Reviewerly/exHarmony huggingface dataset:
Hybrid Cloud Storage Techniques Using Rsa And Ecc

Rivest, Shamir, and Adleman (RSA) is one of the most important types of asymmetric cryptography. In RSA, the key size is larger compared to algorithms in cryptography. In this concept, keys are generated with any two prime numbers. An unauthorized person can easily obtain the keys using a Greatest Common Delvisor (GCD) attack. This is one of the main disadvantages of RSA cryptography. Elliptic Curve Cryptography (ECC) is also an asymmetric cryptography with a small key size. The proposed work aims to overcome the disadvantage of GCD attack in RSA. An RSA private and public key can be combined with an ECC private and public key using Exclusive OR(XOR) to generate a new hybrid private and public key. RSA encryption can be done using a new hybrid public key. RSA decryption can be done using a new hybrid private key. The advantage of the proposed work is to create three key levels for cryptography to prevent GCD attack in RSA. The key strength is higher compared to simple RSA key generation.
Blockchain Technologies &amp; SDG 3:Imperatives For Revamping Health Management Information Systems in Developing Countries

This paper appraises the state of sustainable development goal (SDG) 3 in developing countries to ascertain their performance level as the target date for the SDG draws nigh. In carrying out this objective, the researchers interrogated the state of the health sector in the targeted regions and observed with dismay that it is in a very fragile state with tendencies to degenerate further if not attended to with utmost urgency. One major area we have observed the sector is lagging is the absence of a robust health management information system. In the healthcare industry, the patient's electronic health records (EHR) system is currently faced with the issue of weak security and privacy. This informs our recommendation of integrating blockchain technologies to serve as a remediation tool for the sector. The broad implementation of blockchain in healthcare will provide consumers with broader access to their information, enhance the reliability and confidentiality of electronic health records, increase the accessibility of healthcare services, and lower costs. Adopting a qualitative methodology, we leveraged secondary data sources to study the subject matter empirically. We strongly believe that if developing countries can borrow a leaf from their developed counterparts, their health systems will be revamped and their SDG 3 goal achieved within the shortest time frame.
Agrolend: A Blockchain implementation approach of smart contract farming platform

This study addresses contract farming through blockchain technology using smart contracts. The issues with the current process of acquiring loans from banks, cooperatives and large organizations is the long procedure required and the high interest rates. The new system designed eliminates that process. In the study, a smart contract system was developed to connect farmer with land owners, and supply companies. The design was implemented with Ganache as the local blockchain platform used to store the smart contracts in a bid to make it immutable. The implementation was carried out using Solidity, HTML, CSS, JAVASCRIPT, and Truffle JS. The developed system ensures farmers start agricultural projects without going through the process of applying for loans but rather connecting with a land owner to lease his land for an agreed period of time and also get seeds and fertilizers from harvest companies with little or no capital.
SHA-AES based on Chaining Block Code and Galois Counter Mode Encryptions for the Privacy of Smart Farming Systems

In smart farming, IoT plays a dominant role in diverse data generation, data transmission, and applications such as irrigation estimation, soil and environmental monitoring, food supply chain and field monitoring. The current architecture of IoT is mainly centralized and single-point controlled; thereby opening up privacy and security lapses. Several approaches have favoured advanced data encryption to preserve privacy during transmission or storage. The debilitating challenges of IoT systems sparingly reported in previous works include: security of physical/logical components; software/hardware interoperability; high energy requirements; low processing/storage capacity, and network architecture/scalability. This paper comparatively studied encryption modes of Chaining Block Code (CBC) and Galois Counter Mode (GCM) based on hybrid algorithm of SHA1-256 and AES246 (known as SHA-AES scheme) towards preserving privacy of IoT data. The outcomes revealed that, the SHA-AES scheme based on CBC was better in terms of speed - encryption (8.34%) and decryption (43.42%); storage consumption - secret key sizes (50.00%) and ciphertext sizes (26.88%) against GCM. This implies that, the GCM approach offers the highest privacy protection but, less effective due to sluggish speed and high memory utilization. Also, strong cryptographic schemes interleaved with CBC and GCM hardening schemes offered improved confusion state, diffusion state, complexity, and performances. Future works could consider modifying the complex processes of GCM approach by minimizing key generation time to support resource-constrained devices.
Mandatory COVID-19 Vaccination and Free Self-Determination in the Health Field in Italy

