Reviewerly/exHarmony huggingface dataset:
Internal and External Double-Cycle High-Order Kalman Filter Design

Accurate state estimation depends on the design of a high-precision filter, while the design of a high-precision filter relies on the use of high-order nonlinear information of the model. For a class of nonlinear dynamic systems whose state and measurement model are composed of several nonlinear functions respectively, and each nonlinear function consists of the product of several weak nonlinear multipliers functions, a series of high-order Kalman filter bank constructed with the internal and external double-cycle is designed. In this proposed filter bank, each of the additive nonlinear functions is defined as the overall hidden variable, and the weak nonlinear multipliers in the additive nonlinear functions are defined as the basic hidden variables. Linear dynamic modeling of hidden variables is carried out, an internal cycle high-order Kalman filter bank is designed by implementing the step-by-step linearization for each basic hidden variable, an external cycle high-order Kalman filter bank is designed by making use of all the sequentially estimated overall hidden variables. Original state variables are estimated based on internal and external double-cycle Kalman filter bank. Compared with some existing methods, the effectiveness of the proposed method is verified by comparative experiments.
Graph-based Text Classification by Contrastive Learning with Text-level Graph Augmentation

Text Classification (TC) is a fundamental task in the information retrieval community. Nowadays, the mainstay TC methods are built on the deep neural networks, which can learn much more discriminative text features than the traditional shallow learning methods. Among existing deep TC methods, the ones based on Graph Neural Network (GNN) have attracted more attention due to the superior performance. Technically, the GNN-based TC methods mainly transform the full training dataset to a graph of texts; however, they often neglect the dependency between words, so as to miss potential semantic information of texts, which may be significant to exactly represent them. To solve the aforementioned problem, we generate graphs of words instead, so as to capture the dependency information of words. Specifically, each text is translated into a graph of words, where neighboring words are linked. We learn the node features of words by a GNN-like procedure and then aggregate them as the graph feature to represent the current text. To further improve the text representations, we suggest a contrastive learning regularization term. Specifically, we generate two augmented text graphs for each original text graph, we constrain the representations of the two augmented graphs from the same text close and the ones from different texts far away. We propose various techniques to generate the augmented graphs. Upon those ideas, we develop a novel deep TC model, namely Text-level Graph Networks with Contrastive Learning (TGN cl ). We conduct a number of experiments to evaluate the proposed TGN cl model. The empirical results demonstrate that TGN cl can outperform the existing state-of-the-art TC models.
Bayesian Updating on Time Intervals at Different Magnitude Thresholds in a Marked Point Process and Its Application to Synthetic Seismic Activity

We present a Bayesian updating method on the inter-event time intervals at different magnitude thresholds in a marked point process toward probabilistic forecasting of an upcoming large event using temporal information on smaller events. Bayes’ theorem that yields the one-to-one relationship between intervals at lower and upper magnitude thresholds is presented. This theorem is extended to Bayesian updating for an uncorrelated marked point process that yields the relationship between multiple consecutive lower intervals and one upper interval. We derive the inverse probability density function and the condition under which it has a peak. Further, we derive its approximation function that consists of the kernel part that includes the product of conditional probabilities and a correction term. Bayesian updating is applied numerically to the time series of a seismic activity model. We estimate the time of an upcoming large event using the maximum point of the kernel part and evaluate its accuracy by the relative error with the actual occurrence time. We further evaluate the forecasting effectiveness by the continuity of updates with acceptable accuracy before the large event. Statistical results indicate that forecasting is relatively effective immediately or long after the last major event in which stationarity dominates in time series.
Singularity-Free Flight using Rotor-Distributed Aerial Manipulator

Aerial manipulators have been developed in recent years. The most popular type is the multirotor equipped with arms. By employing these rotor-concentrated manipulators (RCMs), various manipulations have been conducted in high places. Compared with RCMs, rotor-distributed manipulators (RDMs) can generate relatively larger wrenches at the end effector. However, certain types of singular configurations exist during flight. In this letter, we propose design and control methods for the RDM based on previous works [1]-[3]. Additionally, we propose a planning method for optimizing the angles of the thrust vectoring apparatus to overcome singularities and maximize controllability in arbitrary poses. Finally, we verify the feasibility of the proposed methods through experiments. By using a quad-rotor, we achieve stable flight in challenging poses, such as horizontal or vertical line configurations.
Accurate Gaussian-Process-Based Distance Fields With Applications to Echolocation and Mapping

This paper introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc. A distance field provides a continuous representation of the scene defined as the shortest distance from any query point and the closest surface. The key concept of the proposed method is the transformation of a GP-inferred latent scalar field into an accurate distance field by using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reverting</i> function related to the kernel inverse. The latent field can be interpreted as a smooth occupancy map. This paper provides the theoretical derivation of the proposed method as well as a novel uncertainty proxy for the distance estimates. The improved performance compared with existing distance fields is demonstrated with simulated experiments. The level of accuracy of the proposed approach enables novel applications that rely on precise distance estimation: this work presents echolocation and mapping frameworks for ultrasonic-guided wave sensing in metallic structures. These methods leverage the proposed distance field with a physics-based measurement model accounting for the propagation of the ultrasonic waves in the material. Real-world experiments are conducted to demonstrate the soundness of these frameworks.
Flexible Calibration Method for a Quad-Directional Stereo Vision Sensor Based on Unconstraint 3-D Target

