Reviewerly/exHarmony huggingface dataset:
A Framework for Automated Scraping of Structured Data Records From the Deep Web Using Semantic Labeling

The intent of this research is to come up with an automated web scraping system which is capable of extracting structured data records embedded in semi-structured web pages. Most of the automated extraction techniques in the literature captures repeated pattern among a set of similarly structured web pages, thereby deducing the template used for the generation of those web pages and then data records extraction is done. All of these techniques exploit computationally intensive operations such as string pattern matching or DOM tree matching and then perform manual labeling of extracted data records. The technique discussed in this paper departs from the state-of-the-art approaches by determining informative sections in the web page through repetition of informative content rather than syntactic structure. From the experiments, it is clear that the system has identified data rich region with 100% precision for web sites belonging to different domains. The experiments conducted on the real world web sites prove the effectiveness and versatility of the proposed approach.
Indoor Autonomous Powerline Inspection Model

This paper presents the development of an autonomous system that leverages the Quanser Qdrone to perform above-ground indoor autonomous powerline inspections. The powerline infrastructure is exposed to various extreme weather conditions that create an operational concern for utility companies. Frequent inspections ensure the safe operation of a power transmission grid. There are mainly two methods of examination, ground and air [1]. The ground inspections are often slow and challenging due to the rough terrain, utility pole height, and inaccessible remote areas. The aerial inspections are accomplished by deploying helicopters that are expensive to operate, maintain, and repair. As an alternative, Unmanned Aerial Vehicles (UAVs) are being widely adopted for both surveillance and analysis throughout the energy and utility industries. UAVs are being used for inspections of utility towers as well as powerlines as they are energy efficient, user friendly, and convenient. Drone video capturing allows for safer, faster, and more cost-effective solutions to powerline and utility tower inspections as the user does not have to leave the ground aside from repairs. The objective of this project was to develop an autonomous UAV system to detect and track powerlines and utility poles to perform fault inspections of their electrical and material components. The proposed algorithm used a state flow machine paired with an image recognition neural network to make decisions for searching, identifying, and flying along utility poles and powerlines. The proposed system was implemented using MathWorks MATLAB and Simulink with Quarc, a third-party toolbox designed by Quanser, enabling real-time applications with the QDrone. The project yielded an algorithm that would autonomously fly the Quanser QDrone through a scan of the local area, leverage a neural network, PowerNet, to locate an initial tower within the work area, follow attached powerlines if there are any, and locate the secondary tower. Once the inspection was completed, the QDrone would return to the home point and land.
Cache-based Executive Request Dispatching Method in The Distributed Workflow System

A Workflow management system (WfMS) is usually deployed in a Cloud environment in a distributed architecture, and the execution of the process task requires the workflow engine to parse the process definition in its cache. However, the current WfMS seldom considers the problem of the cache hit, which leads to repeated parse and decreases request response time and service quality of WfMS. A task execution dispatching method is proposed in this paper based on cache mechanism. The method enables each task’s requests to be dispatched to the best matching worker, and comprehensively utilizes the cache in the worker or the database. After experiments of practical scenes, compared with two traditional ones commonly used in business, our method is proved to improve the cache hit rate, reduces the response time, and improves system performance.
Covid-19: Marketing Strategies and Innovation In Retail

This study aims to understand the changes and perceptions of retailers related to marketing strategies in innovation in the empirical reality of retail, during the measures of restriction and social and commercial distancing caused by the COVID-19 pandemic. This is a qualitative content analysis study, carried out through interviews. The objects of study of the investigation were companies operating in retail in the city of CriciAoma (SC). The results indicate that the main change in retail companies was the growing use of digital media, including websites, Instagram, Facebook and WhatsApp. The lack of understanding of the theory of innovation in marketing was present in the testimonies of the interviewees. Thus, it is observed that innovation in marketing is something new and still needs to be explored in economic sectors. The testimonies showed that the innovation stems from the marketing need and responsibility of store owners for monitoring the progress of activities, with no sharing of ideas and decisions between the different areas to facilitate decision-making. The and studo contributed to expand the understanding of the marketing strategies of innovation in empirical reality of retailers and how this impacted directly on their business.
Automatic Web Page Robustness Grading

This paper represents a solution to the problem of automatization of a web page robustness score grading. Robustness of a web page is best defined as a property of a specific web page to keep its layout and style of elements after applying different modifications. The rapid development of web pages has enabled a quick creation of numerous web pages, but the question is what is the quality of those web pages in terms of robustness. Automatic grading enables a relatively fast way of creating a metric in terms of the score that specific web pages get after being tested for the level of robustness. The research framework consists of different technologies and concepts that have been used during the implementation of a practical solution. The paper describes data structures that have been used to represent web pages as well as the machine learning methods such as neural networks, used to calculate the robustness score.
Citizen Involvement in Urban Planning - How Can Municipalities Be Supported in Evaluating Public Participation Processes for Mobility Transitions?

