Reviewerly/exHarmony huggingface dataset:
The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry

New approaches into computational quantum chemistry can be developed through the use of quantum computing. While universal, fault-tolerant quantum computers are still not available, and we want to utilize today's noisy quantum processors. One of their flagship applications is the variational quantum eigensolver (VQE)-an algorithm for calculating the minimum energy of a physical Hamiltonian. In this study, we investigate how various types of errors affect the VQE and how to efficiently use the available resources to produce precise computational results. We utilize a simulator of a noisy quantum device, an exact statevector simulator, and physical quantum hardware to study the VQE algorithm for molecular hydrogen. We find that the optimal method of running the hybrid classical-quantum optimization is to: (i) allow some noise in intermediate energy evaluations, using fewer shots per step and fewer optimization iterations, but ensure a high final readout precision; (ii) emphasize efficient problem encoding and ansatz parametrization; and (iii) run all experiments within a short time-frame, avoiding parameter drift with time. Nevertheless, current publicly available quantum resources are still very noisy and scarce/expensive, and even when using them efficiently, it is quite difficult to perform trustworthy calculations of molecular energies.
A Comparative Study on Performance of SVM and CNN in Tanzania Sign Language Translation Using Image Recognition

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Integrating Owicki–Gries for C11-Style Memory Models into Isabelle/HOL

Abstract Weak memory presents a new challenge for program verification and has resulted in the development of a variety of specialised logics. For C11-style memory models, our previous work has shown that it is possible to extend Hoare logic and Owicki–Gries reasoning to verify correctness of weak memory programs. The technique introduces a set of high-level assertions over C11 states together with a set of basic Hoare-style axioms over atomic weak memory statements (e.g. reads/writes), but retains all other standard proof obligations for compound statements. This paper takes this line of work further by introducing the first deductive verification environment in Isabelle/HOL for C11-like weak memory programs. This verification environment is built on the Nipkow and Nieto’s encoding of Owicki–Gries in the Isabelle theorem prover. We exemplify our techniques over several litmus tests from the literature and two non-trivial examples: Peterson’s algorithm and a read–copy–update algorithm adapted for C11. For the examples we consider, the proof outlines can be automatically discharged using the existing Isabelle tactics developed by Nipkow and Nieto. The benefit here is that programs can be written using a familiar pseudocode syntax with assertions embedded directly into the program.
Lightweight mediated semi-quantum key distribution protocol with a dishonest third party based on Bell states

The mediated semi-quantum key distribution (MSQKD) protocol is an important research issue that lets two classical participants share secret keys securely between each other with the help of a third party (TP). However, in the existing MSQKD protocols, there are two improvable issues, namely (1) the classical participants must be equipped with expensive detectors to avoid Trojan horse attacks and (2) the trustworthiness level of TP must be honest. To the best of our knowledge, none of the existing MSQKD protocols can resolve both these issues. Therefore, this study takes Bell states as the quantum resource to propose a MSQKD protocol, in which the classical participants do not need a Trojan horse detector and the TP is dishonest. Furthermore, the proposed protocol is shown to be secure against well-known attacks and the classical participants only need two quantum capabilities. Therefore, in comparison to the existing MSQKD protocols, the proposed protocol is better practical.
A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees

In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A natural way to explain the classifications of the models is to transform them into propositional rules. In this work, we focus on random forests and gradient-boosted trees. Specifically, these models are converted into an ensemble of interpretable MLPs from which propositional rules are produced. The rule extraction method presented here allows one to precisely locate the discriminating hyperplanes that constitute the antecedents of the rules. In experiments based on eight classification problems, we compared our rule extraction technique to “Skope-Rules” and other state-of-the-art techniques. Experiments were performed with ten-fold cross-validation trials, with propositional rules that were also generated from ensembles of interpretable MLPs. By evaluating the characteristics of the extracted rules in terms of complexity, fidelity, and accuracy, the results obtained showed that our rule extraction technique is competitive. To the best of our knowledge, this is one of the few works showing a rule extraction technique that has been applied to both ensembles of decision trees and neural networks.
Witnessing latent time correlations with a single quantum particle

