Reviewerly/exHarmony huggingface dataset:
The taste of video: Facebook videos as multi-sensory experiences

Recipe videos are among the most viral genres of videos on social media. Yet, little research has been done on their aesthetic and formal attributes, especially on how they operate within the frameworks of the attention economy and embodied interaction specific to social media interfaces. This paper examines recipe videos published on Tasty, one of the most popular Facebook pages in the world. We analyze these videos through a three-dimensional model that integrates their semiotic characteristics (visual, auditory, and textual), their interactive and haptic qualities, and their invitation to perceptual engagement and sensorimotor response. We conclude that Facebook recipe videos are exemplary of a broader category of social media videos which we call hyper-sensory videos: these create heightened multisensory experiences that take precedence over informational use or narrative involvement. Hyper-sensory videos present a cultural response to broader questions regarding materiality, presence, and embodied relations within a highly mediated social reality.
RTA-IR: A runtime assurance framework for behavior planning based on imitation learning and responsibility-sensitive safety model

Current research on artificial intelligence (AI) algorithms in safety–critical areas remains extremely challenging due to their inability to be fully verified at design time. In this paper, we propose an RTA-IR architecture, which bypasses the formal verification of the AI algorithm by incorporating runtime assurance (RTA) and provides safety assurances for the AI controllers of complex autonomous vehicles (such as those obtained using neural networks) without excessive performance sacrifice. RTA-IR consists of a high-performance and unproven advanced controller and two verifiable safety controllers and a decision module designed based on the Responsibility Sensitive Safety Model (RSS). The advanced controller is designed based on attention generating adversarial imitation learning(GAIL), which can imitate the efficient policies of experts from a set of expert demonstrations. RSS provides verifiable safety criteria and switching logic for the decision module, and RTA-IR provides safety for autonomous vehicles when the advanced controller produces unsafe control, as well as restoring control of the vehicle by the advanced controller under conditions that confirm safety. We tested and evaluated RTA-IR separately for two levels of traffic density in one driving task. Experiments have shown that RTA-IR exhibits superior performance in terms of both safety and efficiency compared to the baseline method.
Interpretable generalized additive neural networks

We propose Interpretable Generalized Additive Neural Networks (IGANN), a novel machine learning model that uses gradient boosting and tailored neural networks to obtain high predictive performance while being interpretable to humans. We derive an efficient training algorithm based on the theory of extreme learning machines, that allows reducing the training process to solving a sequence of regularized linear regressions. We analyze the algorithm theoretically, provide insights into the rate of change of so-called shape functions, and show that the computational complexity of the training process scales linearly with the number of samples in the training dataset. We implement IGANN in PyTorch, which allows the model to be trained on graphics processing units (GPUs) to speed up training. We demonstrate favorable results in a variety of numerical experiments and showcase IGANN’s value in three real-world case studies for productivity prediction, credit scoring, and criminal recidivism prediction.
Multiparty semiquantum key agreement with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1093" altimg="si340.svg"><mml:mi>d</mml:mi></mml:math>-level single-particle states

In this paper, we propose a three-party semiquantum key agreement (SQKA) protocol based on d-level single-particle states first, where one quantum communicant and two semiquantum communicants can generate a final shared key via the quantum channel equally. The semiquantum communicants need to perform quantum measurement operations and encode their secret keys with the qudit shifting operation. The security analysis turns out that this protocol can resist both the external attack and the participant attack. Compared with the only previous SQKA protocol within d-level quantum system (Chin. Phys. B, 2020, 29:110304), the proposed three-party SQKA protocol has the following advantages: (1) it can establish a final shared key between one quantum party and two semiquantum parties; and (2) it does not employ permutation operations from the semiquantum party. Finally, this protocol is extended to its counterpart of N-party.
MultiTLS: using multiple and diverse ciphers for stronger secure channels

