OpenRaiser/Intern-Atlas huggingface dataset:
Recent Advances and Trends in Multimodal Deep Learning: A Review
Venue: arXiv cs.CV 2021
Year: 2021

Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better when various senses are engaged in the processing of information. This paper focuses on multiple types of modalities, i.e., image, video, text, audio, body gestures, facial expressions, and physiological signals. Detailed analysis of past and current baseline approaches and an in-depth study of recent advancements in multimodal deep learning applications has been provided. A fine-grained taxonomy of various multimodal deep learning applications is proposed, elaborating on different applications in more depth. Architectures and datasets used in these applications are also discussed, along with their evaluation metrics. Last, main issues are highlighted separately for each domain along with their possible future research directions.
Belief Space Planning: A Covariance Steering Approach
Venue: arXiv cs.RO 2021
Year: 2021

A new belief space planning algorithm, called covariance steering Belief RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion planning of dynamical systems under simultaneous motion and observation uncertainties. CS-BRM extends the probabilistic roadmap (PRM) approach to belief spaces and is based on the recently developed theory of covariance steering (CS) that enables guaranteed satisfaction of terminal belief constraints in finite-time. The nodes in the CS-BRM are sampled in belief space and represent distributions of the system states. A covariance steering controller steers the system from one BRM node to another, thus acting as an edge controller of the corresponding belief graph that ensures belief constraint satisfaction. After the edge controller is computed, a specific edge cost is assigned to that edge. The CS-BRM algorithm allows the sampling of non-stationary belief nodes, and thus is able to explore the velocity space and find efficient motion plans. The performance of CS-BRM is evaluated and compared to a previous belief space planning method, demonstrating the benefits of the proposed approach.
Hater-O-Genius Aggression Classification using Capsule Networks
Venue: arXiv cs.CL 2021
Year: 2021

Contending hate speech in social media is one of the most challenging social problems of our time. There are various types of anti-social behavior in social media. Foremost of them is aggressive behavior, which is causing many social issues such as affecting the social lives and mental health of social media users. In this paper, we propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets. Tweets are classified into three categories - Covertly Aggressive, Overtly Aggressive, and Non-Aggressive. The proposed architecture is an ensemble of smaller subnetworks that are able to characterize the feature embeddings effectively. We demonstrate qualitatively that each of the smaller subnetworks is able to learn unique features. Our best model is an ensemble of Capsule Networks and results in a 65.2% F1 score on the Facebook test set, which results in a performance gain of 0.95% over the TRAC-2018 winners. The code and the model weights are publicly available at https://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.
Novel ANN method for solving ordinary and fractional Black-Scholes equation
Venue: arXiv cs.LG 2021
Year: 2021

The main aim of this study is to introduce a 2-layered Artificial Neural Network (ANN) for solving the Black-Scholes partial differential equation (PDE) of either fractional or ordinary orders. Firstly, a discretization method is employed to change the model into a sequence of Ordinary Differential Equations (ODE). Then each of these ODEs is solved with the aid of an ANN. Adam optimization is employed as the learning paradigm since it can add the foreknowledge of slowing down the process of optimization when getting close to the actual optimum solution. The model also takes advantage of fine tuning for speeding up the process and domain mapping to confront infinite domain issue. Finally, the accuracy, speed, and convergence of the method for solving several types of Black-Scholes model are reported.
A Practical Consideration on Convex Mutual Information
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we focus on the convex mutual information, which was found at the lowest level split in multilevel coding schemes with communications over the additive white Gaussian noise (AWGN) channel. Theoretical analysis shows that communication achievable rates (ARs) do not necessarily below mutual information in the convex region. In addition, simulation results are provided as an evidence.
Application of Opportunistic Bit to Multilevel Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a new signal organization method to work in the structure of the multi level coding (MLC). The transmit bits are divided into opportunistic bit (OB) and conventional bit (CB), which are mapped to the lower level- and higher level signal in parallel to the MLC, respectively. Because the OB's mapping does not require signal power explicitly, the energy of the CB modulated symbol can be doubled. As the result, the overall mutual information of the proposed method is found higher than that of the conventional BPSK in one dimensional case. Moreover, the extension of the method to the two-complex-dimension shows the better performance over the QPSK. The numerical results confirm this approach.
Assessing perceived organizational leadership styles through twitter text mining
Venue: arXiv cs.CL 2021
Year: 2021

We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.
LineCounter: Learning Handwritten Text Line Segmentation by Counting
Venue: arXiv cs.CV 2021
Year: 2021

