OpenRaiser/Intern-Atlas huggingface dataset:
The two player shortest path network interdiction problem
Venue: arXiv cs.DS 2020
Year: 2020

In this article, we study a biobjective extension of the shortest path network interdiction problem. Each arc in the network is associated with two integer length values and two players compute their respective shortest paths from source to sink independently from each other while an interdictor tries to lengthen both shortest paths by removing arcs. We show that this problem is intractable and that deciding whether a feasible interdiction strategy is efficient, is NP- complete. We provide a solution procedure to solve the problem on two-terminal series-parallel graphs in pseudopolynomial time.
Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion
Venue: arXiv cs.LG 2020
Year: 2020

We propose a novel approach to learn goal-conditioned policies for locomotion in a batch RL setting. The batch data is collected by a policy that is not goal-conditioned. For the locomotion task, this translates to data collection using a policy learnt by the agent for walking straight in one direction, and using that data to learn a goal-conditioned policy that enables the agent to walk in any direction. The data collection policy used should be invariant to the direction the agent is facing i.e. regardless of its initial orientation, the agent should take the same actions to walk forward. We exploit this property to learn a goal-conditioned policy using two key ideas: (1) augmenting data by generating trajectories with the same actions in different directions, and (2) learning an encoder that enforces invariance between these rotated trajectories with a Siamese framework. We show that our approach outperforms existing RL algorithms on 3-D locomotion agents like Ant, Humanoid and Minitaur.
Active-Learning in the Online Environment
Venue: arXiv cs.CY 2020
Year: 2020

Online learning is convenient for many learners; it gives them the possibility of learning without being restricted by attending a particular classroom at a specific time. While this exciting opportunity can let its users manage their life in a better way, many students may suffer from feeling isolated or disconnected from the community that consists of the instructor and the learners. Lack of interaction among students and the instructor may negatively impact their learnings and cause adverse emotions like anxiety, sadness, and depression. Apart from the feeling of loneliness, sometimes students may come up with different issues or questions as they study the course, which can stop them from confidently progressing or make them feel discouraged if we leave them alone. To promote interaction and to overcome the limitations of geographic distance in online education, we propose a customized design, Tele-instruction, with useful features supplement to the traditional online learning systems to enable peers and the instructor of the course to interact at their conveniences once needed. The designed system can help students address their questions through the answers already provided to other students or ask for the instructor's point of view by two-way communication, similar to face-to-face forms of educational experiences. We believe our approach can assist in filling the gaps when online learning falls behind the traditional classroom-based learning systems.
Identifying Weakly Connected Subsystems in Building Energy Model for Effective Load Estimation in Presence of Parametric Uncertainty
Venue: arXiv cs.CE 2020
Year: 2020

It is necessary to estimate the expected energy usage of a building to determine how to reduce energy usage. The expected energy usage of a building can be reliably simulated using a Building Energy Model (BEM). Many of the numerous input parameters in a BEM are uncertain. To ensure that the building simulation is sufficiently accurate, and to better understand the impact of imprecisions in the input parameters and calculation methods, it is desirable to quantify uncertainty in the BEM throughout the modeling process. Uncertainty quantification (UQ) typically requires a large number of simulations to produce meaningful data, which, due to the vast number of input parameters and the dynamic nature of building simulation, is computationally expensive. Uncertainty Quantification (UQ) in BEM domain is thus intractable due to the size of the problem and parameters involved and hence it needs an advanced methodology for analysis. The current paper outlines a novel Weakly-Connected-Systems (WCSs) identification-based UQ framework developed to propagate the quantifiable uncertainty in the BEM. The overall approach is demonstrated on the physics-based thermal model of an actual building in Central New York.
Parallelization Techniques for Verifying Neural Networks
Venue: arXiv cs.LO 2020
Year: 2020

Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification. We introduce an algorithm based on partitioning the verification problem in an iterative manner and explore two partitioning strategies, that work by partitioning the input space or by case splitting on the phases of the neuron activations, respectively. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems. An extensive experimental evaluation shows the benefit of these techniques on both existing benchmarks and new benchmarks from the aviation domain. A preliminary experiment with ultra-scaling our algorithm using a large distributed cloud-based platform also shows promising results.
Learning-to-Rank with BERT in TF-Ranking
Venue: arXiv cs.IR 2020
Year: 2020

This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance. This approach is proved to be effective in a public MS MARCO benchmark [3]. Our first two submissions achieve the best performance for the passage re-ranking task [4], and the second best performance for the passage full-ranking task as of April 10, 2020 [5]. To leverage the lately development of pre-trained language models, we recently integrate RoBERTa [6] and ELECTRA [7]. Our latest submissions improve our previously state-of-the-art re-ranking performance by 4.3% [8], and achieve the third best performance for the full-ranking task [9] as of June 8, 2020. Both of them demonstrate the effectiveness of combining ranking losses with BERT representations for document ranking.
BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces BReG-NeXt, a residual-based network architecture using a function wtih bounded derivative instead of a simple shortcut path (a.k.a. identity mapping) in the residual units for automatic recognition of facial expressions based on the categorical and dimensional models of affect. Compared to ResNet, our proposed adaptive complex mapping results in a shallower network with less numbers of training parameters and floating point operations per second (FLOPs). Adding trainable parameters to the bypass function further improves fitting and training the network and hence recognizing subtle facial expressions such as contempt with a higher accuracy. We conducted comprehensive experiments on the categorical and dimensional models of affect on the challenging in-the-wild databases of AffectNet, FER2013, and Affect-in-Wild. Our experimental results show that our adaptive complex mapping approach outperforms the original ResNet consisting of a simple identity mapping as well as other state-of-the-art methods for Facial Expression Recognition (FER). Various metrics are reported in both affect models to provide a comprehensive evaluation of our method. In the categorical model, BReG-NeXt-50 with only 3.1M training parameters and 15 MFLOPs, achieves 68.50% and 71.53% accuracy on AffectNet and FER2013 databases, respectively. In the dimensional model, BReG-NeXt achieves 0.2577 and 0.2882 RMSE value on AffectNet and Affect-in-Wild databases, respectively.
Moire Image Restoration using Multi Level Hyper Vision Net
Venue: arXiv cs.CV 2020
Year: 2020

A moire pattern in the images is resulting from high frequency patterns captured by the image sensor (colour filter array) that appear after demosaicing. These Moire patterns would appear in natural images of scenes with high frequency content. The Moire pattern can also vary intensely due to a minimal change in the camera direction/positioning. Thus the Moire pattern depreciates the quality of photographs. An important issue in demoireing pattern is that the Moireing patterns have dynamic structure with varying colors and forms. These challenges makes the demoireing more difficult than many other image restoration tasks. Inspired by these challenges in demoireing, a multilevel hyper vision net is proposed to remove the Moire pattern to improve the quality of the images. As a key aspect, in this network we involved residual channel attention block that can be used to extract and adaptively fuse hierarchical features from all the layers efficiently. The proposed algorithms has been tested with the NTIRE 2020 challenge dataset and thus achieved 36.85 and 0.98 Peak to Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) Index respectively.
On the (Un)Reliability of Privacy Policies in Android Apps
Venue: arXiv cs.CR 2020
Year: 2020

Access to privacy-sensitive information on Android is a growing concern in the mobile community. Albeit Google Play recently introduced some privacy guidelines, it is still an open problem to soundly verify whether apps actually comply with such rules. To this aim, in this paper, we discuss a novel methodology based on a fruitful combination of static analysis, dynamic analysis, and machine learning techniques, which allows assessing such compliance. More in detail, our methodology checks whether each app i) contains a privacy policy that complies with the Google Play privacy guidelines, and ii) accesses privacy-sensitive information only upon the acceptance of the policy by the user. Furthermore, the methodology also allows checking the compliance of third-party libraries embedded in the apps w.r.t. the same privacy guidelines. We implemented our methodology in a tool, 3PDroid, and we carried out an assessment on a set of recent and most-downloaded Android apps in the Google Play Store. Experimental results suggest that more than 95% of apps access user's privacy-sensitive information, but just a negligible subset of them (around 1%) fully complies with the Google Play privacy guidelines.
Convergence Behaviour of Bystanders: An Analysis of 2016 Munich Shooting Twitter Crisis Communication
Venue: arXiv cs.SI 2020
Year: 2020