Abstract The COVID-19 pandemic and the issues related to health emergency management have raised concerns about fundamental rights protection. One of the most complex and contentious issues in doctrine and jurisprudence is the introduction of mandatory COVID-19 vaccination for specified categories of subjects, such as healthcare workers and individuals of a specific age range, as occurred in Italy. The debate in Italy over the mandatory COVID-19 vaccination requirement focuses on whether this obligation interferes with the individual’s fundamental right to free self-determination as guaranteed by Article 32 of the Italian Constitution. In jurisprudence and doctrine, conflicting opinions are noted concerning this topic. Part of the doctrine argue that mandatory COVID-19 vaccination compromises some fundamental rights guaranteed by the Italian Constitution, such as the right to free self-determination and respect for human dignity. Other opinions find the basis of the vaccination in the community’s best interests, citing Article 32 of the Italian Constitution, which allows for the imposition of compulsory health treatment to safeguard citizens’ health. In this regard, the issue of mandatory COVID-19 vaccination requires a reflection on the balance of two fundamental rights: the individual right to free self-determination and the community’s interest in terms of protecting public health.
Malvertisements Detection using urlscan.io, Pulsedive, and SucuriSiteCheck

As revenue on online advertisements continues to grow, Cybercriminals are mingling with the technology organisations to publish and run unsolicited adverts. Advertising companies actually have means of maximising visits to the advertiser' website but they have no control on the contents obtained from the ads auctioned to other providers such as yahoo and Facebook. Such contents may contain unauthorised scripts that may be rerouted to malicious sites where malware could be installed or malicious codes executed. This study therefore presents and implements an automated malvertisement detection system (MDS) by employing three common online detection tools or Intrusion Detection Systems (IDSs) – Pulsedive, SucuriSiteCheck, and Urlscan.io - to crawl malicious ads from 450 websites and represent the results on the confusion matrix. The malvertisement system is real and functional but less effective. When performance metrics were applied on the results. None of the IDSs outperformed the others in all the measures, suggesting that a single metrics is insufficient to objectively measure the effectiveness of the IDSs. An improved performance is expected when other set of IDSs is used in combination to build the MDS.
Exploring Solutions for Text-to-Speech Synthesis of Low-Resource Languages

A text-to-speech synthesis system is expected to convert any given text to highly intelligible and natural speech that sounds as human-like as possible. It finds its place in a variety of applications, like virtual assistants, speech-assistive aids, and speech-to-speech translation systems. In recent years several deep learning-based methods have been proposed for text-to-speech synthesis, which result in synthetic speech that is intelligible and very natural. However, these methods require a large amount of speech data, in the order of tens of hours, preferably recorded from a single speaker and in a quiet/studio environment. While such a large amount of data might be easily available for certain languages, like English, which are widely spoken and used globally, this is not the case with several other languages. In this regard, the current work explores state-of-the-art text-to-speech synthesis methods and strategies for handling low-resource languages. The paper ultimately aims to provide insight into developing text-to-speech systems for Indian languages and hence reviews the work done so far on Indian languages as well. Since India is a multilingual country where several languages co-exist, a multilingual synthesizer would be preferable. Further, one recurring suggestion to handle data scarcity is to make use of data from multiple languages too. Therefore, the current work focuses on exploring multilingual text-to-speech synthesis for low-resource languages.
Healthier Aging: A Systematic Review of Different Fall Detection Techniques in Home and Outdoor Environments

Among several disciplines, IoT has provided a promising prospect for Smart Healthcare and has seamlessly paved the way for various modern-day healthcare systems. A significant part of modern-day healthcare revolves around providing the most optimum and cost-effective healthcare services to the elderly, who need a round-the-day monitoring system to provide them with a safe and quality living experience. In this scenario, we concentrate on one of the most crucial causes of accidental injuries: falls. In this paper, we provide a systematic review of the various Fall Detection Systems currently in use or under research, exploring the various technologies that have contributed to their development and hinting at the various drawbacks of each of those systems. We have categorized the various systems under two major categories: Systems for Home Environments and Systems for Outdoor Environments. The various Fall detection systems developed specifically for Home environments employ sensors ranging from wearable sensors such as accelerometers and pulse rate sensors to using RGB-D Cameras to monitor body postures. Some fall detection systems have implemented a threshold-based detection model where the magnitude of the various features considered exceeds a specific limit; the estimator indicates a fall. On the other hand, in many systems, machine learning algorithms such as Random Forest Classifiers and Neural Network models have been implemented to distinguish fall events from non-fall events. This paper explores all such systems, discussing in a broader sense how each has been implemented and what issues have transpired from some of their implementations. This puts into perspective the pathways into which fall detection systems have made significant progress and provides the scope for future research in this field that can bridge the lacunas and pave the way for a more accurate and reliable fall detection system for the healthier aging of tomorrow.
Blockchain and SDN-IoT based secured voting system

