Reviewerly/exHarmony huggingface dataset:
Risk-Aware Path Planning for Unmanned Aerial Systems in a Spreading Wildfire

Path planning for small unmanned aerial systems (SUAS) in the presence of poorly understood and dynamic obstacles is a challenging problem: for example, flight over a spreading wildfire. Evidential information fusion is used to estimate the current wildfire state and the resulting heat aura at flight level. This approach accounts for ignorance, which is a result of conflict among sensors operating in a harsh environment and a computational forecasting agent that uses a fire evolution model of inadequate accuracy. An SUAS is employed to visit locations of high conflict to provide additional situational awareness. Flight-level heat aura is modeled as a keepout zone with probabilistic boundaries for SUAS path planning. A novel unsupervised classification algorithm is developed to identify distinct obstacle boundaries within the estimated heat aura. Path planning is posed as a chance-constrained optimal control problem, which is transcribed to a nonlinear program via pseudospectral discretization. The results show that this approach can yield a family of solutions that elicit the risk associated with each mission design, and the appropriate choice of risk can aid in the generation of “keyhole paths.”
Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process

In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios.
Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode.
Spatiality Sensitive Learning for Cancer Metastasis Detection in Whole-Slide Images

Metastasis detection in lymph nodes via microscopic examination of histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathology images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. Due to the huge size of whole-slide images, most existing approaches split each image into smaller patches and simply treat these patches independently, which ignores the spatial correlations among them. To solve this problem, this paper proposes an effective spatially sensitive learning framework for cancer metastasis detection in whole-slide images. Moreover, a novel spatial loss function is designed to ensure the consistency of prediction over neighboring patches. Specifically, through incorporating long short-term memory and spatial loss constraint on top of a convolutional neural network feature extractor, the proposed method can effectively learn both the appearance of each patch and spatial relationships between adjacent image patches. With the standard back-propagation algorithm, the whole framework can be trained in an end-to-end way. Finally, the regions with high tumor probability in the resulting probability map are the metastasis locations. Extensive experiments on the benchmark Camelyon 2016 Grand Challenge dataset show the effectiveness of the proposed approach with respect to state-of-the-art competitors. The obtained precision, recall, and balanced accuracy are 0.9565, 0.9167, and 0.9458, respectively. It is also demonstrated that the proposed approach can provide more accurate detection results and is helpful for early diagnosis of cancer metastasis.
ModelsKG:A Design and Research on Knowledge Graph of Multimodal Curriculum Based on PaddleOCR and DeepKE

Multimodel deep learning system has attracted more and more attention. The traditional deep learning system mostly focuses on single modal processing and application. However, many applications require various modes to complete a certain task. For example, in the teaching scene, teaching materials are mostly displayed in text mode, video and PPT modes are also used to transfer the content of knowledge points. However, there is often a lack of connection between one mode and another mode, resulting in the fragmentation, complexity and redundancy of knowledge. Based on this consideration, the paper puts forward the design idea and frame of multimodal curriculum knowledge graph based on paddleOCR and DeepKE. Use DeepKE to extract the triple relationship between subject knowledge points and store it in the neo4j graph database, so as to build the knowledge graph of subject knowledge points, then use paddeOCR to identify the text content in the teaching video, generate the video frame description text, use NLP processing technologies such as text similarity to realize the understanding of video segments, and finally link the fine-grained video segments to the text knowledge graph, so as to build the multi-modal curriculum knowledge graph, In order to realize the purpose of intelligent search and intelligent construction of learning link.
Application of tensor factorisation for CAE model preparation from CAD assembly models

Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.
Poisson limit theorems for the Cressie–Read statistics

We derive Poisson limit theorems for the multinomial Cressie–Read goodness-of-fit statistics and some of their modifications under the assumption that as the sample size n goes to infinity, the number of groups mn increases with the sample size. Our results extend the Poisson limit theorems for the Pearson chi-square statistic reported in Rempała and Wesołowski (2016).
Single‐pass stratified importance resampling

