Reviewerly/exHarmony huggingface dataset:
AR and VR in Manufacturing

Digitization is the backbone and major cause in the era of Industry 4.0. Manufacturing industries are in the midst of a transformation regarding the method of manufacturing processing and delivering of goods to customers. Until now the production of various goods done by the manufacturing industries uses the traditional method of old machines and human labor but looking into the plan of industry 4.0 it gives tremendous outcomes in terms of economical aspect as well as safety-wise in the long run.. In the first revolution, the manufacturing sector was mainly based on using mechanical human-operated machines which needed a lot of labors to complete the work, Second revolution is the use of mass production and assembly lines with the help of electricity. The third revolution was mainly based on advancements made in machines by introducing computers and reducing the burden on human shoulders. But now the beginning of industry 4.0 has started and many manufacturing sectors are getting benefited from this. Industry 4.0 is the automation simulation smart system based environment where Augmented reality(AR) and Virtual reality(VR) comes into play. Industry 4.0 has started to change the complete industrial experience and with the introduction of AR and VR in manufacturing sectors the profit-economy growth graph is starting to rise hence establishing a strong foundation and helping to deliver good sufficient advanced products that customer desires with the help of purely Automation and Artificial intelligence. Industry 4.0 is based on data the way it can be gathered, analyzed and deployed in the manufacturing sector with help of Augmented and Virtual reality.
Unsupervised Image Segmentation model based on W Net architecture and Conditional Random Field for Underwater Images

Under water imaging has been extensively used in monitoring the marine ecosystems for quite some time. The automated learning systems that are much explored in marine ecosystem are mainly supervised methods. Supervised methods even though require less human intervention, still demands considerable manual effort for the careful design of the dataset. The large amount of labelled and annotated data needed for proper learning, as well as the attention required for designing the ground truth dataset for efficient training, makes the supervised learning approach less attractive, especially in the underwater scenarios. This work explores the possibility of unsupervised learning methods for the analysis of the underwater images using segmentation algorithms, which does not need ground truth annotations and is much more scalable. Images and videos from Fish4Knowledge database is utilized for this work to train the unsupervised architecture based on W-Net architecture. The autoencoder architecture is analyzed considering the segmented output from the first half and reconstructed image from the second half using soft N-cut loss and reconstruction loss respectively. Post processing method of Conditional Random Field is also applied for smoothing the initial segmentation. The performance is analyzed using the learning curves and the metrics such as VI, PRI and SC.
Understanding Crowd Intelligence in Large-scale Systems: A Hierarchical Binary Particle Swarm Optimization Approach

As an emerging key technology of crowd intelligence, multi-access edge computing, mobile crowdsensing, and Internet of everything, large-scale optimization can offer suboptimal solutions to the binary optimization problems with NP-complete in these fields. Binary Particle Swarm Optimization (BPSO) is a stable and promising approach with controllable computational complexity. However, it is still challenging to solve these problems by using BPSO. In this paper, inspired by the formulation of crowd intelligence, we propose a hierarchical BPSO algorithm (H-BPSO) based on intelligence model for large-scale binary optimization problems. In H-BPSO, we first formulate the particles in the swarm as entities with intelligence, and divide them into different levels according to their intelligence. Then we design a new strategy for the selection of guiding particles when updating particles. Further, in order to make H-BPSO have better adaptability, and can balance between exploration and exploitation during the evolution, we introduce a dynamic level-number selection strategy. Finally, we investigate the performance of our proposed H-BPSO on a well-known benchmark set of high-dimensional Knapsack instances through comparing H-BPSO with several state-of-the-art BPSO algorithms. The experimental results demonstrate that H-BPSO has better performance when solving high-dimensional Knapsack problems in terms of convergence speed and global search capability.
Designing and Implementing Conversational Intelligent Chat-bot Using Natural Language Processing

