Reviewerly/exHarmony huggingface dataset:
Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network

With the rapid development of the Internet, various electronic products based on computer vision play an increasingly important role in people's daily lives. As one of the important topics of computer vision, human action recognition has become the main research hotspot in this field in recent years. The human motion recognition algorithm based on the convolutional neural network can realize the automatic extraction and learning of human motion features and achieve good classification performance. However, deep convolutional neural networks usually have a large number of layers, a large number of parameters, and a large memory footprint, while embedded wearable devices have limited memory space. Based on the traditional cross-entropy error-based training mode, the parameters of all hidden layers must be kept in memory and cannot be released until the end of forward and reverse error propagation. As a result, the memory used to store the parameters of the hidden layer cannot be released and reused, and the memory utilization efficiency is low, which leads to the backhaul locking problem, limiting the deployment and execution of deep convolutional neural networks on wearable sensor devices. Based on this, this topic designs a local error convolutional neural network model for human motion recognition tasks. Compared with the traditional global error, the local error constructed in this paper can train the convolutional neural network layer by layer, and the parameters of each layer can be trained independently according to the local error and does not depend on the gradient propagation of adjacent upper and lower layers. As a result, the memory used to store all hidden layer parameters can be released in advance without waiting for the end of forward and backward propagation, avoiding the problem of backhaul locking, and improving the memory utilization of convolutional neural networks deployed on embedded wearable devices.
Sat: Self-Adaptive Training for Fashion Compatibility Prediction

This paper presents a self-adaptive training (SAT) model for fashion compatibility prediction. It focuses on the learning of some hard items, such as those that share similar color, texture, and pattern features but are considered incompatible due to the aesthetics or temporal shifts. Specifically, we first design a method to define hard outfits and a difficulty score (DS) is defined and assigned to each outfit based on the difficulty in recommending an item for it. Then, we propose a self-adaptive triplet loss (SATL), where the DS of the outfit is considered. Finally, we propose a very simple conditional similarity network combining the proposed SATL to achieve the learning of hard items in the fashion compatibility prediction. Experiments on the publicly available Polyvore Outfits and Polyvore Outfits-D datasets demonstrate our SAT’s effectiveness in fashion compatibility prediction. Besides, our SATL can be easily extended to other conditional similarity networks to improve their performance.
Quantum circuit architectures via quantum observable Markov decision process planning

Abstract Algorithms for designing quantum circuit architectures are important steps toward practical quantum computing technology. Applying agent-based artificial intelligence methods for quantum circuit design could improve the efficiency of quantum circuits. We propose a quantum observable Markov decision process planning algorithm for quantum circuit design. Our algorithm does not require state tomography, and hence has low readout sample complexity. Numerical simulations for entangled states preparation and energy minimization are demonstrated. The results show that the proposed method can be used to design quantum circuits to prepare the state and to minimize the energy.
Multiresolutional Hybrid NLM-Wieneг Filters for X-Ray Image Denoising

With the rapid evolution of computing and data storage technology, medical image denoising techniques have undergone significant improvement in recent years. Much of this success is in fact due to the emergence of multiresolution analysis (MRA) on both mathematical and algorithmic levels. In this article, we propose a hybrid multiresolution denoising approach coupling the two filters Non-Local Means and Wiener as well as a multiscale decomposition approach. A comparative study is carried out between two among the best-known MRA-based decomposition techniques: empirical mode decomposition (EMD) and empirical wavelet transform (EWT). Simulations in a denoising framework of a sample of benchmark X-ray images prove the effectiveness of multiscale denoising, especially when hybrid filtering is coupled to EWT. These results give several signs of their ability to be integrated into real-use scanners in the next few years.
Efficient Localization And Reconstruction Of 3D Objects Using The New Hybrid Squire Moment

In this paper, we present a new method for 3D image reconstruction and localization. The latter is based on two concepts: the first is the use of a new hybrid squared polynomial called the Krawtchouk-Meixner Squared Polynomial (SKMP), which is based on two traditional polynomials, namely Meixner (MP) and Krawtchouk (KP). The second one is the cubic representation of the 3D image (ICR) to speed up the process of computing the new discrete orthogonal moments and improve the performance of reconstruction and localization of 3D images. The simulation results confirm the 3D image reconstruction and localization capability using the proposed approach.
Protecting Blockchain-based Decentralized Timed release of Data from Malicious Adversaries

