Reviewerly/exHarmony huggingface dataset:
UPAYA MENINGKATKAN HASIL BELAJAR SISWA DENGAN MENGGUNAKAN MEDIA GAMBAR DALAM PEMBELAJARAN PADA SD NEGERI 4 SIGLI

This purpose of this research is to increase students’ interest at the elementary level of SD Negeri 4 Sigli by using Pictures in learning Indonesian Language. The type of researched was Classroom Action Research (CAR). The object of the research was the students’ interest at SD Negeri 4 Sigli using pictures as media. The technique of collecting data was observation. The observation was used to identify the fact about students’ interest. The data Analysis used was qualitative description and percentage of students’ learning interest. The result showed that through pictures media the students’ learning interest increased. The earlier observation had 50 %. After doing treatment in cycle 1 the mean value was only 51,1. Most of the students still got lower value in learning achievements and also below the standard criteria i.e. 75 %. Thus it had to be continued to the next cycle. In the cycle II the students’ mean value increased 98,12 %. Most students got highest score in this cycle i.e. 100%. The conclusion was the use of pictures media gave the improvement in students’ learning at SD Negeri 4 Sigli.
A hybrid firefly algorithm based on modified neighbourhood attraction

Some studies reported that firefly algorithm (FA) had high computational time complexity. To tackle this problem, different attraction models were designed including random attraction, probabilistic attraction, and neighbourhood attraction. This paper concentrates on improving the efficiency of neighbourhood attraction. Then, a hybrid FA based on modified neighbourhood attraction (called HMNaFA) is proposed. In our new approach, the best solution selected from the current neighbourhood is used for competition. If the best solution wins the competition, the current solution flies towards the best one; otherwise a new neighbourhood search is employed to produce high quality solutions. Experiments are validated on several classical problems. Simulation results show HMNaFA surpasses FA with neighbourhood attraction and several other FA algorithms.
Physical modelling synthesis of a harmonium

In this paper we present recent work on the development of a physical model of the hand harmonium, a bellow driven free reed instrument popular in South Asian music. Western free reed instruments like the accordion, the harmonica, the reed organ, etc. share many similarities with the hand harmonium in their physical structure and sound timbre. Previous models and experimental works on western free reed instruments are studied and revisions are proposed to the minimal model of free reeds described by Millot and Baumann (2007) to suitably match the physical setup and the sound timbre of a hand harmonium. The proposed changes allow the physical model to have the bellows pressure as a control parameter. A source-filter structure is suggested to model the acoustic effect of the wooden enclosure of a harmonium, which allows the realization of different timbres in the simulated sound.
HOS-YOLOv5: An Improved High-Precision Remote Sensing Image Target Detection Algorithm Based on YOLOv5

Object detection has made great strides in natural images over the past few years. However, due to the characteristics of small size, dense distribution, and different scales of remote sensing images, when they are directly applied to remote sensing images, the detection accuracy of their targets is too low. To this end, we propose HOS-YOLOv5, an improved high-precision remote sensing image target detection algorithm based on YOLOV5, construct the HOS backbone network, add multiple SPD modules and downsampling modules, and introduce the DotD algorithm to solve remote sensing images. The detection accuracy of the target is too low. HOS-YOLOv5 has conducted a large number of experiments on the public dataset DOTA. The experimental results show that compared with the traditional YOLOv5, the mAP of HOS-YOLOv5 is increased by 4.65%, and a good detection effect is achieved.
Image Hide with Invertible Network and Swin Transformer

Image hiding is a way of hiding information by hiding a secret image in a carrier image in an imperceptible way and recovering it. How to effect better hiding of images in images is a problem that is still being studied. In this paper, we propose an invertible neural network based model using the Swin Transformer module to hide images. According to the properties of invertible neural networks, image hiding and revealing can be done by the same network of forward and backward processes. Since image hiding and recovery are forward and backward of the same network, sharing the same set of parameters, a lot of resources are saved accordingly. It is found that hiding secret information in wavelet domain can improve the concealment, so we transform the image to wavelet domain for hiding.
A Comparative Assessment of Deep Learning Approaches for Opinion Mining

