Reviewerly/exHarmony huggingface dataset:
Convolutional Transformer network for fine-grained action recognition

Fine-grained action recognition is one of the critical problems in video processing, which aims to recognize similar actions of subtle interactions between humans and objects. Inspired by the remarkable performance of the Transformer in natural language processing, Transformer has been applied to the fine-grained action recognition task. However, Transformer needs abundant training data and extra supervision to achieve comparable results with convolutional neural networks (CNNs). To address these issues, we propose a Convolutional Transformer Network (CTN), which integrates the merits of CNN (e.g., sharing weights, capturing low-level features in videos and locality) and the benefits of Transformer (e.g., dynamic attention and learning long-range dependencies). In this paper, we propose two modifications to the original Transformer: (i) we propose a video-to-tokens module that can extract tokens from extracted spatial-temporal features in videos by 3D convolutions instead of the direct token embedding from raw input video clips; (ii) we completely replace the linear mapping in multi-head self-attention layer with depth-wise convolutional mapping, which applies a depth-wise separable convolution operation on embedded token maps. With these two modifications, our approach can extract the effective spatial-temporal features from videos and process the long sequences of tokens encountered in videos. Experimental results demonstrate that our proposed CTN can achieve state-of-the-art accuracy on two fine-grained action recognition datasets (i.e., Epic-Kitchens and Diving 48) with a small computational increase.
Ten Meter Walk Test with Mobile Devices: A dataset with accelerometer, magnetometer, and gyroscope

This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.
SOQCS: A Stochastic Optical Quantum Circuit Simulator

Stochastic Optical Quantum Circuit Simulator (SOQCS) is a C++ and Python library which offers a framework to define, simulate and study quantum linear optical circuits in presence of various imperfections typically encountered in experiments. Quantum circuits can be defined from basic components, including emitters, linear optical elements, delays and detectors. The imperfections come from partial distinguishability of photons, lossy propagation media, unbalanced beamsplitters and non-ideal emitters and detectors for example. SOQCS also provides various simulator cores and tools to analyze the output. Furthermore, the configuration of detectors also includes postselection. SOQCS is developed using a modular approach in which different modules are applied in an automated easy to use manner. Furthermore, the modular approach allows for further extensions of the SOQCS capabilities in future.
A fitting model with optimal Multiple Image Hiding effect

Image hiding is a field of research that focuses on covert storage and transmission techniques.It involves embedding a secret image within a container image to create a classified-carrying image that resembles a normal image.Nevertheless,the current image hiding methods based on Invertible neural networks suffer from a critical issue of information loss during the hiding process.This leads to a substantial degradation in the quality of the secret image extracted,thereby preventing the simultaneous achievement of secure transmission,high-capacity transmission,and high fidelity of the secret image in an insecure network environment.To address this issue,we introduce a novel image hiding architecture called FMIN (Fitting Models Based on Invertible Network).FMIN incorporates our innovative fitting model,which fits the loss information and generates a variable z simulating the loss information at the receiver side.This variable z is used as an input to the revealing process,enabling the high-quality extraction of multiple secret images from a single classified loaded image.Additionally,we introduce a novel decoupled training strategy aimed at enhancing the stability of image hiding model during training.Experimental results demonstrate that the image hiding method based on the proposed FMIN architecture in this paper significantly outperforms other SOTA image hiding methods for single-image and multiple-images hiding.
Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Sentiment analysis is a popular natural language processing task that recognizes the opinions or feelings of a piece of text. Microblogging platforms such as Twitter are a valuable resource for finding such people’s opinions. The majority of Arabic sentiment analysis studies indicated that the data utilized to train machine learning algorithms is balanced. In this paper, we investigated the impact of sampling techniques and classification algorithms on an imbalanced Arabic dataset about people’s perceptions of COVID-19, with the majority of opinions reflecting people’s fear and stress about the pandemic, and the minority reflecting the belief that the pandemic was a hoax. The experiments concentrated on analyzing the imbalanced learning of Arabic sentiments using over-sampling and under-sampling techniques on seven single machine learning algorithms and two common ensemble algorithms from the bagging and boosting families, respectively. Results show that resampling-based approaches can overcome the difficulty of an imbalanced dataset, and the use of over-sampled data leads to better performance than that of under-sampled data. The results also reveal that using oversampled data from synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, or adaptive synthetic sampling with random forest classifier is the most effective in addressing this classification problem, with F1-score value of 0.99.
Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.
Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.
Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.
Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods.
Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.
Diverse Structure-Aware Relation Representation in Cross-Lingual Entity Alignment

Cross-lingual entity alignment (CLEA) aims to find equivalent entity pairs between knowledge graphs (KGs) in different languages. It is an important way to connect heterogeneous KGs and facilitate knowledge completion. Existing methods have found that incorporating relations into entities can effectively improve KG representation and benefit entity alignment, and these methods learn relation representation depending on entities, which cannot capture the diverse structures of relations. However, multiple relations in KG form diverse structures, such as adjacency structure and ring structure. This diversity of relation structures makes the relation representation challenging. Therefore, we propose to construct the weighted line graphs to model the diverse structures of relations and learn relation representation independently from entities. Especially, owing to the diversity of adjacency structures and ring structures, we propose to construct adjacency line graph and ring line graph, respectively, to model the structures of relations and to further improve entity representation. In addition, to alleviate the hubness problem in alignment, we introduce the optimal transport into alignment and compute the distance matrix in a different way. From a global perspective, we calculate the optimal 1-to-1 alignment bi-directionally to improve the alignment accuracy. Experimental results on two benchmark datasets show that our proposed method significantly outperforms state-of-the-art CLEA methods in both supervised and unsupervised manners.
The reality of backdoored S-Boxes—An eye opener

