Reviewerly/exHarmony huggingface dataset:
Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

Authors:
raw_author_name: Antonio Candelieri
raw_affiliation_string: Department of Economics, Management, and Statistics, Univesrity of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126, Milan, Italy
raw_author_name: Andrea Ponti
raw_affiliation_string: Department of Economics, Management, and Statistics, Univesrity of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126, Milan, Italy
raw_author_name: Francesco Archetti
raw_affiliation_string: Department of Computer Science, Systems, and Communication, University of Milano-Bicocca, Viale Sarca, 336, 20126, Milan, Italy

Abstract: Abstract It has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.
Inferior, Yet Transformative: the User Experience with Robotic Vacuum Cleaners

Authors:
raw_author_name: Morten Hertzum
raw_affiliation_string: Roskilde University Department of People and Technology, , Roskilde, Denmark

Abstract: Abstract Robotic vacuum cleaners are a prime example of home automation and a rich source of information about how people experience it. On the basis of a 3-week diary study, this article compares householders’ user experience (UX) of robotic vacuum cleaners with their UX of three types of manual vacuum cleaner. The main finding is that robotic vacuum cleaners are inferior in use, yet transform vacuuming. While their inferiority is about their pragmatic qualities, their transformative power relates to their autonomy, agency and hedonic qualities. This contradictory UX involves that robotic vacuum cleaners perform worse or not at all on seven out of nine surfaces and, at the same time, appear to escalate cleanliness standards. The transformation of household chores such as vacuuming is ongoing and calls for further research on engineering and merging the pragmatic and hedonic aspects of UX.
Adaptive temperature scaling for Robust calibration of deep neural networks

Authors:
raw_author_name: Sergio A. Balanya
raw_affiliation_string: AUDIAS Laboratory - Audio, Data Intelligence and Speech, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente 11, 28049, Madrid, Spain
raw_author_name: Juan Maroñas
raw_affiliation_string: Machine Learning Group, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente 11, 28049, Madrid, Spain
raw_author_name: Daniel Ramos
raw_affiliation_string: AUDIAS Laboratory - Audio, Data Intelligence and Speech, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente 11, 28049, Madrid, Spain

Abstract: Abstract In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Despite the plethora of calibration methods proposed, there is no consensus yet on the inherent complexity of the task and, while some authors claim that simple functions solve the problem, others suggest that more expressive models are needed to capture misscalibration. As a first approach, we focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, which we refer to as the Adaptive Temperature Scaling family. We begin by demonstrating that while complex models like neural networks provide an advantage when there is ample data, they fail in scenarios where it is limited, notably common in fields like medical diagnosis. We then show how under this ideal data conditions the more expressive methods learn a relationship between the entropy of a prediction and its level of overconfidence, and based on this observation, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to this relationship. Results show that our method obtains state-of-the-art performance and is robust against data scarcity. Moreover, our proposed model enables a deeper understanding of the calibration process by the interpretation of the entropy as a measure of uncertainty in the network outputs.
Feasibility study of in-line particle image velocimetry

Authors:
raw_author_name: Markus Raffel
raw_affiliation_string: Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Bunsenstr. 10, 37073, Göttingen, Germany
raw_author_name: Johannes N. Braukmann
raw_affiliation_string: Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Bunsenstr. 10, 37073, Göttingen, Germany

Abstract: Abstract This article describes recent tests and developments of imaging and evaluation techniques for particle image velocimetry (PIV) that exploit the forward scattering of tracer particles by placing the camera in-line with the illuminating light source, such as a laser or a light emitting diode. These techniques have been in use for some time in microscopy and in the design of optical instruments in astronomy. However, they have not yet been used for macroscopic PIV flow measurements. This study highlights the most promising approaches of the various recording configurations and evaluation techniques.
Evaluating Target Expansion for Eye Pointing Tasks

Authors:
raw_author_name: Xinyong Zhang
raw_affiliation_string: School of Information , Renmin University of China, No. 59 Zhongguancun Street, Beijing, China

Abstract: 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.
Underwater object detection method based on learnable query recall mechanism and lightweight adapter

Authors:
raw_author_name: Xixia Huang
raw_affiliation_string: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
raw_author_name: Le Wang
raw_affiliation_string: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China

Abstract: 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

Authors:
raw_author_name: Lyudmila Grigoryeva
raw_affiliation_string: Department of Statistics, University of Warwick, Coventry, United Kingdom; Faculty of Mathematics and Statistics, University of St. Gallen, St. Gallen, Switzerland
raw_author_name: Juan-Pablo Ortega
raw_affiliation_string: School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
raw_author_name: Domenico Campolo
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

Abstract: 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

Authors:
raw_author_name: Anatoliy Malyarenko
raw_affiliation_string: Mälardalen University, Universitetsplan 1, 721 23, Västerås, Sweden
raw_author_name: Emilio Porcu
raw_affiliation_string: Department of Mathematics and Biotechnology, Research Center at Khalifa University, Abu Dhabi, UAE

Abstract: 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

Authors:
raw_author_name: Xinyun Zhang
raw_affiliation_string: The University of York, School of Arts and Creative Technologies(2013-2014), York, YO10 5DD, United Kingdom.
raw_author_name: Tao Ding
raw_affiliation_string: 1-19 Torrington Place. University College London, Gower Street, London, WC1E 6BT, United Kingdom.

Abstract: 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

Authors:
raw_author_name: Keiji Tatsumi
raw_affiliation_string: Graduate School of Engineering, Osaka University, Suita, Japan
raw_author_name: Nao Kinoshita
raw_affiliation_string: Graduate School of Engineering, Osaka University, Suita, Japan

Abstract: 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

Authors:
raw_author_name: Noor Ul Ain Tahir
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Zuping Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Muhammad Asim
raw_affiliation_string: EIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
raw_author_name: Mohammed ELAffendi
raw_affiliation_string: EIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

Abstract: 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

Authors:
raw_author_name: Dinh Tai Pham
raw_affiliation_string: Nguyen Tat Thanh University, Vietam
raw_author_name: Hoang Nam Do
raw_affiliation_string: Nguyen Tat Thanh University, Vietam
raw_author_name: Huyen Trang Phan
raw_affiliation_string: Yeungnam University, South Korea

Abstract: 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

Authors:
raw_author_name: Dung Nguyen
raw_affiliation_string: University of Sciences, Hue University, Hue City, Vietnam

Abstract: 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

Authors:
raw_author_name: Dongshu Xu
raw_affiliation_string: Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China; National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan 430072, China
raw_author_name: Jun Chen
raw_affiliation_string: Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China; National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan 430072, China
raw_author_name: Xiaoyu Chai
raw_affiliation_string: Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China; National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan 430072, China

Abstract: 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.