Reviewerly/exHarmony huggingface dataset:
Object Tracking on Satellite Videos: A Correlation Filter-Based Tracking Method With Trajectory Correction by Kalman Filter

Object tracking toward satellite videos faces various challenges, such as small size of the moving object, few texture, background similarities, etc. In this article, we propose a high-speed correlation filter (CF)-based tracker for object tracking on satellite videos. It takes advantage of the global motion characteristics of the moving target in satellite videos to constrain the tracking process, which is achieved by applying a Kalman filter (KF) to correct the tracking trajectory of the moving target. Thus, our tracker is named CFKF. Besides, a tracking confidence module is designed to pass information from the CF-based position detector to the KF-based trajectory corrector, and a constant optimized model update frequency is studied to speed up the tracker, as well as improve its performance. Furthermore, the target's orientation during the tracking process can be obtained by utilizing an orientation detector based on slope calculation. Experiments conducted on the satellite video datasets demonstrate that our tracker CFKF outperforms the other representative CF-based tracking methods in terms of accuracy and robustness and is also fast in speed.
Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

Deploying deep learning (DL) models across multiple compute devices to train large and complex models continues to grow in importance because of the demand for faster and more frequent training. Data parallelism (DP) is the most widely used parallelization strategy, but as the number of devices in data parallel training grows, so does the communication overhead between devices. Additionally, a larger aggregate batch size per step leads to statistical efficiency loss, i.e., a larger number of epochs are required to converge to a desired accuracy. These factors affect overall training time and beyond a certain number of devices, the speedup from DP scales poorly. This work explores hybrid parallelization, where each data parallel worker comprises more than one device to accelerate each training step by exploiting model parallelism. We show that at scale, hybrid training will be more effective at minimizing end-to-end training time than exploiting DP alone. We project that, for Inception-V3, GNMT, and BigLSTM, the hybrid strategy provides an end-to-end training speedup of at least 26.5%, 8%, and 22%, respectively, compared to what DP alone can achieve at scale.
Trinition the complex number with two imaginary parts: Fractal, chaos and fractional calculus

Human being live in three-dimensional space; they can accurately visualize processes taking place in one, two and three dimensions. Although the set of bi-complex numbers and quaternion have attracted attention of many researchers in physics and related branches, they do not really represent processes taking place in the space where human being are located. We suggested a new set of complex number called "the Trinition". The new set is comprised between complex number with one imaginary part and complex number with three imaginary parts called quaternion/bi-complex numbers. We established a bijection between the new set and the three-dimensional space. We presented some important properties of the new set. We showed that all chaotic attractors in three dimension are simply three-dimensional mapping in the new set. Fewer examples of mapping in such set were presented. A new methodology that can be used to obtain more strange attractors are equally suggested. The methodology combines fractional chaotic models and some fractal mapping within the new set. Some illustrative figures are presented.
Unsupervised Domain Adaptation to Improve Image Segmentation Quality Both in the Source and Target Domain

Domain adaptation is becoming more and more important with the advancing development of machine learning and the ever-increasing diversity of available data. The advancement of autonomous driving depends very much on progress in machine learning, which relies heavily on vast amounts of training data. It is well known that the performance of such models drops, as soon as the data used during inference stems from a different domain as the training data. To avoid the need to label a separate dataset for each new domain, e.g., each new camera sensor, methods for domain adaptation are necessary. Most interesting are unsupervised domain adaptation approaches since they do not require costly labels for the target domain. In this paper we adapt a known domain adaptation approach to work in an unsupervised fashion for semantic segmentation on high resolution data and provide some analysis of the learned representations. With our domain-adapted semantic segmentation we were able to achieve a significant 15 % absolute increase in mean intersection over union (mIoU), securing a surprisingly good 5th rank on the target domain KITTI test set without having used any KITTI labels during training. In addition to that, we even improved quality on the source domain data.
Shaping Belief States with Generative Environment Models for RL

When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive algorithm with an expressive generative model can form stable belief-states in visually rich and dynamic 3D environments. More precisely, we show that the learned representation captures the layout of the environment as well as the position and orientation of the agent. Our experiments show that the model substantially improves data-efficiency on a number of reinforcement learning (RL) tasks compared with strong model-free baseline agents. We find that predicting multiple steps into the future (overshooting), in combination with an expressive generative model, is critical for stable representations to emerge. In practice, using expressive generative models in RL is computationally expensive and we propose a scheme to reduce this computational burden, allowing us to build agents that are competitive with model-free baselines.
Surrogate-assisted classification-collaboration differential evolution for expensive constrained optimization problems

