Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Based on Social Networks Using Support Expectation-Maximization for E-Commerce Applications

In recent years, the use of social networks has increased rapidly. More people and companies are becoming dependent on Data Mining (DM) technology on social networking to promote the reform of unstructured data. It may be likely to put them in a systematic model. Social media has played an important role in promoting different products, generating reviews and highlighting customers' opinions. Reviews provided by the customer help to establish a good name for the product and also help to improve the quality of the product. Reviews also help customer select the best product among available products. For this reason, the provision of a particular product’s customer reviews facilitates the false-positive product review by providing a false negative of product reviews, to make it a possible downgrade which speaks about the project organization itself. The proposed Support Expectation-Maximization (SEM) algorithm is used to analyse the reviews based on sentimental lexical words to customers select the best products to resolve this problem. It collects the dataset from E-commerce online websites or applications such as Flip-kart or Amazon. The collected datasets are trained into the pre-processing process to analyse the stemming, Tokenization and Stop-word Elimination. Then, feature selection is done to select the best features of customer reviews sentiment lexical words weightages to reduce the dimensionality. A feature selection dataset is trained into the proposed Support Expectation-Maximization (SEM) algorithm to classify the reviews about the product, and it helps select the best product.
Mandarin Learning System Based on Chatbot

Using chatbot technology combined with theoretical knowledge points in spoken Mandarin to correct students' pronunciation mistakes in the learning system. It can better solve the problem of insufficient oral learning in the current Mandarin learning software and correct students' oral pronunciation mistakes. The purpose of this paper is to study Mandarin learning system based on chatbot. This paper mainly introduces the design principle, overall function design and detailed design and implementation of online learning system based on chatbot. A feasible general solution is designed for each module, and the system effect is verified. The test results show more than 80% of students think that the system Pinyin learning module can meet their Mandarin learning needs. 78% of students believe that the speech correction learning module can meet the voice enhancement needs, and most students believe that the system can help them achieve their expected learning goals.
Demo: Velox: Enhancing P2P Real-Time Communication in Browsers

Peer-to-peer (P2P) real-time communication is a fundamental building block for modern pervasive systems and applications, to name a few, mobile edge clouds, device-to-device coordination, and intelligent transportation. This paper introduces Velox, a suite of tools to enhance P2P real-time communication in browsers. It consists of a customized JavaScript compiler, a control room for managing peer connections and tracking resources using the WebSocket protocol, and clientside scripts for maintaining P2P communication channels using WebRTC and fetching necessary resources and contents. Two design priorities of our design are extensibility and ease of adoption. First, our tools are modular so that each component can be easily replaced and extended without touching other parts. Hence, others can build on top of Velox by only modifying the critical component(s). Second, we choose to develop our tools such that they work directly in web browsers. Browsers run smoothly on most personal computing devices; hence, a direct support for browsers will make the adoption and development simpler. Finally, we discuss our plan to demonstrate a P2P caching application using Velox to showcase the effectiveness and functionalities of our tools. In particular, a client can fetch resources cached at other neers.
Validating Quality of Context in Pervasive Computing Systems: Surf Life Saving Use Case

We present context Cost and Quality computational engine 2.0 (conCQeng 2.0, in short); this system addresses a significant drawback in Quality of Context (QoC) measurement models that lead to QoC-aware selection uncertainties in Context Management Platforms (CMPs). Current QoC measurement models rely on the QoC parameters in context (such as time-stamps) to assess QoC metrics, representing the context's usability for the pervasive computing applications and selecting the better-performing context providers. Nevertheless, such parameters are prone to misrepresentation, limiting the credibility of QoC measurement. In this paper, we propose a QoC validation mechanism through which conCQeng 2.0 determines the genuineness of measured QoC-metrics, further contributing to a credible QoC-aware selection. We motivate the proposal through its significance in the surf life saving use case. Our evaluation demonstrates that conCQeng 2.0 improves the credible QoC acquisition through the selection process - with slight but addressable processing overhead compared to its baseline version that attains higher QoC adequacy than heuristic models.
NNTrak: A Neural Network Approach Towards Calculating Air-Writing Trajectories in Real-Time with a Smartwatch

