Reviewerly/exHarmony huggingface dataset:
Addressing religious hate online: from taxonomy creation to automated detection

Abusive language in online social media is a pervasive and harmful phenomenon which calls for automatic computational approaches to be successfully contained. Previous studies have introduced corpora and natural language processing approaches for specific kinds of online abuse, mainly focusing on misogyny and racism. A current underexplored area in this context is religious hate, for which efforts in data and methods to date have been rather scattered. This is exacerbated by different annotation schemes that available datasets use, which inevitably lead to poor repurposing of data in wider contexts. Furthermore, religious hate is very much dependent on country-specific factors, including the presence and visibility of religious minorities, societal issues, historical background, and current political decisions. Motivated by the lack of annotated data specifically tailoring religion and the poor interoperability of current datasets, in this article we propose a fine-grained labeling scheme for religious hate speech detection. Such scheme lies on a wider and highly-interoperable taxonomy of abusive language, and covers the three main monotheistic religions: Judaism, Christianity and Islam. Moreover, we introduce a Twitter dataset in two languages-English and Italian-that has been annotated following the proposed annotation scheme. We experiment with several classification algorithms on the annotated dataset, from traditional machine learning classifiers to recent transformer-based language models, assessing the difficulty of two tasks: abusive language detection and religious hate speech detection. Finally, we investigate the cross-lingual transferability of multilingual models on the tasks, shedding light on the viability of repurposing our dataset for religious hate speech detection on low-resource languages. We release the annotated data and publicly distribute the code for our classification experiments at https://github.com/dhfbk/religious-hate-speech.
Microstructure of Epoxy-Based Composites: Fractal Nature Analysis

Polymers and polymer matrix composites are commonly used materials with applications extending from packaging materials to delicate electronic devices. Epoxy resins and fiber-reinforced epoxy-based composites have been used as adhesives and construction parts. Fractal analysis has been recognized in materials science as a valuable tool for the microstructural characterization of composites by connecting fractal characteristics with composites’ functional properties. In this study, fractal reconstructions of different microstructural shapes in an epoxy-based composite were performed on field emission scanning electron microscopy (FESEM) images. These images were of glass fiber reinforced epoxy as well as a hybrid composite containing both glass and electrospun polystyrene fibers in an epoxy matrix. Fractal reconstruction enables the identification of self-similarity in the fractal structure, which represents a novelty in analyzing the fractal properties of materials. Fractal Real Finder software, based on the mathematical affine fractal regression model, was employed to reconstruct different microstructure shapes and calculate fractal dimensions to develop a method of predicting the optimal structure–property relations in composite materials in the future.
Online distance music teaching platform based on Internet Plus

In order to overcome the low response speed and security problems of traditional music teaching platform, a new online distance music teaching platform based on Internet Plus is proposed. Based on Internet Plus, online distance music teaching server and online distance music teaching client is designed to complete the hardware design of the platform. Through the construction of students' personalised learning evaluation model and online distance music teaching program, the software design of the platform is completed. Combined with the platform's hardware and software, the design of online distance music teaching platform is realised. The experimental results show that online music teaching platform based on Internet Plus can shorten response time, and the response time is only 290 ms, which greatly improves the security of the platform.
Neural Photo-Finishing

Image processing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data; however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.
Depth of Field Aware Differentiable Rendering

Cameras with a finite aperture diameter exhibit defocus for scene elements that are not at the focus distance, and have only a limited depth of field within which objects appear acceptably sharp. In this work we address the problem of applying inverse rendering techniques to input data that exhibits such defocus blurring. We present differentiable depth-of-field rendering techniques that are applicable to both rasterization-based methods using mesh representations, as well as ray-marching-based methods using either explicit [Yu et al. 2021] or implicit volumetric radiance fields [Mildenhall et al. 2020]. Our approach learns significantly sharper scene reconstructions on data containing blur due to depth of field, and recovers aperture and focus distance parameters that result in plausible forward-rendered images. We show applications to macro photography, where typical lens configurations result in a very narrow depth of field, and to multi-camera video capture, where maintaining sharp focus across a large capture volume for a moving subject is difficult.
A Programmable On-Chip Hopf Bifurcation Circuit