Computer communication is at the foundation of how the modern world works, connecting people and machines over public infrastructure. For this reason, communication is exposed to attacks, either by passive listening, or by active interference in the communication. Security protocols like TLS (Transport Layer Security) play a crucial role in ensuring the confidentiality, integrity, and authenticity of the communication. However, like in all technologies, there may be flaws in the design, implementation, or cryptography of TLS that compromise the security of the communication channel. Remediation of such vulnerabilities takes time, leaving valuable services exposed to potential attacks. In this article, we present MultiTLS, a middleware based on cipher diversity and network tunneling that enables secure communication even when new vulnerabilities are discovered. MultiTLS creates a secure communication tunnel through the encapsulation of k TLS channels, where each one uses a different cipher suite. This approach allows the communication channel to remain protected, even when k−1 cipher suites become vulnerable, because of the remaining cipher suite. The diversity of cipher suites tolerates cryptography faults. We evaluated the implementation of MultiTLS and concluded that it is easy to use and to maintain up-to-date, since it does not require code changes to any of its dependencies. We also evaluated its performance in practical use cases and proved that it is viable an useful for various personal and corporate contexts using Internet communications.
Variance reduced Shapley value estimation for trustworthy data valuation

Data valuation, especially quantifying data value in algorithmic prediction and decision-making, is a fundamental problem in data trading scenarios. The most widely used method is to define the data Shapley and approximate it by means of the permutation sampling algorithm. To make up for the large estimation variance of the permutation sampling that hinders the development of the data marketplace, we propose a more robust data valuation method using stratified sampling, named variance reduced data Shapley (VRDS for short). We theoretically show how to stratify, how many samples are taken at each stratum, and the sample complexity analysis of VRDS. Finally, the effectiveness of VRDS is illustrated in different types of datasets and data removal applications.
Distributed power storage and converter system health monitoring Internet of Things under blockchain

This study to solve the information storage problem in the current medical system analyzes the privacy and security effects of the blockchain (BC) in the Internet of Medical Things (IoMT) and accurately predicts the health status of IoMT users. First, a microgrid is designed to supply power to BC, and then BC is applied to IoMT so that medical data can be stored safely and secure information can be shared. Then, deep learning is used to predict and analyze the characteristics of users' health data. Besides, the IoMT model under BC and improved AlexNet are constructed. Finally, the model is evaluated based on an example. According to the results, the calculation overhead of the model keeps stable at 0.13 s, and the usage of central processing unit is always lower than 17.62%. Regarding data transmission security, the average delay of the model reported keeps stable at 350 ms, and the area under the characteristic curve of receiver operating is 0.9217. This algorithm can accurately predict the user's physiological health. In this study, a system with high model efficiency and superior performance is created. This work is of significance for the intelligent development and secure information storage in the medical field and contribute to the development of the medical industry.
Calibration for iron redox state and oxygen fugacity in silicate glasses from x-ray absorption spectroscopy

Prior work to quantify glass Fe-redox ratios in situ using x-ray absorption spectroscopy (XAS) has relied heavily on the pre-edge region. The pre-edge-based methods do not take advantage of other information in the spectra above the pre-edge, up to and past the main edge. This study updates the previous calibration (Dyar et al., 2016a) with additional experimental glasses spanning a wide range of geologically relevant compositions (SiO2 weight % = 38.5–78.0), equilibrated over a range of oxygen fugacity (fO2) values from reducing (IW-2) to oxidizing (air), for which Fe XAS analysis has been collected for the whole spectral region (pre-edge, main edge, EXAFS). The resulting dataset was used to evaluate multiple multivariate statistical models for their ability to accurately predict log fO2 or %Fe3+. Accuracies are estimated using cross-validated root mean square error, for which purpose unseen test data are held out from the regressions. Datasets covering small compositional ranges were tested and compared with datasets covering broad compositional ranges. These ‘stratified’ sets improved predictive accuracy in some cases; however, the extra complications associated with data stratification did not justify the small improvement in accuracy. Overall, a simple partial least squares (PLS) model based on all data collected over the entire XAS region was found to result in a universal calibration for determination of log fO2 and %Fe3+ in glasses.
EAGER: Explainable Question Answering Using Knowledge Graphs

We present EAGER: a tool for answering questions expressed in natural language. Core to EAGER is a modular pipeline for generating a knowledge graph from raw text without human intervention. Notably, EAGER uses the knowledge graph to answer questions and to explain the reasoning behind the derivation of answers. Our demonstration will showcase both the automated knowledge graph generation pipeline and the explainable question answering functionality. Lastly, we outline open problems and directions for future work.
A Demonstration of Interpretability Methods for Graph Neural Networks

