Reviewerly/exHarmony huggingface dataset:
SAR Image Segmentation Using Hierarchical Region Merging With Orientated Edge Strength Weighted Kuiper’s Distance

In this paper, a hierarchical region merging method is proposed for partitioning synthetic aperture radar (SAR) image into un-overlapping scene area, such as forest regions, urban regions, agricultural regions, and so on.The proposed method mainly consists of two steps: initial over-segmentation and hierarchical regions merging.The over-segmentation uses the watershed transform to the thresholded Bhattacharyya-coefficient-based edge strength map (BESM), and the hierarchical regions merging applies a new region merging cost weighted by a gradually increasing orientated edge strength penalty.There is a defect that the ratio-based edge detector widely used in homogeneous SAR image fails to distinguish the transitions between uniform and texture regions in high spatial resolution SAR image, and yields an initial over-segmentation result with some regions straddling multiple uniform or texture areas.To overcome this, the Bhattacharyya coefficient is used to replace the ratio-based edge detector for extracting the ESM of a SAR image by using a bi-rectangle-window configuration.Multi-scale windows are utilized to capture additional edge information.A new region merging cost is proposed based on the Kuiper's distance, weighted by a new gradually increasing orientated edge strength penalty term.The hierarchical region merging criterion is obtained with the increasing of the strength of the edge penalty.The effectiveness of the proposed method is demonstrated by comparing it qualitatively and quantitatively with several state-of-the-art methods.
Coverless Image Steganography Method Based on Feature Selection

A new information hiding technology named coverless information hiding is proposed. It uses original natural images as stego images to represent secret information. The focus of coverless image steganography method is how to ... | Find, read and cite all the research you need on Tech Science Press
Question Generator System of Sentence Completion in TOEFL Using NLP and K-Nearest Neighbor

Test of English as a Foreign Language (TOEFL) is one of learning evaluation forms that requires excellent quality of questions. Preparing TOEFL questions using a conventional way certainly spends a lot of time. Computer technology can be used to solve the problem. Therefore, this research was conducted in order to solve the problem of making TOEFL questions with sentence completion type. The built system consists of several stages: (1) input data collection from foreign media news sites with excellent English grammar quality; (2) preprocessing with Natural Language Processing (NLP); (3) Part of Speech (POS) tagging; (4) question feature extraction; (5) separation and selection of news sentences; (6) determination and value collection of seven features; (7) conversion of categorical data value; (8) target classification of blank position word with K-Nearest Neighbor (KNN); (9) heuristic determination of rules from human experts; and (10) options selection or distraction based on heuristic rules. After conducting the experiment on 10 news, it is obtained that 20 questions based on the results of the evaluation showed that the generated questions had a very good quality with percentage of 81.93% (after the assessment by the human expert), and 70% was the same blank position from the historical data of TOEFL questions. So, it can be concluded that the generated question has the following characteristics: the quality of the result follows the data training from the historical TOEFL questions, and the quality of the distraction is very good because it is derived from the heuristics of human experts.
Robust Image Colorization Using Self Attention Based Progressive Generative Adversarial Network

Automatic image colorization is a very interesting computer graphics problem wherein an input grayscale image is transformed into its RGB domain. However, it is an ill-posed problem as there can be multiple RGB outcomes for a particular grayscale pixel. The problem further complicates if noise is present in the grayscale image. In this paper, we propose a Robust Image Colorization using Self-attention based Progressive Generative Adversarial Network (RIC-SPGAN) which consists of residual encoder-decoder (RED) network and a Self-attention based progressive Generative network (SP-GAN) in a cascaded form to perform the denoising and colorization of the image. We have used self-attention based progressive network to model the long-range dependencies and gradually enhanced the resolution of the colorized image for faster, stable and variation rich features for generation of the image. We also presented the stabilization technique of the presented generative model. Our model has shown exceptional perceptual results on noisy and normal grayscale images. We have trained our model on ILSVRC2012. The visual results on images of DIV2K with and without noise has been presented in the paper along with the failure cases of the model.
Piloting a smartphone-based application for tracking and supply chain management of medicines in Africa