Voting is a democratic rights of every citizen of a democratic country. In recent years, voting techniques such as paper ballot, postal ballot, etc., were used to collect votes from the people. These techniques have many drawbacks as the chances of getting invalid votes are much higher, and are less secured and time-consuming. Researchers have also introduced a new blockchain-based e-voting system in order to tackle these issues. This technique has the potential to solve most of the issues of a traditional voting system by enhancing the security, transparency and trust and reduced the chances of fraudulent votes. This method solved the security and reliability of the voting system technologically. However, the prevention of coerced votes from the people is still a major issue, which cannot be handled from a technical point of view. In this paper, a voting system is proposed which can address this issue by using booth voting system along with the convergence of various technologies like blockchain and SDN-IoT platforms. Each voting booth would be considered as an IoT node to collect votes which is controlled by the controller of the SDN. The voting records of the voters are stored on the blockchain. The Voting booth would be protected by the physical security to prevent forced votes. By this technique, the chances of getting untrusted votes from the voters are greatly reduced.
Effective Detection of Copy Move Forgery Using Surf

Copy move forgery is a technique for altering digital images by copying and pasting contents from the original image into different parts of the same image. It is of great importance to detect tampering as the credibility of digital images are being lost. Keypoint based methods proved to be efficient in localizingforgery. The proposed method uses SURF for feature extractionand improved forgery localization method for efficiently localizing forgery. Finally, comparison with the existing keypoint based methods to prove the efficiency of forgery localization in digital images. The tampered image is displayed as binary image for better understanding. The proposed methods efficiently localize forgery even if the tampered region is rescaled, rotated or blurred. The performance is validated by TPR, FPR and F1 score and comparing the same with the existing methods.
A lightweight semantic model for IoT architecture: Smart water meter usecase

The world is enveloped by numerous smart devices that generate tremendously huge amount of heterogeneous data. This data from IoT devices is essential for making intelligent decisions by smart applications and services with little human intervention. For maximum utilization of such data, accurate methods and techniques are necessary for interoperability across various platforms and applications. One such technique that is very common and popular is using semantics and describing raw IoT data in terms of ontology. In our work we propose mechanisms to accurately define the heterogeneous IoT data in terms of a lightweight IoT ontology and query the resultant data for different purpose to suit multiple applications. We are able to achieve it at minimum time. As a use case we are considering smart water meters as IoT devices for the monitoring of water consumption by different households. The semantically enriched data about water consumption will serve as important service in the smart city domain.
RSA Cryptosystem: Block Cipher or Stream Cipher

In the communication system, it is an important and essential concept that secret information must be kept secured. Cryptography is a branch of study that deals with message security. A public key cryptosystem RSA is an important mile stone in the field of message communication networks. In today’s modern computerized era where most of the messages are communicated through the internet, it is evident that messages are not in limited access between the sender and the receiver only. Third party, that is, any unauthorized person also may get access to the message communicated through any hostile communication channels like internet, telephone line etc. During the last five decades various types of cryptographic algorithms have emerged to protect the message from intruder. A new concept of public key cryptosystem was introduced by W. Diffie and M. E. Hellman in their revolutionary article. Following this concept, three eminent researchers at MIT Laboratory for Computer Science, Cambridge have designed the RSA algorithm which is the first public key cryptosystem. In this paper, a brief review on the RSA cryptosystem is presented.
Electromagnetic Signal Analysis: A Vulnerability to Edge Computing Driven IoT Devices

