OpenRaiser/Intern-Atlas huggingface dataset:
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models
Venue: arXiv cs.CV 2019
Year: 2019

Action recognition is a vital task in computer vision, and many methods are developed to push it to the limit. However, current action recognition models have huge computational costs, which cannot be deployed to real-world tasks on mobile devices. In this paper, we first illustrate the setting of real-time action recognition, which is different from current action recognition inference settings. Under the new inference setting, we investigate state-of-the-art action recognition models on the Kinetics dataset empirically. Our results show that designing efficient real-time action recognition models is different from designing efficient ImageNet models, especially in weight initialization. We show that pre-trained weights on ImageNet improve the accuracy under the real-time action recognition setting. Finally, we use the hand gesture recognition task as a case study to evaluate our compact real-time action recognition models in real-world applications on mobile phones. Results show that our action recognition models, being 6x faster and with similar accuracy as state-of-the-art, can roughly meet the real-time requirements on mobile devices. To our best knowledge, this is the first paper that deploys current deep learning action recognition models on mobile devices.
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems
Venue: arXiv cs.DC 2019
Year: 2019

Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network edge. The services are hosted at various types of edge nodes with both computation and communication capabilities. Due to the heterogeneity of edge node characteristics and user locations, the performance of MEC varies depending on where the service is hosted. In this paper, we consider such a heterogeneous MEC system, and focus on the problem of placing multiple services in the system to maximize the total reward. We show that the problem is NP-hard via reduction from the set cover problem, and propose a deterministic approximation algorithm to solve the problem, which has an approximation ratio that is not worse than $\left(1-e^{-1}\right)/4$. The proposed algorithm is based on two sub-routines that are suitable for small and arbitrarily sized services, respectively. The algorithm is designed using a novel way of partitioning each edge node into multiple slots, where each slot contains one service. The approximation guarantee is obtained via a specialization of the method of conditional expectations, which uses a randomized procedure as an intermediate step. In addition to theoretical guarantees, simulation results also show that the proposed algorithm outperforms other state-of-the-art approaches.
Multiple Analyses, Requirements Once: simplifying testing & verification in automotive model-based development
Venue: arXiv cs.LO 2019
Year: 2019

In industrial model-based development (MBD) frameworks, requirements are typically specified informally using textual descriptions. To enable the application of formal methods, these specifications need to be formalized in the input languages of all formal tools that should be applied to analyse the models at different development levels. In this paper we propose a unified approach for the computer-assisted formal specification of requirements and their fully automated translation into the specification languages of different verification tools. We consider a two-stage MBD scenario where first Simulink models are developed from which executable code is generated automatically. We (i) propose a specification language and a prototypical tool for the formal but still textual specification of requirements, (ii) show how these requirements can be translated automatically into the input languages of Simulink Design Verifier for verification of Simulink models and BTC EmbeddedValidator for source code verification, and (iii) show how our unified framework enables besides automated formal verification also the automated generation of test cases.
Supporting Web Archiving via Web Packaging
Venue: arXiv cs.NI 2019
Year: 2019

We describe challenges related to web archiving, replaying archived web resources, and verifying their authenticity. We show that Web Packaging has significant potential to help address these challenges and identify areas in which changes are needed in order to fully realize that potential.
Hierarchical Soft Actor-Critic: Adversarial Exploration via Mutual Information Optimization
Venue: arXiv cs.LG 2019
Year: 2019

We describe a novel extension of soft actor-critics for hierarchical Deep Q-Networks (HDQN) architectures using mutual information metric. The proposed extension provides a suitable framework for encouraging explorations in such hierarchical networks. A natural utilization of this framework is an adversarial setting, where meta-controller and controller play minimax over the mutual information objective but cooperate on maximizing expected rewards.
CheckNet: Secure Inference on Untrusted Devices
Venue: arXiv cs.LG 2019
Year: 2019

We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the inference has not been manipulated by an attacker. CheckNet is completely transparent to the third party running the computation, applicable to all types of neural networks, does not require specialized hardware, adds little overhead, and has negligible impact on model performance. CheckNet can be configured to provide different levels of security depending on application needs and compute/communication budgets. We present both empirical and theoretical validation of CheckNet on multiple popular deep neural network models, showing excellent attack detection (0.88-0.99 AUC) and attack success bounds.
MQTTg: An Android Implementation
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things (IoT) age is upon us. As we look to build larger networks with more devices connected to the Internet, the need for lightweight protocols that minimize the use of both energy and computation gain popularity. One such protocol is Message Queue Telemetry Transport (MQTT). Since its introduction in 1999, it has slowly increased in use cases and gained a huge spike in popularity since it was used in the popular messaging application Facebook Messenger. In our previous works, we focused on adding geolocation to MQTT, to help modernize the protocol into the IoT age. In this paper, we build off our previous work on MQTTg and build an IoT Android Application that can pull geolocation information from the Operating System. We then use the geolocation data to create geofences to help further tailor the use cases of MQTTg.
Eye Gaze Metrics and Analysis of AOI for Indexing Working Memory towards Predicting ADHD
Venue: arXiv cs.HC 2019
Year: 2019

