Reviewerly/exHarmony huggingface dataset:
A scalable dynamic ensemble selection using fuzzy hyperboxes

Authors:
raw_author_name: Reza Davtalab
raw_affiliation_string: École de Technologie Supérieure, Université de Quebéc, Montréal, Québec, Canada
raw_author_name: Rafael M.O. Cruz
raw_affiliation_string: École de Technologie Supérieure, Université de Quebéc, Montréal, Québec, Canada
raw_author_name: Robert Sabourin
raw_affiliation_string: École de Technologie Supérieure, Université de Quebéc, Montréal, Québec, Canada

Abstract: Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers and selecting the most competent ones for the classification of a given test instance during inference time. The majority of dynamic ensemble selection (DES) methods evaluate the competence of classifiers using the K-Nearest Neighbors to the unknown query sample. However, KNN is very sensitive to local data distribution and needs to store all data in memory. Moreover, it performs several computations for each individual query sample. Thus, relying on the KNN technique hampers the use of DES approaches for large-scale problems and situations where data distributions are non-uniform. This article introduces a novel DES framework called FH-DES, which employs fuzzy hyperboxes to generate a competence map or incompetence map for each classifier. The competence map is generated from correctly classified samples to indicate the competence level of the classifier at each data point in the feature space, whereas the incompetence map, which shows regions where the classifier has low accuracy, is generated from misclassified samples. In this way, we can assess the competence or incompetence level of the classifier just by using the map without having to process previous samples. This feature results in a more accurate dynamic selection system with lower computational complexity compared to other dynamic selection methods. Moreover, we introduce several hyperbox expansion and contraction strategies that add incremental learning capability to the framework while keeping the computational cost low. Experimental results demonstrate that FH-DES achieves high classification accuracy with lower complexity than state-of-the-art dynamic selection methods. The source code for FH-DES is available at https://github.com/redavtalab/FH-DES.
GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes

Authors:
raw_author_name: Mateus Maia
raw_affiliation_string: Department and Organization Maynooth University, Maynooth, Ireland; Hamilton Institute and Department of Mathematics and Statistics
raw_author_name: Keefe Murphy
raw_affiliation_string: Department and Organization Maynooth University, Maynooth, Ireland; Hamilton Institute and Department of Mathematics and Statistics
raw_author_name: Andrew C. Parnell
raw_affiliation_string: Hamilton Institute and Department of Mathematics and Statistics; Insight Centre for Data Analytics,Maynooth University, Maynooth, Ireland

Abstract: The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of an explicit covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. The Gaussian processes Bayesian additive regression trees (GP-BART) model is an extension of BART which addresses this limitation by assuming Gaussian process (GP) priors for the predictions of each terminal node among all trees. The model's effectiveness is demonstrated through applications to simulated and real-world data, surpassing the performance of traditional modeling approaches in various scenarios.
ProFi: Scalable and Efficient Website Fingerprinting

Authors:
raw_author_name: Patrick Krämer
raw_affiliation_string: Chair of Communication Networks, Technical University of Munich, Munich, Germany
raw_author_name: Alexander Griessel
raw_affiliation_string: Chair of Communication Networks, Technical University of Munich, Munich, Germany
raw_author_name: Oliver Hohlfeld
raw_affiliation_string: Distributed Systems Group, University of Kassel, Kassel, Germany
raw_author_name: Andreas Blenk
raw_affiliation_string: T CED INW-DE, Siemens AG, Munich, Germany
raw_author_name: Martin Mieth
raw_affiliation_string: Deep Packet Inspection Development, Ipoque GmbH - A Rohde &#x0026; Schwarz Company, Leipzig, Germany
raw_author_name: Wolfgang Kellerer
raw_affiliation_string: Chair of Communication Networks, Technical University of Munich, Munich, Germany

Abstract: Website Fingerprinting (WFP) attacks infer the websites or webpages a user is visiting from encrypted traffic. To date, it remains uncertain if WFP can attack many users from a central location in an online scenario. We close this gap with PROFI, a WFP attack that detects websites based on the initial TLS connection from the client to the server using at most the connection’s first 30 packets. PROFI achieves a precision and recall of 86.51 % and 85.35 % in a closed-world, and 68.90 % and 78.71 % in an open-world scenario, which is competitive to state-of-the-art (SoA) WFP attacks, while taking a fraction of the time of SoA attacks to classify a webpage. Further, we implement PROFI as a micro service-based prototype and evaluate the attack in an online scenario with real traffic traces. We show that PROFI can monitor up to 100 websites at 10 G, corresponding to up to 424 webpages per second. We also show that PROFI has the potential to interfere with a victim’s webpage access.
Dynamic gradient reactivation for backward compatible person re-identification

