Reviewerly/exHarmony huggingface dataset:
MODELING AND ANALYSIS OF CRANKSHAFT

Virtual Reality is a Vast Topic today we are going to discuss it some may be interested some may not but my main goal is to make you understand the simple or basic things about VR so if you are interested in it buckle up and get your popcorns ready cause it is going to be Interesting. Introduction: Hello, Today I am going to provide you with some information about Virtual Reality. So You might have many questions What is Virtual Reality? How does it work? Etc. today I am gonna answer all your questions and give you proper Knowledge about Virtual Reality.
The Language Difficulty of the Original Version and the Rewritten Version of The Time Machine: A Corpus-based Study

This study examines the language difficulty of the original version and rewritten version of The Time Machine from the perspective of lexical complexity, syntactic complexity, and semantic complexity. We aim to answer the following two research questions: (1) Is there a significant difference between the language difficulty of the original version and rewritten version of The Time Machine? If yes, in what ways? (2) Is the rewritten version well-targeted at its intended readers? The data consisted of three corpora, the original version of The Time Machine, the rewritten version, and passages from English subjects in the National College Entrance Examination (NCEE) of China. The results demonstrate a significant difference between the original and rewritten versions on most measures of language difficulty, but in many respects, the rewritten version still leaves much to work on before becoming suitable reading material for its target readers. With such analysis carried out, we seek to offer some pedagogical implications to better facilitate English learning in China.
PROYEKSI AKUNTABILITAS KEUANGAN SERTA IMPLIKASINYA TERHADAP KINERJA PERGURUAN TINGGI SWASTA

This study aims to examine and analyze the effect of the implementation of the internal quality assurance system, the implementation of the accounting information system and the commitment of the foundation leadership to financial management accountability and its implications for the performance of the tridharma of private universities. The object of research is private universities under the Regional IV Higher Education Service Institute for West Java and Banten (LLDikti IV). The sample used in the study was 219 universities. The data was obtained by distributing questionnaires to the respondents, namely the leaders of private universities in the LLDikti IV area. Data were analyzed using structural equation modeling (SEM). There is no significant effect between the implementation of the internal quality assurance system and financial management accountability (2) there is a positive and significant effect between the implementation of the internal quality assurance system and the performance of the higher education tridharma (3) there is a positive and significant effect between the implementation of the accounting information system and financial management accountability (4) there is no significant effect between the implementation of the accounting information system and the performance of the higher education tridharma (5) there is no significant effect between the commitment of the foundation leadership and financial management accountability (6) there is a positive and significant effect between the commitment of the foundation leadership and the performance of the higher education tridharma (7) there is a positive and significant effect between financial management accountability and the performance of higher education tridharma
CHATTING WITH EASE: DESIGNING AND IMPLEMENTING A USER-FRIENDLY CHAT APPLICATION

This paper suggests designing a chat application that would allow users to interact with each other instantly.An end-to-end chat application allows users to communicate with each other in real-time.Developing such an application requires the use of a range of tools and technologies, etc.The application will be built using a clientserver architecture, where the server will handle all the data processing and communication between clients.The application will be designed with a Graphical User Interface (GUI) and will include features such as text messaging, file sharing, etc.The application will be developed using MERN where MongoDB is used as a database.The application will also support various security measures to protect the data of its users.The development of this application will leverage existing open-source libraries and frameworks to deliver a robust, secure, and feature-rich experience to its users.In addition to facilitating real-time communication, chat applications also offer a number of other benefits.For example, many chat applications allow users to create groups where they can discuss specific topics or share information with a larger group of people.This can be useful for teams and organizations, as it allows members to collaborate and share ideas more easily.
Analysis of Satellite Rain Data UsageOn The Rationalization Activities of The Rain Post Network (Case Study: Rationalization of The Jelai Watershed Rain Post Network)

