Reviewerly/exHarmony huggingface dataset:
Color image steganalysis based on channel gradient correlation

It is one of the potential threats to the Internet of Things to reveal confidential messages by color image steganography. The existing color image steganalysis algorithm based on channel geometric transformation measures owns higher accuracy than the others, but it fails to utilize the correlation between the gradient amplitudes of different color channels. Therefore, this article points out that the color image steganography weakens the correlation between the gradient amplitudes of different color channels and proposes a color image steganalysis algorithm based on channel gradient correlation. The proposed algorithm extracts the co-occurrence matrix feature from the gradient amplitude residuals among different color channels and then combines it with the existing color image steganalysis features to train the ensemble classifier for color image steganalysis. The experimental results show that, for WOW and S-UNIWARD steganography, compared with the existing algorithms, the proposed algorithm outperforms the existing algorithms.
Tag-aware recommendation based on Bayesian personalized ranking and feature mapping

Collaborative filtering recommendation with implicit feedbacks (i.e., clicks, views, check-ins) has been gaining increasing attention in various real applications. Tagging information is the common resource to complement implicit feedbacks to assist collaborative filtering recommendation. However, existing tag-aware recommendation methods still suffer from the problem of high dimension and sparsity of tagging information. They also fail to realize that recommendation is inherent a ranking-oriented optimization task. To this end, we propose a novel tag-aware recommendation framework by incorporating tag mapping scheme into ranking-based collaborative filtering model, to boost ranking-oriented personalized recommendation performance. We first build ranking-oriented optimization model based on Bayesian personalized ranking optimization criterion with matrix factorization, by leveraging implicit feedbacks to learn the latent feature vectors of users and items. Then, we propose an explicit-to-implicit feature mapping scheme, mapping the high-dimensional and sparse explicit tags (i.e., user-tag weighting matrix and item-tag weighting matrix) to low-dimensional and compact implicit features of uses and items. This could serve as the regularization constraint of latent features derived from implicit feedbacks. To further enhance recommendation performance, we also introduce users' neighbor relationships to regularize user latent features based on manifold learning. Experiments on real-world recommendation datasets show that the proposed recommendation method outperformed competing methods on ranking-oriented recommendation performance.
Joint Multi-Label Attention Networks for Social Text Annotation

Hang Dong, Wei Wang, Kaizhu Huang, Frans Coenen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Adaptive Convolution for Text Classification

Byung-Ju Choi, Jun-Hyung Park, SangKeun Lee. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A 1.17 TOPS/W, 150fps Accelerator for Multi-Face Detection and Alignment

Face detection and alignment are highly-correlated, computation-intensive tasks, without being flexibly supported by any facial-oriented accelerator yet. This work proposes the first unified accelerator for multi-face detection and alignment, along with the optimizations on multi-task cascaded convolutional networks algorithm, to implement both multi-face detection and alignment. First, the clustering non-maximum suppression is proposed to significantly reduce intersection over union computation and eliminate the hardware-interfer-ence sorting process, bringing 16.0% speed-up without any loss. Second, a new pipeline architecture is presented to implement the proposal network in more computation-efficient manner, with 41.7% less multiplier usage and 38.3% decrease in memory capacity compared with the similar method. Third, a batch schedule mechanism is proposed to improve hardware utilization of fully-connected layer by 16.7% on average with variable input number in batch process. Based on the TSMC 28 nm CMOS process, this accelerator only consumes 6.7ms at 400 MHz to simultaneously process 5 faces for each image and achieves 1.17 TOPS/W power efficiency, which is 54.8× higher than the state-of-the-art solution.
An object counting network based on hierarchical context and feature fusion

