OpenRaiser/Intern-Atlas huggingface dataset:
3D Lip Event Detection via Interframe Motion Divergence at Multiple Temporal Resolutions
Venue: arXiv cs.CV 2021
Year: 2021

The lip is a dominant dynamic facial unit when a person is speaking. Detecting lip events is beneficial to speech analysis and support for the hearing impaired. This paper proposes a 3D lip event detection pipeline that automatically determines the lip events from a 3D speaking lip sequence. We define a motion divergence measure using 3D lip landmarks to quantify the interframe dynamics of a 3D speaking lip. Then, we cast the interframe motion detection in a multi-temporal-resolution framework that allows the detection to be applicable to different speaking speeds. The experiments on the S3DFM Dataset investigate the overall 3D lip dynamics based on the proposed motion divergence. The proposed 3D pipeline is able to detect opening and closing lip events across 100 sequences, achieving a state-of-the-art performance.
Docking-based Virtual Screening with Multi-Task Learning
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data, in this work, we apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multiple targets, the model trained by multi-task learning shows a better ability to adapt to a new target. Additional empirical study shows that other problems in drug discovery, such as the experimental drug-target affinity prediction, may also benefit from multi-task learning. Our results demonstrate that multi-task learning is a promising machine learning approach for docking-based virtual screening and accelerating the process of drug discovery.
Blockchain Interoperability in UAV Networks: State-of-the-art and Open Issues
Venue: arXiv cs.CR 2021
Year: 2021

The breakthrough of blockchain technology has facilitated the emergence and deployment of a wide range of Unmanned Aerial Vehicles (UAV) network-based applications. Yet, the full utilization of these applications is still limited due to the fact that each application is operating on an isolated blockchain. Thus, it is inevitable to orchestrate these blockchain fragments by introducing a cross-blockchain platform that governs the inter-communication and transfer of assets in the UAV networks context. In this paper, we provide an up-to-date survey of blockchain-based UAV networks applications. We also survey the literature on the state-of-the-art cross blockchain frameworks to highlight the latest advances in the field. Based on the outcomes of our survey, we introduce a spectrum of scenarios related to UAV networks that may leverage the potentials of the currently available cross-blockchain solutions. Finally, we identify open issues and potential challenges associated with the application of a cross-blockchain scheme for UAV networks that will hopefully guide future research directions.
Adaptive Shrink-Mask for Text Detection
Venue: arXiv cs.CV 2021
Year: 2021

Existing real-time text detectors reconstruct text contours by shrink-masks directly, which simplifies the framework and can make the model run fast. However, the strong dependence on predicted shrink-masks leads to unstable detection results. Moreover, the discrimination of shrink-masks is a pixelwise prediction task. Supervising the network by shrink-masks only will lose much semantic context, which leads to the false detection of shrink-masks. To address these problems, we construct an efficient text detection network, Adaptive Shrink-Mask for Text Detection (ASMTD), which improves the accuracy during training and reduces the complexity of the inference process. At first, the Adaptive Shrink-Mask (ASM) is proposed to represent texts by shrink-masks and independent adaptive offsets. It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results. Then, the Super-pixel Window (SPW) is designed to supervise the network. It utilizes the surroundings of each pixel to improve the reliability of predicted shrink-masks and does not appear during testing. In the end, a lightweight feature merging branch is constructed to reduce the computational cost. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.
On Generalized Galois Cyclic Orbit Flag Codes
Venue: arXiv cs.IT 2021
Year: 2021

Flag codes that are orbits of a cyclic subgroup of the general linear group acting on flags of a vector space over a finite field, are called cyclic orbit flag codes. In this paper we present a new contribution to the study of such codes started in arXiv:2102.00867, by focusing this time on the generating flag. More precisely, we examine those ones whose generating flag has at least one subfield among its subspaces. In this situation, two important families arise: the already known Galois flag codes, in case we have just fields, or the generalized Galois flag codes in other case. We investigate the parameters and properties of the latter ones and explore the relationship with their underlying Galois flag code.
IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration
Venue: arXiv cs.CV 2021
Year: 2021

