Reviewerly/exHarmony huggingface dataset:
Keyphrase generation algorithm based on co-occurrence graph

Abstract In the keyphrase prediction task, it is usually difficult for a single corpus information to help the model learn enough core information, so this paper improves the current keyphrase generation method based on the corpus. From the developments of the co-occurrence, the use of the word conformity is mentioned in the original selection of the relevant text, and the purpose of achieving the maximum utilization of the spending information is achieved. At the same time, this paper proposes the CG-Net model, which uses the GAT network to learn co-present diagrams and implements a replication mechanism, which ultimately surpasses the current optimal method on the NUS test set.
Ink Mismatch Detection in Hyperpectral Image using K – means Clustering Algorithm

&lt;p&gt;In forensic analysis of any document, Ink mismatch detection provides a very important clue to see whether the document is corrupted or not. In hyperspectral imaging (HSI) ink mismatch detection is used to check whetherthe specific document is written with same pen/ink or different types of pens/inks have been used. In this paper, we show that aHSI of handwritten notes can differentiate between the ink sused to produce the document which are very similar in appearance. The HSI cube we have used for our research has 149 Bands. By using an unsupervised clustering algorithm, we have analyzed the spectral responses of the ink pixels to differentiate between the number of inks used in document. After the successful ink mismatch detection, we have used color labelling to classify the text written with different inks. This technique when applied to a RGB document, is not capable to differentiate between the number of inks used in the document. We hope that our findings and technique will help to examine HSI documents for forensic analysis. &lt;/p&gt;
Moving Object Detection based on Reverse Projection Error Model

Abstract A novel moving feature point detection method is proposed in this paper in order to solve the problems in existing methods. Firstly, the coordinates of feature points are normalized by using spherical coordinates in order to solve the imaging distortion of the fish-eye camera. Secondly, based on the spherical normalization coordinates of feature points the reverse projection error model of the fish-eye camera is deduced and a moving feature point’s discriminant is derived to improve the detection integrity of moving object. Finally, according to the law of object motion, the detected potential moving feature points are clustered to further remove outliers. Experimental results show the proposed moving feature point detection method has better detection results compared with the commonly used methods.
3d Image Processing Robot

Abstract: For an 3D IMAGE PROCESSING robot to truly succeed, there must be a variety of ways for robot to interpret its surrounding environment, both quickly and accurately. This report focuses on investigating improvements for a sub- problem of environmental perception, namely 3D image processing, referring to localizing and classifying objects of interests in a specified environment. The objects of interests in a process security scan of baggage or to analyse scans of materials to understand their structure. Recently python3 &amp; machine learning programming approaches have made significant progress in the field of 3d object detection. Thus, several techniques have been developed for the real time tasks such as the autonomous robot. However, as images lack some information about depth which is essential for environmental perception for autonomous robot, they struggle a lot to preserve distance between object and robot. So, in this project in order to avoid such delays we have used high quality camera OF 1080p. This Report investigates the performance of both the minicomputers and as well as the cameras being mounted on the robot for 3d analysis which uses the state-of-the-3d image processing models. The end-to-end models of python3 and OpenCV are ultimately performed and experimented various times to perform 3D Detections. The models were trained and evaluated on the recently released data set and the most promising model was able to detect the 3d image of the object
Application of Buddhist Philosophy in Nepal: A General Review

The application of Buddhist Philosophy in Nepal can be analyzed in Economic Development, Gender Studies, Livelihood, Peace, Spirituality, Sustainable Development, Tantrism, Tourism, and many more. To find out the application of Buddhism in Nepal the study has been conducted. The philosophy of peace, reality, love, and compassion are universal. Thus Buddhism is a practical philosophy of the middle way path of humanity. Thus it is applicable everywhere. The study is the general review of selected research conducted in Nepal. It is a qualitative descriptive content review of the list presented in reference. Thus in conclusion Buddhism is a way of peaceful life. It is applicable in every sector of development and humanity.
From the experience of holding the regional olympiad of schoolchildren in native languages and literature of the indigenous peoples of the north in the Khanty-Mansi Autonomous Okrug - Yugra

