OpenRaiser/Intern-Atlas huggingface dataset:
Publish, subscribe, and federate!
Venue: arXiv cs.NI 2020
Year: 2020

Connecting usual things/objects to the Internet allows the monitoring and control of such things from anywhere, which is usually referred to as the Internet of Things (IoT). Things communicate among themselves or with other entities (e.g., a server) so that information can be gathered from things whilst proper actions can be taken upon them. A prominent communication approach adopted by many IoT applications is related to the Publish/Subscribe (P/S) paradigm. Any communication entity willing to provide some data announces its intention to a server (broker), establishing itself as a publisher for such data/topic. Entities that are willing to receive any published data, register themselves to the broker as subscribers. While employing just one broker might lead to a bottleneck and a single point of failure, when having multiple brokers one could end up having difficulties with their management. This work presents a scalable and efficient proposal for the federation of independent brokers, by allowing subscribers to get all their publications no matter to which broker publishers and subscribers are associated with.
State Action Separable Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulations, Markov Decision Process (MDP) and state-action-value function are the basis for the problem modeling and policy evaluation. However, several challenging issues still remain. Among most cited issues, the enormity of state/action space is an important factor that causes inefficiency in accurately approximating the state-action-value function. We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition. In this regard, we propose a new learning paradigm, State Action Separable Reinforcement Learning (sasRL), wherein the action space is decoupled from the value function learning process for higher efficiency. Then, a light-weight transition model is learned to assist the agent to determine the action that triggers the associated state transition. In addition, our convergence analysis reveals that under certain conditions, the convergence time of sasRL is $O(T^{1/k})$, where $T$ is the convergence time for updating the value function in the MDP-based formulation and $k$ is a weighting factor. Experiments on several gaming scenarios show that sasRL outperforms state-of-the-art MDP-based RL algorithms by up to $75\%$.
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias and to improve the coverage of long-tail items in recommendations given to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items across recommendation lists produced by the system. Unfortunately, attempts to increase aggregate diversity often result in lower recommendation accuracy for end users. Thus, addressing this problem requires an approach that can effectively manage the trade-offs between accuracy and aggregate diversity. In this work, we propose a two-sided post-processing approach in which both user and item utilities are considered. Our goal is to maximize aggregate diversity while minimizing loss in recommendation accuracy. Our solution is a generalization of the Deferred Acceptance algorithm which was proposed as an efficient algorithm to solve the well-known stable matching problem. We prove that our algorithm results in a unique user-optimal stable match between items and users. Using three recommendation datasets, we empirically demonstrate the effectiveness of our approach in comparison to several baselines. In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
Venue: arXiv cs.CL 2020
Year: 2020

In natural language, often multiple entities appear in the same text. However, most previous works in Relation Extraction (RE) limit the scope to identifying the relation between two entities at a time. Such an approach induces a quadratic computation time, and also overlooks the interdependency between multiple relations, namely the relation of relations (RoR). Due to the significance of RoR in existing datasets, we propose a new paradigm of RE that considers as a whole the predictions of all relations in the same context. Accordingly, we develop a data-driven approach that does not require hand-crafted rules but learns by itself the RoR, using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12\% on the ACE05 dataset and +2.55\% on SemEval 2018 Task 7.2, which is a substantial improvement on the two competitive benchmarks.
Applied Awareness: Test-Driven GUI Development using Computer Vision and Cryptography
Venue: arXiv cs.SE 2020
Year: 2020

Graphical user interface testing is significantly challenging, and automating it even more so. Test-driven development is impractical: it generally requires an initial implementation of the GUI to generate golden images or to construct interactive test scenarios, and subsequent maintenance is costly. While computer vision has been applied to several aspects of GUI testing, we demonstrate a novel and immediately applicable approach of interpreting GUI presentation in terms of backend communications, modeling "awareness" in the fashion employed by cryptographic proofs of security. This focus on backend communication circumvents deficiencies in typical testing methodologies that rely on platform-dependent UI affordances or accessibility features. Our interdisciplinary work is ready for off-the-shelf practice: we report self-contained, practical implementation with both online and offline validation, using simple designer specifications at the outset and specifically avoiding any requirements for a bootstrap implementation or golden images. In addition to practical implementation, ties to formal verification methods in cryptography are explored and explained, providing fertile perspectives on assurance in UI and interpretability in AI.
Bankrupt Covert Channel: Turning Network Predictability into Vulnerability
Venue: arXiv cs.CR 2020
Year: 2020

