OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Principled Approach for Dynamic Analysis of Android's Middleware
Venue: arXiv cs.CR 2021
Year: 2021

The Android middleware, in particular the so-called systemserver, is a crucial and central component to Android's security and robustness. To understand whether the systemserver provides the demanded security properties, it has to be thoroughly tested and analyzed. A dedicated line of research focuses exclusively on this task. While static analysis builds on established tools, dynamic testing approaches lack a common foundation, which prevents the community from comparing, reproducing, or even re-using existing results from related work. This raises questions about whether the underlying approach of any proposed solution is the only possible or optimal one, if it can be re-used as a building block for future analyses, or whether results generalize. In this work, we argue that in order to steer away from incompatible custom toolchains and towards having comparable analyses with reproducible results, a more principled approach to dynamically analyzing the Android system is required. As an important first step in this direction, we propose a unified dynamic analysis platform that provides re-usable solutions for common challenges as the building blocks for future analyses and allows to compare different approaches under the same assumptions.
Parameterizing Activation Functions for Adversarial Robustness
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks are known to be vulnerable to adversarially perturbed inputs. A commonly used defense is adversarial training, whose performance is influenced by model capacity. While previous works have studied the impact of varying model width and depth on robustness, the impact of increasing capacity by using learnable parametric activation functions (PAFs) has not been studied. We study how using learnable PAFs can improve robustness in conjunction with adversarial training. We first ask the question: how should we incorporate parameters into activation functions to improve robustness? To address this, we analyze the direct impact of activation shape on robustness through PAFs and observe that activation shapes with positive outputs on negative inputs and with high finite curvature can increase robustness. We combine these properties to create a new PAF, which we call Parametric Shifted Sigmoidal Linear Unit (PSSiLU). We then combine PAFs (including PReLU, PSoftplus and PSSiLU) with adversarial training and analyze robust performance. We find that PAFs optimize towards activation shape properties found to directly affect robustness. Additionally, we find that while introducing only 1-2 learnable parameters into the network, smooth PAFs can significantly increase robustness over ReLU. For instance, when trained on CIFAR-10 with additional synthetic data, PSSiLU improves robust accuracy by 4.54% over ReLU on ResNet-18 and 2.69% over ReLU on WRN-28-10 in the $\ell_{\infty}$ threat model while adding only 2 additional parameters into the network architecture. The PSSiLU WRN-28-10 model achieves 61.96% AutoAttack accuracy, improving over the state-of-the-art robust accuracy on RobustBench (Croce et al., 2020).
GM-Livox: An Integrated Framework for Large-Scale Map Construction with Multiple Non-repetitive Scanning LiDARs
Venue: arXiv cs.RO 2021
Year: 2021

With the ability of providing direct and accurate enough range measurements, light detection and ranging (LiDAR) is playing an essential role in localization and detection for autonomous vehicles. Since single LiDAR suffers from hardware failure and performance degradation intermittently, we present a multi-LiDAR integration scheme in this article. Our framework tightly couples multiple non-repetitive scanning LiDARs with inertial, encoder, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. Primarily, we formulate a precise synchronization strategy to integrate isolated sensors, and the extracted feature points from separate LiDARs are merged into a single sweep. The fused scans are introduced to compute the scan-matching correspondences, which can be further refined by additional real-time kinematic (RTK) measurements. Based thereupon, we construct a factor graph along with the inertial preintegration result, estimated ground constraints, and RTK data. For the purpose of maintaining a restricted number of poses for estimation, we deploy a keyframe based sliding-window optimization strategy in our system. The real-time performance is guaranteed with multi-threaded computation, and extensive experiments are conducted in challenging scenarios. Experimental results show that the utilization of multiple LiDARs boosts the system performance in both robustness and accuracy.
Are you doing what I say? On modalities alignment in ALFRED
Venue: arXiv cs.CL 2021
Year: 2021