This effort discusses a programmable Hopf bifurcation element based on Transconductance Amplifiers (TA) that can be directly implemented in configurable hardware (e.g. an SoC FPAA). The TA Hopf bifurcation circuit shows the complete dynamics of a Van der Pol circuit, a Hopf bifurcation prototype. This Low-Pass Filter (LPF) TA-implemented ODE shows the necessary linear dynamics, as well as stable and oscillatory nonlinear dynamics expected near a Hopf bifurcation. This component directly uses the dynamics of the TA devices not translating equations, reducing the implementation complexity.
LED Screen-Based Intelligent Hand Gesture Recognition System

Common human–computer interaction (HCI) modes rely on various sensors or cameras for operation. This study proposes a gesture recognition method and device based on light sensing characteristics, which removes the need for external sensors with only light-emitting diode (LED) screens. The key technique is that the photoelectric sensing ability of LED is applied to detect the information change of light without interference to the display function, thus indicating the variation of gestures. This system is integrated by using six LED screen modules with the field-programmable gate array (FPGA) to control display and collect data for deep-learning analysis. Depending on both time- and frequency-domain characteristics of gestures, the recognition algorithm is implemented based on static bidirectional long short-term memory (S-Bi-LSTM). As revealed by the experiments conducted on hand gestures of different people and under the context of complex background illumination, the accuracy of gesture recognition reaches as high as 93.60%. As for dynamic gesture actions, an optimized dynamic bidirectional long short-term memory (D-Bi-LSTM) algorithm is also proposed with the method of frame segmentation. This method divides dynamic gestures into three parts to feed different classifiers, so as to enhance the accuracy of dynamic gestures recognition up to 91.67%. The device possesses all the structures of a large LED screen, which verifies the feasibility of pattern recognition for large LED screens from an experimental perspective. Without any need for traditional sensors, it is contactless, secure, and easy to promote.
Hands-on operation with a Rolling Alphabet-AR System improves English learning achievement

The aim of this study is to use augmented reality and hands-on activity with phonics cubes to create an augmented reality English learning system named the Rolling Alphabet-AR System to investigate its impact on English learning, flow experience, and English self-efficacy for sixth graders. This study adopted quasi-experimental research on 114 sixth graders from Taiwan, which comprised experimental and control groups. The experimental group used the Rolling Alphabet-AR System to learn, and the control group used flash cards and picture cards. Before and after the experiments, these two groups took the Phonics vocabulary, flow experience, and English self-efficacy scales as pre- and posttests. The study was conducted 7 times in 7 weeks for a total of 280 minutes. The pre- and posttest results were analysed by independent sample t tests in SPSS 20.0. Using the Rolling Alphabet-AR System in English learning positively impacts English learning effectiveness and flow experience for sixth graders. This study combined AR learning with manual operations to prove that this combination can effectively improve students’ English learning results.
FPGA Implementation of Low-Latency Recursive Median Filter

The recursive median filter has stronger noise at-tenuation capability than the median filter, especially for high-intensity and irregularly distributed noise. However, the recursive operation prevents recursive median filter from being pipelined, which leads to the recursive median filter being not real-time enough to be widely applied. This paper presents an FPGA implementation of low-latency recursive median filter. The proposed architecture completes the median calculation of the current window and the data pre-processing of the next window in one clock cycle, thereby reducing the calculation complexity of each median. The results show that for 5x5 window, the proposed recursive median filter core operates at a maximum frequency of 334 MHz on a zynq ultrascale+ FPGA device, which meets the real-time processing requirements for Full High Definition(FHD) images.
Optimal Encodings to Elliptic Curves of \(\boldsymbol{j}\)-Invariants 0, 1728

.This article provides new constant-time encodings \(\mathbb{F}_{q}^{*} \to E(\mathbb{F}_{q})\) to ordinary elliptic \(\mathbb{F}_{q}\) -curves \(E\) of \(j\) -invariants 0, 1728 having a small prime divisor of the Frobenius trace. Therefore all curves of \(j=1728\) are covered. This circumstance is also true for the Barreto–Naehrig curves BN512, BN638 from the international cryptographic standards ISO/IEC 15946-5, TCG Algorithm Registry, and FIDO ECDAA Algorithm. Many \(j=1728\) curves, as well as BN512, BN638, are not appropriate for the most efficient prior encodings. So, in fact, only the universal Shallue–van de Woestijne one was previously applicable to them. However, this encoding (in contrast to the new ones) cannot be computed at the cost of one exponentiation in the field \(\mathbb{F}_{q}\) .Keywordscongruent elliptic curvesencodings to (hyper)elliptic curvesisogeniesmedian value curvesoptimal coversWeil pairingMSC codes14H3014G1514G0514G5014H4514Q05
Privacy Protection Scheme for the Internet of Vehicles Based on Private Set Intersection