Public participation processes allow citizens to engage in municipal decision-making processes by expressing their opinions on specific issues.Municipalities often only have limited resources to analyze a possibly large amount of textual contributions that need to be evaluated in a timely and detailed manner.Automated support for the evaluation is therefore essential, e.g. to analyze arguments.In this paper, we address (A) the identification of argumentative discourse units and (B) their classification as major position or premise in German public participation processes.The objective of our work is to make argument mining viable for use in municipalities.We compare different argument mining approaches and develop a generic model that can successfully detect argument structures in different datasets of mobility-related urban planning.We introduce a new data corpus comprising five public participation processes.In our evaluation, we achieve high macro F 1 scores (0.76 -0.80 for the identification of argumentative units; 0.86 -0.93 for their classification) on all datasets.Additionally, we improve previous results for the classification of argumentative units on a similar German online participation dataset.
Low-illumination environment vehicle detection based on optimized adaptive image enhancement

With the increasing number of vehicles, vehicle detection has become an important part of intelligent transportation system. At present, most detection algorithms are only suitable for normal light conditions, but the detection performance is poor for low illumination conditions. In order to achieve effective vehicle detection under low illumination conditions, this paper proposes an image enhancement algorithm to increase the contrast of the image, thereby greatly improving the effect of vehicle detection. First, the image contrast is enhanced through an adaptive contrast stretching algorithm. Secondly, the bilateral filtering algorithm is used to filter out the noise in the image. Finally, the detection system based on Haar features and AdaBoost classifier is used to detect vehicles. Experimental results show that the proposed algorithm can effectively enhance image contrast, highlight vehicle information, and the vehicle detection accuracy rate under low illumination conditions reaches 87.04%.
NPABT: Naming Pattern Analysis Of Bengali Text To Detect Various Community Using Machine Learning Approach

Natural Language processing is an important part of Artificial Intelligence which enriches languages. It creates a bridge between humans and machines to communicate. In this paper, we have proposed a method that can automatically predict a person's community where he may belong by using their name. Based on the research available on online literature, we are the first to do research on it. We have collected more than 8,000 names of the four major communities of Bangladesh, including both males and females. Then data has been preprocessed to clean those texts by following the natural language processing approach. The six most popular machine learning classifiers have been used to train and test this data. Usually, human names have some prefix or suffix of their community. From that prefix or suffix, this method determines the community of that person's or their predecessors' community. The used approaches worked really well for these data and give higher accuracy for the models. The total performance of those classifiers is up to 65.12%-78.25%. Random Forest (RF) classifiers achieved the highest accuracy which is 78.25%.
Interacting with FEM Simulated Tubes in AR

This paper presents the results of a study the authors conducted to identify the opportunities and challenges of interfacing complex engineering simulations run on dedicated systems with mobile devices visualizing the simulation results and providing inputs in real-time. We build upon an existing finite element method (FEM) simulation, which we tuned to deliver snapshots to an Augmented Reality (AR) application for real-time visualization. On this basis we develop interaction designs for tablets and Optical See-Through Head-Mounted Displays (OST-HMD) utilizing the interaction methods available on such mobile devices to manipulate FEM simulated tubes in AR, as well as an evaluation of these interaction designs.Our design goal is an intuitive workflow concept that allows usage of an AR application without training overhead. To this end we introduce movable proxies on the virtual tube that provide the simulation with input data. Since the described client devices belong to very different device classes, we show intuitive interaction designs for all of them.Our client application is supposed to be run at the tube installation site, so we can not rely on stable high bandwidth connections to the simulation server in every case. Therefore, we implemented an asynchronous communication, enabling the use with reduced update rates.Our system enables the user to quickly reconfigure tube configurations and compare different approaches. At the same time the interaction is intuitive so training the user is unnecessary.
LABS ONLINE – An Opportunity to Access High Quality Laboratory During COVID Breakout

