Reviewerly/exHarmony huggingface dataset:
Viewpoint-Aware Progressive Clustering for Unsupervised Vehicle Re-Identification

Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi-776 and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
An Imaging Information Estimation Network for Underwater Image Color Restoration

Computer vision plays an important role in scientific research, resource exploration, and other underwater applications. However, it suffers from the severe color distortion, which is caused by the scattering and absorption of light in the water. In this article, an underwater image color restoration network (UICRN) is proposed to obtain the real color of the image by estimating the main parameters of the underwater imaging model. First, an encoder neural network is applied to extract features from the input underwater image. Second, three independent decoders are used to estimate the direct light transmission map, backscattered light transmission map, and veiling light. Third, the loss functions and the training strategy are designed to improve the performance of restoration. As we know, the learning-based method would require a paired data set for training. An underwater image generation method is also proposed in this article to obtain the data set consisting of color-distorted images and corresponding ground truth. The method combines the inherent optical properties and apparent optical properties with structure information to generate the paired data set. More than 20 000 pairs of underwater images are generated based on the method. Finally, the UICRN method is quantitatively evaluated through various experiments, such as color chart testing in the South China Sea and natural underwater image evaluation. It demonstrates that the UICRN method is competitive with previous state-of-the-art methods in color restoration and robustness.
Deep Reinforcement Learning

This chapter starts by covering the basic concepts involved in reinforcement learning and then describes how to solve reinforcement learning tasks by using basic and deep learning-based solutions. It also provides a brief overview of the typical algorithms central to the deep learning-based solutions, namely DQN, DDPG, and A3C.
Bell-state generation for spin qubits via dissipative coupling

Magnet-mediated dissipative coupling between spin qubits can be exploited to imprint long-lived quantum entanglement. This work points to a new direction for the application of spintronic schemes in future quantum information technologies, with an eye on the dynamical phase transitions associated with non-Hermitian dynamics of the reduced density matrix.
Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.
Humour and (mock) aggression: Distinguishing cyberbullying from roasting

This paper postulates a distinction between two types of discursive aggression in the context of humour production. This theoretical argument is supported by a qualitative (meta)pragmatic analysis of posts on the RoastMe subreddit devoted to an online community's original humorous practice based on jocular insults. The study of emic (community members') and etic (outsiders') metapragmatic evaluations of the content on the subreddit yields divergent insider/outsider interpretations of the practice at hand; it also uncovers some ambivalence in the polysemous folk concepts of roasting and cyberbullying and the relationship between them. “Roasting” and “cyberbullying” are formally distinguished based on the intended form of aggression on which each relies. Also, the points of convergence between humour and aggression are surveyed. First, mock aggression underlies benevolent humour (typified by roasting proper); second, genuine aggression (exemplified by cyberbullying proper) may amuse individuals other than the target of discursive aggression.
One TTS Alignment to Rule Them All

Speech-to-text alignment is a critical component of neural text-to-speech (TTS) models. Autoregressive TTS models typically use an attention mechanism to learn these alignments on-line. However, these alignments tend to be brittle and often fail to generalize to long utterances and out-of-domain text, leading to missing or repeating words. Most non-autoregressive end-to-end TTS models rely on durations extracted from external sources. In this paper we leverage the alignment mechanism proposed in RAD-TTS and demonstrate its applicability to wide variety of neural TTS models. The alignment learning framework combines the forward-sum algorithm, Viterbi algorithm, and an efficient static prior. In our experiments, the framework improves all tested TTS architectures, both autoregressive (Flowtron, Tacotron 2) and non-autoregressive (FastPitch, FastSpeech 2, RAD-TTS). Specifically, it improves alignment convergence speed, simplifies the training pipeline by eliminating need for external aligners, enhances robustness to errors on long utterances and improves the perceived speech synthesis quality, as judged by human evaluators.
FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening

Breast mass segmentation in mammograms is still a challenging and clinically valuable task. In this paper, we propose an effective and lightweight segmentation model based on convolutional neural networks to automatically segment breast masses in whole mammograms. Specifically, we first developed feature strengthening modules to enhance relevant information about masses and other tissues and improve the representation power of low-resolution feature layers with high-resolution feature maps. Second, we applied a parallel dilated convolution module to capture the features of different scales of masses and fully extract information about the edges and internal texture of the masses. Third, a mutual information loss function was employed to optimise the accuracy of the prediction results by maximising the mutual information between the prediction results and the ground truth. Finally, the proposed model was evaluated on both available INbreast and CBIS-DDSM datasets, and the experimental results indicated that our method achieved excellent segmentation performance in terms of dice coefficient, intersection over union, and sensitivity metrics.
Adherence and Association of Digital Proximity Tracing App Notifications With Earlier Time to Quarantine: Results From the Zurich SARS-CoV-2 Cohort Study

