OpenRaiser/Intern-Atlas huggingface dataset:
Evolutionary Stochastic Policy Distillation
Venue: arXiv cs.LG 2020
Year: 2020

Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals. In this work, we propose a new formulation of GCRS tasks from the perspective of the drifted random walk on the state space, and design a novel method called Evolutionary Stochastic Policy Distillation (ESPD) to solve them based on the insight of reducing the First Hitting Time of the stochastic process. As a self-imitate approach, ESPD enables a target policy to learn from a series of its stochastic variants through the technique of policy distillation (PD). The learning mechanism of ESPD can be considered as an Evolution Strategy (ES) that applies perturbations upon policy directly on the action space, with a SELECT function to check the superiority of stochastic variants and then use PD to update the policy. The experiments based on the MuJoCo robotics control suite show the high learning efficiency of the proposed method.
Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science
Venue: arXiv cs.CY 2020
Year: 2020

Modern science, technology, and politics are all permeated by data that comes from people, measurements, or computational processes. While this data is often incomplete, corrupt, or lacking in sufficient accuracy and precision, explicit consideration of uncertainty is rarely part of the computational and decision making pipeline. The CCC Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science explored this problem, identifying significant shortcomings in the ways we currently process, present, and interpret uncertain data. Specific recommendations on a research agenda for the future were made in four areas: uncertainty quantification in large-scale computational simulations, uncertainty quantification in data science, software support for uncertainty computation, and better integration of uncertainty quantification and communication to stakeholders.
A Microscopic Epidemic Model and Pandemic Prediction Using Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2020
Year: 2020

This paper introduces a microscopic approach to model epidemics, which can explicitly consider the consequences of individual's decisions on the spread of the disease. We first formulate a microscopic multi-agent epidemic model where every agent can choose its activity level that affects the spread of the disease. Then by minimizing agents' cost functions, we solve for the optimal decisions for individual agents in the framework of game theory and multi-agent reinforcement learning. Given the optimal decisions of all agents, we can make predictions about the spread of the disease. We show that there are negative externalities in the sense that infected agents do not have enough incentives to protect others, which then necessitates external interventions to regulate agents' behaviors. In the discussion section, future directions are pointed out to make the model more realistic.
Social and Emotional Skills Training with Embodied Moxie
Venue: arXiv cs.RO 2020
Year: 2020

We present a therapeutic framework, namely STAR Framework, that leverages established and evidence-based therapeutic strategies delivered by the Embodied Moxie, an animate companion to support children with mental behavioral developmental disorders (MBDDs). This therapeutic framework jointly with Moxie aims to provide an engaging, safe, and secure environment for children aged five to ten years old. Moxie delivers content informed by therapeutic strategies including but not limited to naturalistic Applied Behavior Analysis, graded cueing, and Cognitive Behavior Therapy. Leveraging multimodal input from a camera and microphones, Moxie is uniquely positioned to be a first-hand witness of a child's progress and struggles alike. Moxie measures skills captured in state-of-the-art assessment scales, such as the Social Responsiveness Scale and Social Skill Improvement Scale, and augments those measures with quantitatively measured behavior skills, such as eye contact and language skills. While preliminary, the present study (N=12) also provides evidence that a six-week intervention using the STAR Framework and Moxie had significant impact on the children's abilities. We present our research in detail and provide an overview of the STAR Framework and all related components, such as Moxie and the companion app for parents.
ML-driven Malware that Targets AV Safety
Venue: arXiv cs.CR 2020
Year: 2020

Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption. However, security attacks that violate safety constraints and cause accidents are a significant deterrent to achieving public trust in AVs, and that hinders a vendor's ability to deploy AVs. Creating a security hazard that results in a severe safety compromise (for example, an accident) is compelling from an attacker's perspective. In this paper, we introduce an attack model, a method to deploy the attack in the form of smart malware, and an experimental evaluation of its impact on production-grade autonomous driving software. We find that determining the time interval during which to launch the attack is{ critically} important for causing safety hazards (such as collisions) with a high degree of success. For example, the smart malware caused 33X more forced emergency braking than random attacks did, and accidents in 52.6% of the driving simulations.
A scalable and efficient convolutional neural network accelerator using HLS for a System on Chip design
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a configurable Convolutional Neural Network Accelerator (CNNA) for a System on Chip design (SoC). The goal was to accelerate inference of different deep learning networks on an embedded SoC platform. The presented CNNA has a scalable architecture which uses High Level Synthesis (HLS) and SystemC for the hardware accelerator. It is able to accelerate any Convolutional Neural Network (CNN) exported from Python and supports a combination of convolutional, max-pooling, and fully connected layers. A training method with fixed-point quantized weights is proposed and presented in the paper. The CNNA is template-based, enabling it to scale for different targets of the Xilinx Zynq platform. This approach enables design space exploration, which makes it possible to explore several configurations of the CNNA during C- and RTL-simulation, fitting it to the desired platform and model. The CNN VGG16 was used to test the solution on a Xilinx Ultra96 board using PYNQ. The result gave a high level of accuracy in training with an auto-scaled fixed-point Q2.14 format compared to a similar floating-point model. It was able to perform inference in 2.0 seconds, while having an average power consumption of 2.63 W, which corresponds to a power efficiency of 6.0 GOPS/W.
The Dark Side of Unikernels for Machine Learning
Venue: arXiv cs.DC 2020
Year: 2020

This paper analyzes the shortcomings of unikernels as a method of deployment for machine learning inferencing applications as well as provides insights and analysis on future work in this space. The findings of this paper advocate for a tool to enable management of dependent libraries in a unikernel to enable a more ergonomic build process as well as take advantage of the inherent security and performance benefits of unikernels.
Computing the Boolean product of two n\times n Boolean matrices using O(n^2) mechanical operation
Venue: arXiv cs.DS 2020
Year: 2020

We study the problem of determining the Boolean product of two n\times n Boolean matrices in an unconventional computational model allowing for mechanical operations. We show that O(n^2) operations are sufficient to compute the product in this model.
Machine Learning Based Mobile Network Throughput Classification
Venue: arXiv cs.LG 2020
Year: 2020

Identifying mobile network problems in 4G cells is more challenging when the complexity of the network increases, and privacy concerns limit the information content of the data. This paper proposes a data driven model for identifying 4G cells that have fundamental network throughput problems. The proposed model takes advantage of clustering and Deep Neural Networks (DNNs). Model parameters are learnt using a small number of expert-labeled data. To achieve case specific classification, we propose a model that contains a multiple clustering models block, for capturing features common for problematic cells. The captured features of this block are then used as an input to a DNN. Experiments show that the proposed model outperforms a simple classifier in identifying cells with network throughput problems. To the best of the authors' knowledge, there is no related research where network throughput classification is performed on the cell level with information gathered only from the service provider's side.
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we propose a fast transient hydrostatic stress analysis for electromigration (EM) failure assessment for multi-segment interconnects using generative adversarial networks (GANs). Our work leverages the image synthesis feature of GAN-based generative deep neural networks. The stress evaluation of multi-segment interconnects, modeled by partial differential equations, can be viewed as time-varying 2D-images-to-image problem where the input is the multi-segment interconnects topology with current densities and the output is the EM stress distribution in those wire segments at the given aging time. Based on this observation, we train conditional GAN model using the images of many self-generated multi-segment wires and wire current densities and aging time (as conditions) against the COMSOL simulation results. Different hyperparameters of GAN were studied and compared. The proposed algorithm, called {\it EM-GAN}, can quickly give accurate stress distribution of a general multi-segment wire tree for a given aging time, which is important for full-chip fast EM failure assessment. Our experimental results show that the EM-GAN shows 6.6\% averaged error compared to COMSOL simulation results with orders of magnitude speedup. It also delivers 8.3X speedup over state-of-the-art analytic based EM analysis solver.
Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning based methods have achieved impressive results in many applications for image-based diet assessment such as food classification and food portion size estimation. However, existing methods only focus on one task at a time, making it difficult to apply in real life when multiple tasks need to be processed together. In this work, we propose an end-to-end multi-task framework that can achieve both food classification and food portion size estimation. We introduce a food image dataset collected from a nutrition study where the groundtruth food portion is provided by registered dietitians. The multi-task learning uses L2-norm based soft parameter sharing to train the classification and regression tasks simultaneously. We also propose the use of cross-domain feature adaptation together with normalization to further improve the performance of food portion size estimation. Our results outperforms the baseline methods for both classification accuracy and mean absolute error for portion estimation, which shows great potential for advancing the field of image-based dietary assessment.
A linearised consistent mixed displacement-pressure formulation for hyperelasticity
Venue: arXiv cs.CE 2020
Year: 2020

