Reviewerly/exHarmony huggingface dataset:
Web 3.0 Decentralized Application Using Blockchain Technology

The use of blockchain technology is growing every day, as are its applications. It is a cutting-edge technology that enables users to interact without the use of a reliable intermediary. This research paper explores the development of a decentralized application (Dapp) and the design considerations involved in building a secure, scalable, and userfriendly application on a blockchain or distributed ledger technology. The paper discusses the importance of decentralization, security, user experience, scalability, interoperability, and governance in the development of a Dapp. The research also explores the implementation of smart contracts, the use of standardized protocols and APIs, and the role of decentralized autonomous organizations (DAOs) in creating a Dapp that allows for decentralized decision-making and community governance. The findings of this research provide insights and recommendations for developers and stakeholders interested in building Dapps, particularly in the context of blockchain technology.
Hypergraph Regularized Lp Smooth Nonnegative Matrix Factorization for Data Representation

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph regularized Lp smooth nonnegative matrix factorization (HGSNMF), by incorporating hypergraph regularization and Lp smoothing constraint terms into the standard NMF. The hypergraph regularization term can capture the intrinsic geometry structure of the high dimension space data more comprehensively than simple graph, the Lp smoothing constraint may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of HGSNMF is theoretically investigated. The experimental results on four different data sets show that the proposed method has better clustering effect than the related state-of-the-art methods in the vast majority of cases.
Pseudo-Label Noise Prevention, Suppression and Softening for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID), including fully unsupervised ReID and unsupervised domain adaptive ReID, remains a challenge for the fields of biometrics and computer vision due to its difficulty in learning with unlabeled target domain data. Existing state-of-the-art methods, most of which generate pseudo-labels via unsupervised clustering for model optimization, are inevitably hampered by the under-explored problem of pseudo-label noise. Motivated by this, we propose a novel joint framework termed pseudo-label Noise Prevention, Suppression, and Softening (NPSS) for unsupervised person re-identification. Instead of refining generated label noise after clustering as many existing methods do, we start solving this issue from the source of pseudo-label noise by proposing a new Dynamic Camera-Adaptive Clustering (DCAC), which dynamically involves camera information to prevent noise caused by cross-camera variance, thus improving their quality during clustering. Moreover, we propose an Online Domain Union (ODU) mechanism for the classification model learning on the target domain via involving source domain data with their ground-truth labels, which effectively suppresses the indelible noisy pseudo-labels. Furthermore, we present the Self-Consistency Constraint (SCC) to soften the label noise in a single model with reduced computation and network parameter cost, which achieves intra-sample knowledge ensembling with our global-local SCC and cross-sample knowledge ensembling with our inter-instance SCC. Experiments demonstrate the effectiveness of our method as it surpasses state-of-the-art methods by a large margin on Market-1501, DukeMTMC-ReID, and MSMT17 benchmarks. The code is available at https://github.com/hjwang-824/NPSS.
A Model for Albanian Speech Recognition Using End-to-End Deep Learning Techniques

End-to-end Automatic Speech Recognition (ASR) system folds the acoustic model (AM), language model (LM), and pronunciation model (PM) into a single neural network. The joint optimization of all these components optimizes performance of the model. In this paper, we introduce a model for Albanian speech recognition (SR) using end-to-end deep learning techniques. The two main modules that build this model are: Residual Convolutional Neural Networks (ResCNN), which aims to learn the relevant features and Bidirectional Recurrent Neural Networks (BiRNN) aiming to leverage the learned ResCNN audio features. To train and evaluate the model, we have built a corpus for Albanian Speech Recognition (CASR), which contains 100 hours of audio data along with their transcripts. During the design of the corpus we took into account the attributes of the speaker such as: age, gender, and accent, speed of utterance and dialect, so that it is as heterogeneous as possible. The evaluation of the model is done through word error rate (WER) and character error rate (CER) metrics. It achieves 5% WER and 1% CER.
A Kind of (t, n) Threshold Quantum Secret Sharing with Identity Authentication

Quantum secret sharing (QSS) is an important branch of quantum cryptography. Identity authentication is a significant means to achieve information protection, which can effectively confirm the identity information of both communication parties. Due to the importance of information security, more and more communications require identity authentication. We propose a d-level (t,n) threshold QSS scheme in which both sides of the communication use mutually unbiased bases for mutual identity authentication. In the secret recovery phase, the sharing of secrets that only the participant holds will not be disclosed or transmitted. Therefore, external eavesdroppers will not get any information about secrets at this phase. This protocol is more secure, effective, and practical. Security analysis shows that this scheme can effectively resist intercept–resend attacks, entangle–measure attacks, collusion attacks, and forgery attacks.
Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system