Opinion mining is a branch of artificial intelligence being used in a variety of applications that transforms human emotions into digital form and tries to better understand the customers. Good customer relationship is a key formula for business intelligence. Deep learning is a subset of machine learning and is comparatively more powerful. Opinion mining techniques illustrated in the last few years have shown that noble performance can be achieved by the use of neural networks. Automation performs good time utilization with the help of illustrated online and offline tools. This study emphasizes the reviews of research in opinion analysis using deep learning models with their advantages and disadvantage. It also investigated different neural networks, their architecture, dataset, and other techniques.
Thermo-Optic Phase Shifter for Photonic Neural Networks

We experimentally demonstrate a compact and power efficient ( P π = 21 mW) silicide-based thermo-optic phase shifter of length 96 µm for photonic neural networks.
Bandgap engineering to control amplification in photonic time crystals

We present an unconventional way to manipulate light dynamics in photonic time crystals by introducing isolated temporal defects as a new degree of control over the amplification of propagating pulse with momentum lying within bandgap.
Device-Aware Quantum Photonic Simulator for Gaussian States

We demonstrate the ability to simulate Gaussian quantum photonic circuits using compact models of real devices and open-source PIC simulation tools. This allows rapid iteration of integrated quantum photonic circuit designs before fabrication.
Continuous-Wave Characterisation of Photon Indistinguishability and Nanophotonic Coupling

We use continuous-wave excitation of a single molecule to measure the full temporal wavepacket indistinguishability of emitted photons, and show that continuous-wave light can determine the coupling of quantum emitters to arbitrary nanophotonic structures.
Research on the Influence of Online Games on Juvenile Delinquency and the Countermeasures

With the rapid development of online games, the number of online game users is growing rapidly, among which the number of minor players is growing at a noticeable speed.With rich content, strong player-controlling and unlimited timeliness, online games have prompted a large number of minors to become addicted.Influenced by the content and culture displayed therein, online games have triggered changes in behavior and psychology of minors, and even further developed criminal behavior.There are many types of juvenile delinquency under the influence of online games, and they have their own characteristics.By analyzing their inducements and combining their psychological and physiological characteristics, we can further propose targeted measures to prevent minors from committing crimes under the influence of online games.
An approach to autonomous learning of advanced English vocabulary

With the development of science and technology and the development of various learning software, the learning methods of advanced English are more and more diversified, and there are more and more platforms for learning (such as corpus retrieval software).This paper expounds the data-driven learning method and the concept of independent learning from the perspective of development, discusses the role of modern networked and intelligent social data-driven learning method in advanced English learning, and the positive role of combining with independent learning in advanced English learning, aiming to provide some reference suggestions for college students to learn advanced English.
Use of content management system in academic libraries: An analysis of Central University library websites in India

This website provides information about the library's activities and services. The creation and maintenance of an interactive library website, however, requires technical knowledge, money, and time. Traditionally, even library staffi with technical skills have difficulty maintaining the library website. With the Content Management System (CMS), library professionals can now take up the design, development, and management of library websites without much dependence on their organization's information technology department. CMSs providefree and paid ready-to-use themes and plugins for library -websites to be designed and developed. A study of the web technologies used by central university library websites is presented in this paper. The study revealed that out of 39 central university library -websites in India, 17 use content management systems (CMS). Among the most used CMS are Drupal, WordPress, and Joomla. For identification of web technology used in library websites, WAAPPALYZER API is used. Drupal is the most popular.
Cloud Data Intelligence Detection Based on Decision Tree Algorithm

At present, many countries and regions are strengthening the construction of digital cities, expecting to obtain valuable intelligence from big data by means of information technology, and the massive accumulated security video and alarm information can be analyzed to discover the laws of the data and form correlated data to provide reference for police work.In this paper, from the actual needs of intelligence analysis, cloud computing and big data technology is used to design a cloud data(CD) intelligence investigation system(IIS) to meet the requirements of information development, by building an intelligence information center, using decision tree algorithm(DTA) to analyze and classify massive data, solve the problem of insufficient human resources for on-site investigation, effectively improve the efficiency of investigation and crime solving, and provide technical support for the construction of a harmonious and stable social environment.
A Study of Factor’s Impact on Speaking of L2 Learner’s