Abstract Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired target. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
Design and Implementation of Smart ATM under IDLE Application

Now a day’s use of ATMs to withdraw funds has grown over time. At the same moment, number of theft and robbery events has grown, requiring the need for a safer ATM with extra security features. For the successful working we are using the raspberry pi 3b+, RFID, Web cam and GSM module. Raspberry pi varies from different aspects of 8051 Micro Controller. This agency’s purpose is to create a safety smart ATM that employs RFID and fingerprint authentication to get access. The user’s RFID identification and fingerprint data are obtained, followed by the recognized card details, approval status, and access location being sent on for validation against database information. The connected creditor receives a message if the information is validated against the record details obtained. The registered owner is also notified about the circumstance, date, and time of the access. It is also advised to install a web camera and use OTP authentication for enhanced security. To guarantee absolute safety, the profile of the customer that use the ATM card is documented in the unit using a camera, as well as the timeline of access, which may be used in the instance of suspicion and any suspicious authentication is performed a buzzer ring automatically. The whole working is done under the IDLE Software.
The interpretability and scalability of linguistic-rule-based systems for solving regression problems

This study applies the idea of computing with words to develop an advanced genetic method to design the Linguistic Rule Base Systems (LRBSs) from a given dataset to solve the dataset regression problem, whose rules can be considered human knowledge. It has two primary specific features. The first is its ability to ensure the uncertain equality of two dataset contents assigned to each of its designed rules: the dataset content the users capture when reading it and the one the proposed method computes and assigns to it. Then, Tarski et al.'s interpretability concept in the math logics field does require the designed fuzzy sets to be isomorphic images of their assigned words. It implies their soundness in representing their words. The second is its ability to ensure the LRBs' scalability, an essential feature of human knowledge stating that it can grow while maintaining its existing one. Then, the proposed method can utilize the existing LRBSs optimality to design a new optimized LRBSs generation to increase the regression precision by allowing the existing L-attributes' word sets to grow. Compared to existing methods, the conducted experimental study can justify its performance and benefits in solving the regression problems using benchmark datasets in this area.
Towards enhanced threat modelling and analysis using a Markov Decision Process

The complexity of socio-technical systems using Ambient Intelligence (AmI) and the Internet of Things (IoT) is growing exponentially, involving numerous entities, such as humans, infrastructures, and cyber systems. Achieving and maintaining a specified level of security and privacy in such systems is challenging and crucial. Attack Tree is a powerful technique used in safety and reliability engineering. In this paper, we attempted to enhance Attack Tree analysis by transforming it into a Markov Decision Process (MDP) model. We propose an algorithm to transform an Attack Tree into an MDP model. We argue that formal methods, such as probabilistic model checking can significantly improve the security analysis capabilities. Moreover, the mixture of MDP and probabilistic model checking can overcome the limitations of Attack Trees, such as state explosion, scalability, and manual interaction. We used a probabilistic model checker, namely PRISM to model an attack scenario and perform security analysis on it. To demonstrate the significance, we took a real-world use case and performed a probabilistic analysis on it. The results revealed that formal analysis can prove certain properties, which were not possible to verify using attack trees.
Optimizing Grid Layouts for Level‐of‐Detail Exploration of Large Data Collections

Abstract This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level‐of‐detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search‐based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black‐Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.
A Process Model for Dashboard Onboarding

Abstract Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non‐expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real‐world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.
Robust Coverless Image Steganography Based on Neglected Coverless Image Dataset Construction

Most of the existing image selection-based coverless image steganography methods mainly focus on improving the capacity and robustness under the assumption that the corresponding dataset is available. But they ignore how to successfully construct the coverless image dataset, which is the foundation of such methods and has a critical impact on the capacity. In this paper, a coverless image steganography is proposed that considers how to efficiently construct the coverless image dataset. In the proposed method, the CNN-based deep hash is extracted from the image and a specific mapping rule is designed to map the high-dimensional deep hash to the low-dimensional secret message. In addition, an unsupervised clustering algorithm is adopted to construct the coverless image dataset, which makes the construction of the coverless image dataset efficient and improves the robustness of the proposed steganography method. To our best knowledge, this is the first attempt to improve the construction efficiency of the coverless image dataset in the field of coverless image steganography. Experimental results show that the construction of a large coverless image dataset is feasible and reliable, and the proposed method has better robustness and higher dataset utilization rate compared with the state-of-the-art methods.
Reaction-diffusion model applied to enhancing U-Net accuracy for semantic image segmentation

