Reviewerly/exHarmony huggingface dataset:
FUNCTIONALITY ENHANCING IN BANKING SYSTEM

Banking system can be considered as the one of the great tool supporting many customers as well as banks and financial institutions to make may banking activities through online.Every day banks need to perform many activities related to users which needs huge infrastructure with more staff members etc.But the banking system allows the banks to perform these activities in a simpler way without involving the employees for example consider online banking, mobile banking and ATM banking.But banking system needs to be more secure and reliable because each and every task performed is related to customer's money.Especially authentication and validation of user access is the major task in the banking systems.In this project I tried to show the working of a banking account system and cover the basic functionality of a Bank Account Management System.To develop a project for solving financial applications of a customer in banking environment in order to nurture the needs of an end banking user by providing various ways to perform banking tasks.Also to enable the user's work space to have additional functionalities which are not provided under a conventional banking project.The main aim of this project is to develop system application for Bank.This project has been developed to carry out the processes easily and quickly, which is not possible with the manuals systems, which are overcome by this software.This project is developed using JAVA language and File System use for database connection.Creating and managing requirements is a challenge of IT, systems and product development projects.
Fake profile detection in Twitter

In the present generation, online social networks (OSNs) have become increasingly popular, people's social lives has become more associated with these sites.They use OSNs to keep in touch with each other, share news, organize events, and even run their own e-business.The rapid growth of OSNs and the massive amount of personal data of its subscribers have attracted attackers, and imposters to steal personal data, share false news, and spread malicious activities.On the other hand researchers have started to investigate efficient technique to detect abnormal activities and fake accounts relying on accounts features, and classification algorithms.However, some of the account's exploited features have negative contribution in the final results or have no impact.We used three classification algorithm namely Support Vector Machine, Neural Network and random Forest for detect fake profiles.we are also provide a comparison among these algorithm in terms of their accuracy.
Pledging My Time VI: scraping and analysis of race results in R

I’ve posted in the past about analysing race results in R (most recently here). I ran the 2023 MK Marathon and wanted to have a look at the finishing times. The days of race results being made available as a csv or xls for easy analysis seem to be behind us. Instead they tend to be served up on multiple webpages of 50 athletes’ results at a time. Oh no, 29 pages of results and now Download option…. let’s scrape the data! The code We need {rvest} to do the scraping. We also need...
Yet Another Movie: IMDB Top 250 movies

I’m not a big movie person. Nonetheless I have a media library with quite a few films in and I wondered how many “films to see before you die”-type movies I had in the collection, and how many were missing. I used R to find the answers. I’ve described previously how to get a plain text dump of a Plex database using WebTools-NG. I did that for the Movies library of my Plex Media Server. Now, for the list of “films to see before you die”. I searched a bit and found a few text files which...
Tips From The Blog XIX: initialising a new RStudio project

I like to set up a standardised directory structure for RStudio projects. The idea came from here. In brief, the structure is: Data/ Output/Data/ Output/Plots/ Script/ My typical workflow is therefore to: select File &amp;gt; New Project in RStudio make a new directory and RProj file then use this R script or these shell commands to setup the directories. So far, so good. However, this process is a bit tedious. I find it irritating to open up RStudio (which opens up a previous...
Latency-aware Neural Architecture Performance Predictor with Query-to-Tier Technique

Neural Architecture Search (NAS) is a powerful tool for automating effective image and video processing DNN designing. The ranking of the accuracy has been advocated to design an efficient performance predictor for NAS. The previous contrastive method solves the ranking problem by comparing pairs of architectures and predicting their relative performance. However, it only focuses on the rankings between the two involved architectures and neglects the overall quality distributions of the search space, which may suffer generalization issues. On the contrary, we propose to let the performance predictor concentrate on the global quality level of specific architecture, and learn the tier embeddings of the whole search space automatically with learnable queries. The proposed method, dubbed as Neural Architecture Ranker with Query-to-Tier technique (NARQ2T), explores the quality tiers of the search space globally and classifies each individual to the tier they belong to. Thus, the predictor gains knowledge of the performance distributions of the search space which helps to generalize its ranking ability to the datasets more easily. Thanks to the encoder-decoder design, our method is able to predict the latency of the searched model without deteriorating the performance prediction. Meanwhile, the global quality distribution facilitates the search phase by directly sampling candidates according to the statistics of quality tiers, which is free of training a search algorithm, e.g., Reinforcement Learning or Evolutionary Algorithm, thus it simplifies the NAS pipeline and saves the computational overheads. The proposed NARQ2T achieves state-of-the-art performance on two widely used datasets for NAS research. Moreover, extensive experiments have validated the efficacy of the designed method.
Chatbot Music recommendation Using Spotify API