ALFRED is a recently proposed benchmark that requires a model to complete tasks in simulated house environments specified by instructions in natural language. We hypothesize that key to success is accurately aligning the text modality with visual inputs. Motivated by this, we inspect how well existing models can align these modalities using our proposed intrinsic metric, boundary adherence score (BAS). The results show the previous models are indeed failing to perform proper alignment. To address this issue, we introduce approaches aimed at improving model alignment and demonstrate how improved alignment, improves end task performance.
Uplink NOMA For STAR-RIS Networks
Venue: arXiv cs.IT 2021
Year: 2021

A simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enhanced uplink non-orthogonal multiple access (NOMA) communication system is proposed. A total power consumption minimization problem is formulated by jointly optimizing the transmit-power of users, receive-beamforming vectors at the base station (BS), STAR-beamforming vectors at the STAR-RIS and time slots. Here, the STAR-beamforming introduced by STAR-RIS consists of transmission- and reflection-beamforming. To solve the formulated non-convex problem, an efficient penalty-based alternating optimization (P-AltOp) algorithm is proposed. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the total power consumption.
Auditing Robot Learning for Safety and Compliance during Deployment
Venue: arXiv cs.RO 2021
Year: 2021

Robots of the future are going to exhibit increasingly human-like and super-human intelligence in a myriad of different tasks. They are also likely going to fail and be incompliant with human preferences in increasingly subtle ways. Towards the goal of achieving autonomous robots, the robot learning community has made rapid strides in applying machine learning techniques to train robots through data and interaction. This makes the study of how best to audit these algorithms for checking their compatibility with humans, pertinent and urgent. In this paper, we draw inspiration from the AI Safety and Alignment communities and make the case that we need to urgently consider ways in which we can best audit our robot learning algorithms to check for failure modes, and ensure that when operating autonomously, they are indeed behaving in ways that the human algorithm designers intend them to. We believe that this is a challenging problem that will require efforts from the entire robot learning community, and do not attempt to provide a concrete framework for auditing. Instead, we outline high-level guidance and a possible approach towards formulating this framework which we hope will serve as a useful starting point for thinking about auditing in the context of robot learning.
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Venue: arXiv cs.CL 2021
Year: 2021

Majority voting and averaging are common approaches employed to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. Annotator disagreements may capture important nuances in such tasks that are often ignored while aggregating annotations to a single ground truth. In order to address this, we investigate the efficacy of multi-annotator models. In particular, our multi-task based approach treats predicting each annotators' judgements as separate subtasks, while sharing a common learned representation of the task. We show that this approach yields same or better performance than aggregating labels in the data prior to training across seven different binary classification tasks. Our approach also provides a way to estimate uncertainty in predictions, which we demonstrate better correlate with annotation disagreements than traditional methods. Being able to model uncertainty is especially useful in deployment scenarios where knowing when not to make a prediction is important.
Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms
Venue: arXiv cs.CL 2021
Year: 2021

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. Zhu et al. (2021) introduced the creation of the OntoGUM corpus for evaluating geralizability of the latest neural LM-based end-to-end systems. This paper covers details of the mapping process which is a set of deterministic rules applied to the rich syntactic and discourse annotations manually annotated in the GUM corpus. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables
Venue: arXiv cs.LG 2021
Year: 2021

Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming. State-of-the-art unsupervised remedies investigated to alleviate the burdens of data annotations in HAR mainly explore training autoencoder frameworks. In this paper: we explore generative adversarial network (GAN) paradigms to learn unsupervised feature representations from wearable sensor data; and design a new GAN framework-Geometrically-Guided GAN or Guided-GAN-for the task. To demonstrate the effectiveness of our formulation, we evaluate the features learned by Guided-GAN in an unsupervised manner on three downstream classification benchmarks. Our results demonstrate Guided-GAN to outperform existing unsupervised approaches whilst closely approaching the performance with fully supervised learned representations. The proposed approach paves the way to bridge the gap between unsupervised and supervised human activity recognition whilst helping to reduce the cost of human data annotation tasks.
Seamless Copy Move Manipulation in Digital Images
Venue: arXiv cs.CV 2021
Year: 2021