This article is an analytical review of the main stages of organizing and holding a regional Olympiad for schoolchildren of the Khanty-Mansiysk Autonomous Okrug Yugra in native languages and literature of the indigenous peoples of the North. The goal is to focus the attention of not only teachers, specialists in the field of education, but also the entire public as a whole on the problem of preserving native languages in modern conditions through various forms of activity, in particular, holding an Olympiad for schoolchildren in native languages and literature of the indigenous peoples of the North. To show the role of the Olympiad as one of the means of the work of the school and teachers to identify gifted students who strive for greater knowledge of their native language, culture and its preservation. The article presents information and analytical materials of the Olympiad for schoolchildren in native languages and literature of the indigenous peoples of the North in the Khanty-Mansiysk Autonomous Okrug Yugra for three years (2019, 2020, 2021). Attention is focused on the format of the Olympiad. Since 2017, the regional stage of the Olympiad for schoolchildren in native languages and literature has been held in full-time format using distance technologies. When writing the article, the reporting documentation on the results of the Olympiads was used (final protocols with rating tables attached, summary evaluation sheets, analytical reports of the jury), including the effectiveness of the participants in the Olympiad tasks, analysis of the developed tasks and analysis of the quality of the Olympiad tasks in native languages. The reasons for the decline in the quality of the performance of Olympiad tasks in native languages and ways and methods to improve the quality of assignments and involve more students in the Olympiad are considered.
ANALISIS SURVIVAL PENDERITA GAGAL GINJAL DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Survival analysis is a statistical method where the outcome variable that is considered is the time until the occurrence of an event or called an event. Survival analysis in the medical world can be used to test the survival of patients suffering from a disease, one of which is kidney failure. Based on the results of the 2013 Basic Health Research, 0.2% of the total population of Indonesia, namely 499,800 people, experienced kidney failure. Kidney failure is a process of decreasing kidney function that requires renal replacement therapy, in the form of hemodialysis or kidney transplantation. If the survival data involve independent variables that are thought to affect the survival time, then the Cox Proportional Hazard (PH) regression approach can be developed which can be developed for the Multivariate Adaptive Regression Spline (MARS) approach. The MARS approach is a nonparametric regression approach with high-dimensional data, namely data with an independent variable of 3≤𝑝≤20 and a data sample of 50≤N≤1000. This study aims to describe the characteristics of patients with kidney failure, identify the factors that affect the survival time of patients with kidney failure and determine the MARS model. The MARS approach is obtained from a combination of functional basis, maximum interaction, and minimum observation by trial and error. Based on the model that has been formed using the MARS approach, it is found that the most influential variables on the survival time of patients with kidney failure are age, urea levels, and creatine levels.&#x0D; Key words: Renal failure, hemodialysis, survival analysis, nonparametric, MARS&#x0D;
Experimental Measurement and Markov Chain Modelling of Stroke Order Intuition

Abstract As a feature of Chinese characters, stroke order plays an important role in Chinese character education, Chinese character recognition and handwriting identification. However, in recent years, the research on stroke order is limited to elaboration and supplementation of existing stroke order and stroke order rules. There is no special research that focuses on the principle behind stroke order. To quantify the internal regularity of stroke order, we start with the stroke order intuition in the actual writing. Because stroke order has individual differences, we select a group of students with small differences as testers. In this paper, we design an algorithm to generate random strokes and collect the timing data during testers copying strokes. After pre-processing the collected data, a Markov chain is introduced to model stroke order intuition, which can divide the stroke sequences into three states. And then we classify them into three situations according to the stroke distribution. Next, we compare them with some well-known stroke order rules. The results show that the probability distribution of strokes in different situations is not always consistent with the empirical rules, as well as the relations between them.
Audio Assistant Based Image Captioning System Using RLSTM and CNN

Abstract-- As we know, visually impaired or partially sighted people face a lot of problems reading or identifying any local scenarios. To vanquish this situation, we developed an audio-based image captioner that will identify the objects in an image and form a meaningful sentence that gives the output in the aural form. Image processing is a widely used method for developing many new applications. It isalso open source, so developers can use it easily. We used NLP (Natural Language Processing) to understand the description of an imageand convert the text to speech. A combination of R-LSTM and CNN is used, which is nothing but a reference based long-short term memory which matches different text data and takes it as reference and gives the output. Some of the other applications of image captioning are social media platforms like Instagram, etc., virtual assistants, and video editing software.
N-Most Interesting Location-based Recommender System

