Reviewerly/exHarmony huggingface dataset:
Remote Sensing Semantic Segmentation via Boundary Supervision-Aided Multiscale Channelwise Cross Attention Network

High spatial resolution (HSR) remote sensing images inevitably pose the challenge of multi-scale transformation, as small objects such as cars and helicopters may occupy only a few pixel points. This incurs a significant hurdle for global context modeling, particularly in backbone networks with large downsampling coefficients. Simple summation or concatenation techniques, such as skip connections, fail to address semantic gaps and even impose negative impacts on multi-scale feature fusion. Meanwhile, due to the complexity of foreground objects, the boundary details of HSR remote sensing images are easy to lose in sampling operations. To overcome these challenges, we propose a Multi-scale Channel-wise Cross Attention Network (MCCANet) assisted by boundary supervision. Technically, MCCA captures the channel attention with various scales, which allows dynamic and adaptive feature fusion in a contextual scale-aware manner and focuses on both large and small objects distributed throughout the inputs. Besides, a Channel and Context Strainer (CCS) module is proposed and embedded in MCCA, filtering channels and contexts for the mitigation of intra-class differences. In addition, we apply a Boundary Supervision (BS) module to recover boundary contour, avoiding the blurring effect during the construction of contextual information. The refined boundary allows for the effective recognition of surrounding pixels, ensuring a better segmentation performance. Extensive experiments on iSAlD, ISPRS Potsdam, and LoveDA datasets demonstrate that our proposed MCCANet achieves a good balance of high accuracy and efficiency. Code will be available at: https://github.com/ZhengJianwei2/MCCANet.
Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the “new oil” of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO.
SECBlock-IIoT: A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

The IoT is widely used in a number of industries and generates large amounts of data. The data are processed, computed, and stored through distributed computing for analytical purposes. This invokes serious security and privacy concerns, and presents scalability issues. This paper describes a secure P2P and group communication supportive edge computing framework for IIoT systems, a consortium blockchain, and IPFS-based immutable data storage system, and an intelligent threat detection model to protect confidential data and identify cyber-attacks. Secure communications were ensured using a hybrid security scheme that included modified ECC, PUF, and Lagrange interpolation. We utilized a modified PoV consensus algorithm to resolve latency issues due to overhead and point of failure errors during block mining. The threat intelligence model used an autoencoder to transform data into a new format which was then fed into an RNN-DL to identify cyber-attacks. The model detected normal and anomalous activity, and then identified the category of detected malicious activity. We evaluated the framework according to various metrics and compared it with ECC, PoV, and ML-based classifiers. The results showed that the proposed system demonstrated a higher efficiency and improved scalability than conventional frameworks.
A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images

The identification of human epithelial type-2 mitotic cell patterns in the indirect immunofluorescence images (IIF HEp-2) is a critical step for autoimmune diseases computer-aided diagnosis (CAD) systems. Recognition of HEp-2 cells in the mitotic phase is clinically vital for validating the HEp-2 samples and assisting in diagnosing specimens with mixed patterns. Typically, mitotic cells are rarely observed in the HEp-2 specimen images, resulting in a significant skewness of the available medical datasets towards the majority of interphase (non-mitotic) patterns. In this paper, a deep learning framework is proposed based on a self-attention deep cross-residual network with an efficient generative adversarial network (GAN). The proposed framework consists of two consecutive steps. First, the problem of imbalanced data of minority mitotic against the majority interphase cells is remedied using Info-WGANGP approach in combination with the conventional data augmentation methods. Second, a downstream end-to-end deep learning Att-DCRNet is developed to classify the mitotic and interphase cell patterns of the IIF HEp-2 medical images. A comprehensive experimental study is performed to validate the effectiveness of the proposed framework against other state-of-the-art methods over the public medical dataset of UQ-SNP_HEp-2 Task-3. The proposed framework demonstrates competitive classification results attaining a maximum performance of 84.10% F1-score, 84.70% Matthew's correlation coefficient (MCC), and 99.0% balanced class accuracy (BcA), which proves its applicability for automatically supporting an accurate diagnosis decision regarding the HEp-2 mitotic and interphase cell patterns. The source code is available at this link: https://github.com/Anaam-dl/AttDCRNet_with_InfoWGANGP.
An efficient 3D object detection method based on Fast Guided Anchor Stereo RCNN