Objectives: We aimed to evaluate the effectiveness of the SwissCovid digital proximity tracing (DPT) app in notifying exposed individuals and prompting them to quarantine earlier compared to individuals notified only by manual contact tracing (MCT). Methods: A population-based sample of cases and close contacts from the Zurich SARS-CoV-2 Cohort was surveyed regarding SwissCovid app use and SARS-CoV-2 exposure. We descriptively analyzed app adherence and effectiveness, and evaluated its effects on the time between exposure and quarantine among contacts using stratified multivariable time-to-event analyses. Results: We included 393 SARS-CoV-2 infected cases and 261 close contacts. 62% of cases reported using SwissCovid and among those, 88% received and uploaded a notification code. 71% of close contacts were app users, of which 38% received a warning. Non-household contacts notified by SwissCovid started quarantine 1 day earlier and were more likely to quarantine earlier than those not warned by the app (HR 1.53, 95% CI 1.15–2.03). Conclusion: These findings provide evidence that DPT may reach exposed contacts faster than MCT, with earlier quarantine and potential interruption of SARS-CoV-2 transmission chains.
An efficient identity-based signature scheme with provable security

Many identity-based digital signature schemes are proved secure in the random oracle model. However, the application of the random oracle may lead to security risks. The used hash function is specific and the response result of the query is not always random, hence it may cause the insecurity of the scheme. To solve the above issues, this paper presents an efficient identity-based signature scheme which is proved secure under the standard model. The security of the proposed scheme is reduced to the well-known computational Diffie–Hellman (CDH) assumption. Furthermore, compared with the related identity-based signature schemes, the scheme proposed in this paper has great advantages in the computation cost of signing and verification.
Semi-device independent randomness generation based on quantum state’s indistinguishability

Semi-device independent (Semi-DI) quantum random number generators (QRNG) gained attention for security applications, offering an excellent trade-off between security and generation rate. This paper presents a proof-of-principle time-bin encoding semi-DI QRNG experiments based on a prepare-and-measure scheme. The protocol requires two simple assumptions and a measurable condition: an upper-bound on the prepared pulses' energy. We lower-bound the conditional min-entropy from the energy-bound and the input-output correlation, determining the amount of genuine randomness that can be certified. Moreover, we present a generalized optimization problem for bounding the min-entropy in the case of multiple-input and outcomes in the form of a semidefinite program (SDP). The protocol is tested with a simple experimental setup, capable of realizing two configurations for the ternary time-bin encoding scheme. The experimental setup is easy-to-implement and comprises commercially available off-the-shelf (COTS) components at the telecom wavelength, granting a secure and certifiable entropy source. The combination of ease-of-implementation, scalability, high-security level, and output-entropy make our system a promising candidate for commercial QRNGs.
COVID-19 and Phishing: Effects of Human Emotions, Behavior, and Demographics on the Success of Phishing Attempts During the Pandemic

Phishing is an online scam where criminals trick users with various strategies, with the goal of obtaining sensitive information or compromising accounts, systems, and/or other personal or organisational Information Technology resources. Multiple studies have shown that human factors influence the success of phishing attempts. However, these studies were conducted before the COVID-19 pandemic, which is significant because security reports show that the numbers of phishing attacks have been rapidly increasing since the start of COVID-19. This study investigates the extent to which users' fear, anxiety and stress levels regarding COVID-19, impact falling for common and COVID-19 themed phishing scams during the outbreak period. Prior studies have depicted the effects of human behaviour on phishing attacks before the pandemic, such as risk-taking preferences and users' demographic factors, hence this study also focuses on the effects of those factors on the likelihood of phishing victimisation. More concretely, we present the results of a scenario-based roleplay experiment to study the relationship between fear, anxiety, stress, risk-taking, and demographic factors and the success of phishing attacks during the pandemic. The findings indicate that fear of COVID-19 influences the success of COVID-19 specific themed phishing scams, while anxiety, stress, and risk-taking influences the success of both the COVID-19 themed and common phishing scams. Our findings also suggest that the users' education level impacts common phishing attacks during the pandemic.
Toward the Minimum Number of Wearables to Recognize Signer-Independent Italian Sign Language With Machine-Learning Algorithms

Sign languages (SL) can aid in improving the communication between deaf and hearing communities, but only a limited number of hearing individuals understand it. SL recognition systems tackle this issue by using sensors to acquire gesture data, later converted into spoken or written language by means of machine learning algorithms. Within this frame, we propose a wearable electronic-based system for achieving automatic signer-independent recognition of Italian SL. The system is used for recognizing ten signs performed by 17 inexperienced signers. The wearable electronics measure hand, arm, and forearm gestures, by means of a sensory glove (equipped with ten flex sensors) and six Inertial Measurement Units (IMUs). We adopt three classifiers: Artificial Neural Network (ANN), k-Nearest Neighbors and Support Vector Machine, with ANN outperforming the others with an accuracy of 95.07%. However, the real key and novel aspect of this work concerns exploring the importance of each sensor for the overall classification accuracy, with the aim of determining the minimum number of sensors (and so minimizing the complexity of the wearable system) necessary to guarantee an acceptable accuracy. As a result, we could drop out three of the six IMUs, obtaining an accuracy as high as 93.91%. In addition, this valuable result was obtained using a signer-independent approach, which makes the application closer to a real-life situation.
A Smart Capsule Equipped with Artificial Intelligence for Autonomous Delivery of Medical Material through Drones

