Reviewerly/exHarmony huggingface dataset:
Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (classical computer), digital annealing (Fujitsu’s Digital Annealing Unit), and quantum annealing (D-Wave Advantage). Starting from Markowitz’s theory on portfolio optimization, our classical preprocessing step finds the most promising assets within a set of possible assets to choose from. We then modify existing QUBO models for portfolio optimization, such that there are no limitations on the number of assets that can be invested in. Furthermore, our QUBO model enables an investor to also place an arbitrary amount of money into each asset. We apply this modified QUBO to the set of promising asset candidates we generated previously via classical preprocessing. A solution to our QUBO model contains information about what percentage of the whole available capital should be invested into which asset. For the evaluation, we have used publicly available real-world data sets of stocks of the New York Stock Exchange as well as common ETFs. Finally, we have compared the respective annealing results with randomly generated portfolios by using the return, variance, and diversification of the created portfolios as measures. The results show that our QUBO formulation is capable of creating well-diversified portfolios that respect certain criteria given by an investor, such as maximizing return, minimizing risk, or sticking to a certain budget.
Revisiting QUIC attacks: a comprehensive review on QUIC security and a hands-on study

Abstract Built on top of UDP, the recently standardized QUIC protocol primarily aims to gradually replace the TCP plus TLS plus HTTP/2 model. For instance, HTTP/3 is designed to exploit QUIC’s features, including reduced connection establishment time, multiplexing without head of line blocking, always-encrypted end-to-end security, and others. This work serves two key objectives. Initially, it offers the first to our knowledge full-fledged review on QUIC security as seen through the lens of the relevant literature so far. Second and more importantly, through extensive fuzz testing, we conduct a hands-on security evaluation against the six most popular QUIC-enabled production-grade servers. This assessment identified several effective and practical zero-day vulnerabilities, which, if exploited, can quickly overwhelm the server resources. This finding is a clear indication that the fragmented production-level implementations of this contemporary protocol are not yet mature enough. Overall, the work at hand provides the first wholemeal appraisal of QUIC security from both a literature review and empirical standpoint, and it is therefore foreseen to serve as a reference for future research in this timely area.
Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT

The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.
Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information

With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with huge variations in the appearance of objects and complex backgrounds have opened a new direction of work for researchers. The task of semantic segmentation becomes more challenging when capturing inherent features in the global and local context for UAV images. In this paper, we proposed a transformer-based encoder-decoder architecture to address this issue for the precise segmentation of UAV images. The inherent feature representation of the UAV images is exploited in the encoder network using a self-attention-based transformer framework to capture long-range global contextual information. A Token Spatial Information Fusion (TSIF) module is proposed to take advantage of a convolution mechanism that can capture local details. It fuses the local contextual details about the neighboring pixels with the encoder network and makes semantically rich feature representations. We proposed a decoder network that processes the output of the encoder network for the final semantic level prediction of each pixel. We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61.93% and 73.65%, respectively.
True Random Number Generation Capability of a Ring Oscillator PUF for Reconfigurable Devices

This paper presents the validation of a novel approach for a true-random number generator (TRNG) based on a ring oscillator–physical unclonable function (RO-PUF) for FPGA devices. The proposal takes advantage of the different noise sources that affect the electronic implementation of the RO-PUF to extract the entropy required to guarantee its function as a TRNG, without anything more than minimal changes to the original design. The new RO-PUF/TRNG architecture has been incorporated within a hybrid HW/SW embedded system designed for devices from the Xilinx Zynq-7000 family. The degree of randomness of the generated bit streams was assessed using the NIST 800-22 statistical test suite, while the validation of the RO-PUF proposal as an entropy source was carried out by fulfilling the NIST 800-90b recommendation. The features of the hybrid system were exploited to carry out the evaluation and validation processes proposed by the NIST publications, online and on the same platform. To establish the optimal configuration to generate bit streams with the appropriate entropy level, a statistical study of the degree of randomness was performed for multiple TRNG approaches derived from the different implementation modes and configuration options available on the original RO-PUF design. The results show that the RO-PUF/TRNG design is suitable for secure cryptographic applications, doubling its functionality without compromising the resource–efficiency trade-off already achieved in the design.
Anomaly Detection Based on Time Series Data of Hydraulic Accumulator

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.
Writer Retrieval and Writer Identification in Greek Papyri

The analysis of digitized historical manuscripts is typically addressed by paleographic experts. Writer identification refers to the classification of known writers while writer retrieval seeks to find the writer by means of image similarity in a dataset of images. While automatic writer identification/retrieval methods already provide promising results for many historical document types, papyri data is very challenging due to the fiber structures and severe artifacts. Thus, an important step for an improved writer identification is the preprocessing and feature sampling process. We investigate several methods and show that a good binarization is key to an improved writer identification in papyri writings. We focus mainly on writer retrieval using unsupervised feature methods based on traditional or self-supervised-based methods. It is, however, also comparable to the state of the art supervised deep learning-based method in the case of writer classification/re-identification.
An investigation of Hertzian contact in soft materials using photoelastic tomography

