Reviewerly/exHarmony huggingface dataset:
Bi-directional Image–Text Matching Deep Learning-Based Approaches: Concepts, Methodologies, Benchmarks and Challenges

Abstract Nowadays, image–text matching (retrieval) has frequently attracted attention due to the growth of multimodal data. This task returns the relevant images to a textual query or descriptions that describe a visual scene and vice versa. The core challenge is how to precisely determine the similarity computation between the text and image, which requires understanding the different modalities by extracting the related information accurately. Although many approaches are established for matching textual data and visual content utilizing deep learning (DL) approaches, a few reviews of the studies of image–text matching are obtainable using DL. In this review study, we contribute to present and clarify the modern techniques based on DL in the image–text matching problem by providing an extensive study of the existing matching models, different current architectures, benchmark datasets, and evaluation methods. First, we explain the matching task and illustrate frequently used architecture. Second, we classify present approaches according to two important concepts the alignment between image and text, and the learning approach. Third, we report standard datasets and evaluation techniques. Finally, we show up current challenges to serve as an inspiration to new researchers in this field.
Virtual reality (VR) treatments for anxiety disorders are unambiguously successful, so why are so few therapists using it? Barriers to adoption and potential solutions

Anxiety disorders are a significant cause of disability globally, yet only one in ten sufferers receives adequate quality treatment. Exposure-based therapies are effective in reducing symptoms associated with a number of anxiety disorders. However, few therapists use exposure techniques to treat these conditions, even when they are adequately trained in them, often because of concerns about provoking distress, drop out, logistical barriers, and other concerns. Virtual reality exposure therapy (VRET) can address many of these concerns, and a large body of research decisively shows that VRET is as efficacious for treating these conditions as in vivo exposures. Yet, use of VRET remains low. In this article, we discuss several factors we believe are contributing to low VRET adoption among therapists and raise potential solutions to address them. We consider steps that VR experience developers and researchers might take, such as leading studies of VRET's real-world effectiveness and treatment optimization trials and continuing to improve the fit of platforms with clinicians' workflows. We also discuss steps to address therapist reservations using aligned implementation strategies, as well as barriers for clinics, and the roles that professional organizations and payers could have in improving care by encouraging adoption of VRET.
Effective software security enhancement using an improved PointNet++

As common three-dimensional (3D) data, point clouds have wide application prospects in many fields. The point cloud disorder problem is solved by the PointNet neural network using a symmetric function, which insensitivity to the input order enables PointNet to process the original point cloud data directly. The ability to extract local features was enhanced by introducing the PointNet++, furnishing a better solving capacity of 3D vision problems and improved intelligent driving software security. This paper analyzes the PointNet++ implementation principles and improves its local feature extraction capability by the proposed density-related farthest point sampling (DR-FPS) algorithm, mitigating some limitations of the conventional FPS algorithm so that the sampling results can better express the feature information of point cloud data. The accuracy of the proposed DR-FPS algorithm applied to five-category and ten-category classification tasks exceeded that of the conventional FPS by 4.4 and 5.6%, respectively. Finally, a positive correlation between the accuracy increment and the number of classification categories was revealed. The results are instrumental to intelligent driving software security enhancement.
ATOM: Self-supervised human action recognition using atomic motion representation learning

Self-supervised learning (SSL) is a promising method for gaining perception and common sense from unlabelled data. Existing approaches to analyzing human body skeletons address the problem similar to SSL models for image and video understanding, but pixel data is far more challenging than coordinates. This paper presents ATOM, an SSL model designed for skeleton-based data analysis. Unlike video-based SSL approaches, ATOM leverages atomic movements within skeleton actions to achieve a more fine-grained representation. The proposed architecture predicts the action order at the frame level, leading to improved perceptions and representations of each action. ATOM outperforms state-of-the-art approaches in two well-known datasets (NTU RGB + D and NTU-120 RGB + D), and its weight transferability enables performance improvements on supervised and semi-supervised tasks, up to 4.4% (3.3% p.p.) and 14.1% (6.3% p.p.), respectively, in Top-1 Accuracy.
Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation

With latest developments in deep learning approaches, automated, accurate, fast, and generalized segmentation model for left atrium, left ventricle, right ventricle, and myocardium from MR images is becoming increasingly desirable. In deep learning-based approaches, model generalizability is an essential concern. The strength of an approach that has proven competent for one dataset, and then executed for other without fine-tuning, started to decline. In medical image segmentation, U-Net-based architecture with its skip-connection has attained the highest levels of success. This article, therefore presents a novel Attention-guided Residual W-Net (ARW-Net), a deep learning-based segmentation approach with residual links, attention gates at each feature dimensions, and upgraded deep supervision for cardiac MRI (CMRI) segmentation. As the basic building block, the W-Net, a unique methodology for medical image segmentation (centred on U-Net layout), is used. In a W-Net-based structure, ARW-Net suggests residual connections in encoder and attention gates at each feature dimension in decoder. Combination of Dice and Cross-entropy losses is used for model training. Four CMRI datasets, (i) ACDC 2017, (ii) 2018 ASC, (iii) M&Ms 2020, and (iv) LAScarQS 2022, which are publicly available, have been utilized to examine effectiveness of ARW-Net. It accomplished improved segmentation results and was among the top two for several metrics. Numerous comparisons have demonstrated that ARW-Net is profoundly promising, with several segmentation findings delivering innovative state-of-the-art outcomes on four datasets. The results of experiments demonstrate that ARW-Net is able to effectively segment cardiac MR images, as demonstrated by comparisons with existing algorithms.
Two-neighbor-wavelength phase-shifting approach for high-accuracy rapid 3D measurement

This paper presents a two-neighbor-wavelength fringe projection approach for high-accuracy fast 3D measurement. Different from the traditional two-wavelength fringe projection approach, the proposed approach encodes four fringe images, wherein two are short-wavelength images and the other two are long-wavelength images, which significantly reduces the time for the camera to collect images. And the short-wavelength pattern has one more fringe period than the long-wavelength pattern, which decreases the frequency ratio and extends the noise tolerance. Through a concise algorithm, the one-period reference phase and multiple periods wrapped phase are extracted simultaneously. The wrapped phase is unfolded through phase stairs derived from the reference phase, so as to retrieve the unwrapped phase. Both static and dynamic experiments demonstrate the effectiveness and superiority of our method.
Object detection and classification from compressed video streams

Abstract Video Analytics is widely used by the internet‐based platforms to govern the mass consumption of videos. Traditionally, it is carried out from the decoded format of the videos. This requires the analytics server to perform both decoding and analytics computation. This process can be made fast and efficient if performed over the compressed format of the videos as it reduces the decoding stress over the analytics server. The field of video analytics from the binarized formats using modern deep learning techniques is still emerging and needs further exploration. This proposed work is based on the same notion. In this work, two analytics tasks that is, classification and object detection are carried out from the binarized videos. The binarized formats are produced by using an already‐designed end‐to‐end video compression network. The experiments have been carried out over standard datasets. The proposed MobileNetv2‐based classification network shows an accuracy of 66% over the YouTube UGC dataset and the YOLOX‐S‐based detection network shows mAP of 45% over IMAGENet datasets. The proposed work shows competitiveness and improvement in the detection outcomes on compressed data and also provides further motivation for the adoption of deep learning‐based video compression in practical analytics domains.
Preparing for a future with quantum technologies: an innovative approach to accessible quantum education

Quantum science and technology has potential to revolutionize applications in metrology, sensing, imaging, computing, and communication. Preparation for quantum-classical technology integration is crucial for the future scientists. Formal theoretical training in quantum electrodynamics usually requires several hundred hours of coursework, notwithstanding the physics background, which would not be feasible for learners from diverse background. We present an online Experimental-based Quantum Technologies course that makes this education more accessible. The module has six lectures and hands-on experiments. The participants enter the lab using VR goggles and 360&deg; live video conferencing and perform the experiment over a virtual network connection. Experiments focus on observing physical effects and conceptualize findings. We'll share our insights from this course's that enables self-paced remote-learning.
TMO-Det: Deep tone-mapping optimized with and for object detection

Detecting objects in challenging illumination conditions is critical for autonomous driving. Existing solutions detect objects with standard or tone-mapped Low Dynamic Range (LDR) images. In this paper, we propose a novel adversarial approach that jointly optimizes tone-mapping (mapping High Dynamic Range (HDR) to LDR) and object detection. We analyze different ways to combine the feedback from tone-mapping quality and object detection quality for training such an adversarial network. We show that our deep tone-mapping operator jointly trained with an object detector achieves the best tone-mapping quality as well as detection quality compared to alternative approaches.
WPE: Weighted prototype estimation for few-shot learning

