Reviewerly/exHarmony huggingface dataset:
Using Internet of Things Application for Energy-Efficient and Lightweight Internet of Drones Networks

The Internet of Drones (IoD) is a rapidly growing technology with the potential to revolutionize various industries, but it also raises concerns about security and efficiency in communication between drones. Blockchain technology has the potential to solve these issues but conventional blockchains face performance, computation, and scalability issues. This article proposes using Internet of Things Application (IOTA) distributed ledger technology (DLT) to address these concerns in the IoD. IOTA offers a highly energy-efficient alternative to major DLTs such as Bitcoin and Ethereum while providing fast and secure solutions. Our results show that IOTA not only outperforms Bitcoin but also low-energy DLTs like Ethereum by a huge margin. The use of IOTA DLTs in large-scale systems including the IoD can improve the efficiency and speed of tasks such as surveillance, delivery, and inspection. The article also discusses the potential challenges and future work for integrating IOTA DLTs in the IoD.
LedgerMaze: An Efficient Privacy-Preserving Noninteractive Zero-Knowledge Scheme Over Account-Model Blockchain

The prosperity of blockchain has pushed various decentralized applications, e.g., cross-regional finance, due to its advantages of openness, immutability, and decentralization. The feature of openness inevitably leads to a serious privacy breach. Recently, various privacy-enhanced works (e.g., Zcash, Monero) were proposed focusing on this problem. However, most existing solutions either aim for the unspent transaction output (UTXO) model, or fail to provide full privacy protection for the account-based model with efficient performance. In this paper, we put forward <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LedgerMaze</monospace> , an efficient privacy-preserving non-interactive zero-knowledge (NIZK) scheme over account-model blockchain. We design a novel scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cheque</i> mechanism to cut the link between the sender/receiver relationship. Namely, a sender transfers money to a receiver's cheque, then the receiver can retrieve the cheque among a set of cheques for obfuscation without revealing the original one. We construct several efficient NIZK proofs for initializing the mechanism. Moreover, we further analyze the security properties of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LedgerMaze</monospace> . Experimental results show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LedgerMaze</monospace> achieves comparable performance in communication and computation costs while retaining a full privacy guarantee, compared to previous similar constructions.
Embedded Artificial Intelligence: Intelligence on Devices

Embedded artificial intelligence (AI) seamlessly integrates AI into everyday devices. By bringing AI closer to the data source, embedded AI empowers real-time decision making, enhanced efficiency, and new possibilities across diverse domains.
Underwater polarization de-scattering imaging based on orthogonal polarization decomposition with low-pass filtering

Previous polarization imaging methods tend to assume that the degree of polarization of the target reflected light is a constant, while the targets with nonuniform polarization characteristics are less considered. The required background area or prior knowledge hinders the practical application of underwater polarization imaging technology. In this paper, we analyzed the basic physical model of underwater imaging, and proposed a new underwater polarization de-scattering method by considering orthogonal polarization decomposition of both the target reflected and backscattered light. The polarized-difference intensity of the backscattered light can be directly calculated from the polarization orientation angle of the two horizontal decomposition components. Meanwhile, a low-pass filter was used to suppress noise and globally evaluate the degree of polarization of the backscattered light. The proposed method can automatically distinguish the underwater targets polarization characteristics, and no background area or any prior information is required. The laboratory simulation experimental results demonstrate that this method can effectively improve the quality of underwater images under different conditions.
A Taxonomy of Uncertainty Events in Visual Analytics

Visual analytics (VA) has become a standard tool to process and analyze data visually to generate novel insights. Unfortunately, each component can introduce uncertainty in the visual analytics process. These uncertainty events can originate from many effects and need to be differentiated. In this work, we propose a taxonomy of potential uncertainty events in the visual analytics cycle. Here, we structure the taxonomy along the components included in the visual analytics cycle. Based on this taxonomy, we provide a list of dependencies between these events. At last, we show how to use our taxonomy by providing a real-world example.
Training Robust Deep Collaborative Filtering Models via Adversarial Noise Propagation

