Reviewerly/exHarmony huggingface dataset:
Temporal segment dropout for human action video recognition

Authors:
raw_author_name: Yu Zhang
raw_affiliation_string: School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education), Southeast University, Nanjing, 211189, China
raw_author_name: Zhengjie Chen
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China
raw_author_name: Tianyu Xu
raw_affiliation_string: School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education), Southeast University, Nanjing, 211189, China
raw_author_name: Junjie Zhao
raw_affiliation_string: School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education), Southeast University, Nanjing, 211189, China
raw_author_name: Xin Geng
raw_affiliation_string: School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education), Southeast University, Nanjing, 211189, China
raw_author_name: Min-Ling Zhang
raw_affiliation_string: School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education), Southeast University, Nanjing, 211189, China

Abstract: Temporal information is important for human action video recognition. With the widely used spatio-temporal neural networks, researchers have found that the learned high-level features preserve overfitted spatial information and limited temporal information, leading to inferior performance. This is because existing networks lack efficient regularization for the temporal structure. To learn more robust temporal features, we propose a temporal regularization method named Temporal Segment Dropout (TSD). TSD drops the most salient spatial features in order to enhance the temporal features in a clip of temporal segments. Without learning from complex examples, TSD can be easily deployed in existing networks. In the experiment, TSD is extensively evaluated on benchmark action recognition datasets, which brings consistent improvements over the baselines, especially for the action-centric classes.
Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

Authors:
raw_author_name: Zhang, Cong
raw_author_name: Xiao, Guanghua
raw_author_name: Chen, Min
raw_author_name: Li, Qiwei

Abstract: Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this article, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value <0.001). Supplementary materials for this article are available online.
Joint Multi-View Unsupervised Feature Selection and Graph Learning

Authors:
raw_author_name: Dong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Yong Tang
raw_affiliation_string: School of Computer Science, South China Normal University, Guangzhou, China

Abstract: Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either</i> cluster structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</i> similarity structure to guide the feature selection, which neglect the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either</i> global structure learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</i> local structure learning, which lack the capability of graph learning with both global and local structural awareness. In light of this, this article presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</i> oint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ulti- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> iew unsupervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> eature selection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> raph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. The cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments on a variety of real-world multi-view datasets demonstrate the superiority of our approach for both the multi-view feature selection and graph learning tasks. The code is available at https://github.com/huangdonghere/JMVFG.
Reducing redundancy in the bottleneck representation of autoencoders

Authors:
raw_author_name: Laakom, Firas
raw_affiliation_string: Faculty of Information Technology and Communication Sciences, Tampere University, Finland
raw_author_name: Raitoharju, Jenni
raw_affiliation_string: Faculty of Information Technology, University of Jyväskylä, Finland; Quality of Information, Finnish Environment Institute, Finland
raw_author_name: Iosifidis, Alexandros
raw_affiliation_string: Department of Electrical and Computer Engineering, Aarhus University, Denmark
raw_author_name: Gabbouj, Moncef
raw_affiliation_string: Faculty of Information Technology and Communication Sciences, Tampere University, Finland

Abstract: Autoencoders (AEs) are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the representation at the bottleneck using a decoder. Both encoder and decoder are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only the information in input data required to reconstruct them and to reduce redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pairwise covariances of the network units, which complements the data reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. We tested our approach across different tasks, namely dimensionality reduction, image compression, and image denoising. Experimental results show that the proposed loss leads consistently to superior performance compared to using the standard AE loss.
The formation of innovative behavior values in new type high schools - national research universities

Authors:
raw_author_name: Zulfiya Kadeeva
raw_affiliation_string: Kazan national research technological university

Abstract: Abstract An important task of the education system in modern market conditions is the preparation and retraining of personnel for innovative activity. Current methods of training specialists do not fully provide the solution of the problem, most of the management training given to the theoretical knowledge. It is necessary to organize context educational process with the participation of managers, owners of existing enterprises, startups and enterprises on the way of modernization of various sectors of the economy that will also help to practice the innovative and entrepreneurial skills. Strategic partnership of Kazan national research technological University and all-Russian public organization "Support of Russia" in the training of personnel for innovative activity in Russia creates a new resource - innovative knowledge, achievements and technologies of the future, creating conditions for the implementation of the national development strategy of the Russian Federation.
Fostering Interest and Knowledge in the Information Security Industry for K-12 Students Using Virtual Machines