This experimental study will explore the impact of various factors on speaking of English language learners at undergraduate level of students. It addressed the issues such as: relationship between external and internal factors, impact of factors on different departmental students, impact of these factors on gender, and different language speakers. The setting of this research is centred on experimental research and the study followed the quantitative approach. The quantitative data was collected through the instrument i.e. questionnaire, sampling of this study is random, 100 second language learners were selected for the given responses from four departments i.e. English, Chemistry, IT, BBA. The finding of this study proved that there is a positive type relationship between external and internal factors that impact on English language learners. The result also shows that these factors have various influences on different departmental students, gender, and different languages speakers. The recommendations are also obvious; the affecting factors on speaking skill of the learners have been limited to only eight factors under the category of external and internal, there might be some other factors which influence on speaking.
The communicative role of silence in Nzema language and culture

This paper looks at the role of silence within the Nzema speech community. Nzema is a Niger-Congo language of the Kwa language family spoken mainly in the South-western part of Ghana and some parts of the La Côte d`Ivoire both in West Africa. It consists of five main dialects namely; Jomoro, Ellembele, Ajomoro, Egila and Evaloe. The paper discusses linguistic and semiotic aspects of silence. It outlines the various communicative situations in Nzema society in which silence is employed, focusing cultural, social and linguistic aspects.
LSTM based Ensemble Network to enhance the learning of Long-term Dependencies in Chatbot

A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language. With the advent of AI, chatbots have developed from being minor guideline based models to progressively modern models. A striking highlight of the current chatbot frameworks is their capacity to maintain and support explicit highlights and settings of the discussions empowering them to have a human contact through the span of involvement. The paper expects to build up a detailed database with respect to the models utilized to deal with the learning of long haul conditions in a chatbot. The paper proposes a crossbreed Long Short Term Memory based Ensemble Network arrangement model to save the continuation of the specific situation. The proposed model uses a characterized number of Long Short Term Memory Networks as a major aspect of the amassed model working as one to create the aggregate forecast class for the info inquiry handled.
Rao-Blackwellized Particle Filter for Scintillation Detection and Position/Bias Estimation of Low-Latitude GNSS Receivers

In this paper, we use a Rao-Blackwellized Particle Filter (RBPF) for GNSS localization subject to ionospheric scintillation. The proposed filter assumes a mixed state vector containing continuous state variables related to the tridimensional (3D) aircraft position and GNSS receiver bias/drift, and a discrete scintillation switching variable for each satellite in view that indicates the presence or absence of scintillation. The dynamics of the scintillation switching variable is modeled by a Markov Chain with known a priori transition probabilities. The variance of the GNSS pseudorange measurement noise changes then as a function of the scintillation variable. Simulation results show that the proposed RBPF achieves a reduced root mean-square 3D position estimation error that is below the corresponding errors for the optimistic and the pessimistic Extended Kalman Filters, which assume, respectively, absence and presence of scintillation at all time instants.
Polarization compensation method based on the wave plate group in phase mismatch for free-space quantum key distribution

Abstract Maintaining the polarization state in communication terminals is vital for polarization-encoding free-space quantum key distribution (QKD). Wave plate group phase mismatch caused by manufacturing errors, complex environmental effects, and the working wavelength deviation can reduce the polarization compensation effect. We found in theoretical analysis, that increasing phase mismatch of wave plates leads to the compensation method failure and reduces robustness. We propose a complementary polarization compensation method, which can effectively improve the robustness. Experimental results show that this method can improve the compensation effect by 50% at a slight phase mismatch, and realize a polarization extinction ratio exceeding 250:1 at the ergodic area even if the phase deviates to 0.27 π . This method is beneficial to the high-stability design of free-space QKD systems and has the potential to be applied to QKD systems operating at multiple wavelengths.
Recommendation for Decision Support on Cloud Data Center based on Operation and Maintenance Knowledge Graph

