Reviewerly/exHarmony huggingface dataset:
Evaluating Target Expansion for Eye Pointing Tasks

Abstract The idea of target expansion was proposed two decades ago for manual target acquisition, but it is not feasible to implement this idea in traditional user interfaces as the interactive system cannot know exactly which target is the desired one and should be expanded among several candidates. With the increasing maturity of eye tracking technology, gaze input has moved from an academically promising technique to an input method with built-in support in Windows 10; and target expansion has already become very feasible in the context of gaze input, as the user’s eye gaze is inherently an indicator of the desired target due to the natural eye-hand coordination in everyday tasks. However, a comprehensive evaluation is still lacking. In this study, two experiments were conducted, each with a different group of subjects, to investigate the effects of target expansion under different expansion feedback styles (visible vs. invisible), expansion factors, as well as different target appearances (i.e., circular vs. rectangular). The experimental results indicated that (1) the index of difficulty in eye pointing tasks (IDeye) does not depend on the initial size of the target, but on its final size, and that the corresponding human performance can be accurately predicted using the IDeye model instead of Fitts’ law; and that (2) the visible expansion style could disrupt the user’s fixations, making the measured human performance less efficient to some extent, but overall the theoretical predictions using the IDeye model were almost the same as the baselines. Following the experimental results, this study also provided some practical suggestions for UI design.
BinDMO: a new Binary Dwarf Mongoose Optimization algorithm on based Z-shaped, U-shaped, and taper-shaped transfer functions for CEC-2017 benchmarks

Abstract Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems.
Underwater object detection method based on learnable query recall mechanism and lightweight adapter

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L 1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.
Tracing curves in the plane: Geometric-invariant learning from human demonstrations

The empirical laws governing human-curvilinear movements have been studied using various relationships, including minimum jerk, the 2/3 power law, and the piecewise power law. These laws quantify the speed-curvature relationships of human movements during curve tracing using critical speed and curvature as regressors. In this work, we provide a reservoir computing-based framework that can learn and reproduce human-like movements. Specifically, the geometric invariance of the observations, i.e., lateral distance from the closest point on the curve, instantaneous velocity, and curvature, when viewed from the moving frame of reference, are exploited to train the reservoir system. The artificially produced movements are evaluated using the power law to assess whether they are indistinguishable from their human counterparts. The generalisation capabilities of the trained reservoir to curves that have not been used during training are also shown.
Multivariate Random Fields Evolving Temporally Over Hyperbolic Spaces

Abstract Gaussian random fields are completely characterised by their mean value and covariance function. Random fields on hyperbolic spaces have been studied to a limited extent only, namely for the case of scalar-valued fields that are not evolving over time. This paper challenges the problem of the second-order characteristics of multivariate (vector-valued) random fields that evolve temporally over hyperbolic spaces. Specifically, we characterise the continuous space–time covariance functions that are isotropic (radially symmetric) over space (the hyperbolic space) and stationary over time (the real line). Our finding is the analogue of recent findings that have been shown for the case where the space is either the n -dimensional sphere or more generally a two-point homogeneous space. Our main result can be read as a spectral representation theorem, and we also detail the main result for the subcase of covariance functions having a spectrum that is absolutely continuous with respect to the Lebesgue measure (technical details are reported below).
Style classification of media painting images by integrating ResNet and attention mechanism

The progress of deep learning technology has made image classification an important application field. Image style classification is a complex task involving the recognition of the whole picture, including the recognition of salient features and detailed features. This study is based on the ResNet algorithm and has improved its Resnet 50 version with excellent performance. In the model architecture, we introduce blur pool operation and replace the traditional Relu function with Celu activation function. In addition, the triplet attention mechanism was integrated to further enhance the model performance. Through a series of experiments, it is found that the improved ResNet50 model has the highest classification accuracy of 80.6% on large-scale image data sets, which is 11.7% higher than the traditional ResNet50 model. In terms of recognition of similar style images, the model incorporating triplet attention demonstrated higher average accuracy (74%) and recall (82%). This improvement has achieved certain results and has certain technical reference value for various styles of image classification fields.
Analysis of dependence of grey wolf optimizer to shift-transformations and its shift-invariant improved methods adaptively controlling the search areas

