Reviewerly/exHarmony huggingface dataset:
Comparison of Open Source Data Extraction Tools

Millions of people are using android and many are dependent on their Android for day to day activities. Android is really a boon as well as the threat to the users. Even though Android is very useful, the rate of cybercrime is increased gradually. To analyze and to extract the data in the android many open source tools are available. This paper focusses on open source tools such as Fone Lab, Dr. Fone, Fonepaw, UltData for Android and Autopsy. Tools were used with the device Redmi 7 and compare the artifacts extracted.
Efficient Methods to Improve the Performance of Supervised Learning Models

Supervised Learning is defined as training a model with input data that includes the result itself. There are large number of supervised learning algorithms and great number of models. Each model has its own merits and demerits and performs differently. There are many data preprocessing techniques and hence the combination of several data preprocessing techniques can increase the performance of the present supervised learning models. Primary data can not be fed directly to the learning model because it can hold a lot of noise. It needs to be preprocessed using various data preprocessing techniques. We have analysed and compared different data preprocessing techniques and their combinations. Comparison is done using various performance metrics and the combination of different data preprocessing is applied to different model. We have done categorical data handling, missing value treatment, feature scaling and feature extraction as the data preprocessing steps. Through the comparison, we found which technique is better for which type of models. We used the California census data for our study.
An Efficient Hybrid Approach to Detecting and Correcting Auxiliary Word Errors in Chinese Text

Chinese spell checking is an important research topic, and it is needed in several applications, such as optical character recognition, speech recognition, and search engines. Due to the specialties of Chinese characters, such as shape and pronunciation similarity, it is still a problem for the computer to detect and correct Chinese spell errors automatically. In this paper, we propose a hybrid approach to detecting and correcting a common class of Chinese word errors, called auxiliary word errors. First, to address the lack of dataset containing Chinese auxiliary errors, we generate artificial dataset of auxiliary errors by an auxiliary confusion set and a large Web corpus. Second, we propose a neural network detection model which adopts BERT as the embedding layer, and combines BiLSTM with CRF. Third, we utilize an auxiliary confusion set and a recurrent neural network language model (RNNLM) to correct auxiliary errors in text. Experimental results on different test datasets show our hybrid approach achieves better performance than traditional baseline methods.
‘그림자 북 만들기’ 활동을 통한 초등 예비교사들의 융합 프로그램에 대한 인식

The purpose of the study is to investigate the perception of elementary pre-service teachers about convergence program after conducting 'Making book using my creative story' activity. The program consists elementary pre-service teachers to creat their own stories and using them to make a shadow book. The program was conducted on 52 elementary pre-service teachers for eight weeks. After the end of the program, they were examined for their perception of the convergence program with the Likert scale questions and essay questions. The analysis results of the Likert scale questions showed that they scored very high on the objectives, contents and methods of the program, and the satisfaction, necessity and willingness to re-participate were also high. In addition, the analysis results based on keywords of essay questions about how the program operates, and its advantages and improvements made it possible to categorize them into instructional content, convergence education, teaching competency, thinking ability, psychology, and emotional experience. Through this study, we could see that by experiencing the convergence program ran by cooperative professors of different majors, elementary pre-service teachers naturally recognized the meaning, importance and necessity of convergence education, understood the contents of related subjects easily, and improved teaching competency. But, they also mentioned various improvements due to the situation of non-face-to-face classes.
Extracting Notes From Youtube Video

In the world of technology, every B.tech student in our college uses Youtube to study the night before the exam, it will be of better use if there is an algorithm that can also provide notes for that video to look for last-minute revision. Hand-written notes are to be carried in a notebook, tedious to maintain for a long time. If a student wishes to pause and take notes, it will consume double the regular time of that streaming video. Those notes might or might not be used in the future or maybe not give proper context if the original content of the video is forgotten. In this project we are extracting frames from a presentation-based youtube video after every fixed time interval, using optical character recognition techniques on these images to make notes of the content written in a youtube video. Users will give the link of the desired youtube video and a pdf/doc file containing all the sentences shown in a video will be in the output. Students will just have to edit and delete some extra material that is not needed in notes. In the upcoming era education will bend more towards paperless, so to maintain the standards all the notes will be generated using this concept. Though it may sound simple it is a bit complex as to know when to extract data from given frame or how to handle cases when some person come in between the video. All this challenges will be major challenges in the future and will require a solution our idea is the first step towards this future.
A tool for executable code complexity visualization

