Reviewerly/exHarmony huggingface dataset:
Selective Kernel Res-Attention UNet: Deep Learning for Generating Decorrelation Mask With Applications to TanDEM-X Interferograms

Decorrelation is one of the main limitations for InSAR.Masking decorrelated pixels is crucial for retrieving information from SAR interferograms.However, for traditional masking methods, manually drawing masks is time-consuming and may be unfeasible when decorrelation areas are with complicated and blurred boundaries.Setting a single coherence threshold is also difficult, if not impossible, to mask out all decorrelated pixels without losing valid phases.Here, we propose a deep-learning segmentation network (Mask Net) based on Selective Kernel Res-Attention UNet, for generating decorrelation masks with applications to TanDEM-X interferograms.We conduct several experiments to determine the training strategy and parameters, including sample size, batch size, loss function and down-sampling scheme, to optimize network performance.Afterwards, we compare the performance of Mask Net with other classical segmentation networks.Our evaluation metrics show that Mask Net outperforms the best performance of other segmentation networks by IoU of 6.32% and F1 Score of 3.97%, respectively.It also possesses the fastest inferring speed, 0.4505s on sample size of 1024-by-1024 pixels, which is at least ~50% faster than other segmentation networks.We applied Mask Net to three TanDEM-X interferograms of Kīlauea crater in Hawaii, metropolitan region of Wuhan, and Muztagata Glacier in China.Our results show that comparing with coherence threshold method, Mask Net can clearly mask out all decorrelation regions, rarely causing loss of valid phases.It also exhibits better segmentation performance than other deep-learning segmentation networks, especially for those complex decorrelation boundaries, with less computational time.
Graph Attention Neural Network for Image Restoration

Self-similarity underpins modern non-local attention mechanism, which has been verified to be an effective prior for image restoration. However, most existing non-local attention restorers are implemented based on pixels, which tend to be biased due to image degeneration. Furthermore, most non-local methods for image restoration are restricted to construct fully-connected correlations in a regular Euclidean space so that all features within the search region have to participate in the feature aggregation process, no matter how similar the key feature is to the query feature. To rectify these weaknesses, in this paper, we propose a novel graph attention network for image restoration, dubbed GATIR, which establishes the non-local attention based on feature patches and utilizes the graph convolution to perform feature aggregation selectively in a non-Euclidean space. Experimental results demonstrate that our GATIR can achieve state-of-the-art performance on synthetic image denoising, real image denoising, image demosaicing, and compression artifact reduction tasks.
A three-stage learning approach to cross-domain person re-identification

Cross-domain person re-identification (Re-id) is far less discussed than supervised Re-id, since there are not labeled data that can be used to guide the person Re-id. Even though some works have been done on unsupervised Re-id, cross-domain Re-id is much challenging, and needs further research works. In this paper, without background transfer and other data enhancement of source domain training set, we propose a three-stage cross-domain Re-id model which considers the domain adaptation, self-supervised clustering re-training and joint loss training in one schema. In order to improve the adaptability of the model trained on the source data, cross-domain and cross-camera losses are considered. Then we do the self-supervised learning of clustering re-training in target dataset to learn the discriminative features in the target domain on the basis of the pre-trained model. Finally, we train the target data using the pseudo labels obtained by clustering with joint metric learning and representation learning. In particular, we replace the classic cross-entropy loss with label smoothing regularization loss so as to reduce fitting of false pseudo labels. Performance evaluation on benchmark datasets demonstrates the effectiveness of the proposed approach.
Towards secure and practical machine learning via secret sharing and random permutation

With the increasing demand for privacy protection, privacy-preserving machine learning has been drawing much attention from both academia and industry. However, most existing methods have their limitations in practical applications. On the one hand, although most cryptographic methods are provable secure, they bring heavy computation and communication. On the other hand, the security of many relatively efficient privacy-preserving techniques (e.g., federated learning and split learning) is being questioned, since they are non-provable secure. Inspired by previous work on privacy-preserving machine learning, we build a privacy-preserving machine learning framework by combining random permutation and arithmetic secret sharing via our compute-after-permutation technique. Our method is more efficient than existing cryptographic methods, since it can reduce the cost of element-wise function computation. Moreover, by adopting distance correlation as a metric for evaluating privacy leakage, we demonstrate that our method is more secure than previous non-provable secure methods. Overall, our proposal achieves a good balance between security and efficiency. Experimental results show that our method not only is up to 5× faster and reduces up to 80% network traffic compared with state-of-the-art cryptographic methods, but also leaks less privacy during the training process compared with non-provable secure methods.
Weakly supervised instance segmentation using multi-prior fusion

