Reviewerly/exHarmony huggingface dataset:
Trust Management Strategy for Digital Twins in Vehicular Ad Hoc Networks

As an essential part of mobile networks, vehicular ad hoc networks (VANETs) are beneficial to the improvement of traffic efficiency and safety through real-time information sharing between vehicles. Digital Twins (DT) have been utilized to facilitate the design, testing, and deployment of VANETs. However, constructing Digital Twins still faces interference from malicious vehicles. Despite most vehicles following communication rules honestly, the reliability and authenticity of traffic messages cannot be guaranteed due to the network’s openness and vulnerability. Meanwhile, vehicles may suffer tracking attacks during the interaction without an effective privacy-preserving method, leading to the leakage of sensitive data. To address these issues, a decentralized trust management scheme embedded with blockchain that considers identity authentication is proposed to detect malicious DT-vehicles. In our method, each vehicle in the Digital Twin of VANETs (DT-VANETs) is equipped with a certificate recorded on the blockchain as a legal identity, which is also served as a pseudonym for security during message transmission. The trustworthiness of the vehicle is evaluated based on direct trust and recommendation trust. Direct interaction between vehicles consists of message authenticity verification and active detection, which are the basis of direct trust calculation. For other vehicles, these direct trust opinions are treated as second-hand information to obtain recommendation trust. Unreliable recommendations are filtered by our proposed RTF algorithm, further resisting cooperation attacks. Vehicles judged to be malicious will have their certificates revoked and removed from DT-VANETs, providing a guarantee for the establishment of trust in DT-VANETs. Experimental results show that the proposed scheme can effectively resist malicious attacks in DT-VANETs.
Blockchain-Aware Rollbackable Data Access Control for IoT-Enabled Digital Twin

The rapid development of Internet of Things (IoT) enables digital twin (DT) technology to precisely represent a real product in a virtual space by generating a multitude of IoT data items to record many aspects of the product. To support various DT-based applications, the generated IoT data items need to be shared among multiple parties involving the lifecycle of the product, which raises increasing demand for data access control. The decentralization and tamper-proofing properties of blockchain enable it a promising technology to support immutability protection of shared IoT data items. Meanwhile, to protect the confidentiality of the shared IoT data items, attribute-based encryption (ABE) can be used as a common tool to construct a cryptographic enforced data access control scheme. However, its adoption has been severely hindered by the incompatibility between the immutability of blockchain and secure authority update of cryptographic enforced data access control. In this paper, a blockchain-aware rollbackable data access control scheme (Bdacs) is proposed to reconcile the above tension. Bdacs uses two novel encryption schemes named hierarchical encryption scheme and privacy-preserving rollback re-encryption scheme to realize secure dynamic access control while preserving the immutability of blockchain. We prove the security of Bdacs and evaluate it through theoretical comparison and experimental analysis to confirm its efficiency. This work can serve as a basis of development of future DT-based applications to enable privacy-preserving IoT data-sharing systems deployed on blockchain.
Exponential Information Bottleneck Theory Against Intra-Attribute Variations for Pedestrian Attribute Recognition

Multi-label pedestrian attribute recognition (PAR) involves assigning multiple attributes to pedestrian images captured by video surveillance cameras. Despite its importance, learning robust attribute-related features for PAR remains a challenge due to the large intra-attribute variations in the image space. These variations, which stem from changes in pedestrian poses, illumination conditions, and background noise, make extracted attribute-related features susceptible to irrelevant information or noise interference. Existing PAR methods rely on body prior extractors or attention mechanisms to locate attribute-correlation regions for extracting robust features. However, these methods may not be robust to intra-attribute variations, which limits their effectiveness. To address this challenge, we propose a novel and flexible PAR framework that leverages the exponential information bottleneck (ExpIB) approach. Our ExpIB-Net uses mutual information compression as the main penalty during the early stage of training, thereby eliminating irrelevant information. As training progresses, the mutual information penalty weakens and the Binary Cross-Entropy Loss (BCELoss) contributes to improving the PAR recognition accuracy. Our method can also be integrated into an attention module to form the AttExpIB-Net, which better handles intra-attribute variations for better performance. Additionally, our model-agnostic ExpIB approach is plug-and-play, requiring no additional computational overhead during inference. Experiments on several challenging PAR datasets show that our method outperforms state-of-the-art approaches.
DRDet: Dual-Angle Rotated Line Representation for Oriented Object Detection

