Reviewerly/exHarmony huggingface dataset:
Uncovering hidden vulnerabilities in CNNs through evolutionary-based Image Test Libraries

Over the past years, Convolutional Neural Networks (CNNs) have become highly prominent in diverse artificial intelligence applications, proving remarkable performance across numerous tasks. Nonetheless, as CNNs find greater integration in safety-critical domains, the need to ensure their safety and reliability has emerged as a crucial issue. Efforts are underway to establish safety standards that specifically address the unique challenges posed by CNNs. As an example, these standards emphasize the importance of detecting in field permanent faults that may occur during the operational phase of devices. However, it is worth noting that CNNs have an intrinsic masking ability that complicates the in-field detection of permanent faults. This peculiar feature is a result of the distributed and parallel structure of artificial neural networks, that can stop the propagation of some hardware-induced faults by masking their effects. The aim of this research is to develop a software-based testing solution that can effectively reduce the masking effect of CNNs. This will be achieved by generating Image Test Libraries (ITLs) specifically designed to expose, when processed by a fully configured CNN, hard-to-detect faults and ensure their propagation to the output of the CNN. The proposed solution intends to uncover hidden vulnerabilities and enhance the overall reliability of CNNs through targeted testing strategies. An evolutionary algorithm is exploited to generate ITLs able to effectively detect the occurrence of faults at the output of the neural network. Results gathered from experiments targeting permanent faults on weights using ResNet-18, ResNet-34, and DenseNet-161 demonstrate the suitability of the proposed approach.
QA4C: An Intelligent Question and Answering System for the C Programming Language Based on Knowledge Graph

A knowledge graph (KG) can help construct a clear and systematic course knowledge framework, describing knowledge points and their structural relationships. It also presents them to students in a visual form, which can enhance teaching effectiveness and learning quality. This paper uses C programming language as an example to design and implement a KG-based intelligent Question and Answer (Q&A) system. Specifically, the ontology that describes the C programming language course knowledge framework is constructed firstly. Next, it is stored in a graph database to create a complete and updatable KG to support knowledge queries. Finally, the Aho-Corasick algorithm and information retrieval algorithm are used to implement the intelligent Q&A system, named QA4C. The experiment shows that the proposed construction method of the KG-based intelligent Q&A system for C programming language is effective, generalizable and scalable.
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations

Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privacy. In this study, we consider the challenge of preserving input sparsity in such approaches to retain the associated computational efficiency enhancements. First, we find a lower bound on the weight of coding, i.e., the number of submatrices to be combined to obtain coded submatrices to provide the resilience to the maximum possible number of stragglers (for given number of nodes and their storage constraints). Next we propose a distributed matrix computation scheme which meets this exact lower bound on the weight of the coding. Further, we develop controllable trade-off between worker computation time and the privacy constraint for sparse input matrices in settings where the worker nodes are honest but curious. Numerical experiments conducted in Amazon Web Services (AWS) validate our assertions regarding straggler mitigation and computation speed for sparse matrices.
On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging

Super-resolution is an important technique in various fields, particularly in medical imaging, where it plays a crucial role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. However, obtaining high-resolution images often faces practical limitations pertaining to acquisition device limitations, patient motion, or longer acquisition times. The remarkable success of deep learning methods has recently opened doors to their application in image super-resolution tasks as well. These deep learning-based methods heavily rely on a substantial amount of data, which is often unavailable, especially in the case of Magnetic Resonance Imaging (MRI) scans. Particularly in magnetic Resonance super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To address this, deep learning approaches simulate low-resolution images using many image degradation methods mimicking low-resolution images to create training image pairs from the available few high-resolution images. However, models trained on specific degradation simulations exhibit bias, leading to poor performance in real-world scenarios. In this paper, we hypothesize that such deep learning models trained on specific training image pairs with a specific degradation model are biased, we systematically study such biases with different types of degradation, different deep learning frameworks, and training losses. Finally, we advocate ensuring the diversity of degradation models to generate training image pairs controls such biases resulting in a more robust learning framework for MR image super-resolution.
High Probability Guarantees For Federated Learning