The real-life incidents researched in academia have revealed that (possibly) state-level efforts are made to camouflage the intentional flaws in the mathematical layer of an S-Box for exploiting the information-theoretic properties, i.e., Kuznyechik. To investigate the common features in the intentionally weakened mappings, this research thoroughly examines the backdoored structures from the perspective of 24 cryptanalytic attack vectors available in the open literature. We have debunked the earlier claims by the backdoor engineers that the proposed designs are stealthy against statistical distinguishers. Although a backdoored architecture fulfils the notions of randomness, it lacks the strength to resist sophisticated cryptanalytic attacks. Our analysis has revealed that during the backdoor insertion phase, a malicious designer compromises vital cryptographic properties, prominently the algebraic degree, differential trails, avalanche characteristics and leaving the open ground for hybrid cryptanalytic attacks. It is also observed that these mappings attain the upper bound of BCT, FBCT and DLCT, paving the way for hybrid attacks with high probability and lower data complexity.
HITS-based Propagation Paradigm for Graph Neural Networks

In this article, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a “self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods, which rely solely on the idea of authorities (in-links), HITS considers the relevance of both authorities (in-links) and hubs (out-links), thereby allowing for a more informative graph learning process. To segregate node prediction and propagation, we use a Multilayer Perceptron in combination with a HITS-based propagation approach and propose two models: HITS-GNN and HITS-GNN+. We provided additional validation of our models’ efficacy by performing an ablation study to assess the performance of authority-hub in independent models. Moreover, the effect of the main hyper-parameters and normalization is also analyzed to uncover how these techniques influence the performance of our models. Extensive experimental results indicate that the proposed approach significantly improves baseline methods on the graph (citation network) benchmark datasets by a decent margin for semi-supervised node classification, which can aid in predicting the categories (labels) of scientific articles not exclusively based on their content but also based on the type of articles they cite.
Illumination variation robust circular target based on digital image correlation method

Circular targets, as one of typical image features, are widely used in image-based measurement, such as photogrammetry, displacement sensing, and vibration monitoring. However, its positioning accuracy is usually sensitive to intensity changes in ambient lighting, particularly in outdoor applications and long-term monitoring scenarios. In this paper, a speckle-based circular target (SbCT) is introduced as a solution to the sensitivity of localization accuracy of traditional circular targets in illumination variation environment. By incorporating a speckle pattern on the circular target and utilizing digital image correlation method, the SbCT achieves insensitivity to illumination variations when an appropriate correlation function is chosen. This enables accurate and reliable measurements even in lighting varying environment. This paper initially outlines the design strategy of the SbCT and demonstrates its extraction procedure. Subsequently, simulations are conducted to analyze the positioning accuracy of SbCT under linearly varying illumination situations. Additionally, experiments are performed to study the accuracy and robustness of SbCT with the comparison to concentric circular targets under different lighting variation cases. The results from both simulation and experiment demonstrate the illumination robustness of SbCT, of which the stability is better than 0.01 pixel. According to the displacement tracking experiment, the relative error obtained by SbCT is smaller than 0.2%. Therefore, it can be concluded that SbCT is an illumination-robust feature, offering automatic recognition and accurate localization performance even in illumination varying situations.
Observations from the 2023 Kahramanmaraş earthquake sequence in Türkiye and Syria

Early last year major earthquakes devastated parts of Türkiye and Syria, killing nearly 60 000 people and destroying 84 000 buildings. Seismic experts Ziggy Lubkowski and Kubilây Hiçyılmaz of Arup summarise the various international reports on the disaster and the lessons learned.
Dual Sparse Structured Subspaces and Graph Regularisation for Particle Swarm Optimisation-Based Multi-Label Feature Selection

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes.
Quantitative bounds to propagation of quantum correlations in many-body systems

We investigate how much information about a quantum system can be simultaneously communicated to independent observers, by establishing quantitative limits to bipartite quantum correlations in many-body systems. As recently reported in Phys. Rev. Lett. 129, 010401 (2022), bounds on quantum discord and entanglement of formation between a single quantum system and its environment, e.g., a large number of photons, dictate that independent observers which monitor environment fragments inevitably acquire only classical information about the system. Here, we corroborate and generalize those findings. First, we calculate continuity bounds of quantum discord, which establish how much states with a small amount of quantum correlations deviate from being embeddings of classical probability distributions. Also, we demonstrate a universally valid upper bound to the bipartite entanglement of formation between an arbitrary pair of components of a many-body quantum system. The results confirm that proliferation of classical information in the Universe suppresses quantum correlations.