Reviewerly/exHarmony huggingface dataset:
Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

Completely automated public turing test to tell computers and humans apart (CAPTCHA) is widely used to prevent malicious automated attacks on various online services. Text- and image-CAPTCHAs have shown broader acceptability due to usability and security factors. However, recent progress in deep learning implies that text-CAPTCHAs can easily be exposed to various fraudulent attacks. Thus, image-CAPTCHAs are getting research attention to enhance usability and security. In this work, the neural-style transfer (NST) is adapted for designing an image-CAPTCHA algorithm to enhance security while maintaining human performance. In NST-rendered image-CAPTCHAs, existing methods inquire a user to identify or localize the salient object (e.g., content) which is solvable effortlessly by off-the-shelf intelligent tools. Contrarily, we propose a Style Matching CAPTCHA (SMC) that asks a user to select the style image which is applied in the NST method. A user can solve a random SMC challenge by understanding the semantic correlation between the content and style output as a cue. The performance in solving SMC is evaluated based on the 1368 responses collected from 152 participants through a web-application. The average solving accuracy in three sessions is 95.61%; and the average response time for each challenge per user is 6.52 s, respectively. Likewise, a Smartphone Application (SMC-App) is devised using the proposed method. The average solving accuracy through SMC-App is 96.33%, and the average solving time is 5.13 s. To evaluate the vulnerability of SMC, deep learning-based attack schemes using Convolutional Neural Networks (CNN), such as ResNet-50 and Inception-v3 are simulated. The average accuracy of attacks considering various studies on SMC using ResNet-50 and Inception-v3 is 37%, which is improved over existing methods. Moreover, in-depth security analysis, experimental insights, and comparative studies imply the suitability of the proposed SMC.
Characterization of variational quantum algorithms using free fermions

We study variational quantum algorithms from the perspective of free fermions. By deriving the explicit structure of the associated Lie algebras, we show that the Quantum Approximate Optimization Algorithm (QAOA) on a one-dimensional lattice -- with and without decoupled angles -- is able to prepare all fermionic Gaussian states respecting the symmetries of the circuit. Leveraging these results, we numerically study the interplay between these symmetries and the locality of the target state, and find that an absence of symmetries makes nonlocal states easier to prepare. An efficient classical simulation of Gaussian states, with system sizes up to $80$ and deep circuits, is employed to study the behavior of the circuit when it is overparameterized. In this regime of optimization, we find that the number of iterations to converge to the solution scales linearly with system size. Moreover, we observe that the number of iterations to converge to the solution decreases exponentially with the depth of the circuit, until it saturates at a depth which is quadratic in system size. Finally, we conclude that the improvement in the optimization can be explained in terms of better local linear approximations provided by the gradients.
An in silico study on the detectability of field cancerization through parenchymal analysis of digital mammograms

Parenchymal analysis has shown promising performance for the assessment of breast cancer risk through the characterization of the texture features of mammography images. However, the working principles behind this practice are yet not well understood. Field cancerization is a phenomenon associated with genetic and epigenetic alterations in large volumes of cells, putting them on a path of malignancy before the appearance of recognizable cancer signs. Evidence suggests that it can induce changes in the biochemical and optical properties of the tissue.The aim of this work was to study whether the extended genetic mutations and epigenetic changes due to field cancerization, and the impact they have on the biochemistry of breast tissues are detectable in the radiological patterns of mammography images.An in silico experiment was designed, which implied the development of a field cancerization model to modify the optical tissue properties of a cohort of 60 voxelized virtual breast phantoms. Mammography images from these phantoms were generated and compared with images obtained from their non-modified counterparts, that is, without field cancerization. We extracted 33 texture features from the breast area to quantitatively assess the impact of the field cancerization model. We analyzed the similarity and statistical equivalence of texture features with and without field cancerization using the t-test, Wilcoxon sign rank test and Kolmogorov-Smirnov test, and performed a discrimination test using multinomial logistic regression analysis with lasso regularization.With modifications of the optical tissue properties on 3.9% of the breast volume, some texture features started to fail to show equivalence (p < 0.05). At 7.9% volume modification, a high percent of texture features showed statistically significant differences (p < 0.05) and non-equivalence. At this level, multinomial logistic regression analysis of texture features showed a statistically significant performance in the discrimination of mammograms from breasts with and without field cancerization (AUC = 0.89, 95% CI: 0.75-1.00).These results support the idea that field cancerization is a feasible underlying working principle behind the distinctive performance of parenchymal analysis in breast cancer risk assessment.
A Novel Method to Generate Auto-Labeled Datasets for 3D Vehicle Identification Using a New Contrast Model

Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensor fusion, it is intended to auto-label new datasets while maintaining the consistency of the ground truth. The performance of the model, with respect to the manually labeled KITTI images, achieves an F-Score of 0.957, 0.927 and 0.740 in the easy, moderate and hard images of the dataset. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labeling system. The proposed methodology is tested under boundary conditions and the results show that this approximation can be easily adapted to a wide variety of problems when labeled datasets are not available.
A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

Summary Federated learning (FL) is widely used in internet of things (IoT) scenarios such as health research, automotive autopilot, and smart home systems. In the process of model training of FL, each round of model training requires rigorous decryption training and encryption uploading steps. The efficiency of FL is seriously affected by frequent encryption and decryption operations. A scheme of key computation and key management with high efficiency is urgently needed. Therefore, we propose a group key agreement technique to keep private information and confidential data from being leaked, which is used to encrypt and decrypt the transmitted data among IoT terminals. The key agreement scheme includes hidden attribute authentication, multipolicy access, and ciphertext storage. Key agreement is designed with edge‐cloud collaborative network architecture. Firstly, the terminal generates its own public and private keys through the key algorithm then confirms the authenticity and mapping relationship of its private and public keys to the cloud server. Secondly, IoT terminals can confirm their cryptographic attributes to the cloud and obtain the permissions corresponding to each attribute by encrypting the attributes. The terminal uses these permissions to encrypt the FL model parameters and uploads the secret parameters to the edge server. Through the storage of the edge server, these ciphertext decryption parameters are shared with the other terminal models of FL. Finally, other terminal models are trained by downloading and decrypting the shared model parameters for the purpose of FL. The performance analysis shows that this model has a better performance in computational complexity and computational time compared with the cited literature.
An Ultra-Short-Term PV Power Forecasting Method for Changeable Weather Based on Clustering and Signal Decomposition

Photovoltaic (PV) power shows different fluctuation characteristics under different weather types as well as strong randomness and uncertainty in changeable weather such as sunny to cloudy, cloudy to rain, and so on, resulting in low forecasting accuracy. For the changeable type of weather, an ultra-short-term photovoltaic power forecasting method is proposed based on affinity propagation (AP) clustering, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN), and bi-directional long and short-term memory network (BiLSTM). First, the PV power output curve of the standard clear-sky day was extracted monthly from the historical data, and the photovoltaic power was normalized according to it. Second, the changeable days were extracted from various weather types based on the AP clustering algorithm and the Euclidean distance by considering the mean and variance of the clear-sky power coefficient (CSPC). Third, the CEEMDAN algorithm was further used to decompose the data of changeable days to reduce its overall non-stationarity, and each component was forecasted based on the BiLSTM network, so as to obtain the PV forecasting value in changeable weather. Using the PV dataset obtained from Alice Springs, Australia, the presented method was verified by comparative experiments with the BP, BiLSTM, and CEEMDAN-BiLSTM models, and the MAPE of the proposed method was 2.771%, which was better than the other methods.
High Dynamic Range Image Tone Mapping: Literature review and performance benchmark

