Reviewerly/exHarmony huggingface dataset:
Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation

Quantum cryptography is a well-stated field within quantum applications where quantum information is used to set secure communications, authentication, and secret keys. Now used in quantum devices with those purposes, particularly Quantum Key Distribution (QKD), which proposes a secret key between two parties free of effective eavesdropping, at least at a higher level than classical cryptography. The best-known quantum protocol to securely share a secret key is the BB84 one. Other protocols have been proposed as adaptations of it. Most of them are based on the quantum indeterminacy for non-orthogonal quantum states. Their security is commonly based on the large length of the key. In the current work, a BB84-like procedure for QKD based on double quantum teleportation allows the sharing of the key statement using several parties. Thus, the quantum bits of information are assembled among three parties via entanglement, instead of travelling through a unique quantum channel as in the traditional protocol. Asymmetry in the double teleportation plus post-measurement retains the secrecy in the process. Despite requiring more complex control and resources, the procedure dramatically reduces the probability of success for an eavesdropper under individual attacks, because of the ignorance of the processing times in the procedure. Quantum Bit Error Rate remains in the acceptable threshold and it becomes configurable. The article depicts the double quantum teleportation procedure, the associated control to introduce the QKD scheme, the analysis of individual attacks performed by an eavesdropper, and a brief comparison with other protocols.
Chirotonia: A Scalable and Secure e-Voting Framework based on Blockchains and Linkable Ring Signatures

In this paper we propose a comprehensive and scalable framework to build secure-by-design e-voting systems. Decentralization, transparency, determinism, and untamperability of votes are granted by dedicated smart contracts on a blockchain, while voter authenticity and anonymity are achieved through (provable secure) linkable ring signatures. These, in combination with suitable smart contract constraints, also grant protection from double voting. Our design is presented in detail, focusing on its security guarantees and the design choices that allow it to scale to a large number of voters. Finally, we present a proof-of-concept implementation of the proposed framework, made available as open source.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search

Neural Architecture Search (NAS) has attracted growing interest. To reduce the search cost, recent work has explored weight sharing across models and made major progress in One-Shot NAS. However, it has been observed that a model with higher one-shot model accuracy does not necessarily perform better when stand-alone trained. To address this issue, in this paper, we propose Progressive Automatic Design of search space, named PAD-NAS. Un-like previous approaches where the same operation search space is shared by all the layers in the supernet, we formulate a progressive search strategy based on operation pruning and build a layer-wise operation search space. In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity. During the search, we also take the hardware platform constraints into consideration for efficient neural network model deployment. Extensive experiments on ImageNet show that our method can achieve state-of-the-art performance.
A Drive Through Computer-Aided Diagnosis of Breast Cancer: A Comprehensive Study of Clinical and Technical Aspects

Breast cancer is a very common and life-threatening disease in women worldwide. The number of breast cancer cases is increasing with time. Prevention of this disease is very challenging and still remains a question at large, but if detected in advance, the survival rate can be increased. The advances in deep learning have demonstrated a lot of changes in the development of Computer-Aided Diagnosis (CAD) of breast cancer. With the noteworthy progress of the new development of artificial intelligence which is deep neural networks, the diagnostic potentialities of deep learning methods are closely approaching the expertise of a human. Although deep learning has substantial improvements and advancements, especially Convolutional Neural Networks (CNN), there are still some challenges that are required to be addressed to build an effective CAD system that can serve as a “second opinion” tool for practitioners. A comprehensive review of clinical aspects of breast cancer like risk factors, breast abnormalities, and BIRADS (Breast Imaging Reporting and Data System) is presented in the paper. This paper also presents CAD systems that are recently developed for breast cancer segmentation, detection, and classification. An overview of mammography datasets used in literature and challenges in applying CNN for medical images are also discussed in the paper.
All-Higher-Stages-In Adaptive Context Aggregation for Semantic Edge Detection

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Semantic Cameras for 360-Degree Environment Perception in Automated Urban Driving

The European UP-Drive project addresses transportation-related challenges by providing key contributions that enable fully automated vehicle navigation and parking in complex urban areas, which results in a safer, inclusive, affordable and environmentally friendly transportation system. For this purpose, the project consortium developed a prototype electrical vehicle equipped with cameras and LiDARs sensors that is capable to autonomously drive around the city and find available parking spots. In UP-Drive, we created an accurate, robust and redundant multi-modal environment perception system that provides 360° coverage around the vehicle. This paper summarizes the work of the project related to the surround view semantic perception using fisheye and narrow field-of-view semantic virtual cameras. Deep learning-based semantic, instance and panoptic segmentation networks, which satisfy requirements in accuracy and efficiency have been developed and integrated into the final prototype. The UP-Drive automated vehicle has been successfully demonstrated in urban areas after extensive experiments and numerous field tests.
A comprehensive Turkish accent/dialect recognition system using acoustic perceptual formants