Timed-release of information refers to releasing protected sensitive data at a future point of time while securely protecting the information until the release time. Blockchain-based self-emerging data infrastructures consist of a group of blockchain accounts that jointly take charge of protecting and transferring the data at the release time. Existing solutions have focused on fully rational adversarial environments in which all peer accounts are rational. However, such protection disregards scenarios in which malicious peer accounts also exist. In our work, we focus on protecting blockchain-based timed-release service in mixed adversarial environments in which both rational peer accounts and malicious peer accounts exist. We introduce our blockchain-based timed-release framework designed for mixed adversarial environments and illustrate two concrete attacks, namely drop attack and release-ahead attack, and discuss our reputation-based solution.
Simulation Experiments of Different Metaheuristics Algorithms using Benchmark Functions: A Performance Study

Metaheuristics have been commonly used in several engineering optimizations, they prerequisite reduced time to converge and produce a better-improved solution. The most applied are Evolutionary Algorithms. Many complex problems are no longer considered difficult due to swarm intelligent optimization algorithms that supply rapid and reliable methods for solutions. and this returns to its features such as robustness, flexibility, self-organization, parallel, and distributive. In this paper, a comparative study among five metaheuristics algorithms in terms of convergence, robustness, and computing time is accomplished, and three benchmark functions are applied to perform simulation experiments with Genetic Algorithm (GA), Firefly Algorithm (FA), Particle Swarm Algorithm (PSO), Invasive Weed optimization (IWO), and Grey Wolf optimizer (GWO). The experimental results show that GE provides more accuracy for complex optimization problems, while the GWO and PSO are better in terms of convergence speed.
Image Super-Resolution via Lightweight Attention-Directed Feature Aggregation Network

The advent of convolutional neural networks (CNNs) has brought substantial progress in image super-resolution (SR) reconstruction. However, most SR methods pursue deep architectures to boost performance, and the resulting large model sizes are impractical for real-world applications. Furthermore, they insufficiently explore the internal structural information of image features, disadvantaging the restoration of fine texture details. To solve these challenges, we propose a lightweight architecture based on a CNN named attention-directed feature aggregation network (AFAN), consisting of chained stacking multi-aware attention modules (MAAMs) and a simple channel attention module (SCAM), for image SR. Specifically, in each MAAM, we construct a space-aware attention block (SAAB) and a dimension-aware attention block (DAAB) that individually yield unique three-dimensional modulation coefficients to adaptively recalibrate structural information from an asymmetric convolution residual block (ACRB). The synergistic strategy captures multiple content features that are both space-aware and dimension-aware to preserve more fine-grained details. In addition, to further enhance the accuracy and robustness of the network, SCAM is embedded in the last MAAM to highlight channels with high activated values at low computational load. Comprehensive experiments verify that our proposed network attains high qualitative accuracy while employing fewer parameters and moderate computational requirements, exceeding most state-of-the-art lightweight approaches.
Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction

Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low source availability, most of the models fail to learn MC and get zero or very low F1 scores on MCs. Previous studies, however, have rather focused on micro F1 scores and MCs have not been addressed adequately. To tackle high mis-classification errors for MCs, we introduce (1) a minority class attention module (MCAM), and (2) effective augmentation methods specialized in RE. MCAM calculates the confidence scores on MC instances to select reliable ones for augmentation, and aggregates MCs information in the process of training a model. Our experiments show that our methods achieve a state-of-the-art F1 scores on TACRED as well as enhancing minority class F1 score dramatically.
A fusion-based contrastive learning model for cross-modal remote sensing retrieval

With the rapid growth of cross-modal data, cross-modal retrieval has become a research hotspot in the field of remote sensing, and remote-sensing image–text retrieval (RSITR) has attracted extensive attention for its flexible and efficient way to get the interested information and its available application. However, most of the existing methods cannot adequately extract fine unimodal features and are poor at exploring potential correlations between different modalities, leading to unsatisfactory performance. Besides, the majority of the existing datasets and methods for image–text retrieval are based on English, and few researches are focused on Chinese captions, but the application of image–text retrieval in the remote-sensing field should not be restricted by the language. In this article, we introduce a novel fusion-based contrastive learning model (FBCLM) for RSITR to cope with the problems of unimodal feature extracting and correlation exploring of remote-sensing image–text pairs, and the model is available for image–text retrieval on both English and Chinese caption datasets. Our model employs the unimodal encoder containing the self-attention module to extract the fine-grained features of the single modal and further utilizes the cross-modal fusion module to improve the discriminative ability of feature representation, which uses the cross attention mechanism. Furthermore, contrastive loss is applied to the method to enhance the image–text retrieval performance by exploring the underlying semantic relationship between visual and textual representations. In addition, we construct several remote-sensing image Chinese caption datasets for RSITR. The experimental results on several public RSITR datasets and the proposed datasets demonstrate the outperformance of our model in the cross-modal remote-sensing image–text retrieval task.
SLOVA: Uncertainty estimation using single label one-vs-all classifier

