Reviewerly/exHarmony huggingface dataset:
Overview of Blockchain Based IoT Trust Management

Over the past few years, Internet of Things (IoT) has become one of the most influential innovative technologies in the world. A huge number of heterogeneous and dynamic devices work together to provide collaborative services and applications across various industries. Nevertheless, Trust Management systems remain an important task to be investigated since it leads to several challenges and issues. In this paper, Blockchain is presented as an emerging decentralized and distributed technology, with its key features make it the most suitable candidate to solve Trust Management issues in IoT. Furthermore, various research works related to Blockchain-based IoT Trust Management were discussed along with some open challenges and future directions.
Digital Modulation Recognition Method Based on High-Order Cumulant Feature Learning

Automatic modulation recognition (AMR) of communication signals is an important research topic in the processing of intercepted signals. In this paper, aiming for the automatic recognition of modulated signals, we propose a feature learning and classification method based on the high-order cumulants, which effectively suppresses Gaussian white noise. Six digital modulation schemes including BPSK, QPSK, 8PSK, 8QAM, 16QAM, and 64QAM can be recognized by comparing the feature with the threshold. During the experiments, we plot the confusion matrix under different conditions. Moreover, it is derived and verified with simulation experiments and actual data acquisition.
Design and Implementation on FPGA of a HW Accelerator for Post-Quantum RLWE Polynomial Operations

Ring learning with errors (RLWE) is largely adopted in Post-Quantum cryptography and Homomorphic encryption schemes. RLWE cryptosystems are defined over polynomial quotient rings, where polynomial additions/subtractions and multiplication are required. In this paper we propose the implementation of a hardware accelerator for polynomial operations required in RLWE cryptosystems. Such hardware accelerator includes a Arithmetic Logic Unit (ALU) able to perform polynomial additions/subtractions and multiplications. The latter adopts Negative Wrapped Convolution (NWC) and Number Theoretic Transform (NTT) techniques. The hardware accelerator has been integrated in a hardware system for accelerating the NewHope-1024 algorithm, and a demo on a Intel DE4 FPGA board has been developed to test the functionality of the system. Synthesis results on different FPGA technologies are proposed too, showing competitive performances respect to the State of Art.
Recommendation by Multiscale Semantic-Visual Analysis of User Reviews and Product Images

Recommendation for items that users have not interacted with is often based on the predicted ratings. However, predicting an item rating can be challenging due to a fact that the user-item paired data are not easily. Many studies addressed this problem by modeling latent factors of users (and/or items) using semantic reviews, as they are available on most ecommerce websites. However, this approach ignored the visual features of items, or product images, which reflect users’ personal preference. In this paper, we present a novel method that exploits multiscale semantic-visual representations (MSVR) to perform rating predictions with two paralleled submodules. The first submodule identifies the review features with respect to various aspects through word-aware and scale-aware attention mechanisms. The second submodule extracts block-level visual features at multi-scales with a pre-trained deep net, and then embeds the features with visual word vocabularies followed by a projection layer to reduce dimensions and an attention layer to re-weight multiscale blocks. Extensive experiments performed on 22 Amazon datasets demonstrates that our model significantly outperforms several state-of-the-art recommendation methods.
Decentralized Federated Learning Methods for Reducing Communication Cost and Energy Consumption in UAV Networks

Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost.
A Comparative Analysis of Chaotic and Quantum Chaotic Encryption Mechanisms

The technique for protecting information whether data or image is called cryptography. In cryptography a data or image is encrypted in such format that it hides the original message in a form which is different and on decryption gives the same message as aftermath. Thus, cryptography can also be referred as an art which is used to secure the data (text/image) by transforming it into a different form. The encrypted form of data or image is called cipher text or image. It is also important for an encryption technique to be efficient; it is said to be efficient if it has high robustness against attacks, has high security and it owes low correlation between encrypted and original image. An ample of survey papers and techniques could be found in literatures taking into account few approaches but still an unerring analysis is needed, that thoroughly takes into account a number of variety of approaches and draw comparison between them highlighting the multiple performance constraints under the variety of probable menaces. In this paper multiple approaches for conventional, modern, chaotic and quantum-chaotic are considered and an absolute examination has performed based on multiple performance constraints. To measure the effectiveness, the techniques are implemented on PYTHON-3.8.8. It is analyzed that the techniques based of Chaos and Quantum-Chaos are magnificent in comparison to others implemented in literatures under most of extensive menaces and also, they are able to resist brute force attacks as a large key size is provided by these approaches.
MIA-Leak: Exploring Membership Inference Attacks in Federated Learning Systems