As a metaheuristic method for the continuous optimization problem, the grey wolf optimizer (GWO) has attracted much attention from researchers because the method is reported to be superior to other methods. However, some works show that the GWO is too specialized only to problems having the zero-optimal solution, which can lead to a significant deterioration of the efficiency for other problems. In this paper, we, first, theoretically prove the shift-dependence of the GWO, which is the underlying cause of the over-specialization of the GWO, and we experimentally analyze the property by using a larger number of problems. Secondly, we propose am shift-invariant GWO, GWO-SR, and, modify the GWO-SR by adding two methods: an adjustment technique the size of the search area and a mutation process to enhance the diversity of the search (GWO-AS) Finally, we show advantages of two proposed GWOs by comparing them with other metaheuristic methods.
PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.
Rule-enriched Decision Tree Classifier for Conditional Sentence Sentiment Analysis

Conditional sentences are often used when people have to choose with some requirements. Conditional sentences account for more than 8% of user opinions. Although accounting for a considerable amount, for sentiment analysis methods, the sentiment expressed in conditional sentences is still analyzed as typical narrative sentences. This causes the approaches to fail to achieve maximum performance. To solve this problem, although some studies have proposed separate approaches to sentiment extraction and analysis for conditional sentences, there are very few, and the performance still needs to improve. This study proposes a new classifier based on a decision tree classifier model enriched with rules (called Rule-enriched Decision Tree Classifier (ReDTC)) to extract and analyze sentiments expressed in conditional sentences. ReDTC has been experimented on a dataset collected from English teaching websites. The performance gain demonstrates that the proposed ReDTC method significantly improved the performance in sentiment extraction and analysis in conditional sentences.
Transfer Learning for Abnormal Object Detection

In today's world, smart surveillance plays an important role in protecting security and creating a safe living environment. For abnormal objects in the smart surveillance system, this is an important issue, requiring attention and timely response from managers and supervisors. To address this issue, the paper uses transfer learning techniques on modern object detection models to detect abnormal objects such as guns, knives, etc. in public places. We experimented with the transfer learning method on the DETR model with a small dataset, and the model results showed a fairly fast convergence speed. Through this method, we hope to help reduce the burden of public security monitoring and warning work for managers, while technicians can use transfer learning techniques that are deployed in practice.
An Orientation-Aware Attention Network for Person Re-Identification

Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. However, discriminative clues corresponding to location differences cannot be discovered, so persons with similar appearances are often confused because of their alike components. To address the above problem, we introduce pixel-wise relative positions for the invariance of their orientations in viewpoint changes. To cope with the scale change of relative position, we combine relative positions with self-attention modules that perform on multi-level features. Moreover, in the data augmentation stage, mirrored images are given new labels due to the conversion of the relative position along a horizontal orientation and change in visual chirality. Extensive experiments on four challenging benchmarks demonstrate that the proposed approach shows its superiority and effectiveness in discovering discriminating features.
Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction.
Smartphones enabled up to 58 s strong-shaking warning in the M7.8 Türkiye earthquake

Public earthquake early warning systems (PEEWSs) have the potential to save lives by warning people of incoming seismic waves up to tens of seconds in advance. Given the scale and geographical extent of their impact, this potential is greatest for destructive earthquakes, such as the M7.8 Pazarcik (Türkiye) event of 6 February 2023, which killed almost 60,000 people. However, warning people of imminent strong shaking is particularly difficult for large-magnitude earthquakes because the warning must be given before the earthquake has reached its final size. Here, we show that the Earthquake Network (EQN), the first operational smartphone-based PEEWS and apparently the only one operating during this earthquake, issued a cross-border alert within 12 s of the beginning of the rupture. A comparison with accelerometer and macroseismic data reveals that, owing to the EQN alerting strategy, Turkish and Syrian EQN users exposed to intensity IX and above benefitted from a warning time of up to 58 s before the onset of strong ground shaking. If the alert had been extended to the entire population, approximately 2.7 million Turkish and Syrian people exposed to a life-threatening earthquake would have received a warning ranging from 30 to 66 s in advance.
A Survey on Adaptive Cameras

