Reviewerly/exHarmony huggingface dataset:
Sanitizable Cross-System Authorization for Secure Communication in Intelligent Connected Vehicle

In Vehicular networks, Intelligent Connected Vehicles (ICVs) perform data exchange and sharing with others via wireless channels for comfortable and environment-friendly driving. Since the open nature of wireless channels, it is necessary to encrypt sensitive data before sharing to ensure data security. However, existing efforts for secure data sharing still encounter efficiency and security bottle-neck because of the following issues: 1) absence of an effective policy to cross-system dynamically assign ciphertexts to multiple recipients. 2) Difficulty in hindering data disclosure resulting from malicious behaviors of a malicious data publisher. With the motivation to address the above issues simultaneously, in this article, we propose a sanitizable cross-system ciphertext authorization scheme (SI-APRE) for secure communication in ICVs. Our SI-APRE enables the conversion of the ciphertext generated with the original recipient's identity into a new ciphertext bound to the access policy, so that other new authorized recipients can also access the raw data. Meanwhile, our SI-APRE can check and sanitize the original ciphertext to prevent malicious publishers from encrypting data in a harmful way. Experimental and theoretical analysis demonstrate that our SI-APRE solution enjoys impressive performance regarding efficiency, which makes it more feasible and practical in reality.
Real-time video streaming in medicine using virtual reality

Nowadays, medical science is faced with many challenges that prevent doctors and researchers from working quickly and efficiently. Such a challenge is, for example, the evaluation of digitized 3D pathological serial sections in a consultative manner. This selected example brings to the surface several obstacles, the solution of which can improve everyday work and medical education in other medical fields as well. One of these difficulties is overcoming the distance, and the other is reducing the need for hardware resources since not all users can be expected to have the most modern computer components. Our goal was to design and implement a solution that enables users (doctors, researchers, educators, medical students) from anywhere in the world to participate in the evaluation of 3D digitized pathological samples, even with the use of limited hardware resources, all in an environment provided by virtual reality technology within.
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.
Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

With the rapid development of artificial intelligence and computer vision, deep learning has become widely used for aircraft detection. However, aircraft detection is still a challenging task due to the small target size and dense arrangement of aircraft and the complex backgrounds in remote sensing images. Existing remote sensing aircraft detection methods were mainly designed based on algorithms employed in general object detection methods. However, these methods either tend to ignore the key structure and size information of aircraft targets or have poor detection effects on densely distributed aircraft targets. In this paper, we propose a novel multi-task aircraft detection algorithm. Firstly, a multi-task joint training method is proposed, which provides richer semantic structure features for bounding box localization through landmark detection. Secondly, a multi-task inference algorithm is introduced that utilizes landmarks to provide additional supervision for bounding box NMS (non-maximum suppression) filtering, effectively reducing false positives. Finally, a novel loss function is proposed as a constrained optimization between bounding boxes and landmarks, which further improves aircraft detection accuracy. Experiments on the UCAS-AOD dataset demonstrated the state-of-the-art precision and efficiency of our proposed method compared to existing approaches. Furthermore, our ablation study revealed that the incorporation of our designed modules could significantly enhance network performance.
Human Activity Recognition Based on Radar and Video Surveillance Sensor Fusion

Activity monitoring is an actual problem in the prevention of life-threatening conditions. This question is especially important for people over 65 who fall at least once a year. Unstable gait is one of the predictors of falls and the future progression of cognitive diseases. Hence, abnormal gait detection can potentially be a useful tool for the prevention of falls and the diagnosis of cognitive impairment. In this paper, a sensor fusion approach for human activity recognition is presented. Radar and video surveillance data of eight volunteers (19–37 years old) were used for training the deep learning model for the binary classification problem (normal/unstable gait). Radar data were processed using a wavelet transform, and a pre-trained AlexNet neural network was fitted using a training dataset. Information about skeletal key points was extracted from video frames by means of a BlasePose neural network and used for training the recurrent neural network with long-short-term memory units. Then, a sensor fusion approach at the classification level was applied. The accuracy and Cohen's kappa of the radar-only model were 0.931 and 0.859, respectively. The accuracy and Cohen's kappa of the video-only model were 0.966 and 0.930, respectively. Finally, the accuracy and Cohen's kappa for the sensor fusion-based model were 0.931 and 0.859, respectively. The results of our work can be potentially used for the design of the system for human activity recognition in the smart home technology paradigm.
GRuM — A flexible model-driven runtime monitoring framework and its application to automated aerial and ground vehicles