An alternative solution to the availability of inadequate rain data as hydrological data input is with the help of Global Precipitation Measurement (GPM) satellite rainfall data using remote sensing technology (satellite). The purpose of this study was to find correlations and corrections of data and validate GPM satellite data with rainfall data at rain stations and observation data in the Jelai watershed. The corrected GP M rain data validation results in Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Relative Error (KR). The validation results resulted in NSE values of 0.33, RMSE 48.54, Correlation Coefficient (R) of 0.75, and Relative Error of 0.19 for 2019 and yielded NSE values of -0.14, RMSE 100.24, Correlation Coefficient (R) of -0.36, and Relative Error of 0.23 for 2020. The overall analysis shows that GPM data can be used as an alternative to rain data if in a watershed there is a small number of rain posts that do not meet the WMO criteria. As a suggestion for further research, it is necessary to calibrate and validate by distinguishing between rain data in wet years and dry years
Bangladeshi Currency Identification and Fraudulence Detection Using Deep Learning and Feature Extraction

There are persistent rumors about counterfeit money all across the world. There is a massive loop of producing counterfeit currency that is developing alongside technology. Counterfeit currency production has gotten easier and more advanced day by day, so the detection process has gotten more challenging. Utilizing a variety of user-friendly counterfeit detection tools or software is the only method to stop fraud. Many people still do not have access to these softwares or tools for detecting fakes. Therefore, some of these programs or tools are not accurate, dependable, and free. This paper describes a potential software solution for detecting fake Bangladeshi banknotes. The most important thing is that it is totally free of cost and usable by all average people. The primary purpose of this software is to identify different currencies and determine whether it is real or fake. In this paper, Convolutional Neural Network (CNN) and FLANN-based Matcher with the Scale-Invariant Feature Transform (SIFT) algorithms have been implemented in the deep learning process to recognize the currency and determine whether it is genuine or counterfeit, to make the entire process functional. 1, 2, 5, 10, 20, 50, 100, 200, 1000, and 5000 notes from Bangladesh have been used in this research. One of the most suitable methods to detect fake currency is color-changeable holographic thread, The Portrait of Bangabandhu Sheikh Mujibur Rahman and “Bangladesh Bank (in Bengali)” written on the level of intaglio ink, printed hidden below the middle of the note, “Amount in Bengali” at the right bottom of the note, Some parallel lines or objects of intaglio ink, and color changeable ink OVI and SPARK. Additionally, this procedure makes it possible to quickly identify fake money. To make the system quick and easy for everyone, the entire system has been developed into a mobile based and as well as a web-based application.
Traffic Sign Identification Using Modified LeNet-5 CNN

Abstract: In 21st century, it is obvious that automobiles have become the standard mode of transportation. With the spread of their usage, road traffic becomes more intricate; hence road safety must be promising enough avoiding accidents. Drivers may sometimes overlook the sign boards along the roads which might lead to undesirable casualties. This could be helped by a system that could assist the driver in keeping an eye on the traffic sign boards on the way. It can be achieved using CNN i.e. Convolutional Neural Networks. CNNs are preferred when the applications are concerned with computer vision or image recognition. A deep learning CNN model is built in the LeNet-5 architecture with some modifications and is trained with traffic signs images from GTSRB dataset which stands for German traffic sign recognition benchmark with the help of OpenCV, Tensorflow, Keras and other libraries. The trained model is tested with test data and is observed to be recognizing any of the traffic signs which were already learnt by the network. Vehicle cameras come handy to capture the real-time sign boards. Implementation of this supervised learning model in real-time would aid road safety through vehicle control.
A Study on the Effectiveness of k-NN Algorithm for Career Guidance in Education

This study aims to evaluate the performance of k-NN algorithm in recommending career paths for students based on their interests, past courses, and career goals. The k-NN algorithm was applied to a dataset of student information and its performance was evaluated using quantitative or qualitative measures such as accuracy or user satisfaction. The results indicated that the algorithm provided accurate recommendations and that the choice of k and the use of Euclidean distance measure were crucial for the performance of the algorithm. However, the study also highlighted the limitations of the research, such as the size and diversity of the dataset used, which could have affected the generalizability of the results. This study emphasizes the potential of data-driven approaches in career guidance in education and the k-NN algorithm as a valuable tool in this field. Future research could include incorporating additional factors such as student demographics or academic performance into the algorithm and using more diverse and larger datasets
SceneScape: Text-Driven Consistent Scene Generation