Graph neural networks (GNNs) are widely used in many downstream applications, such as graphs and nodes classification, entity resolution, link prediction, and question answering. Several interpretability methods for GNNs have been proposed recently. However, since they have not been thoroughly compared with each other, their trade-offs and efficiency in the context of underlying GNNs and downstream applications are unclear. To support more research in this domain, we develop an end-to-end interactive tool, named gInterpreter, by re-implementing 15 recent GNN interpretability methods in a common environment on top of a number of state-of-the-art GNNs employed for different downstream tasks. This paper demonstrates gInterpreter with an interactive performance profiling of 15 recent GNN inter-pretability methods, aiming to explain the complex deep learning pipelines over graph-structured data.
Low-light image enhancement base on brightness attention mechanism generative adversarial networks

With the development of the field of deep learning, image recognition, enhancement and other technologies have been widely used.However, dark lighting environments in reality, such as insufficient light at night, cause or block photographic images in low brightness, severe noise, and a large number of details are lost, resulting in a huge loss of image content and information, which hinders further analysis and use. Such problems not only exist in the traditional deep learning field, but also exist in criminal investigation, scientific photography and other fields, such as the accuracy of low-light image. However, in the current research results, there is no perfect means to deal with the above problems. Therefore, the study of low-light image enhancement has important theoretical significance and practical application value for the development of smart cities. In order to improve the quality of low-light enhanced images, this paper tries to introduce the luminance attention mechanism to improve the enhancement efficiency. The main contents of this paper are summarized as follows: using the attention mechanism, we proposed a method of low-light image enhancement based on the brightness attention mechanism and generative adversarial networks . This method uses brightness attention mechanism to predict the illumination distribution of low-light image and guides the enhancement network to enhance the image adaptiveness in different luminance regions. At the same time, u-NET network is designed and constructed to improve the modeling process of low-light image. We verified the performance of the algorithm on the synthetic data set and compared it with traditional image enhancement methods (HE, SRIE) and deep learning methods (DSLR). The experimental results show that our proposed network model has relatively good enhancement quality for low-light images, and improves the overall robustness, which has practical significance for solving the problem of low-light image enhancement.
Patients electronic heath records safeguarding mechanism based on data hiding and blockchain

The diagnosis of patients is significantly influenced by medical imaging. These images are frequently transmitted over the network for timely and accurate diagnosis. On the external networks, these medical images are prone to attack. Therefore, medical images are encrypted before transmission; this single layer of protection is not sufficient. Recently, the idea of embedding patient personal information, such as patient identity and medical records, in medical images via reversible data hiding (RDH) in the encrypted image has been proposed. Still, these schemes suffer from low embedding rates. In this paper, a RDH mechanism based on an interpolation scheme and histogram shifting is highlighted. In the proposed method, the achievable embedding capacity (EC) is 1 bit per pixel (bpp). The image encryption is achieved using blockchain technology based on 3 keys. The proposed blockchain mechanism is safe from external attacks. The results are compared with the state-of-the-art methods for both digital and medical images to validate the usefulness of the proposed method.
Interferometric dynamic laser speckle imaging

We developed interferometric dynamic laser speckle imaging (iDLSI) capable of three-dimensional volumetric measurements of the blood flow. Here, we present the numerical and analytical model for g 2,iDLSI (τ) and perform preliminary measurement in the mouse brain.
A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.
Dance Video Motion Recognition Based on Computer Vision and Image Processing

