Reviewerly/exHarmony huggingface dataset:
Use Case Examples of Ethereum Non-Fungible Tokens Tied to Assets Using ERC-4519

Ethereum is a dynamic blockchain that grows every day thanks to a community that creates and decides on various of its issues. The community participates through Ethereum Improvement Proposals (EIPs) at many levels, from proposals that describe new standards for the creation of new tokens to proposals that define new ways for the Ethereum main network to generate new blocks. This paper describes how an Ethereum Request for Comments (ERC)-type EIP, the ERC-4519, was proposed last year to standardize the way to define non-fungible tokens (NFTs) representing assets that can generate their own Ethereum addresses and obey users and owners. Advantages provided by ERC-4519 in several use cases are illustrated. The examples show the facilities and the security improvements introduced in the management of both physical and digital assets by their owners and users. Particularly, use cases with physical assets such as IoT devices are illustrated.
Example-Based Query To Identify Causes of Driving Anomaly with Few Labeled Samples

Driving anomaly detection is important for advanced driver assistance systems (ADAS) to increase driving safety and avoid traffic accidents. However, driving anomaly detection faces many challenges such as numerous and uncertain abnormal patterns observed on the road, sparsity of real anomaly cases documented with accurate labels, and rigid existing systems that rely on manually set thresholds and rules. Previous studies have proposed unsupervised methods for driving anomaly detection in the driver’s behaviors or the road condition by identifying deviations from normal driving conditions. A challenge with unsupervised models is the lack of interpretability, where the cause of the anomaly is not always clear. We address this problem with an example-based query method that combines unsupervised anomaly detection methods with the multi-label k-nearest neighbors (ML-KNN) algorithm to interpret the detected driving anomalies by identifying their possible causes (e.g., surrounding objects or driver’s errors). Our approach relies on a few manually labeled driving segments that are efficiently used as anchors to retrieve the causes of driving anomalies in a given driving segment. These anchors are projected into the embedding created by unsupervised driving anomaly detection systems. The experimental results show that this method can effectively identify the causes of driving anomalies, even for abnormal driving segments triggered by multiple causes. The evaluation shows the flexibility of our proposed solution, where we successfully implement the ML-KNN approach with three alternative feature representations.
Mining Implicit Relations Among Image Channels for Few-Shot Semantic Segmentation

The goal of few-shot semantic segmentation (FSS) is to segment the foreground image of an unseen class in the query image by using a few labeled support images. Existing two-branch models mine support and query image information to improve segmentation results by employing support prototypes, calculating the similarity between support and query images, or fusing multi-scale features. Such methods only focus on the spatial information of the query image in the initial feature extraction and subsequent processes. Meanwhile, limited by the sample size, their ability to extract channel information is insufficient, thus leading to the information loss of the query image. To solve the issues, we propose an implicit channel relation based few-shot semantic segmentation method entitled MANGO. The implicit relation mining process is implemented after the initial feature extraction and before the two branches interact to fully mine the query image information. Specifically, the query channel features are taken as nodes to construct the graph structure to establish the relationship between nodes. The network motif is used to quantity the attribute features and structural features of nodes to enhance the relationship between channels. Finally, we aggregate the two features and mine the implicit relationship of nodes through graph representation learning. Experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and FSS-1000 datasets demonstrate that our proposed method outperforms the state-of-the-art methods.
Robustness Evaluation in Hand Pose Estimation Models using Metamorphic Testing

Hand pose estimation (HPE) is a task that predicts and describes the hand poses from images or video frames. When HPE models estimate hand poses captured in a laboratory or under controlled environments, they normally deliver good performance. However, the real-world environment is complex, and various uncertainties may happen, which could degrade the performance of HPE models. For example, the hands could be occluded, the visibility of hands could be reduced by imperfect exposure rate, and the contour of hands prone to be blurred during fast hand movements. In this work, we adopt metamorphic testing to evaluate the robustness of HPE models and provide suggestions on the choice of HPE models for different applications. The robustness evaluation was conducted on four state-of-the-art models, namely MediaPipe hands, OpenPose, BodyHands, and NSRM hand. We found that on average more than 80% of the hands could not be identified by BodyHands, and at least 50% of hands could not be identified by MediaPipe hands when diagonal motion blur is introduced, while an average of more than 50% of strongly underexposed hands could not be correctly estimated by NSRM hand. Similarly, applying occlusions on only four hand joints will also largely degrade the performance of these models. The experimental results show that occlusions, illumination variations, and motion blur are the main obstacles to the performance of existing HPE models. These findings may pave the way for researchers to improve the performance and robustness of hand pose estimation models and their applications.
Recover the Structure of Monocular Depth Estimation base ViT-CNNs

