OpenRaiser/Intern-Atlas huggingface dataset:
Performance of an Astrophysical Radiation Hydrodynamics Code under Scalable Vector Extension Optimization
Venue: arXiv cs.DC 2022
Year: 2022

We present results of a performance study of an astrophysical radiation hydrodynamics code, V2D, on the Arm-based A64FX processor developed by Fujitsu. The code solves sparse linear systems, a task for which the A64FX architecture should be well suited. We performed the performance analysis study on Ookami, an Apollo 80 platform utilizing the A64FX processor. We explored several compilers and performance analysis packages and found the code did not perform as expected under scalable vector extension optimization, suggesting that a "deeper dive" into analyzing the code is worthwhile. However, a simple driver program that exercised basic sparse linear algebra routines used by V2D did show significant speedup with the use of the scalable vector extension optimization. We present the initial results from the study which used V2D on a relatively simple test problem that emphasized the repeated solution of sparse linear systems.
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference. Moreover, the timespan between interacted items is not sufficiently utilized by previous models, restricting SR performance gains. To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences. Specifically, a timespan-aware sequence graph (TSG) for the target user is first constructed with the timespan among interacted items. Next, all original nodes in TSG are softly clustered into factor nodes, each of which represents a certain factor of the user's preference. At last, all factor nodes' representations are used together to predict SR results. Our extensive experiments upon two datasets justify that our HGNN-based factorial user modeling obtains better SR performance than the state-of-the-art SR models.
Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations
Venue: arXiv cs.CV 2022
Year: 2022

In many robotic applications, the environment setting in which the 6-DoF pose estimation of a known, rigid object and its subsequent grasping is to be performed, remains nearly unchanging and might even be known to the robot in advance. In this paper, we refer to this problem as instance-specific pose estimation: the robot is expected to estimate the pose with a high degree of accuracy in only a limited set of familiar scenarios. Minor changes in the scene, including variations in lighting conditions and background appearance, are acceptable but drastic alterations are not anticipated. To this end, we present a method to rapidly train and deploy a pipeline for estimating the continuous 6-DoF pose of an object from a single RGB image. The key idea is to leverage known camera poses and rigid body geometry to partially automate the generation of a large labeled dataset. The dataset, along with sufficient domain randomization, is then used to supervise the training of deep neural networks for predicting semantic keypoints. Experimentally, we demonstrate the convenience and effectiveness of our proposed method to accurately estimate object pose requiring only a very small amount of manual annotation for training.
Social Live-Streaming Use & Well-being: Examining Participation, Financial Commitment, Social Capital, and Psychological Well-being on Twitch.tv
Venue: arXiv cs.SI 2022
Year: 2022

This study examines how active participation, financial commitment, and passive participation in the leading social live-streaming service, Twitch.tv, relate to individuals' psychological well-being. The three dimensions of social capital-structural, relational, and cognitive-as well as parasocial relationship are explored as mediators. Cross-sectional survey data from 396 respondents was analyzed by comparing two fully saturated structural equation models. Findings indicate actively participating in a favorite streamers' Chat is positively associated with increased well-being. Structural social capital, or having more social interaction ties, positively mediates the relationship between active participation and well-being, as well as financial commitment and well-being. Greater cognitive social capital, or shared values and goals with a favorite streamer, is related to decreased well-being. Parasocial relationship does not significantly mediate the relationship between use and well-being. Our results demonstrate the importance of tangible social ties over the perceived relationships or identification with a favorite streamer.
DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking
Venue: arXiv cs.CR 2022
Year: 2022