The recommendation performance of deep collaborative filtering models drops sharply under imperceptible adversarial perturbations. Some methods promote the robustness of recommendation systems by adversarial training. However, these methods only study shallow models and lack the exploration of deep models. Furthermore, the way these methods add adversarial noise to the weight parameters of users and items is not fully applicable to deep collaborative filtering models, because the adversarial noise is not sufficient to fully affect its network structure with multiple hidden layers. In this article, we propose a novel adversarial training framework, Random Layer-wise Adversarial Training (RAT), which trains a robust deep collaborative filtering model via adversarial noise propagation. Specifically, we inject adversarial noise into the output of the hidden layer in a random layer-wise manner. The adversarial noise propagates forward from the injected position to obtain more flexible model parameters during the adversarial training process. We validate the effectiveness of RAT on multilayer perceptron (MLP) and implement RAT on MLP-based and convolutional neural networks-based deep collaborative filtering models. Experiments on three publicly available datasets show that the deep collaborative filtering model trained by RAT not only defends against adversarial noise but also guarantees recommendation performance.
Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.
Detection of preexisting quantum entanglements between dipole–photon discrete observables

Abstract We performed measurements of laser beam power polarized in different angles as 0 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> , 45 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> and 90 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> passing close to a symmetric shielded capacitor with parallel plates and through a polaroid filter configured with angles as 0 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> , 45 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> and 90 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> . An unusual light power variation was detected during the charging and discharging of the high voltage capacitor even considering the suppression of known electromagnetic interaction between laser beam and capacitor. The light power variation curves also indicated a typical pattern of charge and discharge of capacitors. The combination of the angles of the laser beam polarization and polaroid filter surprisingly indicated the measurement of correlations between the polarizations of photons and electric dipoles of the capacitor dielectric. These novel results can only be explained supposing the preexisting condition of quantum entanglements between discrete observables of electric dipoles and photons.
A User Study on the Comparison of View Interfaces for VR-AR Communication in XR Remote Collaboration

Previous studies have investigated which context-sharing interfaces are effective in improving task performance; however, consistent results have yet to be found. In this study, we developed a convenient remote collaboration system that provides multiple context-sharing interfaces in a single platform (2D video, 360° video, and 3D reconstruction view). All interfaces can reflect live updates of environmental changes, and we aimed to clarify the effect of the interface on the quality of remote collaboration. Thirty participants were recruited to perform a target-finding-and-placing scenario. Using both objective and subjective metrics, we compared the task performance among the three interface conditions. The results showed that participants completed the task faster and reported a better collaborative experience in the 3D interface condition. Moreover, we found a strong preference for the 3D view interface. These results suggest that providing 3D reconstructed spatial information can enable remote experts to instruct local workers more effectively.
Geometric quantum discord signals non-factorization

A bstract We propose the information-theoretic quantity of geometric quantum discord (GQD) as an indicator of the factorization properties of a given quantum system. In particular, we show how non-vanishing discord implies that the corresponding partition function does not factorize, both for generic pure states and the thermofield double state as a state with a known geometric dual in light of the AdS/CFT correspondence. Via this analysis, we give a novel interpretation to the thermomixed double state as the best purely classical approximation of the Einstein-Rosen bridge. We connect the non-vanishing of GQD with the existence of wormhole microstates.
Design principles for social exchange in social virtual reality-enabled virtual teams

Abstract Social virtual reality (SVR) is a novel technology that can simulate and potentially enhance our face-to-face interactions. However, our understanding of interpersonal communication in SVR is still limited. To address this research gap, we describe how SVR enables social exchange (i.e., fundamental communication patterns of trust and reciprocity between individuals), which is closely related to virtual team performance. We present an information systems design theory for social exchange in SVR-enabled virtual teams (SE-SVR). Drawing from affordance theory and social exchange theory, we describe how SVR material properties (i.e., avatars, virtual objects, virtual space, and verbal and nonverbal communication features) enable and foster social exchange in SVR. As a theoretical contribution, we propose design principles for social exchange in SVR and connect them with testable theoretical propositions. Furthermore, we present the concept of interacting with presence, which facilitates users’ affordance perceptions in SVR. We conceptually validate our design principles and illustrate our design through an artifact instantiation: XR Campus, which is a minimum viable product of a collaborative platform for the ECIU University. Our SE-SVR theory has important research and practice implications because it explains how critical aspects of organizational remote communication can be considered in SVR design.
Optimization of U-shaped pure transformer medical image segmentation network

