Reviewerly/exHarmony huggingface dataset:
Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

A common assumption of coverage path planning research is a static environment. Such environments require only a single visit to each area to achieve coverage. However, some real-world environments are characterised by the presence of unexpected, dynamic obstacles. They require areas to be revisited periodically to maintain an accurate coverage map, as well as reactive obstacle avoidance. This paper proposes a novel swarm-based control algorithm for multi-robot exploration and repeated coverage in environments with unknown, dynamic obstacles. The algorithm combines two elements: frontier-led swarming for driving exploration by a group of robots, and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both repeated coverage performance and obstacle avoidance ability. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.
A Features-Based Privacy Preserving Assessment Model for Authentication of Internet of Medical Things (IoMT) Devices in Healthcare

Internet of Things (IoT) devices have drawn significant attention over the last few years due to their significant contribution to every domain of life, but the major application of these devices has been witnessed in the healthcare sector. IoT devices have changed the complexion of healthcare set-up, however, the major limitation of such devices is susceptibility to many cyberattacks due to the use of embedded operating systems, the nature of communication, insufficient software updates, and the nature of backend resources. Similarly, they transfer a huge amount of sensitive data via sensors and actuators. Therefore, the security of Internet of Health Things (IoHT) devices remains a prime concern as these devices are prone to various cyberattacks, which can lead to compromising and violating the security of IoT devices. Therefore, IoT devices need to be authenticated before they join the network or communicate within a network, and the applied method of authentication must be robust and reliable. This authentication method has to be evaluated before being implemented for the authentication of IoT devices/equipment in a healthcare environment. In this study, an evaluation framework is introduced to provide a reliable and secure authentication mechanism based on authentication features. The proposed framework evaluates and selects the most appropriate authentication scheme/method based on evaluating authentication features using a hybrid multicriteria decision-making approach. It completes this in two steps: in the first step, the analytic hierarchy process (AHP) method is applied for assigning criteria weights; and in the second step, the technique for order preference by similarity to ideal solution (TOPSIS) approach selects the best authentication solution for IoHT devices based upon identified authentication features. This is the first attempt to present a features-based authentication model for selecting the improved authentication solution employed in IoHT devices.
Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as the development set, while those from the other institution (5456 WSIs) were used as the unseen test set. Label distribution learning (LDL) was used to address a difference in label characteristics between the development and test sets. A combination of EfficientNet (a deep learning model) and LDL was utilized to develop an automatic prediction system. Quadratic weighted kappa (QWK) and accuracy in the test set were used as the evaluation metrics. The QWK and accuracy were compared between systems with and without LDL to evaluate the usefulness of LDL in system development. The QWK and accuracy were 0.364 and 0.407 in the systems with LDL and 0.240 and 0.247 in those without LDL, respectively. Thus, LDL improved the diagnostic performance of the automatic prediction system for the grading of histopathological images for cancer. By handling the difference in label characteristics using LDL, the diagnostic performance of the automatic prediction system could be improved for prostate cancer grading.
Probabilistic solar nowcasting based on all-sky imagers

The interest in shortest-term solar irradiance forecasts (nowcasts) increases steadily with the increase share of distributed solar power generation. Such solar irradiance nowcasts are beneficial for different stakeholders, from generation to transmission and demand, in order to tackle challenges caused by the variability of solar irradiance. In space and time highly resolved nowcasts can be obtained by all sky imager (ASI) systems, which analyze the sky conditions from sky images. Deterministic nowcasts from ASI systems are subject to significant uncertainties. Reliable uncertainty information are very helpful for any application, in order to derive practical benefit from nowcasts. Therefore, such nowcasts should be probabilistic in nature, which provide probability distributions. Meaningful indicators for the uncertainties at hand are provided by prediction intervals for distinct confidence levels derived from the probability distributions. Thus, a real time capable nonparametric probabilistic quantile nowcasting method based on deterministic ASI nowcast is developed. The method takes irradiance variabilities as main predictor of nowcast uncertainties into account. A benchmark against three distinct baseline models is conducted over an extensive data set, using a variety of recently recommended scores. Overall average continuous ranked probability skill scores (Clear-Sky Dependent Climatology as baseline) for nowcasts up to 20 min ahead of 0.72 ± 0.08 (direct normal irradiance) and 0.62 ± 0.09 (global horizontal irradiance) are reached. For a better evaluation of the actual performance of the probabilistic nowcasts, a discretization of the validation data set into eight irradiance variability conditions is performed. All scores are determined for each of these distinct conditions.
Enhancing sequential recommendation with contrastive Generative Adversarial Network