The functionality of a deep learning (DL) model can be stolen via model extraction where an attacker obtains a surrogate model by utilizing the responses from a prediction API of the original model. In this work, we propose a novel watermarking technique called DynaMarks to protect the intellectual property (IP) of DL models against such model extraction attacks in a black-box setting. Unlike existing approaches, DynaMarks does not alter the training process of the original model but rather embeds watermark into a surrogate model by dynamically changing the output responses from the original model prediction API based on certain secret parameters at inference runtime. The experimental outcomes on Fashion MNIST, CIFAR-10, and ImageNet datasets demonstrate the efficacy of DynaMarks scheme to watermark surrogate models while preserving the accuracies of the original models deployed in edge devices. In addition, we also perform experiments to evaluate the robustness of DynaMarks against various watermark removal strategies, thus allowing a DL model owner to reliably prove model ownership.
Towards Clear Expectations for Uncertainty Estimation
Venue: arXiv cs.LG 2022
Year: 2022

If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols. We claim this inconsistency results from the unclear requirements the community expects from UQ. This opinion paper offers a new perspective by specifying those requirements through five downstream tasks where we expect uncertainty scores to have substantial predictive power. We design these downstream tasks carefully to reflect real-life usage of ML models. On an example benchmark of 7 classification datasets, we did not observe statistical superiority of state-of-the-art intrinsic UQ methods against simple baselines. We believe that our findings question the very rationale of why we quantify uncertainty and call for a standardized protocol for UQ evaluation based on metrics proven to be relevant for the ML practitioner.
Camouflaged Object Detection via Context-aware Cross-level Fusion
Venue: arXiv cs.CV 2022
Year: 2022

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://github.com/Ben57882/C2FNet-TSCVT.
MagGlove: A Haptic Glove with Movable Magnetic Force for Manipulation Learning
Venue: arXiv cs.HC 2022
Year: 2022

Recently, haptic gloves have been extensively explored for various practical applications, such as manipulation learning. Previous glove devices have different force-driven systems, such as shape memory alloys, servo motors and pneumatic actuators; however, these proposed devices may have difficulty in fast finger movement, easy reproduction, and safety issues. In this study, we propose MagGlove, a novel haptic glove with a movable magnet mechanism that has a linear motor, to solve these issues. The proposed MagGlove device is a compact system on the back of the wearer's hand with high responsiveness, ease of use, and good safety. The proposed device is adaptive with the modification of the magnitude of the current flowing through the coil. Based on our evaluation study, it is verified that the proposed device can achieve finger motion in the given tasks. Therefore, MagGlove can provide flexible support tailored to the wearers' learning levels in manipulation learning tasks.
A Geometric Approach to Passive Localisation
Venue: arXiv cs.MA 2022
Year: 2022

In this paper, we present a geometric framework for the passive localisation of static emitters. The objective is to localise the position of the emitters in a given area by centralised coordination of mobile passive sensors. This framework uses only the geometry of the problem to minimise the maximal bounds of the emitters' locations without using a belief or probability distribution. This geometric approach provides effective boundaries on the emitters' position. It can also be useful in evaluating different decision-making strategies for coordinating mobile passive sensors and complementing statistical methods during the initialisation process. The effectiveness of the geometric approach is shown by designing and evaluating a greedy decision-making strategy, where a sensor selects its future position by minimising the maximum uncertainty on its next measurement using one of the global objective functions. Finally, we analyse and discuss the emergent behaviour and robustness of the proposed algorithms.
Dynamic Shielding for Reinforcement Learning in Black-Box Environments
Venue: arXiv cs.LG 2022
Year: 2022

It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to reduce undesired behaviors during learning, most of these techniques require prior system knowledge, and their applicability is limited. This paper aims to reduce undesired behaviors during learning without requiring any prior system knowledge. We propose dynamic shielding: an extension of a model-based safe RL technique called shielding using automata learning. The dynamic shielding technique constructs an approximate system model in parallel with RL using a variant of the RPNI algorithm and suppresses undesired explorations due to the shield constructed from the learned model. Through this combination, potentially unsafe actions can be foreseen before the agent experiences them. Experiments show that our dynamic shield significantly decreases the number of undesired events during training.
Adaptive sampling for scanning pixel cameras
Venue: arXiv cs.CV 2022
Year: 2022