We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To tackle the pivotal challenge of achieving 3D consistency, i.e., synthesizing videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene. The depth maps are used to construct a unified mesh representation of the scene, which is progressively constructed along the video generation process. In contrast to previous works, which are applicable only to limited domains, our method generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles.
Friendly paths for finite subsets of plane integer lattice. I

For a given finite subset P of points of the lattice Z^2, a friendly path is a monotone (uphill or downhill) lattice path which splits points in half; points lying on the path itself are discarded. The purpose of this paper (and its sequel) is to fully describe all configurations of n points in Z^2 which do not admit a friendly path. We say that such an n-set is inseparable. There are, up to the lattice symmetry, exactly c(n) such sets. If only lattice shifts are counted, there are \^c(n) of them. Both sequences are new entries into OEIS (A369382 and, respectively, A367783). In particular, n=27 is the first odd numbers with c(n)=1. No example was known so far. This solves problem 11484(b)* posed in American Mathematical Monthly (February 2010). In this paper we also show that inseparable n-set exist for all even numbers n>=12 and almost all odd numbers.
Linear Neural Network as a Fast Solver for Dictionary Learning

&lt;p&gt;Traditional numerical solvers for linear algebra problems such as dictionary learning (DictL) operating on large data face challenges because of super-linear computational complexity and massive memory requirement. This does not make it easily solvable on commonly available computers. A linear neural network (NN) is proposed to be employed as an alternate solver for such problems. Specifically, we demonstrate a linear fully-connected (FC) NN-based solver for DictL. It is employed to learn the dictionary atoms instead of the classically used k-singular value decomposition (K-SVD), while the sparse coefficients are learned using the classical orthogonal matching pursuit (OMP) approach. We compare the computational complexity of the classical vs our proposed FCNN-based approach implemented on both CPU and GPU using synthetically generated datasets with varying sizes. Further, we demonstrate practical utility in image denoising with DictL while comparing the equivalence of solution obtained from our FCNN-based solver with the traditional approach with K-SVD. We achieve a notable speedup compared to the traditional technique on both CPU and GPU implementations.&lt;/p&gt;
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations

&lt;p&gt;Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial approach, in which a convolution or message-passing operation is conducted based on a hypergraph algebraic descriptor. While many HyperGNNs have been proposed and achieved state-of- the-art performance on broad applications, there have been limited attempts at exploring high dimensional hypergraph descriptors (tensors) and joint node interactions carried by hyperedges. In this paper, we depart from hypergraph matrix representations and present a new tensor-HyperGNN framework (T-HyperGNN) with cross-node interactions. The T-HyperGNN framework consists of T-spectral convolution, T-spatial convo- lution, and T-message-passing HyperGNNs (T-MPHN). The T- spectral convolution HyperGNN is defined under the t-product algebra that closely connects to the spectral space. To im- prove computational efficiency for large hypergraphs, we localize the T-spectral convolution approach to formulate the T-spatial convolution and further devise a novel tensor message-passing algorithm for practical implementation by studying a compressed adjacency tensor representation. Compared to the state-of-the- art approaches, our T-HyperGNNs preserve intrinsic high-order network structures without any hypergraph reduction and model the joint effects of nodes through a cross-node interaction layer. These advantages of our T-HyperGNNs are demonstrated in a wide range of real-world hypergraph datasets. &lt;/p&gt;
Computation and Information Theory of Chess Games

&lt;p&gt;We provide two methodologies in the area of computation theory to solve optimal strategies for games such as Watermelon chess and Go. From experimental results, we find relevance to graph theory, group representation, and mathematical consciousness. We prove that the decision strategy of movement for Watermelon chess and Chinese checker games belongs to a matrix that is a noncommutative ring or an abelian group over set Y={-1,0,1}. Additionally, the movement for any chess game with two players belongs to a noncommutative ring or an abelian group from Occam’s razor principle. We derive the closed form of the transition matrix for any chess game with two players and discover that the element of the transition matrix belongs to a rational number. We propose a different methodology based on abstract algebra to analyze the complexity of chess games in their entirety, instead of being limited solely to endgame results. It is probable that similar decision processes of people may also belong to a noncommutative ring or an abelian group.&lt;/p&gt;
On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data

