Reviewerly/exHarmony huggingface dataset:
Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours

One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.
Eye and head movements while encoding and recognizing panoramic scenes in virtual reality

One approach to studying the recognition of scenes and objects relies on the comparison of eye movement patterns during encoding and recognition. Past studies typically analyzed the perception of flat stimuli of limited extent presented on a computer monitor that did not require head movements. In contrast, participants in the present study saw omnidirectional panoramic scenes through an immersive 3D virtual reality viewer, and they could move their head freely to inspect different parts of the visual scenes. This allowed us to examine how unconstrained observers use their head and eyes to encode and recognize visual scenes. By studying head and eye movement within a fully immersive environment, and applying cross-recurrence analysis, we found that eye movements are strongly influenced by the content of the visual environment, as are head movements-though to a much lesser degree. Moreover, we found that the head and eyes are linked, with the head supporting, and by and large mirroring the movements of the eyes, consistent with the notion that the head operates to support the acquisition of visual information by the eyes.
On the use of Blockchain to enable a highly scalable Internet of Things Data Marketplace

As data becomes the new fuel of the economy and a key asset to address our societal challenges, we cannot afford to have the data of businesses, public sector and individuals stored and kept unexploited or, what is worse, exploited by others that actually have the resources and capacity to do it. This is affecting not only our economic performance but also our security, safety and sovereignty. Among the plethora of data sources that exist nowadays, the Internet of Things (IoT) is recognised as a game-changer technology that expands its applicability to a huge variety of domains. Its main asset is, precisely, the data that the myriad of sensors embedded in the environment are constantly generating. The diffusion of platforms for IoT data sharing and monetisation is one of the key success factors which may help to drive the data economy and industrial transformation. In this paper, we are presenting a data sharing platform based on Blockchain, so-called Blockchain-based IoT Data Marketplace (BIDM) over which data producers and data consumers are able to share data in a decentralised and trustworthy manner. The BIDM enables a data marketplace where owners of IoT infrastructures can expose the observations that their devices generate while retaining control over who accesses each observation and directly getting revenues according to the price they have set. The evaluation that we have carried out of the BIDM's behaviour and performance in terms of operational execution times and scalability has been the basis for the discussion that we are presenting on the shortcomings that are typically associated with the use of Blockchain technologies as enablers for data marketplaces. This discussion also includes the evaluation of the challenges that must be considered for the creation of secure and interoperable Data Spaces based on Blockchain.
Spatio-temporal hard attention learning for skeleton-based activity recognition

The use of skeleton data for activity recognition has become prevalent due to its advantages over RGB data. A skeleton video includes frames showing two- or three-dimensional coordinates of human body joints. For recognizing an activity, not all the video frames are informative, and only a few key frames can well represent an activity. Moreover, not all joints participate in every activity; i.e., the key joints may vary across frames and activities. In this paper, we propose a novel framework for finding temporal and spatial attentions in a cooperative manner for activity recognition. The proposed method, which is called STH-DRL, consists of a temporal agent and a spatial agent. The temporal agent is responsible for finding the key frames, i.e., temporal hard attention finding, and the spatial agent attempts to find the key joints, i.e., spatial hard attention finding. We formulate the search problems as Markov decision processes and train both agents through interacting with each other using deep reinforcement learning. Experimental results on three widely used activity recognition benchmark datasets demonstrate the effectiveness of our proposed method.
Comparison of heuristic approaches to PCI planning for Quantum Computers