Deep neural networks present impressive performance, yet they cannot reliably estimate their predictive confidence, limiting their applicability in high-risk domains. We show that applying a multi-label one-vs-all loss reveals classification ambiguity and reduces model overconfidence. The introduced SLOVA (Single Label One-Vs-All) model redefines typical one-vs-all predictive probabilities to a single label situation, where only one class is the correct answer. The proposed classifier is confident only if a single class has a high probability and other probabilities are negligible. Unlike the typical softmax function, SLOVA naturally detects out-of-distribution samples if the probabilities of all other classes are small. The model is additionally fine-tuned with exponential calibration, which allows us to precisely align the confidence score with model accuracy. We verify our approach on three tasks. First, we demonstrate that SLOVA is competitive with the state-of-the-art on in-distribution calibration. Second, the performance of SLOVA is robust under dataset shifts. Finally, our approach performs extremely well in the detection of out-of-distribution samples. Consequently, SLOVA is a tool that can be used in various applications where uncertainty modeling is required.
Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend

Deep vision multimodal learning aims at combining deep visual representation learning with other modalities, such as text, sound, and data collected from other sensors. With the fast development of deep learning, vision multimodal learning has gained much interest from the community. This paper reviews the types of architectures used in multimodal learning, including feature extraction, modality aggregation, and multimodal loss functions. Then, we discuss several learning paradigms such as supervised, semi-supervised, self-supervised, and transfer learning. We also introduce several practical challenges such as missing modalities and noisy modalities. Several applications and benchmarks on vision tasks are listed to help researchers gain a deeper understanding of progress in the field. Finally, we indicate that pretraining paradigm, unified multitask framework, missing and noisy modality, and multimodal task diversity could be the future trends and challenges in the deep vision multimodal learning field. Compared with existing surveys, this paper focuses on the most recent works and provides a thorough discussion of methodology, benchmarks, and future trends.
Modified YOLOv4 Framework with Thermal Images for Pedestrian Detection

A modified YOLOv4 framework is proposed for pedestrian detection which is trained and tested with a portion of ZUT-FIR-ADAS thermal image dataset. The effectiveness of mosaic and cutmix data augmentation techniques have been utilized to improve the generalization capability of this framework. The mean average precision of 73% and average loss of 1.8062 have been achieved through this framework with thermal images. It yields a precision of 0.92, recall of 0.96, F1 score of 0.94, average intersection of union of 72.25%. The experimental results show the effectiveness of the modified YOLOv4 framework for pedestrian detection under diverse weather conditions.
Clustering longitudinal data using R: A Monte Carlo Study

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">The analysis of change within subjects over time is an ever more important research topic. Besides modelling the individual trajectories, a related aim is to identify clusters of subjects within these trajectories. Various methods for analyzing these longitudinal trajectories have been proposed. In this paper we investigate the performance of three different methods under various conditions in a Monte Carlo study. The first method is based on the non-parametric k-means algorithm. The second is a latent class mixture model, and the third a method based on the analysis of change indices. All methods are available in R. Results show that the k-means method performs consistently well in recovering the known clustering structure. The mixture model method performs reasonably well, but the change indices method has problems with smaller data sets.
Finding the Middle Ground: Measuring Passwords for Security and Memorability

Passwords are a ubiquitous element of our digital age, and the need for secure passwords is indispensable. However, traditionally secure passwords tends to be very difficult to remember, leading users to frustration in having to remake them or even abandoning secure ones for the sake of memorability. On the other hand, passwords that are considered memorable have a tendency to be less secure. Despite many studies on passwords, the process of the users' perceiving password memorability is still abstract. This security and memorability trade-off brings out the need to find a middle ground leaving the research question on the ground whether finding passwords that are both memorable and secure is a possibility or not. In this paper, we address this very question by conducting a survey and a user-study of memorability for certain categories of popular password styles. Next, we take the most memorable of these passwords and determine their security via a bits of entropy calculation commonly used to determine a password's strength against a traditional brute force attack. Finally, we find the best performers of both memorability and security to find the middle ground of password security and memorability. Our findings present a collection of both memorable and secure styles of passwords including an infrequent but effective tactic for password remembrance.
PAC-PL: Enabling Control-Flow Integrity with Pointer Authentication in FPGA SoC Platforms

