OpenRaiser/Intern-Atlas huggingface dataset:
Gromit: Benchmarking the Performance and Scalability of Blockchain Systems
Venue: arXiv cs.DC 2022
Year: 2022

The growing number of implementations of blockchain systems stands in stark contrast with still limited research on a systematic comparison of performance characteristics of these solutions. Such research is crucial for evaluating fundamental trade-offs introduced by novel consensus protocols and their implementations. These performance limitations are commonly analyzed with ad-hoc benchmarking frameworks focused on the consensus algorithm of blockchain systems. However, comparative evaluations of design choices require macro-benchmarks for uniform and comprehensive performance evaluations of blockchains at the system level rather than performance metrics of isolated components. To address this research gap, we implement Gromit, a generic framework for analyzing blockchain systems. Gromit treats each system under test as a transaction fabric where clients issue transactions to validators. We use Gromit to conduct the largest blockchain study to date, involving seven representative systems with varying consensus models. We determine the peak performance of these systems with a synthetic workload in terms of transaction throughput and scalability and show that transaction throughput does not scale with the number of validators. We explore how robust the subjected systems are against network delays and reveal that the performance of permissoned blockchain is highly sensitive to network conditions.
Applying Eigencontours to PolarMask-Based Instance Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Eigencontours are the first data-driven contour descriptors based on singular value decomposition. Based on the implementation of ESE-Seg, eigencontours were applied to the instance segmentation task successfully. In this report, we incorporate eigencontours into the PolarMask network for instance segmentation. Experimental results demonstrate that the proposed algorithm yields better results than PolarMask on two instance segmentation datasets of COCO2017 and SBD. Also, we analyze the characteristics of eigencontours qualitatively. Our codes are available at https://github.com/dnjs3594/Eigencontours.
Differential evolution variants for Searching D- and A-optimal designs
Venue: arXiv cs.NE 2022
Year: 2022

Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely minimizing the determinant and trace of the inverse Fisher information matrix. Due to the flexibility and ease of implementation, traditional evolutionary algorithms (EAs) are applied to deal with a small part of experimental optimization design problems without mathematical derivation and assumption. However, the current EAs remain the issues of determining the support point number, handling the infeasible weight solution, and the insufficient experiment. To address the above issues, this paper investigates differential evolution (DE) variants for finding D- and A-optimal designs on several different statistical models. The repair operation is proposed to automatically determine the support point by combining similar support points with their corresponding weights based on Euclidean distance and deleting the support point with less weight. Furthermore, the repair operation fixes the infeasible weight solution into the feasible weight solution. To enrich our optimal design experiments, we utilize the proposed DE variants to test the D- and A-optimal design problems on 12 statistical models. Compared with other competitor algorithms, simulation experiments show that LSHADE can achieve better performance on the D- and A-optimal design problems.
Improved Scheduling of Morphing Edge Drawing
Venue: arXiv cs.DS 2022
Year: 2022

Morphing edge drawing (MED), a graph drawing technique, is a dynamic extension of partial edge drawing (PED), where partially drawn edges (stubs) are repeatedly stretched and shrunk by morphing. Previous experimental evaluations have shown that the reading time with MED may be shorter than that with PED. The morphing scheduling method limits visual clutter by avoiding crossings between stubs. However, as the number of intersections increases, the overall morphing cycle tends to lengthen in this method, which is likely to have a negative effect on the reading time. In this paper, improved scheduling methods are presented to address this issue. The first method shortens the duration of a single cycle by overlapping a part of the current cycle with the succeeding one. The second method duplicates every morph by the allowable number of times in one cycle. The third method permits a specific number of simultaneous crossings per edge. The effective performances of these methods are demonstrated through experimental evaluations.
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting
Venue: arXiv cs.CV 2022
Year: 2022

Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer vision techniques rely on deep learning-based algorithms that estimate pedestrian densities in still, individual images. Only a bunch of works take advantage of temporal consistency in video sequences. In this work, we propose a spatio-temporal attentive neural network to estimate the number of pedestrians from surveillance videos. By taking advantage of the temporal correlation between consecutive frames, we lowered state-of-the-art count error by 5% and localization error by 7.5% on the widely-used FDST benchmark.
Distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans
Venue: arXiv cs.MS 2022
Year: 2022

The modern workflow for radiation therapy treatment planning involves mathematical optimization to determine optimal treatment machine parameters for each patient case. The optimization problems can be computationally expensive, requiring iterative optimization algorithms to solve. In this work, we investigate a method for distributing the calculation of objective functions and gradients for radiation therapy optimization problems across computational nodes. We test our approach on the TROTS dataset -- which consists of optimization problems from real clinical patient cases -- using the IPOPT optimization solver in a leader/follower type approach for parallelization. We show that our approach can utilize multiple computational nodes efficiently, with a speedup of approximately 2-3.5 times compared to the serial version.
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
Venue: arXiv cs.CV 2022
Year: 2022

Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper proposed an effective and efficient multi-task learning network to simultaneously perform the task of traffic object detection, drivable road area segmentation and lane detection. Our model achieved the new state-of-the-art (SOTA) performance in terms of accuracy and speed on the challenging BDD100K dataset. Especially, the inference time is reduced by half compared to the previous SOTA model. Code will be released in the near future.
DynaVINS: A Visual-Inertial SLAM for Dynamic Environments
Venue: arXiv cs.RO 2022
Year: 2022

Visual inertial odometry and SLAM algorithms are widely used in various fields, such as service robots, drones, and autonomous vehicles. Most of the SLAM algorithms are based on assumption that landmarks are static. However, in the real-world, various dynamic objects exist, and they degrade the pose estimation accuracy. In addition, temporarily static objects, which are static during observation but move when they are out of sight, trigger false positive loop closings. To overcome these problems, we propose a novel visual-inertial SLAM framework, called DynaVINS, which is robust against both dynamic objects and temporarily static objects. In our framework, we first present a robust bundle adjustment that could reject the features from dynamic objects by leveraging pose priors estimated by the IMU preintegration. Then, a keyframe grouping and a multi-hypothesis-based constraints grouping methods are proposed to reduce the effect of temporarily static objects in the loop closing. Subsequently, we evaluated our method in a public dataset that contains numerous dynamic objects. Finally, the experimental results corroborate that our DynaVINS has promising performance compared with other state-of-the-art methods by successfully rejecting the effect of dynamic and temporarily static objects. Our code is available at https://github.com/url-kaist/dynaVINS.
Theoretical and Practical Bounds on the Initial Value of Skew-Compensated Clock for Clock Skew Compensation Algorithm Immune to Floating-Point Precision Loss
Venue: arXiv cs.NI 2022
Year: 2022

A clock skew compensation algorithm was recently proposed based on the extension of Bresenham's line drawing algorithm (Kim and Kang, IEEE Commun. Lett., vol. 26, no. 4, pp. 902--906, Apr. 2022), which takes into account the discrete nature of clocks in digital communication systems and mitigates the effect of limited floating-point precision on clock skew compensation. It lacks, however, a theoretical analysis of the range of the initial value of skew-compensated clock, which is also an initial condition for the proposed algorithm. In this letter, we provide practical as well as theoretical bounds on the initial value of skew-compensated clock based on a systematic analysis of the errors of floating-point operations, which replace the approximate bounds in Theorem 1 of the prior work.
Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor
Venue: arXiv cs.LG 2022
Year: 2022

Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression model. The distance based model used is K-Nearest Neighbors Regressor which is a supervised non-parametric method. The point that we want to prove is the effect of k parameter of the model and its fluctuations affecting the metrics. The metrics that we use are Root Mean Squared Error and R-Squared Goodness of Fit with their visual representation of values with respect to k values.
Apple Counting using Convolutional Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Estimating accurate and reliable fruit and vegetable counts from images in real-world settings, such as orchards, is a challenging problem that has received significant recent attention. Estimating fruit counts before harvest provides useful information for logistics planning. While considerable progress has been made toward fruit detection, estimating the actual counts remains challenging. In practice, fruits are often clustered together. Therefore, methods that only detect fruits fail to offer general solutions to estimate accurate fruit counts. Furthermore, in horticultural studies, rather than a single yield estimate, finer information such as the distribution of the number of apples per cluster is desirable. In this work, we formulate fruit counting from images as a multi-class classification problem and solve it by training a Convolutional Neural Network. We first evaluate the per-image accuracy of our method and compare it with a state-of-the-art method based on Gaussian Mixture Models over four test datasets. Even though the parameters of the Gaussian Mixture Model-based method are specifically tuned for each dataset, our network outperforms it in three out of four datasets with a maximum of 94\% accuracy. Next, we use the method to estimate the yield for two datasets for which we have ground truth. Our method achieved 96-97\% accuracies. For additional details please see our video here: https://www.youtube.com/watch?v=Le0mb5P-SYc}{https://www.youtube.com/watch?v=Le0mb5P-SYc.
"Please help share!": Security and Privacy Advice on Twitter during the 2022 Russian Invasion of Ukraine
Venue: arXiv cs.CR 2022
Year: 2022

The Russian Invasion of Ukraine in early 2022 resulted in a rapidly changing (cyber) threat environment. This changing environment incentivized the sharing of security advice on social media, both for the Ukrainian population, as well as against Russian cyber attacks at large. Previous research found a significant influence of online security advice on end users. We collected 8,920 tweets posted after the Russian Invasion of Ukraine and examined 1,228 in detail, including qualitatively coding 232 relevant tweets and 140 linked documents for security and privacy advice. We identified 221 unique pieces of advice which we divided into seven categories and 21 subcategories, and advice targeted at individuals or organizations. We then compared our findings to those of prior studies, finding noteworthy similarities. Our results confirm a lack of advice prioritization found by prior work, which seems especially detrimental during times of crisis. In addition, we find offers for individual support to be a valuable tool and identify misinformation as a rising threat in general and for security advice specifically.
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery
Venue: arXiv cs.CV 2022
Year: 2022

The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training deep learning methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature representation learning in many computer-vision tasks, this study proposes an online deep clustering method using crop label proportions as priors to learn a sample-level classifier based on government crop-proportion data for a whole agricultural region. We evaluate the method using two large datasets from two different agricultural regions in Brazil. Extensive experiments demonstrate that the method is robust to different data types (synthetic-aperture radar and optical images), reporting higher accuracy values considering the major crop types in the target regions. Thus, it can alleviate the burden of large-scale image annotation in EO applications.
Generative Adversarial Network (GAN) based Image-Deblurring
Venue: arXiv cs.CV 2022
Year: 2022

This thesis analyzes the challenging problem of Image Deblurring based on classical theorems and state-of-art methods proposed in recent years. By spectral analysis we mathematically show the effective of spectral regularization methods, and point out the linking between the spectral filtering result and the solution of the regularization optimization objective. For ill-posed problems like image deblurring, the optimization objective contains a regularization term (also called the regularization functional) that encodes our prior knowledge into the solution. We demonstrate how to craft a regularization term by hand using the idea of maximum a posterior estimation. Then, we point out the limitations of such regularization-based methods, and step into the neural-network based methods. Based on the idea of Wasserstein generative adversarial models, we can train a CNN to learn the regularization functional. Such data-driven approaches are able to capture the complexity, which may not be analytically modellable. Besides, in recent years with the improvement of architectures, the network has been able to output an image closely approximating the ground truth given the blurry observation. The Generative Adversarial Network (GAN) works on this Image-to-Image translation idea. We analyze the DeblurGAN-v2 method proposed by Orest Kupyn et al. [14] in 2019 based on numerical tests. And, based on the experimental results and our knowledge, we put forward some suggestions for improvement on this method.
A Review of Knowledge Graph Completion
Venue: arXiv cs.AI 2022
Year: 2022