The popular and ubiquitous location-based social networks (LBSNs) ap-peal many users for sharing interesting locations with other users. As the collected data (such as users' profiles, location comment, and suggestion) become a lot larger in size, location-based recommender systems require more effective fillters to be able to suggest potentially preferable locations to users. Location recommendation is more difficult and challenging especially if users have few or no check-in histories as a new user. Therefore, instead of depending on users' check-in histories, previous works focused on creating recommended location lists by leveraging the information given by other users who check in locations in each area. However, previous studies took into account only the frequencies of users for creating recommendation lists and have su˙ered from the cold-start problem where new users have few or none histories. As a result, the recommenders hardly suggest any locations matching the users' preference. In this paper, we propose an enhanced location-recommendation approach called N-most interesting location-based recommender system (NILR) to recommend interesting lo-cations for new users. Our approach can be divided into three phases. First, the NILR discovers interesting locations by taking into account both the visiting frequencies and the preferences of users already in the system. Second, a ranking procedure is executed to create a final recommendation list based on two interestingness scores: one obtained from the HITS-based model (as adopted by [1] and [2]) and the other from our proposed method. Finally, we re-filter interesting locations based on the current location of the new user. Experimental results reveal the NIRL can reach better precision, recall, average ranking and NDGC than HITS by 6%, 6%, 30%and 8% for Tokyo and 24%, 30%, 43% and 15% for New York dataset, respectively.
The ultra-spatial resolution power microscopy beyond the Rayleigh diffraction limit using the Laser propagating through an optical fiber

Abstract In this paper, we use the Laser propagating through an optical fiber to achieve a multi-photons ultra-spatial resolution power microscopy beyond the Rayleigh diffraction limit. The uncertainty principle for multi-photons satisfies\(\frac{ \varDelta x}{n}n\varDelta {p}_{x}\ge \hslash ,\frac{\varDelta y}{n}n\varDelta {p}_{y}\ge \hslash\). The Rayleigh criterion becomes to\(0.61\lambda /n\tilde{n}\text{sin}[\theta ]\), furthermore, spatial resolving power will be improved as much as \(n\)times.
SISTEM INFORMASI AKUNTANSI (SIA) PENERIMAAN KAS PERUMDAM TIRTA DHARMA PURABAYA KABUPATEN MADIUN

An accounting information system is a process for collecting, processing, and storing financial-related data for making decisions. The reason of this study is to determine the monetary course of payment receipts from customers. To find out the finances of the customer by interviewing the finance department employee. The outcomes of this observe are in the form of a flowchart at the form of images that have meaning and writing.
Systematic Review of Agile Methodologies for Software Development

Abstract: With the increase in complexity and innovation in IT products and services, developing them, that to by the shared efforts of team members brings in the need of some standardized development model or approach. Due to several advantages of agile methodologies over the traditional waterfall based models, it is the preferred choice. Many a times, a single agile method is also not enough to meet the current scenario of product development. We therefore propose a Competitor Driven Development (CDD) model, a hybrid agile process model for IT product development by abstracting practices from Extreme Programming (XP) and Feature Driven Reuse Development (FDRD) agile methods. This model is based on self-realizing requirements generation for Product development by keeping an eye watch on competitor’s upcoming launch of the product and market response to it rather than customer explicitly specifying the requirements. This development model can be generally used by the organizations who intend to develop IT product for mass targeted customers rather than an individual or an organization.
Evolutionary Random Graph for Bitcoin Overlay and Blockchain Mining Networks

Abstract The world economy is experiencing the novel adoption of distributed currencies that are free from the control of central banks. Distributed currencies suffer from extreme volatility, and this can lead to catastrophic implications during future economic crisis. Understanding the dynamics of this new type of currencies is vital for empowering supervisory bodies from current reactive and manual incident responders to more proactive and well-informed planners. Bitcoin, the first and dominant distributed cryptocurrency, is still notoriously vague, especially for a financial instrument with market value exceeding $1 trillion. Modeling of bitcoin overlay network poses a number of important theoretical and methodological challenges. Current measuring approaches, for example, fail to identify the real network size of bitcoin miners. This drastically undermines the ability to predict forks, the suitable mining difficulty and most importantly the resilience of the network supporting bitcoin. In this work, we developed Evolutionary Random Graph, a theoretical model that describes the network of bitcoin miners. The correctness of this model has been validated using simulated and measure bitcoin data. We then predicted forking, optimal mining difficulty, network size and consequently the network’s inability to stand a drastic drop in bitcoin price using the current mining configuration.
Recovering Wavelengths from highly compressed speckle patterns by Deep learning

