OpenRaiser/Intern-Atlas huggingface dataset:
Traffic Lane Detection using FCN
Venue: arXiv cs.CV 2020
Year: 2020

Automatic lane detection is a crucial technology that enables self-driving cars to properly position themselves in a multi-lane urban driving environments. However, detecting diverse road markings in various weather conditions is a challenging task for conventional image processing or computer vision techniques. In recent years, the application of Deep Learning and Neural Networks in this area has proven to be very effective. In this project, we designed an Encoder- Decoder, Fully Convolutional Network for lane detection. This model was applied to a real-world large scale dataset and achieved a level of accuracy that outperformed our baseline model.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Venue: arXiv cs.AI 2020
Year: 2020

Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of "why" and "how", beyond the dominant "what" and "where" framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the "dark matter" of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.
Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, retrieval, and completion. However, this approach has been limited by the need to have query shapes in the same canonical scale and pose as those observed during training, restricting its effectiveness on real world scenes. We present a formulation to overcome this issue by jointly estimating shape and similarity transform parameters. We conduct experiments to demonstrate the effectiveness of this formulation on synthetic and real datasets and report favorable comparisons to the state of the art. Finally, we also emphasize the viability of this approach as a form of 3D model compression.
Securing Internet Applications from Routing Attacks
Venue: arXiv cs.NI 2020
Year: 2020

Attacks on Internet routing are typically viewed through the lens of availability and confidentiality, assuming an adversary that either discards traffic or performs eavesdropping. Yet, a strategic adversary can use routing attacks to compromise the security of critical Internet applications like Tor, certificate authorities, and the bitcoin network. In this paper, we survey such application-specific routing attacks and argue that both application-layer and network-layer defenses are essential and urgently needed. While application-layer defenses are easier to deploy in the short term, we hope that our work serves to provide much needed momentum for the deployment of network-layer defenses.
CatSIM: A Categorical Image Similarity Metric
Venue: arXiv cs.CV 2020
Year: 2020

We introduce CatSIM, a new similarity metric for binary and multinary two- and three-dimensional images and volumes. CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, regions of two images are rated higher than using simple matching. The metric can also compare arbitrary regions inside images. CatSIM is evaluated on artificial data sets, image quality assessment surveys and two imaging applications
Further Evidence Towards the Multiplicative 1-2-3 Conjecture
Venue: arXiv cs.DM 2020
Year: 2020

The product version of the 1-2-3 Conjecture, introduced by Skowronek-Kazi{ó}w in 2012, states that, a few obvious exceptions apart, all graphs can be 3-edge-labelled so that no two adjacent vertices get incident to the same product of labels. To date, this conjecture was mainly verified for complete graphs and 3-colourable graphs. As a strong support to the conjecture, it was also proved that all graphs admit such 4-labellings. In this work, we investigate how a recent proof of the multiset version of the 1-2-3 Conjecture by Vu{\v c}kovi{ć} can be adapted to prove results on the product version. We prove that 4-chromatic graphs verify the product version of the 1-2-3 Conjecture. We also prove that for all graphs we can design 3-labellings that almost have the desired property. This leads to a new problem, that we solve for some graph classes.
Distributed Weighted Min-Cut in Nearly-Optimal Time
Venue: arXiv cs.DS 2020
Year: 2020

Minimum-weight cut (min-cut) is a basic measure of a network's connectivity strength. While the min-cut can be computed efficiently in the sequential setting [Karger STOC'96], there was no efficient way for a distributed network to compute its own min-cut without limiting the input structure or dropping the output quality: In the standard CONGEST model, existing algorithms with nearly-optimal time (e.g. [Ghaffari, Kuhn, DISC'13; Nanongkai, Su, DISC'14]) can guarantee a solution that is $(1+ε)$-approximation at best while the exact $\tilde O(n^{0.8}D^{0.2} + n^{0.9})$-time algorithm [Ghaffari, Nowicki, Thorup, SODA'20] works only on *simple* networks (no weights and no parallel edges). Here $n$ and $D$ denote the network's number of vertices and hop-diameter, respectively. For the weighted case, the best bound was $\tilde O(n)$ [Daga, Henzinger, Nanongkai, Saranurak, STOC'19]. In this paper, we provide an *exact* $\tilde O(\sqrt n + D)$-time algorithm for computing min-cut on *weighted* networks. Our result improves even the previous algorithm that works only on simple networks. Its time complexity matches the known lower bound up to polylogarithmic factors. At the heart of our algorithm are a clever routing trick and two structural lemmas regarding the structure of a minimum cut of a graph. These two structural lemmas considerably strengthen and generalize the framework of Mukhopadhyay-Nanongkai [STOC'20] and can be of independent interest.
Discovering Business Process Simulation Models in the Presence of Multitasking
Venue: arXiv cs.SE 2020
Year: 2020

Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.
Recurrent Convolutional Neural Networks help to predict location of Earthquakes
Venue: arXiv cs.LG 2020
Year: 2020

We examine the applicability of modern neural network architectures to the midterm prediction of earthquakes. Our data-based classification model aims to predict if an earthquake with the magnitude above a threshold takes place at a given area of size $10 \times 10$ kilometers in $10$-$60$ days from a given moment. Our deep neural network model has a recurrent part (LSTM) that accounts for time dependencies between earthquakes and a convolutional part that accounts for spatial dependencies. Obtained results show that neural networks-based models beat baseline feature-based models that also account for spatio-temporal dependencies between different earthquakes. For historical data on Japan earthquakes our model predicts occurrence of an earthquake in $10$ to $60$ days from a given moment with magnitude $M_c > 5$ with quality metrics ROC AUC $0.975$ and PR AUC $0.0890$, making $1.18 \cdot 10^3$ correct predictions, while missing $2.09 \cdot 10^3$ earthquakes and making $192 \cdot 10^3$ false alarms. The baseline approach has similar ROC AUC $0.992$, number of correct predictions $1.19 \cdot 10^3$, and missing $2.07 \cdot 10^3$ earthquakes, but significantly worse PR AUC $0.00911$, and number of false alarms $1004 \cdot 10^3$.
VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle re-identification is a challenging task due to high intra-class variances and small inter-class variances. In this work, we focus on the failure cases caused by similar background and shape. They pose serve bias on similarity, making it easier to neglect fine-grained information. To reduce the bias, we propose an approach named VOC-ReID, taking the triplet vehicle-orientation-camera as a whole and reforming background/shape similarity as camera/orientation re-identification. At first, we train models for vehicle, orientation and camera re-identification respectively. Then we use orientation and camera similarity as penalty to get final similarity. Besides, we propose a high performance baseline boosted by bag of tricks and weakly supervised data augmentation. Our algorithm achieves the second place in vehicle re-identification at the NVIDIA AI City Challenge 2020.
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
Venue: arXiv cs.CL 2020
Year: 2020

The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual annotations by experts. In this work, we introduce a BERT-based approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations. We demonstrate superior performance of a biomedically pretrained BERT model first trained on annotations of a rule-based labeler and then finetuned on a small set of expert annotations augmented with automated backtranslation. We find that our final model, CheXbert, is able to outperform the previous best rules-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.
Enabling and Enforcing Social Distancing Measures using Smart City and ITS Infrastructures: A COVID-19 Use Case
Venue: arXiv cs.CY 2020
Year: 2020

Internet of Things is a revolutionary domain that has the caliber to impact our lives and bring significant changes to the world. Several IoT applications have been envisioned to facilitate data driven and smart application for the user. Smart City and Intelligent Transportation System (ITS) offer a futuristic vision of smart, secure and safe experience to the end user, and at the same time efficiently manage the sparse resources and optimize the efficiency of city operations. However, outbreaks and pandemics like COVID-19 have revealed limitations of the existing deployments, therefore, architecture, applications and technology systems need to be developed for swift and timely enforcement of guidelines, rules and government orders to contain such future outbreaks. This work outlines novel architecture, potential use-cases and some future directions in developing such applications using Smart City and ITS.
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm
Venue: arXiv cs.CR 2020
Year: 2020