Quantum Computing (QC) provides the possibility to develop new approaches to tackle complex problems. Real-world applications, however, cannot yet be managed directly due to the limitation of present and near-future noisy intermediate-scale quantum (NISQ) computers. Decomposition into smaller and manageable subproblems is often needed to take advantage of QC even when using hybrid (classical-quantum) solvers or solvers that already apply decomposition techniques. In this paper, heuristic decomposition algorithms to solve the Physical Cell Identifier (PCI) problem in 4G cellular networks in a way suitable for QC are presented. The PCI problem can be viewed as a map coloring problem with additional constraints and has been represented in a Quadratic Unconstrained Binary Optimization (QUBO) model, a form that, for instance, a quantum annealing machine could crunch. We propose two strategies, with variable decomposition granularity. The first one solves the problem recursively through bisection (max-cut problem), to use only one qubit to represent the status of the objects, avoiding one-hot encoding and thus minimizing the qubit requirement. The second is a multi-step approach, finally solving sets of randomized modified max-k-cut problems of customizable qubit size. We executed the algorithms on real cellular networks of one of the main Italian national telecom operators (TIM). The results show that all proposed QUBO approaches can be effectively applied to very large problems with similar or better performance of the reference classical algorithm, paving the way for the use on NISQ computers.
Haptic Interactions for Extended Reality

This research investigates whether the interaction methods of eXtended Reality (XR) headsets can be improved by using haptic feedback. As a first and most common technique, indirect interactions are considered. Indirect interactions correspond to manipulations of virtual objects from a virtual distance using pre-defined hand gestures. As a second interaction technique, direct interaction (namely Direct Interaction Method (DIM)) has been implemented where the user manipulates objects by virtually touching these with their hands. A third interaction method extends the previous one with haptic feedback (namely Haptic Enhanced Direct Interaction Method (HEDIM)). These 3 methods are compared with each other based on objective and subjective user tests, also taking into account financial considerations. This research concludes that the DIM improves upon the standard indirect method. Additionally, it has been observed that haptic feedback could enhance the DIM in specific situations. Nevertheless, when considering the current financial cost, our subjects were not convinced of the small improvements haptic feedback brings.
Does Private Blockchain Make Sense?

In this study, we propose a permissionless private blockchain framework and clarify that this framework is effective for blockchains self-managed and maintained by a single organization. The idea of our framework is to make a private blockchain permissionless, so that it can be verified by many unspecified users, thereby suppressing fraud by a single organization whose trust is important. Therefore, we argue that it makes sense for a single organization to use a permissionless private blockchain from a security perspective.
Game Model of Optimal Quality Experience Strategy for Internet of Vehicles Bandwidth Service Based on DAG Blockchain

With the rapid development of the Internet of Vehicles (IoV), vehicle networks will generate large amounts of data. Vehicle-to-Vehicle (V2V) data sharing can improve driving experience and quality of service, and Vehicle-to-Roadside unit (V2R) communication can improve road transport efficiency. A certain amount of bandwidth is consumed in the process of data sharing between V2V and V2R, and when multiple vehicles request data from the RSU at the same time, there may be network delay or congestion. Therefore, the vehicle obtains data faster by requesting high bandwidth from the RSU. However, since the bandwidth resources of RSUs are limited, optimal bandwidth service strategies need to be investigated. In addition, in order to ensure the safety, efficiency and traceability of data sharing between V2V and V2R, a blockchain-supported IoV needs to be established. Therefore, we design a blockchain-based framework for V2V and V2R transaction framework that integrates DAG blockchain and modern cryptography. The framework ensures security and scalability during the execution of data exchange and completion of bandwidth transactions, improves the service quality of vehicles and realizes decentralized payment. In this work, a two-stage Stackelberg game is established under two pricing schemes of uniform pricing and discriminatory pricing to jointly maximize the benefits of vehicles and RSUs. In Stage I, the RSU sets the corresponding bandwidth service and pricing strategy. In Stage II, the vehicle decides the service demand according to the observed pricing strategy. The optimal bandwidth allocation and pricing strategy between the RSU and the vehicle is determined through the game of both parties, and the existence and uniqueness of the two-stage subgame Nash equilibrium is analyzed by applying backward induction. The correctness of the model is verified by experiments, and the comparative analysis shows that the proposed model has high throughput.
A Framework for a Blockchain-Based Decentralized Data Marketplace