Abstract Recovering the wavelengths from disordered speckle patterns has become an exciting prospect as a wavelength measurement method due to its high resolution and simple design. In previous studies, panel cameras were often utilized as the speckle images receiver. However, high cost (especially at near infrared range), large size and low speed limit its application in optical communications, metrology and optical sensing. In this work, the speckle patterns were highly compressed into four intensities by using a quadrant detector (QD). And wavelengths still can be recovered through only four pixels instead of millions of pixels of a camera. A new CNN based demodulation algorithm, shallow residual network (SRN), was proposed to recognize the wavelengths from the highly compressed speckle images. Finally, a wavelength precision of 4 fm (~ 0.5 MHz) with an updating speed of ~ 1 kHz was achieved in the demonstrations. In addition, the SRN shows a broad measurement range and good noise robustness. Compared with camera based system, the QD detection scheme associated with the CNN algorithm provides a compact, high speed, and low cost method to examine the speckle patterns, which opens new routes in many other fields.
3D Geometrical Untangling of Heterogeneous Fabric Darcy’s Flow using the Morphology Decoder

Abstract Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (hydrocarbons) and water resources become waste. During the first decades of the 21st century, permeability quantification from nano-microporous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To efficiently and consistently predict permeability with high quality, we propose the morphology decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-ray microcomputerized tomography (µCT) and nuclear magnetic resonance (MRI). For 3D vision, we introduce a controllable measurable volume as a new supervised semantic segmentation, in which a unique set of voxel intensities corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphology boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes described in this paper. These novel processes are (1) geometrical: 3D permeability governing equation, (2) machine learning: guided 3D property recognition of rock morphology, (3) analytical: 3D image properties integration model for permeability, (4) experimental: MRI permeability imager, and (5) morphology decoder (the process that integrates the other four novel processes).
Random Matrix Theory-Based ROI Identification for Wireless Networks

The identification of region of interests (ROIs) in wireless networks holds the potential to resolve the challenging problems of resource allocation and network traffic prediction for large scale traffic data generated by mobile applications. The rationale is that ROIs are capable of gathering single regions that share similar network characteristics, which promotes better network traffic prediction performance. Previous studies show that spatiotemporal information in network traffic data, such as user behaviors and network status, is nontrivial to ROI identification. However, the modeling between these clues regarding spatiotemporal information is not yet fully explored. To this end, we propose a random matrix theory-based ROI identification (RRI) approach. By observing the intensification or diminution of network characteristic differences, i.e., divergence, between adjacent single regions, the ROIs can be identified. Firstly, we leverage the spatiotemporal information of area network traffic data with a spike model which can be described as a zero mean random matrix with a deterministic perturbation matrix. Then, we put forward an average divergence capacity model for ROI identification by estimating the divergent degree of adjacent regions. Case studies on three real-world network traffic datasets demonstrate the effectiveness of our proposed RRI method. The ROI identification greatly improves the network traffic prediction performance, yielding a decrease of root mean square error and mean absolute error by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>36.87</mn> <mi>%</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>52.26</mn> <mi>%</mi> </math> , respectively.
Hyperledger Fabric Consensus Optimization Method Based on Endorsing Node

Abstract Aiming at a series of security risks caused by the transparency and scarcity of endorsing nodes information in Hyperledger fabric, a non-interactive and verifiable optimization scheme for randomly selecting endorsing nodes is proposed, that is, the fabric model based on endorsement-ordering-verification. By introducing the candidate set of endorsing nodes and using the verifiable random function based on the elliptic curve SM2 algorithm to randomly select endorsing nodes as transactions for endorsement strategy, the model realizes the non-interactive random selection function of endorsing nodes in fabric, which can effectively prevent hacker attacks on the network. Experimental results show that the proposed scheme has higher security than the comparison method and the scheme before optimization.
NIME Consortium Proposal: Developing a design framework for constructing an accessible musical instrument from an autoethnographic point of view

What are the specific needs to consider when creating a bespoke instrument designed for myself as a disabled
B-LSTM-NB BASED COMPOSITE SEQUENCE LEARNING MODEL FOR DETECTING FRAUDULENT FINANCIAL ACTIVITIES

Deep Learning (DL) in finance is widely regarded as one of the pillars of financial services sectors since it performs crucial functions such as transaction processing and computation, risk assessment, and even behavior prediction. As a subset of data science, DL can learn and develop from their experience, which does not require constant human interference and programming, implying that the technology will improve quickly. By loading an Ensemble Model (EM), a Deep Sequential Learning (DSL)model, and additional upper-layer EM classifier in the correct order, a new “Contained-In-Between (C-I-B)” composite structured DSL model is recommended in this article. In cases like Fraud Detection System (FDS), where the data flow comprises vectors with complex interconnected characteristics, DL models with this structure have proven to be highly efficient. Finally, by utilizing optimized transaction eigenvectors, a NB classifier is trained. This strategy is more effective than most standard approaches in identifying transaction fraud. The proposed model is evaluated for its accuracy, Recall and F-score, and the results show that the model has better performance against its counterparts.