Reviewerly/exHarmony huggingface dataset:
Flexible DIC System for Wind Tunnel Testing of Lightweight Structures

We propose an adjustable digital image correlation (DIC) system for the wind tunnel testing of small and large-scale aerospace and civil structures. A novel mounting rig with azimuth and pitch rotation fixtures was designed and developed for the DIC setup that can be installed from outside of the wind tunnel test section. The independent mounting rig makes the DIC system adjustable as per the shape and size of the test structure, which can reduce the installation and calibration efforts in wind tunnel testing. Telecentric lenses optimize the far-field optics of the DIC system. It allows the user to implement a rigorous calibration procedure to study magnification effects. We calibrated the DIC system at various fields of view and depths of field to ensure its suitability for wind tunnel experiments in which accuracy is a prime concern. The DIC measurements can be verified with the Euler transformation algorithm with a suitable combination of azimuth, pitch, and camera optical parameters. Case studies included a large-scale parachute model and a bioinspired flexible flapping wing to ensure the applicability of proposed system for both large- and small-scale structures. The inflated parachute diameter and the wing deformation fields were comparable to those predicted by the Euler transformation relationship. The DIC system can be coupled and synchronized with other measurement systems such as sting balances and particle image velocimetry for the real-time study of structural deformation, aerodynamic forces, and flow characteristics in the vicinity of structures.
Adversarial transformer network for classification of lung cancer disease from CT scan images

Lung cancer is a dreadful disease that affects both men and women; an early prognosis is highly required to prolong human life. Recently, the computer-aided diagnosis (CAD) system has played a vital role in effectively diagnosing lung-related diseases. However, this system often fails to identify the type of cancers and is subjected to high error and computation complexity. Hence, this article introduces a novel deep learning (DL) technique to classify different types of lung cancers using chest CT images. The proposed study undertakes three major stages: pre-processing, feature selection and classification. The guided bilateral filtering (GWF) technique is initially introduced in the pre-processing stage to eliminate the noises from the raw CT images. Then, dimensionality reduction is performed using the weighted least absolute shrinkage and selection vector regression (We-LASSVR) technique to avoid over-fitting issues in the network model. In addition, the weighted mean of vector optimization (We-WMVO) technique is introduced to develop the performance of a proposed We-LASSVR technique. Finally, the transformer-aided generative adversarial network (T-GAN) technique is proposed to effectively classify the different types of lung cancers. Moreover, the dynamic levy flight chimp optimization (DyLF-CO) technique is proposed to tune the network model. The proposed model is employed in Python, and a chest CT image from the Kaggle website is collected and processed in this study. In the experimental section, the proposed method obtains an accuracy of 0.997, precision of 0.996, specificity of 0.998, RMSE of 0.104 and time complexity of 120 s.
Group Oriented Attribute-Based Encryption Scheme from Lattices with the Employment of Shamir’s Secret Sharing Scheme

This paper delivers a post-quantum construction for group-oriented attribute-based encryption (GO-ABE) using lattice-based cryptography. The GO-ABE scheme enables users from the same group to combine their attributes to satisfy a decryption policy without revealing their secret keys. GO-ABE is particularly useful when no single user can fulfill the decryption policy alone, but a group of users can satisfy it together. Li et al. introduced the idea of GO-ABE at NSS 2015, discussing its importance in accessing patient data during emergencies. However, since Li et al.’s scheme uses bilinear mappings, it is not secure against quantum attacks. To ensure security against quantum attacks, we construct the GO-ABE scheme using the post-quantum cryptographic primitive lattices, and employ Shamir’s secret sharing scheme to meet the GO-ABE requirements.
Agnostic Label-Only Membership Inference Attack

Abstract In recent years we are witnessing the diffusion of AI systems based on powerful Machine Learning models which find application in many critical contexts such as medicine and financial market. In such contexts, it is important to design Trustworthy AI systems while guaranteeing privacy protection. However, some attacks on the privacy of Machine Learning models have been designed to show the threats of exposing such models. Membership Inference is one of the simplest privacy threats faced by Machine Learning models. It is based on the assumption that an adversary, observing the confidence of the model prediction, can infer whether a particular record was used for training the classifier. A variant, called Label-Only attack, exploits the adversary’s knowledge of the training data statistics to infer the record membership without accessing the confidence score of the prediction. In this paper, we propose a variant of the Label-Only attack, called Aloa , which estimates the prediction confidence exploiting a mechanism that is completely agnostic to the input data distributions. In fact, it requires neither statistical knowledge of the data nor the type of variables. Experimental results show better performance of our attack with respect to the competitors.
CFMB-T: A cross-frequency multi-branch transformer for low-quality infrared remote sensing image super-resolution