Abstract In machine learning (ML), association patterns in the data, paths in decision trees, and weights between layers of the neural network are often entangled due to multiple underlying causes, thus masking the pattern-to-source relation, weakening prediction, and defying explanation. This paper presents a revolutionary ML paradigm: pattern discovery and disentanglement (PDD) that disentangles associations and provides an all-in-one knowledge system capable of (a) disentangling patterns to associate with distinct primary sources; (b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern and entity clustering; and (c) organizing knowledge for statistically supported interpretability for causal exploration. Results from case studies have validated such capabilities. The explainable knowledge reveals pattern-source relations on entities, and underlying factors for causal inference, and clinical study and practice; thus, addressing the major concern of interpretability, trust, and reliability when applying ML to healthcare, which is a step towards closing the AI chasm.
Smart Wheelchair

Mobile platforms, by means of Bluetooth technology, the use of autonomous systems in the world to perform important and delicate task is rapid growing.However, its application in various fields cannot be over emphasized.This paper presents an obstacle detection and avoidance system for an fixed Lawnmower.The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller and a gear DC motor.The ultrasonic and infrared sensors are implemented to detect obstacles on the robot's route by sending signals to an interfaced microcontroller.The micro-controller redirects the robot to move in an alternate direction by actuating the motors in order to escape the detected obstacle.The performance evaluation of the system indicates an accuracy of 85% and 0.15 chance of failure respectively.In conclusion, an obstacle detection circuit was successfully implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller which clarify the output and makes the robot to pause.
An Overview of Voice Enabled Personal Assistant Using Python

People now connect with computers in novel ways thanks to personal assistants, conversational interfaces, and chat bots.A personal virtual assistant may even perform certain basic duties like launching apps, reading out news, taking notes, etc. with just a voice command.Users can ask enquiries to them in the same way they would to a real person.Personal assistants like Siri, Google Assistant, and Alexa operate on text-to-speech technology.Python is being used to create a voice assistant that will enable users to complete any activity without needing a keyboard.The purpose of this Project is to examine the intelligent behaviour of voice assistants and how they might be applied to both academic and daily tasks.
Gesture Language Recognition

One of the non-verbal communication methods adopted in gesture language is hand gestures.It is mostly used by deaf and dumb individuals to communicate with other people or among themselves when they have hearing or speech issues.Many producers across the world have created numerous gesture language systems; however, they are neither adaptable nor economical for end users.As a result, the program shows a system prototype that can automatically detect gesture language to aid deaf and dumb individuals to communicate more efficiently with each other and other people.Normal people often find it difficult to comprehend and converse with dumb individuals, hence they are typically denied regular social interaction.Long-term research has led to a breakthrough in aiding deaf-mute people: gesture language recognition.Every study, sadly, has constraints that prevent it from being applied economically.Some studies have been proven to be effective in understanding gesture language, but they cost a lot of money to market.Researchers are receiving increasing attention these days for creating commercially viable Gesture Language Recognition.Research is conducted in a variety of methods.It begins with the data collection techniques.The cost of a suitable device necessitates a variety of data-collecting techniques; nonetheless, a low-cost technique is required for the commercialization of the Gesture Language Recognition System.Additionally, academics' approaches to creating Gesture Language Recognition differ.
A Review Article on Authentication Protocols in Cloud Computing

Cloud computing technology provides on demand computing resources like software, hardware and storage in pay-per-use model. Cloud computing is very beneficial for today’s organizations, as there is no need to maintain physical infrastructure. These services and resources can be accessed from anywhere anytime over the internet. It is cost effective as users only need to pay for resources they use. Many individuals and organizations have shifted themselves over cloud environment as it is cheaper and convenient. As huge amount of sensitive data is being stored and processing in cloud computing environment so there is major concern of security. Lack of security may cause several issues. It may result in loss of organisation’s data and may even leak confidential data of user and organisation to unwanted access. To implement security in cloud computing various authentication and encryption protocol exists. This aim of this paper is to review various authentication protocols given by various researchers all over the world.
Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study