Control-flow integrity (CFI) is an effective technique to enhance the security of software systems. Processor designers recently started to provide hardware-based support to efficiently implement CFI, such as the pointer authentication (PA) feature provided by ARM starting from ARMv8.3-A processor architectures. These CFI mechanisms are also accompanied by support in the mainline codebase of popular compilers (such as GCC and LLVM) and the Linux operating system. As such, they are expected to establish as widespread security mechanisms. Nevertheless, many commercial chips still do not support hardware-assisted CFI, even some of the ones that just entered the market. This paper presents PAC-PL, a solution to enable hardware-assisted CFI on heterogeneous platforms that include a field-programmable gate array (FPGA) fabric, such as the Xilinx Ultrascale+ and Versal. PAC-PL comes with compiler-and OS-level support, is compatible with ARM’s PA, and enables advanced key management and attack detection strategies. A timing analysis for PAC-PL is also presented. PAC-PL was experimentally evaluated with state-of-the-art benchmarks in terms of run-time overhead, memory footprint, and FPGA resource consumption, resulting in a practical solution for implementing CFI.
Needle in a Haystack: Spotting and recognising micro-expressions “in the wild”

Computational research on facial micro-expressions has long focused on videos captured under constrained laboratory conditions due to the challenging elicitation process and limited samples that are publicly available. Moreover, processing micro-expressions is extremely challenging under unconstrained scenarios. This paper introduces, for the first time, a completely automatic micro-expression “spot-and-recognize” framework that is performed on in-the-wild videos, such as in poker games and political interviews. The proposed method first spots the apex frame from a video by handling head movements and unconscious actions which are typically larger in motion intensity, with alignment employed to enforce a canonical face pose. Optical flow guided features play a central role in our method: they can robustly identify the location of the apex frame, and are used to learn a shallow neural network model for emotion classification. Experimental results demonstrate the feasibility of the proposed methodology, establishing good baselines for both spotting and recognition tasks – ASR of 0.33 and F1-score of 0.6758 respectively on the MEVIEW micro-expression database. In addition, we present comprehensive qualitative and quantitative analyses to further show the effectiveness of the proposed framework, with new suggestion for an appropriate evaluation protocol. In a nutshell, this paper provides a new benchmark for apex spotting and emotion recognition in an in-the-wild setting.
Multi-Level Distributed Caching on the Blockchain for Storage Optimisation

Blockchain has attracted considerable attention as a solution to the challenges of privacy, security and decentralisation for many applications.However, these characteristics of the blockchain result in ever growing ledger size, which is one of the major barriers to blockchain adoption in large-scale networks such as the Internet of Things (IoT).In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access pattern.MLDC divides nodes into storage classes (SCs) by their node availability, and assigns each SC a different Access Frequency (AF) to remove data from the local storage.Over time, each node only stores frequently accessed data, so MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and data query delay.
Spam image detection based on convolutional block attention module

Digital communication platforms, such as Gmail and Yahoo, are become essential in our professional and personal lives. In addition to the low cost of e-mails, they are fast. Despite the advantages of these tools, spammers try to send unsolicited e-mail, known as spam, daily. Recently, image spam, a new type of spam e-mail, is developed by spammers in order to avoid detection based on text-based spam filtering systems. Image spam contains more complex information as compared to text spam. For this reason, the detection of image spam is still a challenging task for researchers. Most of the developed image spam filtering systems are based on hand-crafted features and machine learning techniques, which are time-consuming and less efficient. In addition, these systems do not focus on the important features, which can have an impact on the detection process. In this paper, we apply the convolutional block attention module (CBAM) model in order to address the problem of image spam. The experiments are conducted on the available dataset, called image spam hunter (ISH). The results obtained are then compared, using the CBAM model, to other existing state-of-the-art methods. The results obtained have demonstrated that the convolutional block attention module (CBAM) is efficient for image spam detection.