In our current paper we are introducing a new method to enhance semantic image segmentation accuracy of a U-Net neural network model by integrating it with a mathematical model based on reaction-diffusion equations.The methods currently used for semantic image segmentation, including U-Net neural networks, are processing images as blocks of pixels in which the boundaries, the colors and patterns are all mixed together as inputs to the transformations that take place inside the layers of the convolutional neural networks. In our method we are modifying the architecture of a U-Net network and introduce a new data input feed in parallel to the image feed that needs to be segmented. The new input feed is mathematically extracted from the input image and contains the edges (shape) information of the image to be processed. The new input feed it's used during the U-Net decoding phase in order to help shape more precisely the up-scaled output edges, thus leading to improved accuracy performance of the network.Introducing the parallel feed shows an improvement of accuracy metrics up to 4% (if compared to the U-Net model) and has a limited impact on computational resources consumed at training, because we are only adding a small number of new parameters to be calculated.
Criminal protection of corporate websites: An analytical study

In the light of the state’s economic revolution and tremendous techniques sweeping the communication in the recent years which seeks to establish the concept of e­government practically, and the huge increase in using the Internet by all member of society including companies that depend on e-commerce, the UAE legislator is keen to report on criminal protection of a website by issuing the Federal Decree-Law No. 34 for the year 2021 on Combating Rumours and Cybercrime. This research aims to demonstrate the effectiveness of the UAE law in combating hackers who attack and spy on sensitive data of financial, commercial, or economic establishments. To achieve that, the researcher examined the concept, legal nature, and components of a website similar to a previous study conducted in Ukraine (Nekit, Ulianova, &amp; Kolodi, 2019). The researcher opted to check what the website means and state its advantages and disadvantages, and then dealt with all forms of penal protection of the website in the UAE law as well as the general rules of responsibility for the crimes of hacking, and finished the research by conclusion including the most important findings and recommendations such as that the UAE legislator needs to amend the text of Article 75 of the Code of Criminal Procedure, so as to monitor wired and wireless conversations including the website. A command should be issued from the judge, so as to be justified and useful in revealing the truth
A Fourier-Based Semantic Augmentation for Visible-Thermal Person Re-Identification

This letter introduces a novel Fourier-based data augmentation strategy for visible-thermal person re-identification (VT-ReID). Different from some existing methods which are proposed from the perspective of network structure and loss functions, our method aims to fully consider the semantic information from the perspective of data preprocessing. The main hypothesis is that the phase component in the Fourier domain contains high-level semantic information and the amplitude component contains low-level modality awareness information. In order to make the model pay more attention to semantic information learning, we design a simple but effective Fourier-based semantic augmentation (FSA) module, which can be inserted seamlessly into any existing models. Extensive experiments on RegDB and SYSU-MM01 datasets have shown that our proposed method can improve the VT-ReID performance significantly and achieve state-of-the-art performance.
Collision-free trajectory design for dance choreography of virtual drones in hierarchical structure

This paper proposes a collision-free trajectory design for dance choreography of virtual drones using a hierarchical design structure. The trajectory on two levels is designed, i.e., on the level of individual virtual drones and on the level of their centralized coordination. The design on the drone level through reinforcement learning has been carried out, with which dance steps of virtual drones in their motion can be achieved. The coordination level contains a prediction algorithm, which guarantees the avoidance of collision, even at the faultiness of some virtual drones. The effectiveness of the hierarchical design on the example of a Viennese waltz dance choreography in augmented reality has been illustrated.