Tracking arm motion in real-time with wearables like smartwatches has proved to be a challenging task. Ex-isting approaches either require additional sensing modality affecting ubiquity, and/or are not efficient enough for real-time tracking, especially on low-power embedded devices with limited computing capabilities. In addition, tracking air-writing trajectories while the user is on the move, e.g. walking, adds up to the challenges. ‘NNTrak’ is our attempt to address these issues focusing on practicality, with current work exploring air-writing scenarios. Inertial data from a smartwatch is used to train a computationally inexpensive Neural Network. It learns air-writing dynamics by modeling multi-direction straight line patterns of arm movement, providing 2-D directional inferences while air-writing irrespective of plane of writing around the user. This effects a real-time trajectory output. Multiple air-writing use-cases are tested to establish proof of concept, aiming at a broader 3D tracking in future. NNTrak's air-writing trajectories are compared with Kinect V2 trajectories. Independent-user evaluation is performed for use-cases like contact-less mouse pointer control with air-writing achieving a throughput of 2.4 bits/s and an average movement time of 1.7s for “medium” difficulty ISO task. A training-free gesture recognizer is designed using NNTrak and shape matching, achieving gesture detection accuracy of 93.6 % in a free-living environment. All experiments were performed on a computationally constrained Raspberry Pi 3B. As a first in domain, we also demonstrate NNTrak's capability in challenging scenarios like air-writing while the user is walking on stairs, doing light jogging etc. with interesting results.
Mathematical aspects of distortion calibration for digital cameras

Nowadays, most human processes are automated by means of computerization. This process has not spared the automotive industry. The latest developments in this field give promise that in the near future, cars will be completely autonomous. However, before that, there is an urgent need to address a number of issues, such as increasing the angle of view for greater coverage of the road with minimal space curvature. It is known that when the angle of view increases, so does the distortion (a mismatch in geometric similarity between an object and its image). This mismatch significantly reduces the accuracy of recognition algorithms. To solve this problem, this paper proposes a calibration method for cameras. The paper deals with mathematical aspects of distortion and the method of calibration of automobile chambers, and calibration errors. The method of static calibration without the use of points at infinity using static templates is proposed. A conventional camera has a small field of view (about 80 degrees). Such angle of view does not provide full scanning of the space; as a result, some of the necessary information remains outside the field of view, which is crucial for automotive industry because road signs, pedestrians, traffic lights might be missed. Therefore, in case of car cameras, the angle of view is increased by introducing distortion aberration. In this case, the field of view increases, yet the geometric similarity between the object and its image is affected. This, in turn, can affect the accuracy of computer vision algorithms. In order to keep the field of view angle large and the similarity between the subject and the object, it is necessary to calibrate the camera. In this paper, a calibration method for wide-angle car cameras is proposed and considered.
Eye-Tracking Movements—A Comparative Study

Eye tracking has been a topic of interest in research in recent years because it provides convenience to a wide range of applications. It is acknowledged as an important non-traditional method of human–computer interaction. Eye tracking is a useful tool for determining where and when people devote visual attention to a scene, and it helps to understand cognitive functioning. Nowadays, eye-tracking technology is making its way from the lab to the real world, collecting more data at a faster rate and with a greater variety of data kinds. Eye tracking will become closer to big data if the current trend continues. A real-time model is created using machine learning methodology, which tests a high-accuracy hypothesis. Eye tracking with parameters looks into a participant’s eye movements while presenting them with a variety of options. Using machine learning to analyze eye movements and extract attributes to assess eye behavior. K-nearest neighbor, Naive Bayes, decision trees, and random forests are machine learning algorithms that produce models with improved accuracy. In this paper, we have reviewed different eye-tracking technologies to obtain eye movement parameters and classifiers for categorization, such as machine learning and deep learning toward recognition of cognitive processes involved in learning.
Metaverse and Blockchain

One of the defining features of the metaverse is the built-in economic system, which is essential for the smooth operation of the creators’ economy and the realization of the concept of co-creation, co-rule, and sharing among the participants. Blockchain technology-based digital currency systems are being proposed as a solution to this, as they provide stability and transparency through smart contracts and a decentralized settlement platform. This allows for value transfers within the digital world and supports the regular operation of the metaverse. This chapter will provide an overview of the concept, principles, and applications of blockchain and the different types and characteristics of digital currencies, as well as how they contribute to the economic system within the digital world. Overall, the implementation of blockchain technology in the metaverse economy serves as a key driving force for contributing to an economic system in the metaverse.
ON THE ISSUE OF THE POSSIBLE INTEGRATION OF THE INFORMATION RESOURCES OF THE ARCHIVES, LIBRARIES AND MUSEUMS OF THE RUSSIAN FEDERATION

