Reviewerly/exHarmony huggingface dataset:
DIGITAL MEDIA LITERACY AND FAKE NEWS AWARENESS AMONG DIGITAL IMMIGRANTS OF TIRUNELVELI DISTRICT

The advancement of technologies and the effect of globalization make our world into a small village. This effect has also undergone significant changes in the media. It is made available to everyone around the world without any limitations. As a result, the mass media experienced tremendous growth. It takes a lot of time of ours with itself. And also, it has vast content to explore. We can get a million results on the tab with one click on the internet. So, it is called "information overload." Because of this, it will quickly change our thoughts and actions until we do not get the desired results. The only way to solve this problem is to educate the public on using digital media effectively. That means, “Digital Media Literacy”. Some countries, like India, have a greater need for it than others. This is due to their dismal showing on a global proficiency index with digital media. However, not everybody was independently proficient in digital media. Economic, political, or geographical reasons all have a role in creating this chasm. Consequences include an increase in cybercrime and the proliferation of disinformation. These things have a significant bearing on our culture. Governments like India struggle to address these socioeconomic problems. It directly impacts people and completely upends their lives. Therefore, it is necessary to explore the connection between digital media literacy and the dissemination of fake news. Consequently, the purpose of this study is to analyse the issue by employing the quantitative research method. The data for the study will be collected using Survey Method in the Tirunelveli District of Tamil Nadu State.
Backdoor-Resistant Public Data Integrity Verification Scheme Based on Smart Contracts

This article analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted method, where each user randomly selects a key to encrypt the outsourced data. Such a method results in varying ciphertexts for the same data by different users, leading to additional storage costs for the cloud server. Third, users’ devices, if poorly designed or even intentionally backdoored, can potentially exfiltrate secrets and compromise the security of schemes. To address these issues, we propose the first backdoor-resistant public data integrity verification scheme based on smart contracts (ASSIST). The key idea is to introduce a new entity (a whistleblower) to periodically monitor the state of verification results recorded in the blockchain. This allows for timely notification of data corruption to users. ASSIST requires users to encrypt their data with a cryptographic primitive called message-locked encryption (MLE), which motivates different users to produce the same ciphertext for the same data and reduces storage costs for cloud servers. We also deploy a cryptographic reverse firewall between users’ devices and the external to rerandomize interactive messages, making the exfiltration impossible. We provide rigorous security proofs to demonstrate the security of ASSIST. The performance evaluation shows that ASSIST is efficient regarding computation and communication costs.
Quantum Pontryagin Neural Networks in Gamkrelidze Form Subjected to the Purity of Quantum Channels

We investigate a time and energy minimization optimal control problem for open quantum systems, whose dynamics is governed through the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) master equation. The dissipation is Markovian time-independent, and the control is governed by the Hamiltonian of a quantum-mechanical system. We are specifically interested to study the purity in a dissipative system constrained by state and control inputs. We deal with the state constraints through Gamkrelidze revisited method, while handling control constraints through the idea of saturation functions and system extensions. This is the first time that quantum purity conservation is formulated in such framework. We obtain the necessary conditions of optimality through the Pontryagin Minimum Principle. Finally, the resulted boundary value problem is solved by a Physics-Informed Neural Network (PINN) approach, a technique that is also new in quantum control context. We show that these PINNs play an effective role in learning optimal control actions.
A Privacy-Preserving Image Retrieval Scheme Based on 16×16 DCT and Deep Learning

In recent years, people tend to upload images to cloud servers, which provide storage and retrieval functions. To prevent users’ privacy from leaking to the server, research on cipher-image retrieval has attracted much attention. This work presents a novel encrypted image retrieval method. With this scheme, we perform encryption during the JPEG compression process by applying 16×16 DCT (Discrete Cosine Transform) for blocks’ transformation, followed by coefficients distribution and 8×8 blocks’ permutation. For the retrieval part, when an encrypted query image is sent by an authorized user, the server extracts its DCT histograms as features and inputs them into our trained network model, which incorporates transpose Multilayer perceptron modules ( <inline-formula><tex-math notation="LaTeX">$Transpose$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$MLP$</tex-math></inline-formula> ), for retrieval. Experimental results show that our scheme, compared with related schemes, can improve the retrieval performance significantly, when ensuring compression friendliness and no feature information leakage. Moreover, our scheme enables cipher-image retrieval from multiple image owners.
Learning Multitask Gaussian Process Over Heterogeneous Input Domains