Data is critical for improving an individual’s quality of life. Its value provides opportunities for users to profit from data sales and purchases. Data marketplace users, on the other hand, must share and trade data in a secure and trusted environment while maintaining their privacy. The paper’s first major contribution is to identify enabling technologies and challenges to the development of decentralized data marketplaces. The second major contribution is the proposal of a blockchain-based decentralized data marketplace framework. The proposed framework allows sellers and buyers to transact with greater confidence. The system employs a novel approach to enforcing honesty in data exchange among anonymous individuals by requiring a security deposit. The system has a time frame before the transaction is considered complete.
Physical-priors-guided DehazeFormer

Single-image dehazing is a challenging task in several machine-vision applications. Methods based on physical models and prior knowledge fail under certain conditions, resulting in defects such as color distortion. Transformer-based methods have a strong representation ability owing to their self-attention mechanism that can effectively obtain global information. However, this approach is computationally expensive, and its weak inductive bias capability increases the risk of overfitting on small-sample datasets. To address these problems, in this study, we propose a novel DehazeFormer guided by physical priors, named SwinTD-Net, which is trained according to supervised and self-supervised learning, and combines the advantages of physical priors and transformers. The proposed DehazeFormer learns features guided by physical priors, which improves the generalization ability of the network and enables it to achieve good restoration effects on both synthetic and real-world hazy images. In addition, we propose a more appropriate prior input to better use physical priors, and we design a multi-scale dark-light enhancement algorithm for image restoration post-processing, which can improve the visual perception quality for human observers while performing some local enhancements. Extensive experiments illustrate that the proposed method outperforms state-of-the-art methods. The code and pre-trained models are available to academics so that they can reproduce our results and test them (https://github.com/hocking-cloud/SwinTD_Net).
Look at Adjacent Frames: Video Anomaly Detection Without Offline Training

We propose a solution to detect anomalous events in videos without the need to train a model offline. Specifically, our solution is based on a randomly-initialized multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information. Based on the information shifts between adjacent frames, an incremental learner is used to update parameters of the multilayer perceptron after observing each frame, thus allowing to detect anomalous events along the video stream. Traditional solutions that require no offline training are limited to operating on videos with only a few abnormal frames. Our solution breaks this limit and achieves strong performance on benchmark datasets.
Adversarial Vulnerability of Temporal Feature Networks for Object Detection

Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally fused neural networks are vulnerable to deliberately generated perturbations, i.e. adversarial attacks, or whether temporal history is an inherent defense against them. In this work, we study whether temporal feature networks for object detection are vulnerable to universal adversarial attacks. We evaluate attacks of two types: imperceptible noise for the whole image and locally-bound adversarial patch. In both cases, perturbations are generated in a white-box manner using PGD. Our experiments confirm, that attacking even a portion of a temporal input suffices to fool the network. We visually assess generated perturbations to gain insights into the functioning of attacks. To enhance the robustness, we apply adversarial training using 5-PGD. Our experiments on KITTI and nuScenes datasets demonstrate, that a model robustified via K-PGD is able to withstand the studied attacks while keeping the mAP-based performance comparable to that of an unattacked model.
Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data balanced. Therefore, the imbalanced data classification problem has gradually become a problem that needs to be studied. Aiming at the sentiment information mining of an imbalanced short text review dataset, this paper proposed a fusion multi-channel BLTCN-BLSTM self-attention sentiment classification method. By building a multi-channel BLTCN-BLSTM self-attention network model, the sample after word embedding processing is used as the input of the multi-channel, and after fully extracting features, the self-attention mechanism is fused to strengthen the sentiment to further fully extract text features. At the same time, focus loss rebalancing and classifier enhancement are combined to realize text sentiment predictions. The experimental results show that the optimal F1 value is up to 0.893 on the Chnsenticorp-HPL-10,000 corpus. The comparison and ablation of experimental results, including accuracy, recall, and F1-measure, show that the proposed model can fully integrate the weight of emotional feature words. It effectively improves the sentiment classification performance of imbalanced short-text review data.
Introducing Virtual DIC to Remove Interpolation Bias and Process Optimal Patterns

