Reviewerly/exHarmony huggingface dataset:
Augmented Reality and Virtual Reality in E-Governance

In recent years, augmented reality &amp; virtual reality (AR/VR) has been seen as a technology with huge potential to give companies an operational and competitive advantage. But despite the use of new technologies, companies still face challenges and cannot immediately achieve performance. In addition, companies must adopt attractive technologies and analyse the areas where these technologies can be adopted, which emphasizes the importance of establishing appropriate e-government practices. This study explores how AR/VR governance is applied to support the development of sustainable AR/VR applications and analyse the negative impacts based on application domains. The study illustrates what practices are used to obtain information that helps engage technologies by overcoming obstacles with recommended actions that lead to desired results. The research helps to identify the most important scopes and limitations of AR/VR in e-governance.
Impact of the Coronavirus Pandemic on the Retail Industry and Its IoT Applications' Security Vulnerabilities

The global retail industry changed drastically over the recent years as online shopping, led by gigantic Amazon, harvests an ever-increasing large share of the total retail market with the coronavirus pandemic making the future uncertain for traditional brick-and-mortar retailers. Today's customers are increasingly expecting a tailored, personalized shopping experience. They expect to be able to shop anywhere, at any time, and from any location. In this context, information and communication technology (ICT) is ushering in a new dawn for retail customers. The internet of things (IoT) is the new generation of internet-connected ICT systems in a digital environment that plays an essential role in retail business process automation. However, security issues in IoT-based retail applications have caught significant attention in academia and industry. Therefore, there is a need to educate retail business operation managers about the potential security risks of IoT-based business information systems.
The Blockchain-Empowered Application

Blockchain is an acquired information framework that incorporates a square chain. Blockchain capacities as a common record or as an overall record keeping arrangement of all exchanges on an incorporated blockchain. Blockchain is a successful framework for putting away and taking care of records of exchange at its center. All the more explicitly, blockchain is a typical, extremely durable record of circulated trades working from connected trade squares and putting them away in a high level data set. Blockchain relies upon developed cryptographic strategies to permit any member in an organization to convey, (for example, putting away, trading, and showing information), without earlier certainty between the collections. All members become mindful of participation with the blockchain and permit framework affirmation before information is incorporated, working with trustless coordinated exertion between associations. Blockchain is virtual digital money Bitcoin's spine innovation.
Convergence of Machine Learning and Blockchain Technology for Smart Healthcare Applications

The technology boom in Industry 4.0 has resulted in the generation of large volume of medical data. Smart healthcare systems have burgeoned with the convergence of blockchain, machine learning, and internet of things (IoT). The amalgamation of privacy-preserving blockchain technology and predictive diagnosis by machine learning models produces an enhanced healthcare system. This chapter aims to shed light on the machine learning algorithms and blockchain technologies currently used in the healthcare industry. The advantages of opting for blockchain for preserving data integrity and privacy are highlighted. A few applications and steps involved in implementing blockchain and machine learning in healthcare, as well as the challenges faced while doing so, are discussed. Finally, a theoretical blockchain-machine learning internet of medical things(IoMT) framework is proposed, drawing from the recent research advancements.
Identification of Fake Printed Medicine Packaging from a SVM Approach and Dots Shape Features

Medicine package printing becomes critical nowadays as fake medicines can be easily packed and sold if printed with the proper color combination.Hence, it is important to identify whether a printed package is printed by original medicine manufacturers or their authorized printers or it is printed by counterfeiters.Scanning or photographing the package and reprinting it is one approach for forging an authentic package sample.In this work, the microscopic examination of printed foils has been carried out to verify whether it is original or not.Blister foil is widely used in the pharmaceutical packaging sector and hence it is chosen as the substrate.The microscopic dot pattern can be viewed as a distinct signature of printing processes.In this study, the reference target chart IT 8.7/3 is printed using three different gravure printers (P1, P2, P3).The images of the print samples are then captured using camera.The images are printed again in the same three printers and then the samples are named as reprint samples (R1, R2, R3).Different shape descriptors index parameters such as dot area, perimeter, circularity, eccentricity, solidity, major and minor axis of dots have been used for classification between the print sample and scanned reprint sample.The study demonstrated that print and reprint dot shape descriptor index parameters might be utilized to distinguish at a microscopic scale.A multiclassification-based method Support Vector Machine have been utilized using shape descriptor index features for print and reprint source identification.The suggested method successfully classified the print and reprint samples at different dot percentages with a high rate of accuracy.
Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot

According to the requirements for the rapid scanning and measurement of the geometric shape during the process of chemical milling of an aviation engine casing, the scanning path of the casing is planned and studied. This paper introduces the principle and method of the tracking scanner and automatic measuring system and analyzes the scanning area range, approach distance, and wide angle of the field. The casing process is modeled by applying part of the machine, obtaining a series of scanning path point and synthesizing the scanning trajectory. On this basis, the entire scanning process is divided into two alternating actions: scanning measurement and posture adjustment, and the mathematical model of the annular scanning path on the outer surface of the casing part is obtained. Adjusting the scan height was used to solve the repeated scan area problem, and the results show that the adjustment method effectively shortened the scan path’s length and time. The simulation method verifies the planned finite ring-scanning path, which verifies the correctness and feasibility of the mathematical model. Through the automatic scanning reconstruction process test, the reconstruction rate of the ring scanning trajectory reaches 85%, which is 80% higher than the manual detection efficiency.
Active contour and texture features hybrid model for breast cancer detection from ultrasonic images

Abstract Breast ultrasound is commonly used in the early detection of breast cancer. The existing geodesic‐based methods use pre‐defined filters that necessitate extensive prior knowledge to achieve the region of interest in input image. Furthermore, the majority of ultrasound images suffer from noise and acoustic shadowing, which reduces the accuracy of tumor detection. To make the breast ultrasound image more informative, the discriminative features can also be extracted to improve detection accuracy. This article proposes a method to combine Active Contour and Texture Feature Vectors to find discriminative patterns. A comprehensive set of discriminative features for cancer detection in ultrasound images is created by combining the two learning models. The Breast Ultrasound Images dataset is used to evaluate the suggested method and compare it to recently created algorithms. Experimental results reveal that the proposed approach outperforms the existing algorithms in terms of accuracy, recall, precision, Jaccard index, and F1 score.
Wasserstein Distance-Based Deep Leakage from Gradients

Federated learning protects the privacy information in the data set by sharing the average gradient. However, "Deep Leakage from Gradient" (DLG) algorithm as a gradient-based feature reconstruction attack can recover privacy training data using gradients shared in federated learning, resulting in private information leakage. However, the algorithm has the disadvantages of slow model convergence and poor inverse generated images accuracy. To address these issues, a Wasserstein distance-based DLG method is proposed, named WDLG. The WDLG method uses Wasserstein distance as the training loss function achieved to improve the inverse image quality and the model convergence. The hard-to-calculate Wasserstein distance is converted to be calculated iteratively using the Lipschit condition and Kantorovich-Rubinstein duality. Theoretical analysis proves the differentiability and continuity of Wasserstein distance. Finally, experiment results show that the WDLG algorithm is superior to DLG in training speed and inversion image quality. At the same time, we prove through the experiments that differential privacy can be used for disturbance protection, which provides some ideas for the development of a deep learning framework to protect privacy.
Is This What Krishna Meant?

Abstract This chapter examines the exoticism of two significant twentieth-century figures, T.S. Eliot and J. Robert Oppenheimer, during World War II. Eliot, who had been a serious student of Sanskrit, addressed key themes of the Gītā in The Waste Land, his plays, and in a poem on Indian soldiers written during the war. This chapter juxtaposes Eliot’s reception of the Gītā to that of his contemporary Oppenheimer, who was devoted to the Sanskrit poem as a guide to life and specifically as an inspiration for him during his directorship of the Manhattan Project. The Gītā was instrumental in helping Oppenheimer bring the Atomic Bomb implementation to completion.
Translation Theories

Abstract This chapter examines the various translation theories and methodologies that inform our readings of the reception of this Sanskrit examined in this volume. It looks at how these theories permitted seemingly contradictory messages. It also asks a more general hermeneutical question regarding translation itself. How much transformation can a text undergo before it becomes interpretation? Translation here is understood as a fundamental hermeneutical event. This chapter outlines the history of translation theory, focusing on issues of equivalence, accuracy, fidelity, commensurability, and falsification. It summarizes the thoughts on translation by major theorists on translation in both India and the West.
Understanding the utility of force to protect civilians from violence

Abstract This chapter describes how force—in theory—can be used effectively to protect civilians from physical violence in armed conflict. It introduces four potential causal conditions for successful military protection operations in UN missions: deterrent presence, willingness to accept risk, pre-emption, and matching the perpetrators of violence. The chapter also includes a threat-typology—categorizing perpetrators of violence into eight distinct scenarios—ranging from genocide to mob violence. The typology—developed by Alexander Beadle—helps unpack the types of threats that UN troops can be expected to protect civilians from and how force can be employed to reduce their negative impact on civilian security in armed conflicts.
Protection by military force in UN peace operations