Expensive Constrained Optimization Problems (ECOPs) widely exist in various scientific and industrial applications. Surrogate-Assisted Evolutionary Algorithms (SAEAs) have recently exhibited great ability in solving these expensive optimization problems. This paper proposes a Surrogate-Assisted Classification-Collaboration Differential Evolution (SACCDE) algorithm for ECOPs with inequality constraints. In SACCDE, the current population is classified into two subpopulations based on certain feasibility rules, and a classification-collaboration mutation operation is designed to generate multiple promising mutant solutions by not only using promising information in good solutions but also fully exploiting potential information hidden in bad solutions. Afterwards, the surrogate is utilized to identify the most promising offspring solution for accelerating the convergence speed. Furthermore, considering that the population diversity may decrease due to the excessive incorporation of greedy information brought by the classified solutions, a global search framework that can adaptively adjust the classification-collaboration mutation operation based on the iterative information is introduced for achieving an effective global search. Therefore, the proposed algorithm can strike a well balance between local and global search. The experimental results of SACCDE and other state-of-the-art algorithms demonstrate that the performance of SACCDE is highly competitive.
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies

We study the relationship between the frequency of a function and the speed at which a neural network learns it. We build on recent results that show that the dynamics of overparameterized neural networks trained with gradient descent can be well approximated by a linear system. When normalized training data is uniformly distributed on a hypersphere, the eigenfunctions of this linear system are spherical harmonic functions. We derive the corresponding eigenvalues for each frequency after introducing a bias term in the model. This bias term had been omitted from the linear network model without significantly affecting previous theoretical results. However, we show theoretically and experimentally that a shallow neural network without bias cannot represent or learn simple, low frequency functions with odd frequencies. Our results lead to specific predictions of the time it will take a network to learn functions of varying frequency. These predictions match the empirical behavior of both shallow and deep networks.
Provably Secure and Generalized Signcryption With Public Verifiability for Secure Data Transmission Between Resource-Constrained IoT Devices

The Internet of Things (IoT) is revolutionizing our modern lives by introducing active connection between smart devices. However, IoT devices are repeatedly exhibiting many security flaws, which will inevitably lead to eavesdropping and impersonation attacks. Thus, providing a proper security in IoT becomes a prime focus for the researchers. In cryptography, certificateless signcryption (CLSC) is one of the recent public key techniques for the security requirements of the authenticity and confidentiality of any message between the parties. In this article, a new generalized CLSC (gCLSC) is introduced to provide the functions of digital signature and encryption to fulfill the authenticity and confidentiality for the resource-constrained IoT devices. Besides, the gCLSC supports the property of public verifiability and security of an ideal signcryption under the strong Diffie-Hellman and bilinear Diffie-Hellman inversion problems without random oracle model. Performance assessment of the gCLSC gives satisfactory results after comparing with other competitive CLSC schemes in terms of its functionality. Therefore, the gCLSC can be adopted in the IoT networks where authenticity, confidentiality, and lightweight are the essential factors.
Knowledge-based question answering by tree-to-sequence learning

In recent years, many knowledge bases have been constructed or populated. These knowledge bases link real-world entities by their relationships on a large scale, serving as good resources to answer factoid questions. To answer a natural language question using a knowledge base, the main task is mapping it to a structured query of the same meaning, whose results from the knowledge base will be used as the question's answers. This mapping task is non-trivial since different questions can express a same meaning and many queries can arise from a knowledge base. To fulfill the task, an important thing is to model a query's structure as it conveys a part of the meaning and affects word orders in the question. However, state-of-the-art methods based on deep learning have neglected query structures and focused only on capturing semantic correlations between a question and a simple relation chain. In this paper, we instead take a query as a tree, and encode the orders of entities and relations into its representations to better distinguish candidate queries of a given question. Overall, we first construct candidate queries of a given question from its linked entities, types and number operations. Then, we adopt an encoder-decoder framework which encodes a candidate query and decodes it into the given question, where the decoding probability is used to select the best query from candidates. During encoding, we propose a tree-based LSTM to properly model contexts of an entity or relation in a query, and one such tree-based LSTM runs from all leafs to the root while another runs reversely. During decoding, we propose to mix a generating mode and a referring mode to capture different levels of correlations between queries and questions. Experiments on two popular datasets demonstrate the effectiveness of both the tree-based encoder and the mixed-mode decoder.
Chinese postgraduate EFL learners’ self-directed use of mobile English learning resources

Despite the increasing ownership of mobile devices among Chinese postgraduate EFL learners, relevant studies regarding mobile English learning resources (MELR) use by postgraduate learners are still lacking. This study tries to understand the uses and the motivation behind language learners´ selection of MALL resources. In this research, 95 Chinese postgraduate students from four university levels took part in a questionnaire, and eight of them in an interview. The results show that “passing exams” was the top reason for using MELR and expanding one’s English vocabulary was the aspect learners aimed to improve. The portability of mobile devices enabled learners to use MELR during short time intervals11 In this study, “short time intervals” is the opposite of “a long period”, usually referring to a small window of time in people’s daily lives (e.g., waiting for a bus and queueing)., which suggests that MALL applications should target this behaviour. However, as a type of supplementary material, few students used MELR for more than one hour per day, and they were not regularly and actively involved in using MELR. Few learners were able to select suitable MELR to meet their current English learning needs, and they relied on recommendations from social media and authoritative education experts. Due to the importance of vocabulary, mobile dictionaries and vocabulary learning applications were the learners’ favourite type of MELR. As the participants suggested, enjoyment and interactivity were the two driving factors behind MELR selection and use. On the basis of Framework for the Rational Analysis of Mobile Education (FRAME, Koole, M. (2009). A model for framing mobile learning. In M. Ally (Ed.), Mobile learning: Transforming the delivery of education and training. Athabasca: Athabasca University Press, 25–47) and Technology Acceptance Model (TAM [Davis, F. D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems, 13, 319–339]; TAM 2 [Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204]), a new theoretical model for better understanding the complex nature of mobile-assisted language learning (MALL) and the importance of learners in the self-directed, non-formal English language learning setting is proposed in this study.
Hand Gesture Recognition with Convolution Neural Networks

Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications. Various computer vision algorithms have employed color and depth camera for hand gesture recognition, but robust classification of gestures from different subjects is still challenging. I propose an algorithm for real-time hand gesture recognition using convolutional neural networks (CNNs). The proposed CNN achieves an average accuracy of 98.76% on the dataset comprising of 9 hand gestures and 500 images for each gesture.
Adversarial training based lattice LSTM for Chinese clinical named entity recognition

Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chinese CNER. However, these models do not make full use of the information in EHR, for these models are either word-based or character-based. In addition, neural models tend to be locally unstable and even tiny perturbation may mislead them. In this paper, we firstly propose a novel adversarial training based lattice LSTM with a conditional random field layer (AT-lattice LSTM-CRF) for Chinese CNER. Lattice LSTM is used to capture richer information in EHR. As a powerful regularization method, AT can be used to improve the robustness of neural models by adding perturbations to the training data. Then, we conduct experiments on the proposed neural model with dataset of CCKS-2017 Task 2. The results show that the proposed model achieves a highly competitive performance (with an F1 score of 89.64%) compared to other prevalent neural models, which can be a reinforced baseline for further research in this field.
Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images

Nuclei segmentation in histopathology images plays a crucial role in the morphological quantitative analysis of tissue structure and has become a hot research topic. Though numerous efforts have been tried in this research area, the overlapping and touching nuclei segmentation remains a challenging problem. In this paper, we present a novel and effective instance segmentation method for tackling this challenge by integrating Deep Convolutional Neural Networks with Marker-controlled Watershed. Firstly, we design a novel network architecture with multiple segmentation tasks, called Deep Interval-Marker-Aware Network, for learning the foreground, marker, and interval of nuclei, simultaneously. Then the learned interval between overlapping nuclei is used to refine the foreground result of nuclei by using the logical operators. Finally, the learned marker result and the nuclei segmentation result refined by interval are transmitted into the Marker-controlled Watershed for splitting the touching nuclei. The experiments on the standard public datasets demonstrate that our method achieves a substantial improvement compared with state-of-the-art methods. Source codes are available at https://github.com/appiek/Nuclei_Segmentation_Experiments_Demo.
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.
Scalable Neural Architecture Search for 3D Medical Image Segmentation

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space. Our NAS framework searches the structure of each layer including neural connectivities and operation types in both of the encoder and decoder. Since optimizing over a large discrete architecture space is difficult due to high-resolution 3D medical images, a novel stochastic sampling algorithm based on a continuous relaxation is also proposed for scalable gradient based optimization. On the 3D medical image segmentation tasks with a benchmark dataset, an automatically designed architecture by the proposed NAS framework outperforms the human-designed 3D U-Net, and moreover this optimized architecture is well suited to be transferred for different tasks.
Ensuring electronic medical record simulation through better training, modeling, and evaluation

Abstract Objective Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that (1) enhances the learning model, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process. Materials and Methods We propose a new electronic health record generator using a GAN with a Wasserstein divergence and layer normalization techniques. We designed 2 utility measures to characterize similarity in the structural properties of real and simulated EMRs in the original and latent space, respectively. We applied a filtering strategy to enhance GAN training for low-prevalence clinical concepts. We evaluated the new and existing GANs with utility and privacy measures (membership and disclosure attacks) using billing codes from over 1 million EMRs at Vanderbilt University Medical Center. Results The proposed model outperformed the state-of-the-art approaches with significant improvement in retaining the nature of real records, including prediction performance and structural properties, without sacrificing privacy. Additionally, the filtering strategy achieved higher utility when the EMR training dataset was small. Conclusions These findings illustrate that EMR simulation through GANs can be substantially improved through more appropriate training, modeling, and evaluation criteria.
SceneCode: Monocular Dense Semantic Reconstruction Using Learned Encoded Scene Representations

Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities. However, while there has been much work on the correct formulation for geometrical estimation, state-of-the-art systems usually rely on simple semantic representations which store and update independent label estimates for each surface element (depth pixels, surfels, or voxels). Spatial correlation is discarded, and fused label maps are incoherent and noisy. We introduce a new compact and optimisable semantic representation by training a variational auto-encoder that is conditioned on a colour image. Using this learned latent space, we can tackle semantic label fusion by jointly optimising the low-dimenional codes associated with each of a set of overlapping images, producing consistent fused label maps which preserve spatial correlation. We also show how this approach can be used within a monocular keyframe based semantic mapping system where a similar code approach is used for geometry. The probabilistic formulation allows a flexible formulation where we can jointly estimate motion, geometry and semantics in a unified optimisation.