Federated learning has achieved significant success in both academia and industry scenarios since it can train a joint model among unbalanced datasets while protecting the training data privacy. Recent research has shown that, by inferring whether a given data record belongs to the model’s training dataset, the membership information could be leaked by malicious participants. However, when deploying member inference attacks in federated learning, the core problem is how to obtain the membership inference attack data with the same distribution as the training data. In this paper, to tackle this problem, we mainly focus on exploring membership inference attacks in federated learning based on the data augmentation method. Specifically, we present two types of membership inference attacks based on the generative adversarial nets, in which a class-level attack aims to infer the global model and a user-level attack tries to focus on a specific victim. We conduct extensive experiments to evaluate the effectiveness of our proposed two types of membership inference attacks on two benchmark datasets. The experimental results have shown that both class-level and user-level attacks can achieve extraordinary attack accuracy on federated learning.
Blockchain Technology in Finance: A Literature Review

This article is a contribution to the studies carried out on blockchain technology in finance. We aim to review academic research in the field of financial technology. Our study presents and intensifies the opportunities and constraints of the implementation of blockchain technology in finance around the world. The article presents the results of a systematic literature review on blockchain technology, as advanced in articles published between 2017 and 2021. In the course of reviewing the articles, we found that blockchain technology brings several opportunities to corporate financial services, banks and institutions, despite the existence of several regulatory hurdles. This study contributes to the understanding of blockchain technology for industry, academia, and regulators.
The Use of Artificial Intelligence and Blockchain in Healthcare Applications: Introduction for Beginning Researchers

The healthcare sector is evolving daily thanks to fast evolution and rapid advancements. It benefits from novel technologies such as Artificial Intelligence (AI) and Blockchain. These technologies are causing significant revolutions in various areas of healthcare. This paper addresses the fundamentals and key concepts of AI and Blockchain and focuses on their main applications in healthcare. Then, it presents some research studies that use their integration. This work aims to demystify AI and Blockchain for readers. It will help especially beginning researchers to get started with these technologies and have an overview of their applications in healthcare.
CCC-Transformation: Novel Method to Secure Passwords Based on Hash Transformation

Authentication is one of the essential elements that constitutes the security of online users, and guarantees the protection of their confidential information against cyber-attacks, offering increasingly effective methods and algorithms. One of the forms that we encounter daily is the use of passwords to confirm identity; however, the most relevant challenge is to secure their storage and retrieval. Throughout this document, we will present a method of securing these passwords, which is based on the hash function and its advantage of guaranteeing data integrity. By applying a color-coded transformation of the generated hash, we paralyze the attacker to decipher the passwords despite the possession of access to the database and the application of the most known attacks such as the brute force and the Rainbow-table attack.
Smart Contracts: An Emerging Business Model in Decentralized Finance

Decentralized finance, also known as DeFi refers to an open and transparent alternative financial infrastructure based on blockchain technology; it has recently gained a lot of attention from experts from major banks, startups, and academia. The rapid evolution of blockchain technology is currently reshaping the financial landscape and allowing for the emergence of decentralized business models and platforms, such as decentralized currencies, payment services, and fundraising. One of the most innovative business models in decentralized finance is smart contracts, which have earned considerable attention across the financial industry thanks to their characteristics such as time efficiency, autonomy, immutability, and cost reduction. In this paper, we will first briefly introduce the concept of decentralized finance (DeFi) and blockchain technology. We will define the core functioning and innovative characteristics of Smarts contracts, and we will explore some of the potential areas of applications of smart contracts in the financial and banking industry. Finally, we will outline some of the key challenges and risks associated with smart contracts that could hamper their practical adoption. For the purpose of this paper, we conducted a thorough review of the current state of the literature, including the use of a pertinent and relevant scientific-based research database in order to identify the potential opportunities as well as challenges, and limitations associated with smart contracts adoption in the financial services industry.
Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study

In order to estimate daily solar radiation, this paper proposes Elman (ENN) and Feed forward backpropagation (FNN) neural networks. The time series data from the location of Kenitra City, Morocco is used to train the created models. Fletcher-Powell Conjugate Gradient (CGF), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquardt (LM), Polak-Ribi´ere Conjugate Gradient (CGP), and One Step Secant (OSS) are also used with both, the ENN and FNN to identify the best and effective training function for each. The models with various training algorithms are tested by using evaluation metrics root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The study showed that the Elman Neural Network had an excellent daily solar radiation prediction for Kenitra city. Good results have been obtained with Levenberg-Marquardt algorithm.
New Invariant Meixner Moments for Non-uniformly Scaled Images

Invariants of discrete orthogonal moments have been applied in several research fields of image processing and pattern recognition due to their ability of representing digital images. Generally, moment invariants are invariant only for image translation, rotation and uniform scaling. These moments are not invariant when an image is scaled non-uniformly in the $$x$$ - and $$y$$ -axes directions. In this paper, we propose a new set of discrete orthogonal moments namely Meixner moments, which are invariant when an image is scaled uniformly/non-uniformly. The proposed invariants are completely independent of scale factors unlike some existing invariant moments in the literature. The experimental results show the efficiency of the proposed descriptors.
Stoking the Flames of Conflict in Zimbabwe’s Electoral Terrain: Detecting and Reporting Hate Speech in Online Media

The chapter explores how hate speech was detected, framed and reported in Zimbabwe’s unregulated web-based online media outlets such as ZimEye, Zimbabwe Mail and New Zimbabwe. I used ( Noriega, C. & Iribarren, F. (2011) Quantifying hate speech on commercial Talk Radio: A Pilot Stu dy (CSRC Working Paper, Number 1). Ucla Chicano Studies, Research Centre.) categories of hate speech namely dehumanizing metaphors, flawed argumentation and divisive language to identify and analyse inflammatory language from the publications websites. The publications endorsed and amplified hate speech made by politicians from the ruling party, the Zimbabwe African National Union-Patriotic Front (ZANU PF) and the main opposition Citizens Coalition for Change (CCC). The politicians used false arguments, linguistic conventions such as name calling and metaphoric constructions of animals and insects to dehumanize their opponents. The reportages took place during Zimbabwe’s local and parliamentary by-elections held from March to October 2022, widely considered as a test run to the forthcoming 2023 general elections. Critical Discourse Analysis provided important insights to interrogate how meanings and perceptions were structured and shaped from hate speech. The study observed that the unregulated online media publications failed to uphold journalism ethics and were complicit in fanning political violence, when they quoted verbatim vitriol from politicians, without subjecting it to editorial gatekeeping processes. I showed that abusive and inflammatory hate speech published in the purposively sampled publications, compromised the human dignity of the victims and could have incited violence that broke out after incendiary speeches from politicians.
Microcontroller Based Edge Computing for Pipe Leakage Detection

In the embedded system field a correct resource management is crucial, especially in systems that use Machine Learning (ML) algorithms. The resources in that case are in terms of memory, footprint and time used to compute the tasks. The system should be able to be both accurate and compact although the precision is directly proportional to the memory used to storage data. In this paper we describe a comparison between three ML models implemented in a microcontroller, with an application scenario devoted to monitor a Water Distribution Network by using vibrations input and trying to investigate the computational complexity of each tested solution.
An Image Processing Algorithm to Optimize the Output Configuration of a Photonic Integrated Circuit

The interest in silicon photonics as a quantum enabling technology is rapidly growing, and Photonic Integrated Chips (PICs) have been proven to be a robust and viable solution in such research fields. As this technology applied to the quantum world is relatively young, some areas of interest remain uninspected, especially the control and output optimization. In this work, we propose an image processing tool to control and optimize a PIC based solely on images captured by a camera and without invasive output detectors. We tested this architecture on a Silicon Oxynitride (SiON) PIC where several Mach-Zehnder interferometers can be voltage driven by Titanium-Titanium Nitride (TiTiN) thermistors. By comparing the results of the image processing algorithm with those retrieved by silicon photodetectors on the same chip, we have proven that our approach can match or even outperform the traditional approach of sensing outputs with silicon photodetectors.
The Accomplishment of Quantum Key Distribution via BB84 Protocol with Optical Simulator