Compared to conventional digital imaging, high dynamic range (HDR) image tone mapping technology can faithfully reproduce real-world scenes on common displays to provide a better viewing experience. The research on HDR image tone mapping has evolved over three decades and has been widely employed in many applications like photography, movie, and animation rendering. Nowadays, HDR image tone mapping is still an active area, especially with the successful use of deep neural networks in this field. This paper presents a literature review and constructs benchmarks on the existing tone mapping methods to pave the way for further developments. While there are previous reviews summarizing tone mapping algorithms, they are getting outdated as novel methods appear constantly. Therefore, we propose an updated literature review that includes state-of-the-art image tone mapping works to fill the gap. The reviewed methods are discussed and categorized based on the processed image channels and employed models. Furthermore, we summarize available HDR image datasets and objective metrics used to evaluate the quality of tone-mapped images. Two benchmarks are constructed to assess the reviewed tone mapping algorithms based on two large-scale datasets and seventeen quality metrics. Visual test is also conducted to evaluate the reviewed methods subjectively. This review concludes with a discussion of challenges that remain to be addressed and possible future research directions in HDR image tone mapping.
Adaptive Kernel Graph Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extensions have received much attention from various researchers due to their promising performance. However, the graph similarity matrix of the existing methods is often predefined in the original space of data and kept unchanged during the matrix-factorization procedure, which leads to non-optimal graphs. To address these problems, we propose a kernel-graph-learning-based, nonlinear, nonnegative matrix-factorization method in this paper, termed adaptive kernel graph nonnegative matrix factorization (AKGNMF). In order to automatically capture the manifold structure of the data on the nonlinear feature space, AKGNMF learned an adaptive similarity graph. We formulated a unified objective function, in which global similarity graph learning is optimized jointly with the matrix decomposition process. A local graph Laplacian is further imposed on the learned feature subspace representation. The proposed method relies on both the factorization that respects geometric structure and the mapped high-dimensional subspace feature representations. In addition, an efficient iterative solution was derived to update all variables in the resultant objective problem in turn. Experiments on the synthetic dataset visually demonstrate the ability of AKGNMF to separate the nonlinear dataset with high clustering accuracy. Experiments on real-world datasets verified the effectiveness of AKGNMF in three aspects, including clustering performance, parameter sensitivity and convergence. Comprehensive experimental findings indicate that, compared with various classic methods and the state-of-the-art methods, the proposed AKGNMF algorithm demonstrated effectiveness and superiority.
A dynamic resource-aware endorsement strategy for improving throughput in blockchain systems

Low system latency is of crucial importance for endorsement phase in Hyperledger Fabric systems. Currently, in the blockchain system Hyperledger Fabric, the endorsement nodes can be selected by clients to verify user-submitted proposals by static configuration or service discovery. However, when a large number of proposal requests arrive, nodes with high resource consumption in the endorsers can have an impact on the performance of the system and produce a high energy consumption. To address this issue, a dynamic resource-aware endorsement strategy (Dra-Fabric) is proposed to optimize endorsement phase in Hyperledger Fabric. The proposed strategy is characterized by dynamically selecting endorsement nodes with low resource consumption at the endorsement stage of the transaction proposals. We further design a two-stage resource evaluation scheme for selection of endorsement nodes, which can calculate the weight between the attributes of resource consumption in the endorsement nodes by the CRITIC algorithm and then determine the priority of the endorsement nodes to validate the transactions by PROMETHEE algorithm. Dra-Fabric is implemented on top of Hyperledger Fabric platform and evaluated on metrics such as system throughput, response latency and energy consumption in a simulated Hyperledger Fabric environment. With a comprehensive evaluation, the proposed Dra-Fabric system provides promising improvements compared to the existing Hyperledger Fabric's endorsement strategy.
Underestimation modification for intrinsic dimension estimation

The intrinsic dimension is the dimension of the low-dimensional manifold where the high-dimensional data is located. Accurately estimating the intrinsic dimension of the data set is helpful for data-dimensionality reduction and preprocessing. Due to the unknown spatial distribution of data and the limited sample size of a dataset, estimation methods which only use distance information tend to underestimate the intrinsic dimension of dataset. To reduce the estimation complexity and improve the accuracy, two estimation algorithms based on ID(κ) are proposed, where κ is the scaling ratio of the neighborhood radius of the sample point. First, according to the selection criteria of parameter κ, an improved algorithm for selecting the optimal scaling ratio κ is proposed, which reduces the computational complexity and improves the stability of estimation. Second, using simulation datasets with the same sample size and known intrinsic dimensions, the relationship between the estimated dimension and the true intrinsic dimension is obtained, and an underestimation modification method for intrinsic dimension estimation is proposed. Results of comparative experiments on simulation and real datasets indicate that the underestimation modification algorithm has high estimation accuracy and robustness.
Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method