While convergence behaviour archetypes can explain behaviour of individuals who actively converge on and participate in crises, less is known about individuals who converge on an event and choose to remain passive i.e. "bystanders". Bystanders are important, because of their proximity to an event and their function as an "eye-witness". To investigate the role of bystanders in crisis communications we analysed Twitter communication generated from the 2016 Munich Shooting event. Our findings revealed the impassive convergence behaviour archetype could influence an event as a passive and rational "eye-witness", by gathering and sharing information close to where the event is occurring.
Time Adaptive Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios.
Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study
Venue: arXiv cs.RO 2020
Year: 2020

Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module
Venue: arXiv cs.CV 2020
Year: 2020

Security inspection often deals with a piece of baggage or suitcase where objects are heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited items detection in X-ray images. In the literature, there have been rare studies and datasets touching this important topic. In this work, we contribute the first high-quality object detection dataset for security inspection, named Occluded Prohibited Items X-ray (OPIXray) image benchmark. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. The test set is further divided into three occlusion levels to better understand the performance of detectors. Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors. Despite the heavy occlusion in X-ray imaging, shape appearance of objects can be preserved well, and meanwhile different materials visually appear with different colors and textures. Motivated by these observations, our DOAM simultaneously leverages the different appearance information of the prohibited item to generate the attention map, which helps refine feature maps for the general detectors. We comprehensively evaluate our module on the OPIXray dataset, and demonstrate that our module can consistently improve the performance of the state-of-the-art detection methods such as SSD, FCOS, etc, and significantly outperforms several widely-used attention mechanisms. In particular, the advantages of DOAM are more significant in the scenarios with higher levels of occlusion, which demonstrates its potential application in real-world inspections. The OPIXray benchmark and our model are released at https://github.com/OPIXray-author/OPIXray.
Dual Embedding Expansion for Vehicle Re-identification
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors. Modern systems deploy CNNs to produce unique representations from the images of each vehicle instance. Most work focuses on leveraging new losses and network architectures to improve the descriptiveness of these representations. In contrast, our work concentrates on re-ranking and embedding expansion techniques. We propose an efficient approach for combining the outputs of multiple models at various scales while exploiting tracklet and neighbor information, called dual embedding expansion (DEx). Additionally, a comparative study of several common image retrieval techniques is presented in the context of vehicle re-ID. Our system yields competitive performance in the 2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx when combined with other re-ranking techniques, can produce an even larger gain without any additional attribute labels or manual supervision.
Adaptive Attention Span in Computer Vision
Venue: arXiv cs.CV 2020
Year: 2020

Recent developments in Transformers for language modeling have opened new areas of research in computer vision. Results from late 2019 showed vast performance increases in both object detection and recognition when convolutions are replaced by local self-attention kernels. Models using local self-attention kernels were also shown to have less parameters and FLOPS compared to equivalent architectures that only use convolutions. In this work we propose a novel method for learning the local self-attention kernel size. We then compare its performance to fixed-size local attention and convolution kernels. The code for all our experiments and models is available at https://github.com/JoeRoussy/adaptive-attention-in-cv
Enhancing Pharmacovigilance with Drug Reviews and Social Media
Venue: arXiv cs.CL 2020
Year: 2020

