Reviewerly/exHarmony huggingface dataset:
Enhanced Interactivity in VR-based Telerobotics: An Eye-tracking Investigation of Human Performance and Workload

Virtual Reality (VR) is gaining ground in the robotics and teleoperation industry, opening new prospects as a novel computerized methodology to make humans interact with robots. In contrast with more conventional button-based teleoperations, VR allows users to use their physical movements to drive robotic systems in the virtual environment. The latest VR devices are also equipped with integrated eye-tracking, which constitutes an exceptional opportunity for monitoring users' workload online. However, such devices are fairly recent, and human factors have been consistently marginalized so far in telerobotics research. We thus covered these aspects by analyzing extensive behavioral data during simulated guidance of an industrial robot in VR through a pick-and-place task. Users drove the robot via button-based and action-based controls and under low (single-task) and high (dual-task) mental demands. We collected self-reports, performance and eye-tracking data. Specifically, we asked i) how the interactive features of VR affect users' performance and workload, and additionally tested ii) the sensibility of diverse eye parameters in monitoring users' vigilance and workload throughout the task. Users performed faster and more accurately, while also showing a lower mental workload, when using an action-based VR control. Among the eye parameters, pupil size was the most resilient indicator of workload, as it was highly correlated with the self-reports and was not affected by the user's degree of physical motion in VR. Our results bring a fresh human-centric overview of human-robot interactions in VR, and systematically demonstrate the potential of VR devices for monitoring human factors in telerobotics contexts.
Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.
Collective excitation of spatio-spectrally distinct quantum dots enabled by chirped pulses

Abstract Nanoscale bright sources that produce high-purity single photons and high-fidelity entangled photon pairs are the building blocks to realize high security quantum communication devices. To achieve high communication rates, it is desirable to have an ensemble of quantum emitters that can be collectively excited, despite their spectral variability. In case of semiconductor quantum dots, Rabi rotations are the most popular method for resonant excitation. However, these cannot assure a universal, highly efficient excited state preparation, due to the sensitivity to excitation parameters. In contrast, adiabatic rapid passage (ARP), relying on chirped optical pulses, is immune to quantum dot spectral inhomogeneity. Here, we show that the robustness of ARP holds true for the simultaneous excitation of the biexciton states in multiple, spatially separated and spectrally different quantum dots. For positive chirps, we also find a regime where the influence of phonons relax the sensitivity to spectral detunings and lower the needed excitation power. Being able to generate high-purity photons from spatially multiplexed quantum dot sources using the biexciton to ground state cascade is a big step towards the implementation of high photon rate, entanglement-based quantum key distribution protocols.
FL-Enhance: A federated learning framework for balancing non-IID data with augmented and shared compressed samples

Federated Learning (FL), which enables multiple clients to cooperatively train global models without revealing private data, has gained significant attention from researchers in recent years. However, the data samples on each participating device in FL are often not independent and identically distributed (IID), leading to significant statistical heterogeneity challenges. In this paper, we propose FL-Enhance, a novel framework to address the non-IID-ness data issue in FL by leveraging established solutions such as data selection, data compression, and data augmentation. FL-Enhance, specifically, utilizes cGANs that are trained locally on the server level, which represents a relatively novel approach within the FL framework. Also, data compression techniques are applied to preserve privacy during data sharing between clients and servers. We compare our framework with the commonly used SMOTE data augmentation technique and test it with different FL algorithms, including FedAvg, FedNova, and FedOpt. We conducted experiments using both image and tabular data to evaluate the effectiveness of our proposed framework. The experimental findings show that FL-Enhance can substantially enhance the performance of the trained models in situations of severe pathological clients while still preserving privacy, which is the fundamental requirement in the FL context.
Lightweight Instruction Set for Flexible Dilated Convolutions and Mixed-Precision Operands

