Reviewerly/exHarmony huggingface dataset:
S-Vectors and TESA: Speaker Embeddings and a Speaker Authenticator Based on Transformer Encoder

One of the most popular speaker embeddings is x-vectors, which are obtained from an architecture that gradually builds a larger temporal context with layers. In this paper, we propose to derive speaker embeddings from Transformer&#x2019;s encoder trained for speaker classification. Self-attention, on which Transformer&#x2019;s encoder is built, attends to all the features over the entire utterance and might be more suitable in capturing the speaker characteristics in an utterance. We refer to the speaker embeddings obtained from the proposed speaker classification model as s-vectors to emphasize that they are obtained from an architecture that heavily relies on self-attention. Through experiments, we demonstrate that s-vectors perform better than x-vectors. In addition to the s-vectors, we also propose a new architecture based on Transformer&#x2019;s encoder for speaker verification as a replacement for speaker verification based on conventional probabilistic linear discriminant analysis (PLDA). This architecture is inspired by the next sentence prediction task of bidirectional encoder representations from Transformers (BERT), and we feed the s-vectors of two utterances to verify whether they belong to the same speaker. We name this architecture the Transformer encoder speaker authenticator (TESA). Our experiments show that the performance of s-vectors with TESA is better than s-vectors with conventional PLDA-based speaker verification.
HERS: Homomorphically Encrypted Representation Search

We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HERS</i> (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimal loss</i> of accuracy (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) efficiently searching against a gallery of encrypted representations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly in the encrypted domain, without decrypting them</i> . Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (500 seconds; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$275\times $ </tex-math></inline-formula> speed up over state-of-the-art for encrypted search against a gallery of 100 million). Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/human-analysis/hers-encrypted-image-search</uri> .
Inductive Graph Representation Learning for fraud detection

Graphs can be seen as a universal language to describe and model a diverse set of complex systems and data structures. However, efficiently extracting topological information from dynamic graphs is not a straightforward task. Previous works have explored a variety of inductive graph representation learning frameworks, but despite the surge in development, little research deployed these techniques for real-life applications. Most earlier studies are restricted to a set of benchmark experiments, rendering their practical generalisability questionable. This paper evaluates the proclaimed predictive performance of state-of-the-art inductive graph representation learning algorithms on highly imbalanced credit card transaction networks. More specifically, we assess the inductive capability of GraphSAGE and Fast Inductive Graph Representation Learning in a fraud detection setting. Credit card transaction fraud networks pose two crucial challenges for graph representation learners: First, these networks are highly dynamic, continuously encountering new transactions. Second, they are heavily imbalanced, with only a small fraction of transactions labelled as fraudulent. This paper contributes to the literature by (i) proving how inductive graph representation learning techniques can be leveraged to enhance predictive performance for fraud detection and (ii) demonstrating the benefit of graph-level undersampling for representation learning in imbalanced networks.
A simple embed over encryption scheme for DICOM images using Bülban Map

With the onset of any pandemic, the medical image database is bound to increase. These medical images are prone to attack by hackers for their medical data and patient health information. To safeguard these medical images, a new algorithm is proposed. The algorithm involves secretly embedding the patient identification number into the medical image and encrypting the medical image, protecting the patient's identity and the patient's medical condition from hackers. The encryption algorithm involved a single stage of confusion and two stages of diffusion. The confusion operation was performed using the key generated by the Bülban map. The first stage of diffusion was done in the transform domain, using 5/3 transformation. The second diffusion stage was performed in the spatial domain by altering the pixel values using the key. The algorithm was tested on over 30 DICOM (Digital Imaging and Communications in Medicine) images taken from Open Science Framework (OSF), a public database for COVID-19 patients. The algorithm could resist the statistical attacks upon analysis, providing a PSNR of 7.084 dB and entropy of 15.9815 bits for the cipher image. The correlation coefficients for the cipher image were 0.0275, -0.0027, 0.018 in horizontal, vertical and diagonal directions. The keyspace was 2((M-1) ×N)×16, with M the number of rows and N the number of columns in the image. The key sensitivity was high. The test results and metric analysis prove that the algorithm is an effective one for embedding and encryption.
Distributed State Fusion Using Sparse-Grid Quadrature Filter With Application to INS/CNS/GNSS Integration

