Reviewerly/exHarmony huggingface dataset:
Variational quantum linear solver with a dynamic ansatz

Variational quantum algorithms have found success in the NISQ era owing to their hybrid quantum-classical approach which mitigate the problems of noise in quantum computers. In our study we introduce the dynamic ansatz in the Variational Quantum Linear Solver for a system of linear algebraic equations. In this improved algorithm, the number of layers in the hardware efficient ansatz circuit is evolved, starting from a small and gradually increasing until convergence of the solution is reached. We demonstrate the algorithm advantage in comparison to the standard, static ansatz by utilizing fewer quantum resources and with a smaller quantum depth on average, in presence and absence of quantum noise, and in cases when the number of qubits or condition number of the system matrix are increased. The numbers of iterations and layers can be altered by a switching parameter. The performance of the algorithm in using quantum resources is quantified by a newly defined metric.
EMU: Effective Multi-Hot Encoding Net for Lightweight Scene Text Recognition With a Large Character Set

Deploying a lightweight deep model for scene text recognition task on mobile devices has great commercial value. However, the conventional softmax-based one-hot classification module becomes a cumbersome obstacle when handling multi-languages or languages with large character set ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , Chinese) due to the rapid expansion of model parameters with the number of classes. To this end, we propose an Effective Multi-hot encoding and classification modUle (EMU) for scene text recognition in the scenario of multi-languages or languages with large character set. Specifically, EMU generates a binary multi-hot label for each class with a real-valued sub-network in training stage and produces the prediction by calculating the inner product between the multi-hot code and the multi-hot label. Compared to the softmax-based one-hot classifier, EMU reduces the storage requirement and the time cost in inference stage significantly, retaining similar performance. Furthermore, we design a convolution feature based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Light</b> weight Trans <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Former</b> to learn the effective features for EMU and consequently develop a lightweight scene text recognition framework, termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Light-Former-EMU</b> . We conduct extensive experiments on seven public English benchmarks and two real-world Chinese challenge benchmarks. Experimental results verify the effectiveness of the proposed EMU and demonstrate the promising performance of the proposed Light-Former-EMU.
Low-Complexity Intra Coding in Versatile Video Coding

Versatile Video Coding (VVC) was finalized in 2020 and offered promising coding efficiency with a bitrate reduction of about 50% for same video quality as High Efficiency Video Coding. However, its high encoding complexity is a heavy burden on real-time applications. In particular, the very high complexity in intra coding can be a big barrier into market entry. This paper presents an efficient low-complexity intra coding scheme, which employs downsampling and upsampling processes. The downsampling is simply performed by reducing the resolution of an original video in both horizontal and vertical directions. In the upsampling, convolutional neural network based super-resolution is used to increase the resolution of the reconstructed video. In addition, this paper thoroughly analyzes the performance and complexity of all intra coding tools in VVC. Experimental results demonstrate that the significantly high reduction of the encoding complexity can be achieved with acceptable video quality.
Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions

Abstract Path-following control systems for ships can be designed using both heading and course angle autopilots in conjunction with a proportional line-of-sight (LOS) guidance law. Ships are usually equipped with a gyrocompass from a safety perspective since magnetic compasses are susceptible to magnetic disturbances. Unfortunately, the gyrocompass is an expensive device, and smaller vessels and boats cannot afford to use this as the primary device for steering. An alternative solution is to compute the course over ground (COG) and speed over ground (SOG) from global navigation satellite systems (GNSS) and use these signals for feedback control. This article presents course autopilot design for path following and a five-state extended Kalman filter (EKF) to estimate the COG and SOG efficiently. Even though many algorithms exist for computation of the COG and SOG, it is advantageous to design an EKF since a state estimator can be extended to include other sensory systems such as Doppler Velocity Log (DVL), radar, attitude rate sensors, computer vision systems, etc. This is in contrast to proprietary systems that do not allow the user to modify the software. The convergence and accuracy of the EKF are significantly improved by using target-tracking models in combination with kinematic equations. A high-fidelity model of a MARINER class cargo ship is used in the path-following case study. From the simulation study, it can be concluded that the EKF successfully estimates the COG and the SOG from GNSS measurements during path following. The solution is remarkably robust and accurate, and when combined with a course autopilot, the need for a compass is eliminated during path following.
Digital Healthcare - Cyberattacks in Asian Organizations: An Analysis of Vulnerabilities, Risks, NIST Perspectives, and Recommendations