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. View Full-Text
PEER: A Collaborative Language Model
Venue: arXiv cs.CL 2022
Year: 2022

Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process, today's language models are trained to generate only the final result. As a consequence, they lack several abilities crucial for collaborative writing: They are unable to update existing texts, difficult to control and incapable of verbally planning or explaining their actions. To address these shortcomings, we introduce PEER, a collaborative language model that is trained to imitate the entire writing process itself: PEER can write drafts, add suggestions, propose edits and provide explanations for its actions. Crucially, we train multiple instances of PEER able to infill various parts of the writing process, enabling the use of self-training techniques for increasing the quality, amount and diversity of training data. This unlocks PEER's full potential by making it applicable in domains for which no edit histories are available and improving its ability to follow instructions, to write useful comments, and to explain its actions. We show that PEER achieves strong performance across various domains and editing tasks.
Efficient Heterogeneous Video Segmentation at the Edge
Venue: arXiv cs.CV 2022
Year: 2022

We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across multiple dimensions of specifications for the neural architectures and operations on top of already light-weight backbones, targeting commercially available edge inference engines. We further analyze and optimize the heterogeneous data flows in our systems across the CPU, the GPU and the NPU. Our approach has empirically factored well into our real-time AR system, enabling remarkably higher accuracy with quadrupled effective resolutions, yet at much shorter end-to-end latency, much higher frame rate, and even lower power consumption on edge platforms.
Polarimetric Inverse Rendering for Transparent Shapes Reconstruction
Venue: arXiv cs.CV 2022
Year: 2022

In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues. Most of the existing methods usually lack sufficient constraints and suffer from the over-smooth problem. Hence, we introduce polarization information as a complementary cue. We implicitly represent the object's geometry as a neural network, while the polarization render is capable of rendering the object's polarization images from the given shape and illumination configuration. Direct comparison of the rendered polarization images to the real-world captured images will have additional errors due to the transmission in the transparent object. To address this issue, the concept of reflection percentage which represents the proportion of the reflection component is introduced. The reflection percentage is calculated by a ray tracer and then used for weighting the polarization loss. We build a polarization dataset for multi-view transparent shapes reconstruction to verify our method. The experimental results show that our method is capable of recovering detailed shapes and improving the reconstruction quality of transparent objects. Our dataset and code will be publicly available at https://github.com/shaomq2187/TransPIR.
Lidar SLAM for Autonomous Driving Vehicles
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SLAM system leads to the conclusion that the system remains observable upon a geometrical condition on the alignment of the landmarks.
Deep Learning-based approaches for automatic detection of shell nouns and evaluation on WikiText-2
Venue: arXiv cs.CL 2022
Year: 2022

In some areas, such as Cognitive Linguistics, researchers are still using traditional techniques based on manual rules and patterns. Since the definition of shell noun is rather subjective and there are many exceptions, this time-consuming work had to be done by hand in the past when Deep Learning techniques were not mature enough. With the increasing number of networked languages, these rules are becoming less useful. However, there is a better alternative now. With the development of Deep Learning, pre-trained language models have provided a good technical basis for Natural Language Processing. Automated processes based on Deep Learning approaches are more in line with modern needs. This paper collaborates across borders to propose two Neural Network models for the automatic detection of shell nouns and experiment on the WikiText-2 dataset. The proposed approaches not only allow the entire process to be automated, but the precision has reached 94% even on completely unseen articles, comparable to that of human annotators. This shows that the performance and generalization ability of the model is good enough to be used for research purposes. Many new nouns are found that fit the definition of shell noun very well. All discovered shell nouns as well as pre-trained models and code are available on GitHub.