OpenRaiser/Intern-Atlas huggingface dataset:
Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories
Venue: arXiv cs.LG 2022
Year: 2022

Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.
Focal-PETR: Embracing Foreground for Efficient Multi-Camera 3D Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

The dominant multi-camera 3D detection paradigm is based on explicit 3D feature construction, which requires complicated indexing of local image-view features via 3D-to-2D projection. Other methods implicitly introduce geometric positional encoding and perform global attention (e.g., PETR) to build the relationship between image tokens and 3D objects. The 3D-to-2D perspective inconsistency and global attention lead to a weak correlation between foreground tokens and queries, resulting in slow convergence. We propose Focal-PETR with instance-guided supervision and spatial alignment module to adaptively focus object queries on discriminative foreground regions. Focal-PETR additionally introduces a down-sampling strategy to reduce the consumption of global attention. Due to the highly parallelized implementation and down-sampling strategy, our model, without depth supervision, achieves leading performance on the large-scale nuScenes benchmark and a superior speed of 30 FPS on a single RTX3090 GPU. Extensive experiments show that our method outperforms PETR while consuming 3x fewer training hours. The code will be made publicly available.
A systematic literature review on Robotic Process Automation security
Venue: arXiv cs.RO 2022
Year: 2022

The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution.
Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit
Venue: arXiv cs.NE 2022
Year: 2022

The General Associative Memory Model (GAMM) has a constant state-dependant energy surface that leads the output dynamics to fixed points, retrieving single memories from a collection of memories that can be asynchronously preloaded. We introduce a new class of General Sequential Episodic Memory Models (GSEMM) that, in the adiabatic limit, exhibit temporally changing energy surface, leading to a series of meta-stable states that are sequential episodic memories. The dynamic energy surface is enabled by newly introduced asymmetric synapses with signal propagation delays in the network's hidden layer. We study the theoretical and empirical properties of two memory models from the GSEMM class, differing in their activation functions. LISEM has non-linearities in the feature layer, whereas DSEM has non-linearity in the hidden layer. In principle, DSEM has a storage capacity that grows exponentially with the number of neurons in the network. We introduce a learning rule for the synapses based on the energy minimization principle and show it can learn single memories and their sequential relationships online. This rule is similar to the Hebbian learning algorithm and Spike-Timing Dependent Plasticity (STDP), which describe conditions under which synapses between neurons change strength. Thus, GSEMM combines the static and dynamic properties of episodic memory under a single theoretical framework and bridges neuroscience, machine learning, and artificial intelligence.
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Venue: arXiv cs.LG 2022
Year: 2022

Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an E-commerce platform with scarce records or diagnoses for rare diseases on a patient similarity graph. To tackle such challenging label scarcity issues in the non-Euclidean graph domain, meta-learning has become a successful and predominant paradigm. More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed. In this work, we empirically demonstrate the potential of an alternative framework, \textit{Transductive Linear Probing}, that transfers pretrained node embeddings, which are learned from graph contrastive learning methods. We further extend the setting of few-shot node classification from standard fully supervised to a more realistic self-supervised setting, where meta-learning methods cannot be easily deployed due to the shortage of supervision from training classes. Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. We hope this work can shed new light on few-shot node classification problems and foster future research on learning from scarcely labeled instances on graphs.
Accelerating Self-Supervised Learning via Efficient Training Strategies
Venue: arXiv cs.CV 2022
Year: 2022

Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations. While the performance gap between supervised and self-supervised has been narrowing, the time for training self-supervised deep networks remains an order of magnitude larger than its supervised counterparts, which hinders progress, imposes carbon cost, and limits societal benefits to institutions with substantial resources. Motivated by these issues, this paper investigates reducing the training time of recent self-supervised methods by various model-agnostic strategies that have not been used for this problem. In particular, we study three strategies: an extendable cyclic learning rate schedule, a matching progressive augmentation magnitude and image resolutions schedule, and a hard positive mining strategy based on augmentation difficulty. We show that all three methods combined lead up to 2.7 times speed-up in the training time of several self-supervised methods while retaining comparable performance to the standard self-supervised learning setting.
Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge
Venue: arXiv cs.RO 2022
Year: 2022

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.
Development of Personalized Sleep Induction System based on Mental States
Venue: arXiv cs.HC 2022
Year: 2022

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection
Venue: arXiv cs.CV 2022
Year: 2022

Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Existing methods are unable to adapt to these difficult cases while maintaining acceptable performance. In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time. CircleNet is implemented as a set of feature pyramids and uses weight sharing path augmentation for better feature fusion. It targets at reciprocating feature adaptation and iterative object detection using multiple top-down and bottom-up pathways. To take full advantage of the feature adaptation capability in CircleNet, we design an instance decomposition training strategy to focus on detecting pedestrian instances of various resolutions and different occlusion levels in each cycle. Specifically, CircleNet implements feature ensemble with the idea of hard negative boosting in an end-to-end manner. Experiments on two pedestrian detection datasets, Caltech and CityPersons, show that CircleNet improves the performance of occluded and low-resolution pedestrians with significant margins while maintaining good performance on normal instances.
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design
Venue: arXiv cs.CV 2022
Year: 2022