In aerial scenes, oriented object detection is sensitive to the orientation of objects, which makes the formulation of orientation-aware object representation become a critical problem. Existing methods mostly adopt rectangle anchor or discrete points as object representation, which may lead to the feature aliasing between overlapping objects and ignore the orientation information of objects. To solve these issues, we propose a novel anchor-free oriented object detection network named DRDet, which adopts Dual-angle Rotated Lines (DRL) as object representation. Different from other object representations, DRL can adaptively rotate and extend to the boundary of the object according to its orientation and shape, which explicitly introduces the orientation information into the formulation of object representation. And it can adaptively cope with the geometric deformation of objects. Based on the dual-angle rotated lines, we design an Orientation-guided Feature Encoder (OFE) to encode discriminant object feature along each rotated line, respectively. Instead of encoding rectangle feature, the OFE module adopts line features for orientation-guided feature encoding, which can alleviate the feature aliasing between neighboring objects or background. To further enhance the flexibility of dual-angle rotated lines, we design a Dual-angle Decoder (DD) that predicts two angle offsets according to the orientation-guided feature and converts the angle offsets and regression offsets into dual-angle rotated line representation, which can help to guide the adaptive rotation of each rotated line, respectively. Our proposed method achieves consistent improvement on both DOTA and HRSC2016 datasets. Extensive experimental results verify the effectiveness of our method in oriented object detection.
A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

The vast majority of today’s data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the classification’s accuracy. Feature selection is considered a multi-objective optimization problem with two objectives: boosting classification accuracy while decreasing the feature count. To efficiently handle the feature selection process, we propose in this paper a novel algorithm inspired by the behavior of waterwheel plants when hunting their prey and how they update their locations throughout exploration and exploitation processes. The proposed algorithm is referred to as the binary waterwheel plant algorithm (bWWPA). In this particular approach, the binary search space as well as the technique’s mapping from the continuous to the discrete spaces are both represented in a new model. Specifically, the fitness and cost functions that are factored into the algorithm’s evaluation are modeled mathematically. To assess the performance of the proposed algorithm, a set of extensive experiments were conducted and evaluated in terms of 30 benchmark datasets that include low, medium, and high dimensional features. In comparison to other recent binary optimization algorithms, the experimental findings demonstrate that the bWWPAperforms better than the other competing algorithms. In addition, a statistical analysis is performed in terms of the one-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests to examine the statistical differences between the proposed feature selection algorithm and compared algorithms. These experiments’ results confirmed the proposed algorithm’s superiority and effectiveness in handling the feature selection process.
Passive Haptic Feedback for More Realistic and Efficient Grasping Movements in Virtual Environments

Achieving natural interaction in virtual environments is essential to create realistic simulations in various fields, including healthcare and education. The ability to interact in Virtual Reality (VR) in a natural way, through a combination of visual and physical feedback can greatly enhance the experience and effectiveness of these simulations. Recent works have shown that the lack of haptic and tactile feedback produces significant differences in grasping actions performed in immersive VR, with respect to the same actions performed in the real world. The passive haptics approach, which relies on physical proxies to introduce tactile feedback in VR, has been explored to address this issue. This work focuses on a specific interaction task that involves both hand movements and grasping: pouring coffee into a cup and mimicking the action of drinking it. We take into account three different scenarios: a traditional VR environment where virtual objects don’t have any real counterparts; an MR environment that uses an ecological object substitution technique where the user can interact with real objects that are tracked in real-time and see a virtual counterpart; and the corresponding real scenario. We compute the Minimum Jerk Cost and the Dynamic Time Warping distance between trajectories as metrics to compare movements in the different modalities in terms of their smoothness and trajectory shape, respectively. Our results show that movements in MR environments are smoother and produce more similar trajectories to real-world movements compared to classical VR environments. This indicates that MR with passive haptic feedback could produce more realistic and efficient human movements in virtual environments.
EXIT-Chart Aided Design of Irregular Multiple-Rate Quantum Turbo Block Codes