In recent years, motion recognition has become a hot research topic in the field of computer vision and has great research worth. Scholars and research institutions at home and abroad have made a lot of research and achieved good results. However, the research results of applying motion recognition technology to dance video are relatively few, mainly because the complexity of human motion in dance video is relatively high. In article, an optimized design scheme for motion recognition research of dance video based on computer vision and image processing is proposed. The collected dance video is preprocessed, including grayscale, background subtraction and filter denoising, so as to extract the character features in the video image. Then, the method of self-organizing mapping neural network (SOM) in computer vision is used to realize the recognition of dance movements. Finally, the simulation test and analysis are carried out. The simulation results show that the proposed arithmetic has a certain accuracy, which is 9.34% higher than the traditional arithmetic. The research of motion recognition method based on dance video will also play a certain reference role for the research of human motion recognition in a large number of real and complex environments, and enrich the application field of motion recognition technology.
Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Measuring public opinion about the quality of transit services is a key factor in understanding and addressing customer dissatisfaction and increasing customer loyalty and ridership. Sentiment analysis using social media—in particular Twitter—is a relatively cheap and potentially powerful complement to traditional survey methods, which are expensive and limited in sample size. This study aims to evaluate customer response to the introduction of Calgary Transit’s MAX routes. We compared customer satisfaction expressed on Twitter with measured service reliability in the form of on-time performance. We also employed a qualitative research approach using content analysis from Twitter to gauge rider satisfaction over several service attributes before and after the service change. A transit-specific sentiment lexicon was developed to support this study using a hybrid approach. This lexicon outperformed generic sentiment lexicons traditionally used in transit studies with regard to both accuracy (18.4%) and F1-score (7.1%). We found that the overall perception of on-time performance from riders using Twitter was similar to the actual performance in the field. This was also observed for one individual route on which stops with poor schedule adherence were linked with negative feedback. This study concludes that combining customer-oriented measures from Twitter with operational-oriented ones would enable transit agencies to make better-informed decisions for planning and operational purposes.
A systematic review of Automatic Term Extraction: What happened in 2022?

Abstract Automatic Term Extraction (ATE) systems have been studied for many decades as, among other things, one of the most important tools for tasks such as information retrieval, sentiment analysis, named entity recognition, and others. The interest in this topic has even increased in recent years given the support and improvement of the new neural approaches. In this article, we present a follow-up on the discussions about the pipeline that allows extracting key terms from medical reports, presented at MDTT 2022, and analyze the very last papers about ATE in a systematic review fashion. We analyzed the journal and conference papers published in 2022 (and partially in 2023) about ATE and cluster them into subtopics according to the focus of the papers for a better presentation.
Meta Bi-classifier Gradient Discrepancy for noisy and universal domain adaptation in intelligent fault diagnosis

Unsupervised domain adaptation fuels knowledge transfer in fault diagnosis, but it depends entirely on a clean source domain and a common label set across domains. Nevertheless, due to the absent knowledge on the label set, the source domain may cover the utmost in classes and unknown classes also come in the target domain, making UDA utterly inapplicable and often impractical. Besides, the ubiquity of incorrect annotations is prone to occur in industrial dataset curations, but it is largely overlooked and left unsolved in UDA. To address these issues, towards a noisy and universal domain adaptation, a Meta Bi-classifier Gradient Discrepancy (MBGD) method is proposed. Specifically, a reliable dual-stage sample selection approach is first initiated to select clean and reusable source samples. Then, to handle private samples on both sides, a coupled divergence criterion is presented to separate target private samples, and a residual bi-classifier with learning constraints is constructed to weaken source private samples. Finally, with a bi-classifier gradient discrepancy, a meta-adversarial domain adaptation is proposed to empower a safe domain alignment on common samples. Comprehensive experiments on various noisy and universal tasks validate the efficacy of MBGD.
High-fidelity GAN inversion by frequency domain guidance

Generative Adversarial Network (GAN) inversion aims to invert the input image into the latent space of a pre-trained GAN to obtain a latent code, and then apply it to downstream visual tasks. Several studies have shown that deep neural networks suffer from spectral bias, making it difficult for the generative network to learn the high-frequency distribution of images, and thus lead to poor high-frequency detail in the generated images. To overcome the aforementioned limitations, we propose a high-frequency information generation guidance branch to achieve more fidelity image reconstruction. Specifically, we first utilize the frequency domain gap between the initial reconstructed image and the original image, and then apply them to guide the generator to reconstruct the high-frequency part of the hard-to-recover image. In this manner, the high-frequency information content of the image can be better reconstructed. In addition, to learn the high-frequency information of the image that is not learned previously more quickly, we invoke focal frequency loss to accelerate the learning process of the network. Extensive experimental results on several benchmark datasets demonstrate that our method outperforms the state-of-the-art, while the network can converge faster.