Reviewerly/exHarmony huggingface dataset:
Galaxy morphology classification with deep convolutional neural networks

We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), is applied to a sample of 28790 galaxy images from the Galaxy Zoo 2 dataset, to classify galaxies into five classes, i.e., completely round smooth, in-between smooth (between completely round and cigar-shaped), cigar-shaped smooth, edge-on and spiral. Various metrics, such as accuracy, precision, recall, F1 value and AUC, show that the proposed network achieves state-of-the-art classification performance among other networks, namely, Dieleman, AlexNet, VGG, Inception and ResNets. The overall classification accuracy of our network on the testing set is 95.2083% and the accuracy of each type is given as follows: completely round, 96.6785%; in-between, 94.4238%; cigar-shaped, 58.6207%; edge-on, 94.3590% and spiral, 97.6953%. Our model algorithm can be applied to large-scale galaxy classification in forthcoming surveys, such as the Large Synoptic Survey Telescope (LSST) survey.
Adaptive Image-Based Visual Servoing With Temporary Loss of the Visual Signal

Image-based visual servoing (IBVS) can reach a desired position for a relatively stationary target using continuous visual feedback. Proper feature extraction and appropriate servoing control laws are essential to performance for IBVS. IBVS control can be interrupted or interfered abruptly if no features are extracted when the observed object is occluded. To address the problem of missing feature points in current images during a visual navigation task, a homography method that uses a priori visual information is proposed to predict all of the missing feature points and to ensure the execution of IBVS. The mixture parameter for the image Jacobian matrix can also affect the control of IBVS. The settings for the mixture parameter are heuristic so there is no a systematic approach for most IBVS applications. An adaptive control approach is proposed to determine the mixture parameter. The proposed method uses a reinforcement learning (RL) method to adaptively adjust the mixture parameter during the robot movement, which allows more efficient control than a constant parameter. A logarithmic interval state-space partition for RL is used to ensure efficient learning. The integrated visual servoing control system is validated by several experiments that involve wheeled mobile robots reaching a target with a desired configuration. The results for simulation and experiment demonstrate that the proposed method has a faster convergence rate than other methods.
Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters

Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.
Asymmetric color cryptosystem using chaotic Ushiki map and equal modulus decomposition in fractional Fourier transform domains

A single channel optical asymmetric cryptosystem for color image in fractional Fourier transform (FrFT) domain is presented. Instead of commonly used RGB channels encryption system, the color image is encoded into grayscale format and encrypted in a single optical channel system. For the design of asymmetric approach, the effective trapdoor one-way function is calculated by employing equal modulus decomposition (EMD). To enhance the security of the proposed cryptosystem, the Ushiki chaotic system is performed to generate the random phase mask in FrFT and a random sequence for scrambling the private key. The sensitive initial values and chaotic data can be regarded as the additional keys and public key. Various numerical experiments are given to verify the validity and capability of the proposed color cryptosystem.
Quantum coherence of multiqubit states in correlated noisy channels

The long-time maintenance of quantum coherence is crucial for its practical applications. We explore decoherence process of a multiqubit system passing through a correlated channel (phase flip, bit flip, bit-phase flip, and depolarizing). The results show that the decay of coherence was evidently delayed when the consecutive actions of the channel on the sequence of qubits has some classical correlations. In particular, the relative entropy of coherence for a system with large number of qubits is more robust than that with small number of qubits. We also provide an explanation for the delayed decoherence by exploring the interplay between the change of the unlocalized quantum coherence and the total correlation gain of the multiqubit system.
MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data

Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC’17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC’17 dataset when compared with start-of-the-art methods.
Mining Likely Analogical APIs Across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding

Establishing API mappings between third-party libraries is a prerequisite step for library migration tasks. Manually establishing API mappings is tedious due to the large number of APIs to be examined. Having an automatic technique to create a database of likely API mappings can significantly ease the task. Unfortunately, existing techniques either adopt supervised learning mechanism that requires already-ported or functionality similar applications across major programming languages or platforms, which are difficult to come by for an arbitrary pair of third-party libraries, or cannot deal with lexical gap in the API descriptions of different libraries. To overcome these limitations, we present an unsupervised deep learning based approach to embed both API usage semantics and API description (name and document) semantics into vector space for inferring likely analogical API mappings between libraries. Based on deep learning models trained using tens of millions of API call sequences, method names and comments of 2.8 millions of methods from 135,127 GitHub projects, our approach significantly outperforms other deep learning or traditional information retrieval (IR) methods for inferring likely analogical APIs. We implement a proof-of-concept website (https://similarapi.appspot.com) which can recommend analogical APIs for 583,501 APIs of 111 pairs of analogical Java libraries with diverse functionalities. This scale of third-party analogical-API database has never been achieved before.
A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Bubble segmentation is the most popular method for bubble size measurement. However, due to the complexity of the froth image, the present image segmentation methods cannot get a satisfactory result. In this paper, a watershed segmentation algorithm with an optimal marker is proposed. The marker extraction method is based on the sub-images, and thus an improved sub-image classification model is built. To reduce the under-segmentation, a lumped together marker checking method is also developed based on the skeletons. Then, the optimal marker is obtained by the data fusions with the three marker regions, the light reflection character of the bubble and the class information of sub-images. The industrial experiments show the effectiveness of the proposed method, in which the accuracy is improved by 11.44% and 9.10%, and the robustness is improved by 41.48% and 57.95%, respectively, compared with the two other present methods.
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results

Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8, 599 images (6, 471 for training, 548 for validation, and 1, 580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (e.g., machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone-DET2018 challenge and the comparison results of 38 detectors on the released dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/ . We expect the challenge to largely boost the research and development in object detection in images on drone platforms.
A Differential Approach for Gaze Estimation

Most non-invasive gaze estimation methods regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as subject dependent biases. Thus, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to her actual gaze. In this article, we introduce a novel approach, which works by directly training a differential convolutional neural network to predict gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by comparing eye images of the same user, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Furthermore, the differential network itself can be adapted via finetuning to make predictions consistent with the available user reference pairs. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or those relying on subject specific gaze adaptation.
Investigating End-to-end Speech Recognition for Mandarin-english Code-switching

Code-switching is a common phenomenon in many multilingual communities and presents a challenge to automatic speech recognition (ASR). In this paper, three approaches are investigated to improve end-to-end speech recognition on Mandarin-English code-switching task. First, multi-task learning (MTL) is introduced which enables the language identity information to facilitate Mandarin-English code-switching ASR. Second, we explore wordpieces, as opposed to graphemes, as English modeling units to reduce the mod-eling unit gap between Mandarin and English. Third, we employ transfer learning to utilize larger amount of monolingual Mandarin and English data to compensate the data sparsity issue of a code-switching task. Significant improvements are observed from all three approaches. With all three approaches combined, the final system achieves a character error rate (CER) of 6.49% on a real Mandarin-English code-switching task.
Hierarchical Transfer Learning for Multi-label Text Classification

Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy.MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category.We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child category classification task.In HTrans, we use a Gated Recurrent Unit (GRU)-based deep learning architecture coupled with attention.Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1% on micro-F1 and 3% on macro-F1 on the RCV1 dataset.Our experiments also show that binary classifiers trained from scratch are significantly better than single multi-label models.
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel

Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When dealing with online or large data, existing batch SVDD methods have to be rerun in each iteration. We propose an incremental learning algorithm for SVDD that uses the Gaussian kernel. This algorithm builds on the observation that all support vectors on the boundary have the same distance to the center of sphere in a higher-dimensional feature space as mapped by the Gaussian kernel function. Each iteration involves only the existing support vectors and the new data point. Moreover, the algorithm is based solely on matrix manipulations; the support vectors and their corresponding Lagrange multiplier αi’s are automatically selected and determined in each iteration. It can be seen that the complexity of our algorithm in each iteration is only O(k2), where k is the number of support vectors. Experimental results on some real data sets indicate that FISVDD demonstrates significant gains in efficiency with almost no loss in either outlier detection accuracy or objective function value.
Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism

Learning user’s preference from check-in data is important for POI recommendation. Yet, a user usually has visited some POIs while most of POIs are unvisited (i.e., negative samples). To leverage these “no-behavior” POIs, a typical approach is pairwise ranking, which constructs ranking pairs for the user and POIs. Although this approach is generally effective, the negative samples in ranking pairs are obtained randomly, which may fail to leverage “critical” negative samples in the model training. On the other hand, previous studies also utilized geographical feature to improve the recommendation quality. Nevertheless, most of previous works did not exploit geographical information comprehensively, which may also affect the performance. To alleviate these issues, we propose a geographical information based adversarial learning model (Geo-ALM), which can be viewed as a fusion of geographic features and generative adversarial networks. Its core idea is to learn the discriminator and generator interactively, by exploiting two granularity of geographic features (i.e., region and POI features). Experimental results show that Geo- ALM can achieve competitive performance, compared to several state-of-the-arts.
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototype propagation network (PPN) trained on few-shot tasks together with data annotated by coarse-label. Given a category graph of the targeted fine-classes and some weakly-labeled coarse-classes, PPN learns an attention mechanism which propagates the prototype of one class to another on the graph, so that the K-nearest neighbor (KNN) classifier defined on the propagated prototypes results in high accuracy across different few-shot tasks. The training tasks are generated by subgraph sampling, and the training objective is obtained by accumulating the level-wise classification loss on the subgraph. On two benchmarks, PPN significantly outperforms most recent few-shot learning methods in different settings, even when they are also allowed to train on weakly-labeled data.
An End-to-End Architecture for Class-Incremental Object Detection with Knowledge Distillation

Recent efforts have been made on continuously learning concepts over time, i.e., class-incremental learning, which, however, is still an open question to the task of object detection. For the first time, this paper systematically studies the task of learning an end-to-end class-incremental object detection model. Remarkably, this work tailors Faster R-CNN and proposes a Class-Incremental Faster R-CNN (CIFRCN) model that can dynamically add new classes by only using a few labeled images of new objects. Particularly, the domain of "foreground" in a Region Proposal Network is expanded to generate accurate bounding box proposals, and the classifier in Fast R-CNN is enlarged using knowledge distillation to accurately classify the proposals. Extensive experiments on two challenging datasets are conducted to demonstrate the efficacy of the proposed model for adding new objects in a class-incremental way, whereas other detectors quickly fail.
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning

Before sufficient training data is available, fine-tuning neural networks pre-trained on large-scale datasets substantially outperforms training from random initialization. However, fine-tuning methods suffer from two dilemmas, catastrophic forgetting and negative transfer. While several methods with explicit attempts to overcome catastrophic forgetting have been proposed, negative transfer is rarely delved into. In this paper, we launch an in-depth empirical investigation into negative transfer in fine-tuning and find that, for the weight parameters and feature representations, transferability of their spectral components is diverse. For safe transfer learning, we present Batch Spectral Shrinkage (BSS), a novel regularization approach to penalizing smaller singular values so that untransferable spectral components are suppressed. BSS is orthogonal to existing fine-tuning methods and is readily pluggable to them. Experimental results show that BSS can significantly enhance the performance of representative methods, especially with limited training data.
Tac-Simur: Tactic-based Simulative Visual Analytics of Table Tennis

Simulative analysis in competitive sports can provide prospective insights, which can help improve the performance of players in future matches. However, adequately simulating the complex competition process and effectively explaining the simulation result to domain experts are typically challenging. This work presents a design study to address these challenges in table tennis. We propose a well-established hybrid second-order Markov chain model to characterize and simulate the competition process in table tennis. Compared with existing methods, our approach is the first to support the effective simulation of tactics, which represent high-level competition strategies in table tennis. Furthermore, we introduce a visual analytics system called Tac-Simur based on the proposed model for simulative visual analytics. Tac-Simur enables users to easily navigate different players and their tactics based on their respective performance in matches to identify the player and the tactics of interest for further analysis. Then, users can utilize the system to interactively explore diverse simulation tasks and visually explain the simulation results. The effectiveness and usefulness of this work are demonstrated by two case studies, in which domain experts utilize Tac-Simur to find interesting and valuable insights. The domain experts also provide positive feedback on the usability of Tac-Simur. Our work can be extended to other similar sports such as tennis and badminton.