In most binocular 3D detection algorithms, a large number of anchor points need to be selected, which leads to the problem of slow feature extraction. To solve this problem, an anchor-guided 3D object detection algorithm for autonomous driving is proposed based on Stereo Recurrent Convolutional Neutral Network (Stereo RCNN), which is called Fast Guided Anchored Stereo RCNN (FGAS RCNN). The proposed FGAS framework is divided into two stages. In the first stage, a probability map is generated for the left and right input images to determine the foreground position. Sparse anchor points and corresponding sparse anchor boxes are generated from the prior information. Left and right anchors are used as a whole to generate a 2D preselection box. In the second stage, a Feature Pyramid Network (FPN) based on key point generation network is used to generate key points, which are combined with stereo regression to generate 3D preselected boxes. Finally, instance-level disparity estimation is proposed to solve the problem of pixel-level information loss in the original image. Instance-level disparity is combined with instance segmentation masks to improve the accuracy of center depth on the 3D bounding box. Extensive experiments on the challenging Kitti dataset and NuScences dataset show that the proposed method reduces the computational cost while maintaining a high regression rate without any depth information and prior information of position. Compared to other methods, the proposed method has higher efficiency, better robustness and stronger generalization ability.
CuRobo: Parallelized Collision-Free Robot Motion Generation

This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization technique to solve this problem and demonstrate its effectiveness on massively parallel GPUs. We show that combining simple optimization techniques with many parallel seeds leads to solving difficult motion generation problems within 53ms on average, 62x faster than SOTA trajectory optimization methods. We achieve SOTA performance by combining L-BFGS step direction estimation with a novel parallel noisy line search scheme and a particle-based optimization solver. To further aid trajectory optimization, we develop a parallel geometric planner that is atleast 28x faster than SOTA RRTConnect implementations. We also introduce a collision-free IK solver that can solve over 9000 queries/s. We are releasing our GPU accelerated library CuRobo that contains core components for robot motion generation. Additional details are available at sites.google.com/nvidia.com/curobo.
Sample-Driven Connectivity Learning for Motion Planning in Narrow Passages

Sampling-based motion planning works well in many cases but is less effective if the configuration space has narrow passages. In this paper, we propose a learning-based strategy to sample in these narrow passages, which improves overall planning time. Our algorithm first learns from the configuration space planning graphs and then uses the learned information to effectively generate narrow passage samples. We perform experiments in various 6D and 7D scenes. The algorithm offers one order of magnitude speed-up compared to baseline planners in some of these scenes.
Variable Admittance Interaction Control of UAVs via Deep Reinforcement Learning

A compliant control model based on reinforcement learning (RL) is proposed to allow robots to interact with the environment more effectively and autonomously execute force control tasks. The admittance model learns an optimal adjustment policy for interactions with the external environment using RL algorithms. The model combines energy consumption and trajectory tracking of the agent state using a cost function. Therein, an Unmanned Aerial Vehicle (UAV) can operate stably in unknown environments where interaction forces exist. Furthermore, the model ensures that the interaction process is safe, comfortable, and flexible while protecting the external structures of the UAV from damage. To evaluate the model performance, we verified the approach in a simulation environment using a UAV in three external force scenes. We also tested the model across different UAV platforms and various low-level control parameters, and the proposed approach provided the best results.
Informable Multi-Objective and Multi-Directional RRT<sup>*</sup> System for Robot Path Planning

Multi-objective or multi-destination path planning is crucial for mobile robotics applications such as mobility as a service, robotics inspection, and electric vehicle charging for long trips. This work proposes an anytime iterative system to concurrently solve the multi-objective path planning problem and determine the visiting order of destinations. The system is comprised of an anytime informable multi-objective and multi-directional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm to form a simple connected graph, and a solver that consists of an enhanced cheapest insertion algorithm and a genetic algorithm to solve approximately the relaxed traveling salesman problem in polynomial time. Moreover, a list of waypoints is often provided for robotics inspection and vehicle routing so that the robot can preferentially visit certain equipment or areas of interest. We show that the proposed system can inherently incorporate such knowledge to navigate challenging topology. The proposed anytime system is evaluated on large and complex graphs built for real-world driving applications. C++ implementations are available at: https://github.com/UMich-BipedLab/IMOMD-RRTStar.
Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

Many individuals, including researchers, professors, and students, encounter difficulties when searching for scholarly documents, papers, and journals within a specific domain. Consequently, scholars have begun to focus on document classification problem, offering various methods to address this issue. Researchers have utilized diverse data sources, such as citations, metadata, content, and hybrids, in their approaches.In these sources, the meta-data-based approach stands out for research paper classification due to its availability at no cost. Various scholars have employed different metadata parameters of research articles, including the title, abstract, keywords, and general terms, for research paper classification. In this study, we chose four meta-data-based features such as, title, keyword, abstract, and general terms from the SANTOS dataset, which was prepared by ACM. To represent these features numerically, we employed a semantic-based model called BERT instead of the commonly used count-based models. BERT generates a 768-dimensional vector for each record, which introduces significant time complexity during computation. Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. For classification purposes, we employed GNB and SVM classifiers. The outcomes of our study exposed that the combination of title and keywords outperformed other combinations.
Direct and indirect effects of social dominance orientation on hate speech perpetration via empathy and moral disengagement among adolescents: A multilevel mediation model

