Reviewerly/exHarmony huggingface dataset:
Robust thermal correlations induced by spin–orbit interactions

In this work, we demonstrate that the thermal nonclassical correlations can enhance and become more stable under the magnetic field and second-order spin–orbit interaction effect. Also, we show that the local quantum uncertainty measure can reveal purely quantum correlations beyond other quantifiers of correlations. Our analytical model describes a Heisenberg XYZ two-spin system with an inhomogeneous magnetic field and spin–orbit interaction. A special emphasis is devoted to local quantum uncertainty, concurrence, and Bell-nonlocality measures to investigate the correlations of the bipartite-system at the equilibrium temperature. Our results indicate that temperature, magnetic field, and magnetic field inhomogeneity with DMI and Kaplan–Shekhtman–Entin-Wohlman–Aharony (KSEA) exchanges all play a role in determining the degree of correlation between the quantum spins to some extent. The KSEA interaction greatly enhances the quantum correlation in the system whereas quantum quantifiers are more stable under the influence of the external magnetic field, especially when inhomogeneous and homogeneous magnetic field are near to zero. Furthermore, these findings suggest that the thermal LQU captures a stronger quantum correlation than the Bell-nonlocality and entanglement measures.
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuits to configure quantum neural networks (QNN). The effectiveness of AutoQML is validated by an in-house experiment for human pose recognition, achieving state-of-the-art performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters.
Hierarchical Correlations Replay for Continual Learning

Continual Learning (CL) aims at incrementally learning new knowledge from an infinite stream of data while preserving old knowledge. A perfect CL method is expected to retain the old knowledge greedily to alleviate the catastrophic interference caused by the new knowledge. However, most existing methods only focus on exploring the knowledge carried by the instance itself but neglect the correlations of inter-instance and inter-class, which are also valuable for preserving knowledge. To this end, we propose a novel method dubbed Hierarchical Correlations Replay (HCR) consisting of an Instance-level Correlation Replay (ICR) module and a Class-level Correlation Replay (CCR) module, which retain both the instance-level and the class-level correlations to consolidate old knowledge. Specifically, the ICR module employs a correlation matrix to represent the instance-level correlation, and a random triplet probability is utilized to construct the class-level correlation in the CCR module. Extensive experiments on five benchmark image datasets show that our HCR is competitive with or superior to state-of-the-art methods under diverse continual learning settings.
A line-segment-based non-maximum suppression method for accurate object detection

Computer vision models are currently making great strides in object detection with the rapid development of deep convolutional detectors. However, generating a large number of anchors is an indispensable step in the object detection models for locating targets, which inevitably leads to redundant detections and low computational efficiency. Detecting contours in an image is a fundamental cognitive ability in human vision system, which offers effective evidences for object detection. This paper proposes a novel and simple method by utilizing the distribution of line segments to facilitate the Non-Maximum Suppression (NMS) for the object detection models. Multiple differentiated metrics are designed for the overlap measure between bounding boxes. As a post-processing technique, the proposed segment-based NMS can be easily applied by various models. Furthermore, the proposed method is verified on multiple benchmarks and extensive experiments have been implemented to illustrate its effectiveness.
An analysis of pathologists’ viewing processes as they diagnose whole slide digital images

