Reviewerly/exHarmony huggingface dataset:
Stealthy Attack on Remote Control System With Local Controller and Its Countermeasures

Cyber–physical systems (CPSs) driven by a local controller and a remote controller have been gaining significant research interest in recent years due to its application scenarios in practice, such as unmanned aerial vehicles (UAVs). In this article, we consider the security issue in the remote control system with a local controller under stealthy attacks. Under this framework, one controller is designed locally based on the limited measurements collected by a local sensor, and the other controller is designed remotely and is transmitted to the actuator through a wireless communication channel, which may suffer malicious attacks due to its openness. To defend attacks on remote control signal, the K–L divergence-based detector or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi^{2}$</tex-math> </inline-formula> detector is often adopted. However, there may be attackers adopting stealthy attacks, which can bypass such detectors. Therefore, we analyze the existence of such attacks, and analytically characterize the worst-estimation performance degradation induced by the remote control signal attack. Further, we construct the optimal attack signal to achieve the upper bound on the estimation performance degradation. In addition, we also give countermeasures against such stealthy attacks. Simulations are provided to illustrate the proposed results.
Measurement Combination Estimator for Multisensor Extended Object Tracking Using Random Matrix

For extended object tracking (EOT), the random-matrix approach has received extensive attention. It is able to jointly estimate the kinematic state and extension of an extended object. Multiple sensors with different characteristics and perspectives can provide new information especially for EOT. However, how to fuse the measurements from multiple sensors to guarantee the improvement of the estimation accuracy of the state and extension still needs further research. This paper proposes a random-matrix approach to elliptical EOT using multiple sensors based on a centralized fusion architecture. In order to handle the different distortions of multiple sensors, a measurement combination (MC) approach is firstly proposed. As a result, a likelihood function with a proper form is obtained, and it can also pre-process the multi-sensor measurements and take the scattering matrix of measurement means into consideration. Based on the likelihood function, the measurement combination estimator (MCE) is derived for multi-sensor EOT in a Bayesian framework. It inherits the analytical and concise scheme of the single-sensor EOT approach. Based on the proposed MCE approach, it is theoretically proved that the estimation accuracy of the state and extension can be improved when the sensor number increases. Effectiveness of the MCE approach is demonstrated by simulation and experimental results compared with the existing multi-sensor EOT approaches using random matrix.
Up- and down-correlations in normal variance mixture models

We study conditional correlations in normal variance mixture models. For several families, we determine explicit formulas for up-(down-)correlations defined as the correlation between the sum of risks and an individual component, conditionally on the sum being large (small).
Adaptive phase measuring profilometry for robustly detecting saturated pixels

In this paper, we propose an adaptive method for phase measuring profilometry to more accurately reconstruct 3D point clouds of high-reflective surfaces. First, we project high-frequency sinusoidal fringe patterns to robustly detect saturated pixels and then obtain their corresponding optimal projection intensity. Second, based on the coordinate mapping relationship between a camera-projector pair, we generate a set of fringe patterns with adaptive intensity. Finally, we project the adaptive fringes patterns to achieve more accurate 3D reconstruction. The experimental results show that our method can more accurately detect saturated pixels, and thus improve the quality of 3D reconstruction.
Improved ShuffleNetV2 for Action Recognition in BPPV Treatment

In clinical examination and repositioning for BPPV(benign paroxysmal positional vertigo), analyzing the head position trajectory at the algorithm level to obtain information on each examination and repositioning posture helps to provide guidance for subsequent treatment, which has important practical value in clinical applications. This paper uses the improved ShuffleNetV2 on the basic the data obtained by the swivel chair for action recognition, and locates the interval where the action occurs. Specific network improvements include: the introduction of the SE channel attention block, which improves the performance of the network with a small amount of parameters and computation; the Hardswish activation function is used instead of ReLU in the original network; by reducing the layers in the network, deleting the second 1*1 convolution in the right branch of the downsampling block, and changing the kernel size of the DW convolution, the network can be lightweight while maximizing the capability of action recognition. The network’s performance has been enhanced, resulting in an accuracy of 98.53% with only 71,444 parameters and 15.76M FLOPs, while maintaining a recognition speed of 97.1 images per second. It achieves a good balance between speed and accuracy, enabling it to effectively accomplish the task of action recognition.
Reproducible experiments for generating pre-processing pipelines for AutoETL