Accent or dialect is one of the hot topics of emerging technology in speech processing. In an audio recording, extracting accent clues from a speech signal can help investigators to have an idea about where speaker is from. It is mostly used for detecting regional origin/ethnicity of speakers in real-time surveillance systems as well as demographic researches about changes in human sounds by geography. In this work, Turkish language accent analysis was performed using formant frequencies (F1, F2 and F3) of vowels. We divided our work into two approaches which are statistical and classification. In both of them, evaluations were done by splitting Turkey's map in to 2 and 3 dialect regions virtually. Totally 112 monolingual university students (72 males, 40 females) have uttered 103 meaningful Turkish syllables. Because formant frequencies can vary depending on genders, males and females were evaluated separately in both statistical and classification analysis. The result surprisingly showed that especially the isolated vowel 'e' is able to classify a male speaker which was pre-known to be from Mediterranean or Eastern Anatolia regions with an accuracy of 90% using KNN classifier.
Communication-Efficient Federated Edge Learning via Optimal Probabilistic Device Scheduling

Federated edge learning (FEEL) is a popular distributed learning framework that allows privacy-preserving collaborative model training via periodic learning-updates communication between edge devices and server. Due to the constrained bandwidth, only a subset of devices can be selected to upload their updates at each training iteration. This has led to an active research area in FEEL studying the optimal device scheduling policy for communication time minimization. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially and indirectly by minimizing either the iteration rounds or per-round latency, while the total communication time is determined by both metrics. To close this research gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis. Building on the novel analytical result, an optimized probabilistic device scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. Extensive experiments based on real-world dataset and a use case on collaborative 3D objective detection in autonomous driving are provided to verify the superiority of the proposed policy over three benchmark policies based on the indirect solution approaches.
Comparative Analysis of Ml Algorithms &amp; Stream Lit Web Application

Prediction of Approval of Consumer Personal Loans Applications using different Machine Learning (ML) Algorithms and convert it into a web app by using stream lit library in a very simple and efficient way. Out of the available ML algorithms a comparative analysis is performed to check which one proves out to be the best possible with maximum accuracy. This project basically provides the assumption or probability of approval or denial of the personal loan any candidate applies for. As soon as the customer provides in his or her details and all domains that are required. Various models are used to test whether the given details of the customer are okay to provide loan or not. If yes, it displays a message" loan approved" and further functionalities can be done. Else it displays a message as "Loan denied". Understanding of Test and Train Data. Implementation of steps like Feature Engineering, Feature Selection, Exploratory Data Analysis (EDA), Model Training etc. Usage of different models to train the data like below. LR, RF, XGB. Compares the accuracy from different models. The customer is asked to fill in the details required whoever wishes to apply for the loan. According to the given data if it’s suitable and all the conditions are satisfied, the user gets a message related approval of the loan else it displays a message of denial.
A recurrent wavelet-based brain emotional learning network controller for nonlinear systems

Conventional control systems often suffer from the coexistence of nonlinearity and uncertainty. This paper proposes a novel brain emotional neural network to support addressing such challenges. The proposed network integrates a wavelet neural network into a conventional brain emotional learning network. This is further enhanced by the introduction of a recurrent structure to employ the two networks as the two channels of the brain emotional learning network. The proposed network therefore combines the advantages of the wavelet function, the recurrent mechanism, and the brain emotional learning system, for optimal performance on nonlinear problems under uncertain environments. The proposed network works with a bounding compensator to mimic an ideal controller, and the parameters are updated based on the laws derived from the Lyapunov stability analysis theory. The proposed system was applied to two uncertain nonlinear systems, including a Duffing-Homes chaotic system and a simulated 3-DOF spherical joint robot. The experiments demonstrated that the proposed system outperformed other popular neural-network-based control systems, indicating the superiority of the proposed system.
Non-Autoregressive ASR Modeling Using Pre-Trained Language Models for Chinese Speech Recognition