The article deals with an analysis of the possibilities of implementing a project for the digital integration of information resources (state information systems) that are currently being created by the federal government departments of the Russian Federation in charge of the Archival, Library and Museum Funds. In the preamble, the author briefly describes foreign experience in implementing such projects; highlights the history of the discussion – within the Russian communities of historians, culturologists, specialists in the field of information technology and security, as well as archivists, librarians and museum workers – of the issue of creating unified archive-library-museum information resources; defines the main goals that could be achieved as a result of the formation of integrated resources. In the main part of the article, the author considers the regulatory and methodological framework of the Archival, Museum and Library spheres in the context of the creation of the sectoral state information systems. The article also deals with the changes that have occurred in the regulatory and methodological framework over the past decades – the changes that are related to the adjustment of the activities for the acquisition, recording, keeping, use and provision of access to the information connected with the «non-core» objects of historical and cultural heritage included in the collections. The author pays special attention to the analysis of methodological and technical approaches, as well as to the solution of the technological issues (standards and formats), which form the basis of the created information systems. It is concluded that the creation of integrated information resources seems unlikely under the present circumstances.
Integration of Smart Glasses for Knowledge Transfer in Industrial Remote Maintenance: Learnings from Practice

The use of remote augmented reality (AR) guidance is seen as an opportunity to address the growing complexity of industrial tasks. Previous research showed benefits of AR for different industrial tasks especially for novice users. However, there is not much work that examines the relation between experience level of users with AR and the nature of the task to be addressed (whether it requires remote AR guidance or not). In this chapter, addressing the field of complex machine maintenance and repair, we look into the question of whether and how remote AR support using smart glasses reduces the maintenance time, and two factors that influence acceptance aspects of AR smart glass technology for productive use. A design science approach was adopted to define the requirements and design and develop a prototype for a state-of-the-art smart glass for a German small and medium enterprise (SME) with complex machinery. An evaluation was conducted with two industries (one with low and the other with high requirement of AR technology) as well as under field conditions. In addition to the reduction in maintenance time, we examined whether task suitability and low privacy concerns increase acceptance of an AR application for remote maintenance. Our field study reveals interesting insights. First, we show that using a remote maintenance application, a time reduction of up to 46% was expected to be achieved by the manufacturer. Second, despite its novelty and the corresponding added time, we found that guidance via AR is more accepted when data privacy concerns are addressed by providing information on the system, and when the enterprise has a higher requirement for AR maintenance. This work shows the potential of AR for remote consultation and maintenance of industrial tasks in practice, and it brings up design suggestions for acceptance of smart glass technology for maintenance.
Organizing Code

Programs are rewritten and modified far more often than they are ever created. That means most of the time developers must change and modify existing code either written by someone else or written by you sometime in the past. Since you may be writing code that you or someone else will eventually modify in the future, you should organize your code to make it easy to understand.
Using GeometryReader

User interfaces for iOS devices must adapt to different screen sizes. Not only are there different-size screens for the iPhone and iPad, but there are also different screen sizes among different iPhone and iPad models on the market. To solve this problem, SwiftUI centers user interface items, but what if you need to precisely place user interface items in specific locations?
Displaying Gauges and Progress Views

When a program is busy doing something such as retrieving data or performing a task, it’s good practice to let the user know the program is still working. Otherwise, the program may appear frozen as if it crashed or froze. To give visual feedback to the user that a program is busy but likely to finish soon, you can use gauges and progress views.
Using Bottom Sheets

User interfaces need to present large amounts of information in a small amount of screen space. The most common solution is to divide a large user interface into multiple screens that can be linked together such as through a NavigationStack that lets users swipe between one screen to another or through a TabView that lets users jump from one screen to another by selecting an icon at the bottom of the screen.
Successful applications of Industrial Engineering students were awarded

Üsküdar University Department of Industrial Engineering and Industry and Leadership Club organized a "Celebration Ceremony for the Success of the Application of the STEAM Based Education Model in Üsküdar University Department of Industrial Engineering".
Using the Tab View

