Reviewerly/exHarmony huggingface dataset:
The impact of <scp>COVID</scp> ‐19 on cybersecurity awareness‐raising and mindset in the southern African development community ( <scp>SADC</scp> )

Abstract At the beginning of 2020, the world came to a stand‐still when governments across the globe decided to enter states of ‘emergency’ or ‘disaster’ over the breakout of the COVID‐19 pandemic. The responses to the pandemic included stringent movement restrictions and hygiene advice preventing face‐to‐face interactions. As a result, many activities, including schooling, working, and shopping were moved online, drastically increasing exposure to cyber threats and risks. It is unclear if and how the rapid increase in internet use corresponded to an improvement in cybersecurity mindset development in countries of the Southern African Development Community (SADC). This paper explores the effect of the increase in digital technology usage due to the COVID‐19 pandemic restrictions on the relationship between cybersecurity awareness‐raising initiatives and the development of higher levels of cybersecurity mindset in Botswana, Lesotho, and Malawi. These three countries have a similar cybersecurity footprint and an average cybersecurity capacity level for the region. The research applies a comparative multiple case study approach relying on a thematic review of the literature and related documents, supported by in‐depth interviews with purposefully selected key informants from the three selected SADC countries. Findings suggest that since the start of the COVID‐19 pandemic, awareness‐raising programs have gained some momentum in our selected countries, but the cybersecurity mindset has not improved. That was attributed to low frequency and poor quality of campaigns added to the lack of training, education and lived experience. The paper highlights the need to increase the frequency and improve the quality of programmes, for greater impact on the development of local cybersecurity mindsets.
The Effectiveness of Electronic Records Information System for Education (ERISE) Applications as a Learning Media of Record Management in the Era of the Industrial Revolution 4.0

ERISE is an electronic archive application developed to be used as a learning media for Record Management subject.This research aims to; 1) knowing the motivation of students during learning using the ERISE application, and 2) knowing the differences in students' cognitive learning achievement between students who take Archival subject learning using the ERISE application and using PowerPoint media.The study was conducted using a quasi-experimental method with a nonequivalent control group pretest-posttest model.The population of this study consisted of class X SMK OTKP in the Special Region of Yogyakarta and Central Java.The selection of research samples using purposive sampling technique with a target of 104 students.The research instrument used was a questionnaire and an objective test.The research data were analyzed using descriptive statistical analysis and non-parametric statistics, namely the Mann Whitney U Test.The results showed that students' motivation was at a high level when participating in learning using ERISE application, and there were differences in cognitive values between the use of ERISE and PowerPoint.The cognitive learning outcomes score of students after learning using the ERISE application is higher than the score of students after learning using PowerPoint media.
DEArt: Dataset of European Art

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image/manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.
Plausibility Verification for 3D Object Detectors Using Energy-Based Optimization

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could compromise the safety of autonomous vehicles. The problem would be amplified even more when uncertainty errors could not propagate into the submodules, if these are not a part of the end-to-end system design. To address these concerns, a parallel module which verifies the predictions of the object proposals coming out of Deep Neural Networks are required. This work aims to verify 3D object proposals from MonoRUn model by proposing a plausibility framework that leverages cross sensor streams to reduce false positives. The verification metric being proposed uses prior knowledge in the form of four different energy functions, each utilizing a certain prior to output an energy value leading to a plausibility justification for the hypothesis under consideration. We also employ a novel two-step schema to improve the optimization of the composite energy function representing the energy model.
SMART CITIES: P2P Energy Trading Using Blockchain