This paper presents a new methodology of distributed state fusion by using the sparse-grid quadrature filter to deal with the fusion estimation problem for multi-sensor nonlinear systems. In this methodology, the sparse-grid quadrature filter is performed in a distributed manner to process the information at each local node; and subsequently, a distributed state fusion approach based on matrix weighting is established in the sense of mean square error, in which a flexible procedure is developed based on the sparse-grid quadrature rule to calculate the cross-covariances between any two local estimators for multi-sensor nonlinear systems. The proposed methodology of distributed fusion can obtain higher fusion estimation accuracy in a flexible way, leading to improved fusion performance for multi-sensor nonlinear systems. The simulations and experiments in INS/CNS/GNSS (inertial navigation system/celestial navigation system/global navigation satellite system) integration verifies the effectiveness of the proposed methodology.
MediSecFed: Private and Secure Medical Image Classification in the Presence of Malicious Clients

Deep learning demonstrates its efficacy and potential to solve challenging computer vision problems in medical and other industrial applications. Federated learning is a learning paradigm that facilitates collaborative learning in a federation of users without exchanging actual data with a single authority like a server. However, federated learning provides only a basic level of privacy and robustness and is vulnerable to model poisoning and model inversion attacks in hostile training environments. Hence, in this article, we propose MediSecFed—a secure framework for federated learning in a hostile environment. Compared to the widely used FedAvg, our method relies on simple and practical ideas from knowledge distillation and model inversion to ensure additional security and privacy features. Our approach achieves knowledge exchange among participating entities without sharing model parameters as FedAvg does, thus protecting the privacy of the local data from the server and significantly reducing communication costs. We evaluate our method on two chest X-ray datasets. Our method outperforms FedAvg by 15% on both datasets in a hostile environment. Our method will also continue to maintain good performance even if the number of malicious participating entities increases. Robustness to learn in a malicious environment while preserving privacy with reduced communication costs makes our method more desirable and efficient than that of FedAvg.
An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

In many applications, selecting the optimal features is a difficult task. Numerous In optimization problems, e.g., feature selection (FS) problem, have been solved using optimization algorithms. In this paper, the most discriminating features were chosen using a new chaotic gradient-based optimizer (CGBO) that combines chaotic maps with searching iterations of the gradient-based optimizer (GBO). Ten chaotic maps were utilised to update the parameters, eliminate a local optimum and premature convergence, accelerate convergence, and enhance the efficiency of GBO. A classifier was used in FS approaches to determine the best subset of characteristics. The proposed CGBO uses the k-nearest neighbor as an objective function for the classification process in FS. Ten datasets from the UCI machine learning repository were used to validate CGBO. In an experiment, CGBO outperformed five other metaheuristic algorithms: particle swarm optimization(PSO), moth flame optimizer(MFO), sine cosine algorithm(SCA), salp swarm algorithm(SSA), and GBO. The results demonstrated the capability of CGBO to find and select the optimal feature subset, which maximized the classification performance and minimized the number of features selected efficiently compared with other metaheuristic algorithms.
A cautionary tale of decorrelating theory uncertainties

Abstract A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature. While this can be an essential technique for enabling background estimation, it may also be useful for reducing uncertainties. We carefully examine theory uncertainties, which typically do not have a statistical origin. We will provide explicit examples of two-point (fragmentation modeling) and continuous (higher-order corrections) uncertainties where decorrelating significantly reduces the apparent uncertainty while the true uncertainty is much larger. These results suggest that caution should be taken when using decorrelation for these types of uncertainties as long as we do not have a complete decomposition into statistically meaningful components.
The Making of Meaning through Dyadic Haptic Affective Touch

Despite the importance of touch in human–human relations, research in affective tactile practices is in its infancy, lacking in-depth understanding needed to inform the design of remote digital touch communication. This article reports two qualitative studies that explore tactile affective communication in specific social contexts, and the bi-directional creation, sending and interpretation of digital touch messages using a purpose-built research tool, the Tactile Emoticon. The system comprises a pair of remotely connected mitts, which enable users in different locations to communicate through tactile messages, by orchestrating duration and level of three haptic sensations: vibration, pressure and temperature. Qualitative analysis shows the nuanced ways in which 68 participants configured these elements to make meaning from touch messages they sent and received. It points to the affect and emotion of touch, its sensoriality and ambiguity, the significance of context, social norms and expectations of touch participants. Findings suggest key design considerations for digital touch communication, where the emphasis shifts from generating ‘recognizable touches’ to tools that allow people to shape their touches and establish common understanding about their meaning.
Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks

Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.
Mathematical model of Information Processing in the Ecological Monitoring Information System

