Reviewerly/exHarmony huggingface dataset:
An optimal algorithm for bilateral filtering

Abstract Noise is unavoidable in digital signal processing, therefore studying effective denoising method is very important. Bilateral filter is one of them as effective filter methods. But the parameters of conventional bilateral filter are often fixed, thus we can’t realize adaptive parameters according to contents of videos or pictures. A video denoising algorithm based on both spatial domain and time domain is proposed in this paper to improve the video quality based on bilateral filter. This algorithm can adaptively estimate noise intensity, edge intensity and apply motion detection, which will be employed to adaptively adjust the bilateral filter strength. Peak signal to noise ratio (PSNR) is elevated significantly by the proposed algorithm after experimental verification..
SISTEM MONITORING SKRIPSI BERBASIS PROGESSIVE WEB APPLICATION DENGAN PUSH NOTIFICATION

The Covid-19 pandemic requires thesis guidance activities to be carried out online. If the tertiary institution does not have a proper thesis monitoring system, the guidance process will not run smoothly and on time. This study aims to create a thesis monitoring system using Progressive Web Application (PWA) technology with a software development method using mobile-D. The development stage begins with the construction of a system architecture in the form of use cases and activity diagrams. Then the initialize stage is translating the system architecture design into an application interface design. Next is the stage of translating the design into an actual application using lumens and mariaDB on the server side and javascript and indexedDB on the client side. After each module is completed, the system functionality modules are integrated into a whole application into a thesis monitoring system. Then the software functionality testing is carried out, especially when it is run on a smartphone. The test results indicate that there are no bugs or errors found when running on laptop or mobile devices
The Technical University Teacher: What Do Students Value?

At present, according to Presidential Address to the Federal Assembly our country has set the task of reforming higher education based on the synthesis of the best practices of the Soviet educational system and the experience of recent decades. This increases the responsibility of technical university teachers, whose competencies have been determined in recent years on the basis of different scientific approaches. In this regard, the problem of identifying the teachers’ qualities that are significant for students is becoming especially relevant. In order to solve the problem, the research of students’ essays obtained as a result of the International Essay Contest in English “My University Engineering Teacher I will Never Forget” was undertaken. The contest was organized by Moscow Automobile and Road Construction State Technical University (MADI) in 2021-22. The hypothesis of the research suggested that the personality traits of the technical university teacher are valued by students not lower than their professional competencies. In order to achieve the goal and verify the hypothesis, four tasks were set and solved during the research: the students’ opinions on the role of the teacher were revealed; the professional competencies of the teacher that are significant for students were determined; the personality traits of the teacher that are significant for students were determined; the significance of the teacher’s professional competencies and personality traits for students was compared. The research methodology provided for qualitative and quantitative evaluation of the students’ essays including their content analysis. The paper describes the main results of the research, which confirmed the hypothesis, as well as their interpretation. The interpretation of the research results allowed us to draw general conclusions concerning reforming the modern system of engineering education. The main conclusion concerns the necessity of returning engineering education to the traditional for the Soviet higher school integration of education and upbringing and the significance of developing in the educational process not only the competencies relevant for employers, but also the personality of future specialists.
All Predictions Matter: an Online Video Prediction Approach

To effectively manage and utilize the massive amount of visual data generated by the surging number of videos, decision-making systems must predict and reason about future outcomes. This paper proposes a novel online approach for video prediction that enables continual learning in the presence of new data, as periodic training of neural networks may not be practical. We utilize all predictions, including intermediate computations obtained during the inference process, to improve the performance of video prediction. To achieve this, we incorporate a weighting scheme in the loss that accounts for all the predictions during the learning process. Additionally, we leverage semantic segmentation to assess the performance of extrapolated frames by focusing on the position of the objects in the scene. Our approach stands out from state-of-the-art methods as it uses intermediate predictions, which are available due to the iterative nature of forecasting future frames. Our method improves the offline counterpart for the same network by 1.45 dB for predicting five steps in the future.
A Privacy-Preserving Approach for Big Data Mining Using RainForest with Federated Learning