In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the "Chest Xray Masks and Labels" dataset, which is better than the full convolutional network or the combination of Transformer and convolution.
Direct comparison of virtual reality and 2D delivery on sense of presence, emotional and physiological outcome measures

Introduction: Virtual-reality (VR) technology has, over the last decade, quickly expanded from gaming into other sectors including training, education, and wellness. One of the most popular justifications for the use of VR over 2D is increased immersion and engagement. However, very little fundamental research has been produced evaluating the comparative impact of immersive VR on the user’s cognitive, physiological, and emotional state. Methods: A within-subject cross-over study design was used to directly compare VR and 2D screen delivery of different subject matter content. Both physiological and self-report data were collected for scenes containing calming nature environments, aggressive social confrontations, and neutral content. Results: Compared to 2D, the VR delivery resulted in a higher sense of presence, higher ratings of engagement, fun, and privacy. Confrontational scenes were rated as more tense whilst calming scenes were rated as more relaxing when presented in VR compared to 2D. Physiological data indicated that the scenes promoted overall states of arousal and relaxation in accordance with the scene subject matter (both VR and 2D). However, heart rate (HR) and galvanic skin response (GSR) were consistently higher throughout the VR delivery condition compared to 2D, including responses during scenes of neutral and calming subject matter. Discussion: This discrepancy between emotional and physiological responses for calming and neutral content in VR suggest an elevated arousal response driven by VR immersion that is independent of the emotional and physiological responses to the subject matter itself. These findings have important implications for those looking to develop and utilize VR technology as a training and educational tool as they provide insights into the impact of immersion on the user.
Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4% in detecting cyberbullying on Twitter dataset and 90.97% on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance.
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class

In this paper, we construct neural networks with ReLU, sine, and as activation functions. For a general continuous defined on with continuity modulus , we construct -sine- networks that enjoy an approximation rate , where are the hyperparameters related to widths of the networks. As a consequence, we can construct -sine- network with the depth 6 and width . that approximates within a given tolerance measured in the norm with , where denotes the Hölder continuous function class defined on with order and constant . Therefore, the -sine- networks overcome the curse of dimensionality in an approximation on . In addition to its super expressive power, functions implemented by -sine- networks are (generalized) differentiable, enabling us to apply stochastic gradient descent to train.
Multi-Channel Visibility Distribution Measurement via Optical Imaging

Calibration of the imaging environment is an important step in computational imaging research, as it provides an assessment of the imaging capabilities of an imaging system. Visibility is an important quantity reflecting the transparency of the atmosphere. Currently, transmissometers and optical scatterometers are the primary methods for visibility measurement. Transmissometers measure visibility along a single direction between the transmitter and receiver but encounter challenges in achieving optical alignment under long baseline conditions. Optical scatterometers measure the visibility within a localized area since they collect only a small volume of air. Hence, both transmissometers and optical scatterometers have limitations in accurately representing the visibility distribution of an inhomogeneous atmosphere. In this work, a multi-channel visibility distribution measurement via the optical imaging method is proposed and validated in a standard fog chamber. By calibrating the attenuation of infrared LED arrays, the visibility distribution over the entire field of view can be calculated based on the atmospheric visibility model. Due to the large angle of divergence of the LED, the need for optical alignment is eliminated. In further discussion, the key factors affecting the accuracy of visibility measurement are analyzed, and the results show that increasing the measurement baseline, increasing the dynamic range of the detector, and eliminating background light can effectively improve the accuracy of visibility measurement.
Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology

Context.— Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading. Objective.— To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed. Data Sources.— The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities. Conclusions.— It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.
High imperceptibility and robustness watermarking scheme for brain MRI using Slantlet transform coupled with enhanced knight tour algorithm

Abstract This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.