Reviewerly/exHarmony huggingface dataset:
Traffic Sign Detection: A Comparative Study Between CNN and RNN

In autonomous driving, the precise detection of traffic signs is crucial. Accurate detection can dramatically reduce problems such as road accidents involving autonomous vehicles. Numerous algorithms and libraries have been developed to implement reliable and safe detection of traffic signs. In this project, we aim to examine the performance of two advanced neural network models, namely, convolutional neural networks and recurrent neural networks, and choose the most suitable one in terms of resistance and effectiveness for this particular application. This paper presents the simulations carried out and the results obtained.
Sentiment Analysis in Social Media Website

Sentiment analysis is the automatic extraction of positive or negative attitude expressions from textual information, and it has gotten a lot of interest from scholars in the last decade. Additionally, the popularity of online review sites, social networks, and personal blogs has grown rapidly in line with the advancement of technology; the users actively use on-line review sites, social networks, and personal blogs to express their opinions. They express people’s, organisations’, locations’, events’, and notions’ positive and negative sentiments. Natural language processing and machine learning, as well as other ways to working with massive amounts of text, have made it possible to start extracting sentiments from social media”. In this paper we will show the design of our social and media website along with different sentiment analysis algorithms like Naïve Bayes, LSTM and VADER; the analysis of these different algorithms will help us conclude for the most suitable algorithm to be applied in the social media website.
Conceptual Modeling and Simulation Application Analysis of In-service Assessment

Firstly, this paper expounds the conceptual connotation of in-service assessment in the new system, then applies modeling and Simulation in the field of in-service assessment, establishes the conceptual model of in-service assessment and its process, and finally analyzes the application of modeling and simulation in the specific links of in-service assessment.
Share Data Between Apps with ContentProvider

In the last chapter, we discussed Android data persistence which includes file, SharedPreferences, and database. You probably notice that all the data mentioned before is only accessible within the app. Although file and SharedPreferences used to provide MODE_WORLD_READABLE and MODE_WORLD_WRITABLE to allow other apps to access data, they got deprecated in Android 4.2. Android introduced safer ContentProvider to replace them.
Ortho Image Mosaicing and Object Identification of UAV Data

Real world applications of UAV imagery are growing at a rate faster than ever. Along with this growth comes the need to process the UAV images and extract useful information from them. This paper illustrates a comprehensive python-based algorithm to stitch multiple images gathered from a single UAV and then perform a landscape scan to identify features and other non-homogeneities in the ortho-mosaiced image. The methodology introduced for image stitching involves key point detection using the SIFT algorithm and key point matching using KNN and RANSAC algorithms. The methodology introduced for object identification involves the computation of intensity changes between blocks of pixels in the horizontal direction (H-Scan). These intensity changes are then sorted and filtered before being mapped to feature types such as house roofs, mud trails, forest cover, etc. depending on the image being analyzed. The results indicate that the algorithm can extract meaningful information such as the location and intensity of features from the ortho-mosaiced image. The computational power required to implement this algorithm is extremely minimal, making it a good preliminary algorithm to use for mosaicing and analyzing a set of overlapping UAV images.
Historian for a Day: A Use Case of Augmented Reality in Civic Engagement

Augmented reality (AR), a type of immersive technologies, goes beyond traditional methods of engagement with the general public. It offers an interactive way to expand visualization and communication techniques in public participation. AR generates distinctive experiences by combining the real physical world with a digital or simulated reality. This paper discusses three mobile AR applications (apps) developed by a partnership among four community-based organizations and educational institutions in the City of Charlotte, USA. These apps use AR to overlay current streetscapes in the physical world with digital images that show the historical views of the exact physical locations. They also help the public understand neighborhood characteristics by presenting quantitative socio-economic data as three dimensional digital infographics. These AR apps support a series of community events including an open-street event and several indoor exhibits focused on neighborhood history. These events are aimed at expanding overall public participation and civic engagement with a goal of increasing awareness of neighborhood history through data visualization and story-telling. Surveys and interviews conducted during these events show that a majority of event participants gained new knowledge about the history of their neighborhoods with the information provided by these AR experiences.
The Informational Effect of Monetary Policy and The Case for Policy Commitment