Federated Learning (FL) in Machine Learning (ML) has become very popular nowadays because it trains classifiers across multiple decentralized devices without transferring data to a central server. It is a decentralized learning approach that amalgamates several different nodes into one. In 2016, Google introduced the concept of FL when the use and misuse of personal data were gaining global attention. In this paper, we have proposed a privacy-preserving approach for mining big personalized data employing scalable decision tree induction with FL. The concept of the RainForest framework refers to addressing big data challenges via a Decision Tree (DT) classifier. The proposed method does not share the data or personal information with the central server. It only transfers the local models' parameter values to the central server. Each individual device or node trains its own local DT classifier and shares the prior and conditional probability values with the central server. We have tried to simulate the proposed concept using five benchmark datasets. The results of the evaluation indicate that the model exhibits exceptional performance and accuracy.
MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> on server side and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.
Combining homomorphic encryption and differential privacy in federated learning

Recent works have investigated the relevance and practicality of using techniques such as Differential Privacy (DP) or Homomorphic Encryption (HE) to strengthen training data privacy in the context of Federated Learning protocols. As these two techniques cover different sources of confidentiality threats (other clients/end-users for the former, aggregation server for the latter), there is a need to consistently combine them in order to bridge the gap towards more realistic deployment scenarios. In this paper, we achieve that goal by means of a novel stochastic quantization operator which allows us to establish DP guarantees when the noise is both quantized and bounded due to the use of HE. The paper is concluded by experiments on the FEMNIST dataset which show that the precision required to get state-of-the art privacy/utility trade-off (which directly impacts HE parameters and, hence, HE operations performances) results in a computation time overhead between 0.2% and 1.1% imputable to HE (depending on the key setup, either single key or threshold), for the whole training of a 500k parameters model and state-of-the-art privacy/utility trade-off.
Text classification based on <scp>PEGCN</scp>: Graph convolution classification using location information and edge features

Abstract The purpose of text classification is to label the text with known labels. In recent years, the method based on graph neural network (GNN) has achieved good results. However, the existing methods based on GNN only regard the text as the set of co‐occurring words, without considering the position information of each word in the statement. At the same time, the method mainly extracts the node features in the graph, and the edge features between the nodes are not used enough. To solve these problems, a new text classification method, graph convolutional network using positions and edges, is proposed. In the word embedding section, a positional encoding input representation is employed to enable the neural network to learn the relative positional information among words. Meanwhile, the dimension of the adjacency matrix is increased to extract the multi‐dimensional edge features. Through experiments on multiple text classification datasets, the proposed method is shown to be superior to the traditional text classification method, and has achieved a maximum improvement of more than 4%.
Comparing Object Tracking Algorithms for Real-Time Applications: Performance Analysis and Implementation Study

In this article, we delve into a comparative analysis of three popular object-tracking algorithms: DeepSORT, StrongSort, and ByteTrack. These algorithms share a common approach, as they are designed to identify objects within video frames and establish connections between them across consecutive frames. However, what sets them apart is their distinctive methodology of association. We will dive into the strengths and weaknesses of each algorithm, offering a comprehensive evaluation. In addition, these methods are thoroughly evaluated on a benchmark data set, with the results revealing the essential balance to be found between accuracy and efficiency when it comes to object tracking.
Residual error correction for reducing the uncertainty of a sample-tracking robotic 3D measurement system

This paper presents a method for post-correction of disturbing relative motion between a robotic 3D measurement system and a sample under test. The active sample-tracking measurement platform carries a high-precision scanning confocal chromatic sensor acts as an end-effector of a robot, targeted for operation in a vibration-prone environment. The proposed method does not require any additional hardware and enables a point-by-point correction of the sequentially acquired 3D point cloud by the measured residual sample-tracking error. For experimental measurements at the maximum frame rate of 1fps in a workshop-like environment, which induces a resonant 47 Hz relative motion, the axial measurement error and uncertainty is reduced by 20% to 0.19µm and 0.25 µm, respectively.
Hand Gesture-based Teleoperation Control of a Mecanum-wheeled Mobile Robot

