Reviewerly/exHarmony huggingface dataset:
Augmented Reality - A Tool to Support Learning in Engineering Schools. Automation Practice Case

Augmented reality is a technology that improves user experiences by allowing digital sensations to be integrated. While virtual reality is an immersive model based on simulated environments using glasses, these simulated environments are built through a server. Everything that the user can see or feel is artificially provoked through images and sounds. Both realities are technological tools that are adapted to the professional development of engineering learning, which have been of significant help during mobility challenges due to the global pandemic. Although virtual and augmented reality are expensive to implement in some countries, there are still opportunities to implement them by giving access to new learning tools and creating opportunities to develop learning even in times of challenges of learning due to separation and distance. This article collects information about virtual reality and augmented reality, both of which have been applied with remarkable success in America and Europe, as well as to propose these means as a learning tool to be used in the automation laboratory of engineering schools, using augmented reality techniques to support and improve the competences necessary for distance education in times of epidemic. The method applied was based on the observation of students from a high grade level as qualitative research, the results will support parallel research to include immersing reality.
Siamese Graph Attention Networks for Robust Visual Object Tracking

Siamese-based trackers usually convert the object tracking task into a similarity matching problem between the target template and the search region. Since fixed or manually updated templates are not robust when tracking moving objects with dramatically changing appearance, this paper proposes an improved siamese graph attention network with adaptive template update called SiamGT. By establishing spatiotemporal and context dependencies between historical images and search regions, a frame selection mechanism is added to improve the richness of information. In addition, a graph attention network with residual connections is used in the template update mechanism which enables the propagation and aggregation of information to generate robust templates. Extensive experimental results on challenging benchmarks such as UAV123, OTB100, and VOT2019 demonstrate that the proposed SiamGT has achieved state-of-the-art performance in visual object tracking.
Big data analysis on yelp user-generated reviews

The goal of this project is to demostrate the use of PySpark and Spark SQL to query and analyze the Yelp Open Dataset. Specifically, the aim is to analyze the Yelp Reviews dataset, which consists of 8.6 million user-generated reviews of businesses on Yelp. we also perform JOIN operations with the Yelp Business and Yelp User datasets to describe relations between review ratings and characteristics of the business, such as geographic location. To perform some of these queries, we demonstrate the use of user-defined functions (UDFs) in Spark SQL queries. Lastly, we briefly examine how partitioning of the underlying data abstraction changes computational speed.
Digitization of Handwritten text using Deep Learning

Handwriting recognition as challenging task for a machine with lot of variations in different aspects like tilt, contrast, strokes being different for every person even being different for the same person. This paper is assessing state of the art models for comparison and provides a simple deep learning model based on convolutional network and Connectionist Temporal Classification (CTC). The accuracy accomplished after implementation is not high up as state-of-the-art models but gives a general architecture to start from for handwritten text recognition (HTR). This work also provides a comparison between two stated decoding schemes based on CTC technique. The model in this paper has been trained on IAM dataset with &#x201C;lines&#x201D; images. The model gives a word error rate (WER) of 37&#x0025; and character error rate of 14.8&#x0025;. The model aims to predict the text of the image line at once instead of segmenting the lines into words
Abnormal Detection Model of Energy Consumption Data in Beneficiation and Metallurgy Enterprises based on Transfer Learning

Abstract The energy consumption data of beneficiation and metallurgy enterprises has the characteristics of multi-dimensional and timing. An energy consumption data anomaly detection model based on transfer learning is proposed to help enterprises monitor the abnormal energy use. The combination of transfer learning and DTW algorithm is used to eliminate the adverse impact of timing on the model, and the ensemble learning is used to realize data detection. Finally, an efficient and accurate energy consumption data anomaly detection model is formed. The detection accuracy of the model is 91.6%, which can effectively meet the requirements of enterprise energy management.
Hybrid Genetic-Environmental Adaptation Algorithm to Improve Parameters of COCOMO for Software Cost Estimation

The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8&#x0025; higher than when these algorithms are separately adopted.
An Approach to Reduction of the Number of Pair-Wise Alternative Comparisons During Individual and Group Decision-Making

Research on improvement of credibility of expert estimates and reduction of the number of pair-wise comparisons during decision-making support is extremely relevant, due to large time expenditures and high cost of experts’ work. Analysis of results of theoretical research of human psycho-physiological limitations, that influence the credibility of expert estimates, indicates that the order of pair-wise comparisons, performed by experts, does influence the credibility of expert session results. We suggest the respective ways of improving the credibility of expert information during decision-making support. We also suggest a procedure for group expert session organization, which uses Combinatorial method of expert estimate aggregation. Based on information on preliminary ranking of alternatives, the procedure allows us to reduce the number of expert pair-wise comparisons, without compromising the credibility of expert session results. Suggested approaches provide the opportunity to improve existing decision-making support methods, and improve the algorithmic principles of pair-wise comparison-based decision support software development.
BareTQL: An Interactive System for Searching and Extraction of Open Data Tables

There has been a plethora of research and commercial activities around extracting structured data from documents (e.g. web pages and scientific articles) and making them available to other applications. Many organizations and government bodies have been also making their data available to public. Despite the progress in many different aspects of table extraction and publishing, querying incomplete data in tables with little or no schema has been a challenge. This paper presents BareTQL, an interactive system for querying open data tables in the presence of the aforementioned challenges.
Research on Intelligent Accounting System Based on Intelligent Financial Data Sheet Analysis System Considering Complex Data Mining

Research on intelligent accounting system based on intelligent financial data sheet analysis system considering complex data mining is conducted in the paper. The expert audit system extracts business records from the business database according to the specified audit conditions, and the program automatically calculates the total amount of the amount data items, and then compares it with the standard or normal business, reflecting the necessary information such as differences and also possible audit trails. In order to find intrusion behaviors and traces, data collection is carried out from multiple points in the network system. The collection content includes system logs, network data packets, important files, and the status and the behavior of the user activities. Furthermore, complex data mining model is combined for the systematic analysis on the system performance. The simulation on the collected data is provided to the validate the performance.
NS's Secure Framework for Healthcare

In recent years, Managing Multidimensional Ever-changing data in healthcare management with privacy and security is an arduous task. The majority of researchers that employed machine learning for disease detection did not place a high value on data privacy and security. Using machine learning and advanced security measures, this project proposes a novel technique for creating a smart and secure healthcare information management system. This project focuses on optimizing storage and data security in order to retain privacy using techniques such as Data Encryption, Granular Access Control (GAC), Activity Monitoring, and Validation of End Point. This system consists of four-layer framework which incorporates KDD (Knowledge Discovery in Databases) process for distributed data along with prediction algorithms, which enables healthcare experts to take effective decisions.
Review of: Probability and forensic evidence: Theory, philosophy, and applications

Journal of Forensic SciencesVolume 67, Issue 3 p. 1328-1329 BOOK REVIEW Review of: Probability and forensic evidence: Theory, philosophy, and applications Bruce S. Weir PhD, Corresponding Author Bruce S. Weir PhD bsweir@u.washington.edu orcid.org/0000-0002-4883-1247 Department of Biostatistics, Epidemiology and Genome Sciences, University of Washington, Seattle, Washington, USASearch for more papers by this author Bruce S. Weir PhD, Corresponding Author Bruce S. Weir PhD bsweir@u.washington.edu orcid.org/0000-0002-4883-1247 Department of Biostatistics, Epidemiology and Genome Sciences, University of Washington, Seattle, Washington, USASearch for more papers by this author First published: 28 March 2022 https://doi.org/10.1111/1556-4029.15034Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare 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 Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume67, Issue3May 2022Pages 1328-1329 RelatedInformation
Global language in the field of maritime – the backbone of global trade

The purpose of the study is to investigate the importance of English language in the field of maritime and the backbone of global trade. Participants of the study were 60 Diploma in Nautical Science (DNS) batch students from SAMS Kappal College, Panapakkam, Chennai. For collecting the data, the participants were asked to write two permission letters of 150 words. Problem Statement of the study is that marine students make lot errors in sentence coherence, connectives and grammar in letter writing. The objectives of the study are: 1. To enhance the letter writing skills of the students. 2. To identify the common errors done in their letter writing. Hypothesis of the study was to enhance the letter writing skills through self-correction and peer-correction techniques. Mixed approach was administered in the study. Research Questions of the study is 1. Will the students improve their letter writing skills through self-correction and peer-correction strategies? As a result of the study, self-correction and peer-correction strategies helped the students to build the right foundation for effective letter writing.
The Future of Blockchain Technology, Recent Advancement and Challenges

Blockchain is now in the news, and a lot of companies are working on blockchain technology. The first time the blockchain come into existence in the case of cryptocurrency where the decentralized architecture is followed for mining and storage. The trend of cryptocurrency is increasing day by day and, in other fields, the blockchain is in development. In this book, the chapter author has discussed the future and recent trends, history of blockchain technology. How to make a block and what is the initial requirement to build up the blockchain. The Book chapter gives an overview of the challenges associated with this technology. Later in the section, you will learn about how the blockchain work and the basic functionality of the block with some case study like smart farming, credit fraud detection, etc.
A Study of Restoration Method Considering TCP Characteristics during Slow-Start

A data restoration method for the large-scale disasters is proposed. Recently, social data, e.g. resident information or medical information, are stored into information systems. The social data are backed up to remote site to protect from data loss. It is important for restoration to be able to utilize the backup data as soon as possible. Thus, a part of backup data, e.g. files, is individually restored (File-lvl Restore). To reduce the time to get backup data by the File-lvl Restore more further, we apply the parallel data downloading. However, the current method does not consider the TCP slow-start processing; thus, the number of chunks of parallel downloading becomes insufficient. In this paper, we propose a data restoration method that decides the number of chunks based on Round Trip Time and the characteristics of TCP slow-start. Our evaluation results show that the time to get the backup data by using the proposed method becomes 1/2 to 1/3 compared to the current method.
Action-Feasibility-Based Pragmatic Understanding of Ambiguous Instructions by Service Robots

The aim of this study is to develop a method that enables a service robot to understand not only explicit user instructions, such as "Bring me the coke from the kitchen," but also ambiguous instructions, such as "I ’m thirsty," and to plan a feasible action sequence according to the environment. To achieve this, we propose a method that enables the robot to predict a goal description from the user’s ambiguous instructions using an instruction understanding module, and to plan a feasible action sequence based on the goal description and environmental information utilizing an action planning module. By setting the goal description between the two modules, map-ping from an ambiguous instruction to a goal description can be learned using Seq2Seq combined with BERT. In addition, an executable action sequence can be planned using ProbLog based on the goal description and environmental information as the result of a probabilistic logic inference. We performed an experiment to evaluate the effectiveness of the proposed method in understanding the user’s ambiguous instructions and plan-ning a feasible action sequence. In the experiment, we compared the proposed method, which considers the goal description and environmental information, with a method that directly predicts an action sequence from the user’s instruction. In addition, an experiment was performed to determine the effectiveness of the proposed method in terms of compositionality. The experimental results demonstrated that the proposed method exhibited performed better in terms of the feasibility of the predicted action sequences than the comparison method. The study confirmed that the proposed method enables the service robot to predict suitable goal descriptions from the user’s ambiguous instructions and plan a feasible action sequence according to the environment.
Development of dialogue system based on deep learning

Firstly, deep learning has been successful in many fields in recent years. As a core application in natural language processing, dialogue system has gradually developed from traditional machine learning to deep learning. Secondly, the development of dialogue systems is briefly summarized, and then the typical dialogue systems based on deep learning are introduced in detail. In addition, we also introduce two developer-oriented dialogue system development platforms, including PyOpenDial and DeepPavlov. Finally, we discuss the development prospects and challenges of the dialogue system, including robustness, personalized, and startup problem. We believe that this review can help researchers better understand the development status in this field.
Quantum Game Theory approach for data network routing: a solution for the congestion problem

