OpenRaiser/Intern-Atlas huggingface dataset:
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
Venue: arXiv cs.AI 2021
Year: 2021

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective norms and our own personal objectives. To create effective AI-human teams, we must equip AI agents with a model of how humans make trade-offs in complex, constrained environments. These agents will be able to mirror human behavior or to draw human attention to situations where decision making could be improved. To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings. In addition, learning soft constraints over states, actions, and state features allows agents to transfer this knowledge to new domains that share similar aspects. We then use the constraint learning method to implement a novel system architecture that leverages a cognitive model of human decision making, multi-alternative decision field theory (MDFT), to orchestrate competing objectives. We evaluate the resulting agent on trajectory length, number of violated constraints, and total reward, demonstrating that our agent architecture is both general and achieves strong performance. Thus we are able to capture and replicate human-like trade-offs from demonstrations in environments when constraints are not explicit.
Exploring Decomposition for Table-based Fact Verification
Venue: arXiv cs.CL 2021
Year: 2021

Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a strong capability in verifying simple statements, they struggle with complex statements that involve multiple operations. In this paper, we improve fact verification by decomposing complex statements into simpler subproblems. Leveraging the programs synthesized by a weakly supervised semantic parser, we propose a program-guided approach to constructing a pseudo dataset for decomposition model training. The subproblems, together with their predicted answers, serve as the intermediate evidence to enhance our fact verification model. Experiments show that our proposed approach achieves the new state-of-the-art performance, an 82.7\% accuracy, on the TabFact benchmark.
Intel Optane DCPMM and Serverless Computing
Venue: arXiv cs.DC 2021
Year: 2021

This report describes 1) how we use Intel's Optane DCPMM in the memory Mode. We investigate the the scalability of applications on a single Optane machine, using Subgraph counting as memory-intensive graph problem. We test with various input graph and subtemplate sizes to determine its performance for different memory and CPU loads, as well as a comparison of performance on a single node Optane with a distributed set of nodes in a cluster using MPI. 2) We investigate the end-to-end execution delays in serverless computing and study concurrent function executions with cold starts. In future work, we will show that persistent memory machines may significantly improve concurrent function invocations in serverless computing including Amazon Lambda, Microsoft Azure Functions, Google Cloud Functions and IBM Cloud Functions (Apache OpenWhisk).
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Venue: arXiv cs.LG 2021
Year: 2021

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.
A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Dense disparities among multiple views is essential for estimating the 3D architecture of a scene based on the geometrical relationship among the scene and the views or cameras. Scenes with larger extents of heterogeneous textures, differing scene illumination among the multiple views and with occluding objects affect the accuracy of the estimated disparities. Markov random fields (MRF) based methods for disparity estimation address these limitations using spatial dependencies among the observations and among the disparity estimates. These methods, however, are limited by spatially fixed and smaller neighborhood systems or cliques. In this work, we present a new factor graph-based probabilistic graphical model for disparity estimation that allows a larger and a spatially variable neighborhood structure determined based on the local scene characteristics. We evaluated our method using the Middlebury benchmark stereo datasets and the Middlebury evaluation dataset version 3.0 and compared its performance with recent state-of-the-art disparity estimation algorithms. The new factor graph-based method provided disparity estimates with higher accuracy when compared to the recent non-learning- and learning-based disparity estimation algorithms. In addition to disparity estimation, our factor graph formulation can be useful for obtaining maximum a posteriori solution to optimization problems with complex and variable dependency structures as well as for other dense estimation problems such as optical flow estimation.
Individual and Collective Autonomous Development
Venue: arXiv cs.AI 2021
Year: 2021

The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level of individual components and at the collective level. In other words, such systems should become able to autonomously develop models of themselves and of their environment. Autonomous development includes: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act collectively, i.e., accounting for the presence of others. In this paper, we introduce the vision of autonomous development in ICT systems, by framing its key concepts and by illustrating suitable application domains. Then, we overview the many research areas that are contributing or can potentially contribute to the realization of the vision, and identify some key research challenges.
Cyber Resilience in IoT network: Methodology and example of assessment through epidemic spreading
Venue: arXiv cs.NI 2021
Year: 2021

Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.
Towards Private On-Chain Algorithmic Trading
Venue: arXiv cs.CR 2021
Year: 2021