We propose a novel quantum turbo short-block code, which subsumes multiple-rate quantum short-block codes (MR-QSBCs) as the outer codes and a quantum unity-rate code (QURC) as the inner code. The proposed design is denoted as MR-QSBC-QURC.More specifically, the proposed design exhibits multiple quantum coding rates despite relying only on a single quantum encoder. Moreover, the flexibility offered by the single-encoder MR-QSBCs enables us to leverage extrinsic information transfer (EXIT)-chart based heuristic optimization for determining the optimal weighting in the fractional encoding of MR-QSBCs. Our simulation results show that the MR-QSBC-QURC scheme conceived performs relatively close to the ultimate limit of the quantum hashing bound. Specifically, when considering the target quantum coding rates of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r<sub>Q</sub></i> = {0.3,0.4,0.5,0.6,0.7}, the MR-QSBC-QURC operates at a distance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> = {0.042,0.029,0.030,0.024,0.017} from the quantum hashing bound, respectively, at a quantum bit error ratio (QBER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> .
Immersive Virtual Reality in Cultural Heritage Dissemination: A Comprehensive Application for Novice Users

This paper discusses the increasing relevance of immersive Virtual Reality (iVR) technology in society and its emerging applications, particularly in Cultural Heritage (CH) dissemination. However, the diversification of iVR experiences has led to a need for an application that summarizes the most common types of experiences to familiarize novice users with iVR. To address this need, an iVR application has been developed that presents the four most common types of iVR experiences: Passive, Explorative, Explorative Interaction, and Interactive. The experience includes different reconstructed CH environments and objects showcasing the possibilities for the dissemination of CH using this technology. The application is divided into four levels, and its key design factors are as follows: 1) one type of iVR experience for each level, 2) simple interactions that become more complex as the user progresses, 3) short duration and time-limited progression between levels, 4) development for standalone iVR devices, and 5) different types of reconstructed heritage showcased at each level. The experience was tested in exhibitions and achieved high performance on standalone iVR devices. Usability results are expected to be achieved in the future.
Realter: An Immersive Simulator to Support Low-Vision Rehabilitation

The project REALTER (wearable egocentric altered reality simulator) exploits immersive technologies and extended reality (XR) environments to support low-vision rehabilitation, by offering an immersive simulator of low-vision conditions. Perceiving and navigating the world as low-vision individuals has the potential of being a useful tool for ophthalmologists and visual rehabilitators to increase empathy with the assisted population and to improve the existing therapeutic techniques. Additionally, by analyzing ocular movements acquired during experimental sessions with healthy-sighted individuals in a condition of simulated low vision, researchers may collect quantitative data to extend the state of the art in understanding the behavioral changes of low-vision persons. The project involved the implementation of an immersive system by using commercial device tools currently available on the market. The hardware consists of an immersive virtual reality (VR) headset with an integrated eye tracker and a pair of external cameras, to provide gaze-contingent altered/extended reality (XR) content by a pass-through modality. The software can realistically simulate several low-vision conditions, such as age-related macular degeneration, glaucoma, and hemianopsia, and simultaneously acquire eye and head movements for data analysis.
Measuring the Effectiveness of Virtual Reality for Stress Reduction: Psychometric Evaluation of the ERMES Project

Among all the technologies that make up the Digital Psychology intervention, Virtual Reality (VR) represents a decisive paradigm of innovation since it allows to work in terms of prevention and monitoring of many psychological issues, such as stress, anxiety, depression and burnout. This paper presents ERMES project "Virtual Reality to promote the well-being of vulnerable people within Hospitals", and a preliminary psychometric assessment of the results obtained from its first experimentation. Specifically, the main goal is to provide a tool that can act on stress levels by practicing Mindfulness sessions, virtual museum exploration, and Art Therapy activities, with the possibility of safely sharing the experience with other patients. During the experimental phase, clinical interviews and psychometric tests were carried out in order to assess the impact of VR therapy after participating in the experimental protocol. This first analysis shows significant changes in the stress levels, that have to be confirmed by the biosignals data acquired during the experimentation, that still needs to be evaluated.
Bayesian network-based quality assessment of blockchain smart contracts