Sequential recommendation models a user's historical sequence to predict future items. Existing studies utilize deep learning methods and contrastive learning for data augmentation to alleviate data sparsity. However, these existing methods cannot learn accurate high-quality item representations while augmenting data. In addition, they usually ignore data noise and user cold-start issues. To solve the above issues, we investigate the possibility of Generative Adversarial Network (GAN) with contrastive learning for sequential recommendation to balance data sparsity and noise. Specifically, we propose a new framework, Enhanced Contrastive Learning with Generative Adversarial Network for Sequential Recommendation (ECGAN-Rec), which models the training process as a GAN and recommendation task as the main task of the discriminator. We design a sequence augmentation module and a contrastive GAN module to implement both data-level and model-level augmentations. In addition, the contrastive GAN learns more accurate high-quality item representations to alleviate data noise after data augmentation. Furthermore, we propose an enhanced Transformer recommender based on GAN to optimize the performance of the model. Experimental results on three open datasets validate the efficiency and effectiveness of the proposed model and the ability of the model to balance data noise and data sparsity. Specifically, the improvement of ECGAN-Rec in two evaluation metrics (HR@N and NDCG@N) compared to the state-of-the-art model performance on the Beauty, Sports and Yelp datasets are 34.95%, 36.68%, and 13.66%, respectively. Our implemented model is available via https://github.com/nishawn/ECGANRec-master.
Object-centric Contour-aware Data Augmentation Using Superpixels of Varying Granularity

Regional dropout strategies have demonstrated to be very effective in improving both the performance and the generalization capability of deep learning models. However, when such strategies are performed in a totally random manner, the background noise and label mismatch problems arise. To tackle such problems, existing approaches typically focus on regions with the highest distinctiveness. Yet, there are two main drawbacks of existing approaches: (I) Many existing region-based augmentation methods can only use rectangular regions, resulting in the loss of object contour information; (II) Deterministic selection of the most discriminative regions leads to poor diversification in data augmentation. In fact, a trade-off is needed between diversification and concentration, which can decrease the undesirable noise. In this paper, we propose a novel object-centric contour-aware CutMix data augmentation strategy with arbitrary- shape and size superpixel supports, which is hereafter referred to as OcCaMix for short. It not only captures the most discriminative regions, but also effectively preserves the contour details of the objects. Moreover, it enables the search of natural object parts of different sizes. Extensive experiments on a large number of benchmark datasets show that OcCaMix significantly outperforms state-of-the-art CutMix based data augmentation methods in classification tasks. The source codes and trained models are available at https://github.com/DanielaPlusPlus/OcCaMix.
Safe semi-supervised learning for pattern classification

Semi-supervised learning (SSL) based on manifold regularization in many fields has attracted widespread attention and research. However, SSL still has two main challenges: On the one hand, studies have shown that unlabeled data may cause performance degradation in semi-supervised classifiers, which means that unlabeled data introduces uncertainty and potential hazards. On the other hand, for samples distributed on different class boundaries, manifold regularization is not necessarily satisfactory, which will result in samples near the boundary that are likely to be misclassified. In response to the above problems, we propose a new SSL framework called safe semi-supervised learning (Sa-SSLJR for short). In Sa-SSLJR, a risk degree regularization term is constructed to estimate the uncertainty and potential risk of unlabeled data in the semi-supervised learning process. Secondly, based on manifold regularization and discriminant regularization, a joint regularization term is developed to solve the second challenge of SSL. Extensive experiments on multiple datasets show that our approach is competitive with state-of-the-art methods in terms of classification performance and feasibility.
Vertex points are not enough: Monocular 3D object detection via intra- and inter-plane constraints

Existed methods for 3D object detection in monocular images focus mainly on the class of rigid bodies like cars, while more challenging detection like the cyclist is less studied. Therefore, we propose a novel 3D monocular object detection method to improve the accuracy of detection objects with large differences in deformation by introducing the geometric constraints of the object 3D bounding box plane. Considering the map relationship of projection plane and the keypoint, we firstly introduce the geometric constraints of the object 3D bounding box plane, adding the intra-plane constraint while regressing the position and offset of the keypoint itself, so that the position and offset error of the keypoint are always within the error range of the projection plane. For the inter-plane geometry relationship of the 3D bounding box, the prior knowledge is incorporated to optimize the keypoint regression allowing for improved the accuracy of depth location prediction. Experimental results show that the proposed method outperforms some other state-of-the-art methods on cyclist class, and obtains competitive results in the field of real-time monocular detection.
LC-YOLO: A Lightweight Model with Efficient Utilization of Limited Detail Features for Small Object Detection

The limited computing resources on edge devices such as Unmanned Aerial Vehicles (UAVs) mean that lightweight object detection algorithms based on convolution neural networks require significant development. However, lightweight models are challenged by small targets with few available features. In this paper, we propose an LC-YOLO model that uses detailed information about small targets in each layer to improve detection performance. The model is improved from the one-stage detector, and contains two optimization modules: Laplace Bottleneck (LB) and Cross-Layer Attention Upsampling (CLAU). The LB module is proposed to enhance shallow features by integrating prior information into the convolutional neural network and maximizing knowledge sharing within the network. CLAU is designed for the pixel-level fusion of deep features and shallow features. Under the combined action of these two modules, the LC-YOLO model achieves better detection performance on the small object detection task. The LC-YOLO model with a parameter quantity of 7.30M achieves an mAP of 94.96% on the remote sensing dataset UCAS-AOD, surpassing the YOLOv5l model with a parameter quantity of 46.61M. The tiny version of LC-YOLO with 1.83M parameters achieves 94.17% mAP, which is close to YOLOv5l. Therefore, the LC-YOLO model can replace many heavyweight networks to complete the small target high-precision detection task under limited computing resources, as in the case of mobile edge-end chips such as UAV onboard chips.
Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future.
An Improved Dense CNN Architecture for Deepfake Image Detection