Instance segmentation usually requires mask annotation, which has a much higher labeling cost than bounding box annotation. In recent years, weakly supervised instance segmentation (WSIS) has drawn great attention with only bounding box annotation needed during training. However, bounding box annotation can only provide limited information for the segmentation task. Without pixel-wise fine-grained supervision, predicted segmentation masks usually have blurred boundaries with poor performance. To address the issue caused by weak bounding box annotation, in this paper, two types of prior knowledge, i.e., bounding box tightness prior and contour prior, are employed to guide the mask prediction. For the bounding box tightness prior, we assume that the objects are annotated tightly with bounding boxes. A multi-instance learning (MIL) approach is adopted with the tightness constraint. For the contour prior, we assume that the mask boundaries should align with strong image gradients. We maximize the inner product of the gradients between the mask proposal and the corresponding image region, which is treated as the contour prior constraint. Finally, both prior constraints are combined for separating targets and background. The experiment results on the PASCAL VOC dataset and the augmented dataset demonstrate that our method outperforms most state-of-the-art WSIS methods.
Second-Order Disjoint Factor Analysis

Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by a tree structure where each internal node represents a specific order of abstraction for the latent concept measured. In this paper, we propose a new latent factor model called second-order disjoint factor analysis in order to model an unknown hierarchical structure of the manifest variables with two orders. This is a second-order factor analysis, which-respect to the second-order confirmatory factor analysis-is exploratory, nested and estimated simultaneously by maximum likelihood method. Each subset of manifest variables is modeled to be internally consistent and reliable, that is, manifest variables related to a factor measure "consistently" a unique theoretical construct. This feature implies that manifest variables are positively correlated with the related factor and, therefore, the associated factor loadings are constrained to be nonnegative. A cyclic block coordinate descent algorithm is proposed to maximize the likelihood. We present a simulation study that investigates the ability to get reliable factors. Furthermore, the new model is applied to identify the underlying factors of well-being showing the characteristics of the new methodology. A final discussion completes the paper.
The challenge of comparing digitally captured signatures registered with different software and hardware

Along with the growing popularity of electronic documents authorised with digitally captured signatures, such evidence has appeared in the work of forensic practitioners. Many different vendors offer signature pads with varying specifications. It is therefore expected that forensic handwriting experts will be called upon to compare questioned and known samples captured with completely or partially different hardware and software combinations. Such cases may be challenging as numerical handwriting data produced by various equipment may differ not only in the type of information captured and its quality, but also in its structure and coding. In this research, numerical data of handwriting – i.e. spatial coordinates, force, and time values – were acquired with 26 different combinations of hardware and software to study characteristics of their coding. The analysis of samples revealed that scaling of numerical data is not only hardware but also software dependent. Therefore, their compliance with the ISO/IEC 19794–7 standard is recommended to improve the data interoperability. This standard emphasizes the importance of supplementing numerical signature data with scaling ratios of the used signing solution. The paper also includes descriptions of several phenomena observed in the acquired data to highlight possible pitfalls in performing inter-solution comparisons in casework.
Feature based opinion analysis on social media tweets with association rule mining and multi‐objective evolutionary algorithms