Object counting is a challenging task in computer vision. In this paper, we propose an object counting network based on hierarchical context and feature fusion called HFNet. HFNet comprises a hierarchical context extraction module and an end-to-end convolution neural network. The hierarchical context extraction module extracts hierarchical features to the main network as context cues, aiming to provide more information to improve counting performance. The main network adds the relatively lower but naturally high-resolution feature maps into higher but semantic feature maps, whose benefits are: one is to reduce the risk of losing detailed information during multi-convolutions; the other is to against the scale variations in this task due to the fusion operation of the multi-scale feature maps. Experiments demonstrate HFNet achieves competitive results on crowd counting including UCF_CC_50 dataset and ShanghaiTech dataset and on vehicle counting including TRANCOS dataset. The contrast experiments also verify the structure rationality of HFNet.
Ontogenetic scaling patterns of lizard skin surface structure as revealed by gel‐based stereo‐profilometry

Abstract The skin surface structure of squamate reptiles varies greatly among species, likely because it plays a key role in a range of tasks, such as camouflage, locomotion, self‐cleaning, mitigation of water loss and protection from physical damage. Although we have foundational knowledge about squamate skin morphology, we still know remarkably little about how intraspecific variation in skin surface structure translates to functional variation. This gap in our understanding can be in part traced back to: (i) our lack of knowledge on how body size determines skin surface structure; and (ii) the lack of means to perform high‐throughput and detailed analysis of the three‐dimensional (3D) anatomy of reptilian skin surfaces in a non‐destructive manner. To fill this gap, we explored the possibilities of a new imaging technique, termed gel‐based stereo‐profilometry, to visualize and quantify the 3D topography of reptilian skin surface structure. Using this novel approach, we investigated intra‐specific and intra‐individual variation in the skin surface morphology of a focal lizard species, Anolis cristatellus . We assessed how various characteristics of surface topography (roughness, skew and kurtosis) and scale morphology (area, height, width and shape) scale with body size across different body regions. Based on an ontogenetic series of A. cristatellus males, we show that skin roughness increases with body size. Skin patches on the ventral body region of lizards were rougher than on the dorsum, but this was a consequence of ventral scales being larger than dorsal scales. Dorsal surface skew and kurtosis varied with body size, but surfaces on the ventral skin showed no such relationship. Scale size scaled isometrically with body size, and while ventral scales differed in shape from dorsal scales, scale shape did not change with ontogeny. Overall, this study demonstrates that gel‐based stereo‐profilometry is a promising method to rapidly assess the 3D surface structure of reptilian skin at the microscopic level. Additionally, our findings of the explanatory power of body size on skin surface diversity provide a foundation for future studies to disentangle the relationships among morphological, functional and ecological diversity in squamate reptile skin surfaces.
CoreTrustSeal: From academic collaboration to sustainable services

National and international digital repositories must design and deliver sustainable services as a foundation for a range of scientific and data management infrastructures while reducing costs and avoiding duplication of effort. The CoreTrustSeal, launched in 2017, defines requirements and offers core level certification for Trustworthy Digital Repositories (TDR) holding data for long-term preservation. This paper traces the journey of the CoreTrustSeal through the Data Seal of Approval (DSA), ICSU World Data System (WDS), Research Data Alliance (RDA) working groups, and community engagement, towards becoming a sustainable service supporting global data infrastructure. We outline the design and delivery of the service, current activities, the benefits of certification to a range of communities, and future plans and challenges. As well as providing a historical narrative and current and future perspectives the CoreTrustSeal experience offers lessons for those developing standards and best practices, or seeking to develop cooperative and community-driven efforts which bridge data curation across academic disciplines and the governmental and private sectors.
MCPL-Based FT-LSTM: Medical Representation Learning-Based Clinical Prediction Model for Time Series Events

Large collections of electronic medical records (EMRs) provide us with a vast source of information on medical practice. However, the utilization of these data to support clinical decisions is still limited. Extracting useful patterns from such data is particularly challenging because the data are variable longitudinal, sparse, and heterogeneous. Therefore, in this paper, we propose the MCPL-based FT-LSTM, a clinical event prediction method based on medical concept representation learning. On one hand, inspired by FASTTEXT, we have developed an interpretative vector representation of medical events in EMRs, which enables us to capture the medical concept information effectively so that the patient's clinical data can be represented more reasonably. On the other hand, we propose a novel time-controlled long short-term memory (LSTM) prediction model, which adds time-control units to the original LSTM model. The model can describe the variable time intervals in EMRs, better capture long-term, and short-term information, and eliminate the strong dependence of clinical data on timestamps; thus, improving the model's prediction performance for clinical events. Through extensive experiments on the MIMICIII dataset, we demonstrate that the MCPL-based FT-LSTM achieves higher precision in the field of clinical event prediction, which is of great significance for the medical information research.
Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling.

Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either do not contribute or negatively impact the DNN's optimization. Modifying the training distribution in a way that excludes such samples could provide an effective solution to both improve performance and reduce training time. In this paper, we propose to scale up ensemble Active Learning (AL) methods to perform acquisition at a large scale (10k to 500k samples at a time). We do this with ensembles of hundreds of models, obtained at a minimal computational cost by reusing intermediate training checkpoints. This allows us to automatically and efficiently perform a training data subset search for large labeled datasets. We observe that our approach obtains favorable subsets of training data, which can be used to train more accurate DNNs than training with the entire dataset. We perform an extensive experimental study of this phenomenon on three image classification benchmarks (CIFAR-10, CIFAR-100 and ImageNet), as well as an internal object detection benchmark for prototyping perception models for autonomous driving. Unlike existing studies, our experiments on object detection are at the scale required for production-ready autonomous driving systems. We provide insights on the impact of different initialization schemes, acquisition functions and ensemble configurations at this scale. Our results provide strong empirical evidence that optimizing the training data distribution can provide significant benefits on large scale vision tasks.
Moving Target Detection and Tracking Algorithm Based on Context Information

To improve the robustness of target tracking algorithms in a complex environment, this paper proposes the moving target detection and tracking algorithm based on context information and closed-loop learning.A context region is composed of the target region and its current neighboring background.For every frame that follows from a video stream, the long-term tracking task is principally decomposed into four parts of synchronous operation: tracking, detection, integration, and learning.First, the tracker obtains the posterior probability of the target location and estimates the target state over succeeding frames by exploiting the spatio-temporal local information.Meanwhile, the detector searches for the target in independent frames combining with the context information of tracker, and automatically reinitialize the tracker when it fails.Then, the integrator attains the best location of the target by merging the output results of tracker and detector together through an optimal strategy.Finally, the learning process is designed as the feedback and generates training samples to update the detector according to the results of tracker and detector.Experimentally, we evaluate the performance against several latest techniques on various benchmarks, and the results demonstrate that the proposed algorithm performs remarkably in terms of robustness and tracking accuracy.
Quantum renewal processes

Abstract We introduce a general construction of master equations with memory kernel whose solutions are given by completely positive trace-preserving maps. These dynamics going beyond the Lindblad paradigm are obtained with reference to classical renewal processes, so that they are termed quantum renewal processes. They can be described by means of semigroup dynamics interrupted by jumps, separated by independently distributed time intervals, following suitable waiting time distributions. In this framework, one can further introduce modified processes, in which the first few events follow different distributions. A crucial role, marking an important difference with respect to the classical case, is played by operator ordering. Indeed, for the same choice of basic quantum transformations, different quantum dynamics arise. In particular, for the case of modified processes, it is natural to consider the time inverted operator ordering, in which the last few events are distributed differently.
Hybrid variational model based on alternating direction method for image restoration

The total variation model is widely used in image deblurring and denoising process with the features of protecting the image edge. However, this model usually causes some staircase effects. To overcome the shortcoming, combining the second-order total variation regularization and the total variation regularization, we propose a hybrid total variation model. The new improved model not only eliminates the staircase effect, but also well protects the edges of the image. The alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Numerical results show that our proposed model can get more details and higher image visual quality than some current state-of-the-art methods.
TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH

Abstract. We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot.
Logistic local hyperplane-Relief: A feature weighting method for classification