The number of Unmanned Aerial Vehicles (UAVs) used in various industries has increased exponentially, and abnormal detection of UAVs is one of the primary technical means to ensure that UAVs can work normally. Currently, most anomaly detection models are trained using on-board logs from drones. However, in some cases, using these logs can be problematic due to data encryption, inconsistent descriptions of characteristics, and imbalanced positive and negative samples. Consequently, the on-board logs of UAVs may not be directly usable for training anomaly detection models. Given the above problems, this paper proposes a Time Line Modeling (TLM) method based on the UAV software-in-the-loop (SITL) simulation environment to obtain and process the on-board failure logs of drones. The Time Line Modeling method includes two stages: the Fault Time Point Anchoring Method and Fault Time Window Stretching Method. First, based on the SITL simulation environment, multiple flight missions were constructed. Failures of several common components of UAVs are designed. Secondly, the fault’s initial location and end location are determined by the method of Fault Time Point Anchoring, and the original collection of tagged UAV’s on-board data is realized. Then, in terms of data processing, the features that are not universal are removed, and the flight data of the UAV is optimized by using the data balance method of Time Window Stretching to achieve the balance of normal data and abnormal data. Finally, use of algorithms such as Sequential Minimal Optimization (SMO), Random Forest (RF), and Convolutional Neural Network (CNN) were used to experiment with the processed data. The experimental results showed that the data set obtained based on this method can be effectively applied to the training of machine learning-based anomaly detection models.
Secure and Efficient Federated Gradient Boosting Decision Trees

In recent years, federated GBDTs have gradually replaced traditional GBDTs, and become the focus of academic research. They are used to solve the task of structured data mining. Aiming at the problems of information leakage, insufficient model accuracy and high communication cost in the existing schemes of horizontal federated GBDTs, this paper proposes an algorithm of gradient boosting decision trees based on horizontal federated learning, that is, secure and efficient FL for GBDTs (SeFB). The algorithm uses locality sensitive hashing (LSH) to build a tree by collecting similar information of instances without exposing the original data of participants. In the stage of updating the tree, the algorithm aggregates the local gradients of all data participants and calculates the global leaf weights, so as to improve the accuracy of the model and reduce the communication cost. Finally, the experimental analysis shows that the algorithm can protect the privacy of the original data, and the communication cost is low. At the same time, the performance of the unbalanced binary data set is evaluated. The results show that SeFB algorithm compared with the existing schemes of horizontal federated GBDTs, the accuracy is improved by 2.53% on average.
Simulation of University Teaching Achievement Evaluation Based on Deep Learning and Improved Vector Machine Algorithm

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RMAML: Riemannian meta-learning with orthogonality constraints

Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior experience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nested optimization framework, in which meta-parameters are optimized (or learned) at the outer-level, while the inner-level optimizes the task-specific parameters. In this paper, we introduce RMAML, a meta-learning method that enforces orthogonality constraints to the bi-level optimization problem. We develop a geometry aware framework that generalizes the bi-level optimization problem to the Riemannian (constrained) setting. Using the Riemannian operations such as orthogonal projection, retraction and parallel transport, the bi-level optimization is reformulated so that it respects the Riemannian geometry. Moreover, we observe that a superior stable optimization and an improved generalization ability can be achieved when the parameters and meta-parameters of the method are modeled using a Stiefel Manifold. We empirically show that RMAML can easily reach competitive performances against several state of the art algorithms for few-shot classification and consistently outperforms its Euclidean counterpart, MAML. For example, by using the geometry of the Stiefel manifold to structure the fully-connected layers in a deep neural network, a 7% increase in single-domain few-shot classification accuracy is achieved. For the cross-domain few-shot learning, RMAML outperforms MAML by up to 9% of accuracy. Our ablation study also demonstrates the effectiveness of RMAML over MAML in terms of higher accuracy with a reduced number of tasks and (or) inner-level updates.
Improve Temporal Action Proposals using Hierarchical Context

Temporal action proposal (TAP) aims to generate accurate candidates of action instances in an untrimmed video. It has been proved that contexts are critically important to this task. In this paper, we propose a novel hierarchical context network (HCN) to further explore the snippet-level and proposal-level contexts, which are used to improve the representations of snippets and proposals, respectively. First, we pinpoint that different scales of snippet-level contexts are not equally important for different action instances. To this end, we incorporate a novel gating mechanism into the U-Net structure to capture the content-adaptive snippet-level contexts. Second, to exploit the proposal-level contexts, we propose a task-specific self-attention model with high efficiency. By stacking multiple attention models, we can deeply explore the proposal-level contexts in a wide range. Finally, to leverage both levels of context, we equip HCN with three branches to evaluate proposals from local to global perspectives. Our experiments on the ActivityNet-1.3 and THUMOS14 datasets show that HCN significantly outperforms previous TAP methods. Additionally, further experiments demonstrate that our method can substantially improve the state-of-the-art action detection performance when combined with existing action classifiers.