Recent years have seen a surge in the number of data leaks despite aggressive information-containment measures deployed by cloud providers. When attackers acquire sensitive data in a secure cloud environment, covert communication channels are a key tool to exfiltrate the data to the outside world. While the bulk of prior work focused on covert channels within a single CPU, they require the spy (transmitter) and the receiver to share the CPU, which might be difficult to achieve in a cloud environment with hundreds or thousands of machines. This work presents Bankrupt, a high-rate highly clandestine channel that enables covert communication between the spy and the receiver running on different nodes in an RDMA network. In Bankrupt, the spy communicates with the receiver by issuing RDMA network packets to a private memory region allocated to it on a different machine (an intermediary). The receiver similarly allocates a separate memory region on the same intermediary, also accessed via RDMA. By steering RDMA packets to a specific set of remote memory addresses, the spy causes deep queuing at one memory bank, which is the finest addressable internal unit of main memory. This exposes a timing channel that the receiver can listen on by issuing probe packets to addresses mapped to the same bank but in its own private memory region. Bankrupt channel delivers 74Kb/s throughput in CloudLab's public cloud while remaining undetectable to the existing monitoring capabilities, such as CPU and NIC performance counters.
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we consider the problem of learning an $ε$-optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with $S$ states, $A$ actions, the discount factor $γ\in (0,1)$, and an approximation threshold $ε> 0$, we provide a model-free algorithm to learn an $ε$-optimal policy with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{ε^2(1-γ)^{5.5}})$ (where the notation $\tilde{O}(\cdot)$ hides poly-logarithmic factors of $S,A,1/(1-γ)$, and $1/ε$) and success probability $(1-p)$. For small enough $ε$, we show an improved algorithm with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{ε^2(1-γ)^{3}})$. While the first bound improves upon all known model-free algorithms and model-based ones with tight dependence on $S$, our second algorithm beats all known sample complexity bounds and matches the information theoretic lower bound up to logarithmic factors.
Unique properties of adversarially trained linear classifiers on Gaussian data
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research community has made remarkable progress in the understanding of the root causes of adversarial perturbations. However, most problems that one may consider important to solve for the deployment of machine learning in safety critical tasks involve high dimensional complex manifolds that are difficult to characterize and study. It is common to develop adversarially robust learning theory on simple problems, in the hope that insights will transfer to `real world datasets'. In this work, we discuss a setting where this approach fails. In particular, we show with a linear classifier, it is always possible to solve a binary classification problem on Gaussian data under arbitrary levels of adversarial corruption during training, and that this property is not observed with non-linear classifiers on the CIFAR-10 dataset.
Knowledge-Based Learning through Feature Generation
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning algorithms have difficulties to generalize over a small set of examples. Humans can perform such a task by exploiting vast amount of background knowledge they possess. One method for enhancing learning algorithms with external knowledge is through feature generation. In this paper, we introduce a new algorithm for generating features based on a collection of auxiliary datasets. We assume that, in addition to the training set, we have access to additional datasets. Unlike the transfer learning setup, we do not assume that the auxiliary datasets represent learning tasks that are similar to our original one. The algorithm finds features that are common to the training set and the auxiliary datasets. Based on these features and examples from the auxiliary datasets, it induces predictors for new features from the auxiliary datasets. The induced predictors are then added to the original training set as generated features. Our method was tested on a variety of learning tasks, including text classification and medical prediction, and showed a significant improvement over using just the given features.
Enforcing Predictive Invariance across Structured Biomedical Domains
Venue: arXiv cs.LG 2020
Year: 2020