A confounding factor for healthcare programmes in African countries is the inability of essential health tools to reach targeted locations and populations, due to poor Logistics Management Information System (LMIS). In a bid to contribute towards addressing these challenges, a pilot study was undertaken to evaluate the tracking ability, reliability and applicability of EASE App, a novel Smart Phone based Application. The App is designed to provide real-time tracking and tracing of commodities as well as curation of data in a cloud based database with restricted access which can be linked with other databases. In this study, NIPRIMAL was labelled with QR codes, and tracked within the Federal Capital Territory, Abuja, Nigeria, using the smartphone based EASE App. Data collected showed that the "EASE App" tracking had accuracy of 100% for date and time of scan, operators' codes and product identity; and 92.83±1.69% and 99.83±0.27% accuracy for GPS mapping label for the city and country, respectively. The GPS mapping label for specific streets, roads or districts, gave an accuracy of about 64.28±3.14%. The technology was able to provide real-time data on user unique identity, user location as well as date/time of use, and the feedback report indicated that it was readily deployable and easy to use. The results demonstrate that the "EASE App" is a promising technology that can support supply chain and related data management challenges in resource poor settings. The potential benefit of the EASE App in strengthening LMIS and distribution chain system in Africa as well as future optimization of the App are discussed.
Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial

Background Most frail older persons are living at home, and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. People could give up their homes for the more reassuring environment of a medicalized living facility. We have developed a low-cost unobtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate older adults’ follow-up in their own homes, and thus support independent living. Objective The primary objective of this prospective open-label study is to evaluate the relevance of the automatic alerts generated by our artificial intelligence–driven monitoring solution as judged by the recipients: older adults, caregivers, and professional support workers. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events. Methods The primary outcome will be evaluated for each successive 2-month follow-up period to estimate the progression of our learning algorithm performance over time. In total, 25 frail or disabled participants, aged 75 years and above and living alone in their own homes, will be enrolled for a 6-month follow-up period. Results The first phase with 5 participants for a 4-month feasibility period has been completed and the expected completion date for the second phase of the study (20 participants for 6 months) is July 2020. Conclusions The originality of our real-life project lies in the choice of the primary outcome and in our user-centered evaluation. We will evaluate the relevance of the alerts and the algorithm performance over time according to the end users. The first-line recipients of the information are the older adults and their care partners rather than health care professionals. Despite the fast pace of electronic health devices development, few studies have addressed the specific everyday needs of older adults and their families. Trial Registration ClinicalTrials.gov NCT03484156; https://clinicaltrials.gov/ct2/show/NCT03484156 International Registered Report Identifier (IRRID) PRR1-10.2196/14245
Weakly labeled fine-grained classification with hierarchy relationship of fine and coarse labels

The current work of fine-grained classification generally depends on a large number of fine labels of images. However, these fine labels are much more difficult to annotate than the coarse labels, which generalize fine labels based on the hierarchy of categories. In this paper, we propose to make fine labels prediction under a weakly supervised setting where a subset of training data is labeled with fine labels and the others only have coarse labels. We aim to explore the hierarchy relationship between coarse classes and fine classes to achieve a better performance on fine-grained classification and meanwhile reduce the heavy dependence on fine labels. To this end, we use convolutional block attention module and multi-scale convolution kernel based feature fusion to generate more effective features from multi-scale convolution kernels and multi-level features. Besides, an adaptive classification module exploits the hierarchy relationship of categories to learn the fine-grained classifier automatically according to the available labels of the training data. Comprehensive experiments on the CIFAR100 dataset, a subset of ImageNet and CUB-200-2011 dataset demonstrate the better fine-grained classification performance of our model.
Social Relationship Recognition Based on A Hybrid Deep Neural Network