Abstract Because of the sustained growth of information and mobile users transmitting a great amount of data packets, modern network performances are being seriously affected by congestion problems. In fact, congestion management is a challenging task that can be roughly summarized as a trade off between transmission latency and cost. In this context, a novel framework is proposed for solving the congestion problem on communication networks using a quantum game theory model, where network packets compete selfishly for the shortest route. Simulations show that final network routing and traveling times achieved with the quantum game version outperform those obtained classically. This leads to the opportunity of developing full-stack protocols that may be capable of taking advantage of the quantum properties for optimizing communication systems. Due to its generality, the game theoretic approach can be applied both in local networks and in more complex networks such as 5G or LTE-A.
IP based Dual Data Security for Cloud Storage Against Denial-of-Service Attack

Abstract The main aim of the attacker is to increase the unwanted packets flooding in the network. It is considered as a king of assaults because it floods the network with an excessive number of useless packets, causing the network capacity to be consumed and data transmission in the network to be hampered. The primary work of DDSCS is to visualize the impact of a DoS attack on a network and identify the node or nodes that are causing the network’s performance to be negatively affected. The attacker ping attempts are very high in network but the data delivery attempts are almost negligible. The DDCSS security system examines the profiles of each node in the network through its IP address and the number of ping attempts. If the attacker is one of the nodes that flooded the network with superfluous packets, the DDSCS method prevented the attacker in the network. The simulation results demonstrate the performance of DoS, SPAS and proposed DDSCS but the DDSCS schemes, indicating that it is successful and demonstrating a 0% attacking rate in the presence of an attacker.
Causal Graphs for Missing Data: A Gentle Introduction

chapter Share on Causal Graphs for Missing Data: A Gentle Introduction Author: Karthika Mohan University of California University of CaliforniaSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 655–666https://doi.org/10.1145/3501714.3501750Online:04 March 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Investigation on speech recognition Accuracy via Sphinx toolkits

Automatic speech recognition (ASR) is the process that concerns the permit of the machines to accept speech input from humans and interpret it with the highest precision probability. The availability of usable open-source software playing a remarkable role in the popularization of this objective. The aim of this paper is to evaluate the Sphinx open-source speech recognition toolkit. Specifically, the decoders Pocketsphinx and Sphinx-4 are compared in terms of usability and expense of recognition accuracy. The evaluation presented in this paper was carried out using Amazigh language. Our system was trained to recognize the ten first Amazigh digits by using 5 Hidden Markov model states (HMMs) with 16 Gaussian mixture models (GMMs) and the Mel frequency spectral coefficients (MFCCs). Our obtained experimental results show that the best recognition rate was recorded by Pocketsphinx toolkit.
OUTOPIA

Before being able to communicate with one another over the Internet, users in messaging applications need to discover each other and learn their IP addresses. Today, this User Discovery process is closely coupled with the communication provider. As a result, these providers are able to find (i) who is talking to whom, (ii) who is friends with whom and (iii) where is everybody located in the Internet address space at any time, even when there was no communication channel ever established, positioning this way themselves as powerful "Big Brothers".
Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge

Within computational reinforcement learning, a growing body of work seeks to express an agent's knowledge of its world through large collections of predictions. While systems that encode predictions as General Value Functions (GVFs) have seen numerous developments in both theory and application, whether such approaches are explainable is unexplored. In this perspective piece, we explore GVFs as a form of explainable AI. To do so, we articulate a subjective agent-centric approach to explainability in sequential decision-making tasks. We propose that prior to explaining its decisions to others, an self-supervised agent must be able to introspectively explain decisions to itself. To clarify this point, we review prior applications of GVFs that involve human-agent collaboration. In doing so, we demonstrate that by making their subjective explanations public, predictive knowledge agents can improve the clarity of their operation in collaborative tasks.