Reviewerly/exHarmony huggingface dataset:
A Framework for an Intelligent RoboticManipulator Coupled with a Wheelchair

This paper proposes a framework for a robotic arm coupled with a wheelchair, to aid amputees in accessing physical objects. The framework comprises primarily the software modules with a hardware prototype that provides an environment to test the functionality. The hardware makes use of a stereo vision camera to provide a video feed of the physical objects present in front of the wheelchair, in addition to obtaining a series of three-dimensional point clouds corresponding to the video feed. The hardware includes a provision for two revolute joints offering pitch and yaw motion, located on a moving chair. The system actuates the motors to control the rotation angles towards picking up the objects. A Convolution Neural Network (CNN) model is used in the vision system for locating and segmenting the physical objects on each frame of the video feed, leading to the drawing of rectangles bounding the detected objects on the frames and displaying them on a touchscreen interface. When the user chooses an object via touch, the coordinates of the object are obtained via the three-dimensional point cloud, which is converted to its corresponding azimuth and elevation angles for actuation. The result formulates the mathematical equations for calculating azimuth and elevation with which the software is realized to establish control of locations for picking up the desired objects. The software running on the hardware prototype acts as a demonstrator to provide an alignment of the actuators with the chosen physical objects.
An Intelligent Travel Application Using CNN-Based Deep Learning Technologies

This paper discusses the design and implementation of an application that provides travel information inquiries, schedule management, and budget management to travelers preparing to travel or already traveling to provide a more efficient, leisurely and rewarding travel experience. In particular, it utilizes YOLOv4’s CNN-based deep learning technologies to perform coin recognition to ensure near real-time processing time, no GPU or low GPU performance, and guarantees minimum average precision. The intelligent travel application can instantaneously classify coins, identify the issuing country and provide exchange rate information. In addition, when a budgeting application is being used, the travel application is designed to conveniently implement and manage the budget, thereby saving the user time.
Adaptive ACOR for solving the TEAM22 benchmark problem

The present paper studies the efficiency of a previously proposed aACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> (adaptive Ant Colony Optimization for Real domains) algorithm for solving the TEAM22 (Testing Electromagnetic Analysis Method) benchmark electromagnetic problem. Several variants of aACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , with adaptive selection, adaptive construction and adaptive selection & construction combined, are applied on the TEAM22 benchmark and compared with the original ACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> . The obtained results are also analyzed in comparison with the best results previously obtained with a PSO (Particle Swarm Optimization) based algorithm for the TEAM22 problem.
The Socialoid: A Computational Model of a City

Abstract A socialoid (our term) is an integrated collection of data and models about a society. As such, and accepting that it can never be complete, it is a computational model of a society. We are in the early stages of building a socialoid for Philadelphia, PA. We call it the Philadelphioid. The Philadelphioid is a diachronic (temporal), mashed, geographic information system (GIS) with an extensive integrated library of integrated analytics tools. The purpose of this chapter is to articulate our design rationale for the Philadelphioid and to illustrate its underlying concepts and premises. Central among these concepts is the principle of solution pluralism, which enjoins us to use analytics and visualization to create and explore multiple solutions to decision problems. We illustrate an application of this philosophy by discussing analysis pertaining to food deserts carried out with the Philadelphioid.
A Fair Method for Distributing Collective Assets in the Stellar Blockchain Financial Network

One of the most popular platforms in token-based banking is the flexible Stellar platform. The wide range of Stellar’s features allows companies to use it in modern cryptocurrency and token-based banking. This network charges a fee for each transaction. A percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among the network members on a collective-vote basis. In this mechanism, network users select an account as the destination to which they wish to transfer assets using their user interface, generally a wallet. This account could be the account of charities that need this help. The target distribution network is then determined based on the voting results of all members. One of the challenges in this network is the targeted and fair distribution of these funds between accounts. In this paper, the first step is a complete infrastructure of a Stellar financial network that will consist of three network-based segments of the core network, an off-chain server, and a wallet interface. In the second step, a context-aware recommendation system is implemented to solve the targeted management of payroll account selection. The results of this study concerning the importance of the targeted division of collective assets show a context-aware recommendation system as a solution to improve the process of Stellar users’ participation in the voting process.
Problem Aspects of Training Students of the Military Training Center for Work with Personnel in the Department

The problem of training students of a military training center to work with personnel is considered, for the solution of which it is important to have high-quality training. The need for a correct approach to interaction with future officers is shown. The problematic aspects of the topic under consideration are related to the fact that the majority of students are representatives of generation Y.
Examining Opaque Infrastructures with the Desktop Odometer