The mathematical model of information processing has been developed in the Ecological Monitoring Information System (EMIS). This model is used to formalize the structure of information and its life cycle, simulate processing processes, formalize the impact on information and the movement of information, taking into account the factors of adaptation of the EMIS to the structure of subject activity. Working with information allows you to process information processes and implement them in algorithmic, software and theoretical-analytical analysis. The presented information processing processes scheme is used to formalize information flows in the ecological monitoring information system. The aim of the study is that agricultural production is under heavy pressure based on world population growth and significant changes in climate. This study discusses the concept of digital agricultural practices and their impact on agricultural productivity. The assessment of the current situation is carried out by digitization in agricultural areas located in the Aral Sea region. The methodology is used in this study as a method to assess the current state of digital applications for agriculture in the Aral Sea region. Agriculture is one of the most suitable methods for it, as digitalization is still in its infancy in the Aral Sea region. Digital agricultural technologies in the Aral Sea region are at an early stage of development, and the topics discussed in the world are aimed at making significant progress in raising awareness about farmers and other sectors of agriculture in the Aral Sea region. Although the current penetration of digital technologies is not large enough in terms of volume and quantity, the analysis of the methods used for agriculture can achieve the expected results as a result of the experience of developed countries.
Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference. In this paper, a single-shot detector namely Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components including Attention Feature Fusion Module (AFFM), Deformable Lateral Connection Module (DLCM) and Anchor-guided Detection Module (ADM), are carefully designed in our method for refining and aligning informative characteristics of aircraft. To represent characteristics of aircraft with less interference, low-level textural and high-level semantic features of aircraft are fused and refined in AFFM throughly. The alignment between aircraft's discrete back-scatting points and convolutional sampling spots is promoted in DLCM. Eventually, the locations of aircraft are predicted precisely in ADM based on aligned features revised by refined anchors. To evaluate the performance of our method, a self-built SAR aircraft sliced dataset and a large scene SAR image are collected. Extensive quantitative and qualitative experiments with detailed analysis illustrate the effectiveness of the three proposed components. Furthermore, the topmost detection accuracy and competitive speed are achieved by our method compared with other domain-specific,e.g., DAPN, PADN, and general CNN-based methods,e.g., FPN, Cascade R-CNN, SSD, RefineDet and RPDet.
Evidence-based Fact-Checking of Health-related Claims

The task of verifying the truthfulness of claims in textual documents, or fact-checking, has received significant attention in recent years.Many existing evidence-based factchecking datasets contain synthetic claims and the models trained on these data might not be able to verify real-world claims.Particularly few studies addressed evidence-based fact-checking of health-related claims that require medical expertise or evidence from the scientific literature.In this paper, we introduce HEALTHVER, a new dataset for evidencebased fact-checking of health-related claims that allows to study the validity of real-world claims by evaluating their truthfulness against scientific articles.Using a three-step data creation method, we first retrieved real-world claims from snippets returned by a search engine for questions about COVID-19.Then we automatically retrieved and re-ranked relevant scientific papers using a T5 relevance-based model.Finally, the relations between each evidence statement and the associated claim were manually annotated as SUPPORT, REFUTE and NEUTRAL.To validate the created dataset of 14,330 evidence-claim pairs, we developed baseline models based on pretrained language models.Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and opendomain claims.Our results and manual analysis suggest that HEALTHVER provides a realistic and challenging dataset for future efforts on evidence-based fact-checking of healthrelated claims.The dataset, source code, and a leaderboard are available at https:// github.com/sarrouti/healthver.
Improve Neural Distinguishers of SIMON and SPECK

Deep learning has played an important role in many fields, which shows significant potential for cryptanalysis. Although these existing works opened a new direction of machine learning aided cryptanalysis, there is still a research gap that researchers are eager to fill. How to further improve neural distinguishers? In this paper, we propose a new algorithm and model to improve neural distinguishers in terms of accuracy and the number of rounds. First, we design an algorithm based on SAT to improve neural distinguishers. With the help of SAT/SMT solver, we obtain new effective neural distinguishers of SIMON using the input differences of high-probability differential characteristics. Second, we propose a new neural distinguisher model using multiple output differences. Inspired by the existing works and data augmentation in deep learning, we use the output differences to exploit more derived features and train neural distinguishers, by splicing output differences into a matrix as a sample. Based on the new model, we construct neural distinguishers of SIMON and SPECK with round and accuracy promotion. Utilizing our neural distinguishers, we can distinguish reduced-round SIMON or SPECK from pseudorandom permutation better.
Human pose, hand and mesh estimation using deep learning: a survey

Abstract Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2 D and 3 D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2 D and 3 D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2 D and 3 D HPE approaches.