OpenRaiser/Intern-Atlas huggingface dataset:
Data Dieting in GAN Training
Venue: arXiv cs.LG 2020
Year: 2020

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g. time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to considering stand-alone GAN training and ensembles of generator models, we also consider reduced data training on an evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner makes GAN training more robust and accurate by exploiting overlapping neighborhood-based training on a spatial 2D grid. We conduct empirical experiments on Redux-Lipizzaner using the MNIST and CelebA data sets.
Structure-preserving neural networks
Venue: arXiv cs.LG 2020
Year: 2020

We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E. 56 (6): 6620-6632]. The method does not need to enforce any kind of balance equation, and thus no previous knowledge on the nature of the system is needed. Conservation of energy and dissipation of entropy in the prediction of previously unseen situations arise as a natural by-product of the structure of the method. Examples of the performance of the method are shown that include conservative as well as dissipative systems, discrete as well as continuous ones.
Solutions for Subset Sum Problems with Special Digraph Constraints
Venue: arXiv cs.DM 2020
Year: 2020

The subset sum problem is one of the simplest and most fundamental NP-hard problems in combinatorial optimization. We consider two extensions of this problem: The subset sum problem with digraph constraint (SSG) and subset sum problem with weak digraph constraint (SSGW). In both problems there is given a digraph with sizes assigned to the vertices. Within SSG we want to find a subset of vertices whose total size does not exceed a given capacity and which contains a vertex if at least one of its predecessors is part of the solution. Within SSGW we want to find a subset of vertices whose total size does not exceed a given capacity and which contains a vertex if all its predecessors are part of the solution. SSG and SSGW have been introduced recently by Gourves et al. who studied their complexity for directed acyclic graphs and oriented trees. We show that both problems are NP-hard even on oriented co-graphs and minimal series-parallel digraphs. Further, we provide pseudo-polynomial solutions for SSG and SSGW with digraph constraints given by directed co-graphs and series-parallel digraphs.
Exogenous Shocks Lead to Increased Responsiveness and Shifts in Sentimental Resilience in Online Discussions
Venue: arXiv cs.SI 2020
Year: 2020

The effects of real-world events on the dynamics and sentiment expressed through online conversations is not entirely understood. In particular, the dynamics of highly polarized communities, deeply invested in the outcome of a particular event naturally tend to have a contrasting emotional sensitivity to exogenous events. In this study, we analyze Twitter conversations during the Mexico vs Germany group match of the 2018 FIFA World Cup, and investigate the effect of the live game outcomes on the conversation dynamics and sentiment. We find that the exogenous events influence conversation volume and virality less, while having high influence on user responsiveness. Interestingly, we observe a shift in the influence that exogenous events have on fans immediately following the only goal scored in the game. The emotional resilience of fans of the advantaged team increased following this exogenous shock. In contrast, the sentiment of the disadvantaged team was left more susceptible to further exogenous events following the exogenous shock. These results support the fact that user engagement and emotional resilience of an online population holding highly polarized stances can be manipulated through an exogenous event of high importance.
$μ$Tiles: Efficient Intra-Process Privilege Enforcement of Memory Regions
Venue: arXiv cs.OS 2020
Year: 2020

With the alarming rate of security advisories and privacy concerns on connected devices, there is an urgent need for strong isolation guarantees in resource-constrained devices that demand very lightweight solutions. However, the status quo is that Unix-like operating systems do not offer privilege separation inside a process. Lack of practical fine-grained compartmentalization inside a shared address space leads to private data leakage through applications' untrusted dependencies and compromised threads. To this end, we propose $μ$Tiles, a lightweight kernel abstraction and set of security primitives based on mutual distrust for intra-process privilege separation, memory protection, and secure multithreading. $μ$Tiles takes advantage of hardware support for virtual memory tagging (e.g., ARM memory domains) to achieve significant performance gain while eliminating various hardware limitations. Our results (based on OpenSSL, the Apache HTTP server, and LevelDB) show that $μ$Tiles is extremely lightweight (adds $\approx 10KB$ to kernel image) for IoT use cases. It adds negligible runtime overhead ($\approx 0.5\%-3.5\%$) and is easy to integrate with existing applications for providing strong privilege separation.
A Resilient AWGR and Server Based PON Data Centre Architecture
Venue: arXiv cs.NI 2020
Year: 2020

