OpenRaiser/Intern-Atlas huggingface dataset:
Performance satisfaction in Harpy, a thruster-assisted bipedal robot
Venue: arXiv cs.RO 2020
Year: 2020

We will report our efforts in designing feedback for the thruster-assisted walking of a bipedal robot. We will assume for well-tuned supervisory controllers and will focus on fine-tuning the desired joint trajectories to satisfy the performance being sought. In doing this, we will devise an intermediary filter based on the emerging idea of reference governors. Since these modifications and impact events lead to deviations from the desired periodic orbits, we will guarantee hybrid invariance in a robust fashion by applying predictive schemes within a short time envelope during the double support phase of a gait cycle. To achieve the hybrid invariance, we will leverage the unique features in our robot, i.e., the thruster.
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
Venue: arXiv cs.CL 2020
Year: 2020

Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment classification. One major issue with this approach is that many sentiment words are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words. Detecting such words and correcting their sentiment for an application domain is very important. In this paper, we propose a graph-based technique to tackle this problem. Experimental results show its effectiveness on multiple real-world datasets.
Analyzing Smart Contracts: From EVM to a sound Control-Flow Graph
Venue: arXiv cs.PL 2020
Year: 2020

The EVM language is a simple stack-based language with words of 256 bits, with one significant difference between the EVM and other virtual machine languages (like Java Bytecode or CLI for .Net programs): the use of the stack for saving the jump addresses instead of having it explicit in the code of the jumping instructions. Static analyzers need the complete control flow graph (CFG) of the EVM program in order to be able to represent all its execution paths. This report addresses the problem of obtaining a precise and complete stack-sensitive CFG by means of a static analysis, cloning the blocks that might be executed using different states of the execution stack. The soundness of the analysis presented is proved.
Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak
Venue: arXiv cs.CY 2020
Year: 2020

As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying low-credibility information, although the majority of volume is generated by likely humans. Some of these accounts appear to amplify low-credibility sources in a coordinated fashion.
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding
Venue: arXiv cs.CL 2020
Year: 2020

Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete. In natural language processing fields, virtual adversarial training is introduced since texts are discrete and cannot be perturbed by gradients directly. Alternatively, virtual adversarial training, which generates perturbations on the embedding space, is introduced in NLP tasks. Despite its success, existing virtual adversarial training methods generate perturbations roughly constrained by Frobenius normalization balls. To craft fine-grained perturbations, we propose a Token-Aware Virtual Adversarial Training method. We introduce a token-level accumulated perturbation vocabulary to initialize the perturbations better and use a token-level normalization ball to constrain these perturbations pertinently. Experiments show that our method improves the performance of pre-trained models such as BERT and ALBERT in various tasks by a considerable margin. The proposed method improves the score of the GLUE benchmark from 78.3 to 80.9 using BERT model and it also enhances the performance of sequence labeling and text classification tasks.
A Survey on Time-Sensitive Resource Allocation in the Cloud Continuum
Venue: arXiv cs.DC 2020
Year: 2020

Artificial Intelligence (AI) and Internet of Things (IoT) applications are rapidly growing in today's world where they are continuously connected to the internet and process, store and exchange information among the devices and the environment. The cloud and edge platform is very crucial to these applications due to their inherent compute-intensive and resource-constrained nature. One of the foremost challenges in cloud and edge resource allocation is the efficient management of computation and communication resources to meet the performance and latency guarantees of the applications. The heterogeneity of cloud resources (processors, memory, storage, bandwidth), variable cost structure and unpredictable workload patterns make the design of resource allocation techniques complex. Numerous research studies have been carried out to address this intricate problem. In this paper, the current state-of-the-art resource allocation techniques for the cloud continuum, in particular those that consider time-sensitive applications, are reviewed. Furthermore, we present the key challenges in the resource allocation problem for the cloud continuum, a taxonomy to classify the existing literature and the potential research gaps.
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
Venue: arXiv cs.CL 2020
Year: 2020

Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019). In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a). We also present a detailed analysis on why deep contextualized neural LMs help and where they may fall short. Source code and resources are made available at https://github.com/bnmin/tdp_ranking.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
Venue: arXiv cs.CL 2020
Year: 2020

Previous works on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate logical level NLG as generation from logical forms in order to obtain controllable, high-fidelity, and faithful generations. We present a new large-scale dataset, \textsc{Logic2Text}, with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which poses great challenges on the model's ability to understand the semantics. We experiment on (1) Fully-supervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code are available at https://github.com/czyssrs/Logic2Text.
Bilateral Attention Network for RGB-D Salient Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Most existing RGB-D salient object detection (SOD) methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module (BAM) with a complementary attention mechanism: foreground-first (FF) attention and background-first (BF) attention. The FF attention focuses on the foreground region with a gradual refinement style, while the BF one recovers potentially useful salient information in the background region. Benefitted from the proposed BAM module, our BiANet can capture more meaningful foreground and background cues, and shift more attention to refining the uncertain details between foreground and background regions. Additionally, we extend our BAM by leveraging the multi-scale techniques for better SOD performance. Extensive experiments on six benchmark datasets demonstrate that our BiANet outperforms other state-of-the-art RGB-D SOD methods in terms of objective metrics and subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$ RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation studies also validate our contributions.
Out-of-the-box channel pruned networks
Venue: arXiv cs.LG 2020
Year: 2020