The information we access on the Internet appears immediately but usually lives far away. The Desktop Odometer is a device that shows users the distance they travel when browsing the web by tracking the total miles between their current location and the server from which they are requesting information. In this work, we investigated internet infrastructures by designing and producing Desktop Odometers, selling them on Amazon.com, and receiving customer reviews. We present our analysis of customer reviews which reveal how customers describe their understandings of internet infrastructures after using the device. We also recount our RtD approach to making the device; we describe frictions we encountered when navigating other opaque infrastructures in our fabrication process, such as the Google Play store. Finally, we reflect on our use of Amazon.com and customer reviews as a method to engage participants in discussion about internet infrastructure through the sale and review process.
Doctoral Colloquium-A Qualitative Study to Explore the Use of Augmented Reality as Performance Support in Industries

When augmented reality (AR) technology is used in the learning and development field, it is often viewed from a training perspective. Human Performance Technology (HPT) professionals start to explore the potential of adopting AR on mobile devices as a performance support tool to help field workers across industries. This paper captures the adoption of how mobile AR is used in organizations to address performance issues. The study will use the Critical Incident Technique (CIT) to interview ten organizations. The purpose is to uncover the critical events on why mobile AR is considered an appropriate intervention, what benefits and challenges the organizations encounter, what processes are followed when designing and implementing mobile AR, and what types and functions of mobile AR are used to assist job performance.
Snapshop-An Image Based Search Application

E-commerce has been growing rapidly over the past few years, Peoples uses them to buy and sell products. Since In these offline stores, they face many problems such as inability. For many online customers, image recognition of clothing and to identify the style, the color, and, in fact, it is a challenge to the sophistication of the fashion industry. In e-commerce, the online platform primarily offers text-based search capabilities. They can search many product searches, but they cannot manage searches based on product features, for example, colors or t-shirt patterns. Often, it is difficult for the user to make this determination features when searching for a product. Furthermore, an increasing number of consumers are depending on social media to make purchasing decisions. Consumers are trying to discover what is going on right now and are looking for the same things. This brings us to the need for a visual commerce platform, or a plan, which recommends products based on users, provided that the product images. The database uses a flexible neural network. You extract data using this deep neural network of image recognition, pattern matching and are very effective in testing fabric prediction.
USER REVOCATION WITH PUBLIC AUDITING FOR SECURE CLOUD DATA SHARING

Cloud storage auditing refers to the verification of exactness of shared data in cloud. Different users from different groups share different data in cloud. Privacy protection becomes the biggest question mark in cloud’s data services. To ensure perfection of the cloud data and for efficient user revocation, Third Party Auditing is to be done with novel Collusion Resistant Proxy Re-signature Scheme. When a user gets revocated, the cloud is much able to resign the data chunks; these data chunks were already acknowledged by the repudiated user. But, now the cloud re-stamps with a re-signing key, and this process enhances the potency of the system. Moreover, Auditing has to be done over shared data which heightens the reliability of cloud data.
A study of complex reasoning: The case of GRE ‘logical’ problems

Complex reasoning, such as that elicited by GRE ‘logical’ reasoning problems, is demanding for human reasoners and beyond the competence of any existing computer program. We report four experiments carried out to investigate the question of what makes these problems difficult. The experiments established three causes of difficulty: the nature of the logical task (Experiment 1), the nature of the incorrect foils (Experiment 2), and the nature of the correct conclusions (Experiments 3 and 4).
Reducing Controller Workload by Automatic Speech Recognition Assisted Radar Label Maintenance

Various new hard- and software centered methods were recently implemented to replace paper flight strips through modern technical solutions. These solutions provide valuable information for other ATM applications about the verbally given guidance instructions from air traffic controllers (ATCos), but also tend to increase ATCo workload. Speech recognition applications, which automatically recognize and input the verbal ATCo instructions into a technical flight strip solution, can compensate the workload increase while maintaining the benefit for other ATM applications. Experiments from the AcListant® project in 2015 have shown that Assistant Based Speech Recognition (ABSR), which combines a conventional speech recognizer with an assis-tant system, can provide adequate recognition quality for the use in air traffic control (ATC) applications. AcListant® used a prototypic radar display implementation and a research proto-type of a speech recognizer. This paper describes the exercise 220 of the SESAR 2020 funded solution PJ.16-04. It used a Commer-cial-Off-The-Shelf (COTS) speech recognition engine instead of a research prototype. Furthermore, a radar display developed by Thales Air Sys served for visualization. Command recognition rates varied greatly between 31% and 82% for different control-lers. However, the concept from ABSR to predict possible ATCo instructions could be integrated with the COTS engine, which significantly decreased the command recognition error rate and led to a variation between only 4.8% and 6.6%, i.e. only a small amount of false recognitions were shown to the ATCo.
Machine Learning-Enabled Human Activity Recognition System for Humanoid Robot