Modern deep neural networks specialized for object detection and semantic segmentation require specific operations to increase or preserve the resolution of their feature maps. Hence, more generic convolution layers called transposed and dilated convolutions are employed, adding a large number of zeros between the elements of the input features or weights. Usually, standard neural network hardware accelerators process these convolutions in a straightforward manner, without paying attention to the added zeros, resulting in an increased computation time. To cope with this problem, recent works propose to skip the redundant elements with additional hardware or solve the problem efficiently only for a limited range of dilation rates. We present a general approach for accelerating transposed and dilated convolutions that does not introduce any hardware overhead while supporting all dilation rates. To achieve this, we introduce a novel precision-scalable lightweight instruction set and memory scheme that can be applied to the different convolution variants. This results in a speed-up of 5 times in DeepLabV3+ outperforming the recently proposed design methods. The support of precision-scalable execution of all workloads further increases the speedup in computation time shown for the PointPillars, DeepLabV3+, and ENet networks. Compared to the state-of-the-art commercial EdgeTPU, the instruction footprint of ResNet-50 of our designed accelerator is reduced by 60 percent.
Steganalysis using Convolutional Neural Networks-Yedroudj Net

Since vestige, steganography has existed in securing delicate data against unapproved sensational activities. By and by, advanced media’s development, uncovers that steganography has been utilized as a device for exercises like psychological oppression. Considering this foundation, steganalysis emerges as a cure to steganography. Steganalysis can be separated into two principle draws near: all inclusive - likewise called blind - and explicit. Explicit strategies demand a past information on the steganographic strategy under investigation. Then again, widespread techniques which can be broadly rehearsed in an assortment of calculations are more versatile to certifiable applications. Hence, it is important to build up much more exact steganalysis strategies equipped for recognizing the secret data coming from the utilization of different steganographic techniques. Taking into account this, a general steganalysis technique i.e. yedroudj network worked in pictures is proposed. The proposed methodology depends upon the common counterattack process of steganography in which the data extraction and the classification process involves. The performance of the proposed methodology is carried on the stego images generated by the tools like xiao and openpuff. It just so happens, the proposed strategy prevails generally, giving good outcomes on shading pictures and promising outcomes on dim scale pictures.
Assessment Based On IoT For Efficient Information Surveillance Regarding Harmful Strikes Upon Financial Collection

Several more individuals consider the internet of things to be the upcoming big idea, however, they are woefully ignorant of its standards as well as advantages. It is believed that by the finish of 2022, anywhere from 50 billion to 60 billion internets things will have been implemented in our rapidly expanding globe. A large number of advantages of the internet of things in history’s rapidly expanding and operating globe. As everyone understands, the internet of things has presently evolved into an essential component of our creation, and it’s experiencing quick advancements in the field. Now that our subject has informed us that innumerable threats have been carried out against the internet of things, we will talk about how the internet of things must be handled or supervised so that an efficient tracking of the information can be carried out, as well as how it must create a secure space or evaluation of the information, and how it ought to defend itself against suspicious threats carried out again on financial collectibles as well as companies.
Countering Active Attacks on RAFT-Based IoT Blockchain Networks

This article considers an Internet-of-Things (IoT) blockchain wireless network consisting of a leader node and various follower nodes which together implement the reliable, replicated, redundant, and fault-tolerant (RAFT) consensus protocol to verify a blockchain transaction, as requested by a blockchain client. Furthermore, two kinds of active attacks, that is, jamming and impersonation, are considered on the IoT blockchain network due to the presence of multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active</i> malicious nodes in the close vicinity. When the IoT network is under a jamming attack, we utilize the stochastic geometry tool to derive the closed-form expressions for the coverage probabilities for both uplink (UL) and downlink (DL) IoT transmissions (which eventually translate to the blockchain transaction success rate). On the other hand, when the IoT network is under an impersonation attack, we propose a novel method that enables a receive IoT node to exploit the pathloss of a transmit IoT node as its fingerprint to implement a binary hypothesis test for transmit node identification. To this end, we also provide the closed-form expressions for the probabilities of false alarms, missed detection, and misclassification. Finally, we present detailed simulation results that indicate the following: 1) the coverage probability (and hence the blockchain transaction success rate) improves as the jammers’ locations move away from the IoT network and 2) the three error probabilities decrease (i.e., chances of corruption of the blockchain ledger data due to false data injection by malicious node decrease) as a function of the quality of the link between the transmit and receive IoT nodes.
Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning

This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained.
Writing statistical methods for ecologists

Abstract The Methods section is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly written Methods sections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made in Statistical Methods sections. We highlight some common dos and don'ts when writing Statistical Methods sections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples.
Unsupervised Anomaly Detection for Cars CAN Sensors Time Series Using Small Recurrent and Convolutional Neural Networks