The existing state-of-the-art point descriptor relies on structure information only, which omit the texture information. However, texture information is crucial for our humans to distinguish a scene part. Moreover, the current learning-based point descriptors are all black boxes which are unclear how the original points contribute to the final descriptor. In this paper, we propose a new multimodal fusion method to generate a point cloud registration descriptor by considering both structure and texture information. Specifically, a novel attention-fusion module is designed to extract the weighted texture information for the descriptor extraction. In addition, we propose an interpretable module to explain the original points in contributing to the final descriptor. We use the descriptor element as the loss to backpropagate to the target layer and consider the gradient as the significance of this point to the final descriptor. This paper moves one step further to explainable deep learning in the registration task. Comprehensive experiments on 3DMatch, 3DLoMatch and KITTI demonstrate that the multimodal fusion descriptor achieves state-of-the-art accuracy and improve the descriptor's distinctiveness. We also demonstrate that our interpretable module in explaining the registration descriptor extraction.
A Case Study on Optimization of Platooning Coordination
Venue: arXiv cs.SE 2021
Year: 2021

In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one platooning coordination strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.
Features selection in NBA outcome prediction through Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

This manuscript is focused on features' definition for the outcome prediction of matches of NBA basketball championship. It is shown how models based on one a single feature (Elo rating or the relative victory frequency) have a quality of fit better than models using box-score predictors (e.g. the Four Factors). Features have been ex ante calculated for a dataset containing data of 16 NBA regular seasons, paying particular attention to home court factor. Models have been produced via Deep Learning, using cross validation.
A Bibliometric Analysis of the BPM Conference Using Computational Data Analytics
Venue: arXiv cs.DL 2021
Year: 2021

The BPM conference has a long tradition as the premier venue for publishing research on business process management. For exploring the evolution of research topics, we present the findings from a computational bibliometric analysis of the BPM conference proceedings from the past 15 years. We used the publicly available DBLP dataset as a basis for the analysis, which we enriched with data from websites and databases of the relevant publishers. In addition to a detailed analysis of the publication results, we performed a content-based analysis of over 1,200 papers from the BPM conference and its workshops using Latent Dirichlet Allocation. This offers insights into historical developments in Business Process Management research and provides the community with potential future prospects.
Hamming Distance Tolerant Content-Addressable Memory (HD-CAM) for Approximate Matching Applications
Venue: arXiv cs.AR 2021
Year: 2021

We propose a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in memory approximate matching applications. HD-CAM implements approximate search using matchline charge redistribution rather than its rise or fall time, frequently employed in state of-the-art solutions. HD-CAM was designed in a 65 nm 1.2 V CMOS technology and evaluated through extensive Monte Carlo simulations. Our analysis shows that HD-CAM supports robust operation under significant process variations and changes in the design parameters, enabling a wide range of mismatch threshold (tolerable Hamming distance) levels and pattern lengths. HD-CAM was functionally evaluated for virus DNA classification, which makes HD-CAM suitable for hardware acceleration of genomic surveillance of viral outbreaks such as Covid-19 pandemics.
Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings
Venue: arXiv cs.LG 2021
Year: 2021

Uncertainty quantification in neural network promises to increase safety of AI systems, but it is not clear how performance might vary with the training set size. In this paper we evaluate seven uncertainty methods on Fashion MNIST and CIFAR10, as we sub-sample and produce varied training set sizes. We find that calibration error and out of distribution detection performance strongly depend on the training set size, with most methods being miscalibrated on the test set with small training sets. Gradient-based methods seem to poorly estimate epistemic uncertainty and are the most affected by training set size. We expect our results can guide future research into uncertainty quantification and help practitioners select methods based on their particular available data.
Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks
Venue: arXiv cs.CV 2021
Year: 2021

Reinforced concrete buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolution neural networks have been adopted in recent research to rapidly quantify the damage state of structures. In this paper, an advanced object detection neural network, named YOLO-v2, is implemented which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLO-v2 is used in combination with the classification neural network, which improves the identification accuracy for critical damage state of reinforced concrete structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the damage states of the structure, and also localize the critical damage features. The identified damage state can then be integrated with the state-of-the-art performance evaluation framework to quantify the financial losses of critical reinforced concrete buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.
A Secure Experimentation Sandbox for the design and execution of trusted and secure analytics in the aviation domain
Venue: arXiv cs.CR 2021
Year: 2021

The aviation industry as well as the industries that benefit and are linked to it are ripe for innovation in the form of Big Data analytics. The number of available big data technologies is constantly growing, while at the same time the existing ones are rapidly evolving and empowered with new features. However, the Big Data era imposes the crucial challenge of how to effectively handle information security while managing massive and rapidly evolving data from heterogeneous data sources. While multiple technologies have emerged, there is a need to find a balance between multiple security requirements, privacy obligations, system performance and rapid dynamic analysis on large datasets. The current paper aims to introduce the ICARUS Secure Experimentation Sandbox of the ICARUS platform. The ICARUS platform aims to provide a big data-enabled platform that aspires to become an 'one-stop shop' for aviation data and intelligence marketplace that provides a trusted and secure 'sandboxed' analytics workspace, allowing the exploration, integration and deep analysis of original and derivative data in a trusted and fair manner. Towards this end, a Secure Experimentation Sandbox has been designed and integrated in the ICARUS platform offering, that enables the provisioning of a sophisticated environment that can completely guarantee the safety and confidentiality of data, allowing to any interested party to utilise the platform to conduct analytical experiments in closed-lab conditions.
Assisted Robust Reward Design
Venue: arXiv cs.RO 2021
Year: 2021

Real-world robotic tasks require complex reward functions. When we define the problem the robot needs to solve, we pretend that a designer specifies this complex reward exactly, and it is set in stone from then on. In practice, however, reward design is an iterative process: the designer chooses a reward, eventually encounters an "edge-case" environment where the reward incentivizes the wrong behavior, revises the reward, and repeats. What would it mean to rethink robotics problems to formally account for this iterative nature of reward design? We propose that the robot not take the specified reward for granted, but rather have uncertainty about it, and account for the future design iterations as future evidence. We contribute an Assisted Reward Design method that speeds up the design process by anticipating and influencing this future evidence: rather than letting the designer eventually encounter failure cases and revise the reward then, the method actively exposes the designer to such environments during the development phase. We test this method in a simplified autonomous driving task and find that it more quickly improves the car's behavior in held-out environments by proposing environments that are "edge cases" for the current reward.
PyTorchVideo: A Deep Learning Library for Video Understanding
Venue: arXiv cs.CV 2021
Year: 2021

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/
Parallel Algorithms for Masked Sparse Matrix-Matrix Products
Venue: arXiv cs.DC 2021
Year: 2021

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". Existing algorithms for Masked SpGEMM usually do not consider mask as part of multiplication and either first compute a regular SpGEMM followed by masking, or perform a sparse inner product only for output elements that are not masked out. In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures. Our evaluations show that factors such as matrix and mask density, mask structure and cache behavior play a vital role in attaining high performance for Masked SpGEMM. We evaluate our algorithms on a large number of matrices using several real-world benchmarks and show that our algorithms in most cases significantly outperform the state of the art for Masked SpGEMM implementations.
Predictive Scheduling of Collaborative Mobile Robots for Improved Crop-transport Logistics of Manually Harvested Crops
Venue: arXiv cs.RO 2021
Year: 2021

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This work presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this work can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.
M2A: Motion Aware Attention for Accurate Video Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Advancements in attention mechanisms have led to significant performance improvements in a variety of areas in machine learning due to its ability to enable the dynamic modeling of temporal sequences. A particular area in computer vision that is likely to benefit greatly from the incorporation of attention mechanisms in video action recognition. However, much of the current research's focus on attention mechanisms have been on spatial and temporal attention, which are unable to take advantage of the inherent motion found in videos. Motivated by this, we develop a new attention mechanism called Motion Aware Attention (M2A) that explicitly incorporates motion characteristics. More specifically, M2A extracts motion information between consecutive frames and utilizes attention to focus on the motion patterns found across frames to accurately recognize actions in videos. The proposed M2A mechanism is simple to implement and can be easily incorporated into any neural network backbone architecture. We show that incorporating motion mechanisms with attention mechanisms using the proposed M2A mechanism can lead to a +15% to +26% improvement in top-1 accuracy across different backbone architectures, with only a small increase in computational complexity. We further compared the performance of M2A with other state-of-the-art motion and attention mechanisms on the Something-Something V1 video action recognition benchmark. Experimental results showed that M2A can lead to further improvements when combined with other temporal mechanisms and that it outperforms other motion-only or attention-only mechanisms by as much as +60% in top-1 accuracy for specific classes in the benchmark.
DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI). The DeMFI-Net jointly performs deblurring and MFI where its baseline version performs feature-flow-based warping with FAC-FB module to obtain a sharp-interpolated frame as well to deblur two center-input frames. Moreover, its extended version further improves the joint task performance based on pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively gathers the distributed blurry pixel information over blurry input frames in feature-domain to improve the overall joint performances, which is computationally efficient since its attentive correlation is only focused pointwise. As a result, our DeMFI-Net achieves state-of-the-art (SOTA) performances for diverse datasets with significant margins compared to the recent SOTA methods, for both deblurring and MFI. All source codes including pretrained DeMFI-Net are publicly available at https://github.com/JihyongOh/DeMFI.