Automotive software is usually extremely complex and consists of numerous software modules. In order to increase the efficiency of programmers and software architects, the tools for code complexity visualization and analysis are being constantly developed and used. However, existing solutions are usually developed for a specific purpose and are often not sufficiently automated so they cannot be easily adapted to certain requirements in automotive software development. The tool for code complexity visualization presented in this paper is based on data obtained from software documentation and data from the corresponding map file obtained by the program build process. The tool displays the structure of software documentation and map file in form of graphs with appropriate nodes representing certain software components like modules. Since one graph is derived from program documentation and the other from the generated map file, the tool allows the comparison of graphs and an indication of potential differences between documentation and actual implementation. This way the tool can help in debugging process of complex automotive software. The functionalities of the tool are shown in three examples that represent possible use case scenarios.
Learning to See and Compute through Multimode Fibers

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods. We also show the reverse, meaning that multimode fibers can be used as a computational tool that harnesses spatiotemporal nonlinear effects to perform end to end learning tasks with unprecedented speed and low power consumption.
Engineering applications of atom search optimization algorithm

In this chapter, the proposed ASO algorithm is successfully applied to parameter estimation for a chaotic system, a circular antenna array design problem, and spread spectrum radar polyphase code design. The experiment results illustrate that the ASO algorithm is effective for these problems.
Novel View Synthesis from only a 6-DoF Camera Pose by Two-stage Networks

Novel view synthesis is a challenging problem in computer vision and robotics. Different from the existing works, which need the reference images or 3D models of the scene to generate images under novel views, we propose a novel paradigm to this problem. That is, we synthesize the novel view from only a 6-DoF camera pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model that could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. GenNet generates a coarse image from a camera pose. RefineNet is a generative adversarial network that refines the coarse image. In this way, we decouple the geometric relationship between mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis.
An Improved Chicken Swarm Optimization Algorithm with Fireworks Factor

Considering the problem that the original chicken swarm optimization algorithm ethnic diversity and easy to fall into local optimum problems, an improved chicken swarm optimization algorithm based on fireworks (FW-ICSO) algorithm was proposed. In this algorithm, roulette was introduced to die out some chickens, produce new chicken's methods is implemented in fireworks algorithm, and adding an inertia factor to balance search capability. Finally, FW-ICSO is tested with twenty benchmark function and compared with other similar algorithms to confirm their effectiveness in terms of the accuracy and convergence rate of the results.
Creation of a Meta-Model for the Generation of a Webmapping Application

Our research focuses on creating a meta-model for generating a web mapping application.It was difficult for non-geomatics developers to implement a webmapping application.Indeed, this type of application uses geospatial data that require geomatics skills.For this reason, in order to help non-geomatics developers to set up a webmapping application, we have designed a meta-model that automatically generates a webmapping application using model-driven engineering.The created meta-model is used by non-geomatics developers to explicitly write the concrete syntax specific to the webmapping application using the xtext tool.This concrete syntax is automatically converted into source code using the xtend tool without the intervention of the non-geomatics developers.
Automatic Sinhala News Classification Approach for News Platforms

Because of generating various news articles in large scale, online sources moved into an automatic categorization mechanism. This research has been conducted using LDA topic modeling approach and using other classification algorithms to establish a news categorization solution. Sinhala news websites have only few news categories and do not have any relationships or hierarchies between the categories. Therefore, some users require to search manually and find the necessary articles which are in those categories. Purpose of this study is to build a news categorization model with categorization hierarchies for Sinhala news articles. The goals of the models are to identify the most suitable news category for a related news article and develop hierarchies using generated news categories and assign the news articles according to the hierarchical structure. The final experiments and evaluations show that the solution performs well to solve the automatic categorization problem in Sinhala news platforms.
Compression of Time Evolutionary Image Data through Predictive Deep Neural Networks

