Reviewerly/exHarmony huggingface dataset:
Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems

Robust motion planning entails computing a global motion plan that is safe under all possible uncertainty realizations, be it in the system dynamics, the robot’s initial position, or with respect to external disturbances. Current approaches for robust motion planning either lack theoretical guarantees, or make restrictive assumptions on the system dynamics and uncertainty distributions. In this paper, we address these limitations by proposing the robust rapidly-exploring random-tree (Robust-RRT) algorithm, which integrates forward reachability analysis directly into sampling-based control trajectory synthesis. We prove that Robust-RRT is probabilistically complete (PC) for nonlinear Lipschitz continuous dynamical systems with bounded uncertainty. In other words, Robust-RRT eventually finds a robust motion plan that is feasible under all possible uncertainty realizations assuming such a plan exists. Our analysis applies even to unstable systems that admit only short-horizon feasible plans; this is because we explicitly consider the time evolution of reachable sets along control trajectories. To the best of our knowledge, this is the most general PC proof for robust sampling-based motion planning, in terms of the types of uncertainties and dynamical systems it can handle. Considering that an exact computation of reachable sets can be computationally expensive for some dynamical systems, we incorporate sampling-based reachability analysis into Robust-RRT and demonstrate our robust planner on nonlinear, underactuated, and hybrid systems.
A Long-Term Perspective of the Internet Susceptibility to Covert Channels

Modern malware now takes advantage of information hiding to avoid detection and implement various offensive and elusive mechanisms. The creation of covert channels, i.e., parasitic communication paths nested within legitimate traffic, is becoming a prime tool to exfiltrate sensitive information or retrieve additional malicious payloads. Despite their impact on the security of the Internet, a precise evaluation of the susceptibility of network traffic to covert channels is missing. Moreover, since the hiding capacity is driven by the targeted protocol and its diffusion, understanding their evolution is vital to engineering countermeasures. To fill such a research gap, this article discusses how the susceptibility to information hiding mechanisms of major Internet protocols evolved from 1999 to 2021. Results suggest that a periodic quantification of the phenomena should be part of the continuous cyber security monitoring.
Inclusive HRI II

Diversity, equality, and inclusion (DEI) are critical factors that need to be considered when developing AI and robotic technologies for people. The lack of such considerations exacerbates and can also perpetuate existing forms of discrimination and biases in society for years to come. Although concerns have already been voiced around the globe, there is an urgent need to take action within the human-robot interaction (HRI) community. This workshop contributes to filling the gap by providing a platform in which to share experiences and research insights on identifying, addressing, and integrating DEI considerations in HRI. With respect to last year, this year the workshop will further engage participants on the problem of sampling biases through hands-on co-design activities for mitigating inequity and exclusion within the field of HRI.
Unsupervised domain adaptation for regression using dictionary learning

Unsupervised domain adaptation aims to generalize the knowledge learned on a labeled source domain across an unlabeled target domain. Most of existing unsupervised approaches are feature-based methods that seek to find domain invariant features. Despite their wide applications, these approaches proved to have some limitations especially in regression tasks. In this paper, we study the problem of unsupervised domain adaptation for regression tasks. We highlight the obstacles faced in regression compared to a classification task in terms of sensitivity to the scattering of data in feature space. We take this issue and propose a new unsupervised domain adaptation approach based on dictionary learning. We seek to learn a dictionary on source data and follow an optimal direction trajectory to minimize the residue of the reconstruction of the target data with the same dictionary. For stable training of a neural network, we provide a robust implementation of a projected gradient descent dictionary learning framework, which allows to have a backpropagation friendly end-to-end method. Experimental results show that the proposed method outperforms significantly most of state-of-the-art methods on several well-known benchmark datasets, especially when transferring knowledge from synthetic to real domains.
DarkDeblur: Learning single-shot image deblurring in low-light condition

Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environments.
A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems

Intrusion detection systems are an essential part of any cybersecurity architecture. These systems are critical in defending networks against a variety of security threats. In recent years, deep neural networks have proved their performance and efficiency in various machine learning tasks, including intrusion detection. However, it is shown that deep learning models are highly vulnerable to adversarial attacks. This paper proposes a new approach for performing an adversarial attack against deep learning-based malicious network activity classification. We use the Jacobian Saliency Map to find the best group of features, with different features and perturbation magnitude, to generate adversarial examples. We evaluate our method on three CIC-IDS2017, CIC-IDS2018, and CIC-DDoS2019 datasets. Our experiments show that our proposed method can achieve better performance while using fewer features in adversarial sample generation than other attacks that depend on a higher number of features. Our technique can generate adversarial samples for more than 18% of samples in CIC-IDS2017, 15% of samples in CIC-IDS2018, and 14% of samples in CIC-DDoS2019, using only three features and 0.1 as the perturbation magnitude. We do a deeper analysis of the attack based on its parameters, distance metrics, and the target model performance. Also, an evaluation model with three criteria, including success rates of the best feature sets, average confidence of the adversarial class, and adversarial samples transferability, is used in our analysis.
Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system’s usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To address these usability-related issues, in this work we focus on the cognitive dimension of users’ perception of explanations and XAI systems. We investigated these perceptions in light of their relationship with users’ characteristics (e.g., expertise) through a questionnaire-based user study involved 44 cardiology residents and specialists in an AI-supported ECG reading task. Our results point to the relevance and correlation of the dimensions of trust, perceived quality of explanations, and tendency to defer the decision process to automation (i.e., technology dominance). This contribution calls for the evaluation of AI-based support systems from a human–AI interaction-oriented perspective, laying the ground for further investigation of XAI and its effects on decision making and user experience.
Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.
Status quo and change characteristics of groundwater resources pollution in the Hami region based on sustainable development strategies

Abstract Water shortage is a universal resource problem worldwide, especially in arid and semi-arid regions. The Hami Basin is one of the most water-scarce areas in China, with low average annual precipitation and high evaporation. In order to effectively monitor groundwater problems, this paper proposes a study on the status and change characteristics of groundwater resource pollution in the Hami area based on sustainable development strategies, aiming to study the changes in groundwater in the Hami area and propose corresponding measures to reduce pollution and save water resources. The method of this article is to introduce the groundwater quality evaluation method, analyze the environmental carrying capacity and then study the evolution process of the groundwater environment in the Hami area. The function of the method is to assess the ecological risk of groundwater and the risk of groundwater pollution, and to analyze the changes in the carrying capacity of the local environmental system. This paper uses groundwater detection experiments to detect the current status of groundwater resource pollution and the characteristics of groundwater changes in the Hami area, and establish a groundwater numerical model in the Hami area.
Improved TV Image Denoising over Inverse Gradient

Noise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising effect is less than ideal. As a result, in this paper, an improved image-denoising algorithm is proposed based on the TV model, which effectively solves the above problems. The L1 regularization term can make the solution generated by the model sparser, thus facilitating the recovery of high-quality images. Reducing the number of estimated parameters, while using the inverse gradient to estimate the regularization parameters, enables the parameters to achieve global adaption and improves the denoising effect of the model in combination with the TV regularization term. The split Bregman iteration method is used to decouple the model into several related subproblems, and the solutions of the coordinated subproblems are derived as optimal solutions. It is also shown that the solution of the model converges to a Karush–Kuhn–Tucker point. Experimental results show that the algorithm in this paper is more effective in both preserving image texture structure and suppressing image noise.
Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags

In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.
NST: A nuclei segmentation method based on transformer for gastrointestinal cancer pathological images

Gastrointestinal cancer is a prevalent disease, and analyzing pathological images is crucial for its diagnosis and treatment. Considering the characteristics of pathological images, we propose a novel cell nucleus segmentation method based on Vision Transform, namely NST. Our proposed method consists of a Deformable Attention Transformer (DAT) encoder capturing four different levels of feature; a Coordinate Attention Module (CAM) handling shallow-level features in different dimensions; a Dense Aggregation Module (DAM) integrating deep-level features; and a Similarity Aggregation Module (SAM) combining features to generate pixel-level segmentation predictions. Meanwhile, to fill the data gap in the field of cell nucleus segmentation, we acquire, annotate and present a new dataset of gastrointestinal cancer pathology images named GCNS. Moreover, we conducted a series of experiments, and the experimental results indicate that our proposed method achieves state-of-the-art performance, as high as a 0.725 Dice Score on the GCNS dataset.
Do Technological Innovation and Financial Development Affect Inequality? Evidence from BRICS Countries

While technological innovation and financial development are broadly credited as important drivers of economic growth of developed nations, their impact on inequality (especially in emerging economies) remains understudied. Thus, the objective of this study is to investigate the impact of technological innovation and financial development on income inequality in BRICS (Brazil, Russia, India, China and South Africa) countries using panel dynamic ordinary least squares (PDOLS) and panel fully modified ordinary least squares (PFMOLS) with annual data sourced from the Standardized World Income Inequality Database, International Monetary Fund (IMF) and World Bank (1990–2017). The results suggest that technological innovation increases income inequality in the BRICS nations, while financial development has an income reducing effect on inequality. Our results are robust, using alternative estimation with various sub-indicators of financial development (such as financial markets and financial institution), including other measures proxied by access to credit provided by commercial banks. The study’s results have important implications for policy and practice in the BRICS countries. By providing a nuanced understanding of the relationship between technological innovation, financial development and inequality, the study will inform the design and implementation of policies aimed at reducing inequality and promoting inclusive growth in these emerging economies.
Robust Interactive Multimodel INS/DVL Intergrated Navigation System With Adaptive Model Set

A robust interactive multiple model (RIMM) algorithm with the adaptive model set is proposed to improve the performance of inertial navigation system (INS) and Doppler velocity log (DVL) integrated navigation system under a complex measurement environment with undesirable heavy-tailed non-Gaussian noise. Specifically, an improved Huber kernel function is applied to the robust error state Kalman filter (ESKF) for its benefit of better resisting larger measurement outliers. In addition, a flexible adaptive model set update strategy is proposed where the model set is determined by the current and historical measurement information stored in the sliding window. Also, for the model set update, the main model is no longer fixed, and it will be determined based on the probability weights corresponding to each model. This innovative RIMM algorithm is compared with ESKF, interactive multiple model (IMM), and hybrid IMM (HIMM) through simulations, autonomous underwater vehicle (AUV) lake trial, and semiphysical simulations. Experimental results show that our proposed algorithm has outstanding accuracy and robustness under a heavy-tailed non-Gaussian noise environment.
TILPDeep: A Lightweight Deep Learning Technique for Handwritten Transformed Invariant Pashto Text Recognition

Pashto is the native language of Afghanistan and one of Pakistan’s most essential and regional languages. The Pashto language has a vast number of native speakers who live in various parts of the world. The handwritten Pashto textual trajectories are hard to recognize and detect due to the cursive style and handwriting variation. The transformation behaviour, i.e., scaling, rotation, and shifting of handwritten text, are the prominent but challenging factors.. A lightweight deep learning-based model construction for low and medium-resource devices in a less-constrained environment is challenging. This paper provides a practical light deep learning-based model for predicting handwritten Pashto words. A massive Pashto-transformed invariant inverted handwritten text dataset is prepared with the help of the Pashtun community. A lightweight MobileNetV2 has been hyperactively tuned for Pashto handwritten text classification, extracting images’ features (MoI). We inverted the dataset to make the model more accurate and restrict it to fifteen epochs. Extensive experiments have been conducted to validate the suggested model’s performance. The proposed transformed invariant lightweight Pashto deep learning (TILPDeep) technique achieves a training accuracy of 0.9839 and a validation accuracy of 0.9405 for transformed invariant Pashto handwritten inverted text using recognition matrices.
Privacy-Preserving Range Query Quantum Scheme With Single Photons in Edge-Based Internet of Things

Range query in cloud-based outsourcing applications is an important data search service, but it can suffer from privacy disclosure. In this paper, we generalize and redefine privacy-preserving range query in edge-based Internet of Things (IoT). Furthermore, to enhance security and privacy of sensitive data, we introduce quantum cryptographic technologies and present a novel quantum approach to solve the problem. First, we present a feasible protocol for Quantum Secret Permutating (QSP) with single photons, which will be later utilized in the final scheme as a basic block. Second, we generalize traditional privacy-preserving range query to a secure one-sided two-party computation, i.e., specific dot product. Third, we present a synchronously grouping strategy based on Quantum Key Distribution (QKD) to reduce the complexity and improve the efficiency of solving this two-party computation, so that it can easily get the desired result by repeatedly using feasible quantum privacy query group by group. Finally, adopting our previously proposed quantum protocols, we design privacy-preserving range query quantum scheme in edge-based IoT. Compared with the classical related schemes, our proposed quantum scheme has higher security, because the security of our proposed protocols is based on the basic physical principles of quantum mechanics, instead of unproven computational difficulty assumptions.
MashFormer: A Novel Multiscale Aware Hybrid Detector for Remote Sensing Object Detection

Object detection is a critical and demanding topic in the subject of processing satellite and airborne images. The targets acquired in remote sensing imagery are at various sizes, and the backgrounds are complicated, which makes object detection extremely challenging. We address these aforementioned issues in this paper by introducing the MashFormer, an innovative multi-scale aware CNN and Transformer integrated hybrid detector. Specifically, MashFormer employs the transformer block to complement the convolutional neural network (CNN) based feature extraction backbone, which could obtain the relationships between long-range features and enhance the representative ability in complex background scenarios. With the intention of improving the detection performance for objects with multi-scale characteristic, since in remote sensing scenarios, the size of object varies greatly. A multi-level feature aggregation component, incoperate with a cross-level feature alignment module is designed to alleviate the semantic discrepancy between features from shallow and deep layers. To verify the effectiveness of the suggested MashFormer, comparative experiments are carried out with other cutting-edge methodologies using the publicly available High Resolution Remote Sensing Detection (HRRSD) and Northwestern Polytechnical University (NWPU) VHR-10 datasets. The experimental findings confirm the effectiveness and superiority of our suggested model by indicating that our approach has greater mean average precision (mAP) than the other methodologies.