In the early days of Artificial Intelligence, it was observed that tasks which humans consider ‘natural’ and ‘commonplace’, such as Natural Language Understanding, Natural Language Generation and Vision were the most difficult task to carry over to computers. Nevertheless, attempts to crack the proverbial NLP nut were made, initially with methods that fall under ‘Symbolic NLP’. One of the products of this era was ELIZA. At present the most promising forays into the world of NLP are provided by ‘Neural NLP’, which uses Representation Learning and Deep Neural networks to model, understand and generate natural language. In the present paper the authors tried to develop a Conversational Intelligent Chatbot, a program that can chat with a user about any conceivable topic, without having domain-specific knowledge programmed into it. This is a challenging task, as it involves both ‘Natural Language Understanding’ (the task of converting natural language user input into representations that a machine can understand) and subsequently ‘Natural Language Generation’ (the task of generating an appropriate response to the user input in natural language). Several approaches exist for building conversational chatbots. In the present paper, two models have been used and their performance has been compared and contrasted. The first model is purely generative and uses a Transformer-based architecture. The second model is retrieval-based, and uses Deep Neural Networks.
A Novel Contactless Bolt Preload Monitoring Method Using Digital Image Correlation

Bolt preload is a critical parameter in bolted connections with a strong influence on their general structural behavior. Traditional bolt preload estimation methods are sensitive to the surface conditions of the fastening elements while more accurate measuring methods might require significant modifications of the connection, such as embedded sensors. In this study, a novel contactless bolt preload monitoring method based on Digital Image Correlation measurements of the bolt elongation was developed. A fixed bolted setup was designed to validate the optical measurements with laser triangulation and three-dimensional point tracking, while the bolt preload was monitored with an embedded load cell. Two M16 bolt classes, 8.8 and 10.9, and multiple bolt lengths were evaluated. The bolt preload and displacement results correlated well with the available bolt theoretical models and an accurate preload estimation was possible. An analysis on the method resolution demonstrates that better resolutions are achieved with slender and long bolts. The method resolution is estimated to be under 2.25% and 1.8% of the recommended bolt preload value for each bolt class, respectively.
A New Email Phishing Training Website

In this paper, we have provided an interactive email phishing training website to complete the presented security strategy for a Qatar organisation based on targeted email phishing which is Email Phishing Training. The problem that leads users to be hacked using phishing emails is a lack of awareness on the domain senders. Over half of the email users do not have knowledge on phishing domain senders. The Email Phishing Training project aims to provide email phishing and targeted phishing awareness to organisations and government institutions through interactive training in the State of Qatar. In this study, we have presented a new idea on how to familiarize people to user domain senders using an interactive website. Training will be presented for each user based on a pre-quiz on the knowledge of the users then a quiz will be conducted based on the learning outcomes. The results show that the presented idea is an effective solution to tackle phishing emails.
Information Hiding in Audio Steganography using LSB Matching Revisited

Abstract Audio file steganography can be used as an effective and efficient method to hide messages. LSB based approach is the simplest steganographic technique among the various types for hiding secret information in audio formats. The approach is decided independent of the relationship between audio samples and the size of the secret messages. Efficiency of the output message is reduced when low data rate is used for hiding the message in lower power audio samples. The performance of data hiding in high power of audio is discussed in this paper. To identify the higher frequency, range from a given sample carrier and to hide the messages in the identified range, the LSBMR algorithm is used. The carrier wave is pre-processed in order to reduce the possibilities of intrusion detection. Experimentation of the adaptive steganography is performed on the files that are in. wav format. PSNR and MSE parameters are used for performance analysis. The threshold value of the audio carrier is depending upon the length of the message is used. The content of the secret information and threshold value of the carrier audio decides the number of samples for evaluation.
A Novel Smart CBT Model for Detecting Impersonators using Machine Learning Technique

The computer-based testing (CBT) platforms for conducting mass-driven examinations over computer networks in order to eliminate certain challenges such as delay in marking, misplacement of scripts, impersonation, monitoring and so on associated with the conventional Pen and Paper Type (PPT) of examination has also been seriously bedeviled with the same issue of impersonation commonly associated with the PPT system. The existing CBT systems relies solely on the CCTV system for monitoring people passively and the human invigilators (Proctors) for going round the examination halls in order to physically confirm the students face against their pictures on their respective system dashboard which takes so many time and effort just to screen people against impersonating and yet impersonation is on the increase with CBT system. The proposed Smart CBT model integrates an intelligent agent assessor to the existing CBT model using K-Nearest Neighbor (KNN) machine learning technique for detecting a likely case of impersonation threat considering the considering the level of accuracy and response time in answering the questions the agent delivers to the students shortly before the actual examination can commence. A total of 3,083 dataset was gathered, and 80% (2,466) of the dataset was used for training the model, while 20% (617) dataset was used in testing the model to enable the model detect unseen data correctly. Results revealed that 99.99% accuracy rate, precision, recall and f-score were obtained. The propose Smart CBT model is recommended for all tertiary institutions and commercial CBT software product adoption.
Predicting Changes in User-Driven Requirements Using Conditional Random Fields in Agile Software Development