Social relations reveal the interpersonal association of human beings. Developing techniques to automatically recognize social relations from visual data has great potential for improving human-computer interaction. In this paper, a hybrid deep network is proposed to predict the social relations between two human beings in an image. Unlike existing methods that typically learn deep learning models from scratch, a VGG-FACE model previously trained for face recognition is fine-tuned on a social relation database and used as branches of a siamese-like network. Moreover, a deep network is proposed to extract scene features that contain high-level information related to social relations from whole images and its predictions are fused with the predictions of the siamese network to generate the final result. Experiments show that the proposed approach saves the effort of pre-training and preparing auxiliary datasets, i.e. facial attribute datasets, and outperforms state-of-the-art methods.
A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space.However, promising solutions/regions are not adequately exploited by a traditional DE algorithm.Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search.This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS).The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage).Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process.A study of the important components of MFDEALS shows that there is a synergy between them.MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively.Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference.The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions.Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.
Optimal survey schemes for stochastic gradient descent with applications to <i>M</i>-estimation

Iterative stochastic approximation methods are widely used to solve M -estimation problems, in the context of predictive learning in particular. In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natural and popular approach to gradient descent in this context consists in substituting the “full data” statistics with their counterparts based on subsamples picked at random of manageable size. It is the main purpose of this paper to investigate the impact of survey sampling with unequal inclusion probabilities on stochastic gradient descent-based M -estimation methods. Precisely, we prove that, in presence of some a priori information, one may significantly increase statistical accuracy in terms of limit variance, when choosing appropriate first order inclusion probabilities. These results are described by asymptotic theorems and are also supported by illustrative numerical experiments.
Deictic Image Mapping: An Abstraction for Learning Pose Invariant Manipulation Policies

In applications of deep reinforcement learning to robotics, it is often the case that we want to learn pose invariant policies: policies that are invariant to changes in the position and orientation of objects in the world. For example, consider a pegin-hole insertion task. If the agent learns to insert a peg into one hole, we would like that policy to generalize to holes presented in different poses. Unfortunately, this is a challenge using conventional methods. This paper proposes a novel state and action abstraction that is invariant to pose shifts called deictic image maps that can be used with deep reinforcement learning. We provide broad conditions under which optimal abstract policies are optimal for the underlying system. Finally, we show that the method can help solve challenging robotic manipulation problems.
Re-Ranking Words to Improve Interpretability of Automatically Generated Topics

Topics models, such as LDA, are widely used in Natural Language Processing.Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models.Conventionally, topics are represented by their n most probable words, however, these representations are often difficult for humans to interpret.This paper explores the re-ranking of topic words to generate more interpretable topic representations.A range of approaches are compared and evaluated in two experiments.The first uses crowdworkers to associate topics represented by different word rankings with related documents.The second experiment is an automatic approach based on a document retrieval task applied on multiple domains.Results in both experiments demonstrate that re-ranking words improves topic interpretability and that the most effective re-ranking schemes were those which combine information about the importance of words both within topics and their relative frequency in the entire corpus.In addition, close correlation between the results of the two evaluation approaches suggests that the automatic method proposed here could be used to evaluate re-ranking methods without the need for human judgements.
Model complex control CMA-ES

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown great performance on nonseparable optimization problems largely due to its rotation-invariant feature. However, as the computational cost of the self-adaption operation is sensitive to the scale of problems, the performance of CMA-ES heavily suffers from the well-known curse of dimensionality, which makes it impractical to many Large Scale Global Optimization (LSGO) problems. In this paper, a correlation coefficient based grouping (CCG) strategy is proposed to detect the correlations between variables in a simple yet efficient way. Then coupled with a model complexity control (MCC) framework, a new variant of CMA-ES, named MCC-CCG-CMAES, is presented for LSGO problems, which suffers less from curse of dimensionality and significantly reduces the computational cost compared with the standard CMA-ES. To the best of our knowledge, this work is the first attempt at enhancing CMA-ES with the MCC framework rather than the cooperative coevolution (CC) framework. Experimental results on the CEC′2010 large-scale global optimization (LSGO) benchmark functions show that the performance of MCC-CCG-CMAES outperforms the state-of-the-art counterparts.
Using Rhetorical Structure Theory to Assess Discourse Coherence for Non-native Spontaneous Speech

This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers. Rhetorical Structure Theory (RST) has been commonly used in the analysis of discourse organization of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Due to the fact that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we conducted research to obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Subsequently, automatic parsers were trained on these annotations to process non-native spontaneous speech. Finally, a set of features were extracted from automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, which were then employed to further improve the validity of an automated speech scoring system.
Mirror, Mirror, on the Wall, Who’s Got the Clearest Image of Them All?—A Tailored Approach to Single Image Reflection Removal

Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem. In this paper, we propose a technique based on a novel optimization problem. First, we introduce a simple user interaction scheme, which helps minimize information loss in the reflection-free regions. Second, we introduce an H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> fidelity term, which preserves fine detail while enforcing the global color similarity. We show that this combination allows us to mitigate the shortcomings in structure and color preservation, which presents some of the most prominent drawbacks in the existing methods for reflection removal. We demonstrate, through numerical and visual experiments, that our method is able to outperform the state-of-the-art model-based methods and compete with recent deep-learning approaches.
Faster Support Vector Machines

The time complexity of support vector machines (SVMs) prohibits training on huge data sets with millions of samples. Recently, multilevel approaches to train SVMs have been developed to allow for time efficient training on huge data sets. While regular SVMs perform the entire training in one - time consuming - optimization step, multilevel SVMs first build a hierarchy of problems decreasing in size that resemble the original problem and then train an SVM model for each hierarchy level benefiting from the solved models of previous levels. We present a faster multilevel support vector machine that uses a label propagation algorithm to construct the problem hierarchy. Extensive experiments show that our new algorithm achieves speed-ups of up to two orders of magnitude while having similar or better classification quality over state-of-the-art algorithms.
Oil Painting Style Rendering Based on Kuwahara Filter

The Kuwahara filter can generate local smoothed color regions similar to hand-painted strokes, and therefore it can be used to image artistic rendering. However, the existing Kuwahara filtering methods often adopt a fixed-sized filter kernel, which leads to a homogeneous size of strokes. In view of this problem, a multi-scale anisotropic Kuwahara filtering method is proposed in this paper. By using image saliency as control information to adjust the size of filter kernel, strokes with abundant varied geometric scales are generated. This paper also presents an automatic rendering framework based on the proposed algorithm that automatically transforms images into an oil painting style. Using the image's orientation field of edge, edge gradient magnitude and image saliency to control the bump mapping, the senses of thickness and layers of oil painting's strokes are generated. By adopting this framework, an artistic oil painting style similar to the “impasto technique” can be simulated well.
Constraint-Based Sequential Pattern Mining with Decision Diagrams

Constraint-based sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram (MDD) representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.
Better and faster hyperparameter optimization with Dask

Nearly every machine learning model requires hyperparameters, parameters that the user must specify before training begins and influence model performance.Finding the optimal set of hyperparameters is often a time-and resource-consuming process.A recent breakthrough hyperparameter optimization algorithm, Hyperband finds high performing hyperparameters with minimal training via a principled early stopping scheme for random hyperparameter selection [LJD + 18].This paper will provide an intuitive introduction to Hyperband and explain the implementation in Dask, a Python library that scales Python to larger datasets and more computational resources.The implementation makes adjustments to the Hyperband algorithm to exploit Dask's capabilities and parallel processing.In experiments, the Dask implementation of Hyperband rapidly finds high performing hyperparameters for deep learning models.