Aiming at the problems of low resolution and fuzzy details of infrared remote sensing images, paper proposed a multi branch super-resolution method based on wavelet decomposition and a dataset processing method based on real degradation process. To improve the super-resolution reconstruction effect, the modulation transfer function is used to add the degradation process of the real environment to the dataset at first, and based on this, CFMB-T is proposed. CFMB-T took SwinIR as the architecture, separated the frequency information of the infrared remote sensing image through wavelet decomposition, and introduced a degenerate prior to focus on the recovery of high-frequency information. Moreover, CFMB-T also introduced a recursive module to ensure the ability of feature extraction while reducing model parameters. The detailed experiments on degraded datasets show that CFMB-T achieves better reconstruction results than other super-resolution models. Compared with SwinIR, with limited data and training rounds, the PSNR and SSIM of CFMB-T are improved by 1.07% and 0.71%, respectively, over SwinIR on the 4x super-resolution task, and 3.28% and 0.09%, respectively, on the 2x super-resolution task. The results demonstrate its great potential for infrared remote sensing super-resolution reconstruction.
MS-LSTM: Exploring spatiotemporal multiscale representations in video prediction domain

The drastic variation of motion in spatial and temporal dimensions makes the video prediction task extremely challenging. Existing RNN models obtain higher performance by deepening or widening the model. They obtain the multi-scale features of the video only by stacking layers, which is inefficient and brings unbearable training costs (such as memory, FLOPs, and training time). Different from them, this paper proposes a spatiotemporal multi-scale model called MS-LSTM wholly from a multi-scale perspective. On the basis of stacked layers, MS-LSTM incorporates two additional efficient multi-scale designs to fully capture spatiotemporal context information. Concretely, we employ LSTMs with mirrored pyramid structures to construct spatial multi-scale representations and LSTMs with different convolution kernels to construct temporal multi-scale representations. We theoretically analyze the training cost and performance of MS-LSTM and its components. Detailed comparison experiments with twelve baseline models on four video datasets show that MS-LSTM has better performance but lower training costs.
Geometric phase analysis for characterization of 3D morphology of carbon fiber reinforced composites

In carbon fiber-reinforced resin matrix composites, differences in the mechanical properties of carbon fibers and matrix resins can lead to differences in height, causing step faults at the interphase during grinding and polishing. Accurately and efficiently characterizing the step height at the interphase is crucial for analyzing the mechanical properties and residual stress distribution of the composite materials. In this paper, an orthogonal grating, with a frequency of 10,000 lines/mm, was prepared on the surface of the composite material using the SEM electron beam grating technique. The surface morphology of the T700 carbon fiber-reinforced epoxy resin matrix composite was analyzed using the geometric phase analysis method. The average step height between the fiber and matrix was found to be 128.6 nm. Moreover, the geometric phase analysis method can provide a morphology distribution map within the range of 155 × 100 μm2. This research will provide an effective way to quantitative characterization of three-dimensional surface morphology of composite materials.
The Effectiveness of Transformer-Based Models for BEC Attack Detection

Business Email Compromise (BEC) attacks are a significant threat to organizations, with attackers using various tactics to acquire sensitive information and cause financial damage to target firms. These attacks are difficult to detect using existing email security systems, as approximately 60% of BEC attacks do not include explicit indicators such as attachments and links. Even state-of-the-art solutions using Natural Language Processing (NLP) rely heavily on such explicit indicators. This study proposes a transformer-based BEC detection method that can capture linguistic properties of emails so that could reduce the reliance on explicit indicators. Our method of combining BERT and BiLSTM offers the advantage of capturing both global context and local interdependence, resulting in a comprehensive and nuanced understanding of email text. In our experiment, the proposed method outperforms the state-of-the-art solutions, achieving a 0.99% accuracy and this highlights the potential of transformer-based models in detecting BEC attacks.
Privacy-Preserving Federated Learning with Hierarchical Clustering to Improve Training on Non-IID Data