Few-shot learning (FSL) is a challenging task that aims to transfer a model trained on base classes with adequate labeled data to accommodate novel classes with only a few training examples. In this paper, we propose a simple but effective weighted prototype estimation (WPE) method to improve FSL. We assume that similar classes have similar distributions in the feature space so that the prototypes of novel classes can be estimated by transferring information from their similar base classes. Specifically, the proposed method learns the Gaussian-like feature distributions of similar base classes with sufficient samples and then transfers the learned distributions to calibrate the prototype of the novel class, which is weighted by its similarities. With the estimated prototype, more robust samples can be generated to improve the FSL task. Comparative experiments are conducted to evaluate the effectiveness of our proposed algorithm on three benchmark FSL datasets. The results show that our proposed method can generate more robust samples and significantly improve FSL, outperforming the state-of-the-art methods on these datasets.
Adapt-Infomap: Face clustering with adaptive graph refinement in infomap

Face clustering is a critical task in computer vision due to the increasing number of applications such as augmented reality or photo album management. The primary challenge in this task arises from the imperfections in image feature representations. Given image features extracted from an existing pre-trained representation model, it remains an unresolved problem that how to leverage the inherent characteristics of similarities among unlabelled images to improve the clustering performance. In order to solve face clustering in an unsupervised manner, we develop an effective and robust framework named as Adapt-Infomap. First, we reformulate face clustering as a process of non-overlapping community detection. Specially, Adapt-Infomap achieves face clustering by minimizing the entropy of information flows (also known as the map equation) on an affinity graph of images. Since the affinity graph of images might contain noisy edges, we develop an outlier detection strategy in Adapt-Infomap to adaptively refine the affinity graph. Experiments with ablation studies demonstrate that Adapt-Infomap significantly outperforms existing methods and achieves new state-of-the-arts on three popular large-scale datasets for face clustering, e.g., an absolute improvement of more than 10% and 3% comparing with prior unsupervised and supervised methods respectively in terms of average of Pairwise F-score.
Image steganalysis method based on cover selection and adaptive filtered residual network

Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost.
Enhancing lifestyle and health monitoring of elderly populations using CSA-TkELM classifier

Nowadays, there is a growing interest among researchers in health monitoring using body sensor data due to its usage in diverse applications. Human Activity Recognition (HAR) utilizes smart wearable devices, particularly in healthcare, to help the elderly population improve their lifestyle by preventing forthcoming exceptional events such as health risks and falls. Although various types of sensors are available to recognize human behaviors, wearable sensors provide consistent information about the lifestyle and functionality of subjects. However, accurate recognition requires intelligent algorithms to extract temporal features of time series data from raw sensor readings. In order to recognize human activities more accurately from time series data, this paper proposes a crossover Aquila-optimized Tri-kernel Extreme Learning Machine (CSA-TkELM) classifier. The sensor data utilized to evaluate the effectiveness of the proposed CSA-TkELM classifier is obtained from the USC Human Activity Dataset (USC-HAD), the Public domain dataset for Real-life human activity recognition using smartphone Sensors (PDS-SS), and the OPPORTUNITY dataset. The direct application of raw time series data to the classifier affects its recognition performance. Therefore, preprocessing steps such as noise removal, handling missing values, standardization, and segmentation are executed. The proposed CSA-TkELM classifier extracts significant temporal features from the time series information and recognizes activities. Several performance metrics, such as recall, precision, accuracy, and F1 score, are measured to investigate the results of the proposed CSA-TkELM classifier. The proposed CSA-TkELM classifier achieves a recall of 98.1%, precision of 97.6%, accuracy of 97.9%, and F1 score of 97.3%, which are higher than those of other compared existing techniques.
Potential of Quantum Technologies in the Energy Sector

