Reviewerly/exHarmony huggingface dataset:
Comparative Study of Pre-trained Language Models for Text Classification in Smart Agriculture Domain

Disruptive technologies (DTs) have infiltrated and revolutionized several fields. The data collected from Internet of Things (IoT) devices can be leveraged for performance review, analysis, and predictive modelling. The agricultural industry has benefited greatly from the use of DTs and information and communication technologies (ICTs). However, the eventual success of an emerging technology is greatly dependent on general knowledge and perceived benefits of its use. This study aims to analyse user perception towards smart farming technologies by performing sentiment analysis on data collected from YouTube using 3 transformer models: generative pre-trained transformer 2 (GPT-2), bidirectional encoder representations from transformers (BERT) for agricultural domain, and DistilBERT. Analysis of the results and statistical testing implies that transformers are a viable option for classifying technical and agricultural text, with the domain-specific pre-trained agriculture BERT outperforming others based on macro-F1-score and accuracy.
Toward More Robust Multiclass Aerial Solar Panel Detection and Classification

The challenge of identifying and monitoring multiple types of solar panels has not been studied. Solar panels can be single, double, or double with a water heater on top. Some are packed closely together. Due to installation requirements, additional solar panels may have any random orientation. When combined with the difficulties of detecting different types of panels, this arbitrary orientation negatively affects the effectiveness of deep learning algorithms by resulting to false positive and erroneous panel classifications. Furthermore, no research on the identification of various solar panel types has been done yet. In this study, we concentrate in on two key problems: first, the detection of different types of solar panels; and second, the arbitrary orientation of these panels. Our method does not use horizontal bounding box, rather it leverages horizontal bounding boxes and generates rotated bounding box during the train time. Using our method, we were able to precisely identify three various types of solar panels with various orientations. We show a comparison of their differences for the identification of three different types of solar panels, including water heater photovoltaic (WPV), farm type photovoltaic (FPV), and SPV, in terms of box loss, objectness loss, classification loss, precision, and recall (single photovoltaic).
A Federated Learning Approach to Converting Photos to Sketch

Worldwide, communication frequently involves the use of images. Numerous applications use images, such as medical imaging, remote sensing, educational imaging, and electronic commerce. In the age of information and the introduction of big data, we require a greater level of security protection for our photographs. Since public networks are used to transfer and view our photographs, in order to train models using input from several clients without forcing each client to provide all of their data to a central server, federated learning (FL), a decentralized, privacy-preserving machine learning technique, is used. In this paper, we provide a novel method that employs machine learning (ML) to train the camera filter and provide generic filter capabilities while taking privacy concerns into account and using the concepts of federated learning and auto-encoding with the help of the CUHK Face Sketch Database (CUFS) dataset to transform any given image into a sketched representation of itself.
Computer Crimes

Computer Crimes surveys the history and examples of computer crimes, their types, costs to society, and strategies of detection and prevention. In the discussion, it is noted that a great number of computer attacks fall into two categories: penetration and denial of service attacks. And these are discussed in depth. Attack motives are also discussed. Are nations, businesses, and individuals prepared for computer attacks? Are they ready to pay the price? We look for answers to these questions as we ponder the costs and consequences of computer crimes. We note also that although it is difficult to estimate the actual costs of e-attacks on physical system resources, progress is being made for better and more accurate estimates. An in-depth discussion of the social and ethical consequences that include psychological effects, moral decay, loss of privacy and loss of trust follows. We end the chapter with recommendations for educating the computing device users in computer ethics. The need to educate the user to be aware of possible sources of computer crimes and what to do if and when one becomes a victim of these crimes is stressed. It is noted that education can go a long way in reducing computer crimes if the users take crime preventive steps every time they use the computer and computer related technologies.
A Bridge into the Metaverse: XR-Supported Bridge Inspections

In this chapter, we present a research project that we conducted from March 2020 to October 2021. We developed XR prototypes in a human-centred, iterative procedure, which help bridge inspectors to perform their task. Whilst we found that the AR in situ prototype offers great potential and added value to our target group, the VR prototype in the office was not perceived as crucial. Thus, we focus on the AR evaluation at a bridge in southern Germany with 17 domain experts and discuss our key findings, also regarding our project work during the COVID-19 pandemic. Furthermore, we share our thoughts on the connection of our proof of concept to the ongoing discussion on the metaverse.
PBDPA: A Task Scheduling Algorithm in Containerized Cloud Computing Environment

