Reviewerly/exHarmony huggingface dataset:
A Key-Recovery Side-Channel Attack on Classic McEliece Implementations

In this paper, we propose the first key-recovery side-channel attack on Classic McEliece, a KEM finalist in the NIST Post-quantum Cryptography Standardization Project. Our novel idea is to design an attack algorithm where we submit special ciphertexts to the decryption oracle that correspond to cases of single errors. Decoding of such ciphertexts involves only a single entry in a large secret permutation, which is part of the secret key. Through an identified leakage in the additive FFT step used to evaluate the error locator polynomial, a single entry of the secret permutation can be determined. Iterating this for other entries leads to full secret key recovery. The attack is described using power analysis both on the FPGA reference implementation and a software implementation running on an ARM Cortex-M4. We use a machine-learning-based classification algorithm to determine the error locator polynomial from a single trace. The attack is fully implemented and evaluated in the Chipwhisperer framework and is successful in practice. For the smallest parameter set, it is using about 300 traces for partial key recovery and less than 800 traces for full key recovery, in the FPGA case. A similar number of traces are required for a successful attack on the ARM software implementation.
Toward interactive bug reporting for (android app) end-users

Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing websites, issue trackers, or in-app built-in bug reporting tools, if available. While these systems have various features that facilitate bug reporting (e.g., textual templates or forms), they often provide limited guidance, concrete feedback, or quality verification to end-users, who are often inexperienced at reporting bugs and submit low-quality bug reports that lead to excessive developer effort in bug report management tasks. We propose an interactive bug reporting system for end-users (Burt), implemented as a task-oriented chatbot. Unlike existing bug reporting systems, Burt provides guided reporting of essential bug report elements (i.e., the observed behavior, expected behavior, and steps to reproduce the bug), instant quality verification, and graphical suggestions for these elements. We implemented a version of Burt for Android and conducted an empirical evaluation study with end-users, who reported 12 bugs from six Android apps studied in prior work. The reporters found that Burt's guidance and automated suggestions/clarifications are useful and Burt is easy to use. We found that Burt reports contain higher-quality information than reports collected via a template-based bug reporting system. Improvements to Burt, informed by the reporters, include support for various wordings to describe bug report elements and improved quality verification. Our work marks an important paradigm shift from static to interactive bug reporting for end-users.
The goat search algorithms

This article develops an evolutionary nature inspired algorithm based on the social behavior of the goat, a pet of a farmer in a village life. In village life, we generally see the shepherds keep their goats free/untie from collar thread for grazing in the early morning and receives them at the end of the day when they come back into the home with their own efforts. But some day the goats did not come back in due time because of overfeeding of grass causing unable to move any more after meeting their grasp and began to get rest there. The shepherd feels more tempted and began to search for his/her goat. After untie, the goat began to graze herself through the walk on the path of the cultivated land and bank of the village ponds. The search process is going on through that path until it is not finally got. To characterize this problem some definitions like false walk, uniform and non-uniform steps, goat’s jump, periodic walk and goodness of fit for various walk functions have been discussed here rigorously. Inspiring from this fact novel metaheuristic algorithms along with pseudocode and hardware specification have been discussed to optimize a benchmark multi-modal objective function having some singularity zones explicitly. Numerical results have been compared with some of the existing state- of -arts under 95% confidence intervals. Also, graphical illustrations are performed to validate the proposed approach. Finally, a conclusion is made followed by scope of future work.
Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain

The advancement of Internet and Communication Technologies (ICTs) has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of IoT-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in Artificial Intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This paper presents a secure multi-party computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.
Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

Malaria is a febrile illness caused by a parasite called plasmodium. This life-threatening disease is preventable and treatable if diagnosed early. The World Health Organization aims to reduce the global malaria incidence and death rates by at least 90% until 2030. This disease is diagnosed by visually analyzing red blood cells with a microscope by experienced radiologists. Therefore, this situation may be erroneous due to subjective interpretations. In this study, red blood cells were trained with deep learning–based convolutional neural networks to diagnose malaria, and thus, their deep features were obtained. These obtained features are also trained with autoencoder networks. Thus, the chi-square feature selection algorithm was used to obtain distinctive features. Finally, the unique feature set obtained is given as an introduction to machine learning algorithms, and then a unique diagnostic model is proposed. As a result, 100% accuracy rate was obtained. The results are promising for the diagnosis of malaria disease.
Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Abstract Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration

The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission.
IT/OT convergence and cyber security

A study by Forrester, commissioned by Fortinet, reveals the growing exposure of industry players to cyberthreats – one of the consequences of digital transformation. 1 The lack of collaboration between IT teams and those in charge of industrial or operational technology (OT) is also a hindrance to cyber security for companies wishing to take full advantage of IT/OT convergence to increase their competitiveness.
Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits

Although near-term quantum computing devices are still limited by the quantity and quality of qubits in the so-called NISQ era, quantum computational advantage has been experimentally demonstrated. Moreover, hybrid architectures of quantum and classical computing have become the main paradigm for exhibiting NISQ applications, where low-depth quantum circuits are repeatedly applied. In order to further scale up the problem size solvable by the NISQ devices, it is also possible to reduce the number of physical qubits by "cutting" the quantum circuit into different pieces. In this work, we experimentally demonstrated a circuit-cutting method for simulating quantum circuits involving many logical qubits, using only a few physical superconducting qubits. By exploiting the symmetry of linear-cluster states, we can estimate the effectiveness of circuit-cutting for simulating up to 33-qubit linear-cluster states, using at most 4 physical qubits for each subcircuit. Specifically, for the 12-qubit linear-cluster state, we found that the experimental fidelity bound can reach as much as 0.734, which is about 19% higher than a direct implementation on the same 12-qubit superconducting processor. Our results indicate that circuit-cutting represents a feasible approach of simulating quantum circuits using much fewer qubits, while achieving a much higher circuit fidelity.
Applications of metaverse-related technologies in the services of US urban libraries

Purpose The purpose of this paper is to understand the applications of metaverse-related technologies in US urban libraries, explore excellent cases of US urban libraries' practices in using metaverse-related technologies to serve patrons and try to find the factor that may affect the application of metaverse-related technologies in libraries at this stage. Design/methodology/approach To gather information about how and whether libraries use metaverse-related technologies such as three-dimensional (3D) technology, radio frequency identification (RFID), virtual reality augmented reality (AR) and artificial intelligence (AI) in their services. Firstly, the authors visit 150 US urban library websites that are members of the Urban Libraries Council. Secondly, the authors calculate the proportion of services provided by urban libraries that use metaverse-related technologies and introduce outstanding cases. Lastly, the authors discuss the factors that influence the application of metaverse-related technologies in urban libraries in the United States based on data published by the Institute of Museum and Library Services. Findings Metaverse-related technologies have been widely used in US urban libraries, but there are differences in the popularity of the applications of different technologies. In all, 84% of libraries use 3D technology, mainly in 3D printing services and 3D model building services; 76% of libraries use virtual and augmented reality technologies in their services, mainly concentrated in head-mounted VR device experiences, AR device experiences, virtual tours and virtual exhibitions; 62% of libraries use Internet of things (IoT) technology, mainly in self-checkout machines and book location services. However, AI technologies are less used in libraries, with 28% of libraries mentioning the applications of AI in their services, mainly focusing on intelligent search, virtual assistants and robot librarians. In addition, this study finds that library operating expenditures and population served do not affect the application of metaverse-related technologies in libraries. Originality/value This paper provides updated statistical data on the use of metaverse-related technologies in US urban libraries and aims to help library managers understand the overall applications and best practices. With this as an inspiration, they could formulate corresponding development plans to better serve their communities with metaverse-related technologies.
Machine learning in precision diabetes care and cardiovascular risk prediction

Abstract Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
FSDE-Forced Strategy Differential Evolution used for data clustering

Differential evolution algorithm has seen various changes through numerous researches. Performance of the various algorithms depends on the changes in mutation and crossover strategies. Here in this paper, we are proposing a new variant of differential evolution named Forced Strategy Differential Evolution (FSDE), by creating a new mutation strategy. This strategy uses two parameters for mutation: a constant parameter and a variable parameter. FSDE will be applied on clustering using the k means technique. Experiments were conducted for various standard benchmark functions. FSDE was compared with the classical DE, GA and PSO in the field of clustering and the cluster quality results are tabulated. The results obtained show that the strategy implemented is more efficient than the other mutation strategies.
Design and Implementation of the Ascend Secure Processor

This paper presents post-silicon results for the Ascend secure processor, taped out in a 32 nm SOI process. Ascend prevents information leakage over a processor's digital I/O pins—in particular, the processor's requests to external memory—and certifies the program's execution by verifying the integrity of the external memory. In secure processor design, encrypting main memory is not sufficient for security because <i>where</i> and <i>when</i> memory is accessed reveals secret information. To this end, Ascend is equipped with a hardware Oblivious RAM (ORAM) controller, which obfuscates the address bus by reshuffling memory as it is accessed. To our knowledge, Ascend is the first prototyping of ORAM in custom silicon. Ascend has also been carefully engineered to ensure its timing behaviors are independent of user private data. In 32 nm silicon, all security components combined (the ORAM controller, which includes 12 AES rounds and one SHA-3 hash unit) impose a moderate area overhead of 0.51 mm <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> . Post tape-out, the security components of the Ascend chip have been successfully tested at 857 MHz and 1.1 V, at which point they consume 299 mW of power.
Twin Neural Networks for the classification of large unbalanced datasets

Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets. The Twin NN also learns an optimal feature map, allowing for better discrimination between classes. We also present an extension of this network architecture for multiclass datasets. Results presented in the paper demonstrate that the Twin NN generalizes well and scales well on large unbalanced datasets.
Quantum-Fourier-transform-based quantum arithmetic with qudits

We present some basic integer arithmetic quantum circuits, such as adders and multipliers-accumulators of various forms, as well as diagonal operators, which operate on multilevel qudits. The integers to be processed are represented in an alternative basis after they have been Fourier transformed. Several arithmetic circuits operating on Fourier transformed integers have appeared in the literature for two level qubits. Here we extend these techniques on multilevel qudits, as they may offer some advantages relative to qubits implementations. The arithmetic circuits presented can be used as basic building blocks for higher level algorithms such as quantum phase estimation, quantum simulation, quantum optimization etc., but they can also be used in the implementation of a quantum fractional Fourier transform as it is shown in a companion work presented separately.
The use of politeness strategies in the classroom context by English university students

Politeness still becomes a major concern in English language teaching. It is considered as one way to maintain effective classroom interaction. Therefore, as one of the important actors in the class, teachers, and students need to practice politeness as a way to create effective classroom interaction. This study aims to explore the politeness strategies of English students at one of the universities in Makassar. The researcher applied a descriptive qualitative research method to explore the politeness phenomena in EFL classroom interaction. The participants of this research were two classes of English literature program consisting of 50 students. The primary sources of data were the individual student presentations which had been recorded. There were fifty transcriptions of the recording which lasted for five to seven minutes for each presentation. The transcriptions were analyzed and discussed based on the theory of politeness of Brown and Levinson (1987). The findings from this study revealed that English students used different kinds of expressions to encode their politeness in the class. Those expressions were in the forms of greetings, thanking, addressing terms, apologizing, and fillers. There were also some terms derived from students’ vernacular language which were used as a softening mechanism for their presentation. These expressions were categorized as positive and negative politeness. The findings of this study might be used as an input for teachers and students in an effort to create effective classroom interaction.
Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Adaptive Shape Priors Extracted From Contour Fragments

We present a novel approach for segmenting overlapping cytoplasm of cells in cervical smear images by leveraging the adaptive shape priors extracted from cytoplasm's contour fragments and shape statistics. The main challenge of this task is that many occluded boundaries in cytoplasm clumps are extremely difficult to be identified and, sometimes, even visually indistinguishable. Given a clump where multiple cytoplasms overlap, our method starts by cutting its contour into a set of contour fragments. We then locate the corresponding contour fragments of each cytoplasm by a grouping process. For each cytoplasm, according to the grouped fragments and a set of known shape references, we construct its shape and, then, connect the fragments to form a closed contour as the segmentation result, which is explicitly constrained by the constructed shape. We further integrate the intensity and curvature information, which is complementary to the shape priors extracted from contour fragments, into our framework to improve the segmentation accuracy. We propose to iteratively conduct fragments grouping, shape constructing, and fragments connecting for progressively refining the shape priors and improving the segmentation results. We extensively evaluate the effectiveness of our method on two typical cervical smear datasets. The experimental results demonstrate that our approach is highly effective and consistently outperforms the state-of-the-art approaches. The proposed method is general enough to be applied to other similar microscopic image segmentation tasks, where heavily overlapped objects exist.
The k conditional nearest neighbor algorithm for classification and class probability estimation

The k nearest neighbor (kNN) approach is a simple and effective nonparametric algorithm for classification. One of the drawbacks of kNN is that the method can only give coarse estimates of class probabilities, particularly for low values of k. To avoid this drawback, we propose a new nonparametric classification method based on nearest neighbors conditional on each class: the proposed approach calculates the distance between a new instance and the kth nearest neighbor from each class, estimates posterior probabilities of class memberships using the distances, and assigns the instance to the class with the largest posterior. We prove that the proposed approach converges to the Bayes classifier as the size of the training data increases. Further, we extend the proposed approach to an ensemble method. Experiments on benchmark data sets show that both the proposed approach and the ensemble version of the proposed approach on average outperform kNN, weighted kNN, probabilistic kNN and two similar algorithms (LMkNN and MLM-kHNN) in terms of the error rate. A simulation shows that kCNN may be useful for estimating posterior probabilities when the class distributions overlap.