Although pathologists have their own viewing habits while diagnosing, viewing behaviors leading to the most accurate diagnoses are under-investigated. Digital whole slide imaging has enabled investigators to analyze pathologists' visual interpretation of histopathological features using mouse and viewport tracking techniques. In this study, we provide definitions for basic viewing behavior variables and investigate the association of pathologists' characteristics and viewing behaviors, and how they relate to diagnostic accuracy when interpreting whole slide images. We use recordings of 32 pathologists' actions while interpreting a set of 36 digital whole slide skin biopsy images (5 sets of 36 cases; 180 cases total). These viewport tracking data include the coordinates of a viewport scene on pathologists' screens, the magnification level at which that viewport was viewed, as well as a timestamp. We define a set of variables to quantify pathologists' viewing behaviors such as zooming, panning, and interacting with a consensus reference panel's selected region of interest (ROI). We examine the association of these viewing behaviors with pathologists' demographics, clinical characteristics, and diagnostic accuracy using cross-classified multilevel models. Viewing behaviors differ based on clinical experience of the pathologists. Pathologists with a higher caseload of melanocytic skin biopsy cases and pathologists with board certification and/or fellowship training in dermatopathology have lower average zoom and lower variance of zoom levels. Viewing behaviors associated with higher diagnostic accuracy include higher average and variance of zoom levels, a lower magnification percentage (a measure of consecutive zooming behavior), higher total interpretation time, and higher amount of time spent viewing ROIs. Scanning behavior, which refers to panning with a fixed zoom level, has marginally significant positive association with accuracy. Pathologists' training, clinical experience, and their exposure to a range of cases are associated with their viewing behaviors, which may contribute to their diagnostic accuracy. Research in computational pathology integrating digital imaging and clinical informatics opens up new avenues for leveraging viewing behaviors in medical education and training, potentially improving patient care and the effectiveness of clinical workflow.
Uncertainty-aware selecting for an ensemble of deep food recognition models

Deep learning is a machine learning technique that has revolutionized the research community due to its impressive results on various real-life problems. Recently, ensembles of Convolutional Neural Networks (CNN) have proven to achieve high robustness and accuracy in numerous computer vision challenges. As expected, the more models we add to the ensemble, the better performance we can obtain, but, in contrast, more computer resources are needed. Hence, the importance of deciding how many models to use and which models to select from a pool of trained models is huge. From the latter, a common strategy in deep learning is to select the models randomly or according to the results on the validation set. However, in this way models are chosen based on individual performance ignoring how they are expected to work together. Alternatively, to ensure a better complement between models, an exhaustive search can be used by evaluating the performance of several ensemble models based on different numbers and combinations of trained models. Nevertheless, this may result in being high computationally expensive. Considering that epistemic uncertainty analysis has recently been successfully employed to understand model learning, we aim to analyze whether an uncertainty-aware epistemic method can help us decide which groups of CNN models may work best. The method was validated on several food datasets and with different CNN architectures. In most cases, our proposal outperforms the results by a statistically significant range with respect to the baseline techniques and is much less computationally expensive compared to the brute-force search.
Data Augmentation Using Bitplane Information Recombination Model

The performance of deep learning heavily depend on the quantity and quality of training data. But in many fields, well-annotated data are so difficult to collect, which makes the data scale hard to meet the needs of network training. To deal with this issue, a novel data augmentation method using the bitplane information recombination model (termed as BIRD) is proposed in this paper. Considering each bitplane can provide different structural information at different levels of detail, this method divides the internal hierarchical structure of a given image into different bitplanes, and reorganizes them by bitplane extraction, bitplane selection and bitplane recombination, to form an augmented data with different image details. This method can generate up to 62 times of the training data, for a given 8-bits image. In addition, this generalized method is model free, parameter free and easy to combine with various neural networks, without changing the original annotated data. Taking the task of target detection for remotely sensed images and classification for natural images as an example, experimental results on DOTA dataset and CIFAR-100 dataset demonstrated that, our proposed method is not only effective for data augmentation, but also helpful to improve the accuracy of target detection and image classification.
Quantum entanglement and coherence of tripartite W state for Dirac fields under noisy channels in non-inertial frames

Abstract We investigate quantum entanglement and coherence of tripartite W state for Dirac fields under phase damping, phase flip and bit flip channels, respectively, in non-inertial frames. Via π -tangle, we numerically compute tripartite entanglement of W state under three channels when two observers are accelerated. We also analytically derive l 1 -norm quantum coherence of W state under the simultaneous influence of Unruh effect and noisy environments. For phase-damping and phase-flip channels, both the acceleration and noisy channels modulate the behaviour of the tripartitle entanglement and quantum coherence. They do not vanish even for infinite acceleration when only A 's qubit is under noisy environment, in contrast with the case when two or more observers’ qubits are under noisy environment. In the case of the bit flip channel, the behavior of the tripartite entanglement is similar to the one of the phase flip channel, but quantum coherence remains unaffected by the environment.
Secure Authentication and Key Agreement Protocol for Cloud-Assisted Industrial Internet of Things

