OpenRaiser/Intern-Atlas huggingface dataset:
A characterization of polynomial time computable functions from the integers to the reals using discrete ordinary differential equations
Venue: arXiv cs.CC 2022
Year: 2022

In a recent article, the class of functions from the integers to the integers computable in polynomial time has been characterized using discrete ordinary differential equations (ODE), also known as finite differences. Doing so, we pointed out the fundamental role of linear (discrete) ODEs and classical ODE tools such as changes of variables to capture computability and complexity measures, or as a tool for programming. In this article, we extend the approach to a characterization of functions from the integers to the reals computable in polynomial time in the sense of computable analysis. In particular, we provide a characterization of such functions in terms of the smallest class of functions that contains some basic functions, and that is closed by composition, linear length ODEs, and a natural effective limit schema.
Improving Aircraft Localization: Experiences and Lessons Learned from an Open Competition
Venue: arXiv cs.NI 2022
Year: 2022

Knowledge about the exact positioning of aircraft is crucial in many settings. Consequently, the opportunistic and independent localization of aircraft based on their communication has been a longstanding problem and subject of much research. Originating from military settings, the capability to conduct aircraft localization has moved first towards the institutional civil aviation domain and can now be undertaken by anyone who has access to multiple cheap software-defined radios. Based on these technological developments, many crowdsourced sensor networks have sprung up, which collect air traffic control data in order to localize aircraft and visualize the airspace. Due to their unplanned and uncontrolled deployment and heterogeneous receiver technology traditional solutions to the Aircraft Localization Problem (ALP) can either not be applied or do not perform in a satisfactory manner. In order to deal with this issue and to find novel approaches to the ALP itself, we have designed and executed a multi-stage open competition, conducted both offline and online. In this paper, we discuss the setup, experiences, and lessons learned from this Aircraft Localization Competition. We report from a diverse set of technical approaches, comprising 72 participating teams over three stages. The participants reached a localization accuracy of up to 25 meters in a setting with fully GPS-synchronized receivers and 78 meters in a largely unsynchronized receiver setting. These results constitute a significant improvement over the previous baseline used in the OpenSky research network. We compare the results of the study, discuss the current state of the art, and highlight the areas that, based on our experience from organizing a competition, need to be improved for optimal adoption of the competitive approach for other scenarios.
A Proactive Uncertainty driven Model for Tasks Offloading
Venue: arXiv cs.NI 2022
Year: 2022

The ever-increasing demands of end-users on the Internet of Things (IoT), often cause great congestion in the nodes that serve their requests. Therefore, the problem of node overloading arises. In this article we attempt to solve the problem of heavy traffic in a node, by proposing a mechanism that keeps the node from overloading, regardless of the load entering in it, and which takes into consideration both the priority and the task demand. More specifically, we introduce a proactive, self-healing mechanism that utilizes fuzzy systems, in combination to a non-parametric statistic method. Through our approach, we manage to ensure the uninterrupted service of high demand or priority tasks, regardless of the load the node may receive, based on a proactive approach. Also, we ensure the fastest possible result delivery to the requestors, through the high priority and high demand sensitive mechanism. A series of experimental scenarios are used to evaluate the performance of the suggested model, while we present the relevant numerical results.
Mixed-domain Training Improves Multi-Mission Terrain Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence. Martian terrain segmentation has been critical for rover navigation and hazard avoidance to perform further exploratory tasks, e.g. soil sample collection and searching for organic compounds. Current Martian terrain segmentation models require a large amount of labeled data to achieve acceptable performance, and also require retraining for deployment across different domains, i.e. different rover missions, or different tasks, i.e. geological identification and navigation. This research proposes a semi-supervised learning approach that leverages unsupervised contrastive pretraining of a backbone for a multi-mission semantic segmentation for Martian surfaces. This model will expand upon the current Martian segmentation capabilities by being able to deploy across different Martian rover missions for terrain navigation, by utilizing a mixed-domain training set that ensures feature diversity. Evaluation results of using average pixel accuracy show that a semi-supervised mixed-domain approach improves accuracy compared to single domain training and supervised training by reaching an accuracy of 97% for the Mars Science Laboratory's Curiosity Rover and 79.6% for the Mars 2020 Perseverance Rover. Further, providing different weighting methods to loss functions improved the models correct predictions for minority or rare classes by over 30% using the recall metric compared to standard cross-entropy loss. These results can inform future multi-mission and multi-task semantic segmentation for rover missions in a data-efficient manner.
Safe Balancing Control of a Soft Legged Robot
Venue: arXiv cs.RO 2022
Year: 2022