Runtime monitoring is critical for ensuring safe operation and for enabling self-adaptive behavior of Cyber-Physical Systems (CPS). Monitors are established by identifying runtime properties of interest, creating probes to instrument the system, and defining constraints to be checked at runtime. For many systems, implementing and setting up a monitoring platform can be tedious and time-consuming, as generic monitoring platforms do not adequately cover domain-specific monitoring requirements. This situation is exacerbated when the System under Monitoring (SuM) evolves, requiring changes in the monitoring platform. Most existing approaches lack support for the automated generation and setup of monitors for diverse technologies and do not provide adequate support for dealing with system evolution. In this paper, we present GRuM (Generating CPS Runtime Monitors), a framework that combines model-driven techniques and runtime monitoring, to automatically generate a customized monitoring platform for a given SuM. Relevant properties are captured in a Domain Model Fragment, and changes to the SuM can be easily accommodated by automatically regenerating the platform code. To demonstrate the feasibility and performance we evaluated GRuM against two different systems using TurtleBot robots and Unmanned Aerial Vehicles. Results show that GRuM facilitates the creation and evolution of a runtime monitoring platform with little effort and that the platform can handle a substantial amount of events and data.
Aerial Fluvial Image Dataset for Deep Semantic Segmentation Neural Networks and Its Benchmarks

Classification of aerial imagery is essential for water channel surveillance and waterfront land cover characterization. It is also beneficial to long-duration collaborative autonomous navigation of both unmanned aerial vehicles (UAVs) and autonomous surface vehicles (ASVs) to fulfill unmanned hydrologic data collection, environmental inspection, and disaster warning tasks. Deep semantic segmentation networks trained on aerial imagery have shown great results, however, they require finely labeled data. Existing aerial image datasets contain mostly urban scenes or fluvial images taken from ground level or collected from the Internet, there are no datasets that incorporate aerial and fluvial scenes with detailed annotation from different perspectives or include waterborne obstacles. To tackle this problem, aerial fluvial image dataset (AFID) is presented with multiple camera perspectives of fluvial scenes and is semantically labeled with emphasis on water and waterborne obstacles. Deep neural networks for binary (water and nonwater) semantic segmentation, with 12 different combinations of five encoders and three decoding architectures, are trained and tested in a curriculum learning scheme. Model performance is benchmarked on AFID, and the accuracy-efficiency tradeoff is discussed with the conclusion that the Unet architecture with a mix transformer encoder achieves the best segmentation performance with moderate computational consumption. The AFID dataset is publicly available to facilitate future work on developing new lightweight semantic segmentation models. Our immediate future plan will focus on the coordination of air and surface-water autonomous systems for navigable water detection and obstacle avoidance in high-risk challenging environments.
A Survey on Population-Based Deep Reinforcement Learning

Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can generalize to policies of other players or humans. In this survey, we shed light on population-based deep reinforcement learning (PB-DRL) algorithms, their applications, and general frameworks. We introduce several independent subject areas, including naive self-play, fictitious self-play, population-play, evolution-based training methods, and the policy-space response oracle family. These methods provide a variety of approaches to solving multi-agent problems and are useful in designing robust multi-agent reinforcement learning algorithms that can handle complex real-life situations. Finally, we discuss challenges and hot topics in PB-DRL algorithms. We hope that this brief survey can provide guidance and insights for researchers interested in PB-DRL algorithms.
Comparative Analysis of the Clustering Methods Application for Detecting Anomalies in the Information Processes of ICS Networks Exposed to Cyberattacks

The rise of industrial automation tools and the integration of industrial and corporate networks for better production management have increased the likelihood of successful cyberattacks. This has necessitated the need to address the practical information security of industrial control systems (ICS). One solution is anomaly detection in ICS information processes during operation, which can detect known and unknown cyberattacks without prior analysis or labeling of training samples. Cluster analysis is used as an anomaly detection method in ICS, with hierarchical cluster analysis applied to data exposed to various cyberattacks. The study solves the issue of determining the optimal methods for secondary analysis of hierarchical clusters in order to mark clusters that includes anomalous data. Different methods such as IsoTree, OPTICS, LOF and ECOD were considered. Among these methods of classification and clustering, IsoTree and LOF methods provided the best results for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SWaT$</tex> dataset for determining clusters both anomalous and normal.
A hybrid thrusting system for increasing the endurance time of multirotor unmanned aerial vehicles

One of the most significant disadvantages of electric multirotor unmanned aerial vehicles is their short flight time compared to fuel-powered unmanned aerial vehicles. This is mainly due to the low energy density of electric batteries. Fuel has much more energy density when compared to batteries. Electric-powered motors in multirotor unmanned aerial vehicles cannot be replaced with fuel-based engines because the stability and control of multirotor unmanned aerial vehicles rely on the high response rates of electric motors. One of the possible solutions to overcome this problem of short endurance times is by using hybrid thrusting systems that combine the advantages of both fuel and electrical propulsion systems, where high maneuverability and long endurance flight time could be achieved. In this work, hybrid thrusting and power systems for multirotor unmanned aerial vehicles are studied. Targeted hybrid thrusting systems consist of combustion engines, electric motors, and their power sources. Then a hybrid thrusting system-based quadrotor unmanned aerial vehicle model is developed. The article presents the altitude and attitude control systems of the developed hybrid thrusting system-based unmanned aerial vehicle. The presented hybrid quadcopter model comprises four electric motors and one fuel engine. The fuel engine used in this work is a 4.07 cc internal combustion engine targeting 2–3 kg unmanned aerial vehicles with up to 5 kg maximum takeoff weight. The developed hybrid quadrotor unmanned aerial vehicle achieved a 139% improvement in flight time when compared with traditional electric-based quadrotor unmanned aerial vehicles. The article also reports on other flight time-related issues such as the optimal fuel mass to battery size ratio to maximize the endurance time of the quadrotor unmanned aerial vehicles.
Efficient Dehazing Method for Outdoor and Remote Sensing Images