Federated learning (FL), as a privacy-enhanced distributed machine learning paradigm, has achieved tremendous success in solving the data silo problem. However, data heterogeneity (Non-IID data) between parties (data owners) poses challenges for the vanilla federated learning aggregation approach (FedAvg), including more interaction rounds and lower accuracy of the global model. To address this challenge, some works make improvements based on FedAvg. However, most of these works do not consider the privacy protection of gradients, which would leak private data information about the parties involved in the training. To protect parties’ privacy and enhance the FL training on Non-IID data at the same time, in this paper, we present PPFL+HC, an efficient, private FL framework. Our PPFL+HC follows the state-of-the-art Non-IID FL method (FL+HC IJCNN’20), which presents a modification to FL by introducing a hierarchical clustering step to separate clusters of parties by the similarity of their local gradients, adapting it to the privacy-preserving context. We design a series of secure cryptographic protocols to ensure the privacy of parties. Specifically, first, we use additive secret sharing to protect local gradients and global gradients privacy, while using pseudorandom generation techniques to reduce half the communication overhead. Second, we design a secure and efficient Euclidean distance computation and Manhattan distance computation protocol to accelerate the secure hierarchical clustering process. Finally, to improve the computational efficiency of the clustering process, we perform randomized gradient cropping to reduce the computational overhead while ensuring the accuracy of clustering. Moreover, experiments conducted on two real-world datasets demonstrate that our PPFL+HC achieves secure and efficient FL training for Non-IID data.
Antifa’s Political Violence on Twitter: a Grounded Theory Approach

Abstract This paper explores antifa activists’ use of doxing on Twitter against individuals perceived as alt-right militants. Following the principles of Grounded Theory, we collected 4690 tweets published by antifa users between September 2019 and September 2020 and analysed a random subsample of 1638. Results show that antifa users perceive alt-right activists as a serious threat to their worldview and seek to neutralise their activism even at the cost of making them social pariahs. To achieve that goal, antifa activists collect personal data on persons they suspect of being alt-right activists to “build a case” and then disseminate that information through different virtual social networks to the largest audience possible. The aim of doxing is to encourage other social actors to react and take actions that could be detrimental to the individual targeted. The article discusses practical and ethical implications of this kind of political-based harassment and suggests that future research on doxing could focus on antifa blogs and websites, which include sensitive information forbidden on Twitter.
Pacemod: parametric contour-based modifications for glyph generation

Abstract Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours’ geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications.
On the use of low-cost 3D stereo depth camera to drive robot trajectories in contact-based applications

Abstract In production systems characterized by small batches and high customization levels, operations are required to be flexible to adapt to different products within the shortest possible time and with the minimum effort for system setup. Contact-based operations such as surface finishing, polishing, deburring, and material deposition are mandatory in the fabrication of numerous products. To maintain consistent performance over time, many of these operations require a high level of accuracy, both in end-effector positioning and contact force level. This paper proposes a robotic solution to generate the robot working trajectory for contact-based operations over the external surface of unknown objects of which a digital model is not available or different from the actual state of the workpiece. The paper introduces the integration process of RGBD images to construct a 3D model and its elaboration to extract the workpiece. Different searching subroutines have been developed to select different areas of the workpiece based on the operation to be carried on and generate the related trajectory. The evaluation metric of the proposed robotic solution is given by the stability of the contact force exerted by the robotic tool and the error between generated and the actually followed trajectory due to the depth estimation of a low-cost camera. A few millimeters of the inaccuracy of the trajectory are obtained; these inaccuracies are compensated using force control. Different tests with different nominal values of the force control loop are carried out. Statistical analysis shows that the mean values of the contact force obtained coincide with the nominal value of the single tests.
Classification model-based and assisted environment selection for evolutionary algorithms to solve high-dimensional expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been proven to be very effective in tackling low-dimensional expensive problems. However, it remains a challenge to solve high-dimensional expensive problems (HEPs) with the curse of dimensionality. Therefore, this paper proposes a surrogate-assisted improved multioperator differential evolution (SA-IMODE) algorithm to address HEPs up to 2000 dimensions. Specifically, this paper proposes a novel relationship classification model-based environment selection strategy (RCES), in which a classification model is used to distinguish between "good" and "bad" solutions to assist in environment selection. By doing this, the "unpromising" solutions are thrown away directly without evaluation to reduce the number of expensive fitness evaluations. Moreover, the solution-improvement data point and error samples are added to construct the training dataset to improve the model's prediction accuracy. Two coordinate systems are utilized to produce five types of DE operators to suit different fitness landscapes. An adaptive strategy is also used to select suitable DE operators to generate promising offspring. Furthermore, a local search mechanism is applied to refine the best solution in the current population to accelerate the algorithm's convergence. The systematic experiment results show SA-IMODE has a significant advantage over thirteen state-of-the-art algorithms on benchmark problems.
A One-Size-Fits-Three Representation Learning Framework for Patient Similarity Search