&lt;p&gt;Quantum Machine Learning (QML) is an emerging technology that only recently has begun to take root in the research fields of Earth Observation (EO) and Remote Sensing (RS), and whose state of the art is roughly divided into one group oriented to fully quantum solutions, and in another oriented to hybrid solutions. Very few works applied QML to EO tasks, and none of them explored a methodology able to give guidelines on the hyperparameter tuning of the quantum part. As a first step in the direction of quantum advantage for RS data classification, this letter opens new research lines, allowing us to demonstrate that there are more convenient solutions to simply increasing the number of qubits in the quantum part. To pave the first steps for researchers interested in the above, the structure of a new hybrid quantum neural network is proposed with a strategy to choose the number of qubits to find the most efficient combination in terms of both system complexity and results accuracy. We sampled and tried a number of configurations, and using the suggested method we came up with the most efficient solution (in terms of the selected metrics). Better performance is achieved with less model complexity when tested and compared with state-of-the-art techniques for identifying volcanic eruptions chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a major problem when training classical models. Lastly, the code is freely available so that interested researchers can reproduce and extend the results.&lt;/p&gt;
Effect of Gadgets On The Development of Interest In Reading

The type of research used is quantitative research. This research was conducted at MTsS Nurul Ihsan school. With a total student population of 66 people, with a total sample of 16 people using the Cluster Random Sampling technique. The instrument used was a questionnaire with a total of 15 questions. The research results showed that there were 3 people in the category of low interest in reading, 10 people in the category of moderate reading interest and 3 people in the category of high reading interest. In the low category, the average value is 40 with a percentage of 18.75%; in the medium category the average value is 47.6 with a percentage of 62.5%; and in the high category, the average value is 53.3 with a percentage of 18.75%. So the study results show that the average value of students' reading interest on the influence of gadgets is in the medium category, with an average total score of 47.6.
Relationship of External Factors Caused Student’s Learning Difficulties and Biology Learning Outcomes in Class XI IPA MAN 3 Padang City

This study aims to determine the external factors that cause student learning difficulties and their influence on the biology learning outcomes of class XI IPA MAN 3 Padang City students. In general, students have learning difficulties due to several factors. One of the influencing factors is external factors, where external factors consist of family, school and community factors. These factors are very influential on student learning outcomes. This type of research is descriptive with quantitative research methods. The research was conducted at MAN 3 Padang City. The sample is all students of class XI IPA as many as 33 students. The sampling technique is a random sampling technique. Collecting data using research instruments in the form of a valid questionnaire of 3.38. The results showed that the external factors of students' learning difficulties, family factors, were 2.42, schools were 2.39 and community factors were 1.8. The highest external factor of learning difficulties is the family factor. There is a significant relationship external factors of learning difficulties to learning outcomes on the 0.05 t test. It can be concluded that t table (3,638&gt;2,040), where Hi is accepted, Ho is rejected, meaning that there is a significant relationship between external factors causing learning difficulties and students' biology learning outcomes. So it is expected for teachers and parents to be able to recognize the learning difficulties that students have, so that learning can run smoothly.
Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network

Facial recognition is a part of Computer Vision that is used to get facial coordinates from an image. Many algorithms have been developed to support Facial Detection such as Cascade Face Detection using Haar-Like features and AdaBoost to classify its Cascade and Convolutional Neural Network (CNN). Face recognition in this study uses the Deep Convolutional Neural Network (DCNN) method, and the output of this method is the measurement value of the face. In the model training process, Triplet Loss from Triplet Network Deep Metric Learning is used to get good face grouping results. The value of this face measurement will then be measured using the Euclidean distance calculation to determine the similarity of the input face from the dataset. This Research is using 6 images of Government officers in Indonesia to determine the accuracy of the model when there is a new picture of these officers inputted into the training machine. The result provides a 94% accuracy level with a variety of face positions and levels of brightness.
Improved Classification of Handwritten Jawi Script Based on Main Part of Script Body

Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.&#x0D;