Human activity recognition (HAR) system plays a very impactful role in providing precise and opportune information on people’s activities and behaviors. It is applicable in many diversified fields like surveillance, human–computer interaction, health care, entertainment and robotics. There are two main streams of HAR systems: vision-based systems and sensor-based systems. The vision-based systems use cameras to capture images or videos to recognize human behavior. But the use of cameras for capturing data is a difficult task due to limited coverage area background noise, viewpoint, lighting and appearance. On the other hand, the sensor-based systems use different types of easily available wearable devices, smart phones to capture the data for HAR. In this context, it would be great, if an AI-enabled HAR application can be explored for predicting different human activities, like walking, talking, standing, and sitting. They will even be additional targeted activities like those varieties of activities performed during a room or on a manufactory floor. The device knowledge could also be remotely recorded, like video, radar or alternative wireless ways. Alternately, knowledge could also be recorded directly on the topic like by carrying custom hardware or good phones that have accelerometers and gyroscopes.
Digitalization of Emoji Emotions in the Pleasure-Arousal-Dominance Space

This study explores a method to obtain a comprehensive analysis of intrinsic emotions in emojis. A PAD Emotional State Model is ingeniously utilized in the analysis of emoji emotions semantics. 40 human-face emoji elaborately picked out as stimuli were presented to 8 subjects whose task was to rate each emoji in pleasure, arousal, dominance three dimensions. The rating data from all subjects shows a stunning low interrater agreement which indicates the unique emotion representation in the intrapsychic emotion space while the k-means clustering result reports a clear clustering graph composed of five independent emoji groups. This paper digitalizes different emotions binding with emoji into a three-dimension vector in the PAD space, which provides an emotion analysis model for the symbol and semantics research field. Focusing on the relationship between modulus of PAD vector and symbol feature, an important “squeeze effect” is found in this research, which deserves further study.
Qualitative assessments of Sub-base deposits of Dibdiba formation and determine their suitability for construction and engineering purposes in selected quarries at Karbala governorate/middle of Iraq

Abstract In this geotechnical study a qualitative assessment was made on the Sub-base deposits of the Dibdiba Formation from selected quarries in Karbala Governorate, central Iraq. Where 4 quarries were selected and 6 samples were taken from each quarry, bringing the total number of samples studied to 24. Physical tests such as (grain size analysis, CBR% test, Modified proctor compaction test, Atterberg limits test, and scraping ratio test) were performed, as well as chemical tests such as (sulfate content SO3%, gypsum percentage CaSO4.2H2O% test, Total soluble salts%, and organic matter% test) and the results showed that the Sub-base deposits in Dibdiba Formation in the study area falls under the class (B) according to the general specification for roads and bridges (SORB/R6,1999). The chemical tests of the studied models showed that the percentage of harmful substances is very low and that the studied Sub-base deposits are very good in terms of suitability for use for construction, engineering and road works. It is worth noting that the results of the scraping ratio test were very close, and therefore this indicates that the origin of these deposits studied is from one source.
Focused crawling application for building corporate knowledge base

Collecting and processing the knowledge in a usable way is a quite important activity for almost every company nowadays. The importance of creating innovative products raises, no matter what the domain is in which the company operates. This cannot be done without building and supporting a knowledge base with existing knowledge and extending it with new harvested or generated knowledge. In this article we provide a review of known types of focused crawlers, which can be used by companies to collect knowledge in the global internet space as well as in the intranet and build their own knowledge base.
Validitas Media Pembelajaran E-learning Berbasis Edmodo dalam Pembelajaran Biologi Pada Sekolah Menengah Atas