Monocular depth estimation is a fundamental vision task that is applied to many vision works such as scene reconstruction, object detection, automatic drive, and Augmented Reality AR/Virtual Reality VR. The monocular depth estimation base on Vision Transformer (ViT) has shown remarkable ability. ViT has an excellent capability to model long dependency and global information. However, it shows weak performance in preserving local information. Local information in the depth maps is an important cue for improving depth accuracy and other vision tasks. Furthermore, the local and detailed information is the key to reconstruct the scene and other works. To overcome this flaw and recover structure information (local and detail) in the depth maps, we propose a model with the ViT encoder and Convolutional Neural Networks CNNs sub-network. The CNNs sub-network is used to prove the local information. ViT captured the global information. However, the gap between the CNNs feature and ViT feature is huge. So we propose an exchange fusion strategy to reduce this gap. Furthermore, to enhance the effectiveness of exchange fusion, mixed attention is designed. Finally, to guide the framework to focus on the local feature, a structure loss function that contains the detail term and outline term is proposed. The detail term and outline term guide the model to focus on the non-edge region with a slight gradient and the edge region having a huge gradient respectively. Extensive experiments prove that our depth maps with a richer and clearer structure of objects and scenes than others. Furthermore, ours achieve state-of-the-art in edge accuracy, excellent accuracy of depth on NUY-depth v2 dataset.
Driver Gaze Fixation and Pattern Analysis in Safety Critical Events

Driver attention is one of the major factors for roadway safety. This paper presents a comprehensive analysis of context-aware driver gaze behavior using large-scale naturalistic data. This paper first demonstrates a Point of Gaze (PoG) estimation system that provides an understanding of the interactions between a driver and the driving environment outside the vehicle. The driver’s PoG positions were mapped to roadway actors that were detected using a road-facing camera, and a driver-facing camera detected the driver’s head and eye motions. This paper presents a new algorithm that can effectively predict gaze fixation to outside objects. The temporal fixation patterns of drivers are studied through a Yarbus-style analysis of PoG. Correlations between gaze direction, salient objects, and the driving scenario are described and analyzed. These correlations have the potential to reveal how drivers behave in various traffic situations and may have the predictive power to aid automated safety improvement systems. Our code is available at: https://github.com/VTTI/gaze-fixation-and-object-saliency and https://github.com/VTTI/Event-Correlation
ICFD: An Incremental Learning Method Based on Data Feature Distribution

Neural network models have achieved great success in numerous disciplines in recent years, including image segmentation, object identification, and natural language processing (NLP). Incremental learning in these fields focuses on training models in a continuous data stream. As time goes by, more new data becomes available, and old data may become unavailable owing to resource constraints such as storage. As a result, when new data is continually arriving, the performance of the neural network model on the old data sample sometimes decreases significantly, a phenomenon known as catastrophic forgetting. Many corresponding strategies have been proposed to mitigate the catastrophic forgetting of neural network models, which are based on parameter regularization, data replay, and parameter isolation. This paper proposes an incremental learning method based on data feature distribution (ICFD). The method uses Gaussian distribution to generate features from old data to train neural network models based on the phenomenon that feature vectors obey multi-dimensional Gaussian distribution in feature space. This method avoids storing a large number of original samples, and the generated old class features contain more sample information. This method combines data playback and parameter regularization in concrete implementation. The experimental results of ICFD on the CIFAR-100 demonstrate that when the incremental step is 5, the average incremental accuracy is increased by 10.4%. When the incremental step is 10, the average incremental accuracy is improved by 8.1%.
Experience Filter: Using Past Experiences on Unseen Tasks or Environments

One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very costly and requires substantial data collection, it becomes computationally intractable to train the agent on every possible environment or task the AV may encounter.This paper introduces a zero-shot filtering approach to interpolate learned policies of past experiences to generalize to unseen ones. We use an experience kernel to correlate environments. These correlations are then exploited to produce policies for new tasks or environments from learned policies. We demonstrate our methods on an autonomous vehicle driving through T-intersections with different characteristics, where its behavior is modeled as a partially observable Markov decision process (POMDP). We first construct compact representations of learned policies for POMDPs with unknown transition functions given a dataset of sequential actions and observations. Then, we filter parameterized policies of previously visited environments to generate policies to new, unseen environments. We demonstrate our approaches on both an actual AV and a high-fidelity simulator. Results indicate that our experience filter offers a fast, low-effort, and near-optimal solution to create policies for tasks or environments never seen before. Furthermore, the generated new policies outperform the policy learned using the entire data collected from past environments, suggesting that the correlation among different environments can be exploited and irrelevant ones can be filtered out.
Long- and Short- Term Sequential Recommendation with Temporal Interval

Sequential recommendation aims to learn the changes of users’ interests according to their historical behaviors and predict the most likely next item. Since user’s historical behavior are sequential actions, user’s interest in different time periods has different emphases. For predicting a user’s next item, not only the recent behavior is important, but also long-term preference with all historical behaviors could not be ignored. Recently, self-attention based user preference modeling has drawn much attention for its advantages of fewer parameters and parallelism. However, most of the existing self-attention based model do not make a good distinction between the long-term and short-term preferences of users. Based on the observations, we design a sequential recommendation model based on a combination of long-term and short-term preference. On the one hand, considering the changes of users’ interests in different periods, we divide the sequence of users’ behaviors into different temporal windows. Then, we use GRU to capture users’ interests in different temporal window. On the other hand, to better combine the global and local information of user’s, we adopt a locally constrained multi-head attention mechanism based on Transformer encoder. On three real-world public datasets, we finally validate the efficacy of our method.
DT-FLDS: DT-enabled Federated Learning Data Sharing Model<sup>*</sup>

Data sharing is crucial to promoting the process of informatization development. How to ensure the security of data and privacy is a major problem faced by data sharing. In this paper, we consider a DT-enabled federated learning data sharing model, named DTFLDS, where DTs capture the characteristics of shared devices to assist federal training. Federated learning is utilized to keep the data training locally and only share the aggregation model instead of the source data, which ensuring data and privacy security. Noting the reverse attack and privacy leakage in model transmission, a security aggregation algorithm based on secret sharing is proposed to ensure the security and efficiency of model aggregation. Numerical results show that the proposed is superior to the benchmark in sharing efficiency.
On the Strengths of Pure Evolutionary Algorithms in Generating Adversarial Examples

Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using white-box strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches based on approximate gradient descent algorithms combined with meta-heuristic search (i.e., the BMI-FGSM algorithm) outperform previously proposed white- and black-box strategies. In this paper, we propose a novel black-box approach purely based on differential evolution (DE), i.e., without using any gradient approximation method. In particular, we propose two variants of a customized DE with customized variation operators: (1) a single-objective (Pixel-SOO) variant generating attacks that fool DL models, and (2) a multi-objective variant (Pixel-MOO) that also minimizes the number of changes in generated attacks. Our preliminary study on five canonical image classification models shows that Pixel-SOO and Pixel-MOO are more effective than the state-of-the-art BMI-FGSM in generating adversarial attacks. Furthermore, Pixel-SOO is faster than Pixel-MOO, while the latter produces subtler attacks than its single-objective variant.
Transformer Tracker by Attention Feature Fusion Module and Online Update Module

In recent years, Transformer has shown strong competitiveness and better performance in the field of object tracking. However, during tracking, object occlusion and similar objects can affect the tracking effect. If only features of the last layer are used, sufficient detail information cannot be obtained, and it is difficult to accurately locate the object. As the ResNet-50 network gets deeper and deeper, feature maps of the last layer obtain high-level semantic information, but the feature maps have low resolution, poor ability to perceive details, and rough locations. The low-level feature maps have high resolution and contain more detailed feature and location information, but the semantic information is not strong. Second, if only the template features of objects in the first frame are learned, factors such as occlusion, deformation, and complex background can easily lead to tracking failure. To address these issues, we propose a Transformer tracker by attention feature fusion module and online update module. Firstly, an attention feature fusion module is designed, which can fully integrate low, medium and high-level features, obtain rich detailed information and semantic information, and improve the ability of features to express objects. Subsequently, we use the transformer structure and add an online update branch to solve the tracking failure caused by only learning the template of first frame and the drift caused by the accumulation of updates. Experimental results show that our algorithm outperforms several well-known tracking algorithms in terms of tracking accuracy and robustness.
Neural Network Calibration of Airborne Magnetometers

Magnetic navigation consists in taking measure-ments of the magnetic field of the Earth which, thanks to a map of magnetic anomalies, allows us to know a precise position. However, these measurements are noisy due to the carrier making the measurements, such as an aircraft. To compensate for the magnetic disturbance of the carrier, it is possible to use the Tolles-Lawson method. However, this method does not work very well when the magnetometers are disturbed by the carrier itself, which is very often the case. To reach an accuracy of the order of ten meters, a better method is needed to manage these effects created by the carrier. Since these effects are mainly nonlinear, neural networks have become a method of choice for correcting the measurements. This paper proposes to use and compare different neural networks such as multi-layer perceptron, convolutional neural networks and recurrent neural networks for the calibration of airborne magnetometers. For this purpose, the dataset provided in the MIT magnetic navigation challenge is used to explore this problem. Compared to the Tolles-Lawson method, the use of neural networks can significantly improve the quality of the correction for aircraft effects on the measurements by up to 43%. All of the methods in this paper using neural networks were found to improve the Tolles-Lawson method. Visualization methods are also proposed to better understand the model and visualize the features that affect its prediction.
SiamDSA: Dual-Branch Self-Attention Siamese Network for Visual Object Tracking

In recent years, object tracking is studied and applied widely in human activity recognition. In object tracking scenarios, the object appearance changes significantly due to various factors, such as scale changes and deformation, which will drastically affect the robustness of the object tracking. To deal with the above issues, we propose a DSA module that integrates the siamese tracking network to implement the siamese network tracking methods based on the dual-branch self-attention (SiamDSA). It fuses object features of different scales to greatly enhance the target information and establish a better target appearance model for improving object tracking robustness. Specifically, a novel DSA module based on self-attention mechanism boosts the feature representation of objects from channel and spatial perspectives. And it is responsible for better focusing on the features of the target. We conduct experiments on GOT-IOK, OTB100 and VOT2018 datasets. The experimental results demonstrate that SiamDSA achieves superior performances and runs at 65 FPS, it further shows strong effectiveness and efficiency in object tracking.
A Smart Card based Approach for Privacy Preservation Authentication of Non-Fungible Token using Non-Interactive Zero Knowledge Proof

The adaption of the NFT to the mainstream web3 marketplace has led to the rise of challenges like authenticity and verification. On the other hand, privacy-preserving is one of the important research topics in the data-driven metaverse due to its anonymity while maintaining transparency and security. As the traditional method does not preserve privacy, there is a dire need for a novel privacy-preserving method for NFT authentication while preserving anonymity, transparency, and security. This paper proposes a smart card-based approach for privacy preservation authentication of Non-Fungible Tokens using Non-Interactive Zero Knowledge Proof (NIZKP). The paper presents a Novel NIZKP response with the integration of challenge and proof with the encapsulation of a time-based one-time password (TOTP) solution to address the most critical problem of the proof of the source of the NFT and its rightful owner. The proposed solution is a lightweight mechanism for preventing unauthorized and hostile agents from the accessibility of the system while maintaining privacy and anonymity along with the verification and authenticity of the NFT. We have implemented our approach and compared the communication overhead and functional security features with related work and its precedence over them. The results show that the proposed approach has less communication overhead than the existing work and fulfills all the functional security and privacy-preserving features. It indicates that the proposed method can be an ideal solution for NFT authentication and transaction among users.
A Continuous Collision Detection Algorithm for Dubins Paths

This paper introduces a geometric algorithm for Continuous Collision Detection (CCD). It can be used for so-called Dubins paths, which are composed of arcs and straight lines. The CCD approach checks for an overlap between the area covered by the vehicle during a full arc and the polytopic obstacles. Previous work has already demonstrated the potential benefits over other methods, which perform collision checks at discrete sample points along the path. Compared to the previous CCD implementation, the proposed geometric algorithm requires only on elementary functions and does not depend on external libraries, e.g., for numerical optimization. A numerical study demonstrates the numerical speed-up, for elementary collision checks and in combination with a basic Rapidly-exploring Random Tree (RRT) path planner.
A Novel Cross-FOV Gaze-Driven Human-Robot Interaction Framework for Service Robots

Gaze-based methods have been widely applied in human-robot interactions (HRI). However, most methods were often limited to scenarios where the human face and the gazed object should be in the same field of view (FOV), which is difficult to adapt to the complex and changeable typical daily life scene where the service robot is located. To improve the limitation, this paper proposes a framework capable of completing tasks even if the person and the gazed object are not in the same FOV, requiring only a single RGB camera. First, a cross-FOV gaze estimation algorithm is designed to get the gaze point from images containing a human face captured by an RGB camera. Then a multi-view fusion decision model is employed to drive the robot to turn to the gaze point and identify the object according to the gaze point. Finally, a robot can complete tasks using this information. Experiments under conditions of different lighting, different scenes and different users on the NAO robot show the effectiveness of our method.