Modern industrial societies rely heavily on electric power systems, which serve as their backbone. Companies in the energy industry face significant challenges due to the unprecedented technological innovation and rapid evolution of systems, processes, and technology required for the development and global delivery of energy. Quantum technologies can offer a solution to many of these problems. Quantum communication, simulation, computing, and sensing have advanced beyond the confines of laboratories and are gradually being adopted by end-users. The article links two worlds: a technological summary of research needs and activities to support the operation and management of energy systems until 2050, and the latest findings from the world of quantum technologies.
Improved Alpha-Information Bounds for Higher-Order Masked Cryptographic Implementations

Embedded cryptographic devices are usually protected against side-channel attacks by masking strategies. In this paper, the security of protected cryptographic implementations is evaluated for any masking order, using alpha-information measures. Universal upper bounds on the probability of success of any type of side-channel attack are derived. These also provide lower bounds on the minimum number of queries required to achieve a given success rate. An important issue, solved in this paper, is to remove the loss factor due to the masking field size.
Coded Computation of Multiple Functions

We consider the problem of evaluating arbitrary multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to provide robustness against stragglers who do not respond in time, adversarial workers who respond with wrong computation and information-theoretic security of dataset against colluding workers. Our scheme introduces a small computation overhead which results in a reduction in download cost and also offers comparable resistance to stragglers over existing solutions.
Achievable error exponents of data compression with quantum side information and communication over symmetric classical-quantum channels

A fundamental quantity of interest in Shannon theory, classical or quantum, is the optimal error exponent of a given channel W and rate R: the constant E(W,R) which governs the exponential decay of decoding error when using ever larger codes of fixed rate R to communicate over ever more (memoryless) instances of a given channel W. Here I show that a bound by Hayashi [CMP 333, 335 (2015)] for an analogous quantity in privacy amplification implies a lower bound on the error exponent of communication over symmetric classical-quantum channels. The resulting bound matches Dalai’s [IEEE TIT 59, 8027 (2013)] sphere-packing upper bound for rates above a critical value, and reproduces the well-known classical result for symmetric channels. The argument proceeds by first relating the error exponent of privacy amplification to that of compression of classical information with quantum side information, which gives a lower bound that matches the sphere-packing upper bound of Cheng et al. [IEEE TIT 67, 902 (2021)]. In turn, the polynomial prefactors to the sphere-packing bound found by Cheng et al. may be translated to the privacy amplification problem, sharpening a recent result by Li, Yao, and Hayashi [arXiv:2111.01075 [quant-ph]], at least for linear randomness extractors.
Linearized Reed-Solomon Codes with Support-Constrained Generator Matrix

Linearized Reed-Solomon (LRS) codes are a class of evaluation codes based on skew polynomials. They achieve the Singleton bound in the sum-rank metric, and therefore are known as maximum sum-rank distance (MSRD) codes. In this work, we give necessary and sufficient conditions on the existence of MSRD codes with support-constrained generator matrix. These conditions are identical to those for MDS codes and MRD codes. Moreover, the required field size for an ${\left[ {n,k} \right]_{{q^m}}}$ LRS codes with support-constrained generator matrix is q⩾ ℓ + 1 and m ⩾ max <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> ∈[ℓ]{k−1+log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> k,n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> }, where ℓ is the number of blocks and n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</inf> is the size of the l-th block. The special cases of the result coincide with the known results for Reed-Solomon codes and Gabidulin codes.
Bounds and Algorithms for Frameproof Codes and Related Combinatorial Structures

In this paper, we study upper bounds on the minimum length of frameproof codes introduced by Boneh and Shaw [3] to protect copyrighted materials. A q-ary (k,n)-frameproof code of length t is a t×n matrix having entries in {0,1,…,q−1} and with the property that for any column c and any other k columns, there exists a row where the symbols of the k columns are all different from the corresponding symbol (in the same row) of the column c. In this paper, we show the existence of q-ary (k,n)-frameproof codes of length $t = O\left( {\frac{{{k^2}}}{q}\log n} \right)$ for q ≤ k, using the Lovász Local Lemma, and of length $t = O\left( {\frac{{{k^2}}}{{\log \left( {q/k} \right)}}\log \left( {n/k} \right)} \right)$ for q > k using the expurgation method. Remarkably, for the practical case of q ≤ k our findings give codes whose length almost matches the lower bound $\Omega \left( {\frac{{{k^2}}}{{q\log k\log n}}} \right)$ on the length of any q-ary (k,n)-frameproof code and, more importantly, allow us to derive an algorithm of complexity O(tn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the construction of such codes.