Abstract This chapter explores the evolving role of military forces as protectors of civilian populations under threat of physical violence in armed conflict, highlights knowledge gaps, demonstrates how this book bridges some of these gaps, defines and unpacks United Nations (UN) military protection operations, explains the importance of understanding perpetrators of violence to tailor effective military protection operations, and argues for why we should care about what UN troops do to protect civilians. While protection of civilians is a key task during military operations, most militaries still link protection to International Humanitarian Law (IHL), imploring them to avoid unlawful and unnecessary harm to civilians during operations. While IHL still provides the most important rules for the conduct of military operations to avoid harm to civilians, the UN has expanded its understanding of protection significantly over the past two decades. Today, the UN asks Blue Helmets to deter, coerce, and even destroy armed groups that deliberately kill, maim, rape, and displace civilians as part of their warfare. This is a new task for all militaries, demanding new insights on when to do what, including on when the use of force to protect can lead to increased harm to civilians. This introductory chapter indicates how the book provides new insights and to what effect on the study and practice of UN peace operations and beyond.
Increasing the utility of force to protect

Abstract This final chapter summarizes the book, and points to implications of the findings for the theory, policy, and practice of protection of civilians in UN peace operations. The core findings indicate that UN peace operations, to a larger degree, must tailor operations to particular threats and to intervene in time to influence the perpetrators’ will and ability to target civilians. Although the UN wields force reluctantly, it is necessary to exploit the military protection tool to a larger degree to obtain more successful outcomes across countries and UN missions. Effective operations are seldom about using more force, rather to use militaries for what they are meant for, to deter, and if necessary, to coerce armed groups that kill, maim, and rape civilians as part of their warfare.
Analyzing empirical patterns of UN military protection operations

Abstract This chapter analyzes potential causal steppingstones to explain successful protection outcomes across time and UN missions with the help of basic statistical analysis of categorical data derived from UNPOCO, before employing cross-tabulations, Chi-square tests, and multivariate linear probability analysis. The aim is to search for statistically significant relationships between causal condition candidates and variations in outcome, that may point in the direction of more consistent explanations of “what works” when force is employed to protect civilians from violence. While statistical analysis may seem far removed from the complexities of protection crises in real life, systematic investigations of the best available data is the only viable way forward to start building generalizable explanations to inform what to do in different situations, from a military perspective.
Protecting Civilians from the M23

Abstract This chapter studies a typical case where Blue Helmets matched the perpetrators’ use of violence against civilians, the 2013 joint operations conducted by the Forces armées de la république démocratique du Congo (FARDC) and the UN’s Force Intervention Brigade (FIB) against the armed insurgent group Mouvement du 23 Mars (M23) in the eastern parts of the Democratic Republic of the Congo. Under an exceptionally strong mandate—where the UN Security council expected the Force Intervention Brigade to use of force to protect civilians—Blue Helmets and Congolese armed forces deterred, coerced, and destroyed the armed group’s ability to conduct operations, ceasing its threats and attacks against civilians. While the case underscores the need for tailored and timely operations to protect, other factors emerge to explain successful outcomes.
Discovering causal pathways to successful protection outcomes

Abstract This chapter explores combinations of conditions—so-called “causal recipes”—that may systematically explain variations in outcomes of UN military protection operations across time and UN missions, applying fuzzy set Qualitative Comparative Analysis (fsQCA) to 126 cases from the UNPOCO dataset. The cases are studied along four conditions frequently identified as causally relevant in existing literature: deterrent presence, willingness to accept risk, pre-emption, and matching. It finds that matching and pre-emption unite in the most promising recipe to explain positive outcomes across cases. Still, many cases remain unexplained, demanding other methods to search for other possible causal conditions, which is performed in later chapters.
Overwhelmed by the White Army

Abstract This chapter explores an apparent failure to protect civilians from violence, the 2011–2012 UNMISS operations in Jonglei to reduce the threat to the Murle communities from the Lou Nuer White Army. This case is the most violent captured in the UNPOCO dataset, with up to 900 civilians killed within a few weeks. Although few civilians were protected from physical violence once the White Army’s attacks commenced, the UN mission probably saved thousands of lived through civilian early warning mechanisms before violence materialized. However, the case also underlines the need to better understand the perpetrators of violence, as the tit-for-tat dynamics led to violent counterattacks against Nuer communities by small Murle militias, significantly increasing the overall death-toll.
Preface