Context: Students and job seekers are not getting any labs or practical experience using machines to be industry ready during the lockdown. Expensive machines are not accessible to these in general resulting in missed opportunities. Holistic remote learning of theory and lab is the need of the hour. With no proper remote training, their entire career will be at stake possibly causing damage to the machine or personal safety.How can a great lab experience be offered remotely? The skills that Aarush has to acquire is challenging without good lab experiences. Solution Approach: OpenGL on the web browser provides a 3D lab like experience to users from the comfort of their home. Using open source technology makes the solution cost effective. Technologies like Blender to create 3D models, A-frame for web framework for VR, Skectchfab for 3D marketplace, WebAR, Coffeescript etc. Users who are students and job seekers get an effective lab experience and worry less about burdening parents financially. Using Google cardboard can be an alternate solution for development in the future. Viability: Futuristic technology that is not too expensive. The solution is viable for any time, however the lockdown has accelerated the urgency of such learning platforms. Collaborative and team learning features in the product stresses the fact “during lockdown, remote learning need not be learning alone, it can be learning along” It is the design for the new normal. The product is driven “by the community - for the community” to reach the masses in general. For example, 3D modellers develop the models, The teachers use the solution to enhance experience, students benefit with the viable solution and be confidently career ready.
Intelligent Medical System for Automatic Medicine Recognition by a Novel Deep Learning Algorithm

This project is based on intelligent medicine recognition using proposed deep learning algorithm. Proposed work is designed for assistance to chronic patients in taking all medicines on time and avoiding taking wrong medicines. This project also stores information of medication as well as chronic patient information. Depending on the patients input medicine the total application works with audio-visual alarm. We prepared MATLAB GUI (graphical user interface) which is easy interface with patients. In this we used 8 types of medicines which are recognized correctly by proposed algorithm.
Analysis of flood vulnerability in the lawo watershed Soppeng Regency

Abstract The Lawo Watershed (DAS) is a watershed located in Soppeng Regency and every year during the rainy season it is prone to flooding. Other factors that cause flood vulnerability are slope, rainfall, soil type, altitude, and inappropriate land use. This study aims to determine the distribution of the level of flood vulnerability in the Lawo watershed, Soppeng Regency. The type of research used is descriptive quantitative. The method of making maps uses overlays and scoring between variables. Each variable will be given a score by giving weights and values according to the classification. Variables that have gone through the scoring stage will be overlaid with other variables using the ArcGIS application so as to produce a map of the level of flood vulnerability. The data analysis technique used descriptive method. The result of the research is a map of the level of flood susceptibility with four levels of vulnerability. There are two dominant levels of flood vulnerability in the Lawo watershed, namely not prone to flooding and prone to flooding. The flood-prone level is located downstream of the Lawo watershed with an area of 13,172 ha or 34.33% of the total watershed area, while the non-flood prone level is located in the upstream part of the watershed with an area of 13,923 ha or 36.28% of the total watershed area. The dominant factor that causes flooding in the Lawo watershed is the slope and land use. Most of the area of the Lawo watershed has a slope of 0-8% with a presentation of 57.22% of the total watershed area, and 32.97% of land use is in the form of rice fields and swamp shrubs.
Components of the Pedagogical Practice of Bachelors Based on the Competence Approach

The chapter discusses the competence-based approach to the implementation of training during the practice period. The author presents in the form of a table the main types of tasks aimed at developing the professional competencies of future teachers. The conditions of pedagogical practice for students and their impact on the development of professional competencies were also studied. The author is of the opinion that practice-oriented training and individualization are the main conditions for the organization and development of the internship program by heads of educational institutions and methodologists of higher education. The author also considers the basic requirements for the organizers of the practice and analyses the current state of the problem of organizing the practice of students based on documents in the field of education and regulatory documents of a particular educational institution.
Design and Evaluation of a Deep Learning Powered Image Regression Sensor for Control Engineering Applications

This paper focuses on the study of Deep Learning based solutions in Computer Vision applications in Control Engineering. Machine Learning (ML) based Systems recently gained a lot of scientific traction, as the latest advances in complex fields like Computer Vision and Natural Language Processing proved their great capabilities in learning abstract and non-linear relationships. To demonstrate the power and operational capability of Machine-Learning algorithms within sensor-applications, an image regression model able to predict the angular position of a servo motor shaft, given only images as the sensors input will be designed and trained. The selected device is well known and used as a common actuator in many control applications. A large dataset consisting of 100.000 labelled images was necessary during training for the model to obtain its final prediction accuracy. The final model and its generalization capabilities underline the potential of Neural Networks to adapt themselves to engineering-tasks which are hardly achievable with traditional methods. The paper concludes with a statistical analysis of the trained sensor to gain some insights at the performance and behaviour of the derived model.
Designing a Security Framework for Enhancement of Electronic Transactions