Abstract Although it is known that social dominance orientation directly affects hate speech perpetration, few studies have explored the mechanisms by which this effect takes place during adolescence. Based on the socio‐cognitive theory of moral agency, we aimed to fill this gap in the literature by exploring the direct and indirect effects of social dominance orientation on hate speech perpetration in offline and online settings. The sample included seventh, eigth, and ninth graders ( N = 3225) (51.2% girls, 37.2% with an immigrant background) from 36 Swiss and German schools who completed a survey about hate speech, social dominance orientation, empathy, and moral disengagement. A multilevel mediation path model revealed that social dominance orientation had a direct effect on offline and online hate speech perpetration. Moreover, social dominance also had indirect effects via low levels of empathy and high levels of moral disengagement. No gender differences were observed. Our findings are discussed regarding the potential contribution to preventing hate speech during adolescence.
Probabilistic motion planning for non-Euclidean and multi-vehicle problems

Trajectory planning tasks for non-holonomic or collaborative systems are naturally modeled by state spaces with non-Euclidean metrics. However, existing proofs of convergence for sample-based motion planners only consider the setting of Euclidean state spaces. We resolve this issue by formulating a flexible framework and set of assumptions for which the widely-used PRM*, RRT, and RRT* algorithms remain asymptotically optimal in the non-Euclidean setting. The framework is compatible with collaborative trajectory planning: given a fleet of robotic systems that individually satisfy our assumptions, we show that the corresponding collaborative system again satisfies the assumptions and therefore has guaranteed convergence for the trajectory-finding methods. Our joint state space construction builds in a coupling parameter 1≤p≤∞, which interpolates between a preference for minimizing total energy at one extreme and a preference for minimizing the travel time at the opposite extreme. We illustrate our theory with trajectory planning for simple coupled systems, fleets of Reeds–Shepp vehicles, and a highly non-Euclidean fractal space.
Path planning and collision avoidance based on the RRT*FN framework for a robotic manipulator in various scenarios

Abstract In this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.
Data augmentation driven by optimization for membrane separation process synthesis

This paper proposes a new hybrid strategy to optimally design membrane separation problems. We formulate the problem as a Non-Linear Programming (NLP) model. A common approach to represent the physical behavior of the membrane is to discretize the system of differential equations that govern the separation process. Instead, we represent the input/output behavior of the single membrane by an artificial neural network (ANN) predictor. The ANN is trained on a dataset obtained through the MEMSIC simulator. The equation form of the trained predictor (shape and weights) is then inserted in the NLP model at the place of the discretized system of differential equations. To improve the ANN accuracy without an excessive computational burden, we propose data augmentation strategies to target the regions where densify the dataset. We compare a data augmentation strategy from the literature with a novel one that densifies the dataset around the stationary points visited by a global optimization algorithm. Our approach was validated using a relevant industrial case study: hydrogen purification. Validation by simulation is performed on the obtained solutions. The computational results show that a data augmentation smartly coupled with optimization can produce a robust and reliable design tool.
Multi-population Black Hole Algorithm for the problem of data clustering

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.
Multiscale Dynamic Feature Learning for Quality Prediction Based on Hierarchical Sequential Generative Network

In industrial processes, long short-term memory (LSTM) is usually used for temporal dynamic modeling of soft sensor. The process data usually have various temporal correlations under different time scales due to the continuous physical and chemical reactions. However, LSTM model can only extract the dynamic features at a specific time scale, which affects the feature learning capability and modeling accuracy. In this article, a new hierarchical sequential generative network (HSGN) is proposed for mining multiscale dynamic features using large amount of unlabeled process data for soft sensor. To extract multiscale dynamic features for quality prediction, the process data are resampled with different sampling rates and then used to pretrain the corresponding self-learning LSTM models at different time scales. Subsequently, they can used to calculate the multiscale hidden feature states for labeled samples, which are further integrated with the original input information and input into a deep belief network (DBN) to construct the prediction model for the output variable. Thus, the HSGN method can take advantage of large number of unlabeled samples to mine multiscale dynamic hidden features and overcome the irregular sampling problem in industrial processes. The application in a real industrial scene shows the effectiveness of the proposed method.
Wavelets and stochastic theory: Past and future

In the paper, authors report on the interdisciplinary and extremely complex link between wavelets and stochastic processes. An insight into the history of wavelets has been provided presenting the fundamental conception of wavelets, as well as wavelet theory that emerged from stochastic processes. The multiresolution analysis corresponds to the Kolmogorov system which is a regular stationary stochastic process. It presents a significant link to the measurement problem in terms of positional notation which the wavelet domain hidden Markov model should be derived from. The optimal representation arises to be an issue requiring further elaboration extended to the general measurement and wavelet frames.