Learning media is part of learning resources which is a combination of software (teaching materials) with hardware (learning tools. One type of learning media is e-learning. E-learning is a learning medium that uses the internet to deliver learning materials from teachers to students. One of the e-learning media is edmodo. Edmodo is a learning platform intended for students, teachers and parents. The purpose of this study was to determine the benefits of using edmodo in the learning process of biology. This type of research is a meta-analysis. Meta analysis is collecting and analyzing data from research results from relevant journals. The data were obtained by studying the literature on the articles on Google Scholar using the keywords "learning media", "e-learning", "edmodo" and “validation”. From the results of the search carried out, several articles were found that were in accordance with the research to be carried out and it was concluded that the use of edmodo was included in the valid and very valid category in several aspects such as content aspects, linguistic aspects, study aspects and graphic aspects
A Framework and Decision Algorithm to Determine the Best Feature Extraction Technique for Supporting Machine Learning-Based Hate Speech Detection

We develop and implement a framework and a decision algorithm to determine the best feature extraction technique (FET) for supporting machine learning-based hate speech detection. Specifically, the contributions of this work are three-fold: (1) a seamless modular pipeline that automatically preprocesses, vectorizes, and classifies whether or not a text message is a hate speech; (2) a decision algorithm that determines the best FET approach among all the possible FET candidates with the linear time complexity O(N); and (3) a preliminary experimental evaluation on the tweets provided by Twitter Sentiment Analysis on Analytics Vidhya to demonstrate that our FET framework and decision algorithm are effective and produce the significant results.
Towards Engineering Inclusiveness For The Disabled Community Into Smart City Response During COVID-19

The current pandemic has demonstrated a lot of weak links in the social and economic fabric of the society. Addressing these issues is paramount to enable any successful discussion of smart city technologies. The health inequity and accompanying problems faced by the disabled community during the pandemic underlined that our current systems are not built for resiliency. it is important for us to understand what has been done towards solving this complex problem and where we could further allocate our resources to tackle this issue. It is important for us to understand what has been done towards solving this complex problem and where we could further allocate our resources to tackle this issue.
Line Learning Software to Improve Learning Interest

Abstract The internet’s role has become crucial. Even when the current Covid-19 is occurring, internet use cannot be avoided. One of the most effective internet services during this pandemic is in education, wherewith the internet can make interactions between teachers and students remotely. Therefore, the purpose of this research is to design online learning applications in Indonesia. This research used the Waterfall method. This method explains the needs of application software in the process of maintaining this application. This application acts as a platform that can help teachers and students. This application provides the teaching staff the freedom to give the material that all students can access freely. This study’s result is an online learning application created as efficiently and effectively as possible for use by teaching staff and their students.
Artificial Companion for Second Language Conversation: Chatbots Support Practice Using Conversation Analysis. SviatlanaHöhn. Cham:Springer International Publishing,2019, XIII + 346 pp. ISBN 978‐3‐030‐15503‐2

International Journal of Applied LinguisticsVolume 32, Issue 1 p. 201-204 BOOK REVIEW Artificial Companion for Second Language Conversation: Chatbots Support Practice Using Conversation Analysis. Sviatlana Höhn. Cham:Springer International Publishing, 2019, XIII + 346 pp. ISBN 978-3-030-15503-2 Tenghua You, Corresponding Author Tenghua You 3challenge@sjtu.edu.cn orcid.org/0000-0002-7438-7397 School of Foreign Languages, Shanghai Jiao Tong University Correspondence Tenghua You, Shanghai Jiao Tong University, Shanghai 200240, China. Email: 3challenge@sjtu.edu.cnSearch for more papers by this author Tenghua You, Corresponding Author Tenghua You 3challenge@sjtu.edu.cn orcid.org/0000-0002-7438-7397 School of Foreign Languages, Shanghai Jiao Tong University Correspondence Tenghua You, Shanghai Jiao Tong University, Shanghai 200240, China. Email: 3challenge@sjtu.edu.cnSearch for more papers by this author First published: 17 June 2021 https://doi.org/10.1111/ijal.12374Read the full textAboutRelatedInformationPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume32, Issue1March 2022Pages 201-204 RelatedInformation RecommendedChatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learningWeijiao Huang, Khe Foon Hew, Luke K. Fryer, Journal of Computer Assisted Learning
2SRS: A Two-Sided Recommender System to Connect Local Businesses to Bus Passengers

Recommender systems are widely used to help customers find the most relevant and personalized products or services tailored to their preferences. However, traditional systems ignore the preferences of the other side of the market, e.g., "product suppliers" or "service providers", towards their customers. In this paper, we present 2SRS a Two-Sided Recommender System that recommends coupons, supplied by local businesses, to passerby while considering the preferences of both sides towards each other. For example, some passerby may only be interested in coffee-shops whereas certain businesses may only be interested in sending coupons to new customers only. Our experimental results show that 2SRS delivers higher satisfaction when considering both sides of the market compared to the baseline methods.