Authors:
raw_author_name: Xiao Pan
raw_affiliation_string: Zhejiang University, China
raw_author_name: Hao Luo
raw_affiliation_string: Zhejiang University, China
raw_author_name: Weihua Chen
raw_affiliation_string: Alibaba Group, China
raw_author_name: Fan Wang
raw_affiliation_string: Alibaba Group, China
raw_author_name: Hao Li
raw_affiliation_string: Alibaba Group, China
raw_author_name: Wei Jiang
raw_affiliation_string: Zhejiang University, China
raw_author_name: Jianming Zhang
raw_affiliation_string: Zhejiang University, China
raw_author_name: Jianyang Gu
raw_affiliation_string: Zhejiang University, China
raw_author_name: Peike Li
raw_affiliation_string: University of Technology, Sydney, Australia

Abstract: We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing features from the old model in galleries. Most of the existing works adopt distillation-based methods, which focus on pushing new features to imitate the distribution of the old ones. However, the distillation-based methods are intrinsically sub-optimal since it forces the new feature space to imitate the inferior old feature space. To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features. Different from previous methods, RBCL only pushes the new features to find best-ranking positions in the old feature space instead of strictly alignment, and is in line with the ultimate goal of backward retrieval. However, the sharp sigmoid function used to make the ranking metric differentiable also incurs the gradient vanish issue, therefore stems the ranking refinement during the later period of training. To address this issue, we propose the Dynamic Gradient Reactivation (DGR), which can reactivate the suppressed gradients by adding dynamically computed constants during the forward step. To further help target the best-ranking positions, we include the Neighbor Context Agents (NCAs) to approximate the entire old feature space during training. Unlike previous works that mainly test on the in-domain settings, we make the early attempt to introduce the cross-domain settings (including both supervised and unsupervised) for the backward compatible person Re-ID task, which are more challenging yet meaningful. The experimental results on all five settings show that the proposed RBCL outperforms previous state-of-the-art methods by large margins.
Event-Based Deep Reinforcement Learning for Quantum Control

Authors:
raw_author_name: Haixu Yu
raw_affiliation_string: Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, China
raw_author_name: Xudong Zhao
raw_affiliation_string: Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, China

Abstract: Deep reinforcement learning (DRL) has been widely used in many quantum control tasks, in which rewards are used to guide the agent in acquiring an optimal control strategy. However, designing suitable rewards is usually challenging, which seriously affects the DRL's application performance, especially for complex quantum control tasks. In this article, we propose a simple and general reward design method by constructing some events, where events are defined by fidelity intervals. In the proposed event-based DRL (EDRL), events are used to describe the evolution of a quantum system. Rewards are designed according to several defined events rather than a large number of specific evolved quantum states, thereby resulting in a reduction in dimensions to be considered. With designed event-attributed rewards, the agent can capture the main evolution characteristics of a quantum system, and then obtain an effective control strategy. The effectiveness of the proposed EDRL method is verified by three quantum systems: i.e., 1) the one-qubit closed quantum system, 2) the two-qubit closed quantum system, and 3) the two-level open quantum system. Numerical comparisons demonstrate that the proposed EDRL outperforms the traditional DRL algorithms (deep Q-network and proximal policy optimization) with some representative reward methods for solving quantum control problems.
Design of English viewing, listening, and speaking mobile teaching software based on an interactive digital media algorithm

Authors:
raw_author_name: Hongyan Zhao
raw_affiliation_string: School of Foreign Language, Chongqing College of Humanities, Science & Technology, Chongqing, China

