Reviewerly/exHarmony huggingface dataset:
Fast anchor graph preserving projections

The existing graph-based dimensionality reduction algorithms need to learn an adjacency matrix or construct it in advance, therefore the time complexity of the graph-based dimensionality reduction algorithms is not less than O(n2d), where n denotes the number of samples, d denotes the number of dimensions. Moreover, the existing dimensionality reduction algorithms do not consider the cluster information in the original space, resulting in the weakening or even loss of valuable information after dimensionality reduction. To address the above problems, we propose Fast Anchor Graph Preserving Projections (FAGPP), which learns the projection matrix, the anchors and the membership matrix at the same time. Especially, FAGPP has a built-in Principal Component Analysis (PCA) item, which makes our model not only deal with the cluster information of data, but also deal with the global information of data. The time complexity of FAGPP is O(nmd), where m denotes the number of the anchors and m is much less than n. We propose a novel iterative algorithm to solve the proposed model and the convergence of the algorithm is proved theoretically. The experimental results on a large number of high-dimensional benchmark image data sets demonstrate the efficiency of FAGPP. The data sets and the source code are available from https://github.com/511lab/FAGPP.
Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real-world problems. Recently, the methods to achieve arbitrary-scale super-resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary-scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning-based methods for arbitrary-scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary-scale super-resolution, i.e., how to perform arbitrary-scale upsampling. Moreover, the performance of existing arbitrary-scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real-world applications. Finally, we briefly discuss the future directions of arbitrary-scale super-resolution, which shows some inspirations for the progress of subsequent works on arbitrary-scale image and video super-resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR.
A scalable dynamic ensemble selection using fuzzy hyperboxes

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

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

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

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

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

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

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

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

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.
ReFuSeAct: Representation fusion using self-supervised learning for activity recognition in next generation networks

Over the years, wearable sensors have gained a lot of attention from the research community due to their non-invasive nature, adoption of sensors by general public, and their applicability in healthcare services. With the advancements in communication networks, machine learning methods, and wearable sensor deployment, it is essential to design a method that could accurately classify human activities while reducing the dependence on annotated data. Traditional machine learning approaches require large-scale annotated data in order to provide a reasonable recognition performance. Recently, self-supervised learning methods are proposed but they are either limited to single sensor devices or fail to model intra-modal correlations within the self-supervised learning paradigm. In this work, we propose Representation Fusion using Self-supervised learning for Activity Recognition (ReFuSeAct) framework that uses modality-specific encoders, attention encoders, and decision-level fusion strategies to address the aforementioned limitations. The self-supervised learning paradigm ensures that the method achieves better performance even with less amount of annotated data. The architecture proposed for modality-specific encoder ensures that extraction of representative features that could help in improving recognition performance. The feature-level fusion performed using the proposed attention encoders enhances the quality of representative features that could be used in supervised learning phase. Finally, the decision-level fusion strategy enhances the activity recognition accuracy in comparison to the single deep learning classifier. Our experimental analysis shows that the proposed approach records 9.1% improvement over semi-supervised learning baselines and more than 2% improvement in comparison to existing self-supervised learning approaches.
Low-light image enhancement using gamma correction prior in mixed color spaces

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.
A study of error correction method based on selective focus for curved rail profile measurement

Rail profile detection is an important means of railway operation and maintenance. Currently, structured light profile measurement technology is mainly used for vehicle-mounted rail profile measurement. The body attitude of the vehicle will change as it passes by a curve, and the laser plane of the rail profile measurement device would be non-perpendicular to the rail in the local longitudinal direction, resulting in errors in rail profile measurement. To address this issue, an error correction method based on selective focus for curved rail profile measurement is proposed. The virtual 3D rail is generated with the section image of a single-beam laser and projected onto the image plane, which is then combined with the projection data of virtual 3D rail and the actual rail from the rail image to build the optimization function. The optimal auxiliary plane perpendicular to the rail's longitudinal direction is then determined. Finally, error correction is achieved by projecting the measured data onto the optimal auxiliary plane. The effectiveness and accuracy of the proposed method are validated through experiments, demonstrating that regardless of the laser plane's relative pitch or yaw motion with respect to the rail, the corrected profile wear measurement error was under 0.10 mm, satisfying the requirement for rail wear measurement accuracy. In this study, the plane perpendicular to the longitudinal direction of the rail was obtained through selective focus, thus providing a new insight into future work on rail profile measurement error correction.
SemiAMR: Semi-Supervised Automatic Modulation Recognition With Corrected Pseudo-Label and Consistency Regularization

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.
Efficient low-rank quaternion matrix completion under the learnable transforms for color image recovery

Recent low-rank quaternion matrix completion (LRQMC) approaches have been extensively studied to recover missing data of color images. However, these methods need to frequently compute the quaternion singular value decompositions (QSVD) of the quaternion matrix, making them unsuitable for large-scale data. In this paper, we suggest an efficient LRQMC model based on the learnable transforms for color image recovery. The key idea is to project the large-scale quaternion matrix to a small-scale quaternion matrix via the semi-orthogonal transforms along each mode, which significantly reduces the computational cost of QSVD. We then apply a nonconvex approximation of rank (i.e., weighted Schatten p-norm) onto the small-scale quaternion matrix to achieve a better quaternion rank estimation. The alternating direction method of multipliers scheme is developed to solve the proposed model, and the weak convergence property of the algorithm is discussed. Experimental results on color images demonstrate that our method is considerably faster than state-of-art approaches while achieving comparative recovery performance.