This chapter describes a distributed smart grid application for smart cities that enables peer-to-peer energy trading to reduce resource consumption and create a greener, cleaner planet. Future smart cities built with blockchain technology will place a strong emphasis on resource conservation in order to create a greener, cleaner planet. Blockchain has a variety of applications that can help smart cities save more energy. A blockchain-based network, for instance, can be used to track citizens’ energy usage. Additionally, residents can exchange surplus electricity for incentives with other members. The chapter evinces a proposed framework for the smart grid. Implementation of work has been done by acutely utilizing blockchain technology as the voguish technology possesses profuse strengths including transparency, open-source, immutability, etc. The paramount part of the microgrid is its controller, implemented by using a smart contract of the blockchain that automatically modulates the smart grid functionality predominantly. Energy producers, consumers, and administrators are indispensable in the system. In this work, the profound implementation including smart contracts has been expounded chronologically. The work is limited by a single smart contract for a single type of grid system. A combination of multiple such types of systems fabricates an absolute smart grid.
Central Bank Digital Currencies: A Critical Review

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Augmented Reality Using Gesture and Speech Accelerates User Interaction

A user interaction technique is needed to manipulate the Augmented Reality (AR) content to make AR deliver realism. However, these problems persist, especially when they involve natural user interaction such as real hands and speech. Speech might be the most crucial part, especially in detecting the user’s voice. The user’s voice usually contains noises that come from the surroundings and every people produces different kinds of voice vibrations. Different genders also produce different voice vibrations. Robust real hand gestures are required to ensure the AR can accelerate the natural user interface. By providing users with more than one way to interact with AR, it tends to produce an efficient way of interaction. This chapter discusses the approach involves three main phases. In the first phase, study the interaction metaphor using gesture and speech in AR. After that, the second phase is carried out to perform a test application for the gesture and speech interaction. This chapter presents an appropriate interaction method that accelerates user interaction, it describes the implementation of gesture and speech interaction to the next level to see how AR could accelerate user interaction.
TransPatch: A Transformer-based Generator for Accelerating Transferable Patch Generation in Adversarial Attacks Against Object Detection Models

Patch-based adversarial attack shows the possibility to black-box physical attacks on state-of-the-art object detection models through hiding the occurrence of the objects, which causes a high risk in automated security system relying on such model. However, most prior works mainly focus on the attack performance but rarely pay attention to the training speed due to pixel updating and non-smoothing loss function in the training process. To overcome this limitation, we propose a simple but novel training pipeline called TransPatch, a transformer-based generator with new loss function, to accelerate the training process. To address the issue of unstable training problem of previous methods, we also compare and visualize the landscape of various loss functions. We conduct comprehensive experiments on two pedestrian and one stop sign datasets on three object detection models, i.e., YOLOv4, DETR and SSD to compare the training speed and patch performance in such adversarial attacks. From our experiments, our method outperforms previous methods within the first few epochs, and achieves absolute $$20\% \sim 30 \%$$ improvements in attack success rate (ASR) using $$10\%$$ of the training time. We hope our approach can motivate future research on using generator in physical adversarial attack generation on other tasks and models.
Benchmarking Robustness Beyond $$l_p$$ Norm Adversaries

Recently, a significant boom has been noticed in the generation of a variety of malicious examples ranging from adversarial perturbations to common noises to natural adversaries. These malicious examples are highly effective in fooling almost 'any' deep neural network. Therefore, to protect the integrity of deep networks, research efforts have been started in building the defense against these anomalies of the individual category. The prime reason for such individual handling of noises is the lack of one unique dataset which can be used to benchmark against multiple malicious examples and hence in turn can help in building a true 'universal' defense algorithm. This research work is an aid towards that goal that created a dataset termed "wide angle anomalies" containing 19 different malicious categories. On top of that, an extensive experimental evaluation has been performed on the proposed dataset using popular deep neural networks to detect these wide-angle anomalies. The experiments help in identifying a possible relationship between different anomalies and how easy or difficult to detect an anomaly if it is seen or unseen during training-testing. We assert that the experiments in seen and unseen category attack training-testing reveals several surprising and interesting outcomes including possible connection among adversaries. We believe it can help in building a universal defense algorithm.
The Effect of Virtual and Augmented Reality on Well-Being: Perspectives in Mental Health Education