Respiratory symptoms are the most common presenting complaint in primary care. Often these symptoms are self resolving, but they can indicate a severe illness. With increasing physician workload and health care costs, triaging patients before in-person consultations would be helpful, possibly offering low-risk patients other means of communication. The objective of this study was to train a machine learning model to triage patients with respiratory symptoms before visiting a primary care clinic and examine patient outcomes in the context of the triage.We trained a machine learning model, using clinical features only available before a medical visit. Clinical text notes were extracted from 1,500 records for patients that received 1 of 7 International Classification of Diseases 10th Revision codes (J00, J10, JII, J15, J20, J44, J45). All primary care clinics in the Reykjavík area of Iceland were included. The model scored patients in 2 extrinsic data sets and divided them into 10 risk groups (higher values having greater risk). We analyzed selected outcomes in each group.Risk groups 1 through 5 consisted of younger patients with lower C-reactive protein values, re-evaluation rates in primary and emergency care, antibiotic prescription rates, chest x-ray (CXR) referrals, and CXRs with signs of pneumonia, compared with groups 6 through 10. Groups 1 through 5 had no CXRs with signs of pneumonia or diagnosis of pneumonia by a physician.The model triaged patients in line with expected outcomes. The model can reduce the number of CXR referrals by eliminating them in risk groups 1 through 5, thus decreasing clinically insignificant incidentaloma findings without input from clinicians.
Analysis of Car Selling Prediction Based On AIML

The purpose of this research paper is to develop a predictive model for car sales using machine learning techniques.We explore various factors that affect car sales and use them as features to train and test our model.We collected data from various sources, including online car listings, car dealerships, and demographic data.Our findings show that demographic factors, such as age, income, and education, play a significant role in predicting car sales.Additionally, car features, such as make, model, and year, also influence sales.Using these features, we developed a model that accurately predicts car sales and can be used by car dealerships to make informed decisions.Predicting car sales is crucial for automotive manufacturers, dealerships, and market analysts to make informed decisions about inventory management, marketing strategies, and overall business performance.In this paper, we present a comparative study on the analysis of car selling prediction using multilayer perception (MLP).We begin by collecting a dataset of historical car sales data, including features such as car make, model, year, mileage, price, and other relevant factors.The dataset is preprocessed to handle missing values, outliers, and categorical variables.We then split the dataset into training and testing sets to train and evaluate the MLP model.Furthermore, we compare the performance of the MLP models with other popular machine learning models such as linear regression, decision tree, and random forest.We evaluate their performance using the same dataset and metrics to understand the relative effectiveness of MLP in predicting car sales.
A New Method for Processing Geochemical Exploration Data: the Geological Connotation Method (GCM)

Exploration geochemistry is an essential method for discover new mineral areas. However, with the increase of exploration degree, relying solely on &amp;quot;strong, broad and multiple&amp;quot; anomalies to guide exploration work has become increasingly challenging. How to effectively identify valuable geochemical information remains a hot and frontier topic in the field. Thus we proposed a geological connotation method (GCM) that provides an objective geological connotation to big geochemical data based on regional geological and geochemical characteristics of known typical deposits. There are mainly two types of geochemical anomalies expression: (1) the metallogenic intensity anomaly map (MIAM) obtained by accumulating specific anomalous elements, which directly reflects the degree of material exchange and superposition modification during mineralization; (2) the metallogenic type anomaly map (MTAM) obtained by accumulating the mineralization concentration coefficients of elements related to a certain genetic type, which can highlight the anomalies caused by a specific genetic type of deposit. These have been tried and applied in the exploration of the Gangdese, North Himalayas, Nyenchen Tanglha and other metallogenic belts in Tibet, and have made a series of breakthroughs in mineral resources exploration represented by Qulong, Zhunuo and Zhaxikang. The advantage of GCM is that it not only solves the problem of missing ore in the verification of &amp;quot;strong, broad and multiple&amp;quot; anomalies, but also can quickly and preferably delineate the strongest mineralized areas, and better overcome the influence of numerous factors on the anomalies, such as the chemical nature of elements, redox environment, geomorphic landscape and weathering and stripping, so that the anomaly evaluation becomes simple, objective and highlights the regularity.
Research on Construction and Application of Ocean Circulation Spatial-Temporal Ontology