This paper studies the resilience of an AWGR and server based PON DCN architecture against link failure scenarios and proposes a modified design for improved resilience. A MILP model is developed to evaluate the performance of the modified design considering different failure scenarios. The results show a limited increase in power consumption and a large increase in delay under failure scenarios compared to the normal state.
Analyze and Development System with Multiple Biometric Identification
Venue: arXiv cs.CV 2020
Year: 2020

Cause of a rapid increase in technological development, increasing identity theft, consumer fraud, the threat to personal data is also increasing every day. Methods developed earlier to ensure personal the information from the thefts was not effective and safe. Biometrics were introduced when it was needed technology for more efficient security of personal information. Old-fashioned traditional approaches like Personal identification number( PIN), passwords, keys, login ID can be forgotten, stolen or lost. In biometric authentication system, user may not remember any passwords or carry any keys. As people they recognize each other by the physical appearance and behavioral characteristics that biometric systems use physical characteristics, such as fingerprints, facial recognition, voice recognition, in order to distinguish between the actual user and scammer. In order to increase safety in 2005, biometric identification methods were developed government and business sectors, but today it has reached almost all private sectors as Banking, Finance, home security and protection, healthcare, business security and security etc. Since biometric samples and templates of a biometric system having one biometric character to detect and the user can be replaced and duplicated, the new idea of merging multiple biometric identification technologies has so-called multimodal biometric recognition systems have been introduced that use two or more biometric data characteristics of the individual that can be identified as a real user or not.
Person Re-Identification via Active Hard Sample Mining
Venue: arXiv cs.CV 2020
Year: 2020

Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models. To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts. Considering that hard samples can provide informative patterns, we first formulate an uncertainty estimation to actively select hard samples to iteratively train a re-ID model from scratch. Then, intra-diversity estimation is designed to reduce the redundant hard samples by maximizing their diversity. Moreover, we propose a computer-assisted identity recommendation module embedded in the active hard sample mining framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out to demonstrate the effectiveness of our method on several public datasets. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model.
Robust Line Segments Matching via Graph Convolution Networks
Venue: arXiv cs.CV 2020
Year: 2020

Line matching plays an essential role in structure from motion (SFM) and simultaneous localization and mapping (SLAM), especially in low-textured and repetitive scenes. In this paper, we present a new method of using a graph convolution network to match line segments in a pair of images, and we design a graph-based strategy of matching line segments with relaxing to an optimal transport problem. In contrast to hand-crafted line matching algorithms, our approach learns local line segment descriptor and the matching simultaneously through end-to-end training. The results show our method outperforms the state-of-the-art techniques, and especially, the recall is improved from 45.28% to 70.47% under a similar presicion. The code of our work is available at https://github.com/mameng1/GraphLineMatching.
Bounding the Mim-Width of Hereditary Graph Classes
Venue: arXiv cs.DS 2020
Year: 2020

A large number of NP-hard graph problems become polynomial-time solvable on graph classes where the mim-width is bounded and quickly computable. Hence, when solving such problems on special graph classes, it is helpful to know whether the graph class under consideration has bounded mim-width. We first extend the toolkit for proving (un)boundedness of mim-width of graph classes. This enables us to initiate a systematic study into bounding mim-width from the perspective of hereditary graph classes. For a given graph $H$, the class of $H$-free graphs has bounded mim-width if and only if it has bounded clique-width. We show that the same is not true for $(H_1,H_2)$-free graphs. We find several general classes of $(H_1,H_2)$-free graphs having unbounded clique-width, but the mim-width is bounded and quickly computable. We also prove a number of new results showing that, for certain $H_1$ and $H_2$, the class of $(H_1,H_2)$-free graphs has unbounded mim-width. Combining these with known results, we present summary theorems of the current state of the art for the boundedness of mim-width for $(H_1,H_2)$-free graphs.
On Multiple Semantics for Declarative Database Repairs
Venue: arXiv cs.DB 2020
Year: 2020