The paper is an analysis of the impact of metareality on the human mind and its adverse and therapeutic effects on psychological well-being thereof. Metaverse is marked by a blurring of boundaries between the actual physical world and the virtual digital world—a metareality reflected in a three-dimensional, multisensory experience. Based on research evidence, the paper discusses how applications within virtual and augmented reality, such as, the gaming platforms present potential risks and significant safety challenges. There are ethical risks in the metaverse that are related to identity crisis, uninhibited behaviour, and unleashing of sexual and aggressive impulses. At the macro level, the real-time robust mechanisms of this simulated parallel universe impact various societal domains, such as, home, family, religion, and spirituality. The literature review presented highlights the need for digital safety and the prevention of online abuse by launching safe zones and safety tools. However, studies highlight that there are dialectical mechanisms inbuilt in metaverse and these are manifest in its innate therapeutic capacity. Researchers have reported that the imaginal, embodied, and connectivity aspects of this digital reality form the basis for treatment of psychological ailments. The conceptual framework developed will serve as a foundation for researchers interested in studying the link between augmented reality and its effect on the psyche of the human user. The paper has implications for mental health awareness and education which include debunking of associated myths, inculcating of active interest in designing interventions for patient recovery, and for individual growth at the societal level in a digitally transformed world.
Development of an Application with Augmented Reality to Improve Spatial Visualization

In this work an application with Augmented Reality (AR) was developed with the intention of accompanying the students of the Graphic Design subject in the 3D visualization of mechanical parts and assemblies. AR provides more information about the parts represented in different drawings, and facilitates the interpretation by visualizing parts or assemblies in 3D. It also helps to understand the assemblies when they need to be used, but thanks to the fact that the parts can be visualized in 3D, the knowledge is fixed and provides greater ease of understanding. In this way, knowledge can be reassured through constant interaction with the parts and assemblies worked on, for example, zooming, rotating, displaying or hiding a plane, part or assembly. Thus, with the help of this technology, students actively participate in the learning/teaching process, carrying out a personal learning itinerary. In addition, the developed application can increase the motivation of students with respect to the subject and not fall behind due to the low ability to visualize parts in 3D.
Critical Analysis of Secure Strategies Against Threats on Cloud Platform

Cloud computing is one of the popular topics of the current internet world. Cloud computing technology no doubt makes life simple and easier by offering computing, storage space, and many more services on demand over the internet but associated threats cannot be ignored while accessing these services online. Various efforts have been made by both user's end and cloud services provider to cope up with security issues. Various researches provide advanced defense mechanisms like cryptosystem authentication and intrusion detection system for the last 15 years. This study provides a critical analysis of the security strategies along with there’s role in circumventing threats. The main contribution of this paper is to make society, cloud users, and research scholars aware of the current scenario of widely used technologies. The critical analysis of prevailing approaches reveals the various used prospects which act as the solution essence against emerging attacks on the cloud, at a glance.
Accurate Rating System Using Blockchain

Most websites’ accurate rating systems provide users with product and service ratings. User satisfaction has suffered as a result of a lack of faith and the manipulation of data. Because it communicates with existing accurate rating systems, all score statistics are saved on a central server. As a result, the manager has the ability to remove, all rating data that must be modified and manipulated in order to sway positive feedback on the service or product provider. As a solution to all of the shortcomings of current systems, an accurate rating system based on distributed ledger technologies is proposed in this chapter. Decentralized distributed ledger, with no institution attempting to centralize them. Distributed ledger technologies come in a variety of flavours. Blockchain technology Because it supports smart contracts, it was used in the proposed rating system. The Ethereum platform became selected from among numerous blockchain systems with a public permission network for the proposed accurate rating system. Raters are not permitted to rate in this system unless they make a request to the gadget are permitted to participate in the correct product score process. The Ethereum platform is notable for its smart contract support, which is to be used to create Solidity’s rating contract. The TestNet blockchain, on the other hand, can be used in the rating system. Due to the high cost of this method. The proposed rating system was then tested.
ArtFacePoints: High-Resolution Facial Landmark Detection in Paintings and Prints

Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods.
Framework for Land Registry System Using Ethereum Blockchain

Some governance system that holds records of land possession must include the land registration system as one of its key components. The current system has a number of problems and gaps that lead to corruption and disagreements. In order to resolve these challenges, a sizeable portion of precious government resources is needed from the judiciary and law enforcement organizations. Blockchain technology offers the ability to close these gaps and address problems with the land registry system, such as record tampering and selling the same plot of land to several buyers. In this chapter, a secure and trustworthy foundation for a blockchain-based land register system is suggested. The suggested architecture provides an algorithm for pre-agreement and incorporates the idea of smart contracts at various land registry phases. We first provide a description of the traditional system of registry land and examine its problems. Then, we presented a framework and discussed the possible advantages of integrating Blockchain technology into the land registry system. A selection of case studies is then given.
A Brief History of Deep Learning-Based Text Generation

A dynamic domain in Artificial Intelligence research, Natural Language Generation centres on the automatic generation of realistic text. To help navigate this vast and swiftly developing body of work, the study provides a concise overview of noteworthy stages in the history of text generation. To this end, the paper describes deep learning models for a broad audience, focusing on traditional, convolutional, recurrent and generative adversarial networks, as well as transformer architecture.
LegitVote: A Blockchain-Based System to Facilitate E-Voting Process

Electronic voting has become a critical issue or concern for many organizations, including federal governments and governmental organizations in the United States and many of the democratic nations. Electronic voting issues and concerns have been precipitated by the dilemma and hullabaloo created by the recent elections in the United States. Blockchain technology has become one of the top technological domains in the twenty-first century with its decentralized character and rich security feature; and many organizations are experimenting with and using their resources either through cryptocurrencies such as Bitcoin or Ethereum or by working on decentralized applications. Applying blockchain to electronic voting has the potential to solve many of the problems and concerns that currently plague the electronic voting process. This research presents a designed model (LegitVote) that uses smart contracts on blockchain technology to facilitate the electronic voting process.
Decentralized Ride Hailing System using Blockchain and IPFS

Ride-hailing applications like Uber and Ola have gained immense popularity in India and across the globe as convenient alternatives to traditional modes of travel such as private vehicles which can be unaffordable many times due to rising fuel prices and maintenance cost etc., buses and trains which are crowded; and taxis and auto rickshaws which can deny you service at the driver's whim. The advancement in internet technology and affordable smartphones have made it convenient to book a ride through our smartphones in just a few clicks. However, the service has also brought with it a slew of other challenges like unpredictable surge-pricing and high intermediary fees. The current research into decentralized systems for vehicles is accelerating rapidly. Even then, implementing a decentralized ride-hailing platform proves to be a difficult challenge due to the inherently centralized nature of traditional ride-hailing systems. Blockchain and other similar decentralized technologies are an attractive choice for this architecture due to their immutability, transparency and fault tolerance. If implemented successfully, decentralization using blockchain technology would remove roadblocks along the way such as intermediary fees and surge charges by third parties as it would offer more transparency. This paper proposes a framework for developing a decentralized ride-hailing architecture implemented on the InterPlanetary File System (IPFS) and Ethereum blockchain platform.
Forensic Techniques to Detect Hidden Data in Alternate Data Streams in NTFS

Alternate Data Streams (ADS) have been a feature of the New Technology File System (NTFS) since its introduction in 1993. Alternate Data Streams (ADS) were introduced to address compatibility within the existing Operating Systems. Lately Hackers/Cyber Criminals have used Alternate Data Streams (ADS) as a means for launching Cyber- Attacks. Alternate Data Streams (ADS) allow data hiding, same being difficult to detect without adequate knowledge. In this paper we shall bring out the various Forensic techniques in which hidden data in Alternate Data Streams (ADS) can be detected. Finally, we compared the Forensic techniques to detect data hidden in Alternate Data Streams (ADS) in both Windows 10 and 11 Operating System.