Edge computing is employed in real-time IoT ap-plications to improve service quality. Edge computing-driven IoT devices are used in smart cities, health care, agriculture, etc. Due to the rapid expansion of edge computing devices, researchers are focusing on securing IoT devices. As a result of security vulnerabilities, attackers compromised IoT devices via security attacks. IoT devices deployed by edge computing are also susceptible to side-channel attacks. Although side-channel attacks, security approaches, and mitigation techniques have been developed for computer systems and other devices, the environment for edge computing is distinct. Side-channel attacks need more investigation in the context of IoT edge devices. In this paper, we analyze electromagnetic side-channel attacks on IoT edge devices and address electromagnetic signal analysis. In addition, we evaluate a performance by capturing and analyzing the electromagnetic signals that are emitted from an air pollution monitoring system while it is operating.
Deep Learning Segmentation Based Attention Mechanism Module for Navigation System

This study implements state-of-the-art segmentation models on the unique conflate Area-of-Focus dataset. The goal is to accurately identify and segment a specific regions in images that contain a large number of objects and obstacles for navigation system. The effectiveness of these models will be evaluated using metrics such as dice loss score and IoU metrics. The study aims to demonstrate the potential of deep learningbased segmentation in removing insignificant portion of the image leaving only the Area-of-Focus. Out of the four model selected, SegNet achieves a dice accuracy rate of 98.98%, dice loss of 1.62% and an IoU of 95.2%.
Prosthetic Face Recognition using a Siamese Neural Network Approach

Neural Network based approaches have been extensively used for facial recognition. The success rate being high, such approaches have also gained popularity. In most facial recognition tasks, the identify is associated with the golden ratio. However, for made up faces, especially with prosthetic makeup, the golden ratio approach does not work. This paper proposes a network based on Siamese Neural Model that can identify faces even on use of prosthetic makeup. The network is tuned to make use of the powerful discriminative features to make a generalization of the predictive power of the network. The model’s ability to recognize faces, even when prosthetics have been used, has been confirmed through experiments with reasonable success under constrained laboratory conditions.
Identification and Analysis of Assamese vowel speech signal using Formant Feature-Fusion and Feed-Forward Neural Network Model

Objective: Vowel identification from speech helps translate spoken words to their textual forms through computational linguistics. Even though the Assamese language has Eight vowel sounds, Eleven vowels are present in the Assamese script. However, four vowels (ই, ঈ, উ, ঊ) are no longer pronounced differently by typical Assamese speakers. Since pronunciation plays a vital role in translating speech to text, the present work aims to identify andanalyse Assamese vowels, which can lead to good Assamese Speech to Text translation. Methods: In this work, we have builta speech dataset by recording all Eleven vowels by 26 speakers needed for the vowel identification purpose. We used the Feed Forward Neural Network to train the model using the Formant Frequencies. Findings: The neural network model developed with the help of formant frequencies gave an accuracy rate of 89parcent for identifying the Assamese vowels. The confusion matrix demonstrates that the vowel set, i.e., [ই, ঈ] and [উ, ঊ], are pronounced in a very similar manner by the informants. Such similar pronunciation can be due to various factors like negligence, lack of linguistic knowledge, popular culture, etc., which led to the misclassification of these vowel sets. Novelty: No prior work has been done in Assamese vowel recognition using a combination of Formant Feature Fusion and Neural Networks.
Parts of Speech Taggers for Indo Aryan Languages: A critical Review of Approaches and Performances

Automatic Parts of Speech (PoS) Tagging is a sequence labeling problem. PoS Tagging research has undergone an evolutionary journey starting with Dictionary Lookup PoS Tagger model, and then using rule based and statistical schemes, and later on adopting hybrid methodology for enhanced performance. Emergence of Machine Learning (ML) has boosted the activities adding newer dimensions to look at the problem with deeper linguistics and computational perspectives, and in recent times this has shifted to completely self-learning models with incorporation of Deep Learning (DL) tools. Here we have recorded and analyzed this trajectory for the PoS tagger development and experimentation for the Indo Aryan languages. Rule based and statistical models performed to an acceptable level, but are not robust and dynamic. ML and DL based models outperformed all other models, and started giving higher performances, with reported accuracy to the tune of upto 97% in few cases. Various customized models using DL have been experimented in very recent days, and different groups have reported best performed models using a variety of combination of pre-processing methods to that with DL tools, substantiating with quantitative performance matrix reports. Structured reports, inclusive methodologies adopted, and quantitative performance evaluation comparisons are elaborated in the paper. This comprehensive and critical review shall act as a foundational backbone for any Indo Aryan language PoS Tagger modeling experiment, either fresh, or attempt to enhance performance.
Economics of Semantic Communication System: An Auction Approach

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this article, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.