Reviewerly/exHarmony huggingface dataset:
Improving the performance of reference-frame-independent quantum key distribution with advantage distillation technology

The reference-frame-independent quantum key distribution (RFI-QKD) has the advantage of tolerating reference frames that slowly vary. It can generate secure keys between two remote users with slowly drifted and unknown reference frames. However, the drift of reference frames may inevitably compromise the performance of QKD systems. In the paper, we employ the advantage distillation technology (ADT) to the RFI-QKD and the RFI measurement-device-independent QKD (RFI MDI-QKD), and we then analyze the effect of ADT on the performance of decoy-state RFI-QKD and RFI MDI-QKD in both asymptotic and nonasymptotic cases. The simulation results show that ADT can significantly improve the maximum transmission distance and the maximum tolerable background error rate. Furthermore, the performance of RFI-QKD and RFI MDI-QKD in terms of the secret key rate and maximum transmission distance are still greatly improved when statistical fluctuations are taken into account. Our work combines the merits of the ADT and RFI-QKD protocols, which further enhances the robustness and practicability of QKD systems.
Malaria Parasite Detection on Microscopic Blood Smear Images with Integrated Deep Learning Algorithms

Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is difficult to detect due to inadequate equipment and technology. Microscopic check-ups of blood smear images by experts help to detect malaria-infected parasites accurately. However, manual analysis is tedious and time-consuming as the experts have to deal with many cases. This paper presents computer assisted malaria parasite detection model by classifying the blood smear image with hybrid deep learning methods that have high accuracy for classification. In the proposed approach the blood smear images are pre-processed using bilateral filtering technique in which features are extracted with the convolutional neural network. These features are selected by the improved grey-wolf optimization, and image classification is performed with the support vector machine. To evaluate the efficiency of the proposed technique, the NIH malaria dataset is utilized and the results are compared with existing approaches in terms of accuracy, F-Measure, recall, precision, and specificity. The outcome reveals that the proposed scheme is accurate and can be more helpful to pathologists for reliable parasite detection.
General monogamy and polygamy properties of quantum systems

Monogamy and Polygamy are important properties of entanglement, which characterize the entanglement distribution of multipartite systems. We study general monogamy and polygamy relations based on the $\alpha$th $(0\leq\alpha\leq \gamma)$ power of entanglement measures and the $\beta$th $(\beta\geq \delta)$ power of assisted entanglement measures, respectively. We illustrate that these monogamy and polygamy relations are tighter than the inequalities in the article [Quantum Inf Process 19, 101], so that the entanglement distribution can be more precisely described for entanglement states that satisfy stronger constraints. For specific entanglement measures such as concurrence and the convex-roof extended negativity, by applying these relations, one can yield the corresponding monogamous and polygamous inequalities, which take the existing ones in the articles [Quantum Inf Process 18, 23] and [Quantum Inf Process 18, 105] as special cases. More details are presented in the examples.
Internal Auditor Competency Gap: Perception Of Students, Academics And Practitioners

The purpose of this study is to investigate internal auditor competency gap by comparing the perceptions between students, academics, and practitioner. Questionnaires were sent to 1009 respondents who meet the criteria using google form. The questionnaire was divided into two parts. The first part, we ask respondents’ perception related with hard skill needed by internal auditor, the second part discussed soft skill required to prepare agile and adaptive internal auditor. This study found two main gaps between three groups of respondents. First, there are competency gap related to technology and emerging risk. Internal auditor must update with agile audit methodologies and data analytics. The universities must enrich the internal audit course with current audit method and data analytic skills. Second, all respondents put emphasis on soft skill more than hard skill, therefore important soft skill for auditor should embedded in curricula. Academics and practitioners agree that hard skill competencies specially related with performance standards as mentioned on international standards is important knowledge that should possessed by internal auditor to performed internal audit role comprehensively.
A comparative study of several classification metrics and their performances on data

Six classification metrics namely, Accuracy, Precision, Recall (Sensitivity), Specificity, F1-Score and Area Under the Curve have been studied in this work. A classification model based on the Support Vector Machine, was used to obtain a confusion matrix, which provided the needed information for calculating the different classification metrics. Twenty different datasets were used to assess the performances of the classification metrics. Accuracy and Area Under the Curve are the two metrics that consistently gave a classification result given each dataset used in the study. Although accuracy appears to be marginally better that AUC, it was discovered that in some cases where sensitivity is zero, accuracy yielded a high correct classification result. This goes further to implying that prior to choosing accuracy as a preferred metric for classification, investigation should be carried out to find out what sensitivity and specificity are. Where there are high values for sensitivity and specificity, the study shows that a choice of accuracy as a preferred classification metric leads to a high percentage of correct classification result.
UnbiasedNets: a dataset diversification framework for robustness bias alleviation in neural networks