Authors:
raw_author_name: Abdullah Konak
raw_affiliation_string: Penn State Berks

Abstract: Abstract The field of information security is growing rapidly as malicious attacks to information systems become more frequent and detrimental to individuals and organizations who have become increasingly dependent upon these systems. With such a dramatic growth in demand in a short timeframe, there exists a shortage in the supply of professionals entering the field. In order to assist in the remediation of this gap, the Penn State Berks Information Sciences and Technology (IST) and Security Risk Analysis (SRA) programs are hosting an annual NSA and NSF sponsored summer camp as part of the GenCyber program. This camp is focused on critical thinking, problem-solving, and igniting interest in information security and other STEM fields through the application of hands-on activities in the security of a virtual machine environment. By implementing this program, Penn State Berks aims to show students the value of information security as early as reasonably possible, share the knowledge of safe practices and principles for using the Internet safely, and instill an interest in cybersecurity, as well as the STEM field as a whole. The evaluation of the program shows that the participants make significant progress towards achieving the learning outcomes of the summer camps.
SO(2)-Equivariant Downwash Models for Close Proximity Flight

Authors:
raw_author_name: Jennifer Gielis
raw_affiliation_string: Department of Computer Science &amp; Technology, University of Cambridge, U.K.
raw_author_name: Jan Blumenkamp
raw_affiliation_string: Department of Computer Science &amp; Technology, University of Cambridge, U.K.
raw_author_name: Amanda Prorok
raw_affiliation_string: Department of Computer Science &amp; Technology, University of Cambridge, U.K.

Abstract: Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms for deploying dense formations. Thus, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> a model for these downwash patterns presents an attractive solution. In this paper, we present a novel learning-based approach for modelling the downwash forces that exploits the latent geometries (i.e. symmetries) present in the problem. We demonstrate that when trained with only 5 minutes of real-world flight data, our geometry-aware model outperforms state-of-theart baseline models trained with more than 15 minutes of data. In dense real-world flights with two vehicles, deploying our model online improves 3D trajectory tracking by nearly 36% on average (and vertical tracking by 56%).
A small object detection algorithm based on feature interaction and guided learning

Authors:
raw_author_name: You-Wei Wang
raw_affiliation_string: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
raw_author_name: Zheng-Wei Bao
raw_affiliation_string: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
raw_author_name: Ji-Yu Wang
raw_affiliation_string: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract: At present, for the diverse target sizes, different angles, and complex environment in aerial images, how to detect small objects effectively in aerial images is still a challenge task. In order to further improve the detection accuracy and reduce the missing rate, in this paper, we proposes a small target detection algorithm based on feature interaction and guided learning. Firstly, the feature guided alignment module is designed to deal with the problem of information loss and overlap. Secondly, the misallocation of small object samples is optimized by introducing gaussian sample allocation strategy. Then the interactive parallel detection header is designed to solve the detection task conflict problem. Finally, the new regression loss function is designed to enhance the localization ability of the target. The experimental results on the Tinyperson dataset and Visdrone dataset show that this model can improve the detection accuracy and reduce the missing rate of small targets effectively.
Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars

Authors:
raw_author_name: Emily R. Bartusiak
raw_affiliation_string: Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
raw_author_name: Moses W. Chan
raw_affiliation_string: Lockheed Martin Corporation, Sunnyvale, CA, USA
raw_author_name: Mary L. Comer
raw_affiliation_string: Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
raw_author_name: Edward J. Delp
raw_affiliation_string: Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