Hertzian contact of a rigid sphere and a highly deformable soft solid is investigated using integrated photoelasticity. The experiments are performed by pressing a styrene sphere of 15 mm diameter against a 44 × 44 × 47 mm3 cuboid made of 5% wt. gelatin, inside a circular polariscope, and with a range of forces. The emerging light rays are processed by considering that the retardation of each ray carries the cumulative effect of traversing the contact-induced axisymmetric stress field. Then, assuming Hertz's theory is valid, the retardation is analytically calculated for each ray and compared to the experimental one. Furthermore, a finite element model of the process introduces the effect of finite displacements and strains. Beyond the qualitative comparison of the retardation fields, the experimental, theoretical, and numerical results are quantitatively compared in terms of the maximum equivalent stress, surface displacement, and contact radius dimensions. A favorable agreement is found at lower force levels, where the assumptions of Hertz theory hold, whereas deviations are observed at higher force levels. A major discovery of this work is that, at the maximum equivalent stress location, all three components of principal stress can be determined experimentally and show satisfactory agreement with theoretical and numerical ones in our measurement range. This provides valuable insight into Hertzian contact problems since the maximum equivalent stress controls the initiation of plastic deformation or failure. The measured displacement and contact radii also reasonably agree with the theoretical and numerical ones. Finally, the limitations that arise due to the linearization of this problem are explored.
Self voting classification model for online meeting app review sentiment analysis and topic modeling

Online meeting applications (apps) have emerged as a potential solution for conferencing, education and meetings, etc. during the COVID-19 outbreak and are used by private companies and governments alike. A large number of such apps compete with each other by providing a different set of functions towards users' satisfaction. These apps take users' feedback in the form of opinions and reviews which are later used to improve the quality of services. Sentiment analysis serves as the key function to obtain and analyze users' sentiments from the posted feedback indicating the importance of efficient and accurate sentiment analysis. This study proposes the novel idea of self voting classification (SVC) where multiple variants of the same model are trained using different feature extraction approaches and the final prediction is based on the ensemble of these variants. For experiments, the data collected from the Google Play store for online meeting apps were used. Primarily, the focus of this study is to use a support vector machine (SVM) with the proposed SVC approach using both soft voting (SV) and hard voting (HV) criteria, however, decision tree, logistic regression, and k nearest neighbor have also been investigated for performance appraisal. Three variants of models are trained on a bag of words, term frequency-inverse document frequency, and hashing features to make the ensemble. Experimental results indicate that the proposed SVC approach can elevate the performance of traditional machine learning models substantially. The SVM obtains 1.00 and 0.98 accuracy scores, using HV and SV criteria, respectively when used with the proposed SVC approach. Topic-wise sentiment analysis using the latent Dirichlet allocation technique is performed as well for topic modeling.
Comprehensive review of watermarking techniques in deep-learning environments

Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information—especially in the form of images—from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.
Multipair-free source of entangled photons in the solid state

We investigate the effect of multiphoton emission on polarization-entangled photon pairs from a coherently driven quantum dot by comparing quantum state tomography and second-order autocorrelation measurements as a function of the excitation power. We observe that the relative (absolute) multiphoton emission probability is as low as ${p}_{m}\phantom{\rule{4pt}{0ex}}=(5.6\ifmmode\pm\else\textpm\fi{}0.6)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}[{p}_{2}=(1.5\ifmmode\pm\else\textpm\fi{}0.3)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}]$ at the maximum source brightness, with a negligible effect on the degree of entanglement. In contrast with probabilistic sources of entangled photons, the multiphoton emission probability and the degree of entanglement remain practically unchanged against the excitation power over multiple Rabi cycles, while observing oscillations in the second-order autocorrelation function by more than one order of magnitude. Our results, explained by a model which links the second-order autocorrelation function to the actual multiphoton contribution in the two-photon density matrix, highlight that quantum dots can be regarded as a multipair-free source of entangled photons in the solid state.
Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high-quality features, which can result in very long processing times. Therefore, this paper proposes a new K-means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU's threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini-MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.
Smart Contract-Based Secure Decentralized Smart Healthcare System

Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.
Quantum error mitigation by hidden inverses protocol in superconducting quantum devices <sup>*</sup>

Abstract We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H <?CDATA ${}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow /> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.
Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification

Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across dispersed clients under such privacy-preserving constraints. Although there is a good chance of success, dealing with non-IID (non-independent and identical distribution) client data, which is a typical circumstance in real-world FL tasks, is still difficult for FL. We use two FL algorithms, FedAvg and FedProx, to manage client heterogeneity and non-IID data in a federated setting. A heterogeneous data split of the cancer datasets with three different forms of cancer—cervical, lung, and colon—is used to validate the efficacy of the FL. In addition, since hyperparameter optimization presents new difficulties in an FL setting, we also examine the impact of various hyperparameter values. We use Bayesian optimization to fine-tune the hyperparameters and identify the appropriate values in order to increase performance. Furthermore, we investigate the hyperparameter optimization in both local and global models of the FL environment. Through a series of experiments, we find that FedProx outperforms FedAvg in scenarios with significant levels of heterogeneity.
Locally finite distance clustering with discriminative information

Partition-based clustering methods, such as point center-based and plane center-based clustering techniques, have drawn much attention due to their simplicity and effectiveness in general clustering tasks. However, most of these methods use an unbounded distance during clustering, which may cause their performance to be sensitive to the defined infinite measure, and almost all of them cannot automatically identify halos. To solve these problems, by adopting a locally finite capped l2,1-norm distance in clustering, this paper proposes a novel clustering method named locally finite distance clustering with discriminative information (LFDC). The LFDC effectively solves the above problems and realizes robust clustering by solving a series of eigenvalue problems. We test the effectiveness of the LFDC on a number of different data, including artificial data, benchmark data, and image segmentation data. The experimental results show that the LFDC is more robust than the compared methods.
Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.