Performing location-based services in a secure and efficient manner that remains a huge challenge for the Internet of Vehicles with numerous privacy and security risks. However, most of the existing privacy protection schemes are based on centralized location servers, which makes them all have a common drawback of a single point of failure and leaking user privacy. The employment of anonymity and cryptography is a well-known solution to the above problem, but its expensive resource consumption and complex cryptographic operations are difficult problems to solve. Based on this, designing a distributed and privacy-secure privacy protection scheme for the Internet of Vehicles is an urgent issue for the smart city. In this paper, we propose a privacy protection scheme for the Internet of Vehicles based on privacy set intersection. Specially, using privacy set intersection and blockchain techniques, we propose two protocols, that is, a dual authentication protocol and a service recommendation protocol. The double authentication protocol not only ensures that both communicating parties are trusted users, but also ensures the reliability of their session keys; while the service recommendation protocol based on pseudorandom function and one-way hash function can well protect the location privacy of users from being leaked. Finally, we theoretically analyze the security that this scheme has, i.e., privacy security, non-repudiation, and anti-man-in-the-middle attack.
Less is More: Efficient Networked VR Transformation Handling Using Geometric Algebra

Abstract As shared, collaborative, networked, virtual environments become increasingly popular, various challenges arise regarding the efficient transmission of model and scene transformation data over the network. As user immersion and real-time interactions heavily depend on VR stream synchronization, transmitting the entire data set does not seem a suitable approach, especially for sessions involving a large number of users. Session recording is another momentum-gaining feature of VR applications that also faces the same challenge. The selection of a suitable data format can reduce the occupied volume, while it may also allow effective replication of the VR session and optimized post-processing for analytics and deep-learning algorithms. In this work, we propose two algorithms that can be applied in the context of a networked multiplayer VR session, to efficiently transmit the displacement and orientation data from the users’ hand-based VR HMDs. Moreover, we present a novel method for effective VR recording of the data exchanged in such a session. Our algorithms, based on the use of dual-quaternions and multivectors, impact the network consumption rate and are highly effective in scenarios involving multiple users. By sending less data over the network and interpolating the in-between frames locally, we manage to obtain better visual results than current state-of-the-art methods. Lastly, we prove that, for recording purposes, storing less data and interpolating them on-demand yields a data set quantitatively close to the original one.
Diverse image inpainting with disentangled uncertainty

Most existing inpainting methods repair a corrupted image to a single output, which gives people no choice to select the most satisfactory result. However, image inpainting is essentially a multi-modal problem because the inpainted results could have multiple possibilities. To generate both diverse and realistic inpainted results, we propose a diverse image inpainting framework with disentangled uncertainty. We disentangle the uncertainty of the missing region into two aspects: structure and appearance. Correspondingly, we divide the process of diverse image inpainting into two stages: diverse structure inpainting and diverse appearance inpainting. In the first stage, we restore the structure of the missing region, producing diverse complete edge maps. In the second stage, using a complete edge map as the guidance, we fill in diverse appearance information of the missing region. We also design a light-weighted disentangling subnetwork to disentangle structure information and appearance information. Besides, we propose a novel style-based masked residual block to better deal with the uncertainty. Experiments on CelebA-HQ, Paris Street View, and Places2 demonstrate that our method can repair the corrupted image with higher fidelity and diversity than other existing methods.
A resilient group session key authentication methodology for secured peer to peer networks using zero knowledge protocol