Many biochemical applications such as molecular property prediction require models to generalize beyond their training domains (environments). Moreover, natural environments in these tasks are structured, defined by complex descriptors such as molecular scaffolds or protein families. Therefore, most environments are either never seen during training, or contain only a single training example. To address these challenges, we propose a new regret minimization (RGM) algorithm and its extension for structured environments. RGM builds from invariant risk minimization (IRM) by recasting simultaneous optimality condition in terms of predictive regret, finding a representation that enables the predictor to compete against an oracle with hindsight access to held-out environments. The structured extension adaptively highlights variation due to complex environments via specialized domain perturbations. We evaluate our method on multiple applications: molecular property prediction, protein homology and stability prediction and show that RGM significantly outperforms previous state-of-the-art baselines.
Bitcoin covenants unchained
Venue: arXiv cs.PL 2020
Year: 2020

Covenants are linguistic primitives that extend the Bitcoin script language, allowing transactions to constrain the scripts of the redeeming ones. Advocated as a way of improving the expressiveness of Bitcoin contracts while preserving the simplicity of the UTXO design, various forms of covenants have been proposed over the years. A common drawback of the existing descriptions is the lack of formalization, making it difficult to reason about properties and supported use cases. In this paper we propose a formal model of covenants, which can be implemented with minor modifications to Bitcoin. We use our model to specify some complex Bitcoin contracts, and we discuss how to exploit covenants to design high-level language primitives for Bitcoin contracts.
Contextual Bandits with Side-Observations
Venue: arXiv cs.LG 2020
Year: 2020

We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in social networks respond to their friends' activity, and hence provide information about each other's preferences. In our model, when a learning algorithm recommends an article to a user, not only does it observe his/her response (e.g. an ad click), but also the side-observations, i.e., the response of his neighbors if they were presented with the same article. We model these observation dependencies by a graph $\mathcal{G}$ in which nodes correspond to users, and edges correspond to social links. We derive a problem/instance-dependent lower-bound on the regret of any consistent algorithm. We propose an optimization (linear programming) based data-driven learning algorithm that utilizes the structure of $\mathcal{G}$ in order to make recommendations to users and show that it is asymptotically optimal, in the sense that its regret matches the lower-bound as the number of rounds $T\to\infty$. We show that this asymptotically optimal regret is upper-bounded as $O\left(|χ(\mathcal{G})|\log T\right)$, where $|χ(\mathcal{G})|$ is the domination number of $\mathcal{G}$. In contrast, a naive application of the existing learning algorithms results in $O\left(N\log T\right)$ regret, where $N$ is the number of users.
Frank-Wolfe optimization for deep networks
Venue: arXiv cs.LG 2020
Year: 2020

Deep neural networks is today one of the most popular choices in classification, regression and function approximation. However, the training of such deep networks is far from trivial as there are often millions of parameters to tune. Typically, one use some optimization method that hopefully converges towards some minimum. The most popular and successful methods are based on gradient descent. In this paper, another optimization method, Frank-Wolfe optimization, is applied to a small deep network and compared to gradient descent. Although the optimization does converge, it does so slowly and not close to the speed of gradient descent. Further, in a stochastic setting, the optimization becomes very unstable and does not seem to converge unless one uses a line search approach.
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
Venue: arXiv cs.LG 2020
Year: 2020

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents $Δ$-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.
Generative Design of Hardware-aware DNNs
Venue: arXiv cs.LG 2020
Year: 2020