In the last decade convolutional neural networks have become gargantuan. Pre-trained models, when used as initializers are able to fine-tune ever larger networks on small datasets. Consequently, not all the convolutional features that these fine-tuned models detect are requisite for the end-task. Several works of channel pruning have been proposed to prune away compute and memory from models that were trained already. Typically, these involve policies that decide which and how many channels to remove from each layer leading to channel-wise and/or layer-wise pruning profiles, respectively. In this paper, we conduct several baseline experiments and establish that profiles from random channel-wise pruning policies are as good as metric-based ones. We also establish that there may exist profiles from some layer-wise pruning policies that are measurably better than common baselines. We then demonstrate that the top layer-wise pruning profiles found using an exhaustive random search from one datatset are also among the top profiles for other datasets. This implies that we could identify out-of-the-box layer-wise pruning profiles using benchmark datasets and use these directly for new datasets. Furthermore, we develop a Reinforcement Learning (RL) policy-based search algorithm with a direct objective of finding transferable layer-wise pruning profiles using many models for the same architecture. We use a novel reward formulation that drives this RL search towards an expected compression while maximizing accuracy. Our results show that our transferred RL-based profiles are as good or better than best profiles found on the original dataset via exhaustive search. We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation
Venue: arXiv cs.CL 2020
Year: 2020

We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions. To facilitate this holistic evaluation, we present Monotonicity NLI (MoNLI), a new naturalistic dataset focused on lexical entailment and negation. In our behavioral evaluations, we find that models trained on general-purpose NLI datasets fail systematically on MoNLI examples containing negation, but that MoNLI fine-tuning addresses this failure. In our structural evaluations, we look for evidence that our top-performing BERT-based model has learned to implement the monotonicity algorithm behind MoNLI. Probes yield evidence consistent with this conclusion, and our intervention experiments bolster this, showing that the causal dynamics of the model mirror the causal dynamics of this algorithm on subsets of MoNLI. This suggests that the BERT model at least partially embeds a theory of lexical entailment and negation at an algorithmic level.
Geometric group testing
Venue: arXiv cs.CG 2020
Year: 2020

Group testing is concerned with identifying $t$ defective items in a set of $m$ items, where each test reports whether a specific subset of items contains at least one defective. In non-adaptive group testing, the subsets to be tested are fixed in advance. By testing multiple items at once, the required number of tests can be made much smaller than $m$. In fact, for $t \in \mathcal{O}(1)$, the optimal number of (non-adaptive) tests is known to be $Θ(\log{m})$. In this paper, we consider the problem of non-adaptive group testing in a geometric setting, where the items are points in $d$-dimensional Euclidean space and the tests are axis-parallel boxes (hyperrectangles). We present upper and lower bounds on the required number of tests under this geometric constraint. In contrast to the general, combinatorial case, the bounds in our geometric setting are polynomial in $m$. For instance, our results imply that identifying a defective pair in a set of $m$ points in the plane always requires $Ω(m^{3/5})$ tests, and there exist configurations of $m$ points for which $\mathcal{O}(m^{2/3})$ tests are sufficient, whereas to identify a single defective point in the plane, $Θ(m^{1/2})$ tests are always necessary and sometimes sufficient.
Question Rewriting for Conversational Question Answering
Venue: arXiv cs.IR 2020
Year: 2020

Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to reformulate ambiguous questions, which depend on the conversational context, into unambiguous questions that can be correctly interpreted outside of the conversational context. We introduce a conversational QA architecture that sets the new state of the art on the TREC CAsT 2019 passage retrieval dataset. Moreover, we show that the same QR model improves QA performance on the QuAC dataset with respect to answer span extraction, which is the next step in QA after passage retrieval. Our evaluation results indicate that the QR model we proposed achieves near human-level performance on both datasets and the gap in performance on the end-to-end conversational QA task is attributed mostly to the errors in QA.
Covid-19 and contact tracing apps: A review under the European legal framework
Venue: arXiv cs.CY 2020
Year: 2020

In this article, we would like to review the main technologies that have been proposed so far to fight the spread of the virus. Also, we would like to give an overview of the policy recommendations that some European organisations have put forward in these regards. Finally, we conclude with some considerations we would like to present to public attention and discussion.
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Venue: arXiv cs.CL 2020
Year: 2020

Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.
Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice
Venue: arXiv cs.DS 2020
Year: 2020

