Reviewerly/exHarmony huggingface dataset:
Impact of Packet Loss Rate on Quality of Compressed High Resolution Videos

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions.
Semi-automatic muscle segmentation in MR images using deep registration-based label propagation

Fully automated approaches based on convolutional neural networks have shown promising performances on muscle segmentation from magnetic resonance (MR) images, but still rely on an extensive amount of training data to achieve valuable results. Muscle segmentation for pediatric and rare diseases cohorts is therefore still often done manually. Producing dense delineations over 3D volumes remains a time-consuming and tedious task, with significant redundancy between successive slices. In this work, we propose a segmentation method relying on registration-based label propagation, which provides 3D muscle delineations from a limited number of annotated 2D slices. Based on an unsupervised deep registration scheme, our approach ensures the preservation of anatomical structures by penalizing deformation compositions that do not produce consistent segmentation from one annotated slice to another. Evaluation is performed on MR data from lower leg and shoulder joints. Results demonstrate that the proposed few-shot multi-label segmentation model outperforms state-of-the-art techniques.
A Lack of Restraint: Comparing Virtual Reality Interaction Techniques for Constrained Transport Seating

Standalone Virtual Reality (VR) headsets can be used when travelling in cars, trains and planes. However, the constrained spaces around transport seating can leave users with little physical space in which to interact using their hands or controllers, and can increase the risk of invading other passengers' personal space or hitting nearby objects and surfaces. This hinders transport VR users from using most commercial VR applications, which are designed for unobstructed 1-2m 360 ° home spaces. In this paper, we investigated whether three at-a-distance interaction techniques from the literature could be adapted to support common commercial VR movement inputs and so equalise the interaction capabilities of at-home and on-transport users: Linear Gain, Gaze-Supported Remote Hand, and AlphaCursor. First, we analysed commercial VR experiences to identify the most common movement inputs so that we could create gamified tasks based on them. We then investigated how well each technique could support these inputs from a constrained 50x50cm space (representative of an economy plane seat) through a user study (N=16), where participants played all three games with each technique. We measured task performance, unsafe movements (play boundary violations, total arm movement) and subjective experience and compared results to a control 'at-home' condition (with unconstrained movement) to determine how similar performance and experience were. Results showed that Linear Gain was the best technique, with similar performance and user experience to the 'at-home' condition, albeit at the expense of a high number of boundary violations and large arm movements. In contrast, AlphaCursor kept users within bounds and minimised arm movement, but suffered from poorer performance and experience. Based on the results, we provide eight guidelines for the use of, and research into, at-a-distance techniques and constrained spaces.
CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors

Integrated sensing and communication is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors and the possibility of being manipulated by users with physical access complicate their protection against spectrum sensing data falsification (SSDF) attacks. Most recent literature suggests using behavioral fingerprinting and Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues. Nevertheless, the applicability of these techniques in resource-constrained devices, the impact of attacks affecting spectrum data integrity, and the performance and scalability of models suitable for heterogeneous sensors types are still open challenges. To improve limitations, this work presents seven SSDF attacks affecting spectrum sensors and introduces CyberSpec, an ML/DL-oriented framework using device behavioral fingerprinting to detect anomalies produced by SSDF attacks. CyberSpec has been implemented and validated in ElectroSense, a real crowdsensing RF monitoring platform where several configurations of the proposed SSDF attacks have been executed in different sensors. A pool of experiments with different unsupervised ML/DL-based models has demonstrated the suitability of CyberSpec detecting the previous attacks within an acceptable timeframe.
The efficiency of quantum teleportation with three-qubit entangled state in a noisy environment

Abstract Quantum teleportation plays a significant role in the field of quantum communication. This paper investigates quantum teleportation through a noisy environment by using GHZ state and non-standard W state as quantum channels. We analyze the efficiency of quantum teleportation by solving analytically a master equation in Lindblad form. Following the quantum teleportation protocol, we obtain the fidelity of quantum teleportation as a function of evolution time. The calculation results show that the teleportation fidelity using non-standard W is higher in comparison to GHZ state at the same evolution time. Moreover, we consider the efficiency of teleportation with weak measurements and reverse quantum measurement under amplitude damping noise. Our analysis suggests that the teleportation fidelity using non-standard W is also more robust to noise than GHZ state in the same conditions. Interestingly, we found that weak measurement and its reverse operation have no positive effect on the efficiency of quantum teleportation by using GHZ and non-standard W state in the amplitude damping noise environment. In addition, we also demonstrate the efficiency of quantum teleportation can be improved by making minor modifications to the protocol.
Fake Profile Identification Using Machine Learning