Adversarial attacks on thermal infrared imaging expose the risk of related applications. Estimating the security of these systems is essential for safely deploying them in the real world. In many cases, realizing the attacks in the physical space requires elaborate special perturbations. These solutions are often \emph{impractical} and \emph{attention-grabbing}. To address the need for a physically practical and stealthy adversarial attack, we introduce \textsc{HotCold} Block, a novel physical attack for infrared detectors that hide persons utilizing the wearable Warming Paste and Cooling Paste. By attaching these readily available temperature-controlled materials to the body, \textsc{HotCold} Block evades human eyes efficiently. Moreover, unlike existing methods that build adversarial patches with complex texture and structure features, \textsc{HotCold} Block utilizes an SSP-oriented adversarial optimization algorithm that enables attacks with pure color blocks and explores the influence of size, shape, and position on attack performance. Extensive experimental results in both digital and physical environments demonstrate the performance of our proposed \textsc{HotCold} Block. \emph{Code is available: \textcolor{magenta}{https://github.com/weihui1308/HOTCOLDBlock}}.
Searching for Effective Multilingual Fine-Tuning Methods: A Case Study in Summarization
Venue: arXiv cs.CL 2022
Year: 2022

Recently, a large number of tuning strategies have been proposed to adapt pre-trained language models to downstream tasks. In this paper, we perform an extensive empirical evaluation of various tuning strategies for multilingual learning, particularly in the context of text summarization. Specifically, we explore the relative advantages of three families of multilingual tuning strategies (a total of five models) and empirically evaluate them for summarization over 45 languages. Experimentally, we not only established a new state-of-the-art on the XL-Sum dataset but also derive a series of observations that hopefully can provide hints for future research on the design of multilingual tuning strategies.
A Light-Weight LiDAR-Inertial SLAM System with Loop Closing
Venue: arXiv cs.RO 2022
Year: 2022

In this work, we propose a lightweight integrated LiDAR-Inertial SLAM system with high efficiency and a great loop closure capacity. We found that the current State-of-the-art LiDAR-Inertial SLAM system has poor performance in loop closure. The LiDAR-Inertial SLAM system often fails with the large drifting and suffers from limited efficiency when faced with large-scale circumstances. In this work, firstly, to improve the speed of the whole LiDAR-Inertial SLAM system, we have proposed a new data structure of the sparse voxel-hashing to enhance the efficiency of the LiDAR-Inertial SLAM system. Secondly, to improve the point cloud-based localization performance, we have integrated the loop closure algorithms to improve the localization performance. Extensive experiments on the real-scene large-scale complicated circumstances demonstrate the great effectiveness and robustness of the proposed LiDAR-Inertial SLAM system.
The Viability of Continuous Experimentation in Early-Stage Software Startups: A Descriptive Multiple-Case Study
Venue: arXiv cs.SE 2022
Year: 2022

Background: Continuous experimentation (CE) has been proposed as a data-driven approach to software product development. Several challenges with this approach have been described in large organisations, but its application in smaller companies with early-stage products remains largely unexplored. Aims: The goal of this study is to understand what factors could affect the adoption of CE in early-stage software startups. Method: We present a descriptive multiple-case study of five startups in Finland which differ in their utilisation of experimentation. Results: We find that practices often mentioned as prerequisites for CE, such as iterative development and continuous integration and delivery, were used in the case companies. CE was not widely recognised or used as described in the literature. Only one company performed experiments and used experimental data systematically. Conclusions: Our study indicates that small companies may be unlikely to adopt CE unless 1) at least some company employees have prior experience with the practice, 2) the company's limited available resources are not exceeded by its adoption, and 3) the practice solves a problem currently experienced by the company, or the company perceives almost immediate benefit of adopting it. We discuss implications for advancing CE in early-stage startups and outline directions for future research on the approach.
Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing
Venue: arXiv cs.CV 2022
Year: 2022

Image-text retrieval in remote sensing aims to provide flexible information for data analysis and application. In recent years, state-of-the-art methods are dedicated to ``scale decoupling'' and ``semantic decoupling'' strategies to further enhance the capability of representation. However, these previous approaches focus on either the disentangling scale or semantics but ignore merging these two ideas in a union model, which extremely limits the performance of cross-modal retrieval models. To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval. Specifically, we design the Bidirectional Scale Decoupling (BSD) module, which exploits Salience Feature Extraction (SFE) and Salience-Guided Suppression (SGS) units to adaptively extract potential features and suppress cumbersome features at other scales in a bidirectional pattern to yield different scale clues. Besides, we design the Label-supervised Semantic Decoupling (LSD) module by leveraging the category semantic labels as prior knowledge to supervise images and texts probing significant semantic-related information. Finally, we design a Semantic-guided Triple Loss (STL), which adaptively generates a constant to adjust the loss function to improve the probability of matching the same semantic image and text and shorten the convergence time of the retrieval model. Our proposed SSJDN outperforms state-of-the-art approaches in numerical experiments conducted on four benchmark remote sensing datasets.
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
Venue: arXiv cs.LG 2022
Year: 2022

Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.
IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks
Venue: arXiv cs.IT 2022
Year: 2022

An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results demonstrate that TAS-OSTBC system with zero-forcing precoding technique outperforms the conventional TAS system with OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly im-proves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the error performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better BER performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.
SyReC Synthesizer: An MQT tool for synthesis of reversible circuits
Venue: arXiv cs.AR 2022
Year: 2022

Reversible circuits form the backbone for many promising emerging technologies such as quantum computing, low power/adiabatic design, encoder/decoder devices, and several other applications. In the recent years, the scalable synthesis of such circuits has gained significant attention. In this work, we present the SyReC Synthesizer, a synthesis tool for reversible circuits based on the hardware description language SyReC. SyReC allows to describe reversible functionality at a high level of abstraction. The provided SyReC Synthesizer then realizes this functionality in a push-button fashion. Corresponding options allow for a trade-off between the number of needed circuit signals/lines (relevant, e.g., for quantum computing in which every circuit line corresponds to a qubit) and the respectively needed gates (corresponding to the circuit's costs). Furthermore, the tool allows to simulate the resulting circuit as well as to determine the gate costs of it. The SyReC Synthesizer is available as an open-source software package at https://github.com/cda-tum/syrec as part of the Munich Quantum Toolkit (MQT).