OpenRaiser/Intern-Atlas huggingface dataset:
Orderly Dual-Teacher Knowledge Distillation for Lightweight Human Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Although deep convolution neural networks (DCNN) have achieved excellent performance in human pose estimation, these networks often have a large number of parameters and computations, leading to the slow inference speed. For this issue, an effective solution is knowledge distillation, which transfers knowledge from a large pre-trained network (teacher) to a small network (student). However, there are some defects in the existing approaches: (I) Only a single teacher is adopted, neglecting the potential that a student can learn from multiple teachers. (II) The human segmentation mask can be regarded as additional prior information to restrict the location of keypoints, which is never utilized. (III) A student with a small number of parameters cannot fully imitate heatmaps provided by datasets and teachers. (IV) There exists noise in heatmaps generated by teachers, which causes model degradation. To overcome these defects, we propose an orderly dual-teacher knowledge distillation (ODKD) framework, which consists of two teachers with different capabilities. Specifically, the weaker one (primary teacher, PT) is used to teach keypoints information, the stronger one (senior teacher, ST) is utilized to transfer segmentation and keypoints information by adding the human segmentation mask. Taking dual-teacher together, an orderly learning strategy is proposed to promote knowledge absorbability. Moreover, we employ a binarization operation which further improves the learning ability of the student and reduces noise in heatmaps. Experimental results on COCO and OCHuman keypoints datasets show that our proposed ODKD can improve the performance of different lightweight models by a large margin, and HRNet-W16 equipped with ODKD achieves state-of-the-art performance for lightweight human pose estimation.
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems
Venue: arXiv cs.LG 2021
Year: 2021

Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning and Actor Critic, using states and rewards based on queue lengths. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay and average queue length. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with existing commercial systems.
HDR-Fuzz: Detecting Buffer Overruns using AddressSanitizer Instrumentation and Fuzzing
Venue: arXiv cs.SE 2021
Year: 2021

Buffer-overruns are a prevalent vulnerability in software libraries and applications. Fuzz testing is one of the effective techniques to detect vulnerabilities in general. Greybox fuzzers such as AFL automatically generate a sequence of test inputs for a given program using a fitness-guided search process. A recently proposed approach in the literature introduced a buffer-overrun specific fitness metric called "headroom", which tracks how close each generated test input comes to exposing the vulnerabilities. That approach showed good initial promise, but is somewhat imprecise and expensive due to its reliance on conservative points-to analysis. Inspired by the approach above, in this paper we propose a new ground-up approach for detecting buffer-overrun vulnerabilities. This approach uses an extended version of ASAN (Address Sanitizer) that runs in parallel with the fuzzer, and reports back to the fuzzer test inputs that happen to come closer to exposing buffer-overrun vulnerabilities. The ASAN-style instrumentation is precise as it has no dependence on points-to analysis. We describe in this paper our approach, as well as an implementation and evaluation of the approach.
A geometric approach to conditioning belief functions
Venue: arXiv cs.AI 2021
Year: 2021

Conditioning is crucial in applied science when inference involving time series is involved. Belief calculus is an effective way of handling such inference in the presence of epistemic uncertainty -- unfortunately, different approaches to conditioning in the belief function framework have been proposed in the past, leaving the matter somewhat unsettled. Inspired by the geometric approach to uncertainty, in this paper we propose an approach to the conditioning of belief functions based on geometrically projecting them onto the simplex associated with the conditioning event in the space of all belief functions. We show here that such a geometric approach to conditioning often produces simple results with straightforward interpretations in terms of degrees of belief. This raises the question of whether classical approaches, such as for instance Dempster's conditioning, can also be reduced to some form of distance minimisation in a suitable space. The study of families of combination rules generated by (geometric) conditioning rules appears to be the natural prosecution of the presented research.
Surface Disinfection using Ultraviolet Lightwith a Mobile Manipulation Robot
Venue: arXiv cs.RO 2021
Year: 2021

Robots are being increasingly used in the fight against highly-infectious diseases such as Ebola, MERS, and SARS-COV-2. Many of the robots that are being used employ ultraviolet lights mounted on a mobile base to inactivate the pathogens. However, these lights are often mounted in a fixed configuration and do not provide adequate decontamination of horizontal surfaces, which can be a major source of cross-contamination. In the paper, we describe the design, implementation, and testing of an Ultraviolet Germicidal Irradiation (UVGI) system implemented on a mobile manipulation robot. A human supervisor designates a surface for disinfection, the robot autonomously plans and executes an end-effector trajectory to disinfect the surface to the required certainty, and then displays the results for the human supervisor to verify. We also provide some background information on UVGI and describe how we constructed and validated mathematical models of Ultraviolet (UV) radiation propagation and accumulation. Finally, we describe our implementation on a Fetch mobile manipulation platform, and discuss how the practicalities of implementation on a real robot affect our models.
Robustness of ML-Enhanced IDS to Stealthy Adversaries
Venue: arXiv cs.CR 2021
Year: 2021

Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect cyber threats' activity with greater accuracy than traditional rule-based IDS. Because these are largely black boxes, their acceptance requires proof of robustness to stealthy adversaries. Since it is impossible to build a baseline from activity completely clean of that of malicious cyber actors (outside of controlled experiments), the training data for deployed models will be poisoned with examples of activity that analysts would want to be alerted about. We train an autoencoder-based anomaly detection system on network activity with various proportions of malicious activity mixed in and demonstrate that they are robust to this sort of poisoning.
Development of Aircraft Spoiler Demonstrators for Cost-Efficient Investigations of SHM Technologies under Quasi-Realistic Loading Conditions
Venue: arXiv cs.CE 2021
Year: 2021

An idealized 1:2 scale demonstrator and a numerical parameter optimization algorithm are proposed to closely reproduce the deformation shape and, thus, spatial strain directions of a real aerodynamically loaded civil aircraft spoiler using only four concentrated loads. Cost-efficient experimental studies on demonstrators of increasing complexity are required to transfer knowledge from coupons to full-scale structures and to build up confidence in novel structural health monitoring (SHM) technologies. Especially for testing novel sensor systems that depend on or are affected by mechanical strains, e.g., strain-based SHM methods, it is essential that the considered lab-scale structures reflect the strain states of the real structure at operational loading conditions. Finite element simulations with detailed models were performed for static strength analysis and for comparison to experimental measurements. The simulated and measured deformations and spatial strain directions of the idealized demonstrator correlated well with the numerical results of the real aircraft spoiler. Thus, using the developed idealized demonstrator, strain-based SHM systems can be tested under conditions that reflect operational aerodynamic pressure loads, while the test effort and costs are significantly reduced. Furthermore, the presented loading optimization algorithm can be easily adapted to mimic other pressure loads in plate-like structures to reproduce specific structural conditions.
Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key
Venue: arXiv cs.SI 2021
Year: 2021

Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication, such as cyberbullying and harassment. Nevertheless, detecting online toxicity is challenging due to its multi-dimensional, context sensitive nature. As exposure to online toxicity can have serious social consequences, reliable models and algorithms are required for detecting and analyzing such communication across the vast and growing space of social media. In this paper, we draw on psychological and social theory to define toxicity. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.
Extracting Adverse Drug Events from Clinical Notes
Venue: arXiv cs.CL 2021
Year: 2021

Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our experimental results demonstrate that the contextualized language model-based approach outperformed other models overall and obtain the state-of-the-art performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an $F_1$ score of 0.94; however, for certain relation types, the rule-based approach obtained a higher Precision and Recall than either learning approach.
Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic
Venue: arXiv cs.SI 2021
Year: 2021

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spreading of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of misinformation and vaccination hesitancy. Additionally, our study shows that only half of the online users exposed to rumors might have seen the fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. Our survey also shows that poorer regions are more susceptible to encountering and believing COVID-19 misinformation. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also highlight fact-checking platforms' role in better identifying and prioritizing claims that are perceived to be believable and have wide exposure. Our findings give insights into better handling of risk communication during the initial phase of a future pandemic.
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem
Venue: arXiv cs.LG 2021
Year: 2021

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to optimize their common goal. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ASTC problem. Here we use independent reinforcement learning (IRL) to solve a complex traffic cooperative control problem in this study. One of the largest challenges of this problem is that the observation information of intersection is typically partially observable, which limits the learning performance of IRL algorithms. To this, we model the traffic control problem as a partially observable weak cooperative traffic model (PO-WCTM) to optimize the overall traffic situation of a group of intersections. Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. We also propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffer, which deals with the problem of experience failure caused by the strategy change of other agents. The latter mechanism increases the weight experiences with high estimation and `leniently' trains the DDQN neural network, which improves the probability of the selection of cooperative joint strategies. Experimental results show that CIL-DDQN outperforms other methods in almost all performance indicators of the traffic control problem.
Enabling Cross-Layer Reliability and Functional Safety Assessment Through ML-Based Compact Models
Venue: arXiv cs.AR 2021
Year: 2021

Typical design flows are hierarchical and rely on assembling many individual technology elements from standard cells to complete boards. Providers use compact models to provide simplified views of their products to their users. Designers group simpler elements in more complex structures and have to manage the corresponding propagation of reliability and functional safety information through the hierarchy of the system, accompanied by the obvious problems of IP confidentiality, possibility of reverse engineering and so on. This paper proposes a machine-learning-based approach to integrate the many individual models of a subsystem's elements in a single compact model that can be re-used and assembled further up in the hierarchy. The compact models provide consistency, accuracy and confidentiality, allowing technology, IP, component, sub-system or system providers to accompany their offering with high-quality reliability and functional safety compact models that can be safely and accurately consumed by their users.
Reinforcement Learning using Guided Observability
Venue: arXiv cs.LG 2021
Year: 2021

Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems. However, an open question is how to make RL cope with partial observability which is prevalent in many real-world problems. Contrary to contemporary RL approaches, which focus mostly on improved memory representations or strong assumptions about the type of partial observability, we propose a simple but efficient approach that can be applied together with a wide variety of RL methods. Our main insight is that smoothly transitioning from full observability to partial observability during the training process yields a high performance policy. The approach, called partially observable guided reinforcement learning (PO-GRL), allows to utilize full state information during policy optimization without compromising the optimality of the final policy. A comprehensive evaluation in discrete partially observableMarkov decision process (POMDP) benchmark problems and continuous partially observable MuJoCo and OpenAI gym tasks shows that PO-GRL improves performance. Finally, we demonstrate PO-GRL in the ball-in-the-cup task on a real Barrett WAM robot under partial observability.
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder
Venue: arXiv cs.RO 2021
Year: 2021

Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires a human expert monitoring the procedure from a console. Data scarcity, on the other hand, hinders what would be a desirable migration towards autonomous robotic-assisted surgical systems. Automated anomaly detection systems in this area typically rely on classical supervised learning. Anomalous events in a surgical setting, however, are rare, making it difficult to capture data to train a detection model in a supervised fashion. In this work we thus propose an unsupervised approach to anomaly detection for robotic-assisted surgery based on deep residual autoencoders. The idea is to make the autoencoder learn the 'normal' distribution of the data and detect abnormal events deviating from this distribution by measuring the reconstruction error. The model is trained and validated upon both the publicly available Cholec80 dataset, provided with extra annotation, and on a set of videos captured on procedures using artificial anatomies ('phantoms') produced as part of the Smart Autonomous Robotic Assistant Surgeon (SARAS) project. The system achieves recall and precision equal to 78.4%, 91.5%, respectively, on Cholec80 and of 95.6%, 88.1% on the SARAS phantom dataset. The end-to-end system was developed and deployed as part of the SARAS demonstration platform for real-time anomaly detection with a processing time of about 25 ms per frame.
Agent vs. Avatar: Comparing Embodied Conversational Agents Concerning Characteristics of the Uncanny Valley
Venue: arXiv cs.HC 2021
Year: 2021

Visual appearance is an important aspect influencing the perception and consequent acceptance of Embodied Conversational Agents (ECA). To this end, the Uncanny Valley theory contradicts the common assumption that increased humanization of characters leads to better acceptance. Rather, it shows that anthropomorphic behavior may trigger feelings of eeriness and rejection in people. The work presented in this paper explores whether four different autonomous ECAs, specifically build for a European research project, are affected by this effect, and how they compare to two slightly more realistically looking human-controlled, i.e. face-tracked, ECAs with respect to perceived humanness, eeriness, and attractiveness. Short videos of the ECAs in combination with a validated questionnaire were used to investigate potential differences. Results support existing theories highlighting that increased perceived humanness correlates with increased perceived eeriness. Furthermore, it was found, that neither the gender of survey participants, their age, nor the sex of the ECA influences this effect, and that female ECAs are perceived to be significantly more attractive than their male counterparts.
Online GANs for Automatic Performance Testing
Venue: arXiv cs.SE 2021
Year: 2021

In this paper we present a novel algorithm for automatic performance testing that uses an online variant of the Generative Adversarial Network (GAN) to optimize the test generation process. The objective of the proposed approach is to generate, for a given test budget, a test suite containing a high number of tests revealing performance defects. This is achieved using a GAN to generate the tests and predict their outcome. This GAN is trained online while generating and executing the tests. The proposed approach does not require a prior training set or model of the system under test. We provide an initial evaluation the algorithm using an example test system, and compare the obtained results with other possible approaches. We consider that the presented algorithm serves as a proof of concept and we hope that it can spark a research discussion on the application of GANs to test generation.
Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes. We sample triangular faces on a reference human mesh, and create an adversarial texture atlas over those faces. The adversarial texture is transferred to human meshes in various poses, which are rendered onto a collection of real-world background images. Contrary to the traditional patch-based adversarial attacks, where prior work attempts to fool trained object detectors using appended adversarial patches, this new form of attack is mapped into the 3D object world and back-propagated to the texture atlas through differentiable rendering. As such, the adversarial patch is trained under deformation consistent with real-world materials. In addition, and unlike existing adversarial patches, our new 3D adversarial patch is shown to fool state-of-the-art deep object detectors robustly under varying views, potentially leading to an attacking scheme that is persistently strong in the physical world.