Cyberattacks on healthcare institutions are on an upsurge all over the world.Recently, Asian hospitals have become targets of numerous cyberattacks.While Western countries like the United States have implemented security-related laws, policies, standards, and other protective measures to deal with the healthcare cyberattacks, Asian countries are lagging.The Healthcare insurance portability and accountability act (HIPAA), enacted by the United States federal government, is a classic example of a law that has been in existence for a quarter-century now.Awareness about electronic health records (EHR) and their importance is increasing in Asia.Many hospitals and healthcare systems successfully implement solutions to protect healthcare data, including sensitive patient data.However, protecting healthcare data involves a sophisticated technology and compliance-driven approach due to the high value associated with the data.In this research, an earnest attempt is made to investigate the recent cyberattacks in Asian healthcare institutions.Based on the investigation, five types of cyberattacks are found to dominate Asian healthcare institutions.A detailed analysis of these attacks, their vulnerabilities, and associated risks are performed as a part of this study.In many countries with higher cybersecurity maturity, risk frameworks are successfully employed to manage the risks associated with healthcare data.In this study, the cyberattacks on Asian healthcare institutions are also analyzed through the lens of the National Institute of standards and technology (NIST) risk framework.Based on the literature review, a few unique recommendations are included in this research study to be used as risk mitigation measures by Asian healthcare organizations and researchers to manage and improve the growing situation of cyberattacks.
A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.
A Novel Joint Dataset and Computation Management Scheme for Energy-Efficient Federated Learning in Mobile Edge Computing

In this letter, a novel joint dataset and computation management (DCM) scheme for energy-efficient federated learning (FL) in mobile edge computing (MEC) is proposed. For this purpose, with respect to the amount of dataset and computation resources, we rigorously formulated analytical models for i) learning efficiency, which considers the estimated global accuracy tendency according to the amount of dataset and service latency, and ii) the overall energy consumption of FL participants, including local training and model parameter transmission. To consider the trade-off between these two factors in the FL procedure with MEC, a theoretical framework for the DCM problem that jointly optimizes the amount of dataset and the computation resources used for local training over multiple FL clients was designed. Additionally, the extensive simulation-based performance evaluations validate the superior performance of the proposed DCM; compared to the various benchmarks in terms of the proposed cost function and test accuracy on the MNIST dataset with independent identically distributed (IID) / non-IID settings.
A new scheme of vehicle detection for severe weather based on multi-sensor fusion

Automated vehicles are prone to traffic accidents in severe weather conditions. Real-time vehicle detection can improve the driving safety of automated vehicles. This paper proposes a new vehicle detection method based on multi-sensor fusion to improve the vehicle detection performance in severe weather conditions. First, an efficient vehicle target extraction method from the radar is proposed that uses supervised learning to train a classifier based on LightGBM. This method does not require complex prior knowledge to determine the target segmentation threshold and transforms the target extraction into a data-driven classification. The vehicle target extraction method based on LightGBM has 95.5% accuracy and a 96% true positive rate. Second, we estimate the potential area of vehicles from infrared images according to the distribution of radar targets and predict the region of interest (ROI) of vehicles based on pixel regression. The ROI extraction method based on radar can avoid complicated calculations and interference of heat sources in the environment, which will greatly improve the speed and accuracy of ROI extraction. Radar-based ROI extraction only takes 4 ms, which is much lower than image-based ROI extraction. Finally, four new Haar-like feature templates are designed to improve the vehicle detection performance, which can improve the detection accuracy by 2.9%. This method has a 92.4% detection accuracy and a 43 Fps detection speed in the road test, which significantly improves the vehicle detection performance in severe weather.
New replay attack detection using iterative adaptive inverse filtering and high frequency band

Replay attack is most vulnerable to automatic speaker verification system, where the frauds get the access by replaying pre-recorded voice samples of the genuine speakers. In this proposed work, we mainly concentrated on glottal excitation and high frequency band. First, we demonstrate the importance of the glottal information of the speech signals to detect the replay attack for speaker verification system along with magnitude based discrimination power features set. Iterative Adaptive Inverse Filtering (IA-IF) technique is used to extract the glottal excitation information of the given speech spectrum which shows the difference in the characteristics of genuine and replayed speech sample. Using this technique the glottal information is gained by eliminating the lip radiation and vocal tract effect by applying the integration and inverse filtering respectively. Secondly, we have shown the prominence and discriminative information to detect the replayed attack which is present in high frequency band of the speech spectrums of genuine and replayed samples. Finally, Constant-Q Cepstral Coefficients (CQCC) is used to extract desired features set from the high-frequency bands of glottal excitation spectrum. From the experimental studies done on ASV spoof-2017 version 2.0 database it shows that the proposed method feature set significantly decreases the Equal Error Rate (EER) to 3.68% and 8.32% for development and evaluation set when compare to other state-of-art method feature set.
Robust Planning for Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations

We consider persistent (long-horizon) surveillance over an environment by using energy-constrained unmanned aerial vehicles (UAVs), which are supported by unmanned ground vehicles (UGVs) serving as mobile charging stations. The goal is to periodically visit a set of monitoring points by the UAVs while minimizing the maximum time between the consecutive visits to any of those points. In general, the optimal planning of UAVs and UGVs in such a persistent surveillance scenario is an NP-hard combinatorial optimization problem. Furthermore, the problem also demands a solution strategy that can successfully handle obstacles, especially on the ground, that are unknown a priori in many real-life scenarios. We present a scalable and robust approximate algorithm that is based on 1) forming uniform UAV-UGV teams, 2) decomposing the environment into maximal partitions that can be covered by the UAVs in a single fuel cycle as long as the UAVs are released sufficiently close to the centers of the partitions, 3) maintaining the teams uniformly distributed over a cyclic path traversing those partitions, and 4) having the UAVs in each team cover their current partition and be transported to the next partition while being recharged by the UGV. We support our proposed algorithm with some theoretical results and simulations.
Squarefree values of polynomial discriminants I

We determine the density of monic integer polynomials of given degree $$n>1$$ that have squarefree discriminant; in particular, we prove for the first time that the lower density of such polynomials is positive. Similarly, we prove that the density of monic integer polynomials f(x), such that f(x) is irreducible and $${\mathbb Z}[x]/(f(x))$$ is the ring of integers in its fraction field, is positive, and is in fact given by $$\zeta (2)^{-1}$$ . It also follows from our methods that there are $$\gg X^{1/2+1/n}$$ monogenic number fields of degree n having associated Galois group $$S_n$$ and absolute discriminant less than X, and we conjecture that the exponent in this lower bound is optimal.
Improved YOLOX-S Abnormal Condition Detection for Power Transmission Line Corridors

In transmission line corridors, there are numerous abnormal conditions, such as bird nests, suspended foreign objects, wildfires, and smog, which always affect the normal operation of transmission systems. Considering the various types of foreign objects, complex backgrounds, occlusion, wildfires, irregular smoke shapes, and the real-time requirements of drone detection, this paper introduces an information aggregation algorithm based on YOLOX-S. This algorithm enhances relevant features and suppresses irrelevant features by aggregating spatial information and channel information in the feature map, which raises the overall learning ability of the network to improve the detection accuracy. Finally, the test on the transmission line environmental data set shows that the detection accuracy of this method in abnormal conditions such as bird's nest, hanging foreign body, wildfire and smog is 88.5%, which is 13.9% higher than that before the improvement, and the increase of reasoning time is very small. The experimental results show that this method has made significant progress in the abnormal environmental condition detection of power transmission line corridors.
Polyp-YOLOv5-Tiny: A Lightweight Model for Real-Time Polyp Detection

Early colonoscopy diagnosis can significantly reduce the mortality rate of colon cancer patients. Deep learning based object detection assists to enhance clinical performance on accurate diagnosis; however, it is challenging to integrate current object detection models on low-performance endoscope hardware devices. This paper presents a lightweight model for real-time polyp detection. By reducing the number of convolutional kernels by half and removing the large-object-detecting head from YOLOv5, our model got a similar precision to YOLOv3-spp (mAP.5:.95 of 0.591 and 0.583 respectively), but with a model size of only 2.8 MB (119.7 MB of YOLOv3-spp). There is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.636 with 13.7MB model size); however, our model still shows a significant advantage on reducing model complexity. Experimental results also indicated that MS COCO pre-training is helpful in polyp detection tasks, and the Mosaic augmentation strongly enhances the precision of YOLO models especially when the training set is small.
A network-based feature extraction model for imbalanced text data