We study the problem of database repairs through a rule-based framework that we refer to as Delta Rules. Delta Rules are highly expressive and allow specifying complex, cross-relations repair logic associated with Denial Constraints, Causal Rules, and allowing to capture Database Triggers of interest. We show that there are no one-size-fits-all semantics for repairs in this inclusive setting, and we consequently introduce multiple alternative semantics, presenting the case for using each of them. We then study the relationships between the semantics in terms of their output and the complexity of computation. Our results formally establish the tradeoff between the permissiveness of the semantics and its computational complexity. We demonstrate the usefulness of the framework in capturing multiple data repair scenarios for an Academic Search database and the TPC-H databases, showing how using different semantics affects the repair in terms of size and runtime, and examining the relationships between the repairs. We also compare our approach with SQL triggers and a state-of-the-art data repair system.
Resources: A Safe Language Abstraction for Money
Venue: arXiv cs.PL 2020
Year: 2020

Smart contracts are programs that implement potentially sophisticated transactions on modern blockchain platforms. In the rapidly evolving blockchain environment, smart contract programming languages must allow users to write expressive programs that manage and transfer assets, yet provide strong protection against sophisticated attacks. Addressing this need, we present flexible and reliable abstractions for programming with digital currency in the Move language [Blackshear et al. 2019]. Move uses novel linear [Girard 1987] resource types with semantics drawing on C++11 [Stroustrup 2013] and Rust [Matsakis and Klock 2014]: when a resource value is assigned to a new memory location, the location previously holding it must be invalidated. In addition, a resource type can only be created or destroyed by procedures inside its declaring module. We present an executable bytecode language with resources and prove that it enjoys resource safety, a conservation property for program values that is analogous to conservation of mass in the physical world.
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
Venue: arXiv cs.RO 2020
Year: 2020

In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-the-art odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-of-curvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models.
The Kinematics of Tracked Vehicles via the Power Dissipation Method
Venue: arXiv cs.RO 2020
Year: 2020

This paper develops a new quasi-static modeling framework for tracked robots based on the power dissipation method. Given a set of track speeds, this method predicts the vehicle's instantaneous rigid body motion. We introduce three specific models: a model for tracked operation on flat ground, a model for vehicle motion when the track's grouser tips touch the ground, and a model for operation on stairs. Experiments show that these models predict tracked vehicle motion more accurately than existing kinematic models, and predict phenomena which are not captured by other models. These novel models provide a basis for new feedback control approaches.
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Venue: arXiv cs.CY 2020
Year: 2020

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, a B-spline chained multiple random matrices representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed model, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.
Simulation-based Lidar Super-resolution for Ground Vehicles
Venue: arXiv cs.RO 2020
Year: 2020

We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which is solved using a deep convolutional neural network. By projecting a point cloud onto a range image, we are able to efficiently enhance the resolution of such an image using a deep neural network. Typically, the training of a deep neural network requires vast real-world data. Our approach does not require any real-world data, as we train the network purely using computer-generated data. Thus our method is applicable to the enhancement of any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in the network and removing the predictions with high uncertainty, our method produces high accuracy point clouds comparable with the observations of a real high resolution lidar. We present experimental results applying our method to several simulated and real-world datasets. We argue for the method's potential benefits in real-world robotics applications such as occupancy mapping and terrain modeling.
Towards Anomaly Detection in Dashcam Videos
Venue: arXiv cs.CV 2020
Year: 2020

Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data.
Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments
Venue: arXiv cs.RO 2020
Year: 2020

Aiming at the application environment of indoor mobile robots, this paper proposes a sparse object-level SLAM algorithm based on an RGB-D camera. A quadric representation is used as a landmark to compactly model objects, including their position, orientation, and occupied space. The state-of-art quadric-based SLAM algorithm faces the observability problem caused by the limited perspective under the plane trajectory of the mobile robot. To solve the problem, the proposed algorithm fuses both object detection and point cloud data to estimate the quadric parameters. It finishes the quadric initialization based on a single frame of RGB-D data, which significantly reduces the requirements for perspective changes. As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters. Experiments have shown that compared with the state-of-art algorithm, especially on the forward trajectory of mobile robots, the proposed algorithm significantly improves the accuracy and convergence speed of quadric reconstruction. Finally, we made available an opensource implementation to replicate the experiments.