While quantitative automation related to trading crypto-assets such as ERC-20 tokens has become relatively commonplace, with services such as 3Commas and Shrimpy offering user-friendly web-driven services for even the average crypto trader, we have not yet seen the emergence of on-chain trading as a phenomenon. We hypothesize that just like decentralized exchanges (DEXes) that by now are by some measures more popular than traditional exchanges, process in the space of decentralized finance (DeFi) may enable attractive online trading automation options. In this paper we present ChainBot, an approach for creating algorithmic trading bots with the help of blockchain technology. We show how to partition the computation into on- and off-chain components in a way that provides a measure of end-to-end integrity, while preserving the algorithmic "secret sauce". Our system is enabled with a careful use of algorithm partitioning, zero-knowledge proofs and smart contracts. We also show that with layer-2 (L2) technologies, trades can be kept private, which means that algorithmic parameters are difficult to recover by a chain observer. Our approach offers more transparent access to liquidity and better censorship-resistance compared to traditional off-chain trading approaches. We develop a sample ChainBot and train it on historical data, resulting in returns that are up to 2.4x the buy-and-hold strategy, which we use as our baseline. Our measurements show that across 1000 runs, the end-to-end average execution time for our system is 48.4 seconds. We demonstrate that the frequency of trading does not significantly affect the rate of return and Sharpe ratio, which indicates that we do not have to trade at every block, thereby significantly saving in terms of gas fees. In our implementation, a user who invests \$1,000 would earn \$105, and spend \$3 on gas; assuming a user pool of 1,000 subscribers.
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation
Venue: arXiv cs.RO 2021
Year: 2021

Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. In particular, the agent learns object-specific behavior by contemplating the specific danger zones to enhance safety around vulnerable object classes. We tested the approach against a benchmark obstacle avoidance approach and found an increase in safety. Furthermore, we demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.
Reinforcement Learning Under Algorithmic Triage
Venue: arXiv cs.LG 2021
Year: 2021

Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other. Under algorithmic triage, a supervised learning model predicts a fraction of the instances and humans predict the remaining ones. In this work, we take a first step towards developing reinforcement learning models that are optimized to operate under algorithmic triage. To this end, we look at the problem through the framework of options and develop a two-stage actor-critic method to learn reinforcement learning models under triage. The first stage performs offline, off-policy training using human data gathered in an environment where the human has operated on their own. The second stage performs on-policy training to account for the impact that switching may have on the human policy, which may be difficult to anticipate from the above human data. Extensive simulation experiments in a synthetic car driving task show that the machine models and the triage policies trained using our two-stage method effectively complement human policies and outperform those provided by several competitive baselines.
Fault Localization in Cloud using Centrality Measures
Venue: arXiv cs.DC 2021
Year: 2021

Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.
Scene Graph Generation for Better Image Captioning?
Venue: arXiv cs.CV 2021
Year: 2021

We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language. To do so, we first generate a scene graph from raw image pixels by identifying individual objects and visual relationships between them. This scene graph then serves as input to our graph-to-text model, which generates the final caption. In contrast to previous approaches, our model thus explicitly models the detection of objects and visual relationships in the image. For our experiments we construct a new dataset from the intersection of Visual Genome and MS COCO, consisting of images with both a corresponding gold scene graph and human-authored caption. Our results show that our methods outperform existing state-of-the-art end-to-end models that generate image descriptions directly from raw input pixels when compared in terms of the BLEU and METEOR evaluation metrics.
MARMOT: A Deep Learning Framework for Constructing Multimodal Representations for Vision-and-Language Tasks
Venue: arXiv cs.CV 2021
Year: 2021

Political activity on social media presents a data-rich window into political behavior, but the vast amount of data means that almost all content analyses of social media require a data labeling step. However, most automated machine classification methods ignore the multimodality of posted content, focusing either on text or images. State-of-the-art vision-and-language models are unusable for most political science research: they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework called multimodal representations using modality translation (MARMOT). MARMOT presents two methodological contributions: it can construct representations for observations missing image or text, and it replaces the computationally expensive pretraining with modality translation. MARMOT outperforms an ensemble text-only classifier in 19 of 20 categories in multilabel classifications of tweets reporting election incidents during the 2016 U.S. general election. Moreover, MARMOT shows significant improvements over the results of benchmark multimodal models on the Hateful Memes dataset, improving the best result set by VisualBERT in terms of accuracy from 0.6473 to 0.6760 and area under the receiver operating characteristic curve (AUC) from 0.7141 to 0.7530.
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
Venue: arXiv cs.CV 2021
Year: 2021

Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a large amount of dense and high-resolution (HR) ground-truth depth maps. However, in practice, color images are usually captured with much higher resolution than depth maps, leading to the resolution-mismatched effect. In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i.e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps. The proposed weakly supervised framework is composed of a sharing weight monocular depth estimation network and a depth reconstruction network for distillation. Specifically, for the monocular depth estimation network the input color image is first downsampled to obtain its LR version with the same resolution as the ground-truth depth. Then, both HR and LR color images are fed into the proposed monocular depth estimation network to obtain the corresponding estimated depth maps. We introduce three losses to train the network: 1) reconstruction loss between the estimated LR depth and the ground-truth LR depth; 2) reconstruction loss between the downsampled estimated HR depth and the ground-truth LR depth; 3) consistency loss between the estimated LR depth and the downsampled estimated HR depth. In addition, we design a depth reconstruction network from depth to depth. Through distillation loss, features between two networks maintain the structural consistency in affinity space, and finally improving the estimation network performance. Experimental results demonstrate that our method achieves superior performance than unsupervised and semi-supervised learning based schemes, and is competitive or even better compared to supervised ones.
A Tree-based Construction for Verifiable Diplomas with Issuer Transparency
Venue: arXiv cs.CR 2021
Year: 2021

Still to this day, academic credentials are primarily paper-based, and the process to verify the authenticity of such documents is costly, time-consuming, and prone to human error and fraud. Digitally signed documents facilitate a cost-effective verification process. However, vulnerability to fraud remains due to reliance on centralized authorities that lack full transparency. In this paper, we present the mechanisms we designed to create secure and machine-verifiable academic credentials. Our protocol models a diploma as an evolving set of immutable credentials. The credentials are built as a tree-based data structure with linked time-stamping, where portions of credentials are distributed over a set of smart contracts. Our design prevents fraud of diplomas and eases the detection of degree mills, while increasing the transparency and trust in the issuer's procedures. Our evaluation shows that our solution offers a certification system with strong cryptographic security and imposes a high level of transparency of the certification process. We achieve these benefits with acceptable costs compared to existing solutions that lack such transparency.
Implementation of interactive tools for investigating fundamental frequency response of voiced sounds to auditory stimulation
Venue: arXiv cs.SD 2021
Year: 2021

We introduced a measurement procedure for the involuntary response of voice fundamental-frequency to frequency modulated auditory stimulation. This involuntary response plays an essential role in voice fundamental frequency control while less investigated due to technical difficulties. This article introduces an interactive and real-time tool for investigating this response and supporting tools adopting our new measurement method. The method enables simultaneous measurement of multiple system properties based on a novel set of extended time-stretched pulses combined with orthogonalization. We made MATLAB implementation of these tools available as an open-source repository. This article also provides the detailed measurement procedure using the interactive tool followed by offline measurement tools for conducting subjective experiments and statistical analyses. It also provides technical descriptions of constituent signal processing subsystems as appendices. This application serves as an example for adopting our method to biological system analysis.
Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we consider the multi-armed bandit problem with high-dimensional features. First, we prove a minimax lower bound, $\mathcal{O}\big((\log d)^{\frac{α+1}{2}}T^{\frac{1-α}{2}}+\log T\big)$, for the cumulative regret, in terms of horizon $T$, dimension $d$ and a margin parameter $α\in[0,1]$, which controls the separation between the optimal and the sub-optimal arms. This new lower bound unifies existing regret bound results that have different dependencies on T due to the use of different values of margin parameter $α$ explicitly implied by their assumptions. Second, we propose a simple and computationally efficient algorithm inspired by the general Upper Confidence Bound (UCB) strategy that achieves a regret upper bound matching the lower bound. The proposed algorithm uses a properly centered $\ell_1$-ball as the confidence set in contrast to the commonly used ellipsoid confidence set. In addition, the algorithm does not require any forced sampling step and is thereby adaptive to the practically unknown margin parameter. Simulations and a real data analysis are conducted to compare the proposed method with existing ones in the literature.
Enhancing SUMO simulator for simulation based testing and validation of autonomous vehicles
Venue: arXiv cs.RO 2021
Year: 2021

Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user expertise and complex inconvenient tutorials for customized scenario creation. Simulation of Urban Mobility (SUMO) simulator, which has been presented as an open-source AV simulator, is used extensively but suffer from similar issues which make it difficult for entry-level practitioners to utilize the simulator without significant time investment. In that regard, we provide two enhancements to SUMO simulator geared towards massively improving user experience and providing real-life like variability for surrounding traffic. Firstly, we calibrate a car-following model, Intelligent Driver Model (IDM), for highway and urban naturalistic driving data and sample automatically from the parameter distributions to create the background vehicles. Secondly, we combine SUMO with OpenAI gym, creating a Python package which can run simulations based on real world highway and urban layouts with generic output observations and input actions that can be processed via any AV pipeline. Our aim through these enhancements is to provide an easy-to-use platform which can be readily used for AV testing and validation.