Multi-directional stereo vision sensors have been paid increasing attention in the fields of environment perception and 3D reconstruction attributed to their advantage of wide field of view (FOV). Vision sensor setup requires the parameters to be calibrated accurately, which remains a challenging problem because of the discontinuity in the camera field. This paper proposes a flexible calibration method using an unconstrained 3D target for a quad-directional stereo vision sensor (QSVS) composed of a single camera and a dual mirror pyramid. We build the measurement models of the local virtual binocular structures and the global virtual multi-camera based on the optical properties of the catadioptric elements to reduce the calibration steps of the mirror poses. On this basis, this paper resolves the following three problems. First, the unique intrinsic parameters of the virtual multi-camera are obtained through the intrinsic calibration of the actual camera. Second, based on the proposed sorting algorithm of the feature points, we can determine the correspondence between the spatial feature points and the mirrored 2D points in the global calibration image. Third, we can determine the global structure parameters of the QSVS from a single calibration image based on the designed unconstrained 3D target with 6-DOF. And we optimize the calibration parameters by flexibly adjusting the target pose. Simulation and physical experiments are conducted to demonstrate the performance of the proposed method. This method also provides a new way to calibrate the miniaturized multi-camera vision systems and omnidirectional vision sensors without an overlapping FOV.
Schema Extraction in NoSQL Databases: A Systematic Literature Review

Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.
Toward Accurate and Real-Time Binocular Vision: A Lightweight Stereo Matching Network for RGB Stereo Images

Existing stereo matching networks with high accuracy generally demand high computational overhead, making them expensive to deploy and unsuitable for many real-time applications, especially on source-constrained hardware platforms. Some recent works have tried to build lightweight stereo matching networks, but their accuracy and inference speed is far from satisfactory when deployed on source-constrained GPUs. This paper aims to build a lightweight stereo matching network with high accuracy. First, we claim that a reasonable disparity upsampling strategy is crucial for lightweight stereo matching to achieve high accuracy, whose importance is underestimated in existing works. Accordingly, an efficient and effective disparity upsampling strategy named Spatial Adaptive Disparity Shuffle (SADS) is proposed to meet the upsampling requirements of disparities within diverse regions. Second, a novel Channel-Disparity-Mixed Attention (CDMA) mechanism is proposed to regularize the high-resolution 4D compact cost volume. The attention values in CDMA are adaptive across channels, pixels and disparity candidates, making CDMA suitable for regularizing the compact cost volume. Based on the proposed methods, a lightweight stereo matching network (SADSNet) is designed that achieves real-time performance with high accuracy on source-limited hardware platforms (e.g., the NVIDIA Jetson TX2). In addition, SADSNet can be easily scaled up or down to suit different application scenarios according to available computational power. Extensive experimental results on multiple datasets show its clear superiority over SOTA lightweight stereo models and even many large accuracy-oriented models.
Quasi-Real-Time Inversion of Large Earthquake Rupture Process Based on Earthquake Early Warning System in China: Applications to 2017 MW 6.5 Jiuzhaigou and 2022 MW 6.7 Menyuan Earthquakes

The study of seismic ruptures is crucial for understanding major earthquake events. Historically, research on large earthquake rupture processes has required a long time to be published. However, recent advancements in earthquake early warning (EEW) systems have allowed for more rapid analyses of earthquake rupture inversions. The widespread and dense distribution of EEW stations provides in China an opportunity to study and invert rupture processes in near-real time. Two notable earthquakes were studied using this technology: the 2017 Jiuzhaigou MW 6.5 earthquake in Sichuan Province, China, and the 2022 Menyuan MW 6.7 earthquake in Qinghai Province, China. In both instances, numerous strong-motion sensors captured the seismic events and transmitted waveform data to data analysis centers in real time. Following automated site selection and preparation procedures, the rupture processes of these earthquakes were analyzed and released within 30[Formula: see text]min of the event origin. This rapid response time demonstrates that the seismic rupture process determined through inversion using existing EEW systems can serve as a guide for emergency rescue work after earthquakes.
MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation

Cross-domain Recommendation is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing methods of cross-domain recommendation mainly focus on overlapping scenarios by assuming users are totally or partially overlapped, which are taken as bridges to connect different domains. However, this assumption does not always hold, since it is illegal to leak users’ identity information to other domains. Conducting Non-overlapping MCR (NMCR) is challenging, since (1) the absence of overlapping information prevents us from directly aligning different domains, and this situation may get worse in the MCR scenario, and (2) the distribution between source and target domains makes it difficult for us to learn common information across domains. To overcome the above challenges, we focus on NMCR and devise MCRPL as our solution. To address Challenge 1, we first learn shared domain-agnostic and domain-dependent prompts and pre-train them in the pre-training stage. To address Challenge 2, we further update the domain-dependent prompts with other parameters kept fixed to transfer the domain knowledge to the target domain. We conduct experiments on five real-world domains, and the results show the advance of our MCRPL method compared with several recent SOTA baselines. Moreover, our source codes have been publicly released. 1
Wizard of Props: Mixed Reality Prototyping with Physical Props to Design Responsive Environments

Driven by the vision of future responsive environments, where everyday surroundings can perceive human behaviors and respond through intelligent robotic actuation, we propose Wizard of Props (WoP): a human-centered design workflow for creating expressive, implicit, and meaningful interactions. This collaborative experience prototyping approach integrates full-scale physical props with Mixed Reality (MR) to support ideation, prototyping, and rapid testing of responsive environments. We present two design explorations that showcase our investigations of diverse design solutions based on varying technology resources, contextual considerations, and target audiences. Design Exploration One focuses on mixed environment building, where we observe fluid prototyping methods. In Design Exploration Two, we explore how novice designers approach WoP, and illustrate their design ideas and behaviors. Our findings reveal that WoP complements conventional design methods, enabling intuitive body-storming, supporting flexible prototyping fidelity, and fostering expressive environment-human interactions through in-situ improvisational performance.
Spectral Total-variation Processing of Shapes—Theory and Applications

We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al. 2020 ] and adaptive anisotropic spectral TV [Biton and Gilboa 2022 ]. A new notion of convexity on surfaces is derived by characterizing structures that are stable throughout the TV flow, performed on surfaces. We establish and numerically demonstrate quantitative relationships between TV, area, eigenvalue, and eigenfunctions of the TV operator on surfaces. Moreover, we expand the shape spectral TV toolkit to include zero-homogeneous flows, leading to efficient and versatile shape processing methods. These methods are exemplified through applications in smoothing, enhancement, and exaggeration filters. We introduce a novel method that, for the first time, addresses the shape deformation task using TV. This deformation technique is characterized by the concentration of deformation along geometrical bottlenecks, shown to coincide with the discontinuities of eigenfunctions. Overall, our findings elucidate recent experimental observations in spectral TV, provide a diverse framework for shape filtering, and present the first TV-based approach to shape deformation.
CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge Graph

Object detection is a widely studied problem in existing works. However, in this paper, we turn to a more challenging problem of “ Covered Object Reasoning ”, aimed at reasoning the category label of target object in the given image particularly when it has been totally covered (or invisible ). To resolve this problem, we propose CoBjeason to seize the opportunity when visual reasoning meets the knowledge graph, where “ empirical cognition ” on common visual contexts have been incorporated as knowledge graph to conduct reinforced multi-hop reasoning via two collaborative agents. Such two agents, for one thing, stand at the covered object (or unknown entity ) to observe the surrounding visual cues in the given image and gradually select entities and relations from the global gallery-level knowledge graph which contains entity-pairs frequently occurring across the entire image-collection, so as to infer the main structure of image-level knowledge graph forward expanded from the unknown entity . In turn, for another, based on the reasoned image-level knowledge graph, the semantic context among entities will be aggregated backward into unknown entity to select an appropriate entity from the global gallery-level knowledge graph as the reasoning result. Moreover, such two agents will collaborate with each other, securing that the above Forward &amp; Backward Reasoning will step towards the same destination of the higher performance on covered object reasoning. To our best knowledge, this is the first work on Covered Object Reasoning with Knowledge Graphs and reinforced Multi-Agent collaboration. Particularly, our study on Covered Object Reasoning and the proposed model CoBjeason could offer novel insights into more basic Computer Vision (CV) tasks, such as Semantic Segmentation with better understanding on the current scene when some objects are blurred or covered, Visual Question Answering with enhancement on the inference in more complicated visual context when some objects are covered or invisible, and Image Caption Generation with the augmentation on the richness of visual context for images containing partially visible objects. The improvement on the above basic CV tasks can further refine more complicated ones involved with nuanced visual interpretation like Autonomous Driving, where the recognition and reasoning on partially visible or covered object are critical. According to the experimental results, our proposed CoBjeason can achieve the best overall ranking performance on covered object reasoning compared with other models, meanwhile enjoying the advantage of lower “ exploration cost ”, with the insensitivity against the long-tail covered objects and the acceptable time complexity.
Enhancing diagnostic capabilities in Tonga: a 12-month analysis of telepathology implementation

Telepathology is suggested as a solution for delivering essential surgical pathology diagnostic support to remote or under-resourced communities. Although the technologies involved are increasingly robust and cost-effective, successful implementation relies on more than just technology.1 We critically review a year-long experience with telepathology, evaluating its effectiveness as a diagnostic tool and as a teaching method in our pathology laboratory.
Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation

The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.