Recent advances in Deep Neural Networks (DNNs) have demonstrated a promising potential in predicting the temporal and spatial proximity of time evolutionary data. In this paper, we have developed an effective (de)compression framework called TEZIP that can support dynamic lossy and lossless compression of time evolutionary image frames with high compression ratio and speed. TEZIP first trains a Recurrent Neural Network called PredNet to predict future image frames based on base frames, and then derives the resulting differences between the predicted frames and the actual frames as more compressible delta frames. Next we equip TEZIP with techniques that can exploit spatial locality for the encoding of delta frames and apply lossless compressors on the resulting frames. Furthermore, we introduce window-based prediction algorithms and dynamically pinpoint the trade-off between the window size and the relative errors of predicted frames. Finally, we have conducted an extensive set of tests to evaluate TEZIP. Our experimental results show that, in terms of compression ratio, TEZIP outperforms existing lossless compressors such as x265 by up to 3.2x and lossy compressors such as SZ by up to 3.3x.
Morphological Analysis Corpus Construction of Uyghur

Morphological analysis is a fundamental task in natural language processing, and results can be applied to different downstream tasks such as named entity recognition, syntactic analysis, and machine translation. However, there are many problems in morphological analysis, such as low accuracy caused by a lack of resources. In this paper, to alleviate the lack of resources in Uyghur morphological analysis research, we construct a Uyghur morphological analysis corpus based on the analysis of grammatical features and the format of the general morphological analysis corpus. We define morphological tags from 14 dimensions and 53 features, manually annotate and correct the dataset. Finally, the corpus provided some informations such as word, lemma, part of speech, morphological analysis tags, morphological segmentation, and lemmatization. Also, this paper analyzes some basic features of the corpus, and we use the models and datasets provided by SIGMORPHON Shared Task organizers to design comparative experiments to verify the corpus’s availability. Results of the experiment are 85.56%, 88.29%, respectively. The corpus provides a reference value for morphological analysis and promotes the research of Uyghur natural language processing.
Hardware Implementation of Fixed-Point Convolutional Neural Network For Classification

The Convolutional Neural Network (CNN) dominates the research area of Field Programmable Gate Arrays (FPGAs) and demonstrates its efficiency on computer vision applications. The correct predicted rate of the CNN is highly dependent on the selection of the activation functions. Thus, we intend to deploy a CNN model on Virtex-7 while varying the activation function such as ReLU, PReLU, and Tanh Exponential (TanhExp) activation functions. To this end, we will use a fixed-point representation concerning the arithmetic numbers and the piecewise linear approximation regarding the TanhExp activation function. We present the speed, accuracy and hardware resources of each model of the CNN.
Identification of thermo optical parameters of 8ocb pure and nano dispersed liquid crystalline compound using image processing based IME method

In the present paper synthesis, characterization and image enhancement studies have been carried out on 8ocb LC with dispersed ZnO nanoparticles in conjunction with identification of thermo optical parameters at various concentrations. The microscopic textures are analyzed with MATLAB tool to obtain optical parameters. Image processing methods are used to identify the behavior of optical transmittance, absorption coefficient and phase retardation calculated using proposed Illumination Map Estimation (IME) technique and compared with the available literature. The proposed image processing-based method is a simple experimentation procedure to identify the thermo optical parameters of liquid crystal compounds for various electro optic applications.
Checking the writing of commas in Slovak

This article focuses on the design of a procedure for solving the correction of commas (incorrectly placed or missing) in the text written in the Slovak language. At the beginning of the article, the text deals with existing third-party applications that address the same issue. Subsequently, we focus on the ways in which the field of neural networks is currently applied to the problem of finding mistakes in the text. Based on the acquired knowledge, we proposed a solution using neural networks. The next part of the article deals with experiments with our neural network and different lengths of the input vector. The last part contains an evaluation of the experiments and an overall summary.
Design and Development of a Telescopic Twin-copter