A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited. It produces data as a sequence of samples extracted from various parts of the scene during the course of a scan. It can provide very detailed images at the expense of samplerates and slow image acquisition time. This paper proposes a new algorithm which allows the sensor to adapt the samplerate over the course of this sequence. This makes it possible to overcome some of these limitations by minimising the bandwidth and time required to image and transmit a scene, while maintaining image quality. We examine applications to image classification and semantic segmentation and are able to achieve similar results compared to a fully sampled input, while using 80% fewer samples
An Automated Testing and Debugging Toolkit for Gate-Level Logic Synthesis Applications
Venue: arXiv cs.SE 2022
Year: 2022

Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. Moreover, when the application fails on a large benchmark, the debugging process may be tedious and time-consuming. In some fields such as compiler construction, automatic testing and debugging tools are well-developed to support developers and provide minimal guarantees on program quality. In this paper, we adapt fuzz testing and delta debugging techniques and specialize them for gate-level netlists commonly used in logic synthesis. Our toolkit improves over similar tools specialized for the AIGER format by supporting other gate-level netlist formats and by allowing a tight integration to provide 10x speed-up. Experimental results show that our fuzzer captures defects in mockturtle, ABC, and LSOracle with 10x smaller testcases and our testcase minimizer extracts minimal failure-inducing cores using 2x fewer oracle calls.
Time to augment self-supervised visual representation learning
Venue: arXiv cs.LG 2022
Year: 2022

Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashion. Such systems learn representations invariant to augmentation operations over images, like cropping or flipping. In contrast, biological vision systems exploit the temporal structure of the visual experience during natural interactions with objects. This gives access to "augmentations" not commonly used in SSL, like watching the same object from multiple viewpoints or against different backgrounds. Here, we systematically investigate and compare the potential benefits of such time-based augmentations during natural interactions for learning object categories. Our results show that time-based augmentations achieve large performance gains over state-of-the-art image augmentations. Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation. Overall, we conclude that time-based augmentations during natural interactions with objects can substantially improve self-supervised learning, narrowing the gap between artificial and biological vision systems.
Adapting the Exploration-Exploitation Balance in Heterogeneous Swarms: Tracking Evasive Targets
Venue: arXiv cs.RO 2022
Year: 2022

There has been growing interest in the use of multi-robot systems in various tasks and scenarios. The main attractiveness of such systems is their flexibility, robustness, and scalability. An often overlooked yet promising feature is system modularity, which offers the possibility to harness agent specialization, while also enabling system-level upgrades. However, altering the agents' capacities can change the exploration-exploitation balance required to maximize the system's performance. Here, we study the effect of a swarm's heterogeneity on its exploration-exploitation balance while tracking multiple fast-moving evasive targets under the Cooperative Multi-Robot Observation of Multiple Moving Targets framework. To this end, we use a decentralized search and tracking strategy with adjustable levels of exploration and exploitation. By indirectly tuning the balance, we first confirm the presence of an optimal balance between these two key competing actions. Next, by substituting slower moving agents with faster ones, we show that the system exhibits a performance improvement without any modifications to the original strategy. In addition, owing to the additional amount of exploitation carried out by the faster agents, we demonstrate that a heterogeneous system's performance can be further improved by reducing an agent's level of connectivity, to favor the conduct of exploratory actions. Furthermore, in studying the influence of the density of swarming agents, we show that the addition of faster agents can counterbalance a reduction in the overall number of agents while maintaining the level of tracking performance. Finally, we explore the challenges of using differentiated strategies to take advantage of the heterogeneous nature of the swarm.
iCub Knows Where You Look: Exploiting Social Cues for Interactive Object Detection Learning
Venue: arXiv cs.RO 2022
Year: 2022