Transformer-based models have led to significant innovation in classical and practical subjects as varied as speech processing, natural language processing, and computer vision. On top of the Transformer, attention-based end-to-end automatic speech recognition (ASR) models have recently become popular. Specifically, non-autoregressive modeling, which boasts fast inference and performance comparable to conventional autoregressive methods, is an emerging research topic. In the context of natural language processing, the bidirectional encoder representations from Transformers (BERT) model has received widespread attention, partially due to its ability to infer contextualized word representations and to enable superior performance for downstream tasks while needing only simple fine-tuning. Motivated by the success, we intend to view speech recognition as a downstream task of BERT, thus an ASR system is expected to be deduced by performing fine-tuning. Consequently, to not only inherit the advantages of non-autoregressive ASR models but also enjoy the benefits of a pre-trained language model (e.g., BERT), we propose a non-autoregressive Transformer-based end-to-end ASR model based on BERT. We conduct a series of experiments on the AISHELL-1 dataset that demonstrate competitive or superior results for the model when compared to state-of-the-art ASR systems.
Privacy and Healthcare During COVID-19

The ambiguity related to transmission of disease in humans and the lack of health infrastructure to tackle the needs and shortage of healthcare providers has resulted in adoption of new means to tackle COVID-19. Technologies were used for contact tracing, case identification, population surveillance, and evaluations. Such rapid responses have leveraged large data sets, connected devices, machine learning, deep learning, and natural language processing. A huge amount of data collected by the processes for healthcare led to many privacy breach incidents. This chapter aims to capture the digital innovations to healthcare during COVID-19 like high user uptake, integration with existing system, ability for impact analysis, notification to agencies, and quarantine for infectious people. Privacy concerns of these healthcare innovations related to location, biometrics, personal information, and comorbidities have been identified. The chapter also presents taxonomies of privacy related to online health collaboration which increased during the COVID-19 era.
An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.
A lightweight data representation for phishing URLs detection in IoT environments

Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). Hence, it is advantageous to detect a phishing attack by analyzing URLs before accessing them. With the raising of the Internet of Things (IoT), phishing attacks are moving to this field due to the number of IoT devices and the amount of personal information they handle. Although several approaches were proposed for phishing attacks detection, the URL-based Machine Learning approaches obtain better performance results, but all of them are dependent on the feature set used. Contradictorily, only a few works on selecting the best-suited feature set for improving the phishing detection process have been published. The present research explores how to obtain a feature set that substantially enhances the phishing detection rate in IoT environments. Hence, a feature selection algorithm was adopted and extended for getting the most representative feature set. When Random Forest is used with the proposed data representation, the phishing URL attacks discovery rate is 99.57%.
Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Graph neural networks take node features and graph structure as input to build representations for nodes and graphs. While there are a lot of focus on GNN models, understanding the impact of node features and graph structure to GNN performance has received less attention. In this paper, we propose an explanation for the connection between features and structure: graphs can be constructed by connecting node features according to a latent function. While this hypothesis seems trivial, it has several important implications. First, it allows us to define graph families which we use to explain the transferability of GNN models. Second, it enables application of GNNs for featureless graphs by reconstructing node features from graph structure. Third, it predicts the existence of a latent function which can create graphs that when used with original features in a GNN outperform original graphs for a specific task. We propose a graph generative model to learn such function. Finally, our experiments confirm the hypothesis and these implications.
The IoT and the new EU cybersecurity regulatory landscape

This article aims to cast light on how the fast-evolving European cybersecurity regulatory framework would impact the Internet of Things (IoT) domain. The legal analysis investigates whether and to what extent existing and proposed sectoral EU legislation addresses the manifold challenges in securing IoT and its supply chain. It firstly takes into account the Cybersecurity Act, being the most recent and relevant EU legal act covering ICT products and cybersecurity services. Then, EU product legislation is scrutinised. The analysis focuses on the delegated act recently adopted by the Commission under the Radio Equipment Directive (RED), strengthening wireless devices’ cybersecurity, the Medical Devices Regulation, the Proposal for a General Product Safety Regulation and the Proposal for a Machinery Regulation. Lastly, the proposal for a revised Network and Information Systems Directive (NIS2) is assessed in terms of its potential impact on the field of IoT cybersecurity. Against this backdrop, the article concludes by advocating the need for a separate horizontal legislation on cybersecurity for connected products. To avoid fragmentation of the EU's Single Market, a horizontal legal act should be based on the principles of the New Legislative Framework, with ex-ante and ex-post cybersecurity requirements for all IoT sectors and products categories.
The convergence of social media and other communication technologies in the promotion of illicit and controlled drugs

Some social media platforms have strict regulations regarding the promotion of illicit and controlled drug on their sites. This study attempts to examine whether social media outlets like Twitter, Flickr and Tumblr have implemented practical measures to stop the active promotion of such drugs. We examined over 2.6 million social media posts taken from these three platforms. By focusing on keyword searches around mobile apps and communication means, we found evidence of ongoing opioid drug promotion, especially on Twitter followed by Flickr and Tumblr; we discuss our approach which effectively identifies posts related to the promotion of opioids and controlled drugs.
An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.