As an atmospheric phenomenon, haze significantly reduces the visibility of outdoor and remote sensing images. As remote sensing and outdoor imaging have different mechanisms, existing dehazing methods are hard to be applied for both outdoor images and remote sensing images. In this article, an efficient dehazing method is proposed which can be applied to both outdoor and remote sensing images. The proposed method has the advantages of both dehazing methods based on image enhancement and image restoration methods. To address the problem of inaccurate calculation of transmittance in existing methods, a Gaussian-weighted image fusion is introduced to obtain a refined transmittance. In addition, to solve the problem of image color distortion after dehazing, an unsharp mask method is used to correct the dehazed image. Experiments on the synthetic dataset and real dataset show that the proposed method is able to dehaze both the outdoor and remote sensing images and outperforms existing methods by visual inspection. On the RICE remote sensing image dataset, the proposed method has an image peak signal-to-noise ratio of 27.08 and structural similarity of 0.94, which are higher than other methods.
Semantic-Enhanced Image Clustering

Image clustering is an important and open challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. To solve the above problems, we propose a novel image clustering method guided by the visual-language pre-training model CLIP, named Semantic-Enhanced Image Clustering (SIC). In this new method, we propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics. Finally, we propose to perform clustering with consistency learning in both image space and semantic space, in a self-supervised learning fashion. The theoretical result of convergence analysis shows that our proposed method can converge at a sublinear speed. Theoretical analysis of expectation risk also shows that we can reduce the expectation risk by improving neighborhood consistency, increasing prediction confidence, or reducing neighborhood imbalance. Experimental results on five benchmark datasets clearly show the superiority of our new method.
cegpy: Modelling with chain event graphs in Python

Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to embed, within its graph and its statistical model, asymmetries exhibited by a process. These asymmetries might be in the conditional independence relationships or in the structure of the graph and its underlying event space. Structural asymmetries are common in many domains, and can occur naturally (e.g. a defendant vs prosecutor's version of events) or by design (e.g. a public health intervention). Whilst two CEG packages exist in R for modelling processes with asymmetric conditional independencies, there currently exists no software that allows a user to leverage the theoretical developments of the CEG model family in modelling processes with structural asymmetries. In this paper, we present cegpy: the first Python implementation of CEGs and the first across all languages to support structurally asymmetric processes. cegpy contains an implementation of Bayesian learning and probability propagation algorithms for CEGs. We illustrate the functionality of cegpy using a structurally asymmetric dataset.
Visual Gaze Labeling for Augmented Reality Studies

Abstract Augmented Reality (AR) provides new ways for situated visualization and human‐computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR. Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches. To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image‐based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.
Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations

Abstract 3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos – or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high‐frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.
Data Stories of Water: Studying the Communicative Role of Data Visualizations within Long‐form Journalism

Abstract We present a methodology for making sense of the communicative role of data visualizations in journalistic storytelling and share findings from surveying water‐related data stories. Data stories are a genre of long‐form journalism that integrate text, data visualization, and other visual expressions (e.g., photographs, illustrations, videos) for the purpose of data‐driven storytelling. In the last decade, a considerable number of data stories about a wide range of topics have been published worldwide. Authors use a variety of techniques to make complex phenomena comprehensible and use visualizations as communicative devices that shape the understanding of a given topic. Despite the popularity of data stories, we, as scholars, still lack a methodological framework for assessing the communicative role of visualizations in data stories. To this extent, we draw from data journalism, visual culture, and multimodality studies to propose an interpretative framework in six stages. The process begins with the analysis of content blocks and framing elements and ends with the identification of dimensions, patterns, and relationships between textual and visual elements. The framework is put to the test by analyzing 17 data stories about water‐related issues. Our observations from the survey illustrate how data visualizations can shape the framing of complex topics.
Ferret: Reviewing Tabular Datasets for Manipulation

Abstract How do we ensure the veracity of science? The act of manipulating or fabricating scientific data has led to many high‐profile fraud cases and retractions. Detecting manipulated data, however, is a challenging and time‐consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically flagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer‐review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these findings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.