Abstract Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. However, even the most accurate NNs can be biased toward a specific output classification due to the inherent bias in the available training datasets, which may propagate to the real-world implementations. This paper deals with the robustness bias , i.e., the bias exhibited by the trained NN by having a significantly large robustness to noise for a certain output class, as compared to the remaining output classes. The bias is shown to result from imbalanced datasets, i.e., the datasets where all output classes are not equally represented . Towards this, we propose the UnbiasedNets framework, which leverages K-means clustering and the NN’s noise tolerance to diversify the given training dataset, even from relatively smaller datasets. This generates balanced datasets and reduces the bias within the datasets themselves. To the best of our knowledge, this is the first framework catering to the robustness bias problem in NNs. We use real-world datasets to demonstrate the efficacy of the UnbiasedNets for data diversification, in case of both binary and multi-label classifiers. The results are compared to well-known tools aimed at generating balanced datasets, and illustrate how existing works have limited success while addressing the robustness bias. In contrast, UnbiasedNets provides a notable improvement over existing works, while even reducing the robustness bias significantly in some cases, as observed by comparing the NNs trained on the diversified and original datasets.
Implications of Cyberbiosecurity in Advanced Agriculture

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, as well as the lacking and fragmented nature of policy dictating cybersecurity stances– the field which intersects both cybersecurity and biosecurity to protect several areas within life sciences (Murch et al, 2018; Duncan et al, 2019; U.S. Department of Agriculture, 2022) . These looming oversights create dangers to advanced agricultural systems, which in turn poses risk to businesses, economies, and individuals. While there are various methods to reduce these risk factors, they ultimately depend on the careful consideration of cyberbiosecurity (CBS) by all involved. This includes the system developers, equipment engineers, and especially the end users - all of us. A conscientious team-effort can work to diminish risks and ultimately provide a safer environment for advanced agriculture and all who depend on it. This analysis explores numerous vulnerabilities within the system of advanced agriculture, discusses potential solutions to the escalating risks they present, and considers the achievable future of an advanced agricultural system which further implements the role of CBS.
Privacy-Preserving and Reliable Decentralized Federated Learning

Conventional federated learning (FL) approaches generally rely on a centralized server, and there has been a trend of designing asynchronous FL approaches for distributed applications partly to mitigate limitations associated with conventional (synchronous) FL approaches (e.g., single point of failure / attack). In this paper, we first introduce two new tools, namely: a quality-based aggregation method and an extended dynamic contribution broadcast encryption (DConBE). Building on these two new tools and local differential privacy, we then propose a privacy-preserving and reliable decentralized FL scheme, designed to support batch joining/leaving of clients while incurring minimal delay and achieving high model accuracy. In other words, our scheme seeks to ensure an optimal trade-off between model accuracy and data privacy, which is also demonstrated in our simulation results. For example, the results show that our aggregation method can effectively avoid low-quality updates in the sense that the scheme guarantees high model accuracy even in the presence of bad clients who may submit low-quality updates. In addition, our scheme incurs a lower loss and the extended DConBE only slightly affects the efficiency of our scheme. With the extended dynamic contribution broadcast encryption, our scheme can efficiently support batch joining/leaving of clients.
Amplitude Estimation from Quantum Signal Processing

Amplitude estimation algorithms are based on Grover&amp;apos;s algorithm: alternating reflections about the input state and the desired outcome. But what if we are given the ability to perform arbitrary rotations, instead of just reflections? In this situation, we find that quantum signal processing lets us estimate the amplitude in a more flexible way. We leverage this technique to give improved and simplified algorithms for many amplitude estimation tasks: we perform non-destructive estimation without any assumptions on the amplitude, develop an algorithm with improved performance in practice, present a new method for unbiased amplitude estimation, and finally give a simpler method for trading quantum circuit depth for more repetitions of short circuits.
FaultMeter: Quantitative Fault Attack Assessment of Block Cipher Software