Federated learning (FL) has emerged as a promising approach for training machine learning models on distributed data while ensuring privacy preservation and data locality. However, one key challenge in FL optimization is the lack of high-probability guarantees, which can undermine the trustworthiness of FL solutions. To address this critical issue, we introduce Federated Averaging with post-optimization (FedAvg-PO) method, a modification to the Federated Averaging (FedAvg) algorithm. The proposed algorithm applies a post-optimization phase to evaluate a short list of solutions generated by several independent runs of the FedAvg method. These modifications allow to significantly improve the large-deviation properties of FedAvg which improve the reliability and robustness of the optimization process. The novel complexity analysis shows that FedAvg-PO can compute accurate and statistically guaranteed solutions in the federated learning context. Our result further relaxes the restrictive assumptions in FL theory by developing new technical tools which may be of independent interest. The insights provided by the computational requirements analysis contribute to the understanding of the scalability and efficiency of the algorithm, guiding its practical implementation.
Construction of Enterprise Financial Risk Pre-alarm System Based on Big Data

Big data has brought new opportunities for enterprise development, and the work efficiency of the financial department has been greatly improved. However, the current combination of big data and enterprise financial risk pre-alarm has some problems, such as unreasonable and irregular data processing. Based on this, this paper puts forward an innovative idea to improve the financial risk pre-alarm model, constructs a new enterprise financial risk pre-alarm model based on big data, and makes a qualitative analysis of the enterprise financial crisis, so as to predict the enterprise financial risk. The test results show that this method can further improve the prediction accuracy of the model, and the accuracy of the algorithm can reach 95.74%, which is about 12% higher than that of the traditional neural network method. This shows that it is effective to use big data method in financial risk pre-alarm. This model enhances the robustness of the algorithm and the rationality of initial weight threshold setting, and effectively improves the prediction performance of the pre-alarm model. Using this model to analyze and predict the capital situation of enterprises can provide reference for enterprises to scientifically formulate development decisions and financial strategies and effectively avoid financial risks, thus ensuring the stable growth of enterprises under big data.
Research on Personalized English Teaching Strategies Based on the Collaborative Filtering Algorithm

The current ELT objectives, concepts and requirements are standardized. Influenced by the traditional exam-oriented education concept, some teachers are used to sticking to the tradition and using a single teaching mode. However, in today’s network and information age, students’ great demand for personalized development, autonomous learning conditions and cross-cultural communicative ability require college English teachers to innovate in teaching. Based on the CFA, this paper studies the personalized ELT strategy, and analyzes it through simulation experiments. The experimental results show that the average number of students in the CFA test is significantly larger than that in the CNN algorithm and the AGA, and the standard deviation is relatively low, indicating that the student’s scores in the CFA test are relatively concentrated, not far from the average. Comparing the learning styles of students tested in the CFA with those in the CNN algorithm and the AGA, it can also be found that the average number of learning styles of students tested in the CFA is also significantly greater than that tested in the CNN algorithm and the AGA, with a lower standard deviation. It can be seen that the algorithm proposed in this paper is more suitable for the ELT.Under the research of the CFA, the initiative of students and teachers can be brought into full play, and learning or teaching can be adjusted and optimized in time according to feedback information, thus changing the pessimistic situation in traditional teaching.
Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks

The reliability of Artificial Neural Networks (ANNs) has emerged as a prominent research topic due to their increasing utilization in safety-critical applications. Long Short-Term Memory (LSTM) ANNs have demonstrated significant advantages in healthcare applications, primarily attributed to their robust processing of time-series data and memory-facilitated capabilities. This paper, for the first time, presents a comprehensive and fine-grain analysis of the resilience of LSTM-based ANNs in the context of gait analysis using fault injection into weights. Additionally, we improve their resilience by replacing faulty weights with zero, enabling ANNs to withstand environments that are up to 20 times harsher while experiencing up to 7 times fewer critical faults than an unprotected ANN.
Diagrammatic Analysis for Parameterized Quantum Circuits

Diagrammatic representations of quantum algorithms and circuits offer novel approaches to their design and analysis.In this work, we describe extensions of the ZX-calculus especially suitable for parameterized quantum circuits, in particular for computing observable expectation values as functions of or for fixed parameters, which are important algorithmic quantities in a variety of applications ranging from combinatorial optimization to quantum chemistry.We provide several new ZX-diagram rewrite rules and generalizations for this setting.In particular, we give formal rules for dealing with linear combinations of ZX-diagrams, where the relative complex-valued scale factors of each diagram must be kept track of, in contrast to most previously studied single-diagram realizations where these coefficients can be effectively ignored.This allows us to directly import a number useful relations from the operator analysis to ZX-calculus setting, including causal cone and quantum gate commutation rules.We demonstrate that the diagrammatic approach offers useful insights into algorithm structure and performance by considering several ansätze from the literature including realizations of hardware-efficient ansätze and QAOA.We find that by employing a diagrammatic representation, calculations across different ansätze can become more intuitive and potentially easier to approach systematically than by alternative means.Finally, we outline how diagrammatic approaches may aid in the design and study of new and more effective quantum circuit ansätze.
Tunable Quantum Neural Networks in the QPAC-Learning Framework

In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework.Tunable neural networks are quantum circuits made of multi-controlled X gates.By tuning the set of controls these circuits are able to approximate any Boolean functions.This architecture is particularly suited to be used in the QPAClearning framework as it can handle the superposition produced by the oracle.In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification.The numerical results show that this approach can efficiently learn concepts from a simple class.
Dynamic Qubit Routing with CNOT Circuit Synthesis for Quantum Compilation

Many quantum computers have constraints regarding which two-qubit operations are locally allowed.To run a quantum circuit under those constraints, qubits need to be mapped to different quantum registers, and multi-qubit gates need to be routed accordingly.Recent developments have shown that compiling strategies based on Steiner tree provide a competitive tool to route CNOTs.However, these algorithms require the qubit map to be decided before routing.Moreover, the qubit map is fixed throughout the computation, i.e. the logical qubit will not be moved to a different physical qubit register.This is inefficient with respect to the CNOT count of the resulting circuit.In this paper, we propose the algorithm PermRowCol for routing CNOTs in a quantum circuit.It dynamically remaps logical qubits during the computation, and thus results in fewer output CNOTs than the algorithms Steiner-Gauss [14] and RowCol [27].Here we focus on circuits over CNOT only, but this method could be generalized to a routing and mapping strategy on Clifford+T circuits by slicing the quantum circuit into subcircuits composed of CNOTs and single-qubit gates.Additionally, PermRowCol can be used in place of Steiner-Gauss in the synthesis of phase polynomials as well as the extraction of quantum circuits from ZX-diagrams.
HEAR: Homomorphically Encrypted Allreduce

Allreduce is one of the most commonly used collective operations. Its latency and bandwidth can be improved by offloading the calculations to the network. However, no way exists to conduct such offloading securely; in state-of-the-art solutions, the data is passed unprotected into the network. Security is a significant concern for High-Performance Computing applications, but achieving it while maintaining performance remains challenging. We present HEAR, the first high-performance system for securing in-network compute and Allreduce operations based on homomorphic encryption. HEAR implements carefully designed and modified encryption schemes for the most common Allreduce functions and leverages communication domain knowledge in MPI programs to obtain decryption and encryption routines with high performance. HEAR operates on integers and floats with no code base and no or little hardware changes. We design and evaluate HEAR, showing its minimal overhead, and open-source our implementation. HEAR represents the first step towards achieving confidential HPC.
Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors

Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnoses if a computerized system is developed to detect breast cancer. Computer-aided design techniques have the potential to help medical professionals to determine the specific location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. The aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. As a result, it provides a method for noise reduction known as quantum wavelet transform (QWT) filtering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classification of mammographic images. The hybrid methodology dubbed QWT-APCNN is compared to earlier methods in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state-of-the-art approaches, the proposed method performed better at noise reduction and segmentation according to different evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specificity, 90% DSS, and ROC and AUC rate of 88.77.
Advances in ACL2 Proof Debugging Tools

The experience of an ACL2 user generally includes many failed proof attempts. A key to successful use of the ACL2 prover is the effective use of tools to debug those failures. We focus on changes made after ACL2 Version 8.5: the improved break-rewrite utility and the new utility, with-brr-data.
State preparation in a Jaynes-Cummings lattice with quantum optimal control