I study how the informational effect of monetary policy changes the optimal conduct of monetary policy. In my model, the private sector extracts information about unobserved shocks from the central bank’s interest rate decisions. The central bank optimally changes the informational effect of the interest rate by committing to a state-contingent policy rule, in which case the Phillips curve becomes endogenous. In a dynamic model, the optimal policy rule overshoots the natural-rate shock and gradually responds to the cost-push shock, which makes the interest rate change expected output growth but not expected inflation.
Non-analytical Reasoning Assisted Deep Reinforcement Learning

Addressing the sparse reward problem in Deep Reinforcement Learning (DRL) using human supplied external knowledge or reasoning is a common practice. Such external knowledge and reasoning should not be so complete that a DRL agent does not almost need to perform any exploration questioning its utility. Non-analytical Reasoning could shape an agent’s actions sufficiently yet take away minimal credit from the DRL exploration process. We generalize the solution approaches to Non-analytical Reasoning Assisted Deep Reinforcement Learning and present an example solution to “Montezuma’s Revenge,” a notorious Atari game, applying such reasoning.
Anomaly Detection of Underwater Sensor Data Based on Temporal and Spatial Correlation

With the development of artificial intelligence, neural networks have been successfully applied to the research and analysis of time series data. Considering the dynamic and spatiotemporal correlation characteristics of underwater sensor data, an anomalous data detection algorithm based on spatiotemporal correlation is proposed. First, the algorithm uses the gated recurrent unit (GRU) to learn the time series characteristics of underwater sensor data. According to the dynamics of the sensor data, the distribution of the difference between the predicted and real values is modeled by using a sliding window, and the abnormal data are preliminarily judged according to the probability density value. Then, for the spatial correlation of data, the algorithm combines its adjacent node data and uses Euclidean distance to further judge the abnormal data. Finally, the analysis of the experimental results shows that our anomalous data detection algorithm performs well at anomaly detection.
Misleading Inference Generation via Proximal Policy Optimization

In this paper, we propose to investigate Misleading Inference Generation, a new natural language generation task. The goal is to generate a counterfactual sentence for a context and a factual sentence. This paper proposes a framework based on BART and reinforcement learning for the misleading inference generation task. The experiment results show our model significantly outperforms the compared models, making our solution a necessary and strong baseline for future research toward misleading inference generation.
Improved Dynamic Embedding Scheme with Adaptive Pixel Replacement

The field of information security has gradually been valued in recent years; steganography and cryptography techniques are two popular methods for protecting data privacy. The image has spatial redundancy that is the neighboring pixels in the picture usually have similar intensity. Even the vector quantization-compressed (VQ) image after VQ encoding also has coding redundancy, so there is also a correlation between the index values. Based on coding redundancy, we proposed a high-capacity IDES-APR scheme for protecting confidential data. We develop a reduced index strategy to recode the VQ index, by searching for similar index values; the original redundant code is reduced to only three bits. Adjust the VQ image by embedding secret data to create stego image block, which become more similar to the original image blocks after embedding. Compared with other similar work, the proposed scheme preforms better regarding the embedding capacity and image quality.
Fraud Triangle Perspective: Artificial Neural Network Used in Fraud Analysis

This study aims to determine the Triangle Perspective: Neural Network Used in Fraud Analysis.Application of the Artificial Neural Network method to investigate and detect fraud in companies.The number of samples used in this study was 102 companies.The population in this study are manufacturing companies listed on the Indonesia Stock Exchange for the 2016-2019 period.The test was carried out using an artificial neural network (ANN) method using the SPSS 25 application.External pressure does not affect fraudulent financial statements, and financial stability significantly affects financial statement fraud.The nature of the industry has a significant effect on financial statement fraud, inventory to sales ratio has a significant effect on financial statement fraud, the ineffective monitoring variable has no significant effect on financial statement fraud, gross profit to total assets has a significant effect on financial statement fraud.Return on assets has no significant effect on financial statement fraud, net profit margin (NPM) has no significant effect on financial statement fraud, firm liquidity (WCTA) has a significant effect on financial statement fraud.
Data Transformation for Super-Resolution on Ocean Remote Sensing Images