The paper presents a Quantum cryptography method QKD using BB84 protocol that transmits telecommunication data using optical fiber with a high level of security that prevents eavesdroppers from intercepting sensitive information due to its use of photon polarization states. Here Quantum Cryptography algorithms are used. Each quantum system generates correlated bits that allow a secret key to be generated between remote sites through quantum key distribution (QKD). When distributed over D-dimensional quantum channels, quantum key distribution offers significant security benefits that grow with D. In this case, we've used a four-channel BB84 protocol QKD Configuration.
A Secure Microgrid Power Transaction Scheme Based on Hyperledger Fabric

As part of the smart grid, distributed power trading based on clean energy generation has attracted a lot of attention and investment in recent years. This paper adopts the Hyperledger Fabric blockchain as the underlying framework, combined with key technologies such as smart contracts, blockchain oracles, bilateral auction mechanisms, and multi-signatures, and proposes a secure microgrid power transaction scheme based on Hyperledger Fabric. Different from the existing schemes, this paper considers the security and integrity issues that may arise in the transaction process. In terms of power trading, this scheme proposes two trading modes that can be carried out simultaneously and don’t affect each other, that is, trading based on predicted power and trading based on reserved power. In terms of transaction protection, the user credit and proof of work algorithm are introduced into the continuous double auction mechanism to solve the possible malicious bidding problem. In addition, the multi-signed address wallet is used to ensure the transaction security of users without a trusted center. Finally, the security and feasibility analysis of the paper proves the effectiveness of the scheme.
Deepening Branding Opportunities in VR-Based Metaverses. A Qualitative Study

Virtual reality-based metaverses are enabling brands to develop engaging approaches to consumers. Despite the growing interest in this innovative technology, the strand of studies about branding opportunities in VR-based metaverses is still in its infancy. Accordingly, our research aims to analyse three key aspects of branding strategies in VR-based metaverses: technical factors, perceptual dimensions, and branding outcomes. Through a qualitative exploratory approach based on in-depth interviews, our study contributes to the academic literature by providing the first holistic view of branding strategies in VR-based metaverses (high-immersive and low-immersive) from a managerial perspective.
Data Cooperatives for Trusted News Sharing in Social Media

The voluntary gathering and pooling of personal data by individuals via legal fiduciaries called data cooperatives is gaining a lot of attention as an approach to secure data management. Data cooperatives and blockchain are an excellent combination since they share fundamental features like decentralization and democratic design. In this paper, we leverage the power of blockchain to design a trusted news-sharing system for social media. We prove our concept by implementing a consumer news coop network on the Ethereum blockchain where members can voluntarily pool news information about their neighborhood for their benefit and also receive incentives in the form of an improved reputation for sharing credible news stories. We enforce honest behavior among the participants by implementing a trust and reputation scheme based on EigenTrust. Our results show that the blockchain approach to implementing a data cooperative is efficient with respect to memory consumption, scalability, and cost while also providing improved trust among participants. Furthermore, the reputation mechanism is effective in ensuring that malicious participants are severely penalized and removed from the system, while honest participants are rewarded. This approach can be used in a much bigger setup like Twitter so that the credibility of a shared post can be verified by a consensus before being shared on the network, thereby mitigating the spread of misinformation.
Scalable Smart Contracts for Linear Regression Algorithm

Linear regression algorithms capture information from previous experiences and build a cognitive model to forecast the future. The information and the cognitive model representing the history of predicting future outputs must be reliable so that expected results are trusted. Furthermore, the algorithms must be explainable and traceable, making the learning process meaningful and trackable. Blockchain smart contracts boost information integrity, providing trust and the provenance of distributed ledger transactions that support such requirements. Smart contracts are traditionally developed to perform simple transactions with integer operations. However, developing learning algorithms such as linear regression with smart contracts mandates complex computation involving floating-point operations, which are not supported by smart contracts. Moreover, smart contract transactions are expensive and time-consuming. In this work, we propose a novel implementation of smart contracts for linear regression algorithms with fraction-based computation that can train and predict on the Ethereum blockchain. Our smart contract-based training and prediction technique with Solidity programming language produced a similar mean square error to the scikit-learn-based prediction model. Moreover, our design strategy saves training costs for linear regression algorithms through off-chain computations with an optimistic roll-up solution. The off-chain training and on-chain prediction strategy demonstrated in our work will help academic and industry researchers to develop cost-effective distributed AI applications in the future.