Multitask Gaussian process (MTGP) is a well-known nonparametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs are usually limited to the multitask scenario defined in the same input domain, leaving no space for tackling the heterogeneous case, i.e., the features of input domains vary over tasks. To this end, this article presents a novel heterogeneous stochastic variational linear model of coregionalization ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HSVLMC</monospace> ) model for simultaneously learning the tasks with varied input domains. Particularly, we develop the stochastic variational framework with Bayesian calibration that: 1) infers posterior domain mappings to consider the effect of dimensionality reduction raised by domain mappings for achieving effective input alignment and 2) employs a residual modeling strategy to leverage the inductive bias brought by prior domain mappings for better-model inference. Finally, the superiority of the proposed model against existing heterogeneous LMC models has been extensively verified on diverse heterogeneous multitask cases and a practical multifidelity steam turbine exhaust case.
Review of Solutions for Securing End-User Data Over Cloud Applications

With more and more organizations working on the cloud over unsecured internet, sharing files and emails and saving them on cloud storage is imperative. Securing the end-user sensitive data in transit has thus started to get maximum priority to protect it from Cloud company staff, hackers, and data thieves. In this study, an attempt is made to review the research on end-user data security. There is an urgent need for solutions for end-users data protection privacy during the times when migrating from one cloud service provider to another. This chapter reviews the challenges in Cloud computing services regarding end-user data, analyzes the issues face, and presents solutions to overcome them. The chapter identifies end-users data security issues when using cloud computing services. The focus is directed to critical issues related to unauthorized access to integrity during data in transit. This can be addressed using Public Key Cryptography or PKI. For Confidentiality and Data Integrity for end-user data over Cloud. Then for migrating from one cloud service provider to another, data security and privacy are addressed by Cloud-aware applications. Lastly, using Multi-Factor Authentication combined with network and application detection systems, Intrusion Detection Systems, and Network traffic routing in case of cyber-attacks can help achieve denial of service attack mitigation or prevent man-in-the-middle and network snooping in Cloud Computing.
Security Algorithms For Cloud Computing

With growing awareness and concerns regarding Cloud Computing and Information Security, there is growing awareness and usage of Security Algorithms in data systems and processes. This chapter presents a brief overview and comparison of Cryptographic algorithms, with an emphasis on Symmetric algorithms should be used for Cloud-based applications and services that require data and link encryption. In this chapter, we review Symmetric and Asymmetric algorithms with an emphasis on Symmetric Algorithms for security consideration on which one should be used for Cloud-based applications and services that require data and link encryption.
Efficient Fault Tolerance in Cloud Environments

With mission-critical web applications and resources being hosted on cloud environments, and cloud services growing fast, the need for having a greater level of service assurance regarding fault tolerance for availability and reliability has increased. The high priority now is ensuring a fault-tolerant environment that can keep the systems up and running. To minimize the impact of downtime or accessibility failure due to systems, network devices, or hardware, the expectations are that such failures must be anticipated and handled proactively, quickly and intelligently. This chapter discusses the fault tolerance system for cloud computing environments and analyzes whether this is effective for Cloud environments.
Designing A Framework For Cloud Service Agreements For Cloud Environments

Cloud Computing has emerged as the prime IT computing model for on.demand access using a pool of shared resources with the least IT support. Cloud computing is starting to replace the legacy office IT infrastructure and helpdesk support system. Corporate and home users alike are hugely turning into cloud service consumers and moving their data and work to the cloud. Therefore, the Cloud Service Agreement (CSA) between cloud service consumers and cloud service providers has a critical significance that can guarantee the highest level of service quality and delivery. The current CSA parameters and CSA terms tend to fall short of the service delivery commitments with no common terminology or standard followed industry-wide by the cloud service providers. Comparing similar service offerings and agreements from multiple cloud service providers continues to be a complex issue. This chapter provides a pragmatic approach to Cloud Service Agreements, comparing the current process with the proposed parameters and the new framework for CSA to determine the role of various elements and terms in the decision-making process for cloud service agreements for SaaS, PaaS, IaaS, and STaaS.
Retrograde Assemblages of Gneisses at Arigidi and Erusu Areas of Southwestern Nigeria