In the last few years, many examples of blood and medicine delivery drones have been demonstrated worldwide, which mainly rely on aeronautical experience that is not common in the medical world. Speaking about drone delivery, attention should focus on the most important thing: the transported lifesaving good. Traditional boxes that monitor temperature are not usually in real time, and are not suitable for drone transportation because they are heavy and bulky. This means that the biomedical characteristics of delivery are of primary importance. A Smart Capsule, equipped with artificial intelligence (AI), is the first system ever proposed to provide a fully autonomous drone delivery service for perishable and high-value medical products, integrating real-time quality monitoring and control. It consists in a smart casing that is able to guide any autonomous aerial vehicle attached to it, specifically designed for transporting blood, organs, tissues, test samples and drugs, among others. The system monitors the conditions of the product (e.g., temperature, agitation and humidity) and adjusts them when needed by exploiting, for instance, vibrations to maintain the required agitation, ensuring that goods are ready to be used as soon as they are delivered. The Smart Capsule also leverages external temperature to reduce energy uptake from the drone, thus improving the drone’s battery life and flight range. The system replaces the need for specialized drivers and traditional road-bound transportation means, while guaranteeing compliance with all applicable safety regulations. A series of 16 experimental tests was performed to demonstrate the possibility of using the smart capsule to manage the flight and internal good delivery. Eighty-one missions were carried out for a total of 364 min of flight. The Smart Capsule greatly improves emergency response and efficiency of healthcare systems by reducing delivery times by up to 80% and costs by at least 28%. The Smart Capsule and its enabling technology based on AI for drone deliveries are discussed in this paper. The aim of this work is to show the possibility of managing drone delivery with an AI-based device.
Sag-based cable tension force evaluation of cable-stayed bridges using multiple digital images

Cable tension is a critical variable for evaluating the structural health and safety of cable-stayed bridges. Although various vision-based systems have been developed for measuring the tension of cables, it is still cumbersome to measure the instantaneous tension of cables, which fluctuates owing to external loadings such as temperature and wind. Therefore, in this paper, a method for evaluating the tension and range of tension using multiple digital images is proposed. A field test was conducted on cables of a real cable-stayed bridge to confirm the applicability and reliability of the proposed method. The cable tension evaluated using the image was compared with the value measured directly through lift-off tests, and the tension evaluated was consistent with that measured directly. The proposed method can provide a time- and cost-effective option for determining the instantaneous cable tension force of cable-stayed bridges compared to existing tension measurement systems.
All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Similarity measures are a vital tool for understanding how language models represent and process language.Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used in static word embedding models to understand how words cluster in semantic space.Recently, these measures have been applied to embeddings from contextualized models such as BERT and GPT-2.In this work, we call into question the informativity of such measures for contextualized language models.We find that a small number of rogue dimensions, often just 1-3, dominate these measures.Moreover, we find a striking mismatch between the dimensions that dominate similarity measures and those which are important to the behavior of the model.We show that simple postprocessing techniques such as standardization are able to correct for rogue dimensions and reveal underlying representational quality.We argue that accounting for rogue dimensions is essential for any similarity-based analysis of contextual language models.
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real time applications. Channel pruning is one important compression strategy, and widely used in slimming various DNNs. Previous gate-based or importance-based pruning methods aim to remove channels whose "importance" are smallest. However, it remains unclear what criteria the channel importance should be measured on, leading to various channel selection heuristics. Some other sampling-based pruning methods deploy sampling strategy to train sub-nets, which often causes the training instability and the compressed model’s degraded performance. In view of the research gaps, we present a new module named Gates with Differentiable Polarization (GDP), inspired by principled optimization ideas. GDP can be plugged before convolutional layers without bells and whistles, to control the on-and-off of each channel or whole layer block. During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin. When training terminates, those zero-gated channels can be painlessly removed, while other non-zero gates can be absorbed into the succeeding convolution kernel, causing completely no interruption to training nor damage to the trained model. Experiments conducted over CIFAR-10 and ImageNet datasets show that the proposed GDP algorithm achieves the state-of-the-art performance on various benchmark DNNs at a broad range of pruning ratios. We also apply GDP to DeepLabV3Plus-ResNet50 on the challenging Pascal VOC segmentation task, whose test performance sees no drop (even slightly improved) with over 60% FLOPs saving.
Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems

In recent years, deep learning (DL) has been widely used in vehicle misbehavior detection and has attracted great attention due to its powerful nonlinear mapping ability. However, because of the large number of network parameters, the training processes of these methods are time consuming. Besides, the existing detection methods lack scalability; thus, they are not suitable for Internet of Vehicles (IoV) where new data are constantly generated. In this article, the concept of the broad learning system (BLS) is innovatively introduced into vehicle misbehavior detection. In order to make better use of vehicle information, key features are first extracted from the collected raw data. Then, a BLS is established, which is able to calculate the connection weight of the network efficiently and effectively by ridge regression approximation. Finally, the system can be updated and refined by an incremental learning algorithm based on the newly generated data in IoV. The experimental results show that the proposed method performs much better than DL or traditional classifiers, and could update and optimize the old model fastly and progressively while improving the system’s misbehavior detection accuracy.