Abstract: China has now embraced the information era, which has had a significant impact on everyday life, employment, and educational practices. Information technology has also had a significant impact on the growth of the education sector, resulting in a fast-paced and resource-rich setting for student interaction. Through the network platform, various interactive software can improve students’ learning methods, especially language teaching software. English audio-visual speaking is software for training English language listening and speaking, which can carry out relevant oral activities and topic discussions according to the imported materials. As a result, you can assist pupils in using the vocabulary and knowledge associated with the subject, which will increase their interest in learning. English teachers can fully prepare for speaking and listening tasks in the classroom by using audio-visual speaking. At the same time, through the learning of TV and movie trailers, English audio-visual speaking can provide readers with background knowledge, which is ready for readers to fully understand the language and content in the video materials. Based on information technology, this paper constructs English audio-visual and oral mobile teaching software, which depends on interactive digital media algorithms. Through the mobile teaching software for English audio-visual speaking, students can form good English listening and reading habits, which will provide important help for English language learning.First, this essay examines the value and benefits of mobile applications for providing English instruction orally and visually, which might help to illustrate the need for software development. The research then suggests various algorithms for English that are related to audio, visual, and oral input that can detect, assess, and correct students’ learning mistakes. Finally, this work develops the fundamental methodology of the audio-visual and verbal mobile software for instruction in English.
Economic and Energy-Efficient Wireless Federated Learning Based on Stackelberg Game

Authors:
raw_author_name: Haitao Zhao
raw_affiliation_string: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Wenchao Xia
raw_affiliation_string: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Yiyang Ni
raw_affiliation_string: School of Mathematics and Information Technology, Jiangsu Second Normal University, Nanjing, China
raw_author_name: Guan Gui
raw_affiliation_string: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Hongbo Zhu
raw_affiliation_string: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China

Abstract: Wireless federated learning (FL) is a distributed learning framework, in which mobile devices (MDs) collaboratively train a model while protecting data privacy. Considering selfishness of MDs, they are unwilling to sacrifice their resources to assist model training without any reward, which may limit performance of FL. In this paper, we propose an incentive mechanism between a base station (BS) and several mobile devices (MDs) modeled as a Stackelberg game. During this game, BS first decides the total reward that can be distributed to MDs, and then each MD determines its number of local iterations according to its utility function. In addition, we derive the closed-form expressions of the optimal reward function of the BS and the number of local iterations of the MDs. Finally, the numerical results validate the efficacy of our proposed scheme.
Collaborative Human Recognition With Lightweight Models in Drone-Based Search and Rescue Operations

Authors:
raw_author_name: Lijuan Xu
raw_affiliation_string: State Key Laboratory of Integrated Services Networks, School of the Telecommunication Engineering, Xidian University, Xi&#x0027;an, China
raw_author_name: Qinghai Yang
raw_affiliation_string: State Key Laboratory of Integrated Services Networks, School of the Telecommunication Engineering, Xidian University, Xi&#x0027;an, China
raw_author_name: Meng Qin
raw_affiliation_string: Pengcheng Laboratory, Shenzhen, China
raw_author_name: Weihua Wu
raw_affiliation_string: School of Physics and Information Technology, Shaanxi Normal University, Xi&#x0027;an, China
raw_author_name: KyungSup Kwak
raw_affiliation_string: Graduate School of Information Technology and Telecommunications, Inha University, Incheon, South Korea

Abstract: Due to the flexibility of drones, it is promising to use them in Search and Rescue (SAR) operations for intelligently searching for lost humans over large areas. However, the limited computing resources of drones pose challenges when deploying deep neural network models. To address this problem, we design a lightweight human recognition model for drones, combining the Ghost Module and the Mobilenetv3 Block. The Ghost Module generates more feature maps with fewer parameters, and the squeeze-and-excitation (SE) attention module in the Mobilenetv3 Block greatly improves recognition accuracy. Furthermore, recognizing that relying solely on the human recognition model offers limited assistance in SAR operations, we propose a collaborative recognition mode between the drone and the rescue command center (RCC). In this collaborative recognition mode, we design an offloading model for deployment on the drone. The offloading model learns from the middle layer perception features of the lightweight recognition model and selectively offloads the vision taken by the drone to the RCC. The recognition results provided by the RCC are used to update the parameters of the offloading model. We introduce a reinforcement learning algorithm, a dual-buffer-based proximal policy optimization algorithm (DBPPO), to train the offloading model with the goal of maximizing accuracy and recall while minimizing the offloading ratio. Specifically, we incorporate an additional data buffer for training the Actor network in the PPO algorithm in a supervised manner, with supervised training interspersed throughout the PPO training process. Eventually, experiments comparing different methods demonstrate the effectiveness of the lightweight recognition model and the collaborative recognition mode in SAR operations.
GDB: Gated Convolutions-based Document Binarization