The importance and relevance of digital image forensics has attracted researchers to establish different techniques for creating as well as detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper frequency domain image manipulation method is being presented.The method exploits the localized nature of discrete wavelet transform (DWT) to get hold of the region of the host image to be manipulated. Both the patch and host image are subjected to DWT at the same level $l$ to get $3l + 1$ sub-bands and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. The resultant manipulated host sub-bands are then subjected to inverse DWT to get the final manipulated host image. The proposed method shows good resistance against detection by two frequency domain forgery detection methods from the literature. The purpose of this research work is to create the forgery and highlight the need to produce forgery detection methods that are robust against the malicious copy-move forgery.
Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties
Venue: arXiv cs.LG 2021
Year: 2021

Due the alarming rate of climate change, the implementation of efficient CO$_2$ capture has become crucial. This project aims to create an algorithm that predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) by using Machine Learning. These values will in turn gauge the efficiency of these MOFs and provide scientists who are looking to maximize the uptake a way to know whether or not the MOF is worth synthesizing. This algorithm will save resources such as time and equipment as scientists will be able to disregard hypothetical MOFs with low efficiencies. In addition, this paper will also highlight the most important features within the data set. This research will contribute to enable the rapid synthesis of CO$_2$ adsorbing MOFs.
Remote Anomaly Detection in Industry 4.0 Using Resource-Constrained Devices
Venue: arXiv cs.IT 2021
Year: 2021

A central use case for the Internet of Things (IoT) is the adoption of sensors to monitor physical processes, such as the environment and industrial manufacturing processes, where they provide data for predictive maintenance, anomaly detection, or similar. The sensor devices are typically resource-constrained in terms of computation and power, and need to rely on cloud or edge computing for data processing. However, the capacity of the wireless link and their power constraints limit the amount of data that can be transmitted to the cloud. While this is not problematic for the monitoring of slowly varying processes such as temperature, it is more problematic for complex signals such as those captured by vibration and acoustic sensors. In this paper, we consider the specific problem of remote anomaly detection based on signals that fall into the latter category over wireless channels with resource-constrained sensors. We study the impact of source coding on the detection accuracy with both an anomaly detector based on Principal Component Analysis (PCA) and one based on an autoencoder. We show that the coded transmission is beneficial when the signal-to-noise ratio (SNR) of the channel is low, while uncoded transmission performs best in the high SNR regime.
Quantifying Cognitive Factors in Lexical Decline
Venue: arXiv cs.CL 2021
Year: 2021

We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors -- semantic, distributional, and phonological -- that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their 'ecological niches'.
Optimizing Ranking Systems Online as Bandits
Venue: arXiv cs.IR 2021
Year: 2021

Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means that the deployed system can serve user requests, e.g., queries in the web search, and optimize the ranking policy by learning from user interactions, e.g., clicks. Bandit is a general online learning framework and can be used in our optimization task. However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization. In this dissertation, we study and propose solutions for four challenges in optimizing ranking systems online: effectiveness, safety, nonstationarity, and diversification. First, the effectiveness is related to how fast the algorithm learns from interactions. We study the effective online ranker evaluation task and propose the MergeDTS algorithm to solve the problem effectively. Second, the deployed algorithm should be safe, which means the algorithm only displays reasonable content to user requests. To solve the safe online learning to rank problem, we propose the BubbleRank algorithm. Third, as users change their preferences constantly, the algorithm should handle the nonstationarity. We formulate this nonstationary online learning to rank problem as cascade non-stationary bandits and propose CascadeDUCB and CascadeSWUCB algorithms to solve the problem. Finally, the contents in ranked lists should be diverse. We consider the results diversification task and propose the CascadeHybird algorithm that considers both the item relevance and results diversification when learning from user interactions.
Balancing Average and Worst-case Accuracy in Multitask Learning
Venue: arXiv cs.LG 2021
Year: 2021