Handwritten Text Line Segmentation (HTLS) is a low-level but important task for many higher-level document processing tasks like handwritten text recognition. It is often formulated in terms of semantic segmentation or object detection in deep learning. However, both formulations have serious shortcomings. The former requires heavy post-processing of splitting/merging adjacent segments, while the latter may fail on dense or curved texts. In this paper, we propose a novel Line Counting formulation for HTLS -- that involves counting the number of text lines from the top at every pixel location. This formulation helps learn an end-to-end HTLS solution that directly predicts per-pixel line number for a given document image. Furthermore, we propose a deep neural network (DNN) model LineCounter to perform HTLS through the Line Counting formulation. Our extensive experiments on the three public datasets (ICDAR2013-HSC, HIT-MW, and VML-AHTE) demonstrate that LineCounter outperforms state-of-the-art HTLS approaches. Source code is available at https://github.com/Leedeng/Line-Counter.
Learning Security Classifiers with Verified Global Robustness Properties
Venue: arXiv cs.CR 2021
Year: 2021

Many recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all inputs. Since data distribution shift is very common in security applications, e.g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier. Therefore, it is more desirable to enforce global robustness properties that hold for all inputs, which is strictly stronger than local robustness. In this paper, we present a framework and tools for training classifiers that satisfy global robustness properties. We define new notions of global robustness that are more suitable for security classifiers. We design a novel booster-fixer training framework to enforce global robustness properties. We structure our classifier as an ensemble of logic rules and design a new verifier to verify the properties. In our training algorithm, the booster increases the classifier's capacity, and the fixer enforces verified global robustness properties following counterexample guided inductive synthesis. We show that we can train classifiers to satisfy different global robustness properties for three security datasets, and even multiple properties at the same time, with modest impact on the classifier's performance. For example, we train a Twitter spam account classifier to satisfy five global robustness properties, with 5.4% decrease in true positive rate, and 0.1% increase in false positive rate, compared to a baseline XGBoost model that doesn't satisfy any property.
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Venue: arXiv cs.LG 2021
Year: 2021

Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for given classifier under a budget constraint. However, in many real settings, different labelers have different labeling costs and can yield different labeling accuracies. Moreover, a given labeler may exhibit different labeling accuracies for different instances. This setting can be referred to as active learning with diverse labelers with varying costs and accuracies, and it arises in many important real settings. It is therefore beneficial to understand how to effectively trade-off between labeling accuracy for different instances, labeling costs, as well as the informativeness of training instances, so as to achieve the best generalization performance at the lowest labeling cost. In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset.
LuvHarris: A Practical Corner Detector for Event-cameras
Venue: arXiv cs.CV 2021
Year: 2021

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only "as-fast-as-possible", i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art; a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.
3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization.
The power of reciprocal knowledge sharing relationships for startup success
Venue: arXiv cs.SI 2021
Year: 2021

Purpose: The purpose of this paper is to examine the innovative capabilities of biotech start-ups in relation to geographic proximity and knowledge sharing interaction in the R&D network of a major high-tech cluster. Design-methodology-approach: This study compares longitudinal informal communication networks of researchers at biotech start-ups with company patent applications in subsequent years. For a year, senior R&D staff members from over 70 biotech firms located in the Boston biotech cluster were polled and communication information about interaction with peers, universities and big pharmaceutical companies was collected, as well as their geolocation tags. Findings: Location influences the amount of communication between firms, but not their innovation success. Rather, what matters is communication intensity and recollection by others. In particular, there is evidence that rotating leadership - changing between a more active and passive communication style - is a predictor of innovative performance. Practical implications: Expensive real-estate investments can be replaced by maintaining social ties. A more dynamic communication style and more diverse social ties are beneficial to innovation. Originality-value: Compared to earlier work that has shown a connection between location, network and firm performance, this paper offers a more differentiated view; including a novel measure of communication style, using a unique data set and providing new insights for firms who want to shape their communication patterns to improve innovation, independently of their location.
Uncertainty-Aware Signal Temporal Logic Inference
Venue: arXiv cs.AI 2021
Year: 2021

Temporal logic inference is the process of extracting formal descriptions of system behaviors from data in the form of temporal logic formulas. The existing temporal logic inference methods mostly neglect uncertainties in the data, which results in limited applicability of such methods in real-world deployments. In this paper, we first investigate the uncertainties associated with trajectories of a system and represent such uncertainties in the form of interval trajectories. We then propose two uncertainty-aware signal temporal logic (STL) inference approaches to classify the undesired behaviors and desired behaviors of a system. Instead of classifying finitely many trajectories, we classify infinitely many trajectories within the interval trajectories. In the first approach, we incorporate robust semantics of STL formulas with respect to an interval trajectory to quantify the margin at which an STL formula is satisfied or violated by the interval trajectory. The second approach relies on the first learning algorithm and exploits the decision tree to infer STL formulas to classify behaviors of a given system. The proposed approaches also work for non-separable data by optimizing the worst-case robustness in inferring an STL formula. Finally, we evaluate the performance of the proposed algorithms in two case studies, where the proposed algorithms show reductions in the computation time by up to four orders of magnitude in comparison with the sampling-based baseline algorithms (for a dataset with 800 sampled trajectories in total).
Pan-sharpening via High-pass Modification Convolutional Neural Network
Venue: arXiv cs.CV 2021
Year: 2021

Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification block. Different from existing methods, the proposed block is designed to learn the high-pass information, leading to enhance spatial information in each band of the multi-spectral-resolution images. To facilitate the generation of visually appealing pan-sharpened images, we propose a perceptual loss function and further optimize the model based on high-level features in the near-infrared space. Experiments demonstrate the superior performance of the proposed method compared to the state-of-the-art pan-sharpening methods, both quantitatively and qualitatively. The proposed model is open-sourced at https://github.com/jiaming-wang/HMB.
TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search
Venue: arXiv cs.CV 2021
Year: 2021

Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this problem in the fine-grained level. However, most relevant methods introduce additional models or complicated training and evaluation strategies, which are hard to use in realistic scenarios. In order to facilitate the practical application, we propose a simple but effective end-to-end learning framework for text-based person search named TIPCB (i.e., Text-Image Part-based Convolutional Baseline). Firstly, a novel dual-path local alignment network structure is proposed to extract visual and textual local representations, in which images are segmented horizontally and texts are aligned adaptively. Then, we propose a multi-stage cross-modal matching strategy, which eliminates the modality gap from three feature levels, including low level, local level and global level. Extensive experiments are conducted on the widely-used benchmark dataset (CUHK-PEDES) and verify that our method outperforms the state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.
On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud
Venue: arXiv cs.RO 2021
Year: 2021

Ground surface detection in point cloud is widely used as a key module in autonomous driving systems. Different from previous approaches which are mostly developed for lidars with high beam resolution, e.g. Velodyne HDL-64, this paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution, e.g. Velodyne VLP-16. The approach is based on the RANSAC scheme of plane fitting. Inlier verification for plane hypotheses is enhanced by exploiting the point-wise tangent, which is a local feature available to compute regardless of the density of lidar beams. Ground surface which is not perfectly planar is fitted by multiple (specifically 4 in our implementation) disjoint plane regions. By assuming these plane regions to be rectanglar and exploiting the integral image technique, our approach approximately finds the optimal region partition and plane hypotheses under the RANSAC scheme with real-time computational complexity.
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Venue: arXiv cs.LG 2021
Year: 2021

Hierarchical Agglomerative Clustering (HAC) is one of the oldest but still most widely used clustering methods. However, HAC is notoriously hard to scale to large data sets as the underlying complexity is at least quadratic in the number of data points and many algorithms to solve HAC are inherently sequential. In this paper, we propose {Reciprocal Agglomerative Clustering (RAC)}, a distributed algorithm for HAC, that uses a novel strategy to efficiently merge clusters in parallel. We prove theoretically that RAC recovers the exact solution of HAC. Furthermore, under clusterability and balancedness assumption we show provable speedups in total runtime due to the parallelism. We also show that these speedups are achievable for certain probabilistic data models. In extensive experiments, we show that this parallelism is achieved on real world data sets and that the proposed RAC algorithm can recover the HAC hierarchy on billions of data points connected by trillions of edges in less than an hour.
BoundarySqueeze: Image Segmentation as Boundary Squeezing
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a novel method for high-quality image segmentation of both objects and scenes. Inspired by the dilation and erosion operations in morphological image processing techniques, the pixel-level image segmentation problems are treated as squeezing object boundaries. From this perspective, a novel and efficient \textbf{Boundary Squeeze} module is proposed. This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation. A bi-directionally flow-based warping process is proposed to generate such squeezed feature representation, and two specific loss signals are designed to supervise the squeezing process. The Boundary Squeeze module can be easily applied to both instance and semantic segmentation tasks as a plug-and-play module by building on top of some existing methods. Moreover, the proposed module is light-weighted, and thus has potential for practical usage. Experiment results show that our simple yet effective design can produce high-quality results on several different datasets. Besides, several other metrics on the boundary are used to prove the effectiveness of our method over previous work. Our approach yields significant improvement on challenging COCO and Cityscapes datasets for both instance and semantic segmentation, and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting. Codes and models will be published at \url{https://github.com/lxtGH/BSSeg}.