Given a CNF formula F on n variables, the problem of model counting or #SAT is to compute the number of satisfying assignments of F . Model counting is a fundamental but hard problem in computer science with varied applications. Recent years have witnessed a surge of effort towards developing efficient algorithmic techniques that combine the classical 2-universal hashing with the remarkable progress in SAT solving over the past decade. These techniques augment the CNF formula F with random XOR constraints and invoke an NP oracle repeatedly on the resultant CNF-XOR formulas. In practice, calls to the NP oracle calls are replaced a SAT solver whose runtime performance is adversely affected by size of XOR constraints. The standard construction of 2-universal hash functions chooses every variable with probability p = 1/2 leading to XOR constraints of size n/2 in expectation. Consequently, the challenge is to design sparse hash functions where variables can be chosen with smaller probability and lead to smaller sized XOR constraints. In this paper, we address this challenge from theoretical and practical perspectives. First, we formalize a relaxation of universal hashing, called concentrated hashing and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain (log m) tight bounds on variance and dispersion index and show that p = O( log(m)/m ) suffices for design of sparse hash functions from {0, 1}^n to {0, 1}^m. We then use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. A comprehensive experimental evaluation of our algorithm on 1893 benchmarks demonstrates that usage of sparse hash functions can lead to significant speedups.
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
Venue: arXiv cs.LG 2020
Year: 2020

This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special characteristics of the tensor in MVSC, we design a novel structured tensor low-rank norm tailored to MVSC. Specifically, we explicitly impose a symmetric low-rank constraint and a structured sparse low-rank constraint on the frontal and horizontal slices of the tensor to characterize the intra-view and inter-view relationships, respectively. Moreover, the two constraints could be jointly optimized to achieve mutual refinement. On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively. Extensive experimental results on five benchmark datasets show that the proposed method outperforms state-of-the-art methods to a significant extent. Impressively, our method is able to produce perfect clustering. In addition, the parameters of our method can be easily tuned, and the proposed model is robust to different datasets, demonstrating its potential in practice.
Randomized Two-Valued Bounded Delay Online Buffer Management
Venue: arXiv cs.DS 2020
Year: 2020

In the bounded delay buffer management problem unit size packets arrive online to be sent over a network link. The objective is to maximize the total weight of packets sent before their deadline. In this paper we are interested in the two-valued variant of the problem, where every packet has either low (1) or high priority weight ($α$ > 1). We show that its randomized competitive ratio against an oblivious adversary is 1 + ($α$ -- 1)/($α$ 2 + $α$).
The Lipschitz Constant of Perturbed Anonymous Games
Venue: arXiv cs.GT 2020
Year: 2020

The worst-case Lipschitz constant of an $n$-player $k$-action $δ$-perturbed game, $λ(n,k,δ)$, is given an explicit probabilistic description. In the case of $k\geq 3$, $λ(n,k,δ)$ is identified with the passage probability of a certain symmetric random walk on $\mathbb Z$. In the case of $k=2$ and $n$ even, $λ(n,2,δ)$ is identified with the probability that two two i.i.d.\ Binomial random variables are equal. The remaining case, $k=2$ and $n$ odd, is bounded through the adjacent (even) values of $n$. Our characterisation implies a sharp closed form asymptotic estimate of $λ(n,k,δ)$ as $δn /k\to\infty$.
Robustness Certification of Generative Models
Venue: arXiv cs.LG 2020
Year: 2020

Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation. However, certifying that all images captured by the resulting path in the image manifold satisfy a given property can be very challenging. This is because this set is highly non-convex, thwarting existing scalable robustness analysis methods, which are often based on convex relaxations. We present ApproxLine, a scalable certification method that successfully verifies non-trivial specifications involving generative models and classifiers. ApproxLine can provide both sound deterministic and probabilistic guarantees, by capturing either infinite non-convex sets of neural network activation vectors or distributions over such sets. We show that ApproxLine is practically useful and can verify interesting interpolations in the networks latent space.
Real-Time Energy Monitoring in IoT-enabled Mobile Devices
Venue: arXiv cs.CR 2020
Year: 2020

With rapid advancements in the Internet of Things (IoT) paradigm, electrical devices in the near future is expected to have IoT capabilities. This enables fine-grained tracking of individual energy consumption data of such devices, offering location-independent per-device billing. Thus, it is more fine-grained than the location-based metering of state-of-the-art infrastructure, which traditionally aggregates on a building or household level, defining the entity to be billed. However, such in-device energy metering is susceptible to manipulation and fraud. As a remedy, we propose a decentralized metering architecture that enables devices with IoT capabilities to measure their own energy consumption. In this architecture, the device-level consumption is additionally reported to a system-level aggregator that verifies distributed information and provides secure data storage using Blockchain, preventing data manipulation by untrusted entities. Using evaluations on an experimental testbed, we show that the proposed architecture supports device mobility and enables location-independent monitoring of energy consumption.