This paper surveys adaptive cameras, i.e. any camera device able to change its geometric settings. We consider their classification in four categories: lensless, dioptric, catadioptric and polydioptric cameras. In each category, we report and describe all the existing adaptive cameras. Then, the known applications of these devices are summarized. Finally, we discuss open research lines for new adaptations of cameras, and their promising uses.
Global biasing using a hardware-based artificial Zeeman term in spinwave Ising machines

A spinwave Ising machine (SWIM) is a recently proposed type of time-multiplexed hardware solver for combinatorial optimization that employs feedback coupling and phase sensitive amplification to map an Ising Hamiltonian into phase-binarized propagating spinwave RF pulses in an Yttrium-Iron-Garnet film. In this work, we increase the mathematical complexity of the SWIM by adding a global Zeeman term to a 4-spin nearest neighbor Hamiltonian using a continuous external electrical signal with the same frequency as the spin pulses and phase locked with one of the two possible states. We are able to induce ferromagnetic ordering in both directions of the spin states despite antiferromagnetic pairwise coupling. Embedding a planar antiferromagnetic spin system in a magnetic field has been proven to increase the complexity of the graph associated with its Hamiltonian, and, thus, this straightforward implementation helps explore higher degrees of complexity in this evolving solver.
Controlling the charge-transfer dynamics of two-level systems around avoided crossings

Two-level quantum systems are fundamental physical models that continue to attract growing interest due to their crucial role as a building block of quantum technologies. The exact analytical solution of the dynamics of these systems is central to control theory and its applications, such as that to quantum computing. In this study, we reconsider the two-state charge transfer problem by extending and using a methodology developed to study (pseudo)spin systems in quantum electrodynamics contexts. This approach allows us to build a time evolution operator for the charge transfer system and to show new opportunities for the coherent control of the system dynamics, with a particular emphasis on the critical dynamic region around the transition state coordinate, where the avoided crossing of the energy levels occurs. We identify and propose possible experimental implementations of a class of rotations of the charge donor (or acceptor) that endow the electronic coupling matrix element with a time-dependent phase that can be employed to realize controllable coherent dynamics of the system across the avoided level crossing. The analogy of these rotations to reference frame rotations in generalized semiclassical Rabi models is discussed. We also show that the physical rotations in the charge-transfer systems can be performed so as to implement quantum gates relevant to quantum computing. From an exquisitely physical–mathematical viewpoint, our approach brings to light situations in which the time-dependent state of the system can be obtained without resorting to the special functions appearing in the Landau–Zener approach.
Using a virtual reality interview simulator to explore factors influencing people’s behavior

Abstract Virtual reality interview simulator (VRIS) is an effective and valid tool that uses virtual reality technology to train people’s interview skills. Typically, it offers candidates prone to being very nervous during interviews the opportunity to practice interviews in a safe and manageable virtual environment and realistic settings, providing real-time feedback from a virtual interviewer on their performance. It helps interviewees improve their skills, reduce their fears, gain confidence, and minimize the cost and time associated with traditional interview preparation. Yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing electrodermal activity and questionnaire, we investigated the influence of five variables: (I) Realism ; (II) Question type ; (III) Interviewer attitude ; (IV) Timing ; and (V) Preparation . As such, an orthogonal design $$L_8(4^1 \times 2^4)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>8</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>4</mml:mn> <mml:mn>1</mml:mn> </mml:msup> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> with eight experiments ( $$O A_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>8</mml:mn> </mml:msub> </mml:mrow> </mml:math> matrix) was implemented, in which 19 college students took part in the experiments. Considering the anxiety, overall experience, and performance of the interviewees, we found that Question type plays a major role; secondly, Realism , Preparation , and Interviewer attitude all have middle influence; lastly, Timing has little to no impact. Specifically, professional interview questions elicited a greater degree of anxiety than personal ones among the categories of interview questions. This work contributes to our understanding of anxiety-stimulating factors during job interviews in virtual reality and provides cues for designing future VRIS.
Time Series Feature Selection Method Based on Mutual Information

Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.