The Dendritic Cell Algorithm (DCA) as one of the emerging evolutionary algorithms is based on the behavior of the specific immune agents; known as Dendritic Cells (DCs). DCA has several potentially beneficial features for binary classification problems. In this paper, we aim at providing a new version of this immune-inspired mechanism acts as a semi-supervised classifier which can be a defensive shield in network intrusion detection problem. Till now, no strategy or idea has already been adopted on the GetAntigen() function on detection phase, but randomly sampling entails the DCA to provide undesirable results in several cycles in each time. This leads to uncertainty. Whereas it must be accomplished by biological behaviors of DCs in tissues, we have proposed a novel strategy which exactly acts based on its immunological functionalities of dendritic cells. The proposed mechanism focuses on two items: First, to obviate the challenge of needing to have a preordered antigen set for computing danger signal, and the second, to provide a novel immune-inspired idea in order to non-random data sampling. A variable functional migration threshold is also computed cycle by cycle that shows necessity of the Migration threshold (MT) flexibility. A significant criterion so called capability of intrusion detection (CID) used for tests. All of the tests have been performed in a new benchmark dataset named UNSW-NB15. Experimental consequences demonstrate that the present schema dominates the standard DCA and has higher CID in comparison with other approaches found in literature.
Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients using Data-Driven Network Analysis
Venue: arXiv cs.SI 2020
Year: 2020

Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years' (2010-2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of a network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analyzed and communities of departments are discovered. The results of this study may help hospital administration to effectively organize and manage the coordination of departments based on their significance, strategic positioning and role in the diagnosis and treatment process which, in turn, nurtures value-based and precision healthcare.
A Novel Multi-Agent System for Complex Scheduling Problems
Venue: arXiv cs.MA 2020
Year: 2020

Complex scheduling problems require a large amount computation power and innovative solution methods. The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains. Independent specialized agents handle small tasks, to reach a superordinate target. Effective coordination is therefore required to achieve productive cooperation. Role models and distributed artificial intelligence are employed to tackle the resulting challenges. We simulate a NP-hard scheduling problem to demonstrate the validity of our approach. In addition to the general agent based framework we propose new simulation-based optimization heuristics to given scheduling problems. Two of the described optimization algorithms are implemented using agents. This paper highlights the advantages of the agent-based approach, like the reduction in layout complexity, improved control of complicated systems, and extendability.
MorphStore: Analytical Query Engine with a Holistic Compression-Enabled Processing Model
Venue: arXiv cs.DB 2020
Year: 2020

In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column-store database systems, but mainly for base data. In particular, during query processing, these systems only keep the data compressed until an operator cannot process the compressed data directly, whereupon the data is decompressed, but not recompressed. Thus, the full potential of compression during query processing is not exploited. To overcome that, we developed a novel compression-enabled processing model as presented in this paper. As we are going to show, the continuous usage of compression for all base data and all intermediates is very beneficial to reduce the overall memory footprint as well as to improve the query performance.
A Generalization of the Allreduce Operation
Venue: arXiv cs.DC 2020
Year: 2020

Allreduce is one of the most frequently used MPI collective operations, and thus its performance attracts much attention in the past decades. Many algorithms were developed with different properties and purposes. We present a novel approach to communication description based on the permutations inspired by the mathematics of a Rubik's cube where the moves form a mathematical structure called group. Similarly, cyclic communication patterns between a set of $P$ processes may be described by a permutation group. This new approach allows constructing a generalization of the widely used Allreduce algorithms such as Ring, Recursive Doubling and Recursive Halving. Using the developed approach we build an algorithm that successfully solves the well-known problem of the non-power-of-two number of processes which breaks down the performance of many existing algorithms. The proposed algorithm provides a general solution for any number of processes with the dynamically changing amount of communication steps between $\lceil \log{P} \rceil$ for the latency-optimal version and $2 \cdot \lceil \log{P} \rceil$ for the bandwidth-optimal case.
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 98% ($\pm$ 3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git
Criteria Based Evaluation Framework for Service Oriented Methodologies
Venue: arXiv cs.SE 2020
Year: 2020

Service Oriented Software Engineering is based on concepts and principles for constructing complex enterprise systems in which services as building block of the system, are distributed in large networks. The main goal of the service oriented methodologies is to define a process for development and maintenance of service based systems. Most of the Service Oriented methodologies are not mature enough compared with traditional software development methodologies such as Object Oriented or Component-Based. Hence, defining an evaluation framework will be useful for comparing methodologies for identifying their strengths and weaknesses, defining new methodologies or extending existing Service Oriented methodologies. At the time being, there is no complete evaluation framework for evaluating Service Oriented methodologies. The principal objective of this paper is to introduce a comprehensive evaluation framework for evaluating Service-Oriented methodologies. This evaluation tool is appropriate for methodology engineers to develop new methodologies, as well as project managers to select an appropriate methodology at a specific project.