This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs). We examined the performance of BERT alongside two variants that are trained on biomedical papers, BioBERT7, and clinical notes, Clinical BERT8. A variety of 8 different BERT models were fine-tuned and compared across three different tasks in order to evaluate their relative performance to one another in the ADR tasks. The tasks include sentiment classification of drug reviews, presence of ADR in twitter postings, and named entity recognition of ADRs in twitter postings. BERT demonstrates its flexibility with high performance across all three different pharmacovigilance related tasks.
A Trustworthy Recruitment Process for Spatial Mobile Crowdsourcing in Large-scale Social IoT
Venue: arXiv cs.SI 2020
Year: 2020

Spatial Mobile Crowdsourcing (SMCS) can be leveraged by exploiting the capabilities of the Social Internet-of-Things (SIoT) to execute spatial tasks. Typically, in SMCS, a task requester aims to recruit a subset of IoT devices and commission them to travel to the task location. However, because of the exponential increase of IoT networks and their diversified devices (e.g., multiple brands, different communication channels, etc.), recruiting the appropriate devices/workers is becoming a challenging task. To this end, in this paper, we develop a recruitment process for SMCS platforms using automated SIoT service discovery to select trustworthy workers satisfying the requester requirements. The method we purpose includes mainly two stages: 1) a worker filtering stage, aiming at reducing the workers' search space to a subset of potential trustworthy candidates using the Louvain community detection algorithm (CD) applied to SIoT relation graphs. Next, 2) a selection process stage that uses an Integer Linear Program (ILP) to determine the final set of selected devices/workers. The ILP maximizes a worker efficiency metric incorporating the skills/specs level, recruitment cost, and trustworthiness level of the recruited IoT devices. Selected experiments analyze the performance of the proposed CD-ILP algorithm using a real-world dataset and show its superiority in providing an effective recruitment strategy compared to an existing stochastic algorithm.
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher
Venue: arXiv cs.LG 2020
Year: 2020

Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time series classification datasets are often small. In order to address this problem, we propose a novel time series data augmentation called guided warping. While many data augmentation methods are based on random transformations, guided warping exploits the element alignment properties of Dynamic Time Warping (DTW) and shapeDTW, a high-level DTW method based on shape descriptors, to deterministically warp sample patterns. In this way, the time series are mixed by warping the features of a sample pattern to match the time steps of a reference pattern. Furthermore, we introduce a discriminative teacher in order to serve as a directed reference for the guided warping. We evaluate the method on all 85 datasets in the 2015 UCR Time Series Archive with a deep convolutional neural network (CNN) and a recurrent neural network (RNN). The code with an easy to use implementation can be found at https://github.com/uchidalab/time_series_augmentation .
On the Unusual Effectiveness of Type-Aware Operator Mutations for Testing SMT Solvers
Venue: arXiv cs.SE 2020
Year: 2020

We propose type-aware operator mutation, a simple, but unusually effective approach for testing SMT solvers. The key idea is to mutate operators of conforming types within the seed formulas to generate well-typed mutant formulas. These mutant formulas are then used as the test cases for SMT solvers. We realized type-aware operator mutation within the OpFuzz tool and used it to stress-test Z3 and CVC4, two state-of-the-art SMT solvers. Type-aware operator mutations are unusually effective: During one year of extensive testing with OpFuzz, we reported 1,092 bugs on Z3's and CVC4's respective GitHub issue trackers, out of which 819 unique bugs were confirmed and 685 of the confirmed bugs were fixed by the developers. The detected bugs are highly diverse -- we found bugs of many different types (soundness bugs, invalid model bugs, crashes, etc.), logics and solver configurations. We have further conducted an in-depth study of the bugs found by OpFuzz. The study results show that the bugs found by OpFuzz are of high quality. Many of them affect core components of the SMT solvers' codebases, and some required major changes for the developers to fix. Among the 819 confirmed bugs found by OpFuzz, 184 were soundness bugs, the most critical bugs in SMT solvers, and 489 were in the default modes of the solvers. Notably, OpFuzz found 27 critical soundness bugs in CVC4, which has proved to be a very stable SMT solver.