When a noisy communication channel is used multiple times, the errors occurring at different times generally exhibit correlations. Classically, these correlations do not affect the evolution of individual particles: a single classical particle can only traverse the channel at a definite moment of time, and its evolution is insensitive to the correlations between subsequent uses of the channel. In stark contrast, here we show that a single quantum particle can sense the correlations between multiple uses of a channel at different moments of time. Taking advantage of this phenomenon, it is possible to enhance the amount of information that the particle can reliably carry through the channel. In an extreme example, we show that a channel that outputs white noise whenever the particle is sent at a definite time can exhibit correlations that enable a perfect transmission of classical bits when the particle is sent at a superposition of two distinct times. In contrast, we show that, in the lack of correlations, a single particle sent at a superposition of two times undergoes an effective channel with classical capacity of at most 0.16 bits. When multiple transmission lines are available, time correlations can be used to simulate the application of quantum channels in a coherent superposition of alternative causal orders, and even to provide communication advantages that are not accessible through the superposition of causal orders.
Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.
Domain-specific NLP system to support learning path and curriculum design at tech universities

The tech sector has been growing at a rapid speed, demanding a higher level of expertise from its labor force. New skills and programming languages are introduced and required by the industry every day, while the university courses are not updated adequately. Finding the high-demand skills and relevant courses to study has become essential to both students and faculty members at tech universities, which leads to a growing research interest in building an intelligence system to support decision making. Leveraging recent development in Natural Language Processing, we built an NLP-based course recommendation system specifically for the computer science (CS) and information technology (IT) fields. In particular, we built (1) a Named Entity Recognition (CSIT-NER) model to extract tech-related skills and entities, then used these skills to build (2) a personalized multi-level course recommendation system using a hybrid model (hybrid CSIT-CRS). Our CSIT-NER model, trained and fine-tuned on a large corpus of text extracted from StackOverflow and GitHub, can accurately extract the relevant skills and entities, outperforming state-of-the-art models across all evaluation metrics. Our hybrid CSIT-CRS can provide recommendations on multiple individualized levels of university courses, career paths with job listings, and industry-required with suitable online courses. The whole system received good ratings and feedback from users from our survey with 201 volunteers who are students and faculty members of tech universities in Australia and Vietnam. This research is beneficial to students, faculty members, universities in CS/IT higher education sector, and stakeholders in tech-related industries.
Freedom of the mixer rotation axis improves performance in the quantum approximate optimization algorithm

Variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA) are particularly attractive candidates for implementation on near-term quantum processors. As hardware realities such as error and qubit connectivity will constrain achievable circuit depth in the near future, new ways to achieve high-performance at low depth are of great interest. In this work, we present a modification to QAOA that adds additional variational parameters in the form of freedom of the rotation-axis in the $XY$-plane of the mixer Hamiltonian. Via numerical simulation, we show that this leads to a drastic performance improvement over standard QAOA at finding solutions to the MAXCUT problem on graphs of up to 7 qubits. Furthermore, we explore the Z-phase error mitigation properties of our modified ansatz, its performance under a realistic error model for a neutral atom quantum processor, and the class of problems it can solve in a single round.
Privacy-Enhanced MQTT Protocol for Massive IoT

The growing expectations for ubiquitous sensing have led to the integration of countless embedded sensors, actuators, and RFIDs in our surroundings. Combined with rapid developments in high-speed wireless networks, these resource-constrained devices are paving the road for the Internet-of-Things paradigm, a computing model aiming to bring together millions of heterogeneous and pervasive elements. However, it is commonly accepted that the Privacy consideration remains one of its main challenges, a notion that does not only encompasses malicious individuals but can also be extended to honest-but-curious third-parties. In this paper, we study the design of a privacy-enhanced communication protocol for lightweight IoT devices. Applying the proposed approach to MQTT, a highly popular lightweight publish/subscribe communication protocol prevents no valuable information from being extracted from the messages flowing through the broker. In addition, it also prevents partners re-identification. Starting from a privacy-ideal, but unpractical, exact transposition of the Oblivious Transfer (OT) technology to MQTT, this paper follows an iterative process where each previous model’s drawbacks are appropriately mitigated all the while trying to preserve acceptable privacy levels. Our work provides resistance to statistical analysis attacks and dynamically supports new client participation. Additionally the whole proposal is based on the existence of a non-communicating 3rd party during pre-development. This particular contribution reaches a proof-of-concept stage through implementation, and achieves its goals thanks to OT’s indistinguishability property as well as hash-based topic obfuscations.
A Year after the Outbreak of COVID-19: Applicants’ Perception of Higher Education Quality in the Context of Digitalization and Blended Learning