ADHD is being recognized as a diagnosis which persists into adulthood impacting economic, occupational, and educational outcomes. There is an increased need to accurately diagnose and recommend interventions for this population. One consideration is the development and implementation of reliable and valid outcome measures which reflect core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity when compared to their peers (Michalek et al., 2014). A reduction in working memory capacity indicates attentional control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as eye tracking technology, to generate a relationship between ADHD and measures of working memory capacity would be useful to advancing our understanding and treatment of the diagnosis in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a working memory capacity task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.
Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods
Venue: arXiv cs.CE 2019
Year: 2019

To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Gauss-Markov-Kalman filter using functional approximation is introduced and employed to estimate the model parameters in the Bayesian setting. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. In the end, the effect of the load path on parameter identification is investigated.
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer
Venue: arXiv cs.LG 2019
Year: 2019

We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). The agent and DNC model are trained in conjunction iteratively. The agent improves its policy in simulations generated by the DNC model and rolls out the policy to the live environment, collecting experiences in new portions or tasks of the environment for further learning. Experiments in two synthetic environments show that DNC models can continually learn from pixels alone to simulate new tasks as they are encountered by the agent, while the agents can be successfully trained to solve the tasks using Proximal Policy Optimization entirely in simulations.
On the System-level Performance of Coordinated Multi-point Transmission Schemes in 5G NR Deployment Scenarios
Venue: arXiv cs.NI 2019
Year: 2019

This paper investigates the system-level performance of dynamic point selection (DPS) and non-coherent joint transmission (NC-JT) coordinated multi-point transmission (CoMP) schemes under different 5G NR deployment scenarios using state-of-the-art system-level simulations. It is observed that at a mid-band carrier frequency, NC-JT does not provide performance gains over DPS or single transmission/reception point (TRP) transmission unless the channel from a TRP is rank deficient. Therefore, benefits with NC-JT are more likely to be found in indoor deployment scenarios where the TRPs are typically equipped with only 2 transmit antenna ports, whereas benefits are less likely to be observed in macro-cell deployments where TRPs typically have a larger number of antennas ports. It is further observed that NC-JT gains tend to diminish with increasing system load, partly due to increased interference, which typically lowers the transmission rank.
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking. NMSR incorporates two novel components: 1) utilizing a deep neural net to parameterize the velocity field between two image frames, and 2) optimizing the parameters of the neural net in a sequential multi-scale fashion to account for large variations within the velocity field. Experiments demonstrate that NMSR yields significantly better registration accuracy than state-of-the-art methods, such as advanced normalization tools (ANTs) and VoxelMorph, for both myocardial and cardiac blood flow dense tracking. Our approach promises to provide a fully automated method for fast and accurate analyses of echocardiograms.
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
Venue: arXiv cs.CV 2019
Year: 2019

3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, abnormal detection, and classification. Currently deep learning algorithms are widely used in medical image segmentation, most algorithms trained models with full annotated datasets. However, obtaining medical image datasets is very difficult and expensive, and full annotation of 3D medical image is a monotonous and time-consuming work. Partially labelling informative slices in 3D images will be a great relief of manual annotation. Sample selection strategies based on active learning have been proposed in the field of 2D image, but few strategies focus on 3D images. In this paper, we propose a sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation. Attention mechanism is used to improve segmentation accuracy and estimate the segmentation accuracy of each slice. The comparative experiments with three different strategies using datasets from the developing human connectome project (dHCP) show that, our strategy only needs 15% to 20% annotated slices in brain extraction task and 30% to 35% annotated slices in tissue segmentation task to achieve comparative results as full annotation.
Asymptotic performance of metacyclic codes
Venue: arXiv cs.IT 2019
Year: 2019

A finite group with a cyclic normal subgroup N such that G/N is cyclic is said to be metacyclic. A code over a finite field F is a metacyclic code if it is a left ideal in the group algebra FG for G a metacyclic group. Metacyclic codes are generalizations of dihedral codes, and can be constructed as quasi-cyclic codes with an extra automorphism. In this paper, we prove that metacyclic codes form an asymptotically good family of codes. Our proof relies on a version of Artin's conjecture for primitive roots in arithmetic progression being true under the Generalized Riemann Hypothesis (GRH).
20 Years of Mobility Modeling & Prediction: Trends, Shortcomings & Perspectives
Venue: arXiv cs.SI 2019
Year: 2019