Ocean circulation serve as the primary channels for transporting material and energy flows throughout the entire ocean system, which plays a crucial role in shaping Earth’s climate, weather patterns, and marine ecosystems. The ocean movements have far-reaching impacts on both the environment and human life. An effective method for semantically modeling the ocean circulation is urgently required to be established. To achieve a unified description of ocean circulation at the semantic level, this paper introduces the theory and methodology of ocean ontology, which is developed through an analysis of domain knowledge in ocean circulation. We focus on analyzing the concepts, temporal relationships, and spatial relationships of ocean circulation. By defining classes, properties, relationships, instances, and constraint conditions within the logical structure of an ontology, it is feasible to formalize the expression of conceptual elements and their relationships. Additionally, semantic inference rules are established to finalize the construction of the ocean circulation ontology. The effectiveness of ontology construction has been verified through practical examples. Furthermore, a specialized knowledge base framework has been developed upon the ontology description of ocean circulation. Some examples of knowledge base queries have been articulated and verified. The results demonstrate that this ontology can effectively represent the relevant knowledge in the domain of ocean circulation and provide a meaningful strategy for investigating semantic integration and knowledge sharing in this field.
Performance Comparison of Response Time Native, Mobile and Progressive Web Application Technology

The development of technology in web-based applications is growing, this creates new problems. The web technology that is currently being discussed is Progressive Web Application (PWA) but is the PWA's performance better than the previous technology. This research is about measuring the performance of the Native Web, Mobile Web and PWA using three testing tools, namely GTMetrix, Lighthouse, and Chrome DevTool. The results of this study show how to measure the performance of a Progressive Web Application (PWA), where PWA can beat the performance of Native Web and Mobile Web if a web page is tested more than once. Test results on the Progressive Web Application (PWA), the minimum number of page files (home) is 217 kB with page loading time of 638 ms, while the medium page (about) is 431 kB with page loading time of 646 ms, and when accessing heavy pages (news) with a size of 41700 kB the page load time is 532 ms.
Cultivation of Music Creation and Creative Ability in Composition Teaching in Colleges and Universities

With the development of social economy, people's initial demand for material has gradually changed into their demand for spirit.Under this background, music, as an art, can relax people's mood and cultivate people's sentiments, and has gradually become an indispensable element in people's lives.However, the inherent mode can no longer meet people's demand for music, and its demand reflects diversified characteristics.Therefore, higher requirements are put forward for music creation, and innovative music creation can continuously meet people's needs.Music works are often the expression of the author's emotion, which contains a strong emotional color, which requires teachers to guide students to integrate their emotions into music creation when teaching composition, and constantly stimulate students' music creation and creative potential, thus presenting more excellent music works.In the composition teaching in colleges and universities, we pay attention to the cultivation of students' music creation and creative ability, and promote the innovation of music works.Based on this, this paper discusses the cultivation of students' music creation and creative ability, analyzes the importance of cultivating students' music creation and creative ability, and puts forward some effective training strategies.
PERBANDINGAN METODE EQUIVALENCE PARTITIONS DAN BOUNDARY VALUE ANALYSIS PADA PENGUJIAN BLACK BOX Literatur Review

This review literature reviews the Equivalence Partitioning and Boundary Value Analysis methods published between 2019 and 2020. Insights that arise from this review can guide researchers in their ongoing investigation of the implementation of Black Box, especially the Equivalence Partitioning and Boundary Value Analysis. From the review of 10 journals, this study found several substantive changes in the field of Black Box research. This includes an increase in the use of theory in research. This review literature aims to study software testing techniques in black box and improve better quality jeminan goals.
Research on Topic Fusion Graph Convolution Network News Text Classification Algorithm

Abstract In the face of a large amount of news text information, how to make a reasonable classification of news text is a hot issue of modern scholars. To solve the problem that only word co-occurrence was considered in the Text Graph Convolutional Network (Text-GCN) method to build a graph model, a news text classification algorithm which fuses themes and is based on Graph Convolution Network, is presented. Firstly, the LDA topic model is used to process the corpus to obtain the distribution of themes of the corpus. Secondly, a graph model is built to construct a global map by using the related topic words and their subject distribution in each article. Finally, the text graph is input into the Graph Convolution Network layers to compute the learning representation of combining feature in order to complete the text classification task. The experimental results show that this method can effectively realize the word level interaction of information in text. In the experiment on Chinese and English datasets, adding theme information improves the accuracy by 1% compared with the Text-GCN method.