To efficiently run DNNs on the edge/cloud, many new DNN inference accelerators are being designed and deployed frequently. To enhance the resource efficiency of DNNs, model quantization is a widely-used approach. However, different accelerator/HW has different resources leading to the need for specialized quantization strategy of each HW. Moreover, using the same quantization for every layer may be sub-optimal, increasing the designspace of possible quantization choices. This makes manual-tuning infeasible. Recent work in automatically determining quantization for each layer is driven by optimization methods such as reinforcement learning. However, these approaches need re-training the RL for every new HW platform. We propose a new way for autonomous quantization and HW-aware tuning. We propose a generative model, AQGAN, which takes a target accuracy as the condition and generates a suite of quantization configurations. With the conditional generative model, the user can autonomously generate different configurations with different targets in inference time. Moreover, we propose a simplified HW-tuning flow, which uses the generative model to generate proposals and execute simple selection based on the HW resource budget, whose process is fast and interactive. We evaluate our model on five of the widely-used efficient models on the ImageNet dataset. We compare with existing uniform quantization and state-of-the-art autonomous quantization methods. Our generative model shows competitive achieved accuracy, however, with around two degrees less search cost for each design point. Our generative model shows the generated quantization configuration can lead to less than 3.5% error across all experiments.
Conditional Neural Architecture Search
Venue: arXiv cs.LG 2020
Year: 2020

Designing resource-efficient Deep Neural Networks (DNNs) is critical to deploy deep learning solutions over edge platforms due to diverse performance, power, and memory budgets. Unfortunately, it is often the case a well-trained ML model does not fit to the constraint of deploying edge platforms, causing a long iteration of model reduction and retraining process. Moreover, a ML model optimized for platform-A often may not be suitable when we deploy it on another platform-B, causing another iteration of model retraining. We propose a conditional neural architecture search method using GAN, which produces feasible ML models for different platforms. We present a new workflow to generate constraint-optimized DNN models. This is the first work of bringing in condition and adversarial technique into Neural Architecture Search domain. We verify the method with regression problems and classification on CIFAR-10. The proposed workflow can successfully generate resource-optimized MLP or CNN-based networks.
Advertising for Demographically Fair Outcomes
Venue: arXiv cs.GT 2020
Year: 2020

Online advertising on platforms such as Google or Facebook has become an indispensable outreach tool, including for applications where it is desirable to engage different demographics in an equitable fashion, such as hiring, housing, civic processes, and public health outreach efforts. Somewhat surprisingly, the existing online advertising ecosystem provides very little support for advertising to (and recruiting) a demographically representative cohort. We study the problem of advertising for demographic representativeness from both an empirical and algorithmic perspective. In essence, we seek fairness in the outcome or conversions generated by the advertising campaigns. We first present detailed empirical findings from real-world experiments for recruiting for civic processes, using which we show that methods using Facebook-inferred features are too inaccurate for achieving equity in outcomes, while targeting via custom audiences based on a list of registered voters segmented on known attributes has much superior accuracy. This motivates us to consider the algorithmic question of optimally segmenting the list of individuals with known attributes into a few custom campaigns and allocating budgets to them so that we cost-effectively achieve outcome parity with the population on the maximum possible number of demographics. Under the assumption that a platform can reasonably enforce proportionality in spend across demographics, we present efficient exact and approximation algorithms for this problem. We present simulation results on our datasets to show the efficacy of these algorithms in achieving demographic parity.
A Scalable Architecture for Monitoring IoT Devices Using Ethereum and Fog Computing
Venue: arXiv cs.DC 2020
Year: 2020

With the recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the internet. Monitoring this huge number of IoT devices that are heterogeneous in terms of underlying communication protocols and data format is challenging. The majority of existing IoT device monitoring solutions heavily rely on centralized architectures. Since using centralized architectures comes at the expense of trusting an authority, it has several inherent drawbacks, including vulnerability to security attacks, lack of data privacy, and unauthorized data manipulation. Hence, a new decentralized approach is crucial to remedy these drawbacks. One of the most promising technologies which is widely used to provide decentralization is blockchain. Additionally, to ease the burden of communication overhead and computational power on resource-constrained IoT devices, fog computing can be exploited to decrease communication latency and provide better network scalability. In this paper, we propose a scalable blockchain-based architecture for monitoring IoT devices using fog computing. To demonstrate the feasibility and usability of the proposed solution, we have implemented a proof-of-concept prototype, leveraging Ethereum smart contracts. Finally, a comprehensive evaluation is conducted. The evaluation results indicate that the proposed solution is significantly scalable and compatible with resource-constrained IoT devices.