With the expansion of the Industrial Internet of Things (IIoT), real-time data collected by smart sensors deployed in factories are shared over open channels , which may cause unauthorized access of transmitted messages by adversaries, thus causing the problem of privacy leakage. User authentication is the first line of defense for security protection in the IIoT environment. In this paper, we propose a cloud—assisted authentication scheme based on Chebyshev polynomial encryption, in which only authorized users can access the sensing devices in the Internet of Things (IoT) to obtain real-time data. The scheme uses fuzzy extraction technology to verify biometric characteristics. There are three factors to verify the user’s login request: the smart card, password and the user’s personal biometrics. The commonly adopted formal security analysis, the ROR model, is applied to prove the semantic security of session key, and a detailed informal security analysis is performed to show that the proposed scheme can withstand multiple known attacks. Compared with other related user authentication schemes, the proposed scheme provides several extra functionality features, including offline sensor node registration, updating user passwords and biometrics, adding new sensor node deployment, user anonymity and untraceability. In addition, the cost of computation, communication and security is compared with similar schemes, and results show that our scheme has more security performance while the cost is acceptable.
On the robustness of information-theoretic authentication in quantum cryptography

Abstract In addition to the quantum channel, an auxiliary classical authentic communication channel is required for quantum key distribution. To provide unconditional security in quantum cryptography, which based on the fundamental laws of quantum mechanics, information-theoretic authentication is required, and not authentication based on computational complexity. The paper provides a security proof of information-theoretic authentication in quantum cryptography using ɛ -ASU 2 hash functions. The internal structure of ɛ -ASU 2 hash functions is not used in the proof, so these hash functions can be implemented by a different composition of other hash functions, for example, including the composition ɛ -AXU 2 functions with a reused key and subsequent encryption of the tag with a one-time pad key, which is taken from the previous session of quantum key distribution. It is also shown that information-theoretic authentication preserves composable security of the keys. This authentication method can also be used for key agrrement in in networks with trusted nodes (Molotkov 2022 Laser Phys. Lett. 19 045201–9; Arbekov and Molotkov 2020 Laser Phys. Lett. 17 055202–8) and with quantum key distribution.
BuHamad - The first Qatari virtual interpreter for Qatari Sign Language

As well as all spoken languages, Sign Languages SL have a structured grammar and syntax. Despite that it was visual, multi-dimensional and mainly based on gestures, SL follows specifics grammatical rules. Consequently, the automatic generation of sign language should follow these rules. It’s for this reason that Mada works on the development of a new framework that aims to support researchers and developers to create new innovant tools for deaf. The objective is essentially to create tools that enhance the development of software using grammatically validated sentences. Signed languages are principally based on manual and facial gestures. For this reason, the automatic generation of SL mainly deals with a 3D virtual signer. In fact, the use of avatars presents a mandatory task on SL generation. Since 2019, Mada worked on the design and the development of a personalized Qatari virtual sign language interpreter. The avatar is now published on Mada website it is used to translate the website content to Qatari SL. On this paper we describe the objective of the project, and we illustrate how it was designed.
Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction

Abstract Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes’ locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features—Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes ( $$M_w \ge 7.0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>M</mml:mi> <mml:mi>w</mml:mi> </mml:msub> <mml:mo>≥</mml:mo> <mml:mn>7.0</mml:mn> </mml:mrow> </mml:math> ), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake’s location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.
More robust object tracking via shape and motion cue integration

Most current trackers utilise an appearance model to localise the target object in each frame. However, such approaches often fail when there are similar looking distractor objects in the surrounding background. This paper promotes an approach that can be combined with many existing trackers to tackle this issue and improve tracking robustness. The proposed approach makes use of two additional cues to target location: shape cues which are exploited through offline training of the appearance model, and motion cues which are exploited online to predict the target object’s future position based on its history of past locations. Combining these additional mechanisms with the existing trackers SiamFC, SiamFC++, Super_DiMP and ARSuper_DiMP all resulted in an increase in the tracking accuracy compared to that achieved by the corresponding underlying tracker alone. When combined with ARSuper_DiMP the resulting tracker is shown to outperform all popular state-of-the-art trackers on three benchmark datasets (OTB-100, NFS, and LaSOT), and produce performance that is competitive with the state-of-the-art on the UAV123, Trackingnet, GOT-10K and VOT2020 datasets.
InkFiltration: Using Inkjet Printers for Acoustic Data Exfiltration from Air-Gapped Networks