Abstract Patient similarity search is an essential task in healthcare. Recent studies adopted electronic health records (EHRs) to learn patient representations for measuring the clinical similarities. These methods outperformed traditional methods, by capturing more information from various sources consisting of multi-modal EHRs, external knowledge and correlations among medical concepts. They often concerned certain type of data without taking full advantage of various information. We propose a graph representation learning framework, denoted by One-Size-Fits-Three ( OSFT ), that takes into account fusion-attention, neighbor-attention and global-attention from three types of information. Extensive experiments are conducted on two real datasets of MIMIC-III and MIMIC-IV, and the results verified the effectiveness and generality of our framework. When compared with baselines on patient similarity search, our framework achieved good effectiveness and comparative efficiency. The results provide new insights about whether the use of various information can better measure the patient similarity. The source codes are available at https://github.com/emmali808/ADDS/tree/master/EHRDeepHelper .
Busting the one-voice-fits-all myth: Effects of similarity and customization of voice-assistant personality

Despite the increasing sophistication of voice assistant (VA) technology, most major VAs subscribe to a one-voice-fits-all model of interaction. This study examines if offering users a VA similar to them, or letting users customize the VA's voice personality, would affect their perceptions and experience. We test this in a unique scenario where a VA delivers misinformation about COVID-19. Data from a pre-registered experiment (N = 401) suggest that extroverted users appreciate being matched with an extroverted VA, whereas introverted users do not have a specific preference. In addition, perceived homophily in voice increases user attraction toward the VA, and enhances credibility perceptions for those who customize their VA. Those not given the option to customize prefer VAs with an extroverted voice. Data also suggest that automated similarity matching of VA personality can evoke user resistance toward the persuasive information provided—in our case, changing as many as 38% of unvaccinated individuals’ mind toward vaccination after exposure to misinformation.
Race and gender intertwined: why intersecting identities matter for perceptions of incivility and content moderation on social media

Abstract Social media users often push back against harmful rhetoric with satirical and aggressive counterspeech. How do the interconnected race and gender identities of the person posting counterspeech and the person viewing it impact evaluations of the comment? Across two online experiments, we manipulate the race (Black or White) and gender (man or woman) of an individual whose tweet opposes ignorance about White privilege to examine if identity influences perceptions of incivility and intentions to flag the tweet for removal among Black and White men and women participants. Results demonstrate White men were most likely to find the tweet uncivil and report it, and this was especially the case when the tweet came from a Black woman, regardless of the tone. These studies highlight the importance of recognizing power and intersectionality in social media content moderation and creating policies that counteract the uniquely severe treatment of Black women by White men.
Exploring the state of the art in legal QA systems

Abstract Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task more challenging, even for human experts. Question answering (QA) systems are designed to generate answers to questions asked in natural languages. QA uses natural language processing to understand questions and search through information to find relevant answers. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and discuss the performance and limitations of these models. Moreover, we have established a public GitHub repository that contains a collection of resources, including the most recent articles related to Legal Question Answering, open datasets used in the surveyed studies, and the source code for implementing the reviewed deep learning models (The repository is available at: https://github.com/abdoelsayed2016/Legal-Question-Answering-Review ). The key findings of our survey highlight the effectiveness of deep learning models in addressing the challenges of legal question answering and provide insights into their performance and limitations in the legal domain.