Authors:
raw_author_name: Zongyuan Yang
raw_affiliation_string: The State Key Lab. of Switching and Networking Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Baolin Liu
raw_affiliation_string: The State Key Lab. of Switching and Networking Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Yongping Xiong
raw_affiliation_string: The State Key Lab. of Switching and Networking Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Guibin Wu
raw_affiliation_string: The State Key Lab. of Switching and Networking Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: Document binarization is a crucial pre-processing step for various document analysis tasks. However, existing methods fail to accurately capture stroke edges, primarily due to the inherent limitations of vanilla convolutions and the absence of adequate boundary-related supervision during stroke edge extraction. In this paper, we formulate text extraction as the learning of gating values and propose an end-to-end network architecture based on gated convolutions, named GDB, to address the problem of imprecise stroke edge extraction. The gated convolutions enable the selective extraction of stroke feature with different attention. Our proposed framework comprises two stages. Firstly, a coarse sub-network with an extra edge branch is trained to enhance the precision of feature maps by incorporating a priori mask and edge information. Secondly, a refinement sub-network is cascaded to enhance the output of the first stage using gated convolutions based on the sharp edges. To effectively incorporate global information, GDB also integrates a parallelized multi-scale operation that combines local and global features. We conduct comprehensive experiments on ten Document Image Binarization Contest (DIBCO) datasets from 2009 to 2019 and Document Deblurring Datasets. Experimental results show that our proposed methods outperform the state-of-the-art methods across all metrics on average. Extensive ablation studys demonstrate the efficacy of key components. Available codes: https://github.com/Royalvice/GDB.
Low-light image enhancement using gamma correction prior in mixed color spaces

Authors:
raw_author_name: Jong Ju Jeon
raw_affiliation_string: Digital Analysis Division, National Forensic Service, 10, Ipchun-ro, Wonju-si, Gangwon-do, 26460, Republic of Korea
raw_author_name: Jun Young Park
raw_affiliation_string: Department of Electronics Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
raw_author_name: Il Kyu Eom
raw_affiliation_string: Department of Electronics Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea

Abstract: In this paper, we propose an efficient and fast low-light image enhancement method using an atmospheric scattering model based on an inverted low-light image. The transmission map is derived as a function of two saturations of the original image in the two color spaces. Due to the difficulty in estimating the saturation of the original image, the transmission map is converted into a function of the average and maximum values of the original image. These two values are estimated from a given low-light image using the gamma correction prior. In addition, a pixel-adaptive gamma value determination algorithm is proposed to prevent under- or over-enhancement. The proposed algorithm is fast because it does not require the training or refinement process. The simulation results show that the proposed low-light enhancement scheme outperforms state-of-the-art approaches regarding both computational simplicity and enhancement efficiency. The code is available on https://github.com/TripleJ2543.
SemiAMR: Semi-Supervised Automatic Modulation Recognition With Corrected Pseudo-Label and Consistency Regularization

Authors:
raw_author_name: Yuanpu Guo
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, China
raw_author_name: Dan Zhong
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, China
raw_author_name: Haixin Sun
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, China
raw_author_name: Zhiye Jiang
raw_affiliation_string: National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing Institute of Space Long March Vehicle, Beijing, China
raw_author_name: Zhenmiao Deng
raw_affiliation_string: School of Electronics and Communications Engineering, Sun Yat-sen University, Shenzhen, China
raw_author_name: Hui Liu
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, China

Abstract: In modern communication systems, automatic modulation recognition (AMR) has broad application prospects but a large amount of training data is usually required. Due to the high cost of adding annotations to signals, it is of great significance to study AMR under the condition of few labeled samples. In this paper, an end-to-end semi-supervised radio signal recognition algorithm is proposed. In view of the poor anti-noise performance of the original IQ data, short-time Fourier transform (STFT) is introduced to mine the stable modulation characteristics. In order to overcome the noise problem caused by pseudo-labels, a mixed sample data augmentation (MSDA) method PFMix is proposed, and the consistency constraint of the model disturbance based on mean teacher (MT) framework and data disturbance based on PFMix is designed. Meanwhile, in order to provide pseudo-labels with high confidence, entropy regularization is constructed by taking the prediction variance of MT framework as correction factor. The experimental results using the open source RML2016.10A dataset show the superior performance of the proposed method. The average recognition accuracy with 1% labeled data is 88.82% at 0 dB, which is a significant improvement compared with state of the art in semi-supervised AMR.