Reviewerly/exHarmony huggingface dataset:
Efficient VVC Encoding Using Hierarchical Parallelization: A Comprehensive Analysis

Authors:
raw_author_name: Valeri George
raw_affiliation_string: Video Communication and Applications Department, Fraunhofer HHI, Berlin 10587, Germany
raw_author_name: Tobias Hinz
raw_affiliation_string: Video Communication and Applications Department, Fraunhofer HHI, Berlin 10587, Germany
raw_author_name: Adam Wieckowski
raw_affiliation_string: Video Communication and Applications Department, Fraunhofer HHI, Berlin 10587, Germany
raw_author_name: Benjamin Bross
raw_affiliation_string: Video Communication and Applications Department, Fraunhofer HHI, Berlin 10587, Germany
raw_author_name: Detlev Marpe
raw_affiliation_string: Video Communication and Applications Department, Fraunhofer HHI, Berlin 10587, Germany

Abstract: This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to ×86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.
Evaluation on model-driven knowledge graph and platform for grid operation and maintenance

Authors:
raw_author_name: Runzhen Yan
raw_author_name: Yating Wang
raw_author_name: Rui Su
raw_author_name: Jinmei Wen

Abstract: With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&amp;M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&amp;M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&amp;M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.
Target-Free Stereo Camera-GNSS/IMU Self-Calibration Based on Iterative Refinement

Authors:
raw_author_name: Shengjie Huang
raw_affiliation_string: College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
raw_author_name: Xiaohui Qin
raw_affiliation_string: State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
raw_author_name: Manjiang Hu
raw_affiliation_string: State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China

Abstract: Camera and GNSS/IMU are widely used in autonomous vehicles and robots due to their light weight and low cost, in these cases, an accurate extrinsic calibration is essential for sensor fusion. Traditionally, specific vehicle movements or scenarios with known fiducial markers are required for the sophisticated calibration process, however, the former is difficult to eliminate the impact of drift made by the techniques used to estimate the motion of each sensor, and the latter is poorly automated and needs specialist human expertise. To tackle above problems, this paper proposes a target-free stereo camera-GNSS/IMU self-calibration method based on iterative refinement. The initial calibration parameters are calculated with relative pose constraints derived from visual odometry. To eliminate the impact of visual odometry drift, parameters are further refined by iteratively using paired relative pose constraints and absolute pose constraints, which are derived from scan-global map matching. Quantitative calibration results on simulated data show the robustness and accuracy of this approach with RMSEs of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> deg and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> m for rotation and translation, respectively. Further experiments in real world confirm the superior of our method in natural environments.
Internal and External Double-Cycle High-Order Kalman Filter Design

Authors:
raw_author_name: Xiaohui Sun
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Xiao He
raw_affiliation_string: Department of Automation, BNRist, Tsinghua University, Beijing, China
raw_author_name: Xinyu Wu
raw_affiliation_string: Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Chenglin Wen
raw_affiliation_string: Department of Automation, Guangdong University of Petrochemical Technology, Maoming, China

Abstract: 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

Authors:
raw_author_name: Ximing Li
raw_affiliation_string: College of Computer Science and Technology, Jilin University, China; College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China
raw_author_name: Bing Wang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, China; College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China
raw_author_name: Yang Wang
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, China
raw_author_name: Meng Wang
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, China

Abstract: 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

Authors:
raw_author_name: Ken Umeno
raw_affiliation_string: Graduate School of Informatics, Kyoto University, Sakyo, Kyoto 606-8501, Japan

Abstract: 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

Authors:
raw_author_name: Takuzumi Nishio
raw_affiliation_string: Department of Mechano-Infomatics, University of Tokyo, Bunkyo-ku, Japan
raw_author_name: Moju Zhao
raw_affiliation_string: Department of Mechanical Engineering, University of Tokyo, Bunkyo-ku, Japan

Abstract: 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

Authors:
raw_author_name: Cedric Le Gentil
raw_affiliation_string: Robotics Institute, University of Technology Sydney, Ultimo, NSW, Australia
raw_author_name: Othmane-Latif Ouabi
raw_affiliation_string: Sysnav, Vernon, France
raw_author_name: Lan Wu
raw_affiliation_string: Robotics Institute, University of Technology Sydney, Ultimo, NSW, Australia
raw_author_name: Cedric Pradalier
raw_affiliation_string: International Research Lab 2958 Georgia Tech-CNRS, Metz, France
raw_author_name: Teresa Vidal-Calleja
raw_affiliation_string: Robotics Institute, University of Technology Sydney, Ultimo, NSW, Australia

Abstract: 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

Authors:
raw_author_name: Yang Liu
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
raw_author_name: Fuqiang Zhou
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
raw_author_name: Wanning Zhang
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
raw_author_name: Lemiao Yang
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
raw_author_name: Zhanshe Guo
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
raw_author_name: Haishu Tan
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China

Abstract: 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

Authors:
raw_author_name: ahmed Zellou
raw_affiliation_string: Mohammed V University Software Project Management Research Team, ENSIAS Rabat Morocco
raw_author_name: Mouna Berquedich
raw_affiliation_string: Mohammed VI Polytechnic University Green Tech Institute Ben Guerir Morocco

Abstract: 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

Authors:
raw_author_name: Zhong Wu
raw_affiliation_string: School of Automation and Information Engineering, Xi&#x2019;an University of Technology, Xi&#x2019;an, China
raw_author_name: Hong Zhu
raw_affiliation_string: School of Automation and Information Engineering, Xi&#x2019;an University of Technology, Xi&#x2019;an, China
raw_author_name: Lili He
raw_affiliation_string: Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng, China
raw_author_name: Jing Shi
raw_affiliation_string: School of Automation and Information Engineering, Xi&#x2019;an University of Technology, Xi&#x2019;an, China

Abstract: 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.