The aim of this study is to design an interface for the teleoperation control of a Mecanum-wheeled Mobile Robot (MMR). To make a cost-effective and non-invasive teleoperation interface approach, a hand gesture recognition strategy via a webcam is adopted. In this study, seven hand gestures are recognized on a desktop PC and then the corresponding velocity commands are sent to the MMR via WiFi. The SqueezeNet architecture is employed for the hand gesture recognition and MMR teleoperation task after various Convolutional Neural Network (CNN) models are compared. The comparison considers some aspects, i.e., accuracy, memory usage, and recognition speed, to find the most feasible method for real-time hand gesture recognition and teleoperation control. Experimental results are presented to show the excellence of the proposed SqueezeNet architecture.
The Effect Of Using The Power Of Two On Students’ Reading Comprehension On Descriptive Text At 1th Grade Of SMA N 1 Pangkalan

This research was done due to several problems on the students’ ability and impact to the score in reading comprehension.The first problem because the students do their task alone, the second problem the students had low motivation to read the text, the last problem the students felt bored with learning model used. The researher expected wether the power of two strategy is able to give significant effect on students’ reading comprehension. This research was an experimental research. The researcher chose quasi experimental design by using the experimental class and control class. The population of this research was 1th grade of SMA N 1 Pangkalan. The sample was X MIPA 1 which was as treated experimental class and X MIPA 2 was treated as control class. To determine the sample, the researcher used cluster sampling because the class have similar characteristic in score of reading test and have similar problems in reading comprehension.The researcher used the reading test as instrument.To analyzed the data, the researcher used Liliefors test for normality test. Then to test the hypothesis, the researcher used t-test and Based on the analysis of the data, the result was also indicated by the mean of experimental class pre-test score which was 60.36. It was higher than the control class pre-test score which was 49.29. The result was also indicated by the mean of experimental class post-test score which was 85.86. It was higher than the control class post-test score which was 61.86. It meant there was significant effect of using the power of two strategy on students reading comprehension. It was concluded that Ha was accepted and H0 was rejected. The students who were taught by using the power of two strategy better than the students who were not taught by using the power of two strategy. To sum up, the power of two strategy has a significant effect on students reading comprehension on descriptive text at 1th grade of SMA N 1 Pangkalan.
Giving names to features in geology; the choice between subjective listing or researching objective natural divisions

In literature the English playwright Shakespeare proposed that “a rose by any other name would smell as sweet”. Many geologist are also flexible about the names they apply to features, and are tolerant of both over-simplification and even making mistakes in the naming of formations or other features. It appears that precision of a single noun is considered subservient to the essence of the narrative, such as evaluating a hydrocarbon prospect, or giving an interpretation of history. Here it is argued that naming is important because geology is so rich in nouns, and consequently compound errors in the use of multiple, poorly-defined nouns rapidly degrades overall meaning. It is proposed that without names that are both natural and consistently applied, but also continually improved, the scientific framework soon falls apart. By this we mean that the geological understanding of exploration risk, or geological history, rapidly become too easily accommodating of any new data and thus loses all rigour. Good, natural names are the results of tested concepts and are themselves open to further testing. From such testing comes recognition that in nature there are both gradational variation in series as well as discrete categories. A search for good names is inexorably linked to a better understanding of the features concerned.
Distributed Database Model for Mobile Health Telemonitoring Applications

Telemonitoring interventions in patients allow to collect and transmit information to health professionals bringing recognized advantages in the personalization of health care services.Those applications deal with sensitive data, raising ethical and legal issues that must be considered.Guarantee the quality and accuracy of the data collected in telemonitoring and ensure confidentiality to protect the patient privacy is a fundamental requirement.The patient's informed consent implies that he is aware of the potential benefits and risks of the system, how telemonitoring will be used, what data will be collected and with whom it will be shared.This work proposes a database model ensuring that sensitive data is handled securely and accessible only to authorized health professionals.
Systematic Literatur Review : Pengaruh Media Pop Up Book Dalam Pembelajaran PPKn Di Sekolah Dasar

The success of any particular educational process is influenced by many different components, but the use of educational media is one of them. Learning through media is an interactive process that serves as a communication hub, making the learning that occurs as a result beneficial to students. One example is the use of Pop-Up Books in PPK instruction in Elementary Schools. The purpose of this study is to understand whether Pop up books exist or not to improve student learning outcomes. The method uses the (SLR) method for data analysis Data from a journal article that has been indexed by Google Scholar and additional research shows that there are 6 articles that discuss PPKN's PPKN Pop Up Book Media in Elementary Schools Learning Based on the analysis, we can conclude that using books Pop-up books have a positive impact on learning outcomes of Civics in public schools.
Cooperative Identification of Multi-Agent Systems in Presence of Integral Action: Insights from a Two-Agent Framework

This article addresses the parameter inference problem on dynamical systems in a cooperative setting. The dynamical systems contain integral action, which is of practical interest within cyber-physical systems. Given this dynamical characteristic of the problem, existing statistical results on system Identification are not straightforwardly applicable and hence adaptations are required. To this end, a formulation of the problem is presented and results highlighting the challenges of the given setup are discussed. Insights from a two-agent framework are used to formulate results and the next research steps on the topic, where the benefit of performing open-loop Identification on the variance of the parameter is highlighted and bias issues are discussed.
Multiagent Rollout with Reshuffling for Warehouse Robots Path Planning

Efficiently solving path planning problems for a large number of robots is critical to the successful operation of modern warehouses. The existing approaches adopt classical shortest path algorithms to plan in environments whose cells are associated with both space and time in order to avoid collision between robots. In this work, we achieve the same goal by means of simulation in a smaller static environment. Built upon the new framework introduced in (Bertsekas, 2021a), we propose multiagent rollout with reshuffling algorithm, and apply it to address the warehouse robots path planning problem. The proposed scheme has a solid theoretical guarantee and exhibits consistent performance in our numerical studies. Moreover, it inherits from the generic rollout methods the ability to adapt to a changing environment by online replanning, which we demonstrate through examples where some robots malfunction.
Set-membership vs. stochastic approaches for target localization with UAVs

Searching and tracking mobile targets rely most often on modeling the uncertainty and various perturbations by stochastic processes. The detection and location of the targets are performed with Bayesian estimation, which reliability and resulting performance are deeply linked to the adequacy of the stochastic models. An alternative approach limits the representation of these perturbations by defining the bounds within which they can vary. Set-membership estimation techniques have been developed to handle this representation. This paper compares the performance of set-membership and stochastic Bayesian estimation techniques for target search and tracking for scenarios integrating false alarms. For this purpose, estimation schemes are presented for each approach. The ability of estimators to find real targets and not to be deceived by false targets or imperfect sensors are compared in simulations.
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks

The widespread use of sensor devices in IoT networks imposes a significant burden on energy consumption at the network’s edge. To address energy concerns, a prompt anomaly detection strategy is required on demand for troubleshooting resource-constrained IoT devices. It enables devices to adapt their configuration according to the dynamic signal quality and transmission settings. However, obtaining accurate energy data from IoT nodes without external devices is unfeasible. This paper proposes a framework for energy anomaly detection of IoT nodes using data transmission analysis. We use a public dataset that contains peer-to-peer IoT communication energy and link quality data. Our framework first utilizes linear regression to analyze and identify the dominant features of data communication for IoT transceivers. Later, a deep neural network modifies the gradient flow to focus on the dominant features. This modification improves the detection accuracy of anomalies by minimizing the associated reconstruction error. Finally, the energy stabilization feedback provides nodes with insight to change their transmission configuration for future communication. The experimental results show that the proposed approach outperforms other unsupervised models in anomalous energy detection. It also proves that redesigning the conventional loss function by enhancing the impact of our dominant features can dramatically improve the reliability of the anomaly detection method.
em-Test for Homogeneity

The modified likelihood ratio test represents a significant advancement over the limitations of conventional likelihood ratio tests. It is applicable to a specific class of finite mixture models, although its success remains somewhat constrained. An interesting variation of this test is the EM-test, which may be seen as a derivative of the modified likelihood ratio test. In contrast to comparing the maximum possible modified likelihood values under null and alternative hypotheses concerning the order of the finite mixture model, the EM-test focuses on how quickly the likelihood increases when using EM-iterations from the best-fitted null model in a specific manner. This chapter introduces the concept and the conclusions related to the simple homogeneity case, with the more complex scenarios addressed in the subsequent chapter. It unveils the somewhat more intricate technical advantages of this approach.