With the popularity of e-commerce websites, online transaction options have too gained prominence among customers. Ensuring security is still a major issue that professionals focus on. Though digital transactions enhance customer-merchant experience, there are various risks and challenges involved. Existing techniques such as Address Verification System (AVS), Card Verification Value (CVV), etc. help in verifying customer authenticity. From a customer's perspective, there are no authentication frameworks that will inform them about dishonest merchants. This paper suggests a system model that will use interactive modules to generate a Trust Factor (TF) which will help in detecting fraudulent merchant accounts at financial institutions. This financial technology will generate reports that can be used for analysis to understand the behavior of fraud activities in real time using sophisticated machine learning algorithms, thereby, mitigating merchant and transactional frauds.
3-way Authentication Approach for Agricultural IOT using IFTTT application

Agricultural is one of the fields which provide the major contribution in fulfilling the food demand for the increasing population. It includes the technological advancement of the recent arena in the agricultural sector. Even though it brings a lot of benefit to the society, the secure and privacy of AIOT has always been the biggest concern. In this paper, we proposed 3-way authentication approach. Firstly, it uses the login request to authenticate. Secondly, it authenticates using PUF(physical unclonable function) function and secret keys. Thirdly, OTP(one-time password) method is used for authentication. The proposed work is proven to be resistant to Man-in-middle attack, Replay attack, Traceability attack, Impersonation attack, Data Leakage, Key attack, Eavesdropping attack, Anonymity and provides confidentiality and integrity. The simulation of the proposed work has been analyzed using SPAN, the Security Protocol Animator for AVISPA with CL-AtSe as its backend. The BAN(Burrows-Abadi-Needham) logic is used for verification of the origin, the trustworthiness of the origin and the freshness of the received message.
A Vision Transformer with Improved LeFF and Vision Combinative Self-attention Mechanism for Waste Image Classification

At present, many challenges exist in the application of waste image classification including high image resolution, background noise interference and low computational efficiency, which makes a great impact on the accuracy of the algorithm. Generally, convolutional neural network related strategy is adopted, which can improve accuracy to a certain extent. But it is prone to getting interfered by irrelevant information and has large size of parameters, which makes it difficult to be deployed for those resource-limited applications. Thus, a Vision Transformer is proposed. By using Vision Transformer, it significantly improves the anti-interference ability and reduces parameter scale. Furthermore, distillation strategy is adopted to improve the generalization ability of the model. In addition, this paper replaces multilayer perceptron (MLP) layer in base Vision Transformer with improved Locally-enhanced Feed-Forward (improved LeFF) to improve the convergence speed and accuracy of the model. This paper also proposes Vision combinative self-attention algorithm to reduce the requirements of computational resources especially for image with high resolution. The results show that in 79002 waste image datasets with 198 types, the accuracy of training set and test set can reach 99.98% and 95.03% respectively. Meanwhile, a relatively high accuracy rate could be achieved within less training epoch and less computational resources are required during the operation.
Plato unmasks hidden limits of tele-education

Plato's powerful metaphor of the Cave, from Republic, further advances a critical assessment of the hidden limits of distance learning. In the Cave, individuals are restrained to see only straight ahead to the images projected from behind them onto the wall in front of them. As in the Cave, in tele-education the dynamism of learning is replaced by passive learning. Not only do learners become largely passive with respect to their teacher, but also to each other. These effects are masked from teacher and learner alike by the technical prowess of distance learning and teaching, a version of Plato's Cave. Tele-education has at least three undeniably salient features: safety, convenience, and cost savings. Two and a half millennia after Plato gave us the concept of mimesis and the metaphor of the Cave, we can use these philosophical tools to unmask hidden limits of tele-education.
A Controllable Multi-Lingual Multi-Speaker Multi-Style Text-to-Speech Synthesis With Multivariate Information Minimization

In this letter, we propose a multivariate information minimization method that disentangles three or more latent representations. We show that control factors can be disentangled by minimizing interactive dependency, which can be expressed as a sum of mutual information upper bound terms. Since the upper bound estimate converges from the early training stage, there is little performance degradation due to auxiliary loss. The proposed technique is applied to train a text-to-speech synthesizer with multi-lingual, multi-speaker, and multi-style corpora. Subjective listening tests validate that the proposed method can improve the synthesizer in terms of quality as well as controllability.