Legged robots constructed from soft materials are commonly claimed to demonstrate safer, more robust environmental interactions than their rigid counterparts. However, this motivating feature of soft robots requires more rigorous development for comparison to rigid locomotion. This article presents a soft legged robot platform, Horton, and a feedback control system with safety guarantees on some aspects of its operation. The robot is constructed using a series of soft limbs, actuated by thermal shape memory alloy (SMA) wire muscles, with sensors for its position and its actuator temperatures. A supervisory control scheme maintains safe actuator states during the operation of a separate controller for the robot's pose. Experiments demonstrate that Horton can lift its leg and maintain a balancing stance, a precursor to locomotion. The supervisor is verified in hardware via a human interaction test during balancing, keeping all SMA muscles below a temperature threshold. This work represents the first demonstration of a safety-verified feedback system on any soft legged robot.
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
Venue: arXiv cs.CV 2022
Year: 2022

Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny ship annotations. Each image covers about 10000 square kilometers of area with 10000X10000 pixels. Considering the extreme characteristics (e.g., small, dim, changeable) of those tiny ships in such challenging scenes, we propose a multi-level TransUNet (MTU-Net) in this paper. Specifically, we design a Vision Transformer (ViT) Convolutional Neural Network (CNN) hybrid encoder to extract multi-level features. Local feature maps are first extracted by several convolution layers and then fed into the multi-level feature extraction module (MVTM) to capture long-distance dependency. We further propose a copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning based SIRST methods in terms of probability of detection, false alarm rate and intersection over union.
A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery
Venue: arXiv cs.LG 2022
Year: 2022

Traffic data chronically suffer from missing and corruption, leading to accuracy and utility reduction in subsequent Intelligent Transportation System (ITS) applications. Noticing the inherent low-rank property of traffic data, numerous studies formulated missing traffic data recovery as a low-rank tensor completion (LRTC) problem. Due to the non-convexity and discreteness of the rank minimization in LRTC, existing methods either replaced rank with convex surrogates that are quite far away from the rank function or approximated rank with nonconvex surrogates involving many parameters. In this study, we proposed a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data recovery, in which a log-based relaxation term was designed to approximate tensor algebraic rank. Moreover, previous studies usually assumed the observations are reliable without any outliers. Therefore, we extended the TC-PFNC to a robust version (RTC-PFNC) by modeling potential traffic data outliers, which can recover the missing value from partial and corrupted observations and remove the anomalies in observations. The numerical solutions of TC-PFNC and RTC-PFNC were elaborated based on the alternating direction multiplier method (ADMM). The extensive experimental results conducted on four real-world traffic data sets demonstrated that the proposed methods outperform other state-of-the-art methods in both missing and corrupted data recovery. The code used in this paper is available at: https://github.com/YoungHe49/T-ITSPFNC.
A Learning-based Honeypot Game for Collaborative Defense in UAV Networks
Venue: arXiv cs.GT 2022
Year: 2022

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fairness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.
Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems
Venue: arXiv cs.LG 2022
Year: 2022

Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications. In this work, the possibility of modeling the blood glucose level of diabetic patients purely on the basis of measured data is described. A combination of the influencing variables insulin and calories are used to find an interpretable model. The absorption speed of external substances in the human body depends strongly on external influences, which is why time-shifts are added for the influencing variables. The focus is put on identifying the best timeshifts that provide robust models with good prediction accuracy that are independent of other unknown external influences. The modeling is based purely on the measured data using Sparse Identification of Nonlinear Dynamics. A differential equation is determined which, starting from an initial value, simulates blood glucose dynamics. By applying the best model to test data, we can show that it is possible to simulate the long-term blood glucose dynamics using differential equations and few, influencing variables.
Rethinking Blur Synthesis for Deep Real-World Image Deblurring
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network architecture design. Deblurring models trained on existing synthetic datasets perform poorly on real blurry images due to domain shift. To reduce the domain gap between synthetic and real domains, we propose a novel realistic blur synthesis pipeline to simulate the camera imaging process. As a result of our proposed synthesis method, existing deblurring models could be made more robust to handle real-world blur. Furthermore, we develop an effective deblurring model that captures non-local dependencies and local context in the feature domain simultaneously. Specifically, we introduce the multi-path transformer module to UNet architecture for enriched multi-scale features learning. A comprehensive experiment on three real-world datasets shows that the proposed deblurring model performs better than state-of-the-art methods.
An Efficient Multitask Learning Architecture for Affective Vocal Burst Analysis
Venue: arXiv cs.SD 2022
Year: 2022

Affective speech analysis is an ongoing topic of research. A relatively new problem in this field is the analysis of vocal bursts, which are nonverbal vocalisations such as laughs or sighs. Current state-of-the-art approaches to address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT features. In this paper, we investigate the use of the wav2vec successor data2vec in combination with a multitask learning pipeline to tackle different analysis problems at once. To assess the performance of our efficient multitask learning architecture, we participate in the 2022 ACII Affective Vocal Burst Challenge, showing that our approach substantially outperforms the baseline established there in three different subtasks.
CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognition
Venue: arXiv cs.CL 2022
Year: 2022

People's conduct and reactions are driven by their emotions. Online social media is becoming a great instrument for expressing emotions in written form. Paying attention to the context and the entire sentence help us to detect emotion from texts. However, this perspective inhibits us from noticing some emotional words or phrases in the text, particularly when the words express an emotion implicitly rather than explicitly. On the other hand, focusing only on the words and ignoring the context results in a distorted understanding of the sentence meaning and feeling. In this paper, we propose a framework that analyses text at both the sentence and word levels. We name it CEFER (Context and Emotion embedded Framework for Emotion Recognition). Our four approach facets are to extracting data by considering the entire sentence and each individual word simultaneously, as well as implicit and explicit emotions. The knowledge gained from these data not only mitigates the impact of flaws in the preceding approaches but also it strengthens the feature vector. We evaluate several feature spaces using BERT family and design the CEFER based on them. CEFER combines the emotional vector of each word, including explicit and implicit emotions, with the feature vector of each word based on context. CEFER performs better than the BERT family. The experimental results demonstrate that identifying implicit emotions are more challenging than detecting explicit emotions. CEFER, improves the accuracy of implicit emotion recognition. According to the results, CEFER perform 5% better than the BERT family in recognizing explicit emotions and 3% in implicit.
Leveraging machine learning for less developed languages: Progress on Urdu text detection
Venue: arXiv cs.CV 2022
Year: 2022

Text detection in natural scene images has applications for autonomous driving, navigation help for elderly and blind people. However, the research on Urdu text detection is usually hindered by lack of data resources. We have developed a dataset of scene images with Urdu text. We present the use of machine learning methods to perform detection of Urdu text from the scene images. We extract text regions using channel enhanced Maximally Stable Extremal Region (MSER) method. First, we classify text and noise based on their geometric properties. Next, we use a support vector machine for early discarding of non-text regions. To further remove the non-text regions, we use histogram of oriented gradients (HoG) features obtained and train a second SVM classifier. This improves the overall performance on text region detection within the scene images. To support research on Urdu text, We aim to make the data freely available for research use. We also aim to highlight the challenges and the research gap for Urdu text detection.
Monitoring ROS2: from Requirements to Autonomous Robots
Venue: arXiv cs.RO 2022
Year: 2022

Runtime verification (RV) has the potential to enable the safe operation of safety-critical systems that are too complex to formally verify, such as Robot Operating System 2 (ROS2) applications. Writing correct monitors can itself be complex, and errors in the monitoring subsystem threaten the mission as a whole. This paper provides an overview of a formal approach to generating runtime monitors for autonomous robots from requirements written in a structured natural language. Our approach integrates the Formal Requirement Elicitation Tool (FRET) with Copilot, a runtime verification framework, through the Ogma integration tool. FRET is used to specify requirements with unambiguous semantics, which are then automatically translated into temporal logic formulae. Ogma generates monitor specifications from the FRET output, which are compiled into hard-real time C99. To facilitate integration of the monitors in ROS2, we have extended Ogma to generate ROS2 packages defining monitoring nodes, which run the monitors when new data becomes available, and publish the results of any violations. The goal of our approach is to treat the generated ROS2 packages as black boxes and integrate them into larger ROS2 systems with minimal effort.
How Far Does the Predictive Decision Impact the Software Project? The Cost, Service Time, and Failure Analysis from a Cross-Project Defect Prediction Model
Venue: arXiv cs.SE 2022
Year: 2022

Context: Cross-project defect prediction (CPDP) models are being developed to optimize the testing resources. Objectives: Proposing an ensemble classification framework for CPDP as many existing models are lacking with better performances and analysing the main objectives of CPDP from the outcomes of the proposed classification framework. Method: For the classification task, we propose a bootstrap aggregation based hybrid-inducer ensemble learning (HIEL) technique that uses probabilistic weighted majority voting (PWMV) strategy. To know the impact of HIEL on the software project, we propose three project-specific performance measures such as percent of perfect cleans (PPC), percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the predictions to calculate the amount of saved cost, remaining service time, and percent of the failures in the target project. Results: On many target projects from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure. In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the HIEL model. Conclusion: For better predictions, we recommend ensemble learning approaches for the CPDP models. And, to estimate the benefits from the CPDP models, we recommend the above project-specific performance measures.
Design Guidelines for Improving User Experience in Industrial Domain-Specific Modelling Languages
Venue: arXiv cs.SE 2022
Year: 2022

Domain-specific modelling languages (DSMLs) help practitioners solve modelling challenges specific to various domains. As domains grow more complex and heterogeneous in nature, industrial practitioners often face challenges in the usability of graphical DSMLs. There is still a lack of guidelines that industrial language engineers should consider for improving the user experience (UX) of these practitioners. The overall topic of UX is vast and subjective, and general guidelines and definitions of UX are often overly generic or tied to specific technological spaces. To solve this challenge, we leverage existing design principles and standards of human-centred design and UX in general and propose definitions and guidelines for UX and user experience design (UXD) aspects in graphical DSMLs. In this paper, we categorize the key UXD aspects, primarily based on our experience in developing industrial DSMLs, that language engineers should consider during graphical DSML development. Ultimately, these UXD guidelines help to improve the general usability of industrial DSMLs and support language engineers in developing better DSMLs that are independent of graphical modelling tools and more widely accepted by their users.