Introduction. The forced transition of Russian universities to distance learning in 2020 and accelerated digital transformation of educational processes in higher education are the first effects of the COVID-19 pandemic. A key aspect of measuring higher education quality is the perception of its formats by students as university change agents. The aim of the study is to identify the factors that determine the applicants’ positive attitude to learning that includes online elements in the context of the Russian universities’ transition to the blended learning model. Materials and Methods. The empirical base of the research includes the results of an online sociological survey conducted among the applicants for Ural Federal University undergraduate and graduate programmes in 2021. The methods of classification, factor analysis, and coefficients of pair correlations were applied. Additionally, for comparison, data from 2015 for a similar sample (1st year bachelor’s degree students) were used. Results. Positive attitudes towards online and blended learning are gradually increasing. The factor analysis of data from 2021 showed that applicants who support the online and blended learning include: those aspiring for master’s degree upon completing their bachelor’s degree course; those who choose their degree field rationally – men who apply for a state-funded education in any Russian university (including participants of federal contests – ‘Academic Olympics’). The above groups are formed mainly under the influence of external factors. Another group includes those oriented towards self-realization – women who choose their degree field relying on their personal inclinations for a future profession (the influence of internal factors). Discussion and Conclusion. The research results contribute to the development of scientific ideas about the blended learning model and emphasize the value of institutional research based on feedback from university students for making informed management decisions on change. The materials of the paper will be useful when designing the educational process in the Russian universities’ transition to the blended learning model.
A Self-Attention Augmented Graph Convolutional Clustering Networks for Skeleton-Based Video Anomaly Behavior Detection

In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and dynamic camera scenes and are naturally constructed as a graph in non-Euclidean space. Particularly, the establishment of spatial temporal graph convolutional networks (ST-GCN) can effectively learn the spatio-temporal relationships of Non-Euclidean Structure Data. However, it only operates on local neighborhood nodes and thereby lacks global information. We propose a novel spatial temporal self-attention augmented graph convolutional networks (SAA-Graph) by combining improved spatial graph convolution operator with a modified transformer self-attention operator to capture both local and global information of the joints. The spatial self-attention augmented module is used to understand the intra-frame relationships between human body parts. As far as we know, we are the first group to utilize self-attention for video anomaly detection tasks by enhancing spatial temporal graph convolution. Moreover, to validate the proposed model, we performed extensive experiments on two large-scale publicly standard datasets (i.e., ShanghaiTech Campus and CUHK Avenue datasets) which reveal the state-of-art performance for our proposed approach when compared to existing skeleton-based methods and graph convolution methods.
Fruit-classification model resilience under adversarial attack