Performing joint interaction requires constant mutual monitoring of own actions and their effects on the other's behaviour. Such an action-effect monitoring is boosted by social cues and might result in an increasing sense of agency. Joint actions and joint attention are strictly correlated and both of them contribute to the formation of a precise temporal coordination. In human-robot interaction, the robot's ability to establish joint attention with a human partner and exploit various social cues to react accordingly is a crucial step in creating communicative robots. Along the social component, an effective human-robot interaction can be seen as a new method to improve and make the robot's learning process more natural and robust for a given task. In this work we use different social skills, such as mutual gaze, gaze following, speech and human face recognition, to develop an effective teacher-learner scenario tailored to visual object learning in dynamic environments. Experiments on the iCub robot demonstrate that the system allows the robot to learn new objects through a natural interaction with a human teacher in presence of distractors.
Membership Inference Attacks via Adversarial Examples
Venue: arXiv cs.LG 2022
Year: 2022

The raise of machine learning and deep learning led to significant improvement in several domains. This change is supported by both the dramatic rise in computation power and the collection of large datasets. Such massive datasets often include personal data which can represent a threat to privacy. Membership inference attacks are a novel direction of research which aims at recovering training data used by a learning algorithm. In this paper, we develop a mean to measure the leakage of training data leveraging a quantity appearing as a proxy of the total variation of a trained model near its training samples. We extend our work by providing a novel defense mechanism. Our contributions are supported by empirical evidence through convincing numerical experiments.
RobotIO: A Python Library for Robot Manipulation Experiments
Venue: arXiv cs.RO 2022
Year: 2022

Setting up robot environments to quickly test newly developed algorithms is still a difficult and time consuming process. This presents a significant hurdle to researchers interested in performing real-world robotic experiments. RobotIO is a python library designed to solve this problem. It focuses on providing common, simple, and well structured python interfaces for robots, grippers, and cameras, etc. These are provided with implementations of these interfaces for common hardware. This enables code using RobotIO to be portable across different robot setups. In terms of architecture, RobotIO is designed to be compatible with OpenAI gym environments, as well as ROS; examples of both of these are provided. The library comes together with a number of helpful tools, such as camera calibration scripts and episode recording functionality that further support algorithm development.
Efficient Search of the k Shortest Non-Homotopic Paths by Eliminating Non-k-Optimal Topologies
Venue: arXiv cs.RO 2022
Year: 2022

An efficient algorithm to solve the $k$ shortest non-homotopic path planning ($k$-SNPP) problem in a 2D environment is proposed in this paper. Motivated by accelerating the inefficient exploration of the homotopy-augmented space of the 2D environment, our fundamental idea is to identify the non-$k$-optimal path topologies as early as possible and terminate the pathfinding along them. This is a non-trivial practice because it has to be done at an intermediate state of the path planning process when locally shortest paths have not been fully constructed. In other words, the paths to be compared have not rendezvoused at the goal location, which makes the homotopy theory, modelling the spatial relationship among the paths having the same endpoint, not applicable. This paper is the first work that develops a systematic distance-based topology simplification mechanism to solve the $k$-SNPP task, whose core contribution is to assert the distance-based order of non-homotopic locally shortest paths before constructing them. If the order can be predicted, then those path topologies having more than $k$ better topologies are proven free of the desired $k$ paths and thus can be safely discarded during the path planning process. To this end, a hierarchical topological tree is proposed as an implementation of the mechanism, whose nodes are proven to expand in non-homotopic directions and edges (collision-free path segments) are proven locally shortest. With efficient criteria that observe the order relations between partly constructed locally shortest paths being imparted into the tree, the tree nodes that expand in non-$k$-optimal topologies will not be expanded. As a result, the computational time for solving the $k$-SNPP problem is reduced by near two orders of magnitude.
Towards Mapping and Assessing Sidewalk Accessibility Across Sociocultural and Geographic Contexts
Venue: arXiv cs.HC 2022
Year: 2022

Despite the important role of sidewalks in supporting mobility, accessibility, and public health, there is a lack of high-quality datasets and corresponding analyses on sidewalk existence and condition. Our work explores a twofold vision: first, to develop scalable mechanisms to locate and assess sidewalks in cities across the world, and second, to use this data to support new urban analyses and mobility tools. We report on two preliminary urban science explorations enabled by our approach: exploring geo-spatial patterns and key correlates of sidewalk accessibility and examining differences in sidewalk infrastructure across regions.