When training and evaluating machine learning models on a large number of tasks, it is important to not only look at average task accuracy -- which may be biased by easy or redundant tasks -- but also worst-case accuracy (i.e. the performance on the task with the lowest accuracy). In this work, we show how to use techniques from the distributionally robust optimization (DRO) literature to improve worst-case performance in multitask learning. We highlight several failure cases of DRO when applied off-the-shelf and present an improved method, Lookahead-DRO (L-DRO), which mitigates these issues. The core idea of L-DRO is to anticipate the interaction between tasks during training in order to choose a dynamic re-weighting of the various task losses, which will (i) lead to minimal worst-case loss and (ii) train on as many tasks as possible. After demonstrating the efficacy of L-DRO on a small controlled synthetic setting, we evaluate it on two realistic benchmarks: a multitask version of the CIFAR-100 image classification dataset and a large-scale multilingual language modeling experiment. Our empirical results show that L-DRO achieves a better trade-off between average and worst-case accuracy with little computational overhead compared to several strong baselines.
MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
Venue: arXiv cs.SD 2021
Year: 2021

Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training. Consequently, several noisy speeches recorded in daily life cannot be used to train the model. Although certain unsupervised learning frameworks have also been proposed to solve the pair constraint, they still require clean speech or noise for training. Therefore, in this paper, we propose MetricGAN-U, which stands for MetricGAN-unsupervised, to further release the constraint from conventional unsupervised learning. In MetricGAN-U, only noisy speech is required to train the model by optimizing non-intrusive speech quality metrics. The experimental results verified that MetricGAN-U outperforms baselines in both objective and subjective metrics.
Sanctuary lost: a cyber-physical warfare in space
Venue: arXiv cs.CR 2021
Year: 2021

Over the last decades, space has grown from a purely scientific struggle, fueled by the desire to demonstrate superiority of one regime over the other, to an anchor point of the economies of essentially all developed countries. Many businesses depend crucially on satellite communication or data acquisition, not only for defense purposes, but increasingly also for day-to-day applications. However, although so far space faring nations refrained from extending their earth-bound conflicts into space, this critical infrastructure is not as invulnerable as common knowledge suggests. In this paper, we analyze the threats space vehicles are exposed to and what must change to mitigate them. In particular, we shall focus on cyber threats, which may well be mounted by small countries and terrorist organizations, whose incentives do not necessarily include sustainability of the space domain and who may not be susceptible to the threat of mutual retaliation on the ground. We survey incidents, highlight threats and raise awareness from general preparedness for accidental faults, which is already widely spread within the space community, to preparedness and tolerance of both accidental and malicious faults (such as targeted attacks by cyber terrorists and nation-state hackers).
Concepts of Self-maintaining Robots and Their Design
Venue: arXiv cs.RO 2021
Year: 2021

This paper proposes an initial theory for robotic systems that can be fully self-maintaining. The new design principles focus on functional survival of the robots over long periods of time without human maintenance. Self-maintaining semi-autonomous mobile robots are in great demand in nuclear disposal sites from where their removal for maintenance is undesirable due to their radioactive contamination. Similar are requirements for robots in various defence tasks or space missions. For optimal design, modular solutions are balanced against capabilities to replace smaller components in a robot by itself or by help from another robot. Modules are proposed for the basic platform, which enable self-maintenance within a team of robots helping each other. The primary method of self-maintenance is replacement of malfunctioning modules or components by the robots themselves. Replacement necessitates a robot team's ability to diagnose and replace malfunctioning modules as needed. Due to their design, these robots still remain manually re-configurable if opportunity arises for human intervention. Apart from the basic principles, an evolutionary design approach is presented and a first mathematical theory of the reliability of a team of self-maintaining robots is introduced.
LaoPLM: Pre-trained Language Models for Lao
Venue: arXiv cs.CL 2021
Year: 2021

Trained on the large corpus, pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. They can benefit multiple downstream natural language processing (NLP) tasks. Although PTMs have been widely used in most NLP applications, especially for high-resource languages such as English, it is under-represented in Lao NLP research. Previous work on Lao has been hampered by the lack of annotated datasets and the sparsity of language resources. In this work, we construct a text classification dataset to alleviate the resource-scare situation of the Lao language. We additionally present the first transformer-based PTMs for Lao with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base, and evaluate it over two downstream tasks: part-of-speech tagging and text classification. Experiments demonstrate the effectiveness of our Lao models. We will release our models and datasets to the community, hoping to facilitate the future development of Lao NLP applications.