Abstract The study is aimed to unravel the presence of low-grade minerals in the migmatite terrain of Southwestern Nigeria. Mineral and textural relationship of mineral assemblages were determined using a petrographic microscope, while chemical composition was analyzed using XRF and LA-ICP-MS. Petrographic studies show the mineral assemblage, biotite + plagioclase + chlorite + epidote + quartz + muscovite + ilmenite ± calcite. There is intense alteration of both biotite and plagioclase. Textural evidence shows chloritization of biotite and epidotization of both biotite and plagioclase. Epidotization of biotite is basically at the edges of biotite while that of plagioclase in more intense at the core of grains. Primary foliation is defined by mineralogical banding of quartzo-feldspathic and mafic minerals, while secondary foliation is defined by both biotite and chlorite. SiO 2 contents are high in both gneisses (&gt; 60 wt %), while there is a high depletion of REE. The results from field observations, mineral assemblages and textural relationship suggests that deformation due to shearing could have enhanced the retrograde chloritization of gneisses. Shearing could have created a path for the influx of fluid (H 2 O &amp; CO 2 ) thus triggering alteration of both biotite and plagioclase. Hydrothermal fluids containing K + can be said to have initiated the retrograde process which gave rise to reactions that produces secondary low grade minerals; chlorite, epidote and muscovite.
Research and application of smart home-based care system based on Internet of Things technology

As the overwhelming choice of Chinese families, home-based care has the advantages of convenience, economy and high efficiency. However, there are also some shortcomings, such as low professional level, poor emergency ability and limited time and space environment. In this regard, the paper aims at improving the effectiveness of home-based care service, and builds a home-based care system based on the Internet of Things technology, which provides a reference prototype for the service model and realization path of smart care. The whole system is B/S architecture, and the front end is an interactive page, which supports community management or medical staff to manage the aged care service. The back-end is the system server, which is developed by the framework of SpringBoot2 under Javaweb technology, aiming at completing the call and control of various data information. In addition, for all kinds of intelligent sensors and monitoring devices involved in home-based care services, real-time data collection and uploading will be completed by WIFI communication technology under MQTT protocol, and the data storage and management will be completed by relying on cloud platform technology. After simulation test, the functions of the system run smoothly and can meet the actual needs of smart home-based care services.
CVGAN: A Cross-View SAR Image Generation Method for Enhancing the View Diversity

Although numerous classification algorithms have been successfully applied to synthetic aperture radar (SAR) automatic target recognition (ATR), the performance of most methods is limited by the lack of SAR images under different views. Considering traditional generative adversarial networks (GAN) generate fake SAR images same as the input, a novel cross-view GAN (CVGAN) is proposed to generate SAR images under disparate views for enhancing view diversity. In this network, a generator is first used to learn the mapping relations of image pairs to generate cross-view SAR images. Then, a pre-trained classifier is utilized to improve the view angle and category authenticity of the generated images. Finally, image pairs are fed into the discriminator to distinguish the generated SAR images from the real SAR images. To make the proposed network better learn the mapping relations of cross-view images, a modified loss function is extended to our network. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of the CVGAN on SAR image generation.
Domain Ontology Representation Model Based on Graph Databases

The article presents an approach to modeling domain ontologies based on graph databases. Ontology is traditionally considered as a means of studying and formalizing the subject area. Based on ontologies, knowledge bases of information systems are formed, which can later be replenished and used to study certain applied aspects of the subject area. At the same time, with the development of NoSQL technologies and graph databases aimed at optimizing work with related data, it becomes possible to design a data warehouse without a strict pre-established domain model. Due to the obvious graph nature of ontologies, graph databases are a promising solution for storing and working with ontologies. However, with all the similarities, the ontology and data models used in graph DBMSs are not so easy to combine. The paper proposes an approach to the integration of ontological and graph models of knowledge representation and considers its application in creating a database for a prototype of a web ontology editor. Visual tools for editing ontologies are described, an algorithm for layer-by-layer rendering of a directed graph is given, a mechanism for generating dynamic forms for editing classes and ontology instances is described, and basic support for ontological design patterns is implemented.
A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms

The paper proposes a method of extracting the feature vector of images, which makes it possible to effectively detect the presence of hidden information in JPEG images embedded by various popular steganography tools. This method is based on the usage of the transition probability matrix. The essence of the method for extracting the feature vector of the image is to use the transition probability matrix and apply the image calibration method to improve the accuracy of steganalysis and reduce the number of false positives. For each image from the training and test sets a feature vector is found in this way, the number of elements is 324. Further, the models were trained on the training dataset by each of machine learning methods separately: decision trees with gradient boosting, linear models, k-nearest neighbors, support vector machines, neural networks, and artificial immune systems. To assess the capacity of the models the following metrics were used: accuracy, the rate of the false positive and false negative errors, and the confusion matrix. The results of classification by each of the above methods are given. For training and testing a dataset IStego100K was used, which consists of 208 thousand images of the same size 1024 x 1024 with different quality values in the range from 75 to 95. One of the J-UNIWARD, nsF5, and UERD steganography algorithms was used to embed a hidden message. As a result, we can observe that the proposed approach to extracting the feature vector makes it possible to detect the presence of hidden information embedded by non-adaptive steganography (Steghide, OutGuess and nsF5) in static JPEG images with high accuracy (more than 95%). However, for adaptive steganography methods (J-UNIWARD, UERD) the accuracy is less (about 50-60%).
Research Data Governance, Roles, and Infrastructure

This chapter explores the concepts, requirements, structures, and processes of data or information governance. Data governance comprises the principles, policies, and strategies that are commonly adopted, the functions and roles that are needed to implement these policies and strategies, and the consequent architectural designs that provide both a home for the data and, less obviously, an operational expression of policies in the form of controls and audits. This speaks to the “What?” and “How?” of data governance, but the “Why?” is what justifies the extraordinary efforts and lengths organizations must go to in the pursuit of effective data governance. This receives a fuller answer in this chapter; in brief, information is a valuable asset whose value is threatened both by loss of integrity, the principal internal threat, and by its potential for theft or leakage, compromising privacy, business advantage, and failure to meet regulatory requirements—the external threats. Internal and external threats are not quite so neatly distinguished in real life, as we shall see in this chapter.
A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages

The stemming process is crucial and significant in the pre-processing step of natural language processing. The stemmer oversees the stemming process. It facilitates the extraction of morphological variants of a root or base word from the provided word. Over the period, several stemmers for various vernacular languages have been proposed. However, very few research studies have comprehensively investigated these available stemmers. This paper makes multifold contributions. First, we discuss the various stemmers of 15 Indian and 17 non-Indian languages describing their key points, benefits, and drawbacks. All the Indian languages for which stemmers have been built are covered in this study. For the non-Indian languages, stemmers of commonly spoken languages have been covered. Second, we present a language-wise comparative analysis of stemmers based on our identified parameters. Third, we discuss the wordnets and dictionaries available for different languages. Fourth, we provide details of the datasets available for various languages. Fifth, we also provide challenges in existing stemmers and future directions for future researchers. The study presented in this paper reveals that significant research has been carried out for the stemmers of influential languages such as English, Arabic, and Urdu. On the other hand, languages with limited resources, such as Farsi, Polish, Odia, Amharic, and others, have received the least attention for research. Moreover, rigorous analysis reveals that most of the stemmers suffer from over-stemming errors. With a complete catalogue of available stemmers, this study aims to assist the researchers and professionals working in the areas such as information retrieval, semantic annotation, word meaning disambiguation, and ontology learning.
Searching for an Appropriate Architecture for the Development of Digital Twins for Hybrid Energy Systems in Isolated Environments Using the TRIZ Evolutionary Approach

This work is devoted to the study of the architecture of digital twins. Of course, it is inevitable that the technology of creating digital twins, borrowed from engineering, realizing specific turnovers, such as the need for massive costly software tools and highly qualified personnel, is clearly visible in the few examples of digital dual power systems. There is a lack of unambiguously interpreted data values in standard machine-readable formats, adequate mathematical models, and instrumentation. The article proposed approaches to overcome the shortcomings in the design and operation of digital twin systems.
Research on application scenarios of vehicles safety warning based on connected technology

The safety warning function of intelligent and connected vehicles is of great significance to improve driving safety. Firstly, based on the coverage of application scenarios, this paper builds an application scenario database which based on different sources, with multi-dimensional scene distribution characteristics and a uniform format. Secondly, the application scenario screening model is constructed to determine the application scenario of the safety warning based on connected technology. Then, the application scenarios are classified from different dimensions. Based on the classification method proposed in this paper, considering extensibility and backward compatibility, an safety warning application scenario database is constructed that conforms to China's traffic patterns and reality.