Abstract Social media platform has achieved wide popularity in presenting the user‐generated information online. The proliferation of user‐generated content through social networking sites can enhance the existing transportation system. This work adds to the existing research by proposing a novel system to assess transit rider's reviews on the quality of transport services using Twitter information. A novel framework of a multi‐objective evolutionary approach with association rule mining is proposed for feature‐based opinion analysis on social media transit reviews. Feature‐based opinion analysis is performed in two steps such as feature extraction and opinion analysis. First, the corpus of association rules is generated with opinion analysis, morphological and syntactic analysis, and semantic representation. Then, multi‐objective flower pollination, multi‐objective gray wolf, multi‐objective moth flame, and multi‐objective cat swarm optimization are applied to discover high‐quality association rules on the transit user's opinion. The experimental results indicate that multi‐objective cat swarm optimization using association rule mining performs better in terms of confidence, coverage, the interestingness of the rules, computational time, mean average support, and mean average confidence than the other proposed as existing approaches. It is observed that MCSO‐ARM achieved an improvement in the range of 0.21%–0.27% in terms of confidence, 0.10%–0.13% in coverage, 0.10%–0.25% in interestingness, the computational time of 27.5 s, mean average support value of 0.10%–0.30% and mean of 0.20%–0.30% compared to the existing MPSO‐ARM and other proposed MGWO‐ARM, MMFO‐ARM, and MFPO‐ARM approaches.
Nonuniqueness of generalized quantum master equations for a single observable

When deriving exact generalized master equations for the evolution of a reduced set of degrees of freedom, one is free to choose what quantities are relevant by specifying projection operators. However, obtaining a reduced description does not always need to be achieved through projections-one can also use conservation laws for this purpose. Such an operation should be considered as distinct from any kind of projection; that is, projection onto a single observable yields a different form of master equation compared to that resulting from a projection followed by the application of a constraint. We give a simple example to show this point and give relationships that the different memory kernels must satisfy to yield the same dynamics.
AdaBLEU: A Modified BLEU Score for Morphologically Rich Languages

Machine Translation (MT) depends upon the MT evaluation for comparing several MT systems and gives a measure of their efficiency in terms of translation sentences. Consequently, MT evaluation has become an integral part of the MT procedure and it has led to the development of several MT evaluation metrics which can automatically assess the MT systems. Lexical metrics like BLEU have been mostly used in MT evaluation. However, these metrics poorly represent lexical relationships and impose strict identity matching, leading to less correlation with human evaluation for morphologically rich languages.To overcome the limitations posed by the BLEU evaluation metric for morphologically rich languages, we propose a MT evaluation score called AdaBLEU that is a modified BLEU evaluation score. The proposed score considers the lexical and syntactical properties for any language including the morphologically rich languages. It considers the Parts-of-Speech tags and Dependency Parsing tags along with the BLEU score of sentences. Our modification to the BLEU score does not require multiple reference sentences for evaluation. The evaluation of the performance of AdaBLEU has been conducted by comparing our proposed metric's performance with several other evaluation metrices on different test datasets for different morphological languages. Experimental results show an improved performance in the case of the proposed score.
Diversity-Aware Entity Exploration on Knowledge Graph

Knowledge graphs are graph-structured knowledge bases containing abundant entities and relations among the entities.Entity exploration can help users understand the overall structure of knowledge graphs, as well as find the entities of interest in an exploratory manner.Being different from typical entitycentric search whose goal is to retrieve the most related entities for a user's specific need, the related entities that present diverse aspects are preferred for user's ambiguous needs in entity exploration.In this paper, we propose a novel diversity-aware entity exploration approach based on random walk model, which naturally mimics human conceptual exploration by surfing a class association graph.This model leverages the diversity, representativeness and relatedness of entity classes to rank these classes in a unified way.Furthermore, for each top ranked class, the associated entities are ranked by combining their diversity and popularity.We compare our algorithm with four baseline algorithms and the experimental results indicate that it outperforms baselines.Furthermore, we conduct a task-based user study to evaluate our approach and the experimental results show that our work provides effective support for entity exploration.
An Ensemble Fuzziness-Based Online Sequential Learning Approach and Its Application

Traditional deep learning algorithms are difficult to deploy on most IoT terminal devices due to their limited computing power. To solve this problem, this paper proposes a novel ensemble fuzziness-based online sequential learning approach to support the local update of terminal intelligent models and improve their prediction performance. Our method consists of two modules: server module and terminal module. The latter uploads the data collected in real-time to the server module, then the server module selects the most valuable samples and sends them back to the terminal module for the local update. Specifically, the server module uses the ensemble learning mechanism to filter data through multiple fuzzy classifiers, while the terminal module uses the online neural networks with random weights to update the local model. Extensive experimental results on ten benchmark data sets show that the proposed method outperforms other similar algorithms in prediction. Moreover, we apply the proposed method to solve the network intrusion detection problem, and the corresponding experimental results show that our method has better generalization ability than other existing solutions.
Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation

Monocular depth estimation is an undirected problem, so constructing a network to predict better image depth information is an important research topic.This paper proposes a mixed-scale Unet network (MAPNet) with a dense atrous pyramid based on the coder-decoder structure widely used in computer vision.We innovatively introduce the Unet++ structure of the image segmentation network for depth estimation.We reset the number of convolutional layers of the network under the framework of the Unet++ network and innovatively connect the decoders densely.Moreover, by choosing the appropriate size of the atrous radius, we form a dense atrous pyramid based on different feature layers to better connect the features in the deep and shallow layers of the network.To verify the effectiveness of the proposed algorithm, we tested the network on the KITTI public dataset and the NYU Depth V2 dataset.We compared it with the current state-of-the-art methods.The proposed method has higher accuracy and has steadily improved relative to the threshold of accuracy and root-mean-square error.We also conduct ablation studies, studies targeting the effectiveness of the network framework, and discussions on the convergence time and parameter complexity of the network.We will open-source the code at https://github.com
Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.
Efficient Analysis of Resource Availability for Cloud Computing Systems to Reduce SLA Violations

Resource availability of cloud computing systems is vital for today's information infrastructures. However, the constituent computing nodes are not fault free. The availability requirements on the delivered computing resources should be defined clearly by using a formal, contractual agreement, known as the Service Level Agreement (SLA), between providers and customers. To reduce the risk of various SLA violations, an efficient analysis of resource availability is important. This paper proposes a new analytical approach based on multi-valued decision diagrams (MDD) for the efficient resource availability analysis of cloud computing systems with heterogeneous, multi-state computing nodes. Particularly, a novel and efficient MDD construction method is presented to generate compact MDD models encoding different amounts of cumulative computing resources. Two detailed case studies are performed to illustrate basics and application of the proposed approach to reduce SLA violations and guarantee the availability requirements on the delivered computing resources. Benchmark studies are further conducted to show efficiency of the proposed MDD-based approach as compared with the continuous-time Markov chains-based method and the universal generation function-based method.
A Modified Random Forest Based on Kappa Measure and Binary Artificial Bee Colony Algorithm

Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest. To improve the accuracy of random forest, decision trees with larger degree of diversity and higher classification accuracy are selected for voting. In this paper, the RF based on Kappa measure and the improved binary artificial bee colony algorithm (IBABC) are proposed. Firstly, Kappa measure is used for pre-pruning, and the decision trees with larger degree of diversity are selected from the forest. Then, the crossover operator and leaping operator are applied in ABC, and the improved binary ABC is used for secondary pruning, and the decision trees with better performance are selected for voting. The proposed method (Kappa+IBABC) are tested on a quantity of UCI datasets. Computational results demonstrate that Kappa+IBABC improves the performance on most datasets with fewer decision trees. The Wilcoxon signed-rank test is used to verify the significant difference between the Kappa+IBABC method and other pruning methods. In addition, Chinese haze pollution is becoming more and more serious. This proposed method is used to predict haze weather and has achieved good results.
Security of reference-frame-independent quantum key distribution with source flaws

Recently, reference-frame-independent quantum key distribution (RFI-QKD) has attracted more attention, since no active calibration of the reference frame is required. Thus, it has potential advantages in practical applications, especially for QKD with a mobile-platform-based quantum channel. However, due to the imperfections of devices, practical security is still one of the main bottlenecks for the application of RFI-QKD. Although, based on ``qubit'' assumption, RFI-QKD with the inaccuracy of encoded state was analyzed and demonstrated, this assumption is hard to be guaranteed in practical situations, and some source flaws could not be considered. In this paper we evaluate the security of RFI-QKD with general source flaws, in which the qubit assumption is removed. Then, almost all the source flaws (not only the inaccuracy of encoded state, but also the side channel, the Trojan horse, and so on) can be considered together.