Nowadays, remarkable growth in the use of social networks, as well as the manner of using them has fundamentally changed the way of using the Internet. This increases the quantity and quality of shared data in these social networks. In addition, the security and confidentiality of data become an important concern to the significance of detecting malicious profiles in OSNs (Online Social Networks), the social network security has offered multiple techniques for detecting malicious profiles and content spam. In this paper, the authors test a new system to detect malicious profiles. First, they propose the identification of false profiles by the security approach based on social honeypots. Second, they suggest detecting those that are similar to the profiles detected by the approach of honeypot using a similarity function based on collaborative filtering. This work uses a hybrid system based on honeypots and a recommender system algorithm for detecting malicious users and those are similar. The approach has greatly improved the quality of abstraction in terms of performance and design. The algorithm is also fast and simple to implement. Experimental results show the stability and accuracy (over 99,99%) of our approach.
A hybrid classical-quantum approach to speed-up Q-learning

We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probability distributions. This quantum routine is then employed, in a reinforcement learning set-up, to encode the distributions that drive action choices. Our routine is well-suited in the case of a large, although finite, number of actions and can be employed in any scenario where a probability distribution with a large support is needed. We describe the routine and assess its performance in terms of computational complexity, needed quantum resource, and accuracy. Finally, we design an algorithm showing how to exploit it in the context of Q-learning.
TTL: transformer-based two-phase transfer learning for cross-lingual news event detection

Abstract Today, we have access to a vast data amount, especially on the internet. Online news agencies play a vital role in this data generation, but most of their data is unstructured, requiring an enormous effort to extract important information. Thus, automated intelligent event detection mechanisms are invaluable to the community. In this research, we focus on identifying event details at the sentence and token levels from news articles, considering their fine granularity. Previous research has proposed various approaches ranging from traditional machine learning to deep learning, targeting event detection at these levels. Among these approaches, transformer-based approaches performed best, utilising transformers’ transferability and context awareness, and achieved state-of-the-art results. However, they considered sentence and token level tasks as separate tasks even though their interconnections can be utilised for mutual task improvements. To fill this gap, we propose a novel learning strategy named Two-phase Transfer Learning (TTL) based on transformers, which allows the model to utilise the knowledge from a task at a particular data granularity for another task at different data granularity, and evaluate its performance in sentence and token level event detection. Also, we empirically evaluate how the event detection performance can be improved for different languages (high- and low-resource), involving monolingual and multilingual pre-trained transformers and language-based learning strategies along with the proposed learning strategy. Our findings mainly indicate the effectiveness of multilingual models in low-resource language event detection. Also, TTL can further improve model performance, depending on the involved tasks’ learning order and their relatedness concerning final predictions.
The Impact of Digital Media Learning Apps on Students' Behaviors in Distance Learning During COVID-19 at the University of Jordan

The Corona pandemic created many challenges for all sectors. However, confronting these challenges was easy due to the availability of Information Communication Technology. ICT penetration and usage are largely facilitated especially, in the educational arenas are attaining several benefactions. In the current research, the researcher focused on the Microsoft Team application and the extent to which students accept the e-learning process through it. To achieve the research objectives, the experimental method was adopted by designing an electronic questionnaire distributed among n= 250 students from the College of Languages, University of Jordan. The researcher further executed Structural Equation Modelling for the data analysis and assessing the study model. The results revealed that usability, perceived ease of use, and usefulness, all are major factors that motivate students to use Microsoft Teams Application. As they widely depend on the Microsoft Teams application, it also helps them to continue their educational activities through digital means.
Special Issue on Current Trends and Future Directions in Voice Acoustics Measurement

The human voice production mechanism implements a superbly rich communication channel that at once tells us what, who, how, and much more [...]
A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers among women. Early detection of the cancer type is essential to help inform subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large datasets and are not developed for small datasets. Although large datasets may lead to more reliable results, their collecting and processing are challenging. Materials and Methods: This paper proposes a new ensemble deep learning model for breast cancer grade detection based on small datasets. Our model uses some basic deep-learning classifiers to grade the breast tumors, including grades I, II, and III. Since none of the previous works focus on the datasets, including breast cancer grades, we have used a new dataset called Databiox to grade the breast cancers in the three grades. Databiox includes histopathological microscopy images from patients with invasive ductal carcinoma (IDC). Results: The performance of the model is evaluated based on the small dataset. We compare the proposed three-layer ensemble classifier with the most common single deep learning classifiers in terms of accuracy and loss. The experimental results show that the proposed model can improve the classification accuracy of the breast cancer grade compared to the other state-of-the-art single classifiers. Conclusion: The ensemble model can be also used for small datasets. In addition, they can improve the accuracy compared to the other models. This achievement is fundamental for the design of classification-based systems in computer-aided diagnosis.
Image complexity in the tracking of DLD

Image complexity in a picture-identification task (PI-task) is shown to affect children's comprehension of relative clauses (RCs), object RCs, in particular.Images depicting reversible thematic relations led to more errors than non-reversible images for 5-7-yearolds.Schoolchildren identified as at risk of Developmental Language Disorder (DLD) had poorer performance than their age-matched control in the comprehension of RCs and which-questions (particularly in object sentences with complex images).Results of a correlation between children's scores on a language assessment test by means of a PI-Task and two non-verbal inhibitory control tasks suggest low inhibitory capacity makes it more difficult for children at risk of DLD to cope with costly sentences.
Deep Blind Image Quality Assessment Powered by Online Hard Example Mining