Predictive maintenance in the car industry is an active field of research for machine learning and anomaly detection. The capability of cars to produce time series data from sensors is growing as the car industry is heading towards more connected and electric vehicles. Unsupervised anomaly detectors are therefore very adapted to process those complex multidimensional time series and highlight abnormal behaviors. We propose to use recurrent and convolutional neural networks based on unsupervised anomaly detectors with simple architectures on real, multidimensional time series generated by the car sensors and extracted from the Controller Area Network bus (CAN). Our method is then evaluated through known specific anomalies. As the computational costs of Machine Learning algorithms are a rising issue regarding embedded scenarios such as car anomaly detection, we also focus on creating anomaly detectors that are as small as possible. Using a state-of-the-art methodology incorporating a time series predictor and a prediction-error-based anomaly detector, we show that we can obtain roughly the same anomaly detection performance with smaller predictors, reducing parameters and calculations by up to 23% and 60%, respectively. Finally, we introduce a method to correlate variables with specific anomalies by using anomaly detector results and labels.
State of the Art in Dense Monocular Non‐Rigid 3D Reconstruction

Abstract 3D reconstruction of deformable (or non‐rigid ) scenes from a set of monocular 2D image observations is a long‐standing and actively researched area of computer vision and graphics. It is an ill‐posed inverse problem, since—without additional prior assumptions—it permits infinitely many solutions leading to accurate projection to the input 2D images. Non‐rigid reconstruction is a foundational building block for downstream applications like robotics, AR/VR, or visual content creation. The key advantage of using monocular cameras is their omnipresence and availability to the end users as well as their ease of use compared to more sophisticated camera set‐ups such as stereo or multi‐view systems. This survey focuses on state‐of‐the‐art methods for dense non‐rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views. It reviews the fundamentals of 3D reconstruction and deformation modeling from 2D image observations. We then start from general methods—that handle arbitrary scenes and make only a few prior assumptions—and proceed towards techniques making stronger assumptions about the observed objects and types of deformations (e.g. human faces, bodies, hands, and animals). A significant part of this STAR is also devoted to classification and a high‐level comparison of the methods, as well as an overview of the datasets for training and evaluation of the discussed techniques. We conclude by discussing open challenges in the field and the social aspects associated with the usage of the reviewed methods.
Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance

This study aimed to assess the moderating effect of strategic planning on the relationship between career path planning and job performance among employees in small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain. A convenience sample consisting of 249 participants was selected. The study used SPSS and Smart-PLS to analyze the data. The findings of the study revealed that strategic planning and career path planning affected SME employees’ performance. In contrast, it was found that strategic planning substantially moderated the relationship between career path planning and job performance. Moreover, the results showed that SP and CPP had the greatest impact on JP. Based on the obtained findings, it is shown that the proposed hypotheses are accepted. The study revealed the impact of the moderator on the relationship between CPP and JP. The results of this study may serve as a guide for the owners and managers of enterprises and decision makers who aim to develop strategic planning and enhance employee performance.
Technology as a Motivational Factor in Foreign Language Learning

Abstract It is a common belief that engagement and motivation are crucial factors in learning and especially in language learning. In particular, increasing motivation can lead to the mobilization of students’ personal, cognitive, emotional and behavioral resources and, consequently to better learning results. As digital technology has become more sophisticated, its tools and applications can be used in and outside the classroom, in both formal and informal settings, in order to increase students’ motivation. Amongst the various factors -tools, methods or strategies- that can lead to increased motivation, this paper examines the role of technology as a motivational factor in foreign language learning. The relationship between the use of technological means such as web tools and services, digital games, mobile apps or communication tools and motivation in language learning context, has been studied extensively, with a wide variety of approaches, and within the framework of several language learning applications. In order to determine the real impact of technology on learners’ motivation, an extensive literature review focusing on studies that have examined the impact of technology use in language learning and teaching on motivation to learn, has been carried out. Furthermore, this paper discusses the concept of motivation in learning context and the relationship between technology and language learning, summarizes some of the numerous studies and researches on this subject, presents a synthesis of the studies examined, and formulates conclusions and perspectives for effective integration of technology as a motivational tool / factor in language learning context.
Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies

The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.