In this paper, we present a comprehensive survey of human-mobility modeling based on 1680 articles published between 1999 and 2019, which can serve as a roadmap for research and practice in this area. Mobility modeling research has accelerated the advancement of several fields of studies such as urban planning, epidemic modeling, traffic engineering and contributed to the development of location-based services. However, while the application of mobility models in different domains has increased, the credibility of the research results has decreased. We highlight two significant shortfalls commonly observed in our reviewed studies: (1) data-agnostic model selection resulting in a poor tradeoff between accuracy vs. complexity, and (2) failure to identify the source of empirical gains, due to adoption of inaccurate validation methodologies. We also observe troubling trends with respect to application of Markov model variants for modeling mobility, despite the questionable association of Markov processes and human-mobility dynamics. To this end, we propose a data-driven mobility-modeling framework that quantifies the characteristics of a dataset based on four mobility meta-attributes, in order to select the most appropriate prediction algorithm. Experimental evaluations on three real-world mobility datasets based on a rigorous validation methodology demonstrate our frameworks ability to correctly analyze the model accuracy vs. complexity tradeoff. We offer these results to the community along with the tools and the literature meta-data in order to improve the reliability and credibility of human mobility modeling research.
Chemotaxis Based Virtual Fence for Swarm Robots in Unbounded Environments
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents a novel swarm robotics application of chemotaxis behaviour observed in microorganisms. This approach was used to cause exploration robots to return to a work area around the swarm's nest within a boundless environment. We investigate the performance of our algorithm through extensive simulation studies and hardware validation. Results show that the chemotaxis approach is effective for keeping the swarm close to both stationary and moving nests. Performance comparison of these results with the unrealistic case where a boundary wall was used to keep the swarm within a target search area showed that our chemotaxis approach produced competitive results.
Determination of Metamaterial Parameters by Means of a Homogenization Approach Based on Asymptotic Analysis
Venue: arXiv cs.CE 2019
Year: 2019

Owing to additive manufacturing techniques, a structure at millimeter length scale (macroscale) can be produced by using a lattice substructure at micrometer length scale (microscale). Such a system is called a metamaterial at the macroscale as the mechanical characteristics deviate from the characteristics at the microscale. As a remedy, metamaterial is modeled by using additional parameters; we intend to determine them. A homogenization approach based on the asymptotic analysis establishes a connection between these different characteristics at micro- and macroscales. A linear elastic first order theory at the microscale is related to a linear elastic second order theory at the macroscale. Relation for parameters at the macroscale is derived by using the equivalence of energy at macro- and microscales within a so-called Representative Volume Element (RVE). Determination of parameters are succeeded by solving a boundary value problem with the Finite Element Method (FEM). The proposed approach guarantees that the additional parameters vanish if the material is purely homogeneous, in other words, it is fully compatible with conventional homogenization schemes based on spatial averaging techniques. Moreover, the proposed approach is reliable as it ensures that such resolved additional parameters are not sensitive to choices of RVE consisting in the repetition of smaller RVEs but depend upon the intrinsic size of the structure.
Text Readability Assessment for Second Language Learners
Venue: arXiv cs.CL 2019
Year: 2019

This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and PCC of $0.938$.
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which plays a key role in many RL algorithms, are either prone to noise or inefficient. To solve this issue, we introduce a novel policy evaluation algorithm, which we call Gap-increasing RetrAce Policy Evaluation (GRAPE). It leverages two recent ideas: (1) gap-increasing value update operators in advantage learning for noise-tolerance and (2) off-policy eligibility trace in Retrace algorithm for efficient learning. We provide detailed theoretical analysis of the new algorithm that shows its efficiency and noise-tolerance inherited from Retrace and advantage learning. Furthermore, our analysis shows that GRAPE's learning is significantly efficient than that of a simple learning-rate-based approach while keeping the same level of noise-tolerance. We applied GRAPE to control problems and obtained experimental results supporting our theoretical analysis.
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish
Venue: arXiv cs.CL 2019
Year: 2019

This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, which used shared lower-layers in a deep BiLSTM to predict negation, while the higher layers were dedicated to predicting document-level sentiment. The multi-task component shows promise as a way to incorporate information on negation into deep neural sentiment classifiers, despite the fact that the absolute results on the test set were relatively low for a binary classification task.
Model Explanations under Calibration
Venue: arXiv cs.IR 2019
Year: 2019

Explaining and interpreting the decisions of recommender systems are becoming extremely relevant both, for improving predictive performance, and providing valid explanations to users. While most of the recent interest has focused on providing local explanations, there has been a much lower emphasis on studying the effects of model dynamics and its impact on explanation. In this paper, we perform a focused study on the impact of model interpretability in the context of calibration. Specifically, we address the challenges of both over-confident and under-confident predictions with interpretability using attention distribution. Our results indicate that the means of using attention distributions for interpretability are highly unstable for un-calibrated models. Our empirical analysis on the stability of attention distribution raises questions on the utility of attention for explainability.
The Mathematical Specification of the Statebox Language
Venue: arXiv cs.PL 2019
Year: 2019

This document defines the mathematical backbone of the Statebox programming language. In the simplest way possible, Statebox can be seen as a clever way to tie together different theoretical structures to maximize their benefits and limit their downsides. Since consistency and correctness are central requisites for our language, it became clear from the beginning that such tying could not be achieved by just hacking together different pieces of code representing implementations of the structures we wanted to leverage: Rigorous mathematics is employed to ensure both conceptual consistency of the language and reliability of the code itself. The mathematics presented here is what guided the implementation process, and we deemed very useful to release it to the public to help people wanting to audit our work to better understand the code itself.