Recently, blind image quality assessment (BIQA) models based on deep neural networks (DNNs) have achieved impressive performance on existing datasets. However, due to the intrinsic imbalance property of the training set, not all distortions or images are handled equally well. Online hard example mining (OHEM) is a promising way to alleviate this issue. Inspired by the recent finding that network pruning disproportionately hampers the model's memorization of a tractable subset, atypical, low-quality, long-tailed samples, that are hard-to-memorize during training and easily “forgotten” during pruning, we propose an effective “plug-and-play” OHEM pipeline, especially for generalizable deep BIQA. Specifically, we train two parallel weight-sharing branches simultaneously, where one is full model and other is a “self-competitor” generated from the full model online by network pruning. Then, we leverage the prediction disagreement between the full model and its pruned variant (i.e., the self-competitor) to expose easily “forgettable” samples, which are therefore regarded as the hard ones. We then enforce the prediction consistency between the full model and its pruned variant to implicitly put more focus on these hard samples, which benefits the full model to recover forgettable information introduced by pruning. Extensive experiments across multiple datasets and BIQA models demonstrate that the proposed OHEM can improve the model performance and generalizability as measured by correlation numbers and group maximum differentiation (gMAD) competition. Our code are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangzhihua520/IQA_with_OHEM</uri>
IoT Smart Flooring Supporting Active and Healthy Lifestyles

The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build "Outdoor Smart Objects" (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.
Distributed quantum sensing with optical lattices

In distributed quantum sensing the correlations between multiple modes, typically of a photonic system, are utilized to enhance the measurement precision of an unknown parameter. In this work we investigate the metrological potential of a multi-mode, tilted Bose-Hubbard system and show that it can allow for parameter estimation at the Heisenberg limit of $(N(M-1)T)^{2}$, where $N$ is the number of particles, $M$ is the number of modes, and $T$ is the measurement time. The quadratic dependence on the number of modes can be used to increase the precision compared to typical metrological systems with two atomic modes only, and does not require correlations between different modes. We show that the limit can be reached by using an optimized initial state given as the superposition of all the atoms occupying the first and the last site. Subsequently, we present strategies that would allow to obtain quadratic dependence on $M$ of the Fisher information in a more realistic experimental setup.
The Mediating Effect of Trust on Psychological Safety and Job Satisfaction

This research aims to assess the relationship of psychological safety and trust on employee job satisfaction. Individual job satisfaction is essential to personal and professional accomplishments, impacting team success, and achieving optimal organizational performance. A safe environment is a critical factor to achieve job satisfaction. When employees feel safe, learning occurs more quickly, peers rely on each other, and team performance improves. Trust in peers and management influences psychological safety development. Therefore, understanding the role of psychological safety and trust in the development of engaged employees is essential. In this paper, we approach the idea that psychological safety is a prerequisite to trust, and we believe trust mediates the relationship between psychological safety and job satisfaction. A quantitative approach using correlation and mediation tested the theory of the effect trust has on psychological safety and job satisfaction. Responses from 283 nurses at a large teaching hospital revealed that psychological safety is linked to job satisfaction and mediated by trust. Overall, this study suggests that psychological safety precedes trust and collectively increases individual job satisfaction. Managers can draw upon the potential, but frequently untapped benefits, of cultivating a safe space by developing a sincere and trusting relationship with employee-partners thus increasing job satisfaction.
Realizing symmetry-protected topological phases in a spin-1/2 chain with next-nearest-neighbor hopping on superconducting qubits

Quantum simulation on near-term quantum hardware is a topic of intense interest. The preparation of novel quantum states of matter provides a quantitative assessment of the capabilities of near-term digital quantum computers to implement circuits with structure of relevance to quantum simulation. Here, we conduct a benchmark study by realizing symmetry-protected topological (SPT) phases of a spin-1/2 Hamiltonian with next-nearest-neighbor hopping on up to 11 qubits on a programmable superconducting quantum processor using adiabatic state preparation. Using recompilation techniques to reduce the gate count to around 50 two-qubit gates, we observe clear signatures of the two distinct SPT phases, such as excitations localized to specific edges and finite string-order parameters. We identify a parasitic phase associated with the two-qubit gate as the dominant imperfection that limits the depth of the circuits, indicating a research topic of interest for future hardware development.
A Novel Denoising Algorithm Based on Wavelet and Non-Local Moment Mean Filtering

Denoising is the basis and premise of image processing and an important part of image preprocessing. Denoising can effectively improve image quality, which contributes to subsequent image processing such as image segmentation, feature extraction, and so on. In this paper, we propose a novel image denoising method based on wavelet transform and nonlocal moment mean filtering approach (NMM). The noisy image is firstly denoised by a wavelet-based soft-thresholding denoising technique and NMM is then utilized to further eliminate the rest noises. Meanwhile, the fusion of moment invariants increases the robustness of our denoising algorithm due to the invariance of image scaling, translation, and rotation of color moments. Experiments show that our algorithm achieves a better denoising effect compared with some other denoising approaches.