Abstract: Hypersonic glide vehicles are a new class of vehicles that fly at hypersonic speeds and have high maneuverability. These fast-moving targets exhibit different flight characteristics compared to conventional vehicles, so traditional tracking and defense systems require new methods to contend with them. In this paper, we propose a machine learning method for predicting the behavior of hypersonic glide vehicles. Our method is based on a stochastic grammar, which is a mathematical framework that describes the possible transition patterns of sequences. We use the stochastic grammar to predict the transition patterns in hypersonic glide vehicle trajectories. Given a partial trajectory, our method uses the grammar to predict the hypersonic glide vehicle's future kinematics, such as its altitude, velocity, and acceleration. We evaluate our method on two datasets of simulated hypersonic glide vehicle trajectories and show that it can successfully predict hypersonic glide vehicle behavior, even in the presence of noise. We also show that our method can predict several minutes into the future and can accurately predict future hypersonic glide vehicle behavior based on shorter observation times. Our results suggest that our method has the potential to be a valuable tool for predicting behavior of hypersonic glide vehicles.
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Authors:
raw_author_name: Christian Abbet
raw_affiliation_string: Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
raw_author_name: Behzad Bozorgtabar
raw_affiliation_string: Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland; Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
raw_author_name: Jean-Philippe Thiran
raw_affiliation_string: Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland; Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland

Abstract: Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets, which is costly and time-consuming. In this paper, we demonstrate that the abundance of unlabeled images that can be extracted from Pap smear test whole slide images presents a fertile ground for self-supervised learning methods, yielding performance improvements compared to off-the-shelf pre-trained models for various downstream tasks. In particular, we propose Cervical Cell Copy-Pasting (C3P) as an effective augmentation method, which enables knowledge transfer from public and labeled single-cell datasets to unlabeled tiles. Not only does C3P outperforms naive transfer from single-cell images, but we also demonstrate its advantageous integration into multiple instance learning methods. Importantly, all our experiments are conducted on our introduced in-house dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our long-term objective of deep learning-assisted telecytology for diagnosis in low-resource settings.
Exploring the Learning Difficulty of Data: Theory and Measure

Authors:
raw_author_name: Weiyao Zhu
raw_affiliation_string: Center for Applied Mathematics, Tianjin University, China
raw_author_name: Ou Wu
raw_affiliation_string: Center for Applied Mathematics, Tianjin University, China
raw_author_name: Fengguang Su
raw_affiliation_string: Center for Applied Mathematics, Tianjin University, China
raw_author_name: Yingjun Deng
raw_affiliation_string: Center for Applied Mathematics, Tianjin University, China

Abstract: ‘‘Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples refers to how easy/hard a sample is during a learning procedure. An increasing need of measuring learning difficulty demonstrates its importance in machine learning (e.g., difficulty-based weighting learning strategies). Previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, influential factors for learning difficulty are summarized. Under various situations conducted by summarized influential factors, correlations between learning difficulty and two vital criteria of machine learning, namely, generalization error and model complexity, are revealed. Second, a theoretical definition of learning difficulty is proposed on the basis of these two criteria. A practical measure of learning difficulty is proposed under the direction of the theoretical definition by importing the bias-variance trade-off theory. Subsequently, the rationality of theoretical definition and the practical measure are demonstrated, respectively, by analysis of several classical weighting methods and abundant experiments realized under all situations conducted by summarized influential factors. The mentioned weighting methods can be reasonably explained under the proposed theoretical definition and concerned propositions. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.
An Adaptive Variational Outlier-Robust Filter for Multisensor Distributed Fusion

Authors:
raw_author_name: Yunsheng Fan
raw_affiliation_string: College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
raw_author_name: Shuanghu Qiao
raw_affiliation_string: College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
raw_author_name: Guofeng Wang
raw_affiliation_string: College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
raw_author_name: Haoyan Zhang
raw_affiliation_string: College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China

Abstract: The fusion accuracy of distributed fusion algorithms may degrade in multi-sensor measurements with unknown noise and outliers. To tackle the problem, an improved adaptive feedback covariance intersection fusion algorithm based on the modified slide window variational outlier-robust adaptive Kalman filter is proposed. To estimate unknown noise covariance and eliminate outliers, a modified slide window variational outlier-robust adaptive Kalman filter is proposed. The algorithm devises a simplified slide-window variational adaptive filter according to the Student’s t distribution, which treats the Student’s t distribution as the approximation of the posterior distribution to eliminate the effect of outliers. The adaptive factor is introduced into this algorithm to realize the tradeoff between measurement and prediction. Moreover, an improved adaptive feedback covariance intersection fusion algorithm is developed with respect to multisensor information fusion with uncertain noise. The simulations verify that the improved fusion algorithm outperforms other existing filtering and fusion algorithms.