With the rapid development of Cloud Data Center, the recommendation systems that provide valuable suggestions for the users to address the problem of information over-loaded have provoked a vast amount of attention and research from multiple disciplines.In recent decades, both the matrix factorization (MF) and deep learning methods have achieved fairly good performance for the recommendation.However, in the field of Operation And Maintenance (OAM) cloud data center, due to the intricate nature of the faults data in multi-level and diversified OAM scenarios, the sparsity of data may lead to significant degradation of recommendation performance, which pose huge challenges to the existing recommendation methods.To address these problems, in this paper, we propose a recommendation method for Decision Support on Cloud Data Center based on the operation and maintenance Knowledge Graph.Specifically, fault-based and solution-based representations are learned for Collaborative Filtering (CF), which has been proven to be one of the most commonly applied and successful recommendation approaches.Meanwhile, faults' attributions are combined into the representations by OAM Knowledge Graph for alleviating the sparsity problem.Experimental results demonstrated the effectiveness of our proposed method in the OAM cloud data center for decision support.
Csi-Based Real Time Location Independent Human Activity Recognition Using Deep Learning

Human Activity Recognition (HAR) is a valuable technique in a wide range of applications, including smart homes, health care, the Internet of Things (IoT), and virtual reality gaming. This technology is used to tracks locations and movement based on the signal’s reflection. However, it is fully dependent on location and affected while broadcasting and receiving in a limited range, and is sensitive to noise, making it vulnerable to being blocked, reflected, or scattered by different targets. This work presents real time classification and absolute location-independent sensing with an adaptive algorithm that enables learning various activities in different surroundings. The approach is based on sequential learning activity features in order to simplify the recognition process as well as the variation of human activities among different people. The approach utilizes sequential activity features to enrich the recognition process of human activities among people. This model uses learning technique based on the properties of the Raspberry Pi 4 to extract activity recognition using Channel State Information CSI data. Additionally, it presents a signal segmentation approach to determine the start and endpoint of human activity with the Long Short Term Memory algorithm (LSTM). High accuracy up to 97% was achieved in recognizing eight activities and mapping activities associated with untrained environments.
Research on the Digital Monitoring System of Online Course Teaching Quality Under the Background of Big Data

With the rapid development of digital technology, E-teaching has become a new task of vocational college.The outbreak of COVID-19 has promoted the rapid development of online courses, and also made the quality monitoring of online teaching a new problem to be solved.Based on the analysis of the traditional classroom teaching quality monitoring system and data analysis technology based in the era of big data, this article aims at the need to improve the teaching quality of online courses, and constructs the specific content of the online courses teaching quality digital monitoring system from the perspective of the monitoring elements of curriculum teaching quality through big data analysis technology.And taking the core courses of advertising design major of Anhui Finance and Trade Vocational College as an example to verify the feasibility of the digital monitoring system.The research results could provide a technical reference for the monitoring and management of the teaching quality of university and college courses.
Application of Target Detection Technology Based on Embedded Devices and Convolutional Neural Networks in Power Patrol

In order to ensure safe, stable and efficient operation of power production, regular inspections of power equipment and transmission lines must be carried out.With the update of power patrol technology, large volumes of inspection data increase manual workload, there is therefore an urgent need for intelligent inspection work.This study briefly describes the current state of research in target detection and power patrol, analyses the challenges of target detection technology in power inspections and explore possible solutions, finally take a look at its future development.It is hoped that this study will provide a useful reference for related research.
Semantic-Based Chatbot Application Development to Improve Online Store User Service

The use of chatbot applications as virtual customer service can help potential online store visitors before making transactions due to time constraints in serving customers, ordinary humans cannot answer questions or serve customer requests at any time.Prospective visitors can communicate with the seller about the product to be purchased with virtual customer service.This technology is very supportive to answer various types of questions or problems faced by customers quickly and accurately.Chatbot application developed through A.L.I.C.E (Artificial Internet Linguistic Computer Agency) knowledge based on AIML (Artificial Intelligence Markup Language) equipped with artificial intelligence using a semantic-based ontology.The online shop chatbot application used in this research is batik cloth that sells Malang batik products because the demand for batik from Malang is very high and there are no online stores that provide services by utilizing virtual customer service (chatbot).The development of a chatbot equipped with semantics uses an ontology to process questions that have a more specific meaning, obtaining an accuracy of about 92% from 15 types of relevant questions and answers, then 10 questions answered according to frequently asked questions developed by potential buyers.The use of semantics using ontologies in virtual customer service applications (chatbots) helps in answering questions that have the same word or meaning so as to produce answers that match the question.