The objective of this project is to create a user's emotion-based music recommendation system use the chatbot interface to suggest personalized music recommendations.The system uses Spotify an API to access user's music preferences and recommend songs based on their reported moods via chat.The chatbot user interface facilitates communication in natural language, which makes a difference the referral process is more engaging and interactive.In addition, the project contains a recommendation system that uses Spotify API to suggest some similar songs what the user's selected song.The resulting system is a valuable resource for both music lovers and casual listeners allowing them to explore new music based on their mood preferences.The system also offers a sharing feature so that the user can share their favorite music on social media.
Research on the Ideas and Methods of Multi-Level Sight-Singing and Ear Training Aesthetic Education

Under the background of the continuous improvement of China's modern higher education system, the teaching mode of colleges and universities has been comprehensively innovated, which has greatly improved the quality of education and teaching.In the teaching work of music colleges, in order to improve the teaching effect of sight-singing and ear training, it is necessary to carry out aesthetic education at multiple levels to ensure that students have good aesthetic ability and aesthetic literacy, which plays an important role in promoting students' learning and development.Therefore, in this article, author will conduct an in-depth study and analysis of the ideas and methods of multi-level sight-singing and ear training aesthetic education, and summarize some measures in combination with practical experience, hoping to be helpful to relevant personnel.
The Predicament and Countermeasures of Teacher Training under the High-Quality Development of Rural Education

Under the background of comprehensively promoting the high-quality development of rural education, the construction of rural teachers in our country has been further promoted.Combined with the present situation of the construction of teachers in rural schools, this paper evaluates and analyzes its structural characteristics, professional level and teachers' development cognition, obtains the problems existing in the training of rural teachers, and puts forward the corresponding strategies through attribution analysis.That is, from "change the leadership practice, enhance school care; based on professional ability, establish and improve the mechanism of teacher education and training."Pay attention to the needs of teachers' development and create supportive conditions to realize the innovation of rural teacher management, and also create its foundation for the sustainable development of follow-up teachers.
Policies of STEM Education from the Perspective of International Comparison

Based on an international comparative perspective, the article compares and analyses the STEM education policies of five developed countries, namely the United States, the United Kingdom, Germany, Finland and Israel.On this basis, the article focuses on China's STEM education policies, and compares and analyses STEM education policies at the governmental level and at the level of educational practices in China and abroad.Finally, the article reveals the shortcomings of China's STEM education policies and proposes specific development paths, with a view to providing references for improving China's STEM education policies.
The Application of Mobile Network Technology in the Reform of Physical Education Classroom Teaching in Universities

Mobile network technology is an inevitable product of the development of the times, and has been applied and achieved good results in the field of higher education.It has the characteristics of portability of mobile terminals, convenience of mobile networks, and universality of mobile network application services.In college physical education teaching, the application of this technology will inevitably have a certain impact on college physical education teaching.This paper adopts research methods of literature and Delphi.The study found that the application of mobile network technology in physical education teaching can promote teaching and communication between teachers and students, as well as between students and students, provide support for students' mobile learning, and optimize the management of physical education teaching.In addition, it may also have some negative impacts on the classroom teaching of physical education in universities, students' psychology, and life patterns.Based on the characteristics of mobile internet in teaching application, this paper proposes countermeasures and suggestions for the application of mobile internet technology in physical education teaching in universities.
Implementasi Metode K-Means Clustering Pada Segmentasi Citra Digital

ABSTRACT:Image segmentation is the process of placing a label for each pixel in an image therefore pixels with the same label share certain visual characteristics. One of the algorithms that can be applied in accelerating the segmentation process is K-Means Clustering. K-means is a non-hierarchical clustering method that tries to partition existing data into one or more clusters. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. The implementation of the system uses the Visual Basic 2010 programming language and the method used in this research is the waterfall method. The results of the analysis carried out show that the similarity of the identified images based on the proximity of the color values ​​and the accuracy produced is quite good, especially for objects that have special colors or colors that have become characteristics of the object.&#x0D; Keywords: Digital Image, Segmentation, Clustering, K-Means
The Study of Reading Interest in Indonesian Society and the Shift in Alternative Media as Reading Sources for Finding Innovation in-Library Management