High-resolution ocean remote sensing imaging is of vital importance for research in the field of ocean remote sensing. However, the available ocean remote sensing images are often averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a data transformation method to process remote sensing images in different locations and resolutions. We target satellite-derived sea surface temperature (SST) images as a specific case-study. In detail, we use a modified very deep super-resolution (VDSR) model as our baseline model and propose a data transformation method to improve the robustness of the model. Furthermore, we also illustrates how the degree of difference in the data distribution influences the model’s robustness and also, how our proposed data transformation method can improve the model’s robustness. Experiment results prove that our method is effective and our model is robust.
LITERACY AND PROFESSORS’ PRACTICES: AN ANALYSIS OF LITERATURE

ABSTRACT: This study aims to review Brazilian research on literacy practices for undergraduate professors. We reviewed SciELO and Portal de Periódicos CAPES. From 2013 to 2019, 20 articles were retrieved and fulfilled inclusion criteria. Studies’ main statements were interpreted using a dialogical analysis of the discourse method. Few studies directly addressed the association between literacy practices and undergraduate professional training in Brazilian higher education. Among these studies, there was a predominance of the procedural technical guidance method, which seeks to improve reading and writing practices in undergraduate courses, particularly for human sciences. The relevance of literacy while becoming a higher education professor in Brazil is discussed in light of previous international research. An interdisciplinary approach, guided by a discursive conception of language, may foster improvements for professors’ practices from different areas of knowledge.
Evaluating Natural Language Processing tools for Polish during PolEval 2019

PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. It is organized since 2017 and each year the winning systems become the state-of-the-art in Polish language processing in the respective tasks. In 2019 we have organized six different tasks, creating an even greater opportunity for NLP researchers to evaluate their systems in an objective manner.
New Methods and Forms of Work during Online Maths Lessons

In more than 38 years as a mathematics teacher, I have always tried to look for interesting methods and new forms of work. I wondered how to explain the new material to students so that they would understand and be able to use the information in the future. The previous school year has been a huge challenge in the field of distance learning. From October 2020 to May 2021, all teachers in Poland conducted Online lessons. As a result, we had to switch from traditional classroom teaching to online teaching. So I decided to look for appropriate tools and solutions of how to conduct such lessons. Keywords: online learning, distance learning, applications, computer programs, teaching materials, virtual notes, IT tools, online mathematics.
Modeling a Tangible User Interface for Navigation in an Information Space

The content elements and the connections between elements form an information space that is conceptually similar to a physical space. Navigation is a common problem in information space and in physical space. Using an appropriate metaphor is a key factor in transforming abstract information space into a tangible space that users can accept. This research proposes an interface design approach to navigate an information space, such as the contents of a website or a museum, with a tangible user interface (TUI). The goal of the TUI is to connect the digital and physical space with a visible and tangible form. Tangible objects are used as metaphors to manipulate the information space. Information finding tasks are given to the participants to test user performance and errors, and subjective satisfaction is evaluated with questionnaires. The effects of metaphors and the TUI/graphics user interface (GUI) are to be investigated. The results show that metaphors help users find information with better performance and lower error rates. Users also perceive more usability from interfaces with metaphors and think they can work better. The proposed TUI system can get similar errors and subjective usability as a GUI system, which users are more familiar with.
Cricket Commentary Classification

As technology advances in the field of machine learning (ML), humans will be able to automate everything. Consider a game of cricket. In cricket, there are often two outcomes: the ultimate match outcome, win or lose, and the outcome of each ball in an over. The majority of commentary delivery platforms use manual methods to update the game database. To eradicate this manual method of updating, this research work has attempted to automate the update process of the game database by using natural language processing (NLP). This project consists of a machine learning model called cricket commentary classifier. The proposed ML model is trained by using the commentary data obtained from various cricket score delivery platforms like Cricbuzz, Espn Sports, etc. The commentary delivered by the commentator was captured with a microphone and later that audio is converted into text. The converted text was given as input to the cricket commentary classifier. This classification model classifies the outcome of the ball among four classes: wicket, dot, boundary, and runs. The final prediction will be automatically reflected in the score log or game database by eliminating human involvement using REST API calls.
PROJECT APPROACH AS A MODERN METHOD OF MANAGING THE SPHERE OF CULTURE IN THE CONDITIONS OF TRANSFORMATION OF RUSSIAN SOCIETY