This work is a companion reproducibility paper of the experiments and results reported in Giovanelli et al. (2022), where data pre-processing pipelines are evaluated in order to find pipeline prototypes that reduce the classification error of supervised learning algorithms. With the recent shift towards data-centric approaches, where instead of the model, the dataset is systematically changed for better model performance, data pre-processing is receiving a lot of attention. Yet, its impact over the final analysis is not widely recognized, primarily due to the lack of publicly available experiments that quantify it. To bridge this gap, this work introduces a set of reproducible experiments on the impact of data pre-processing by providing a detailed reproducibility protocol together with a software tool and a set of extensible datasets, which allow for all the experiments and results of our aforementioned work to be reproduced. We introduce a set of strongly reproducible experiments based on a collection of intermediate results, and a set of weakly reproducible experiments (Lastra-Dıaz, 0000) that allows reproducing our end-to-end optimization process and evaluation of all the methods reported in our primary paper. The reproducibility protocol is created in Docker and tested in Windows and Linux. In brief, our primary work (i) develops a method for generating effective prototypes, as templates or logical sequences of pre-processing transformations, and (ii) instantiates the prototypes into pipelines, in the form of executable or physical sequences of actual operators that implement the respective transformations. For the first, a set of heuristic rules learned from extensive experiments are used, and for the second techniques from Automated Machine Learning (AutoML) are applied.
Compressive hyperspectral imaging based on Images Structure Similarity and deep image prior

In this paper, the structural similarity between RGB image and spectral image is studied, and a non-iterative Images Structure Similarity(ISS) method for fast reconstruction of spectral image is proposed. At the same time, the input of the Deep Image Prior (DIP) method is optimized for the first time by using the initial spectral data reconstructed by ISS. It raises the starting value of the iteration. Specifically, we take the RGB image data as the base of the spectral data, and solve the base coefficient by the least square method to quickly estimate the initial hyperspectral image. The Gaussian noise data is replaced by the estimated initial spectral data to constrain the solution space of the network and reduce the number of iterations. Finally, the structural similarity between RGB image and spectral image is used. The RGB three-channel graph is used to filter the iterative results to improve the reconstruction quality. Experimental results show that compared with other hyperspectral imaging methods, the proposed method can improve the quality of reconstruction in both spectral resolution and spatial resolution. In addition, compared with other methods based on Deep Image Prior (DIP), our improvement greatly reduces the reconstruction time and is more suitable for actual snapshot spectral imaging.
The Hybrid Features and Supervised Learning for Batik Pattern Classification

Several countries have traditional textiles as a piece of their cultural heritage. Indonesia has a traditional textile called batik. Central Java is one of the regions producing batik known for its variety of distinctive themes. It has unique designs and several motifs that emphasize the beauty of historic sites. Since the diversity of central java batik motifs and the lack of knowledge from the surrounding community, only a select group of people, especially the batik craftsmen themselves, can recognize these motifs. Consequently, the method to identify the batik according to the primary ornament pattern is required. Therefore, this study proposes a computer vision-based method for classifying batik patterns. The proposed method required discriminating appropriate features to produce optimal results. The discriminating features were constructed based on color, shape, and texture. Those features were derived using the method of Color Moments, Area Based Invariant Moments, Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP). This study’s proposed hybrid features were formed based on the most discriminating and appropriate features. These were yielded by the Correlation-based feature selection (CFS) method. The hybrid features were then fed into several classifiers to determine the batik pattern. The pattern consists of ten classes: Asem Arang, Asem Sinom, Asem Warak, Blekok, Blekok Warak, Gambang Semarangan, Kembang Sepatu, Semarangan, Tugu Muda, and Warak Beras Utah. Based on the experimental results, the most optimal predicted class of the batik pattern was generated using the Artificial Neural Network (ANN) classifier. It was indicated by achieving an accuracy value of 99.76% based on the 3,000 images (each class consists of 300 images) with cross-validation using a k-fold value of 10. This study has proved that the hybrid features incorporated with ANN can be selected as a suitable model to classify the batik patterns.
Privacy Protection for Marginal-Sensitive Community Individuals Against Adversarial Community Detection Attacks

Social networks especially the social communities facilitate the rapid and rich social activities of all individuals in the world. However, advanced community detection brings serious privacy disclosure (e.g., whether two important and sensitive individuals belong to the same community?) to us. For instance, online plainclothes policemen can be regarded as marginal community users who are often in the same community initially and in need of penetrating into as many as possible different communities to collect illegal evidence of network criminals, but adversarial community inference which can maliciously disclose the sensitive user relationships within a target community will expose their privacy and lead to task failure. Thus, privacy protection for the marginal community users becomes an urgent issue which is still open so far. In this work, we aim to study the community privacy protection for target marginal individuals of a community against multiple adversarial community detection (ACD) attacks. First, we define the marginal community user hiding problem and propose a marginal user pair selection strategy. Second, to enhance the privacy effectiveness of conventional methods, we propose a deep graph learning approach to maximally find the minimum link perturbation cost. Finally, we conduct various community detection attacks on many real social graphs, and the experimental results show that our method can more effectively hide the marginal-sensitive user pairs than baselines.
Pedestrian Re-Identification Algorithm Based on Attention Pooling Saliency Region Detection and Matching

