysia. This document will be a roadmap for replacing existing technology utilizing the traditional transaction authorization code (TAC) via a short messaging service (SMS). In addition, this study looks into the elements that influence the new blockchain authentication technology's acceptability in Malaysia. The data was gathered from articles and research papers written by other scholars on blockchain authentication. To examine and categorise the aspects that influence the acceptance of blockchain authentication technology, we used risk management in technology (RMiT) standards to map them. Based on the result, security risk, regulatory support, technology latency, and technology complexity have been established as components of blockchain authentication adoption factors that can be a guideline to implement blockchain authentication in banking and financial institutions in Malaysia. In addition, the findings can contribute as a reference for future researchers to develop models or guidelines for blockchain authentication methods in banking and financial institutions.&lt;/span&gt;
Lepton $g-2$ anomalies in general flavour conserving two Higgs doublets models

The potential discrepancies between the anomalous magnetic moments of the electron and the muon and their Standard Model expectations are addressed in the context of a class of Two Higgs doublets models with tree level neutral flavour conservation. These ``anomalies'' can be explained in two regimes, either scalar masses $>1.3$ TeV and similar vacuum expectation values of the scalar doublets, or scalar masses $<1$ TeV and a strong hierarchy of vacuum expectation values. The electron anomaly is explained through two loops contributions in both regimes; the muon anomaly is explained through two loops contributions for large scalar masses, and through one loop contributions in the second regime with scalar masses $<1$ TeV.
Top-Down Influence Leads to a Reduced Sense of Body Ownership in Individuals With Depersonalization Tendencies: A Focus on Full Body Illusion

Sense of body ownership, that is, the feeling that "my body belongs to me," has been examined by both the rubber hand illusion (RHI) and full body illusion (FBI). In a study that examined the relationship between RHI and depersonalization, a symptom in which people experience a lower sense of body ownership, people with a high depersonalization tendency experienced RHI through the bottom-up process of visual-tactile integration. Why is it that people with depersonalization feel a lower sense of body ownership over their bodies? Case studies of depersonalization suggest that the top-down cognition in people with depersonalization may make them less likely to feel a sense of body ownership. However, the top-down influence on the sense of body ownership in depersonalization has not yet been experimentally demonstrated. By incorporating top-down manipulation (e.g., instructing participants to regard a fake body as their own) into the FBI procedure, we aimed to clarify the cause of the reduced sense of body ownership in people with a high depersonalization tendency. The FBI procedure was conducted in a virtual reality environment using an avatar as a fake body. The avatar was presented from a third-person perspective, and visual-tactile stimuli were presented to create an illusion. To examine the degree of illusion, we measured the skin conductance responses to the fear stimulus presented after the visual-tactile stimuli presentation. The degree of depersonalization was measured using the Japanese version of the Cambridge Depersonalization Scale. To manipulate the top-down influence, we provided self-association instructions before the presentation of the visual-tactile stimuli. We predicted that the higher the degree of depersonalization, the lower the degree of illusion in the self-association instruction. The results showed that participants with a higher depersonalization tendency had a lower degree of illusion (rho = -0.424, p = 0.035) in the self-association condition. This indicates that in people with a high depersonalization tendency, top-down cognition of the body as their own leads to a decrease in the sense of body ownership.
Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination

The main problem regarding nutrition faced by Indonesia is stunting, where Indonesia is ranked fifth in the world with the largest stunting prevalence rate in 2017, which is 29.6% of all Indonesian children. Stunting is a child under five years who has a z-score value of less than -3 standard deviations (SD). Stunting has a negative impact, namely it can disrupt the physical and intellectual development of toddlers in the future. In this case, the examination of stunting status by medical personnel is still carried out manually which takes a long time and is prone to inaccuracies. This study aims to classify stunting status in toddlers by applying the K-Nearest Neighbor method using the Backward Elimination feature selection to get fast and accurate results. Based on the results of this study, the average accuracy produced by the K-Nearest Neighbor algorithm at k=5 is 91.90% with 9 attributes and the average accuracy produced by the K-Nearest Neighbor algorithm with the addition of Backward Elimination is 92.20%. with 8 attributes. These results indicate that the application of Backward Elimination can increase the accuracy value of the K-Nearest Neighbor algorithm and also perform attribute selection.
Decentralized Aggregation for Energy-Efficient Federated Learning via Overlapped Clustering and D2D Communications

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users' private data. In this paper, we propose a decentralized FL scheme that is called \underline{f}ederated \underline{l}earning \underline{e}mpowered \underline{o}verlapped \underline{c}lustering for \underline{d}ecentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. Unlike the star-based FL, the proposed FL-EOCD scheme involves a large number of local devices by reusing the RRBs in different non-adjacent clusters. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.
A Blockchain Platform For Teaching Services Among The Students

The lack of digital literacy in the student community is high and depends on the academic background of the student. Although the skills gap can be filled by hiring additional academic staff and improving learning methods, it is obvious that funds or teaching time are not enough to cope with these changes. Our academic effort proposes a blockchain platform maintained by the students, which allows the students to exchange teaching duties and teach each other digital skills. The students adopt the role of the lecturer or the learner and receive reputation points as a reward. The platform is designed by using a gamified type of blockchain technology because it empowers a trustful and entertaining method for the exchange of teaching duties between the students.
Analysis of Biomechanical Parameters of Martial Arts Routine Athletes’ Jumping Difficulty Based on Image Recognition

This paper uses image segmentation technology to examine the biomechanical parameters of martial arts routine athletes’ whirlwind legs and backflips, two difficult jumping sports. The successful completion of the whirlwind leg, a typical martial arts jumping difficulty, during the buffer period of the take-off stage, the left knee angle flexion, the drop of the body’s center of gravity, and the drop of the horizontal speed of the center of gravity are all significantly correlated, so it is only necessary to grasp airborne altitude and speed from landing. The 720-degree cyclone foot has a flying height of 0.470.11 m, which is 4 cm higher than the 540-degree cyclone foot (0.430.11 m). The antigravity of the last foot is greater than about 1.3 kgf/kg of the left foot during the run-up stage, which allows for a higher rotational angular velocity and completion of the 720-degree difficulty of the whirlwind foot. As a result, it is crucial to pay attention to how you step with your right foot. In the backflip, the coordination of the two legs and the upper body is crucial. The right foot’s effective braking can help to increase the body’s rising angle. The trunk inclination angle in the flying stage is between 110° and 120°, the knee angle is between 60° and 70°, and the angle between the two legs is between 35° and 35°. When lifting off the ground and landing, the tibialis anterior muscle discharge is greater than the gastrocnemius muscle discharge, which helps to maintain the balance between the fulcrums. As a result, it is necessary to let the non-supporting leg fall first in order to achieve the goal of a smooth landing.
Semantic Perspective on Lexical Ambiguity in English Tetxbook

The goal of this study is to describe the many types of lexical ambiguity and how they are used in student English textbooks. Ullman Stephen proposed the linguistic ambiguity theory. The essay combined qualitative research with a content analysis technique. The words with lexical ambiguity are the subjects of this study. The data is made up of words, and the source is an English textbook. Reading, comprehending, and coding the text book that contains lexical ambiguity is the procedure for gathering data. The data analysis technique is then organized, familiarized, and interpreted. The findings of the study revealed that the text book contained two types of lexical ambiguity, Polisemy and Homonymy. Lexical Ambiguity can be assumed to exist in everyword with several meanings.&#x0D; Keywords: semantic; lexical ambiguity; textbook
Comparing and Analysis of Geospatial Interpolation Prediction Algorithm: Case Study The Quality of Education of Malang and Batu City, Indonesia

Abstract. The number of schools in Indonesia continues to grow. This must also be balanced with improving the quality of education in accordance with the objectives of the 4 SDGs, which as a whole are to improve the quality of education that is inclusive, equitable and provides lifelong learning opportunities. However, until now it is very difficult to determine differences in the quality of education in an area. From the problem of education quality and education equity, it is necessary to have a regional analysis of the quality of education. This analysis can be performed using various geospatial interpolation methods. Geospatial Interpolation is a technique to find the value of a missing variable in a known data range in an area. The data used for the Geospatial interpolation process in this study are School Quality data taken through research questionnaires, as well as school accreditation data at the junior high school level. The geospatial interpolation method used in this study is the Inverse Distance Weighted, Spline, Kriging and Natural Neighbor methods. The use of different interpolation methods can indicate the best method for this research case study. Measurement validation results from each geospatial interpolation method using RMSE. From the results of this accuracy validation, the most accurate method will be obtained in determining the quality of education contained in an area.