Recent advancements in computer vision processing need potent tools to create realistic deepfakes. A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected media streams. Motivated by this, this paper presents a novel and improved deep-CNN (D-CNN) architecture for deepfake detection with reasonable accuracy and high generalizability. Images from multiple sources are captured to train the model, improving overall generalizability capabilities. The images are re-scaled and fed to the D-CNN model. A binary-cross entropy and Adam optimizer are utilized to improve the learning rate of the D-CNN model. We have considered seven different datasets from the reconstruction challenge with 5000 deepfake images and 10000 real images. The proposed model yields an accuracy of 98.33% in AttGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> , 99.33% in GDWCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>b</i></sup> , 95.33% in StyleGAN, 94.67% in StyleGAN2, and 99.17% in StarGAN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>c</i></sup> real and deepfake images, that indicates its viability in experimental setups.
A Novel Security Survival Model for Quantum Key Distribution Networks Enabled by Software-Defined Networking

Quantum-key-distribution (QKD) networks can theoretically generate quantum secret keys that are completely secure for an entire communication system. The rate of key distribution is low and decreases exponentially with increasing distance. A classic trusted relay (CTR) uses additional keys to enhance security distance in QKD networks. In practice, the assurance of security for certain relay nodes is still lacking, despite the fact that CTR requires that all nodes be trusted. Owing to channel unreliability, system faults accumulate during the key relay, thereby increasing the probability of CTR failing to distribute the secret key. The failure of a successful key relay would then result in the subsequent destruction of all the keys involved in the process, which leads to the wasting of the quantum secret key and reduction system encryption. Accordingly, reducing the impact of CTR failures and realising the key security distribution of remote quantum networks have become new challenges that require the development of a new survivability scheme. In this study, a software-defined networking (SDN) technique is introduced to circumvent this drawback by utilising the flexibility provided by the SDN paradigm for better QKD network management efficiency. In particular, a novel survivability model called software-defined quantum key relay failure (SDQKRF) is proposed in this paper in which a new function is developed and added to the SDN controller. According to the simulation results, SDN over a QKD network using the SDQKRF model is more reliable and performs better in terms of the key generation ratio, key utilisation rate, recovery after failure, avalanche effect, and service blocking rate than a regular QKD network without the SDQTRF model.
Machine Learning Security in Industry: A Quantitative Survey

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze attack occurrence and concern and evaluate statistical hypotheses on factors influencing threat perception and exposure. Our results shed light on real-world attacks on deployed machine learning. On the organizational level, while we find no predictors for threat exposure in our sample, the amount of implement defenses depends on exposure to threats or expected likelihood to become a target. We also provide a detailed analysis of practitioners' replies on the relevance of individual machine learning attacks, unveiling complex concerns like unreliable decision making, business information leakage, and bias introduction into models. Finally, we find that on the individual level, prior knowledge about machine learning security influences threat perception. Our work paves the way for more research about adversarial machine learning in practice, but yields also insights for regulation and auditing.
Evaluating the feeling of control in virtual object translation on 2D interfaces

Abstract Computer-aided design (CAD) plays an essential role in creative idea generation on 2D screens during the design process. In most CAD scenarios, virtual object translation is an essential operation, and it is commonly used when designers simulate their innovative solutions. The degrees of freedom (DoF) of virtual object translation modes have been found to directly impact users’ task performance and psychological aspects in simulated environments. Little is known in the existing literature about the sense of agency (SoA), which is a critical psychological aspect emphasizing the feeling of control, in translation modes on 2D screens during the design process. Hence, this study aims to assess users’ SoA in virtual object translation modes on mouse-based, touch-based, and handheld augmented reality (AR) interfaces through subjective and objective measures, such as self-report, task performance, and electroencephalogram (EEG) data. Based on our findings in this study, users perceived a greater feeling of control in 1DoF translation mode, which may help them come up with more creative ideas, than in 3DoF translation mode in the design process; additionally, the handheld AR interface offers less control feel, which may have a negative impact on design quality and creativity, as compared with mouse- and touch-based interfaces. This research contributes to the current literature by analyzing the association between virtual object translation modes and SoA, as well as the relationship between different 2D interfaces and SoA in CAD. As a result of these findings, we propose several design considerations for virtual object translation on 2D screens, which may enable designers to perceive a desirable feeling of control during the design process.
symQV: Automated Symbolic Verification of Quantum Programs

We present symQV, a symbolic execution framework for writing and verifying quantum computations in the quantum circuit model. symQV can automatically verify that a quantum program complies with a first-order specification. We formally introduce a symbolic quantum program model. This allows to encode the verification problem in an SMT formula, which can then be checked with a delta-complete decision procedure. We also propose an abstraction technique to speed up the verification process. Experimental results show that the abstraction improves symQV's scalability by an order of magnitude to quantum programs with 24 qubits (a 2^24-dimensional state space).