Extract As a soldier and officer and deployed to wars and armed conflicts in Afghanistan, the Balkans, and the Middle East from the mid-1990s to the mid-2000s, I have seen firsthand that military forces often fail to reduce civilian suffering. In fact, much civilian harm is caused by the same forces that are meant to protect them. Yet, I still believe that protecting civilians from violence sometimes demands the use of force. But how can force be used most effectively to protect, without causing more harm in the process? In this book, I combine the military practitioner’s field experience with a researcher’s tools for scientific investigation to seek answers to that question. Toward the end of the 2000s, protecting civilians from violence in armed conflicts—if necessary, with the use of lethal force—became the priority task for almost every United Nations (UN) military peacekeeper. Acutely aware of the UN’s spectacular protection failures of the not-so-distant past in Rwanda, Bosnia, and Somalia, I found it astonishing that the United Nations Security Council (UNSC) yet again relied on military forces to protect those threatened with violence. For decades, one of the bedrock principles of UN peacekeeping had been to avoid using force altogether, except in self-defense. I was one of those Blue Helmets that stood idly by when civilians were threatened with violence. Now, the UNSC expected the Blue Helmets to directly counter armed groups that deliberately killed, raped, and otherwise harmed civilians as part of their warfare. Conventional military doctrine did not capture this new understanding of protection, as it still narrowly related to the rules laid out by International Humanitarian Law (IHL), i.e., to minimize harm to civilians during military operations to avoid so-called collateral damage. Irrefutably, the way the UN now understood protection of civilians in peacekeeping operations constituted a new task for all military forces.
A Class-Incremental Learning Method for SAR Images Based on Self-Sustainment Guidance Representation

Existing deep learning algorithms for synthetic aperture radar (SAR) image recognition are performed with offline data. These methods must use all data to retrain the entire model when new data are added. However, facing the real application environment with growing data, retraining consumes much time and memory space. Class-Incremental Learning (CIL) addresses this problem that deep learning faces in streaming data. The goal of CIL is to enable the model to continuously learn new classes without using all data to retrain the model while maintaining the ability to recognize previous classes. Most of the CIL methods adopt a replay strategy to realize it. However, the number of retained samples is too small to carry enough information. The replay strategy is still trapped by forgetting previous knowledge. For this reason, we propose a CIL method for SAR images based on self-sustainment guidance representation. The method uses the vision transformer (ViT) structure as the basic framework. We add a dynamic query navigation module to enhance the model’s ability to learn the new classes. This module stores special information about classes and uses it to guide the direction of feature extraction in subsequent model learning. In addition, the method also comprises a structural extension module to defend the forgetting of old classes when the model learns new knowledge. It is constructed to maintain the representation of the model in previous classes. The model will learn under the coordinated guidance of old and new information. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our method performs well with remarkable advantages in CIL tasks. This method has a better accuracy rate and performance dropping rate than state-of-the-art methods under the same setting and maintains the ability of incremental learning with fewer replay samples. Additionally, experiments on a popular image dataset (CIFAR100) also demonstrate the scalability of our approach.
Exploring the Long-Term User Experience of an Interactive Loading Screen Using UX Curve and QUIS

User experience can change with time, and achieving a positive long-term user experience is an important goal in a successful product design. The long-term user experience of the interactive loading screen in Honor of Kings was studied by using two evaluation methods, UX Curve and QUIS questionnaire. The experiment included valid responses from a total of 23 participants. The results show that an interactive human-computer interface can achieve a good long-term waiting experience. It is also necessary to make changes to the UI or functions of the interactive screen after a period of time to ensure a good user experience. A toolbox including the QUIS questionnaire and UX Curve was proven effective in measuring the temporal aspects of user experience of the computer interface. The findings obtained in the experiments help understand the long-term user experience of human-computer interfaces. They may also provide a reference for developing different long-term user experience evaluation methods in different product contexts.
Are non‐fungible token coins a good hedge against the stock market volatility?

Abstract We test the hedge property of non‐fungible token (NFT) coins against equity market fluctuations and compare it with the hedge property of Bitcoin. We employ daily the returns of Bitcoin; three NFT coins, namely Theta, Enjin Coin and Decentraland, and three equity market indices: S&amp;P 500, NASDAQ and CAC 40, ranging from 18 January 2018 to 12 January 2021. We estimate the hedge effectiveness of the three NFT coins and Bitcoin against stock market fluctuations. Our results suggest that NFT coins are a better hedge against equity market fluctuations than Bitcoin.