This study aims to determine the visitor's reading material, the reasons for visiting the library, the frequency of visits, and reading sources besides visiting the library.Collecting data using qualitative methods through structured interviews.A total of 200 visitors were randomly selected from several libraries in Singaraja, Buleleng, Bali, Indonesia.Descriptive qualitative data analysis, data checking, and data explanation were used.It was found that the main reading material for visitors was looking for scientific articles, fiction readings, followed by textbooks and newspapers.From the data obtained, it was found that preparing assignments was the main reason for reading articles and books, apart from surfing the internet and reading textbooks as well as consulting with friends and educators.From the data analysis, it was found that the majority of visitors were students, followed by the general public.The reasons for visiting varied from looking for materials to do assignments, adding insight and knowledge, filling spare time, and reading news.This study begins with the author's concern regarding the fact that there are few library visitors and the impression that the Indonesian public has a low reading interest.This study concluded that people's reading interest has increased, but has shifted to using gadgets to surf the internet and watching YouTube.
Realization of Anti-glare Algorithm Based on Stagnant Water Reflection of Automobile Headlights on Rainy Day

This project proposes to develop an algorithm founded on YOLOv5 to detect water accumulation at night, to eliminate the glare that results from car headlights reflecting off of standing water while people are driving at night on rainy days. Use the histogram equalization enhancement algorithm to process the night driving image to improve the contrast between the stagnant water and the road surface. Then use the YOLOv5 target detection algorithm to accurately identify where the water is located. Through the Freescale MCU to control the high and low beam switching and angle adjustment of the automobile headlights, purpose to avoid the glare of stagnant water at night. Experimental findings indicate that the histogram equalization enhancement algorithm can increase the contrast between stagnant water and road surface by 40.35%. The precision of the YOLOv5 target detection algorithm reaches more than 80%, and the precision is sufficient for practical applications. This project has high flexibility as a whole, and at the same time improves the safety of driving on rainy days at night.
Design of Intelligent Classroom Energy Management System

In recent years, with the rapid development of society and technology, the size of teaching facilities in colleges and universities has continued to expand, and most of the energy management in teaching areas is still controlled by people, leading to problems such as low management efficiency and serious energy waste. To address these types of problems, an intelligent classroom management system is proposed in this paper, which is based on the efficient implementation of low-complexity convolutional neural networks in STM32H7 series microprocessors and the coordination of each sensing module. The system has the option to automatically detect changes in the classroom environment to control the switching of various energy sources, or to remotely control the switching of the energy system via Wi-Fi from a mobile phone terminal. After testing, the system can efficiently and automatically manage the classroom energy system.
“I straight up criminalized myself on messenger”: law enforcement risk management among people who buy and sell drugs on social media

Background and Aim Social media drug markets are likely to present people participating in these spaces with new vulnerabilities to law enforcement. There is currently limited research on how people perceive and manage the risk of exposure to law enforcement in social media drug markets. This is particularly notable considering widespread practices of user data collection and the normalization of surveillance as part of social media engagement.Methods We present a thematic analysis of data from anonymous online interviews with people who buy and sell drugs (N = 33) via social media and messaging apps in New Zealand. We use the concept of “imagined surveillance” to explore how participants adapted existing understandings of online surveillance to online risk management strategies to avoid police.Findings Most participants reported low concern for exposure to law enforcement while using social media and messaging apps for drug trading. Nevertheless, almost all participants took active risk management measures. Examples of strategies used included limiting the accumulation of evidence via self-deleting messages or arranging drug trades using code language. Participants often also reported low concern for their digital trace data to be accessed by police.Conclusions Navigating law enforcement risk in social media drug markets is likely to be informed and shaped by more general perceptions of digital privacy risk and related management strategies, particularly in more normalized drug market contexts. The potential for broader and unexpected consequences to result from the use of drug-related digital trace data across public and private contexts is discussed.
An Approach of Fake Videos Detection Based on Haar Cascades and Convolutional Neural Network

Because deep fakes might skew our impression of the truth, we need to come up with a method for better spotting them. This paper proposes a new forensic technique to detect manipulated facial images from videos. It is based on CNNs architecture that can distinguish possible face manipulations in the “real-and-fake-face-detection” dataset offered by Kaggle. The results obtained highlight comparable performances with the state-of-the-art methods. It showed an accuracy of approximately 99 % for this binary classification into fake or real faces. Then to validate this model we added a human face detection technique using the Haar Cascade method to this model in order to detect the manipulated videos from Deep Fake Detection Challenge (DFDC) dataset and we achieve an accuracy of 91 correct predictions out of 100 total videos.
EVALUATING FOUR FACTORS AFFECTING PERCEPTIONS OF STUDENTS OF FACULTY OF LAWS ON THE ACCEPTANCE OF ONLINE COURSES USING PLS-SEM APPROACH

