Reviewerly/exHarmony huggingface dataset:
UAV Path Planning using Rotated TOR in Structured Environment

Path planning is one of the crucial criteria for an unmanned aerial vehicle (UAV) in providing a safe path to fly from any location to the specified goal. Global approach to autonomous path planning often used the harmonic potentials technique to guide the planner. The harmonic potentials are essentially the solutions of Laplace's equation that are employed to model the environment. The computational resources required to obtain these harmonic potentials often involve a large number of mathematical calculations. In the previous work, fast iterative methods that apply the use of full-sweep iteration were suggested. This study examines a fast-iterative numerical approach, namely Rotated Two-Parameter Successive Over-Relaxation (RTOR), to UAV path planning. The algorithm is implemented in a self-developed UAVPlanner Simulator where hills are generated randomly in grid areas. The proposed method was tested using several simulation scenarios which demonstrated the efficiency of the algorithm in finding the path, where it was evaluated in terms of the number of iterations and computational time efficiency with the different number of outdoor static obstacles. The results show that RTOR outperformed the previous methods and gives faster computational time and iteration in generating a path for UAVs.
A Workflow to Convert Live Atmospheric Sensor Data into Linked Data

Today's atmospheric data is generated swiftly as a result of the growth of IoT and sensor technologies and is available via data suppliers' RESTful APIs. However, sensor data mostly consists of live data streams including sensor observations, which are produced in a dispersed manner by several heterogeneous infrastructures, with little or no interoperability. RDF streams incorporating semantic data interoperability have arisen in last years and can be the foundation of intelligent semantic applications (e.g. semantic complex event processing). To enable semantic analysis of live atmospheric data streams, this article proposes a methodology for converting live data streams into Linked Data. The process leverages the most recent technologies for RML semantic mapping, ontology modeling, and Linked Data to extend the semantic usefulness of live atmospheric data, for example, by allowing for easy integration of atmospheric data streams with other live RDF streams.
WAFormer: Ship Detection in SAR Images Based on Window-Aware Swin-Transformer

The research work of synthetic aperture radar (SAR) image target detection based on deep learning has made great progress. However, most of them apply the methods applicable to optical images directly to SAR images, ignoring the characteristics of targets in SAR images. For instance, the size of target in SAR images is usually small and volatile. Meanwhile, the target distribution is relatively sparse and the detection is affected by the complex background noise. In this paper, we propose an improved backbone network, called WAFormer, for ship targets detection in SAR images, based on the latest Swin-Transformer. WAFormer improves the local window attention mechanism of Swin-Transformer by introducing the new window settings. Our model can be more suitable to match the shape of the target, so that it obtains more accurate detection in SAR images. Experimental results show that the WAFormer achieves 74.4% mAP on the Official-SSDD SAR dataset, surpassing Swin-Transformer by +1.0, especially for large targets.
Inverse Covariance Intersection Fusion Steady-state Kalman filter for Uncertain Systems with Multiplicative Noises, Missing Measurements and Linearly Correlated White Noises

This paper is concerned with inverse covariance intersection fusion problem of uncertain linear systems with multiplicative noises, missing measurements and uncertain linearly correlated white noises. By introducing the fictitious noises to compensate the stochastic uncertainties, the system under consideration can be converted into one with only uncertain noise variances. The steady-state Kalman filter is designed by inverse covariance intersection (ICI) fuser. It overcomes the disadvantage that the covariance intersection (CI) fuser has larger conservativeness. The accuracy of the ICI filter is higher than CI filter and that of local filter. A simulation example is given to verify the accuracy relations.
Automatic Digit Hand Sign Detection With Hand Landmark

Sign language is challenging to understand and needs a lot of practice before it can be mastered. With the growth of the deaf and the hard-of-hearing community, researchers are trying to find an effective way to understand sign language. This study will utilize hand landmarks to detect digits in sign language. Three models trained with different features will be used to compare their accuracy. The first model will be trained using the hand images only, the second model will be trained using the hand image and the hand landmarks, and the third model will be trained using the hand landmarks only. Mediapipe will be used to extract the hand landmark features, which is one of the features used by the model. The study results show that the first and second models have better training and testing accuracy than the third. However, the third model is superior when evaluated using the validation dataset with 85% accuracy, compared to 23.30% and 41.70% for the first and second models.
Research on Credit Risk Assessment of Listed Companies in Technology Sector Based on Support Vector Machine Integration

In recent years, the state vigorously support the development of science and technology enterprises, with the transformation of economy and the rapid development of science and technology, science and technology enterprises in our country's economic development play a more and more important role, but the science and technology enterprises are in growth period, exist in the process of the development of a lot of risk, so there are some problems in financing. For banks, credit evaluation of enterprises is very necessary, which can effectively reduce the credit risk of banks. Based on the establishment of the credit evaluation index system of listed companies in the science and technology sector, this paper screens out 12 indicators, uses support vector machine to classify the selected sample enterprises, and trains 10 sub-support vector machines. In order to improve the accuracy, the fuzzy integral based support vector machine ensemble method is used to classify enterprises. The results show that the classification accuracy of both training samples and test samples is improved, and the integrated classification accuracy is higher than that of a single support vector machine, which also shows the feasibility and effectiveness of the model.
Informed Graph Convolution Networks for Multilingual Short Text Understanding

In the open domain environment, the state-of-the-art models cannot process analyze insufficient training data correctly. We propose an adaptive graph convolution network with informed machine learning for multilingual short text understanding to tackle these problems. Specifically, The prior knowledge to guide the graph neural network to extract sentence topics. We construct category anchor words as prior category keywords, prior category keywords and training data as independent information sources, and prior knowledge participates in the training of graph neural network. Moreover, we integrate the attention mechanism in the training process, so that the model can pay attention to task-related information adaptively. We explain the build blocks and present the integrated knowledge representation. The experimental results on the Multilingual Short Text (MST), THUCNews and AGNews datasets show that our method outperforms most of the existing methods.
SUPPLE: A Dialogue Management Approach Based on Conversation Patterns

We propose SUPPLE, a new class of dialogue management systems that takes the core concept of a dialogue sequence as its main starting point. SUPPLE is inspired by the conversation patterns from the Natural Conversation Framework (NCF). While NCF primarily provides a design framework, we propose to automate the selection and updating of dialogue sequences as a central component of the dialogue management module, enabling the dialogue system to build a hierarchical dialogue structure at run-time. The conversation patterns are combined with the key concepts of update strategies and agenda adopted from the Information State Update approach. We formally describe the building blocks of our approach, and show how dialogue competencies like sequence expansion and slot-filling can be performed in our approach. These are further illustrated in a cooking assistant scenario.
Opportunities and Challenges in Metaverse the Rise of Digital Universe

The concept of the metaverse dates to 1992; however, the popularity of this concept has gained increased attention in 2021 following the announcement by Mark Zuckerberg. The topic of the metaverse is a new and attractive topic that is gaining increased insight from researchers. The paper focuses on a broader evaluation of the metaverse and web 3.0 technologies. Moreover, an assessment of the history and potential of these technologies is considered. Additionally, an evaluation of the available literature reviews between 1992 and 2022 was conducted. The assessment identified enormous attention on this scope from diverse multidisciplinary fields ranging from virtual reality, augmented reality, quantum communication, and blockchain. In the next ten years, expected progress is expected concerning metaverse and web 3.0 technologies. Consequently, these technologies will significantly impact how users interact with one another and how they associate with the internet. In this paper, we address the current security challenges and opportunities with the rise of the metaverse.
$\mathit {O(N)}$ Memory-Free Hardware Architecture for Burrows-Wheeler Transform

A novel hardware architecture for Burrows-Wheeler Transform (BWT) scheme is presented. The core idea is to have a memory-free strategy that does not involve any software overhead during BWT operation. This is achieved by introducing a register-file concept and utilizing basic digital logic circuits to perform the entire BWT operation. Additionally, this is a kind of transformation scheme that does not utilize any kind of matrix during transformation, and thereby, it is free from run-time memory consumption. It efficiently handles the string terminating mechanism in the proposed design without involving any extra terminating symbol. This string terminator-free architecture eventually reduces additional operation and storage space to maintain the string, and thereby, the architecture does not necessitate any register read and write operations. This architecture exhibits efficient transformation without involving any indexing method or sorting mechanism during an inverse transformation operation. This architecture achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N)$</tex-math></inline-formula> time complexity compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N^{2})$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N \log N)$</tex-math></inline-formula> as experienced by the existing state-of-the-art approaches.
Solar Tracking System by Utilized Optimized Algorithm Based Deep Learning