Agile development encourages requirements change. The accurate predictions of changes in user requirements could help software evolve in the right direction to increase user satisfaction. Previous research on requirement predictions mostly relies on historical defects or user feedback. In this article, we aim to predict requirement changes based on user–system interactions in agile software development. We focus on the user–system interaction behaviors used to infer user intentions and predict requirement changes to drive the incremental iterations of agile development. Through a prototype system with two incremental iterations, an embedded program in the system captures the user runtime interaction behavior data. We utilize the conditional random fields to explore the user potential intentions and infer the emerging requirements accordingly. The increased accuracy of results in the iterations proves the effectiveness of our approach in predicting user requirement changes.
Online Truthful Mechanism Design in Wireless Communication Networks

The recent upsurge of mobile devices such as smartphones and tablets has caused scarcity of network resources, such as spectrum and computation resources. Moreover, mobile devices ordinarily join or leave networks for their own convenience, resulting in high network dynamics. As a result, developing advanced technologies based on the concepts of cooperation, sharing, and reusing becomes necessary for wireless networks. As one of the promising solutions, online truthful mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. This article first introduces the origination and some basic concepts of the online truthful mechanism, and then provides an exhibition of several online truthful mechanism design methods, which have been widely applied in wireless networks. We primarily present two types of online truthful mechanisms, that is, primal-dual based and time slotted online mechanisms, and then briefly summarize other classes, including Myerson-based, random-based, and matching-based ones.
An Systematic Study on Blockchain Transaction Databases Storage and Optimization

As the peer-to-peer networks, blockchain transaction systems require each node to store a complete collection of Unspent Transaction Output (UTXO) in the in-memory transaction database, so as to ensure new transactions can by verified independently and realize the system decentralization. However, the size of blockchain transaction database is growing rapidly, in order to meet the requirements of transaction verification and system decentralization, the nodes need to constantly expand the memory capacity to adapt to the growing database, which leads to the in-memory storage problem of transaction database. This paper analyzes blockchain transaction databases and proposes a storage optimization scheme. The scheme uses the expiration identification method based on Least Recently Used (LRU) algorithm to separate the blockchain addresses into cold and hot zones. It can achieve storage optimization by moving special UTXOs away from the in-memory database, and it can achieve storage optimization without damaging data integrity and security. We present the theoretical analysis for the optimization method to validate the effectiveness. Designed extensive experiments show our proposed method outperforms the current mechanism for the blockchain transaction databases.
Hybrid Collaborative Filtering Model Using Hierarchical Clustering and PCA

Recommendation system uses different types of algorithms to make recommendations. Collaborative filtering recommendation algorithm which uses the similar types of user with similar likings, but it is not that much efficient while working on big data. Here in our proposed approach we are applying an additional hierarchical clustering technique to the collaborative filtering recommendation algorithm along with the Principle Component Analysis (PCA) method which is applied for dimensionality reduction of data for accuracy of the results. By adding the features of these two methods to the traditional collaborative filtering recommendation algorithm the major components used for recommendations can be improved. This will surely increase the accuracy of the output of the traditional CFRA and will increase the efficiency of the recommendation system. The overall results will be carried out on the dataset of Movie lens, which is used for making recommendations of the movies to the user based on the ratings provided by the particular user.
Action Replication in GTA5 using Posenet Architecture with LSTM Cells

Playing video games by doing physical activity in an environment instead of keyboard or game controllers is not new. There are multiple products accessible in the market which are already doing well. But they all rely on some expensive sensors (Motion sensors, accelerometer, radar, infrared, etc.) with a separate processing unit to control games by physical activity in real-time. They perform excellently but cost too much that not everyone can afford. Despite that, they are compatible with only fewer games and users can't modify them to play games that they want. This paper introduces a method to control games by doing physical activities with just a Smartphone or web camera without any separate processing unit or expensive sensors. The product will be the only software that will use a camera to analyze physical activities with the help of some Deep Learning algorithms to control games in real-time. The user will have the ability to tune the system according to the game and the way they want to play. The network was trained on 70% of data and tested on 30% of the data logging 96.01 % accuracy when validated.
Ensemble of Clustering Approaches for Feature Selection of High Dimensional Data

As a prolific research area in data mining and machine learning, feature selection and associated problems bring an enormous number of proposed solutions. However, many researches are composed in new way, but fail to classify at some time for a particular data. In general, a feature selection methods search out local minima but it's does not obtain optimal minima. So, in this paper, we have proposed a method that comprised of ensemble techniques for feature selection which enable to achieve the minimum classification error. We proposed a hybrid approach, using bits from k-mean techniques and called it Feature Co-association Ensemble (CFE). For experimental analysis, we have used seven benchmark high dimensional datasets from UCI repository and validation proves its worthiness among the exiting approaches of feature selection methods.
An End-to-End Face Compression and Recognition Framework Based on Entropy Coding Model

Recent years, more and more image/video data were produced, which brings great challenge to data storage and transmission. For face recognition and video surveillance scenario, images/videios need to be compressed and transmitted to intelligent back end for analysis. While general image codecs only extract the feature towards pixel or perceptual similarity, ignoring the Rate-Accuracy performance. In this paper, we proposed an learned end-to-end face compression framework based on entropy coding model, jointly optimize face recognition and image compression performance. Compared with traditional codings, such as JPEG and JPEG2000, better Rate-Accuracy and Rate-Distrotion performance can be achieved by the proposed scheme in LFW(Labeled Faces in the Wild) dataset, especially at low bit rate.
Enhancing student achievement using the fungi learning media supported by Numbered Head Together learning

Abstract Even more paper waste was generated daily. It is coming from the workplace or other. In biology topics, paper waste can be reform into learning media, e.g., in fungi topics. The Numbered Head Together (NHT) model can support its application in learning. Examine the effectiveness of fungi learning media supported by NHT learning models to enhance student achievement was the aim of this research. Classroom action research was being used. Eighteen grade X students in a private school were research subjects. Observation sheets were used to observe student activities, while the test was used to measure student achievement. The data were analyzed using the percentage. The results showed that students’ activities in the first cycle were 88.09%, and the second cycle was 94.05%. In the first cycle, student achievement was 44.44%, and in the second cycle increased by 38.89 to 83.33%. Findings revealed that student activities and progress could be enhancing by implementing fungi learning media supported by the NHT learning model. Learning media made from paper waste can be used by teachers to improve their students’ performance.
Knowledge Transfer to Pre-schoolers based on Augmented Reality

Augmented Reality has overwhelmed the world for the last decade but still it has failed to reach the general public. This paper tries to present a system which uses Augmented Reality to enhance knowledge transfer and make the learning experience more immersive and interactive for pre-schoolers. The system recognizes each image to be assembled through object recognition techniques. A 3D object which is modeled with the help of modeling techniques is placed in the real world. In this current COVID-19 pandemic this Augmented Reality application will help students to learn in an effective and beneficial way.
Fast Background Subtraction and Graph Cut for Thermal Pedestrian Detection

Most studies in pedestrian detection from surveillance videos focus on analysing footage from visible image cameras which require external light and are sensitive to illumination changes. The presence or absence of external light determines the possibility of monitoring a scene while variations in illumination determines the degree of detection accuracy. In this paper, pedestrian detection is performed on thermal (infrared) images using a Graph-based background-subtraction technique. First, to address the limitation of thermal images such as polarity changes and halo around objects of extreme temperatures, motion is used as leverage in generating a reliable background which allows for candidate region extraction for further processing. Second, to address the limitations of automatic detection methods in the presence of multiple objects and absence of sharp edges, interactive Graph Cut is used to perform the final labelling of the valid candidate regions. Experiments on the all-inclusive benchmark dataset of thermal imagery from the Ohio State University (OSU) shows the effectiveness of the proposed method.
Subject-centered approach to integrated language-professional training: backgrounds and perspectives

In the context of dynamically updated professionally oriented information, the "erasure" of interdisciplinary boundaries, the demand for metasubject competencies the modern higher education system faces the need not only to choose innovative forms, methods and means of organizing the educational process, but to update the content of education taking into account the requirements for a modern graduate whose personal subject position in the process of his professional activity should be clearly "formalized" upon receiving his diploma. The theoretical study confirmed the demand for updating the content both on the behalf of the pedagogical community, and the employers who note the lack of readiness of most university graduates to make independent decisions in real professional situations and be responsible for them. This fact actualizes the need to study the problem of updating educational content through strengthening interdisciplinary links in the process of integrated language-professional training from the position of a subject-centered approach.