&#x0D; This article discusses the four factors affecting the perceptions of students of the Faculty of Laws on the Acceptance of Online Courses (AOC). These four factors are attitude toward online courses (AT), self-regulation skills (SRL), self-efficacy on online courses (SE), and facilitating conditions for online courses (FC). This study engaged a survey of Students of the Faculty of Laws on the Acceptance of Online Courses Using PLS-SEM Approach. The PLS-SEM method was utilized by using Smart-PLS version 4. The study's findings have confirmed these four factors: Acceptance of Online Courses (AOC). These four factors are attitude toward online courses (AT), self-regulation skills (SRL), self-efficacy on online courses (SE), and facilitating conditions for online courses (FC), which have positive and significant effects on the acceptance of Online Courses (AOC) of the students. Therefore, related implications and suggestions are proposed to help in enhancing the level of Acceptance of Online Courses (AOC) of the students. &#x0D;
Geochemical Society

The Goldschmidt Award is the society's highest honor, presented annually for major achievements in geochemistry over a career.Prof. Rudnick is recognized for her contributions to understanding the composition, origin
Step By Step: recreating a volcano plot in R

We have an analysis routine for proteomics data written for IgorPro. One output is a volcano plot. These plots show the fold change in one sample compared to another and plot that against a p-value to estimate how reproducible any changes observed are. This post is not about that software, but on the topic of how we can recreate this plot in R. What steps need to be considered? Quick note about volcano plots in R There are plenty of ways to make volcano plots in R. One of the best is...
Learning to Correct Erroneous Words for Document Grounded Conversations

Document grounded conversation (DGC) aims to generate informative responses when talking about a document. It is normally formulated as a sequence-to-sequence (Seq2seq) learning problem, which directly maps source sequences, i.e., the context and background documents, to the target sequence, i.e., the response. These responses are normally used as the final output without further polishing, which may suffer from the global information loss owing to the auto-regression paradigm. To tackle this problem, some researches designed two-pass generation to improve the quality of responses. However, these approaches lack the capability of distinguishing inappropriate words in the first pass, which may maintain the erroneous words while rewrite the correct ones. In this paper, we design a scheduled error correction network (SECN) with multiple generation passes to explicitly locate and rewrite the erroneous words in previous passes. Specifically, a discriminator is employed to distinguish erroneous words which are further revised by a refiner. Moreover, we also apply curriculum learning with reasonable learning schedule to train our model from easy to hard conversations, where the complexity is measured by the number of decoding passes. We conduct comprehensive experiments on a public document grounded conversation dataset, Wizard-of-Wikipedia, and the results demonstrate significant promotions over several strong benchmarks.
Search Trajectory Networks Meet the Web: A Web Application for the Visual Comparison of Optimization Algorithms

This paper presents a new web application implementing and automizing a tool called Search Trajectory Networks. This web application is potentially very useful for researchers from the field of stochastic optimization algorithms such as metaheuristics because it allows the visual comparison of such algorithms. Moreover, it helps in gaining an improved understanding of optimization algorithm behaviour. Our web application facilitates the use of the Search Trajectory Networks tool because the previously manual process is automized and important improvements are added. These improvements include a simplified input format, a generalization to more than three algorithms and an integrated search space partitioning scheme.
An Improved Ant Colony Algorithm based on Target-Driven Gravity Force

Addressing the issues of slow convergence, trapping into the locally optimal solutions, and blinding search in conventional ant colony algorithm (ACA) for mobile robot path-planning, we develop an improved ant colony algorithm based on target-driven gravity force (ACA-TDGF). The goal is quickly finding an optimal route in a grid map that connects the starting and ending points, avoiding encountering random obstacles. In the ACA-TDGF algorithm, the ants are classified as strong, medium, or weak according to the valid path length in each iteration, while the ant leaves the updated pheromone along the path. This novel pheromones updating strategy provides diversity in pheromone levels at the initial whilst improving the performance by selecting the strong class, reducing the probability of trapping into the locally optimal solutions. ACA-TDGF algorithm combines the gravitational function with the ant colony algorithm, replacing the first-order gravitational function as a higher-order exponential form so that the algorithm has a faster convergence speed and higher global search ability. Simulation results show superior performance in the tested environment.