High-fidelity preparation of quantum states in an interacting many-body system is often hindered by the lack of knowledge of such states and by limited decoherence times. Here, we study a quantum optimal control (QOC) approach for fast generation of quantum ground states in a finite-sized Jaynes-Cummings lattice with unit filling. Our result shows that the QOC approach can generate quantum many-body states with high fidelity when the evolution time is above a threshold time, and it can significantly outperform the adiabatic approach. We study the dependence of the threshold time on the parameter constraints and the connection of the threshold time with the quantum speed limit. We also show that the QOC approach can be robust against control errors. Our result can lead to advances in the application of the QOC to many-body state preparation.
Research on Lightweight-Based Algorithm for Detecting Distracted Driving Behaviour

In order to solve the existing distracted driving behaviour detection algorithms’ problems such as low recognition accuracy, high leakage rate, high false recognition rate, poor real-time performance, etc., and to achieve high-precision real-time detection of common distracted driving behaviours (mobile phone use, smoking, drinking), this paper proposes a driver distracted driving behaviour recognition algorithm based on YOLOv5. Firstly, to address the problem of poor real-time identification, the computational and parametric quantities of the network are reduced by introducing a lightweight network, Ghostnet. Secondly, the use of GSConv reduces the complexity of the algorithm and ensures that there is a balance between the recognition speed and accuracy of the algorithm. Then, for the problem of missed and misidentified cigarettes during the detection process, the Soft-NMS algorithm is used to reduce the problems of missed and false detection of cigarettes without changing the computational complexity. Finally, in order to better detect the target of interest, the CBAM is utilised to enhance the algorithm’s attention to the target of interest. The experiments show that on the homemade distracted driving behaviour dataset, the improved YOLOv5 model improves the mAP@0.5 of the YOLOv5s by 1.5 percentage points, while the computational volume is reduced by 7.6 GFLOPs, which improves the accuracy of distracted driving behaviour recognition and ensures the real-time performance of the detection speed.
Beyond the Bitcoin Hype: A Structured Study of Blockchain’s Impact and Potential in Today’s Digital World

The development and application of blockchain-based technologies have been overshadowed by the performance of cryptocurrencies and the belief that these are all that this technology has to offer. This idea has led to the question on numerous occasions if this technology is just expectations or if there are indeed applications that solve problems in today’s world. This bibliometric study on the current state of blockchain applications in the context of digital transformation seeks to break down the myth that this technology is just expectations, synthesizing and ordering the wide variety of topics that arise from this relationship. The results showed that the development of blockchain in the context of digital transformation is growing at an exponential rate, and with this, its applications have come to cover a large part of the industries, interact with other technologies, and establish objectives of all kinds. Based on these topics of technologies, industries, operational objectives, and strategic objectives, a structure is proposed that will serve as a guide for the development of new solutions to some problems, research, applications, and integrations with other technologies.
Psychometric Research: Modern Methods and New Opportunities for Education

Qualitative measurement is a fundamental requirement for research practice in the social sciences. The quality of measurements determines the validity of the interpretations, conclusions and decisions we can make based on the data obtained from the measurements. Qualitative measurement in the social sciences requires assessment tools as well as data analysis methods to link observed measurement results to theoretical attributes. The scientific basis for their development is provided by psychometrics.&#x0D; Preceding the special issue of the journal "Voprosy obrazovaniya / Educational Studies Moscow" devoted to psychometrics, the guest editors of this issue cover the main milestones of the history of psychometrics, highlight some significant publications, note the professional institutions and authors who have made their valuable contribution to the development of this branch of science. The authors pay special attention to the history of psychometrics in Russia. Assessing the possibilities, prospects and limitations of psychometrics, the authors express their point of view on the debatable issues of psychometrics, and it does not always coincide with the opinion of the authors of the special issue. This issue presents examples of using modern psychometric methods to solve actual problems in education research, as well as in research at the intersection of education and psychology, education and different spheres of business. All the authors of the presented articles are united by the desire to improve research practice in the social sciences through truly qualitative measurements.&#x0D;