The container is a lightweight and agile virtualization solution that effectively addresses VM issues in cloud infrastructures such as overhead and compatibility. Users' tasks are assigned to containers in the form of cloudlets to ensure efficient management of cloud resources. Task scheduling with large job sizes and strict deadlines is a challenging job in cloud environments. Several jobs may need to be effectively assigned on several containers while minimizing makespan and optimizing resource efficiency. This paper presents a PROMETHEE-II-Based Dynamic Priority Algorithm (PBDPA) that incorporates multi-criteria-based optimization for dealing with multi-objective task scheduling issues. The suggested technique is evaluated using the CloudSim simulator. Experimental results on 3000 tasks successfully demonstrate that the proposed PBDPA algorithm maximizes performance in terms of time, revenue, resource utilization, and quality of service (QoS).
A Framework for an Intelligent Voice-Assisted Language Translation System for Agriculture-Related Queries

Language translation plays a major role in responding to farmers queries in technology-assisted response systems for agriculture. It is imperative that there should exist a system that can handle the queries raised by a farmer in regional language and respond to queries asked with minimal human involvement. The proposed framework accepts the farmers' queries spoken in Kannada language and translates the Kannada query into English query. The translated query obtained in English from the proposed framework is utilized by the response system to retrieve an appropriate query from a sample set chosen from the Kisan Call Centre dataset for Belagavi district which consists of queries and answer pairs asked by the farmers in English language. Further, the corresponding English text answer obtained from the response model is converted to Kannada text answer, which is translated as Kannada voice answer. The Bilingual Evaluation Understudy score is utilized as a metric for analyzing the efficiency of the framework. The BLEU is calculated by comparing a candidate text translation against reference translations. BLEU 1-gram, 2-gram, and 3-gram scores have been analyzed for evaluating the performance of the translation model. Google translator is incorporated in the framework to translate the query from Kannada speech to English text and vice versa. For experimentation, 9 queries corresponding to the most frequently asked categories from around 50,000 KCC queries are considered. The response generated by the system has been compared with 4 reference translations. 83% translation accuracy is obtained by considering the BLEU score for 1-gram.
The Temple of Brunello: Virtual Journeys to Enhance a Unique Product and Its Territory—Brunello di Montalcino Wine

In order to react to a period of uncertainty, consortiums and producers have had to find a proactive response to the pandemic, implementing innovative virtual and immersive journeys, to promote their products and their territories using digital and emotional experiences. In Tuscany, to enhance and celebrate the connection between Brunello di Montalcino wine, the historical roots of its creation process, and the unique territory where it is produced, the Tempio del Brunello—Temple of Brunello—multimedia museum uses different technologies to guide the visitor on a journey of knowledge and emotion. Using augmented and virtual reality, along with projection mapping and touch exhibits, the Temple of Brunello aims to bend the boundaries between cultural heritage, wine knowledge and territories.
Conditional Generative Adversarial Networks for Image Transformation

Conditional adversarial networks are broadly used in picture-to-picture interpretation issues. These networks not only understand the mapping from input picture to the output picture, yet in addition gain proficiency with a misfortune capacity (also known as loss functions) to prepare this cross joining. This will in turn make it conceivable for the application of the very nonexclusive way to deal with issues which customarily would require totally different misfortune capacities (loss functions). The misfortune capacity (loss function) is pointed toward decreasing ancient rarities presented by GANs and guarantee better visual quality and accuracy of precision concerning the ground reality. The generator sub-network is built utilizing the U-Net engineering, though the discriminator is intended to use worldwide and neighborhood data to choose if a picture is genuine/counterfeit. Thus, the exhibition of this approach is compelling at orchestrating photographs from labeled maps and also recreating objects from edge maps.
Comparative Study of Pruning Techniques in Recurrent Neural Networks

In recent years, there has been a drastic development in the field of neural networks. They have evolved from simple feed-forward neural networks to more complex neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used for tasks such as image recognition where the sequence is not essential, while RNNs are useful when order is important such as machine translation. By increasing the number of layers in the network, we can improve the performance of the neural network (Alford et al. in Pruned and structurally sparse neural networks, 2018 [1]). However, this will also increase the complexity of the network, and also training will require more power and time. By introducing sparsity in the architecture of the neural network, we can tackle this problem. Pruning is one of the processes through which a neural network can be made sparse (Zhu and Gupta in To prune, or not to prune: exploring the efficacy of pruning for model compression, 2017 [2]). Sparse RNNs can be easily implemented on mobile devices and resource-constraint servers (Wen et al. in Learning intrinsic sparse structures within long short-term memory, 2017 [3]). We investigate the following methods to induce sparsity in RNNs: RNN pruning and automated gradual pruning. We also investigate how the pruning techniques impact the model's performance and provide a detailed comparison between the two techniques. We also experiment by pruning input-to-hidden and hidden-to-hidden weights. Based on the results of pruning experiments, we conclude that it is possible to reduce the complexity of RNNs by more than 80%.
Comparison of Data Collection Models in an Intelligent Tutoring System for the Inclusive Education of the Learning-Disabled

Intelligent tutoring systems were developed to provide effective learning and educate learners without the human interventions. However, identifying learning disabilities is a challenge that requires an effective data collection process. As, the process of data collection affects the generated learner's details and their learning outcomes in an intelligent tutoring system. Therefore, in the present work, we have compared the data collection process to record the response of the learning-disabled learners for the developed ITS. Also, the machine learning (ML) techniques were explored to extract and select the features related to learning disabilities (dyslexia, dysgraphia, and dyscalculia). This study includes comparison of some popular and effective feature selection and classification algorithms. The feature selection methods utilized in the present study are relief, XGBoost (XGB), elastic net (EN), least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GDBT). The classification algorithms that have been compared based on these feature selection techniques for learning disability prediction are K-nearest neighbors (KNN), logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), Naïve Bayes (NB), and support vector machine (SVM). The result proves both data collection methods were equally effective and accuracy of CART with the feature selection method LASSO yields highest performance for feature selection and classification process.
Metaverse Security

Information security is essential in the field of information technology. The three core components of information security are confidentiality, integrity, and availability. Confidentiality ensures that sensitive information is protected and cannot be obtained without authorization. Integrity ensures that information is not tampered with and can be immediately detected if it has been. Availability ensures that authorized users can access the information when they need it. In the context of the emerging metaverse, information security is even more important as it is an essential prerequisite for its development that relies heavily on data, and without proper information security, social production and life in the metaverse cannot be carried out effectively. Building a basic framework for data security governance and raising public awareness of privacy protection are crucial to enhance the security of the metaverse and ensure its smooth operation. This chapter will provide information on the various types of security threats that exist in the metaverse as well as a basic framework for mitigating these risks and keeping personal information secure. It is intended to raise awareness about the importance of protecting information security in the metaverse.
Stacked Ensemble Architecture to Predict the Metastasis in Breast Cancer Patients

Prediction of metastasis in breast cancer (BC) patients plays a vital role in treatment and to prolong lifespan of BC patients. The critical part is to locate the tumor cells in lymph node. Whole slide image (WSI) of BC patients requires the expertise in the domain, and it will take more time for pathologists. During this observation, a single tumor cell may be skipped from observation, resulting in false label. Automation of identifying the tumor cells in WSI using deep learning architectures will reduce the false labeling and make the classification process faster than the conventional diagnosis system. In this paper, ResNet-50, Efficient Net B3, and MobileNetV2 deep learning models are implemented on the CAMELYON 17 challenge dataset containing lymph node WSI of BC patients. Stacking Ensemble of ResNet-50, Efficient Net B3, and MobileNetV2 deep learning models is done and implemented to improve the accuracy metrics. A comparison of ResNet-50, Efficient Net B3, MobileNetV2 individual, and Stacking ensemble of these architectures was investigated in terms of AUC, accuracy, specificity, and sensitivity. Result shows that ResNet-50 has better AUC, accuracy, sensitivity, and specificity (0.899, 0.949, 0.921, and 0.9539) than other two networks. Stacking ensemble of these networks has better AUC, accuracy, sensitivity, and specificity (0.934, 0.955, 0.9612, and 0.954) than individual networks.
Vaccine-Block: A Blockchain-Based Prevention of COVID-19 Vaccine Misplacement

The World Health Organization has classified COVID-19 as a highly contagious disease. In order to provide a quick and prompt diagnosis, appropriate medical support is essential. Many researchers have already proposed intelligent schemes to identify the disease. However, vaccination is the only feasible way to prevent from COVID-19 virus. Many countries have already developed the vaccine and vaccination is going on till now. In this paper, a secure blockchain-based scheme is designed for vaccine management including the prevention of vaccines from theft and misuse. Blockchain is an emerging technology for the decentralized storage of data. The proposed scheme used blockchain to store the vaccination record and protect from fraud registration or misplacement of vaccines. The performance evaluation is done in terms of true generated response, altered data, and product loss with respect to the number of vaccine units. The proposed approach is evaluated in terms of true generated response, altered data, and product loss ratio with respect to number of vaccine units. The results are compared with other two existing approaches. The comparative analysis shows that the proposed scheme outperforms both the existing schemes.
Hello World: Liveness in Virtual Theatre for the Metaverse

Understanding what audiences want in a new sector of live virtual events requires open and collaborative sharing of projects and work. As the virtual theatre and metaverse live events sectors establish themselves, we need to better understand whether audiences need or want live performance to create better connection to the performance, the story, and the other audience members. This project, produced by Copper Candle as part of Innovate UKs Audience of the Future programme, is a highly technical development of a new live theatre platform which utilises state-of-the-art motion capture live streaming and Unreal Engine game applications for audiences. The project produced a piece of live theatre with research mechanisms built in which allowed the team to understand what an audience wants from this format of entertainment. The findings of the production can identify directions for producers to take forward in their future works.
Enabling the Metaverse with the Development of PopupView: An Augmented Reality Platform

PopupView is an augmented reality (AR) platform that provides audio-visual digital content through mobile phones via visual “triggers” such as photographs and artwork. It builds on Visioning Lab’s vision for the metaverse as integrated virtual and physical environments, conceptualised as “storyworlds”, through which people move freely. AR becomes a tool for viewing and accessing digital content in the metaverse whilst on the move.
Metaverse and Social View

This chapter discusses the concept of the ”social experience” as a characteristic of the metaverse and a key element of the industry chain involving the experience layer. In the Internet era, social networking services have emerged as a way for people to share information, content, and interests and build relationships with others online. This has expanded the boundaries of the human social experience and created new opportunities for social interactions. The emergence of digital life in the metaverse is expected to further break down these boundaries and create new scenarios for social experience in a virtual space. Users can form virtual communities based on shared interests and can communicate with one another in real-time through text, voice, and video. Some metaverse platforms also allow for the creation of virtual events, such as parties or meetups, which can bring people together from all over the world. The social experience in the metaverse is a unique and dynamic one that is constantly evolving as technology and user behavior evolve. It provides users with opportunities to explore a wide range of possibilities for social interaction, communication, and even commerce. The Metaverse has the potential to be a powerful tool for social connection and communication.
Simulation and Synthesis of SHA-256 Using Verilog HDL for Blockchain Applications

Hash algorithms play a major role in ensuring data authenticity and integrity in many applications such as digital signature verification, password hashing, SSL handshake, integrity checking, blockchain applications. Although many popular hash algorithms exist such as MD-5, SHA-0, SHA-1, SHA-2. Among these SHA-2 is the most important hash family. Nowadays, blockchain technology is becoming more and more popular in the Internet world which uses SHA-256 (an algorithm of SHA-2 family). The key factor behind the success of the blockchain is the Proof-of-Work (PoW) consensus algorithm in which a hash value of a specific complexity is computed using SHA-256. It requires extensive computing resources and consumes a lot of power. Therefore, in this paper, FPGA-based hardware architecture for SHA-256 computation is synthesized and simulated on Verilog HDL for better resource management. Along with this the power and time consumed in different FPGA families have been analyzed.
An Extended Reality Solution for Mitigating the Video Fatigue of Online Meetings

Much of the issue with video meetings is the lack of naturalistic cues, together with the feeling of being observed all the time. Video calls take away most body language cues, but because the person is still visible, the brain still tries to compute that non-verbal language. It means that participants are working harder, trying to achieve the impossible. This impacts data retention and can lead to participants feeling unnecessarily tired. The solution presented in this chapter aims to transform the way online meetings happen, by turning off the camera and simplifying the information that our brains need to compute, thus preventing video fatigue. The immersive solution developed, iVXR, consists of cutting-edge augmented reality technology, natural language processing, speech-to-text technologies, and sub-real-time hardware acceleration using high-performance computing.
Data Consumption Behaviour and Packet Delivery Delay Analysis in OTT Services Using Machine Learning Techniques

Network operators must monitor their networks and analyse their customer's consumption patterns. The data gathered on consumption patterns enables the creation of new data plans targeted at specific consumers, as well as a proper network perspective. The high amount of network resources consumed by over-the-top (OTT) apps are well-known. This data is transferred through protocols that can give us valuable information about consumption and time delay. With this in mind, using data mining and traditional machine learning, a concept for tailoring consumption behaviour and time delay for users, has been considered. Data set with 1581 instances and 87 attributes are there which is sufficient to find the behaviour of the user. Two protocols are extracted from the data set on which various machine learning classifiers are implemented to find the best accuracy. Only four attributes are used for the analysis of consumption patterns: Flow ID, Source IP, Destination IP, and timestamp. After reviewing the results, the best suitable algorithm from the traditional approach was Random Forest with an accuracy of 99.67% while the best classifier from the incremental approach is the decision tree with accuracy 99.38%.