The recognition accuracy of the pedestrian re-identification algorithm is affected by factors such as the scale, posture, occlusion level, and appearance of a pedestrian in an image. These factors bring great challenges to the further development of deep learning theory in the field of person re-identification. On the one hand, existing pedestrian re-identification model cannot accurately extract attention information containing contextual information and discriminative pedestrian features. On the other hand, images of the same pedestrian taken with different cameras have the problem of different degrees of occlusion. Therefore, we introduce an attention pooling mechanism, which allows the model to automatically focus on discriminative regions of pedestrian images. The model can then learn attention maps by automatically focusing on visually salient pedestrian regions. It can effectively address the influence of factors on the accuracy of pedestrian re-identification. Furthermore, we propose a pedestrian re-identification method based on saliency region detection and matching. The method first extracts multiple local saliency regions from pedestrian images using the pedestrian joint point detection method, then marks and extracts the positions of the abovementioned saliency regions, and finally performs matching and sorting. It can correct the impact caused by occlusion on the accuracy of pedestrian re-identification. We conduct tests and experiments on three public person re-identification datasets, and the results show that our method not only achieves the best recognition accuracy on the aforementioned datasets but also has better robustness.
Flexible Dual-Branch Siamese Network: Learning Location Quality Estimation and Regression Distribution for Visual Tracking

Anchor-free based trackers introduce an extra branch in addition to classification and regression branches in the network to achieve comparable performance with anchor-based trackers. This extra branch is usually trained independently in the training phase and is used in combination with other branches in the inference phase. However, this can increase the inconsistency between the inference phase and the training phase, potentially degrading the tracking performance. To address this problem, we propose a new Siamese network-based object tracking framework that eliminates this inconsistency by unifying classification and additional branch tasks to achieve learning location quality estimation. Furthermore, regression tasks for bounding boxes are widely formulated based on Dirac <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta$</tex-math> </inline-formula> distribution. Though this assumption works well for many scenarios, it restricts the prediction of regression branches. To overcome this restriction, we propose discretizing the continuous offset of the regression branch into multiple offset predictions, which enables the network to learn more flexible distributions automatically. Meanwhile, the discrete distribution prediction of regression branches is utilized to further guide the classification of the trackers. Extensive experiments on the widely accepted benchmarks demonstrate the effectiveness and efficiency of the proposed model.
Complaint and Severity Identification From Online Financial Content

The automatic detection of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">financial complaints</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FINCORP</i> ) can benefit businesses and online merchants. Compared with manually tagged complaints, they can use this information to monitor and address issues and effectively route them to appropriate teams. This can also promote greater transparency and accountability when dealing with consumer financial products and services, strengthening the firm’s brand value. In linguistic studies, complaints have been classified into severity categories based on the level of risk the complainant is prepared to accept. Furthermore, since emotions influence every speech act, an individual’s emotional state considerably impacts the complaint expression. In this article, we introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FINCORP</i> resource, a collection of annotated complaints arising between financial institutions and consumers expressed in English on Twitter. The dataset has been enriched with the associated emotion, sentiment, and complaint severity classes. The dataset comprises 3149 complaint and 3133 noncomplaint instances spanning over ten domains (e.g., credit cards, mortgages). For a comprehensive evaluation of our dataset, we develop a multitask framework for complaint detection and severity classification with emotion recognition (ER) and sentiment classification as the additional tasks and compare it with several existing baselines. The corpus and code are available here: https://github.com/RohanBh23/FINCORP.
Supervised Local Training With Backward Links for Deep Neural Networks

The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization.Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients.These methods prevent information exchange between modules and result in inferior performance.This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependency and facilitates information to flow backward along with the network.To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module.Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods.For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity, respectively.Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs.Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP.Therefore, our method could create new opportunities for improving training algorithms towards better efficiency for real-time learning applications.Impact Statement-Deep learning models are generally bulky with abundant parameters, demanding long training time and excessive computing power.It poses a great challenge for deploying these models in real-world applications.This study looks into the intrinsic limitation of the standard training algorithm.The proposed method increases information flow in the algorithm and improves image classification accuracy over conventional methods.It can substantially reduce memory cost and hardware runtime by 50%.With the proposed method, an intelligent system can be readily deployed on mobile devices and function to meet practical demands.It also enables computing systems to reduce excessive energy waste and cut down carbon footprints for deep learning applications, such as traffic control and autonomous driving.
Knowledge Graph-Based Reinforcement Federated Learning for Chinese Question and Answering

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model [MSP-bidirectional and auto-regressive transformers (BART)] that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.
Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this paper, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among non-communicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights.