Reviewerly/exHarmony huggingface dataset:
Tight Collision Probability for UAV Motion Planning in Uncertain Environment

Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.
Zeroth Order GreedyLR: An Adaptive Learning Rate Scheduler for Deep Neural Network Training

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machine learning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. The results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning.
Distributed Bayesian Inference for Large-Scale IoT Systems

In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy.
Deep learning-based sign language recognition system using both manual and non-manual components fusion

&lt;abstract&gt;&lt;p&gt;Sign language is regularly adopted by speech-impaired or deaf individuals to convey information; however, it necessitates substantial exertion to acquire either complete knowledge or skill. Sign language recognition (SLR) has the intention to close the gap between the users and the non-users of sign language by identifying signs from video speeches. This is a fundamental but arduous task as sign language is carried out with complex and often fast hand gestures and motions, facial expressions and impressionable body postures. Nevertheless, non-manual features are currently being examined since numerous signs have identical manual components but vary in non-manual components. To this end, we suggest a novel manual and non-manual SLR system (MNM-SLR) using a convolutional neural network (CNN) to get the benefits of multi-cue information towards a significant recognition rate. Specifically, we suggest a model for a deep convolutional, long short-term memory network that simultaneously exploits the non-manual features, which is summarized by utilizing the head pose, as well as a model of the embedded dynamics of manual features. Contrary to other frequent works that focused on depth cameras, multiple camera visuals and electrical gloves, we employed the use of RGB, which allows individuals to communicate with a deaf person through their personal devices. As a result, our framework achieves a high recognition rate with an accuracy of 90.12% on the SIGNUM dataset and 94.87% on RWTH-PHOENIX-Weather 2014 dataset.&lt;/p&gt;&lt;/abstract&gt;
Visual Commonsense-Aware Representation Network for Video Captioning

Generating consecutive descriptions for videos, that is, video captioning, requires taking full advantage of visual representation along with the generation process. Existing video captioning methods focus on an exploration of spatial-temporal representations and their relationships to produce inferences. However, such methods only exploit the superficial association contained in a video itself without considering the intrinsic visual commonsense knowledge that exists in a video dataset, which may hinder their capabilities of knowledge cognitive to reason accurate descriptions. To address this problem, we propose a simple, yet effective method, called visual commonsense-aware representation network (VCRN), for video captioning. Specifically, we construct a Video Dictionary, a plug-and-play component, obtained by clustering all video features from the total dataset into multiple clustered centers without additional annotation. Each center implicitly represents a visual commonsense concept in a video domain, which is utilized in our proposed visual concept selection (VCS) component to obtain a video-related concept feature. Next, a concept-integrated generation (CIG) component is proposed to enhance caption generation. Extensive experiments on three public video captioning benchmarks: MSVD, MSR-VTT, and VATEX, demonstrate that our method achieves state-of-the-art performance, indicating the effectiveness of our method. In addition, our method is integrated into the existing method of video question answering (VideoQA) and improves this performance, which further demonstrates the generalization capability of our method. The source code has been released at https://github.com/zchoi/VCRN.
Query-optimal estimation of unitary channels in diamond distance

We consider process tomography for unitary quantum channels. Given access to an unknown unitary channel acting on a d-dimensional qudit, we aim to output a classical description of a unitary that is $\varepsilon$-close to the unknown unitary in diamond norm. We design an algorithm achieving error $\varepsilon$ using $O\left(\mathrm{~d}^{2} / \varepsilon\right)$ applications of the unknown channel and only one qudit. This improves over prior results, which use $O\left(\mathrm{~d}^{3} / \varepsilon^{2}\right)$ [via standard process tomography] or $O\left(\mathrm{~d}^{2.5} / \varepsilon\right)$ [Yang, Renner, and Chiribella, PRL 2020] applications. To show this result, we introduce a simple technique to “bootstrap” an algorithm that can produce constant-error estimates to one that can produce $\varepsilon$-error estimates with the Heisenberg scaling. Finally, we prove a complementary lower bound showing that estimation requires $\Omega\left(\mathrm{d}^{2} / \varepsilon\right)$ applications, even with access to the inverse or controlled versions of the unknown unitary. This shows that our algorithm has both optimal query complexity and optimal space complexity.
Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.
Linking Transformational Leadership With Organizational Performance: A PLS-SEM Integrated Model Examining the Mediating Role OF Innovative Work Behavior And Motivation

This study investigates the impact of transformational leadership on organizational performance, employee motivation, and innovative work behavior in Saudi Arabia, aligned with Saudi Vision 2030. Research was conducted in Riyadh, Jeddah, and Medina, focusing on large companies with over 500 employees. Data from 214 responses were analyzed using Smart PLS 3.5, employing descriptive statistics, Cronbach's alpha, and composite reliability analysis. Hypotheses were tested using PLS and bootstrapping techniques, and the model was evaluated with Blindfolding in PLS-SEM. Results supported all hypotheses. Transformational leadership positively impacted motivation, innovative work behavior, and organizational performance. Innovative work behavior also positively influenced organizational performance, as did motivation. The study found that innovative work behavior and motivation partially mediate the relationship between transformational leadership and organizational performance, emphasizing their importance. This research underscores the role of transformational leadership in enhancing Saudi organizations' competitiveness and global reputation while aligning with Saudi Vision 2030's goals. It contributes to understanding leadership's impact on employee behavior and organizational outcomes, supporting the nation's aspirations. Finally, this study conducted in Saudi Arabia explores the influence of transformational leadership on organizational performance, innovation, and motivation. It confirms positive relationships between these variables, highlighting the importance of transformational leadership in achieving organizational goals and aligning with Saudi Vision 2030.
Program Abstraction and Re-Engineering: An Agile MDE Approach

In this paper we describe an Agile model-driven engineering (MDE) approach for the re-engineering of legacy systems. This involves the automated abstraction of systems to UML specifications and the interactive application of refactoring and rearchitecting transformations to improve system quality. The approach is demonstrated on Visual Basic and COBOL legacy codes, including a finance industry case. Significant quality improvements are achieved, and translation accuracy over 80 % is demonstrated.
Diffusion-Based 3D Object Detection with Random Boxes

3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the algorithms lack elegance. In recent years, we have witnessed the rise of several generative models, among which diffusion models show great potential for learning the transformation of two distributions. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. During training, the object boxes diffuse from the ground truth boxes to the Gaussian distribution, and the decoder learns to reverse this noise process. In the inference stage, the model progressively refines a set of random boxes to the prediction results. We provide detailed experiments on the KITTI benchmark and achieve promising performance compared to classical anchor-based 3D detection methods.
Using and Abusing Equivariance

In this paper we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to the rotation and reflection symmetries. We focus on the 2D rotations and reflections and investigate the impact of the broken equivariance on network performance. We show that a change in the input dimension of a network as small as a single pixel can be enough for commonly used architectures to become approximately equivariant, rather than exactly. We investigate the impact of networks not being exactly equivariant and find that approximately equivariant networks generalise significantly worse to unseen symmetries compared to their exactly equivariant counterparts. However, when the symmetries in the training data are not identical to the symmetries of the network, we find that approximately equivariant networks can relax their equivariance constraints, matching or outperforming exactly equivariant networks on common benchmarks.
Pragmatic Quantum-Classic Phase Estimation of a Quantum Channel

The ability to estimate the phase offset introduced by a quantum channel is an essential functionality of designing the receiver of a quantum wireless communication or remote-sensing system. Although the foundational theory of quantum phase estimation has been well known for years, appropriate engineering implementation remains an open problem. Assuming transmitting classic information through a quantum channel, after a series of investigations, we suggest that quantum-classic implementation of quantum optical circuitry and classic statistical analysis serves a promising engineering implementation, not only for estimation of small phase offset but also for acquisition of larger phase offset.
A Unified Approach for Occlusion Tolerant 3D Facial Pose Capture and Gaze Estimation using MocapNETs

We tackle the challenging problems of 3D facial capture, head pose and gaze estimation. We do so by extending MocapNET, a highly effective deep learning motion capture framework. By leveraging state-of-the-art RGB/2D joint estimators, the proposed network ensemble converts 2D facial keypoints into a real-time 3D Bio-Vision Hierarchy (BVH) skeleton in an end-to-end fashion, incorporating inverse kinematics computations. Our approach achieves satisfactory performance on benchmark datasets and also architecturally excels in challenging scenarios with significant facial occlusions. Moreover, it runs in real-time on CPU, which makes it an ideal choice for applications requiring low-latency interactions. Overall, our unified approach for facial capture, head pose and gaze estimation provides a robust solution for capturing facial expressions and visual focus, with huge potential in HCI and AR/VR applications. Notably, our approach is naturally integrable with Mocap-NETs for 3D human body and hands pose estimation, offering one of the few state-of-the-art unified approaches that enable holistic recovery of 3D information regarding human gaze, face, upper/lower body, hands, and feet.
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack.Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models.Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work.
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video

We present a novel real-time capable learning method that jointly perceives a 3D scene’s geometry structure and semantic labels. Recent approaches to real-time 3D scene reconstruction mostly adopt a volumetric scheme, where a Truncated Signed Distance Function (TSDF) is directly regressed. However, these volumetric approaches tend to focus on the global coherence of their reconstructions, which leads to a lack of local geometric detail. To overcome this issue, we propose to leverage the latent geometric prior knowledge in 2D image features by explicit depth prediction and anchored feature generation, to refine the occupancy learning in TSDF volume. Besides, we find that this cross-dimensional feature refinement methodology can also be adopted for the semantic segmentation task by utilizing semantic priors. Hence, we proposed an end-to-end cross-dimensional refinement neural network (CDRNet) to extract both 3D mesh and 3D semantic labeling in real time. The experiment results show that this method achieves a state-of-the-art 3D perception efficiency on multiple datasets, which indicates the great potential of our method for industrial applications.
Real time object recognition based on YOLO model

With the rapid development of computer technology, the concept of computer vision has been proposed. Since then, many object recognition methods have been developed to lay the foundation for computer vision. Object recognition is vital in various computer vision applications, such as autonomous driving, surveillance systems, robotics, and other areas. The You Only Look Once (YOLO) model has gained significant attention due to its ability to achieve real-time object detection and localization in images and videos. This paper comprehensively reviews real-time object recognition based on the YOLO model. We discuss the YOLO architecture's underlying principles and advantages over traditional object detection methods. Then, according to the article by Joseph Redmon, the inventor of YOLO, the benefits of each version of the YOLO model and the performance optimization compared to the previous work are briefly introduced in the order of release. Furthermore, this paper explores its applications in different domains.
<i>Can an Embodied Agent Find Your “Cat-shaped Mug”?</i> LLM-Based Zero-Shot Object Navigation

We present LGX (Language-guided Exploration), a novel algorithm for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Language-Driven Zero-Shot Object Goal Navigation</i> (L-ZSON), where an embodied agent navigates to an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniquely described</i> target object in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">previously unseen</i> environment. Our approach makes use of Large Language Models (LLMs) for this task by leveraging the LLM's commonsense-reasoning capabilities for making sequential navigational decisions. Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-world</i> experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.
An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.