Most apps consist of multiple screens. Chapter 17 explained how to create apps that use a Navigation Stack that lets users jump from one screen to another in sequential order. This can be handy for showing more details such as the Settings app in iOS that lets you pick various options and then view multiple screens to choose different settings.
Retrieving Text from Text Fields and Text Editors

User interfaces often need to retrieve text from the user. Sometimes this text can be a single word or short phrase, but other times this text might consist of several paragraphs. To retrieve text from the user, SwiftUI offers three types of views:
Neural Networks and Deep Learning

Artificial neural networks (ANNs) inspire biological networks as powerful artificial intelligence tools. ANN is an object that imitates the neural network constituting the human brain so that the computer can learn and make decisions like a human. On the other hand, deep learning indicates a neural network with more than three layers. Deep neural networks are capable of extracting higher-level features from the raw data to solve complicated optimization problems. In this chapter, we intend to present a summary of deep neural networks and review their background. Then, the activation functions and concept of parameter selection in deep learning are discussed. In the end, the performance of deep neural models is presented, and classic deep learning models, including stacking automatic encoders, convolutional neural networks, deep probabilistic neural networks, and generative adversarial networks, are introduced.
Conversational AI: A Study on Capabilities and Limitations of Dialogue Based System

The conversational chat model here is based on the RASA framework. This framework is used as the base to design the chatbot. The topical Chat dataset used here contains approximately 210,000 utterances, which is more than 4,100,000 words. The chatbot analyzed this chat dataset and revealed how individuals interact with one another while discussing various topics. The TED (Transformer Embedding Dialogue) policy is used by Rasa to ensure that conversations are meaningful and are able to stick to the original intent of placing an order. The three inputs and all the features were fed into the transformer. Then, we built a model so that we can train it with the data and then can be used for the prediction and identify improvement opportunities. We simulated ASR hypotheses for each turn in a conversation using four different settings. System A had a Word Error Rate (WER) of 0.01–0.39 for the dialogues in the test freq audio and test-uncommon audio, whereas system B had an error rate of about 0.08. We trained our Rasa-based model with topical chat data. Topical chat data includes various conversations on a specific topic in the json files. After training the bot we can ask various questions about around 250 entities and the bot will share fun facts about those entities. The report, confusion matrix, and confidence histogram for your intent classification model were then created by the Rasa test script. Any samples that were inaccurately predicted are recorded and preserved in an errors file.
The Concept of Originality as Explored in Georgia

The concept of originality is embedded in copyright law. In all countries, including Georgia, a work is eligible for copyright protection if it is original. What is considered original varies from country to country, and Georgia is no exception. This chapter shows the ways countries in different legal systems have interpreted originality and locates the Georgian approach among them, taking into account its characteristics. It critically asserts the high threshold included in Georgian copyright law requiring that all works shall be the results of the intellectual-creative activity of the author. The chapter describes an extension of the list of originality requirements by the Supreme Court of Georgia and the problems in their application process. Furthermore, it provides recommendations for amending the copyrightability criteria as a logical continuation of the harmonization process of Georgian legislation with the European Union’s acquis in the realm of Georgia’s membership aspirations.
Canal Head Works

A weir is an obstruction to river flow provided with a specific purpose of raising water level in river such that canal can receive water in it by gravity flow. To increase water level, weir crest is raised above river bed and if need be, shutters are also provided on the top of the weir.
Introducing Federated Learning into Internet of Things ecosystems – preliminary considerations

Federated learning (FL) was proposed to train models in distributed environments. It facilitates data privacy and uses local resources for model training. Until now, the majority of research has been devoted to the “core issues”, such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with effects of unbalanced data distribution. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, different issues that need to be considered are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.
A Trustworthy Blockchain-Enabled System for Indoor Contact Tracing in Epidemic Control

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased. There is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. In this context, the paper proposes the Trustworthy Blockchain-enabled system for Indoor COVID-19 Contact Tracing (TB-ICT) framework. The proposed TB-ICT, as a blockchain-enabled indoor CT solution, is trustworthy and secure. For the localization module of the TB-ICT, we capitalize on availability of Internet of Things (IoT) indoor infrastructures. The simulation results illustrate efficacy of the proposed TB-ICT.