Printers have become ubiquitous in modern office spaces, and their placement in these spaces been guided more by accessibility than security. Due to the proximity of printers to places with potentially high-stakes information, the possible misuse of these devices is concerning. We present a previously unexplored covert channel that effectively uses the sound generated by printers with inkjet technology to exfiltrate arbitrary sensitive data (unrelated to the apparent content of the document being printed) from an air-gapped network. We also discuss a series of defense techniques that can make these devices invulnerable to covert manipulation. The proposed covert channel works by malware installed on a computer with access to a printer, injecting certain imperceptible patterns into all documents that applications on the computer send to the printer. These patterns can control the printing process without visibly altering the original content of a document, and generate acoustic signals that a nearby acoustic recording device, such as a smartphone, can capture and decode. To prove and analyze the capabilities of this new covert channel, we carried out tests considering different types of document layouts and distances between the printer and recording device. We achieved a bit error ratio less than 5% and an average bit rate of approximately 0.5 bps across all tested printers at distances up to 4 m, which is sufficient to extract tiny bits of information.
Serious Games: A new Approach to Foster Information and Practices About Covid-19?

The current Covid-19 pandemic poses an unprecedented global challenge in the field of education and training. As we have seen, the lack of proper information about the virus and its transmission has forced the general population and healthcare workers to rapidly acquire knowledge and learn new practices. Clearly, a well-informed population is more likely to adopt the correct precautionary measures, thus reducing the transmission of the infection; likewise, properly educated healthcare workers are better equipped to manage the emergency. However, the need to maintain physical distancing has made it impossible to provide in-presence information and training. In this regard, new technologies have proved to be an invaluable resource by facilitating distance learning. Indeed, e-learning offers significant advantages because it does not require the physical presence of learners and teachers. This innovative method applied to serious games has been considered potentially effective in enabling rapid and large-scale dissemination of information and learning through content interactivity. We will review studies that have observed the development and use of serious games to foster information and practices about Covid-19 aimed at promoting behavioral changes in the population and the healthcare personnel involved on the front line.
Cross-lingual offensive speech identification with transfer learning for low-resource languages

Most of research on the identification of offensive speech on social media platforms exist in English and other rich languages. A series of recently proposed methods for detecting low-resource offensive languages require labeled data. In this work, we propose an unsupervised model that can detect offensive speech for low-resource languages. Our method does not depend on any labeled data of low-resource languages. In detail, we propose an agreement regularized training that combines adversarial learning and transfer learning. Augmenting low-resource training data with sample regeneration methods to maintain the performance of the trained offensive speech identification model from rich-resource to low-resource languages. Extensive experiments on four low-resource languages demonstrate that our model either is on par or outperforms the supervised methods, without employing any annotated data on real-world offensive speech detection tasks for low-resource languages.
A Face Pre-Processing Approach to Evade Deepfake Detector

Recently, various image synthesis technologies have increased the prevalence of impersonation attacks. With the development of such technologies, damages to people such as defamation or fake news have also increased. Deepfakes have already evolved to the point, where people cannot easily distinguish fake from real. This leads to an urgent need for developing detection methods. Currently, in order to detect deepfakes, many deepfake datasets are widely used in deep neural networks. And several methods have been proposed and demonstrated to be effective in detecting deepfakes. In this work, we present pre-processing techniques such as face restoration, edge smoothing, face beautification to mitigate the artifacts of deepfakes and makes them appear more natural to humans, while lowering the deepfake detection performance. Through extensive experiments, our method can significantly lower the performance of the state-of-the-art deepfake detectors and expose the vulnerability of deployed detectors.