Smart contracts (SC) are a key component in the Blockchain. Smart contracts are normal computer programs that are written mostly in solidity object-oriented programming language. Blockchain allows the completion of transactions only if the transaction obeying the smart contract rules. SC is not modifiable once they are deployed into the Blockchain. Thus it is essential to verify the quality of the smart contract before deploying it into the Blockchain. In this chapter, Bayesian Network Model is designed and constructed to measure the SC quality. The benefit of using a Bayesian network is that in addition to getting the probability of each quality measure, it will allow us to recommend the causes for the outcome of each quality measure. The proposed model with Bayesian Network helps in assessing the quality of smart contracts and the success rate is 82%.
Target-Driven One-Shot Unsupervised Domain Adaptation

In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OS-UDA), which aims to adapt to a target domain with only a single unlabeled target sample. Unlike existing approaches that rely on large labeled source and unlabeled target data, our Target-Driven One-Shot UDA (TOS-UDA) approach employs a learnable augmentation strategy guided by the target sample's style to align the source distribution with the target distribution. Our method consists of three modules: an augmentation module, a style alignment module, and a classifier. Unlike existing methods, our augmentation module allows for strong transformations of the source samples, and the style of the single target sample available is exploited to guide the augmentation by ensuring perceptual similarity. Furthermore, our approach integrates augmentation with style alignment, eliminating the need for separate pre-training on additional datasets. Our method outperforms or performs comparably to existing OS-UDA methods on the Digits and DomainNet benchmarks.
VM-NeRF: Tackling Sparsity in NeRF with View Morphing

NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their reliance on data: the fewer the viewpoints, the higher the likelihood of overfitting. This paper addresses this issue by introducing a novel method to generate geometrically consistent image transitions between viewpoints using View Morphing. Our VM-NeRF approach requires no prior knowledge about the scene structure, as View Morphing is based on the fundamental principles of projective geometry. VM-NeRF tightly integrates this geometric view generation process during the training procedure of standard NeRF approaches. Notably, our method significantly improves novel view synthesis, particularly when only a few views are available. Experimental evaluation reveals consistent improvement over current methods that handle sparse viewpoints in NeRF models. We report an increase in PSNR of up to 1.8 dB and 1.0 dB when training uses eight and four views, respectively. Source code: https://github.com/mbortolon97/VM-NeRF .
Active Class Selection for Dataset Acquisition in Sign Language Recognition

Dataset collection for Sign Language Recognition (SLR) represents a challenging and crucial step in the development of modern automatic SLR systems. Typical acquisition protocols do not follow specific strategies, simply trying to gather equally represented classes. In this paper we provide some empirical evidences that alternative, more clever, strategies can be really beneficial, leading to a better performance of classification systems. In particular, we investigate the exploitation of ideas and tools of Active Class Selection (ACS), a peculiar Active Learning (AL) context specifically devoted to scenarios in which new data is labelled at the same time it is generated. In particular, differently from standard AL where a strategy asks for a specific label from an available set of unlabelled data, ACS strategies define from which class it is more convenient to acquire a new sample. In this paper, we show the beneficial effect of these methods in the SLR scenario, where these concepts have never been investigated. We studied both standard and novel ACS approaches, with experiments based on a challenging dataset recently collected for an ECCV challenge. We also preliminary investigate other possible exploitations of ACS ideas, for example to select which would be, for the classification system, the most beneficial signer.
Hand Gesture Recognition Exploiting Handcrafted Features and LSTM

Hand gesture recognition finds application in several heterogeneous fields, such as Human-Computer Interaction, serious games, sign language interpretation, and more. Modern recognition approaches use Deep Learning methods due to their ability in extracting features without human intervention. The drawback of this approach is the need for huge datasets which, depending on the task, are not always available. In some cases, handcrafted features increase the capability of a model in achieving the proposed task, and usually require fewer data with respect to Deep Learning approaches. In this paper, we propose a method that synergistically makes use of handcrafted features and Deep Learning for performing hand gesture recognition. Concerning the features, they are engineered from hand joints, while for Deep Learning, a simple LSTM together with a multilayer perceptron is used. The tests were performed on the DHG dataset, comparing the proposed method with both state-of-the-art methods that use handcrafted features and methods that use learned features. Our approach overcomes the state-of-the-art handcrafted features methods in both 14 and 28 gestures recognition tests, while we overcome the state-of-the-art learned features methods for the 14 gesture recognition test, proving that it is possible to use a simpler model with well engineered features.
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data

The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose OpenFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall. Source code and trained models are publicly available at: https://github.com/aimagelab/open-fashion-clip .