We propose a novel mixed displacement-pressure formulation based on an energy functional that takes into account the relation between the pressure and the volumetric energy function. We demonstrate that the proposed two-field mixed displacement-pressure formulation is not only applicable for nearly and truly incompressible cases but also is consistent in the compressible regime. Furthermore, we prove with analytical derivation and numerical results that the proposed two-field formulation is a simplified and efficient alternative for the three-field displacement-pressure-Jacobian formulation for hyperelastic materials whose strain energy density functions are decomposed into deviatoric and volumetric parts.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Venue: arXiv cs.CL 2020
Year: 2020

Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited. In August 2019, a workshop was held at Carnegie Mellon University in Pittsburgh to attempt to bring together language community members, documentary linguists, and technologists to discuss how to bridge this gap and create prototypes of novel and practical language revitalization technologies. This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw'ida, Kwak'wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.
Inferring Temporal Compositions of Actions Using Probabilistic Automata
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a framework to recognize temporal compositions of atomic actions in videos. Specifically, we propose to express temporal compositions of actions as semantic regular expressions and derive an inference framework using probabilistic automata to recognize complex actions as satisfying these expressions on the input video features. Our approach is different from existing works that either predict long-range complex activities as unordered sets of atomic actions, or retrieve videos using natural language sentences. Instead, the proposed approach allows recognizing complex fine-grained activities using only pretrained action classifiers, without requiring any additional data, annotations or neural network training. To evaluate the potential of our approach, we provide experiments on synthetic datasets and challenging real action recognition datasets, such as MultiTHUMOS and Charades. We conclude that the proposed approach can extend state-of-the-art primitive action classifiers to vastly more complex activities without large performance degradation.
A Photo-Based Mobile Crowdsourcing Framework for Event Reporting
Venue: arXiv cs.CY 2020
Year: 2020

Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.
Why Johnny can't rely on anti-phishing educational interventions to protect himself against contemporary phishing attacks?
Venue: arXiv cs.CR 2020
Year: 2020

Phishing is a way of stealing people's sensitive information such as username, password and banking details by disguising as a legitimate entity (i.e. email, website). Anti-phishing education considered to be vital in strengthening "human", the weakest link in information security. Previous research in anti-phishing education focuses on improving educational interventions to better interact the end user. However, one can argue that existing anti-phishing educational interventions are limited in success due to their outdated teaching content incorporated. Furthermore, teaching outdated anti-phishing techniques might not help combat contemporary phishing attacks. Therefore, this research focuses on investigating the obfuscation techniques of phishing URLs used in anti-phishing education against the contemporary phishing attacks reported in PhishTank.com. Our results showed that URL obfuscation with IP address has become insignificant and it revealed two emerging URL obfuscation techniques, that attackers use lately, haven't been incorporated into existing anti-phishing educational interventions.
Iterative Variable Reordering: Taming Huge System Families
Venue: arXiv cs.LO 2020
Year: 2020

For the verification of systems using model-checking techniques, symbolic representations based on binary decision diagrams (BDDs) often help to tackle the well-known state-space explosion problem. Symbolic BDD-based representations have been also shown to be successful for the analysis of families of systems that arise, e.g., through configurable parameters or following the feature-oriented modeling approach. The state space of such system families face an additional exponential blowup in the number of parameters or features. It is well known that the order of variables in ordered BDDs is crucial for the size of the model representation. Especially for automatically generated models from real-world systems, family models might even be not constructible due to bad variable orders. In this paper we describe a technique, called iterative variable reordering, that can enable the construction of large-scale family models. We exemplify feasibility of our approach by means of an aircraft velocity control system with redundancy mechanisms modeled in the input language of the probabilistic model checker PRISM. We show that standard reordering and dynamic reordering techniques fail to construct the family model due to memory and time constraints, respectively, while the new iterative approach succeeds to generate a symbolic family model.
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
Venue: arXiv cs.AI 2020
Year: 2020

Hanabi is a cooperative game that challenges exist-ing AI techniques due to its focus on modeling the mental states ofother players to interpret and predict their behavior. While thereare agents that can achieve near-perfect scores in the game byagreeing on some shared strategy, comparatively little progresshas been made in ad-hoc cooperation settings, where partnersand strategies are not known in advance. In this paper, we showthat agents trained through self-play using the popular RainbowDQN architecture fail to cooperate well with simple rule-basedagents that were not seen during training and, conversely, whenthese agents are trained to play with any individual rule-basedagent, or even a mix of these agents, they fail to achieve goodself-play scores.
Tree-depth and the Formula Complexity of Subgraph Isomorphism
Venue: arXiv cs.CC 2020
Year: 2020

For a fixed "pattern" graph $G$, the $\textit{colored $G$-subgraph isomorphism problem}$ (denoted $\mathrm{SUB}(G)$) asks, given an $n$-vertex graph $H$ and a coloring $V(H) \to V(G)$, whether $H$ contains a properly colored copy of $G$. The complexity of this problem is tied to parameterized versions of $\mathit{P}$ ${=}?$ $\mathit{NP}$ and $\mathit{L}$ ${=}?$ $\mathit{NL}$, among other questions. An overarching goal is to understand the complexity of $\mathrm{SUB}(G)$, under different computational models, in terms of natural invariants of the pattern graph $G$. In this paper, we establish a close relationship between the $\textit{formula complexity}$ of $\mathrm{SUB}$ and an invariant known as $\textit{tree-depth}$ (denoted $\mathrm{td}(G)$). $\mathrm{SUB}(G)$ is known to be solvable by monotone $\mathit{AC^0}$ formulas of size $O(n^{\mathrm{td}(G)})$. Our main result is an $n^{\tildeΩ(\mathrm{td}(G)^{1/3})}$ lower bound for formulas that are monotone $\textit{or}$ have sub-logarithmic depth. This complements a lower bound of Li, Razborov and Rossman (SICOMP 2017) relating tree-width and $\mathit{AC^0}$ circuit size. As a corollary, it implies a stronger homomorphism preservation theorem for first-order logic on finite structures (Rossman, ITCS 2017). The technical core of this result is an $n^{Ω(k)}$ lower bound in the special case where $G$ is a complete binary tree of height $k$, which we establish using the $\textit{pathset framework}$ introduced in (Rossman, SICOMP 2018). (The lower bound for general patterns follows via a recent excluded-minor characterization of tree-depth (Czerwiński et al, arXiv:1904.13077).) Additional results of this paper extend the pathset framework and improve upon both, the best known upper and lower bounds on the average-case formula size of $\mathrm{SUB}(G)$ when $G$ is a path.
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training
Venue: arXiv cs.DC 2020
Year: 2020

In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update computation is repeated on all replicas, because the weights do not have a batch dimension to partition. This can be a bottleneck for performance and scalability in typical language models with large weights, and models with small per-replica batch size which is typical in large-scale training. This paper presents an approach to automatically shard the weight update computation across replicas with efficient communication primitives and data formatting, using static analysis and transformations on the training computation graph. We show this technique achieves substantial speedups on typical image and language models on Cloud TPUs, requiring no change to model code. This technique helps close the gap between traditionally expensive (ADAM) and cheap (SGD) optimizers, as they will only take a small part of training step time and have similar peak memory usage. It helped us to achieve state-of-the-art training performance in Google's MLPerf 0.6 submission.
Approximating longest common substring with $k$ mismatches: Theory and practice
Venue: arXiv cs.DS 2020
Year: 2020

In the problem of the longest common substring with $k$ mismatches we are given two strings $X, Y$ and must find the maximal length $\ell$ such that there is a length-$\ell$ substring of $X$ and a length-$\ell$ substring of $Y$ that differ in at most $k$ positions. The length $\ell$ can be used as a robust measure of similarity between $X, Y$. In this work, we develop new approximation algorithms for computing $\ell$ that are significantly more efficient that previously known solutions from the theoretical point of view. Our approach is simple and practical, which we confirm via an experimental evaluation, and is probably close to optimal as we demonstrate via a conditional lower bound.