Relief-based algorithms have been widely used for feature selection because of their low computational cost and high accuracy. However, the available Relief-based algorithms have their limitations. To improve the performance of Relief-based methods further, we propose a novel feature selection algorithm based on the logistic iterative-Relief (LI-Relief) and local hyperplane-Relief (LH-Relief) methods, called logistic local hyperplane-based Relief (LLH-Relief). LLH-Relief uses local learning to find neighbor representations for given samples and learns feature weights by solving the optimization problem with logistic regression and the ℓ1-norm regularization terms. To demonstrate the validity and the effectiveness of LLH-Relief for feature selection in supervised learning, we perform extensive experiments on toy and real-world datasets. Experimental results indicate that LLH-Relief is very promising.
Data of SemanticWeb as Unit of Knowledge

In service to the state of the art, advances are required toward redesigning the framework over which web applications are built. The semantic web lies at the intersection of web and machine understandable meaningful data, turning it into intelligent ‘web of data’. The key requirement with any intelligent system has been to find a concrete knowledge representation that can make the inferences within time and space constraints; that is, reasoning effectively and efficiently within the resource constraints posed to the problem at one hand and with insufficient data as well as incomplete knowledge on the other hand. Various Knowledge representation schemes have been proposed in the literature, each having its limitation over the others. Ontology is the key component for semantic web engineering. Ontologies are conceptual knowledge bases providing a systematic and taxonomical description of the concepts and instances under consideration. Conceptual clarity in the computational representation of a concept is vital for holistic thinking and knowledge engineering. In order to meet the needs of an application/enterprise, knowledge should be presented taking care of all possible perspectives; and represented in a hierarchical structure with differing levels of granularity. This paper discusses about bringing all the manifestations of an ontological concept/decision under one umbrella; hence describing the resultant scheme as a unit of knowledge. By representing a concept as a knowledge unit, classical ontology is claimed to be sufficient in dealing with imprecise, vague and heterogeneous knowledge for real-world web applications; and portrays the capability to acquire fresh knowledge through its thorough interaction with the external world in a given working environment.
Detecting new signals under background mismodeling

Searches for new astrophysical phenomena often involve several sources of nonrandom uncertainties that can lead to highly misleading results. Among these, model uncertainty arising from background mismodeling can dramatically compromise the sensitivity of the experiment under study. Specifically, overestimating the background distribution in the signal region increases the chances of missing new physics. Conversely, underestimating the background outside the signal region leads to an artificially enhanced sensitivity and a higher likelihood of claiming false discoveries. The aim of this work is to provide a unified statistical strategy to perform modeling, estimation, inference, and signal characterization under background mismodeling. The method proposed allows one to incorporate the (partial) scientific knowledge available on the background distribution and provides a data-updated version of it in a purely nonparametric fashion without requiring the specification of prior distributions on the unknown parameters. Applications in the context of dark matter searches and radio surveys show how the tools presented in this article can be used to incorporate nonstochastic uncertainty due to instrumental noise and to overcome violations of classical distributional assumptions in stacking experiments.
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study

Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks.However, there is no guarantee that they can always generate sentences without grammatical errors.In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation.We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification.Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring taskoriented improvements in the tasks where target sentences are in a formal style.
Against Reflective Equilibrium for Logical Theorizing

I distinguish two ways of developing anti-exceptionalist approaches to logical revision. The first emphasizes comparing the theoretical virtuousness of developed bodies of logical theories, such as classical and intuitionistic logic. I'll call this whole theory comparison. The second attempts local repairs to problematic bits of our logical theories, such as dropping excluded middle (and modifying elsewhere accordingly) to deal with intuitions about vagueness. I'll call this the piecemeal approach. I then briefly discuss a problem I've developed elsewhere for comparisons of logical theories. Essentially, the problem is that a pair of logics may each evaluate the alternative as superior to themselves, resulting in oscillation between logical options. The piecemeal approach offers a way out of this problem andthereby might seem a preferable to whole theory comparisons. I go on to show that reflective equilibrium, the best known piecemeal method, has deep problems of its own when applied to logic.