Currently, the sphere of culture needs to be rethought, since everything is changing around due to the development of information systems and the creation of a unified economic and socio-cultural system. Recently, many cultural projects have been implemented in Russia, which confirms the importance of the project approach, which contributes to the development of culture. With the active development of this area, there may be a lag in the development of the project base. This primarily concerns the lack of sufficient information regarding culture, the availability of a small supply of scientific projects and research to train project managers.
Intelligent remote system for assessing a subject's health during sleep

A subject's behavior during bed rest gives information about their physical and mental health. This chapter presents a system for identifying the position of a subject during bed rest and the state of agitation using resistive sensors. The purpose of the sensor network is to generate the best possible response in the areas where the human body exerts greater pressure on the horizontal support on which it is located. The data obtained from the sensors are processed and classified using machine learning algorithms. Python in the Spyder Integrated Development Environment is used for data processing.
Sentiment Analysis on Code-Switched Dravidian Languages with Kernel Based Extreme Learning Machines

Code-switching refers to the textual or spoken data containing multiple languages.Application of natural language processing (NLP) tasks like sentiment analysis is a harder problem on code-switched languages due to the irregularities in the sentence structuring and ordering.This paper shows the experiment results of building a Kernel based Extreme Learning Machines(ELM) for sentiment analysis for code-switched Dravidian languages with English.Our results show that ELM performs better than traditional machine learning classifiers on various metrics as well as trains faster than deep learning models.We also show that Polynomial kernels perform better than others in the ELM architecture.We were able to achieve a median AUC of 0.79 with a polynomial kernel.
A Comparative Analysis of Agile Teamwork Quality Measurement Models

Multiple models (or instruments) for measuring Teamwork Quality (TWQ) for Agile Software Development can be found in the literature. Regardless, such models have different constructs and measures, with no empirical evidence for comparing them. This study analyzed two agile TWQ models, resulting in equivalent results. We mapped the models' variables given their definitions.We then collected data using both a Bayesian Network model, namely the TWQ-BN model, and Structural Equation Modeling, namely the TWQ-SEM model. We interviewed 162 team members from two software development companies. We analyzed the data using the Bland-Altman method. We obtained enough evidence to conclude that the results for Communication, Coordination, Cohesion and Mutual Support variables are not equivalent. On the other hand, we did not have enough evidence to claim that the models do not agree for measuring Effort and Balance of member contribution variables. The results of this study detail how two state-of-the-art agile TWQs compare in terms of their measures as well as potential research areas for further investigation.
An Analysis of the Effect of Wireless Network Channel on Radio Fingerprint Authentication

Radio fingerprint aims to provide an authentication and authorization for wireless devices by identification. It improves the security of wireless network at the physical layer which also enhance the time complexity by eliminating the need of authentication at higher layers. Artificial intelligence (AI) has proven its significant improvement in the radio fingerprinting, especially a multidimensional mapping feature of AI enhances the accuracy of radio fingerprint. However, the wireless network channel reduces the accuracy of radio fingerprinting algorithm. Hence, it is important to study the data classification of radio fingerprinting with lager amount of dataset collected from various wireless channels and quantitative analysis of effect of wireless network on radio fingerprint. In this research, 8 terabytes of data are collected from various wireless devices having similar radio frequency circuit. This dataset is used to analyze the effect of wireless network on radio authentication. Experimental results show the effect of wireless network channel on the classification accuracy of artificial intelligence algorithm which varies from 98 to 8% for the collected dataset. It is also observed that balancing I/Q data increases fingerprinting accuracy.
Real-Time-Based COVID-19 Social Distance Detection Using Deep Learning

Pandemic diseases COVID-19 coronavirus outbreak has caused a global disaster with its deadly spreading. In the current situation, as there are no vaccines available, therefore, social distancing is thought to be an adequate precaution against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. In this research, we developed a YOLOv3 (You Only Look Once)-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common cameras at Kyungdong University Global Campus. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. Later, the video frame was transformed into bird’s eye (top-down) view for distance measurement from the video frame. The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red, yellow, and green boundary lines. The proposed method was validated on a pre-recorded video of KDU global campus such as main building, academic building, elevator, stair, dormitory, and foot ware. The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video. The overall tracking accuracy of the model is 94.74% and the real-time speed of video is 24 fps.