The explosive growth of text data has attracted many researchers to explore the efficient method to extract valuable hidden information. Many technologies, especially deep learning methods, have achieved great success in text analysis. However, the most powerful methods always require a considerable quantity of data for training, which may suffer from imbalanced data in some cases. In this paper, we propose a network-based Convolution Neural Network (NCNN) to mitigate the effect of imbalanced data. The proposed model first generates new synthetic samples for the imbalanced data based on the random walking of the network. Then an extra layer called Polar Layer is introduced to connect the output from the network model of the text to the classical CNN. Two electing strategies (n-NCNN and x-NCNN) are proposed to improve the performance of NCNN further. In the experimental section, the proposed model is applied to Reuters 21578 and WebKb. By comparing with six approaches, we prove the effectiveness of the proposed NCNN model on the imbalanced text data.
Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection

In this work, we first propose a deep neural network (DNN) system for the automatic detection of speech in audio signals, otherwise known as voice activity detection (VAD). Several DNN types were investigated, including multilayer perceptrons (MLPs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), with the best performance being obtained for the latter. Additional postprocessing techniques, i.e., hysteretic thresholding, minimum duration filtering, and bilateral extension, were employed in order to boost performance. The systems were trained and tested using several data subsets of the CENSREC-1-C database, with different simulated ambient noise conditions, and additional testing was performed on a different CENSREC-1-C data subset containing actual ambient noise, as well as on a subset of the TIMIT database. An accuracy of up to 99.13% was obtained for the CENSREC-1-C datasets, and 97.60% for the TIMIT dataset. We proceed to show how the final VAD system can be adapted and employed within an utterance-level deceptive speech detection (DSD) processing pipeline. The best DSD performance is achieved by a novel hybrid CNN-MLP network leveraging a fusion of algorithmically and automatically extracted speech features, and reaches an unweighted accuracy (UA) of 63.7% on the RLDD database, and 62.4% on the RODeCAR database.
Garbage Classification System with YOLOV5 Based on Image Recognition

At present, the main technology of garbage identification and classification is the use of traditional machine vision algorithm or the use of sensors for screening and identification of garbage, in garbage sorting, the first accurate identification and classification of garbage is very necessary. By collecting various types of garbage pictures and building detection data sets, we adopt the garbage identification and detection algorithm based on YOLO-V5 and use data enhancement to improve the robustness of the model, to achieve fast and accurate identification of different types of garbage. Experimental results show that this method has high accuracy, short time consumption, and good robustness.
Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning

State estimation methods used in cyber–physical systems (CPSs), such as smart grid, are vulnerable to false data injection attacks (FDIAs). Although substantial deep learning methods have been proposed to detect such attacks, deep neural networks (DNNs) are highly susceptible to adversarial attacks, which modify input of DNNs with unnoticeable but malicious perturbations. This article proposes a method to explore targeted and stealthy FDIAs via adversarial machine learning. We pose FDIAs as sparse optimization problems to achieve initial attack objectives and remain stealthy during attacks. We propose a parallel optimization algorithm to efficiently solve the problems and explore additional sparse-state attacks. The experimental results show that for IEEE 14-bus and 118-bus systems, the success rate of two-state sparse attacks with small-scale targets is as high as 80%. In addition, the attack success rate can continue to increase as the number of attack states increases. The proposed attacks demonstrate that attackers can implement attacks that can bypass both bad data detectors and neural network detectors while keeping the initial attack objectives unchanged, which is a critical and urgent security threat in CPS.
Extended rule-based opinion target extraction with a novel text pre-processing method and ensemble learning

Opinion target extraction (OTE) is the extraction of explicit expressions related to entity aspects interpreted with subjective attributive words in the review sentences using supervised or rule-based approaches. Despite the constraints of syntactic-based relation rules, rule-based approaches can be domain-independently implemented. Although supervised approaches yield better results, more costly due to requiring a large number of labeled samples. This study proposes an unsupervised (rule-based) OTE approach with novel methods and extended rule-based techniques to overcome the aforementioned issues. In this study, first, a novel pattern-based text pre-processing method is proposed to eliminate punctuations that are incompatible with determinative group rules patterns. Then, implemented syntactic-based relation rules on the dependency relation graph are extended with new auxiliary features to extract multi-word expressions which modify each other. The majority voting method is used for optimizing the performance of outputs. Finally, the effectiveness of the proposed approach was tested on a restaurant review dataset. The experimental results show that the proposed approach outperforms all unsupervised approaches. Additionally, it gives comparable results with the supervised approaches, revealing the effectiveness of the proposed approach.