Abstract An accurate and robust fruit image classifier can have a variety of real-life and industrial applications including automated pricing, intelligent sorting, and information extraction. This paper demonstrates how adversarial training can enhance the robustness of fruit image classifiers. In the past, research in deep-learning-based fruit image classification has focused solely on attaining the highest possible accuracy of the model used in the classification process. However, even the highest accuracy models are still susceptible to adversarial attacks which pose serious problems for such systems in practice. As a robust fruit classifier can only be developed with the aid of a fruit image dataset consisting of fruit images photographed in realistic settings (rather than images taken in controlled laboratory settings), a new dataset of over three thousand fruit images belonging to seven fruit classes is presented. Each image is carefully selected so that its classification poses a significant challenge for the proposed classifiers. Three Convolutional Neural Network (CNN)-based classifiers are suggested: 1) IndusNet , 2) fine-tuned VGG16 , and 3) fine-tuned MobileNet . Fine-tuned VGG16 produced the best test set accuracy of 94.82% compared to the 92.32% and the 94.28% produced by the other two models, respectively. Fine-tuned MobileNet has proved to be the most efficient model with a test time of 9 ms/step compared to the test times of 28 ms/step and 29 ms/step for the other two models. The empirical evidence presented demonstrates that adversarial training enables fruit image classifiers to resist attacks crafted through the Fast Gradient Sign Method (FGSM), while simultaneously improving classifiers’ robustness against other noise forms including ‘Gaussian’, ‘Salt and pepper’ and ‘Speckle’. For example, when the amplitude of the perturbations generated through the Fast Gradient Sign Method (FGSM) was kept at 0.1, adversarial training improved the fine-tuned VGG16’s performance on adversarial images by around 18% (i.e., from 76.6% to 94.82%), while simultaneously improving the classifier’s performance on fruit images corrupted with ‘salt and pepper’ noise by around 8% (i.e., from 69.82% to 77.85%). Other reported results also follow this pattern and demonstrate the effectiveness of adversarial training as a means of enhancing the robustness of fruit image classifiers.
UAV-Based Crowd Surveillance in Post COVID-19 Era

To cope with the current pandemic situation and reinstate pseudo-normal daily life, several measures have been deployed and maintained, such as mask wearing, social distancing, hands sanitizing, etc. Since outdoor cultural events, concerts, and picnics, are gradually allowed, a close monitoring of the crowd activity is needed to avoid undesired contact and disease transmission. In this context, intelligent unmanned aerial vehicles (UAVs) can be occasionally deployed to ensure the surveillance of these activities, that health restriction measures are applied, and to trigger alerts when the latter are not respected. Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities. Specifically, we propose a three steps approach. In the first step, captured images by a UAV are analyzed using machine learning to detect and locate individuals. The second step consists of a novel coordinates mapping approach to evaluate distances among individuals, then cluster them, while the third step provides an energy-efficient and/or reliable UAV trajectory to inspect clusters for restrictions violation such as mask wearing. Obtained results provide the following insights: 1) Efficient detection of individuals depends on the angle from which the image was captured, 2) coordinates mapping is very sensitive to the estimation error in individuals’ bounding boxes, and 3) UAV trajectory design algorithm 2-Opt is recommended for practical real-time deployments due to its low-complexity and near-optimal performance.
Recipe Recommendation With Hierarchical Graph Attention Network

Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it. In particular, we propose HGAT, a novel hierarchical graph attention network for recipe recommendation. The proposed model can capture user history behavior, recipe content, and relational information through several neural network modules, including type-specific transformation, node-level attention, and relation-level attention. We further introduce a ranking-based objective function to optimize the model. Thorough experiments demonstrate that HGAT outperforms numerous baseline methods.
Design and Analysis of a Self-Adaptive Zeroing Neural Network for Solving Time-Varying Quadratic Programming

In order to solve the time-varying quadratic programming (TVQP) problem more effectively, a new self-adaptive zeroing neural network (ZNN) is designed and analyzed in this article by using the Takagi-Sugeno fuzzy logic system (TSFLS) and thus called the Takagi-Sugeno (T-S) fuzzy ZNN (TSFZNN). Specifically, a multiple-input-single-output TSFLS is designed to generate a self-adaptive convergence factor to construct the TSFZNN model. In order to obtain finite- or predefined-time convergence, four novel activation functions (AFs) [namely, power-bi-sign AF (PBSAF), tanh-bi-sign AF (TBSAF), exp-bi-sign AF (EBSAF), and sinh-bi-sign AF (SBSAF)] are developed and applied in the TSFZNN model for solving the TVQP problem. Both theoretical proofs and experimental simulations show that the TSFZNN model using PBSAF or TBSAF has the property of converging in a finite time, and the TSFZNN model using EBSAF or SBSAF has the property of converging in a predefined time, which have superior convergence performance compared to the traditional ZNN model.
A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP.