Reviewerly/exHarmony huggingface dataset:
Improve the Performance and Stability of Incremental Learning by a Similarity Harmonizing Mechanism

Incremental learning involves processing continuous streams of information in real time without much incorporation of previous knowledge. It is crucial to humans and machine learning models. In this work, to alleviate the catastrophic forgetting characteristic of incremental learning in low-complexity models, we aim to combine the low complexity of single-task learning (STL) with the stability of multitask learning (MTL) through a two-stage similarity harmonizing mechanism (SHM). In the initialization stage, we construct an explainer by a multitask learning Shapley additive explanation (MTLSHAP) on a pretrained MTL model and return the model feature importance. In the incremental stage, the sample feature importance of incoming data computed by the explainer are compared with the model feature importance to establish disharmonies among samples. Hence, SHM impels the performance of an STL model to approach the designed MTL model. Numerical experiments show that the STL model improved by SHM is more stable while maintaining low complexity due to the acceptable additional cost of the initialization stage.
Vehicle Make and Model Recognizing Simultaneously by HVM Features

In this paper, a method to recognize vehicle makes and models simultaneously from a huge amount of surveillance images is proposed based on the vehicle frontal texture of air inlets. The moment features and de-correlation are utilized in recognition. The inlets of different model vehicle occupy different features even if they are belonged to the same make. In this paper, the inlet texture and moment are computed and these features are utilized to calculate the likelihood among different makes and models. To avoid the influence of correlation, de-correlation technique is utilized to calculate the difference among various makes and models. This method can recognize different makes and models of vehicles even when the makes are of just the same vehicles. In this case, the logos of vehicles are the same, but the inlet textures are different. The experiment verified the perfect performance of this method proposed in this paper when recognizing the makes and models simultaneously.
Joint Auction–Coalition Formation Framework for UAV-Assisted Communication-Efficient Federated Learning

The performance of the Federated Learning suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. In this chapter, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the Internet of Vehicles (IoV) components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. Therefore, we present a joint auction–coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction–coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions. Thereafter, we design an auction scheme to solve the allocation of UAV coalitions. The auction scheme takes into account the preferences of IoV components over heterogeneous UAVs. The simulation results show the communication latency reduction of FL using our proposed scheme.
PENGEMBANGAN BAHAN AJAR MATEMATIKA BERBASIS MULTIMODAL PADA MATERI BARISAN DAN DERET

The purpose of this study is to develop multimodal-based mathematics teaching materials on row and series materials using assistance media in the form of Power Point, iSpring Suite 10 and Website 2 apk builder. The method used in this study is the Waterfall model. A tool used to analyze compilers using Unified Modeling Language (UML). The result of this research is an android application from the development of mathematics teaching materials on multimodal-based lineups and series materials. Multimodal is a way of learning by combining several techniques, be it sound, visual and also writing. Testing this application uses a black box test, which is testing an application, which can be tested on anyone and the android media used in this test is the Samsung Galaxy A51. Based on the test results, all the buttons in the application work as intended. Then it can be concluded that the application of mathematics to the material of rows and series can be used to train students' mathematical reasoning skills, especially in row and series materials. For this application, it is hoped that it can be developed again in various ways, be it animation, design, features, practice questions, various and more varied quiz questions and more effective programming so that it becomes more interesting.
Budaya Baca Tulis Dan Kemampuan Berbahasa Asing Siswa Melalui Program Gerakan Literasi Madrasah (GELEM)

The aim of this research is to create a Madrasah environment to become a lifelong learning environment by cultivating good reading, listening, writing and speaking activities. The research approach is a mixed method combining qualitative and quantitative using random sampling and interview techniques. Sampling is based on the calculation of 10% of the population, thus getting 46 samples. The population in this study were all students of MTsN 16 Jombang. The data collection technique was by distributing questionnaires. The results showed that there was an effect of literacy on students' foreign language skills through the madrasah literacy movement program
Using platinum-group element geochemistry to constrain mid-ocean ridge magmatic processes

Magmatism at the globe-encircling mid-ocean ridges is responsible for 75% of Earth's basaltic volcanism, covering ~70% of the planet's surface.It has long been recognized that mid-ocean ridges are underlain by open magma chambers that experience regular cycles of replenishment, mixing, eruption and fractionation [1][2][3].Existing models, which use major and incompatible trace elements to model these open systems, conclude the initial replenishment rate is high but declines during magmatic evolution [4, 5].We report major and trace element abundances, emphasizing the highly compatible platinum-group elements, for glasses and whole rocks from the East Pacific Rise (EPR), Siqueiros transform zone and Mid-Atlantic Ridge (MAR).Following early sulfide saturation, mid-ocean ridge basalts (MORBs) show no evidence of further decline in Pd with decreasing MgO, which is attributed to the replenishment of primitive magma into the MOR magma chambers.We combine the highly compatible palladium abundances, with strongly incompatible elements (Th and La), to produce a replenishment model that successfully accounts for the variations of both classes of elements in the MORBs.We show that the sulfidesaturated MOR magma chambers experience frequent small replenishments between ~1% and 4%, rather than large initial inputs that systematically decrease in magnitude as the system evolves.
EARLY WARNING SYSTEM FOR FLOOD IN GUNUNGSARI DISTRICT BASED ON IOT WITH TELEGRAM BOT AS A WARNING MESSAGE SENDER

Entering the rainy season with a high level of rainfall will have an impact on vulnerability to floods that will hit a number of areas in various parts of Indonesia. As is often the case in the Gunungsari Sub-district, West Lombok Regency, heavy rains that come early with high intensity for several days often occur, which causes flood disasters and the absence of an automatic system or tool that can detect flooding in the area so that people around the difficulty of detecting floods that come early and cause many people to lose their homes and property due to the flood disaster. The purpose of this study is to provide information related to signs before a flood disaster using the Raspberry Pi 4 as the main tool and the Hc-SR04 ultrasonic sensor as a tool for measuring the distance of an object, where this system can monitor the water level of the river, then disseminate information. related to the water level periodically via Telegram. The test results of the detection sensor system show that the level of accuracy in reading the water level with an average error of 0.48%, indicates that this IoT system has good accuracy.&#x0D;
The Influence of Think, Talk and Write (TTW) Learning Model on The Ability to Write Description and Learning Motivation of Fifth Grader

This research is an experimental study with a design that aims to see the effect of implementing the Think, Talk, and Write (TTW) learning model on the ability to write descriptions and learning motivation of class V Cluster III students, Kahu District, Bone Regency. The population in this study were all students of class V, Group III, Kahu District, Bone Regency. Then two schools were chosen randomly as the research sample. SD Inpres 12/79 Hulo and SD Inpres 5/81 Tompon Patu with 28 students. Data was collected through test, questionnaire, and observation techniques. The collected data were analyzed through descriptive statistical analysis and inferential statistical analysis. The results showed that (1) the students' writing ability at the posttest stage of the experimental class had an average score in the good category while the control class was in the sufficient category; (2) students' learning motivation in the experimental class is in the high category; (3) based on hypothesis testing, the application of the TTW learning model affects the description writing skills and students' learning motivation. So it can be concluded that the Think, Talk, and Write (TTW) learning model has an effect on the description writing skills and learning motivation of the fifth grade students of Cluster III, Kahu District, Bone Regency.
THEORETICAL AND METHODOLOGICAL ASPECTS OF ONTOLOGICAL MODELING OF INFORMATION-ANALYTICAL SYSTEM OF SCIENTIFIC PERSONNEL TRAINING

The development of science and education is now exposed to a huge impact of such civilizational challenges as: climate change, which is due to global warming, excessive and unjustified consumption of material goods, environmental pollution; pandemic COVID 19 and the emergence of new diseases and as the quintessence of human irresponsibility and callousness, activation on a global scale, and in Ukraine in particular, hybrid wars against universal values. This situation causes a massive shift to a remote format of the organization of scientific and educational (research) process, including the training of scientific personnel in higher education institutions and research institutes, using certain networked information and computer technologies and systems.&#x0D; Therefore, the purpose of the study, the current results of which are presented in the article, is to study the problems of applying the methodology of building information-analytical systems (hereinafter – IAS), in particular digital IAS, and to substantiate the basis and highlight in this context the main aspects of creating an ontological model of scientific personnel training (hereinafter – SPT) system as part of a single information space of scientific education. The author raises the problem of the need to apply transdisciplinary methodology of building digital information-analytical system of scientific personnel training in higher education institutions (hereinafter – HEI) and research institutes (hereinafter – RI) of Ukraine in a distance. Particular attention is paid to the ontological modeling of IAS components of the relevant organization of effective scientific and educational (research) process. Also basic definitions of the ontological approach to the training of scientific personnel disclosed. The article considers certain requirements for the design and functioning of intelligent educational systems and programs that provide adaptive information-analytical support of scientific personnel training on the basis of transdisciplinary, system, competence and ontological approaches. The analysis of a certain base of sources (researches and publications) is given. Thus, the methodological basis for building a digital information-analytical system in the specified context is presented and certain fragments of its ontological model are proposed. The presented approbation of applied aspects of the author's research is in the ascertaining stage. Subsequent results of analytical-synthetic and formative stages of the presented doctoral research are passing into the phase of experimental verification and the obtained data will be made public in the following publications of the author.
Data Validity Verification Scheme Applicable to Constrained Devices

With the continuous development of informatization, new challenges of information security have emerged. In the open network environment, when the collected data is transmitted back to the next node, it faces various threats of interception and tampering, and if it is under the condition of constrained devices, some high-security encryption algorithms are difficult to be implemented on the equipment, and the integrity and availability of the data will be threatened to a certain extent, and in the case of constrained devices resources The choice of function for data verification is also crucial in the case of constrained devices resources, so it is urgent to find a solution that requires the acceptance of data to ensure the authenticity and reliability of the data source and guarantee the legitimacy of the data source. In this paper, we propose a solution that combines ultra-lightweight stream cipher with Hash function authentication to provide flexible and efficient legitimacy verification with low computation and low power consumption, so that the receiver can verify the legitimacy of the data and obtain the complete data even if the data transmission is disturbed.
UCRP-miner: Mining Patterns that Matter

The primary goal of analyzing data is to uncover patterns that generate novel, unknown and unexpected knowledge. In data mining, frequent pattern mining is a well-known method of finding patterns that co-occur frequently. There are, however, two significant problems with frequent pattern mining, namely pattern explosion and rare (infrequent) pattern mining. Many algorithms have been proposed to extract a compact representation of patterns in data and detect interesting patterns that infrequently occur. Despite this tendency, traditional methods primarily employ frequency-driven techniques, which may not be suitable for real-world domains characterized by unusual patterns that generate unforeseen knowledge. Unexpected pattern mining is used to detect patterns in data that do not adhere to normal behavior. Recently, unsupervised clustering models have been proposed for identifying unexpected rules. These models have two significant limitations: retrieving redundant ones and failing to discover the complete set of interesting patterns. In addition, their performance is degraded in terms of time and memory. This paper introduces a novel model, UCRP-miner, to efficiently retrieve the complete set of interesting patterns. It utilizes known knowledge (frequent closed patterns) to produce new, known, and unpredictable knowledge. to evaluate the proposed model, we evaluate it using real-life datasets. The experiments show that our model generates the complete set of interesting patterns that are non-redundant and differ from the set of beliefs (frequent patterns), significantly outperforming the state-of-the-art models.
Response Of The Banking Cycle To Economic Fluctuations In Egypt For The Period ( 2004 – 2020)

The researchers aim to analyze the response of the banking cycle to economic fluctuations in Egypt and the ways of banking hedging.The research problem was formulated to answer the question: Do economic fluctuations affect the banking cycle, and what are the reactions of commercial banks toward those fluctuations?Commercial banks suffer from excessive sensitivity to fluctuations.Economic fluctuations were analyzed based on the indicators of the banking cycle utilizing the logarithmic function to produce significant results after analyzing the stability of the time series.Hence, the results indicated a direct relationship between GDP and internal debt with total credit.At the same time, inflation was associated with a negative relationship with credit.The tests of models for the Arab Republic of Egypt proved the effect of independent variables on the dependent variables and, thus, the effect of fluctuations on the banking cycle.The most important of which is seeking to formulate an early warning model or a system for predicting financial and economic crises, to be applied to ensure stability, based on macroeconomic indicators to use them as warning indicators about the possibility of a country falling into a financial crisis resulting from external or internal causes and the non-expansion in granting credit during periods of crisis to avoid the risk of non-payment and the losses that result from it.
The Future of Educational Publishing Industry: Exploration Based on Existing Business Models

To explore how the educational publishing industry is responding to the global digital trend, this paper analyses the changing business models of three of the world's leading educational publishers, including Pearson, McGraw Hill, and Houghton Mifflin Harcourt, and finds that they have all chosen to adopt a reader-centric business model.Faced with the trend towards digital technology, educational publishers have the potential to become competitive employers with their vast book resources and partnership opportunities.However, educational publishers are equally exposed to cross-industry competition, adaptive challenges, and cyber-hacking.
Configuring Azure Cognitive Services Anomaly Detector

Over the past dozen (or so) chapters, we have created a fairly complete outlier detection engine. With this in hand, it makes sense to compare our creation to some of the best general-purpose outlier detection engines available on the market today. In this chapter, we will review a few of the many options available for general-purpose outlier detection, focusing on cloud platform providers. Then, we will configure one of these for our own use: Microsoft’s Azure Cognitive Services Anomaly Detection service.
Implementation of the Tellu Cappa Philosophy in Bugis Bone Political Communication

Political Communication has a vital role in the political process, especially as a bridge to conveyed messages.This process took place at all levels of society.This research was a primary data analysis to investigate the implementation of the tellu cappa philosophy in the political communication process among Bugis Bone politicians.Data analysis used a qualitative paradigm using a phenomenological approach strategy, a research strategy in which researchers investigated the nature of human experience about a particular phenomenon.In this research, the sample was taken using a purposive sampling technique which required that the sample was a person who understands the problem being studied.The data collection techniques used observation, interview and documentation.From the research results, it can be concluded that the tellu cappa philosophy is still being implemented as political Communication among members of the Bone Regency DPRD.
Image Dehazing Algorithm for Relieving Halo Effect and Color Distortion in Smooth Regions

Aiming to solve the problem that the dehazing algorithm based on the dark channel produces halo effect and color distortion in smooth region, a novel dehazing algorithm is proposed using bilateral filter guided homomorphic filtering. Firstly, a nonlinear stretch model is constructed to characterize the contrast relationship among each color component before and after dehazing. The model is proportional to the difference between the original pixels and the atmospheric light value while inversely proportional to the transmittance. Secondly, the coarse transmittance map is decomposed into an atmospheric light component and a reflection component by use of the bilateral filter guided spatial homomorphic filtering. Finally, an adaptive function is defined, taking the ratio of the foreground area as a variable. It is used to calculate a refined transmittance map by linearly weighting the atmospheric light component and the reflection component, and then employed to accomplish the dehazing with the dark channel prior model. The real foggy images in O-HAZE and RESIDE datasets is used as the test data, and the UCIQE, FADE, SSIM, NIQE, and hue restoration ability are adopted as the objective evaluation matrics. The evaluation scores of the proposed algorithm increase by 21.68% on average, compared with those of the state-of-art algorithms. The results show that the proposed algorithm is able to effectively avoid the color distortion in large smooth area, as well as the halo &amp; fake edge effects at the edges and contours, achieves higher subjective and objective quality.
Edukasi Pentingnya Membaca dan Manfaat Literasi Bagi Siswa Kelas 2 Di SDN Besowo 2

Books are one of the sources of knowledge that add insight. By reading a person can find out what is around the world. This makes books often referred to as windows to the world. Reading books is also an activity that has many benefits and is best applied at the earliest possible age or the age of children. But in reality not many children have a high interest in reading due to several factors. Especially like now. where the development of technology is very fast and rapid causing any information can be easily obtained through gadgets. So the activity of reading books is starting to be abandoned. The Teras Baca program aims to restore interest in reading in children, provide reading material for them and as a place for them to study. In this study, researchers used the LITERATURE method. The LITERATURE method is a strategy to shape the creativity of students through its 5 components, namely observing, asking, reasoning, trying, and communicating. Namely by directly holding a mimbel program for children. With this program, it is hoped that it can foster interest and instill reading habits in children.
A Drawing Support System for Sketching Aging Anime Faces

Drawing the facial features of anime characters at different ages is challenging in the creation process. Since characters’ facial features at different ages have obvious differences, it is difficult, especially for novices to accurately illustrate the age features of anime characters. Conventional data-driven drawing interfaces for anime characters focus on the visual features of hair, emotion, and coloring but fail to provide age-specific drawing guidance. To address this gap, we propose AgeFace, an interactive drawing interface that assists users in creating facial features with age-specific features based on user input strokes. We evaluated the usability of AgeFace by a user experience experiment and a comparison experiment with baseline approaches. The results verified that AgeFace could achieve better performance in usability and better support in the creative process than baseline systems.