This novel methodology develops a Pseudo Trust in Peer-to-Peer networks using Lagrange Interpolation and Zero-Knowledge Protocol and creates a non-forgeable pseudonym. Authentication of a peer can be done without disclosing any sensitive information by using Zero-Knowledge Protocol. Each node in a peer maintains a unique I.D. It is not necessary to reveal the credential information at the time of verification. The implementation of distributed trust model is done incrementally in P2P networks. This proposed methodology generates different polynomials at the source and destination, respectively. After creating polynomials, authentication is performed using a challenge-based system (Zero-Knowledge Protocol) by exchanging pseudo-random numbers. Source and destination generate the same polynomial without exchanging any secret information. This polynomial can be used for encryption and decryption. The proposed algorithm is analysed by considering three parameters of network Throughput, the number of packets dropped and the number of packets delivered. These results measure the performance of the proposed algorithm applied in Peer-to-Peer networks over public networks.
Accuracy-diversity optimization in personalized recommender system via trajectory reinforcement based bacterial colony optimization

Personalized recommender systems have been extensively studied in human-centered intelligent systems. Existing recommendation techniques have achieved comparable performance in predictive accuracy; however, the trade-off between recommendation accuracy and diversity poses new challenges, as diversification may lead to accuracy loss, whereas it can solve the over-fitting problem and enhance the user experience. In this study, we propose a heuristic optimization-based recommendation model that jointly optimizes accuracy and diversity performance by obtaining a set of optimized solutions. To establish the best accuracy-diversity balance, a novel trajectory-reinforcement-based bacterial colony optimization algorithm was developed. The improved bacterial colony optimization algorithm was comprehensively evaluated by comparing it with eight popular and state-of-the-art algorithms on ten benchmark testing problems with different degrees of complexity. Furthermore, an optimization-based recommendation model was applied to a real-world recommendation dataset. The results demonstrate that the improved bacterial colony optimization algorithm achieves the best overall performance for benchmark problems in terms of convergence and diversity. In the real-world recommendation task, the proposed approach improved the diversity performance by 1.62% to 8.62% while maintaining superior (1.88% to 40.32%) accuracy performance. Additionally, the proposed personalized recommendation model can provide a set of nondominated solutions instead of a single solution to accommodate the ever-changing preferences of users and service providers. Therefore, this work demonstrates the excellence of an optimization-based recommendation approach for solving the accuracy-diversity trade-off.
A machine learning method for improving liver cancer staging

Liver cancer is a common malignant tumor, and its clinical stage is closely related to the clinical treatment and prognosis of patients. Currently, the BCLC staging system revised by the BCLC group of University of Barcelona is the globally recognized staging system for liver cancer. However, with the deepening of related research, the current staging system can no longer fully meet the clinical needs. In this work, we propose a novel machine learning method for constructing an automatic hepatocellular carcinoma staging model that incorporates far more clinical variables than any existing staging system. Our model is based on random survival forests, which generates a unique hazard function for each patient. B-splines are used to embed hazard functions into vectors in low-dimensional space and hierarchical clustering method groups similar patients to form staging cohorts. The resulting staging system significantly outperforms the BCLC system in terms of distinctiveness between patients in different stages.
A non-uniform illumination image enhancement method based on fusion of events and frames

The dynamic range of standard frame-based cameras is much smaller than that of natural scenes. Images captured by standard cameras in non-uniform illumination scenes may be over-exposure, under-exposure, or a combination of both, limiting the visibility of the images. Enhancing images with uneven illumination is a tough assignment. Although most of the existing non-uniform illumination image enhancement methods can preserve details well in dark areas, they cannot regain details in bright regions and may produce noise and halos. Event cameras have a higher dynamic range and are less affected by scene lighting than standard cameras. In this paper, we utilize the complementary advantages of event cameras and standard cameras to improve the visibility of unevenly illumination images, enhance the brightness of dark areas, and recover details in bright areas. First, events and frames are converted into intensity frames by corresponding preprocessing methods, which are subjected to a gamma correction operation to obtain artificial multi-exposure image sequences. Then, the weight map of each artificially exposed image is designed using several evaluation terms, namely contrast, well-exposure, and average brightness. After that, the weight terms are combined with the Laplacian pyramid algorithm to get a fused image. Finally, the final image is created by color restoration and contrast enhancement. Quantitative and qualitative experimental results demonstrate that our method enhances the details and visibility of non-uniform illumination images more effectively than several contrast algorithms.