Solar tracking system is significant for most PV solar power systems in order to enhance the power production. In this study, a dual-axis solar tracking system-based solar tracking algorithm has been designed and implemented. However, several issues still need to be overcome for using a tracker system that includes power consumption related to the electrical part of the tracker and the estimation of the best path based on tracking technology that is utilized. The solar tracker predication algorithm can predicate the best solar path for each day in the year based on location. For this purpose, Deep Recurrent Neural Network (D-RNN) with Long-Term Short-Term units (LSTM) has been implemented to predict the path that the tracker should move with time for all the years. “Adam” optimizer has also been used in order to update network weights iteratively based on the training data. The proposed model achieved more accuracy than slandered algorithm, in which the average RMSE of proposed algorithm is 0.0512 in the training phase and 0.067 in the testing phase, while the average RMSE (Root Mean Square Error) of the traditional machine learning model is 0.0746 in training phase and 0.086 in testing phase.
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild

Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performance of existing camera pose estimation methods are susceptible to pixels that are not geometrically consistent. To tackle this challenge, we present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence initialized from pairwise optical flow. Our key idea is to optimize long-range video correspondence as dense point trajectories and use it to learn robust estimation of motion segmentation. A novel neural network architecture is proposed for processing irregular point trajectory data. Camera poses are then estimated and optimized with global bundle adjustment over the portion of long-range point trajectories that are classified as static. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories compared to existing state-of-the-art methods. In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories. As the point trajectory representation is general, we further present results and comparisons on in-the-wild monocular videos with complex motion of dynamic objects. Code is available at https://github.com/bytedance/particle-sfm .
Enhancing Classifier Conservativeness and Robustness by Polynomiality

We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression.We then show how polynomiality can remedy the situation.This, among others, leads purposefully to random-level performance in the tails, away from the bulk of the training data.A directly related, simple, yet important technical novelty we subsequently present is softRmax: a reasoned alternative to the standard softmax function employed in contemporary (deep) neural networks.It is derived through linking the standard softmax to Gaussian class-conditional models, as employed in LDA, and replacing those by a polynomial alternative.We show that two aspects of softRmax, conservativeness and inherent gradient regularization, lead to robustness against adversarial attacks without gradient obfuscation.
A Study of Continual Learning Methods for Q-Learning

We present an empirical study on the use of continual learning (CL) methods in a reinforcement learning (RL) scenario, which, to the best of our knowledge, has not been described before. CL is a very active recent research topic concerned with machine learning under non-stationary data distributions. Although this naturally applies to RL, the use of dedicated CL methods is still uncommon. This may be due to the fact that CL methods often assume a decomposition of CL problems into disjoint sub-tasks of stationary distribution, that the onset of these sub-tasks is known, and that sub-tasks are non-contradictory. In this study, we perform an empirical comparison of selected CL methods in a RL problem where a physically simulated robot must follow a racetrack by vision. In order to make CL methods applicable, we restrict the RL setting and introduce non-conflicting subtasks of known onset, which are however not disjoint and whose distribution, from the learner's point of view, is still non-stationary. Our results show that dedicated CL methods can significantly improve learning when compared to the baseline technique of “experience replay”.
Semantic Novelty Detection via Relational Reasoning

Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to recognize unknown objects at deployment time and issue a warning to the user accordingly. Despite the impressive advancements of deep learning research, existing models still need a finetuning stage on the known categories in order to recognize the unknown ones. This could be prohibitive when privacy rules limit data access, or in case of strict memory and computational constraints (e.g. edge computing). We claim that a tailored representation learning strategy may be the right solution for effective and efficient semantic novelty detection. Besides extensively testing state-of-the-art approaches for this task, we propose a novel representation learning paradigm based on relational reasoning. It focuses on learning how to measure semantic similarity rather than recognizing known categories. Our experiments show that this knowledge is directly transferable to a wide range of scenarios, and it can be exploited as a plug-and-play module to convert closed-set recognition models into reliable open-set ones.
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching

Omnidirectional cameras have been used widely to better understand surrounding environments. They are often configured as stereo to estimate depth. However, due to the optics of the fish eye lens, conventional epipolar geometry is inapplicable directly to omnidirectional camera images. Intermediate formats of omnidirectional images, such as equirect-angular images, have been used. However, stereo matching performance on these image formats has been lower than the conventional stereo due to severe image distortion near pole regions. In this paper, to address the distortion problem of omnidirectional images, we devise a novel subdivision scheme of a spherical geodesic grid. This enables more isotropic patch sampling of spherical image information in the omnidirectional camera space. By extending the existing equalarc scheme, our spherical geodesic grid is tessellated with an equalepiline subdivision scheme, making the cell sizes and in-between distances as uniform as possible, i.e., the arc length of the spherical grid cell's edges is well regularized. Also, our uniformly tessellated coordinates in a 2D image can be transformed into spherical coordinates via one-to-one mapping, allowing for analytical forward/backward transformation. Our uniform tessellation scheme achieves a higher accuracy of stereo matching than the traditional cylindrical and cubemap-based approaches, reducing the memory footage required for stereo matching by 20%.
Distributed Training for Deep Learning Models On An Edge Computing Network Using Shielded Reinforcement Learning

With the emergence of edge devices along with their local computation advantage over the cloud, distributed deep learning (DL) training on edge nodes becomes promising. In such a method, the cluster head of a cluster of edge nodes schedules all the DL training jobs from the cluster nodes. Using such a centralized scheduling method, the cluster head knows all the loads of the cluster nodes, which can avoid overloading the cluster nodes, but the head itself may become overloaded. To handle this problem, we first propose a multi-agent RL (MARL) system that enables each edge node to schedule its own jobs using RL. However, without the coordination between the nodes, action collision may occur, in which multiple nodes may schedule tasks to the same node and make it overloaded. To avoid these problems, we propose a system called Shielded ReinfOrcement learning (RL) based DL training on Edges (SROLE). In SROLE, each edge node schedules its own jobs using multi-agent RL. The shield deployed in a node checks action collisions and provides alternative actions to avoid the collisions. As the central shield node for the entire cluster may become a bottleneck, we further propose a decentralized shielding method, in which different shields are responsible for different regions in the cluster and they coordinate to avoid action collisions on the region boundaries. Our container-based emulation experiments show that SROLE reduces training time by up to 59% with 29% lower median resource utilization and reduces the number of action collisions by up to 48% compared to multi-agent RL and the centralized RL. Our real device experiments show that SROLE still reduces the training time by up to 53% with 28% lower median resource utilization than multi-agent RL and the centralized RL.
An Encryption Method of Convmixer Models without Performance Degradation

In this paper, we propose an encryption method for ConvMixer models with a secret key. Encryption methods for DNN models have been studied to achieve adversarial defense, model protection and privacy-preserving image classification. However, the use of conventional encryption methods degrades the performance of models compared with that of plain models. Accordingly, we propose a novel method for encrypting ConvMixer models. The method is carried out on the basis of an embedding architecture that ConvMixer has, and models encrypted with the method can have the same performance as models trained with plain images only when using test images encrypted with a secret key. In addition, the proposed method does not require any specially prepared data for model training or network modification. In an experiment, the effectiveness of the proposed method is evaluated in terms of classification accuracy and model protection in an image classification task on the CIFAR10 dataset.
Optimization of non-Gaussian state generation using tensor networks and automatic differentiation

Tensor networks have been eagerly developed in recent years as a simulation method for quantum computation on classical computers. The efficiency of tensor networks for simulating quantum computation has been shown by solving a task designed for demonstrating quantum computing supremacy. However, such a task is not useful for real application. Therefore, there is the room for research of finding specific applications suitable for exploiting tensor network simulation. As such an application, we propose optimization of circuits to generate non- Gaussian states. Non-Gaussian state is one of the crucial elements for achieving universal continuous variable quantum computation. We consider this problem as a variational optimization of optical circuits to generate desired non-Gaussian states. Tensor networks efficiently simulate the optical circuits, and their compatibility with automatic differentiation provides a heuristic way to optimize the circuits. We propose and report the result of our approach that combines tensor networks and automatic differentiation to perform variational optimization of circuits such that the desired non-Gaussian state is approximately generated. The complexity of contracting tensor networks is scaled by degree of entanglement, so we consider relatively shallow optical circuit. Such a restriction is beneficial for considering devices without error correction method, which are accessible in near-term.