Fault attacks are a potent class of physical attacks that exploit a fault njected during device operation to steal secret keys from a cryptographic device. The success of a fault attack depends intricately on (a) the cryptographic properties of the cipher, (b) the program structure, and (c) the underlying hardware architecture. While there are several tools that automate the process of fault attack evaluation, none of them consider all three influencing aspects.This paper proposes a framework called FaultMeter that builds on the state-of-art by not just identifying fault vulnerable locations in a block cipher software, but also providing a quantification for each vulnerable location. The quantification provides a probability that an injected fault can be successfully exploited. It takes into consideration the cryptographic properties of the cipher, structure of the implementation, and the underlying Instruction Set Architecture’s (ISA) susceptibility to faults. We demonstrate an application of FaultMeter to automatically insert optimal amounts of countermeasures in a program to meet the user’s security requirements while minimizing overheads. We demonstrate the versatility of the FaultMeter framework by evaluating five cipher implementations on multiple hardware platforms, namely, ARM (32 and 64 bit), RISC-V (32 and 64 bit), TI MSP-430 (16-bit) and Intel x86 (64-bit).
Adversarial Sampling for Fairness Testing in Deep Neural Network

In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy.
Neural Airport Ground Handling

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies

Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained to reconstruct the normal only training data with the expectation that, in test time, it can poorly reconstruct the anomalous data. However, previous studies have shown that, even trained with only normal data, AEs can often reconstruct anomalous data as well, resulting in a decreased performance. To mitigate this problem, we propose to limit the anomaly reconstruction capability of AEs by incorporating pseudo anomalies during the training of an AE. Extensive experiments using five types of pseudo anomalies show the robustness of our training mechanism towards any kind of pseudo anomaly. Moreover, we demonstrate the effectiveness of our proposed pseudo anomaly based training approach against several existing state-of-the-art (SOTA) methods on three benchmark video anomaly datasets, outperforming all the other reconstruction-based approaches in two datasets and showing the second best performance in the other dataset.
Multimodal sentiment system and method based on CRNN-SVM

Abstract Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people’s sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people’s needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs.
Multi-generation multi-criteria feature construction using Genetic Programming

The purpose of feature construction is to create new high level features from the original features. When Genetic Programming (GP) is applied to wrapper-based feature construction, especially when the samples size is small, GP generally overfits the training set and generalizes poorly with the deepening of evolution. Overfitting has attracted wide attention in some classification models, however, it is not commonly studied in the field of feature construction. In this paper, a Multi-Generation feature construction method (MG) is developed to preserve the solutions produced by multiple generations of GP. A Multi-Criteria feature construction method (MC) is introduced to use a multi-criteria evaluation function to evaluate GP individuals. Combining the above two methods, a Multi-Generation Multi-Criteria feature construction method (MGMC) is proposed. Experiments on fourteen datasets show that the proposed MG and MC methods can improve the classification performance and overcome overfitting problems of traditional feature construction methods in most cases. The combined MGMC method further improves the classification performance and achieves the best results.
Toward complete coverage planning using deep reinforcement learning by trapezoid-based transformable robot

Shape-shifting robots are the feasible solutions to solve the Complete Coverage Planning (CCP) problem. These robots can extend the covered areas by reconfiguring their shape to different forms according to space conditions. Since energy usage while navigating is constrained by the number of shape-shifting, it is desirable to cover the confined areas by trajectory using minimal robot actions within finite states. This paper presents a CCP method using deep reinforcement learning (DRL) for a reconfigurable robot with a trapezoid shape called Transbot. The framework derives optimal action policy for robot trajectory within the grid-based workspace. DRL model relies on Convolutional Neural Networks (CNNs) with Long Short Temporary Memory (LSTM) layers using Actor-Critic with Experience Replay (ACER) as the decision layers. The trained DRL model simultaneously generates robot shapes and directions with optimal energy cost by maximizing the cumulative reward representing the Transbot actions. By creating the trajectory with less 28.95% energy and 19.42% time in tested simulation and real-world experiments, the proposed CCP framework outperforms the existing tiling-based heuristic optimization techniques.
Switching-free time-domain optical quantum computation with quantum teleportation

Optical switches and rerouting networks are considered essential in optical quantum computers where they are used for injection and dejection of the necessary quantum states into an optical quantum computer. Practical optical switches and rerouting networks are, however, experimentally challenging as they must have extremely low loss, small switching time, high repetition rate, and minimum optical nonlinearity, requirements that are difficult to achieve simultaneously. In this paper, we present an optical quantum computation platform that does not require such optical switches. Our method is based on continuous-variable measurement-based quantum computation where, instead of the typical cluster states, we modify the structure of the quantum entanglement, so that the quantum teleportation protocol can be employed instead of optical switching and rerouting. The quantum entanglement structure in our architecture has additional modes that allow quantum states to be teleported in or out of the computation along the cluster state, a task that normally requires optical switches. We also outline how to combine our platform with Gottesman-Kitaev-Preskill encoding, the currently most promising encoding for a continuous-variable system.