Digital Image Correlation (DIC) is an image-based measurement technique routinely used in experimental mechanics, which provides displacement and strain maps of an observed surface/volume. The metrological performance of DIC has reached its limit which is directly determined by the texture of the imaged surface/volume. This paper proposes a novel DIC strategy, which relies on a virtual image. This image, noiseless and of infinite resolution, is moreover optimized for providing measurements with the best metrological performance. The so-called Virtual DIC retrieves the displacement fields by comparing this virtual image to the experimental images. No interpolation is required and processing optimal textures such as checkerboards is possible. Virtual DIC is first applied on synthetic images for comparison purposes with a usual DIC approach. Outstanding metrological performance is observed thanks to the possibility of processing checkerboard patterns. The proposed Virtual DIC is twofold: (i) thanks to the use of a closed-form expression, built-in DIC operators are elaborated without recurring to noisy and poorly defined real images. Interpolation is therefore avoided; (ii) it makes possible it to process checkerboard patterns, which offers the best metrological performance.
When iota meets lambda

Abstract Definite descriptions are widely discussed in linguistics and formal semantics, but their formal treatment in logic is surprisingly modest. In this article we present a sound, complete, and cut-free tableau calculus $${\textbf{TC}}_{R_{\lambda }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>TC</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mi>λ</mml:mi></mml:msub></mml:msub></mml:math> for the logic $${\textbf{L}}_{R_{\lambda }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mi>λ</mml:mi></mml:msub></mml:msub></mml:math> being a formalisation of a Russell-style theory of definite descriptions with the iota-operator used to construct definite descriptions, the lambda-operator forming predicate-abstracts, and definite descriptions as genuine terms with a restricted right of residence. We show that in this setting we are able to overcome problems typical of Russell’s original theory, such as scoping difficulties or undesired inconsistencies. We prove the Craig interpolation property for the proposed theory, which, through the Beth definability property, allows us to check whether an individual constant from a signature has a definite description-counterpart under a given theory.
Crowd Behavior Analysis and Prediction using the Feature Fusion Framework

The increasing number of people is a major cause of disasters that occur due to overcrowding. The gatherings of crowds in public places are a source of panic, which results in disaster. An analytical study was performed on crowd management. This is highly essential for the design of a well-planned public space, the possibility of surveillance in every area, and transportation systems. The disasters that occur due to uncontrollable crowd behaviour involve loss of property, fatalities, or casualties. To avoid this, the crowd’s behaviour was analysed. A MFF (multi-level feature fusion) framework was designed in this paper to predict behaviour. The first level of multi-level feature fusion employs motion and appearance, the second level employs spatial connections, and the third level employs temporal features. The combination of these characteristics aids in the exploitation of crowd behaviour. Furthermore, MFF was evaluated considering the web dataset, considering accuracy, precision, and recall as parameters. Comparative analysis was carried out with various existing methodologies with an accuracy of above 99 %.
SIR: Self-Supervised Image Rectification via Seeing the Same Scene From Multiple Different Lenses

Deep learning has demonstrated its power in image rectification by leveraging the representation capacity of deep neural networks via supervised training based on a large-scale synthetic dataset. However, the model may overfit the synthetic images and generalize not well on real-world fisheye images due to the limited universality of a specific distortion model and the lack of explicitly modeling the distortion and rectification process. In this paper, we propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of a same scene from different lenses should be the same. Specifically, we devise a new network architecture with a shared encoder and several prediction heads, each of which predicts the distortion parameter of a specific distortion model. We further leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters and exploit the intra- and inter-model consistency between them during training, thereby leading to a self-supervised learning scheme without the need for ground-truth distortion parameters or normal images. Experiments on synthetic dataset and real-world fisheye images demonstrate that our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art (SOTA) methods. The proposed self-supervised method also provides a possible way to improve the universality of distortion models while keeping their self-consistency. Code and datasets will be available at https://github.com/loong8888/SIR.