Telescopic Twin-copter is a well-designed and power-efficient bi-copter with a longer flight time than its competitors. The twin-copter has two main rotors controlled by BLDC (Brushless DC) motors, tilted at required angles using servo motors underneath them. Thus, achieving all six degrees of freedom. The aerodynamic structure of its body, the minimal number of BLDC (Brushless DC) motors contribute to the increased flight time. It can also traverse narrow, tortuous terrains by actively morphing its arms mid-flight. The liberty to morph gives the drone an additional capability to adapt and change its structure as per the flying environment and requirement to gain more stability or faster reactions. Hence, by overcoming many such constraints, we can utilize this drone in photography, surveillance, weather monitoring, and many more.
Application of Directed Graph Algorithm in Formula Relationship of Power Grid

In the operation of the power grid, the amount of data is large, and the intricate relationship between the data formulas brings hidden dangers to the operation of the power grid. In order to solve the problem, this paper uses directed graphs to show the relationship of formulas in the power grid. By optimizing the rank, node order, and edge length of the graph, the complex formula reference relationships are converted into directed graphs to realize human-computer interaction. The practical application shows that this function can effectively reduce the working pressure and risk of disoperation of the staff in the operation of the power grid, and has positive significance for the safe and stable operation of the power grid.
Question Difficulty Priori Evaluation Based on Fuzzy Logic in Programming System

With the increasing number of online programming systems, how to efficiently and accurately assess the difficulty of programming questions is a classic problem in the field of computer education. Therefore, we propose a question difficulty priori evaluation method based on fuzzy logic in this paper. The proposed method considers the lines of code, variables and knowledge points in the answer of question, and uses fuzzy membership functions and fuzzy rules to evaluate the difficulty of questions. It solves the problems of subjective experience and low flexibility of traditional question difficulty evaluation methods. The experimental results show that the proposed method is effective and can obtain more accurate programming question difficulty.
Case Study: The Impact of VR &amp; AR Simulation Project to Decrease Human Exposure in Critical Environments

Abstract Human exposure is a relevant factor when operating in critical environments and depends on a thorough analysis and consideration towards driving the teams to a safer and more productive environment. Reducing such exposure through digital technologies benefits the whole workforce in their decisions and maneuvers, like simulations, training, and other critical activities that can be executed remotely and prior to the actual activity. This paper presents a case study to demonstrate how augmented and virtual reality can be used to create a high fidelity virtual environment emulating the real industrial facility. This approach enriches the Digital Twin with the association of data and the virtual environment. It leverages on display and interaction capabilities of hardware devices, and intelligence and data querying capabilities of industrial software, empowering the workers with enhanced training capabilities and access to information increasing safety and efficiency. A real application of this technology is presented in this paper through the case study of the PredictMain4.0 project of Repsol Sinopec Brazil (RSB), which aimed at the integration of digital technologies, including augmented reality (AR) and virtual reality (VR). The PredictMain4.0 project was executed using data and data models of PETROBRAS’ P-50, a FPSO (Floating Production Storage and Offloading) operating in Brazil, and illustrates how different AR/VR applications can be developed and used in combination with engineering, operation, and maintenance databases. This includes 3D models, digitalized critical procedures, and the ability to integrate field teams into a single virtual environment, allowing real interaction in a digital setting that is linked to the real world. Considering the digitalized procedures, this paper aims to establish how virtual simulation and training can make teams more confident and prepared to execute the same physical asset procedures. After consulting with stakeholders from many different teams, the PredictMain4.0 project team selected three critical operating modules in the FPSO (Power Generation, Water Injection, and Gas Compression). For each one, considered which situations were relevant, should they occur. These situations led to developing a training and simulation framework, allowing instructors to create different scenarios and use advanced features such as digital measurement, real-time data collection, and collaborative sessions. The case study indicates that the development of such applications can save more than $1 million per year in maintenance costs considering the decrease in downtime and avoiding risks of accident.
Passionarity management: adapting the model to modern conditions

Public administration is now quite often identified with state and municipal administration, while the sign of identity between these concepts cannot be put, since public administration is the result of the development of state and municipal administration, its “higher stage of evolution”. The article describes the situations when traditional ways of public management do not work that requires the development of qualitatively new methods of management or adoption of already developed once to the current conditions. The article is aimed to show the need to study the peculiarities of passionarity management as a perspective trend of public management. The authors analyze the existing theories in the field of passionarity management. The authors 'conclusions support the fact that the effectiveness of public management could be determined by the level of management passionarity.