Reviewerly/exHarmony huggingface dataset:
A selectional auto-encoder approach for document image binarization

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a threshold. This approach has proven to outperform existing binarization strategies in a number of document domains.
Epithelium segmentation using deep learning in H&amp;E-stained prostate specimens with immunohistochemistry as reference standard

Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important prerequisite for the development of automated methods for detecting PCa. We propose a new deep learning method to segment epithelial tissue in digitised hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard. We used IHC to create a precise and objective ground truth compared to manual outlining on H&E slides, especially in areas with high-grade PCa. 102 tissue sections were stained with H&E and subsequently restained with P63 and CK8/18 IHC markers to highlight epithelial structures. Afterwards each pair was co-registered. First, we trained a U-Net to segment epithelial structures in IHC using a subset of the IHC slides that were preprocessed with color deconvolution. Second, this network was applied to the remaining slides to create the reference standard used to train a second U-Net on H&E. Our system accurately segmented both intact glands and individual tumour epithelial cells. The generalisation capacity of our system is shown using an independent external dataset from a different centre. We envision this segmentation as the first part of a fully automated prostate cancer grading pipeline.
Symbiotic organisms search algorithm: Theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions.
Assessing stakeholders' risk perception to promote Nature Based Solutions as flood protection strategies: The case of the Glinščica river (Slovenia)

Evidences from flood risk management demonstrated that a deep understanding of the main physical phenomena to be addressed is often not enough but should be also integrated with stakeholders' knowledge and risk perception. Particularly, the effectiveness of flood risk management strategies is highly dependent on stakeholders' perception and attitudes, which play a critical role on how individuals and institutions act to mitigate risks. Furthermore, practitioners and policy-makers realized that grey infrastructures may not be the most suitable solution to reduce flood risk, and that a shift from grey solutions to Nature Based Solutions is required. Within this framework, the present work describes a methodology to enhance the Nature Based Solutions implementation by facilitating the generation, acquisition and diffusion of different stakeholders' risk perceptions. It is based on the combination of Problem Structuring Methods for the elicitation of stakeholders' risk perceptions through individual Fuzzy Cognitive Maps, and Ambiguity Analysis for the investigation of differences in risk perceptions and problem framing. The outputs of the Ambiguity Analysis, used during a participatory workshop, facilitated a dialogue aligning the divergences and promoting the social acceptance of Nature Based Solutions. These results of the implementation of this multi-step methodology in the Glinščica river basin (Slovenia) are discussed.
Iterated feature selection algorithms with layered recurrent neural network for software fault prediction

Software fault prediction (SFP) is typically used to predict faults in software components. Machine learning techniques (e.g., classification) are widely used to tackle this problem. With the availability of the huge amount of data that can be obtained from mining software historical repositories, it becomes possible to have some features (metrics) that are not correlated with the faults, which consequently mislead the learning algorithm and thus decrease its performance. One possible solution to eliminate those metrics is Feature Selection (FS). In this paper, a novel FS approach is proposed to enhance the performance of a layered recurrent neural network (L-RNN), which is used as a classification technique for the SFP problem. Three different wrapper FS algorithms (i.e, Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and Binary Ant Colony Optimization (BACO)) were employed iteratively. To assess the performance of the proposed approach, 19 real-world software projects from PROMISE repository are investigated and the experimental results are discussed. Receiver operating characteristic - area under the curve (ROC-AUC) is used as a performance measure. The results are compared with other state-of-art approaches including Naïve Bayes (NB), Artificial Neural Network (ANN), logistic regression (LR), the k-nearest neighbors (k-NN) and C4.5 decision trees, in terms of area under the curve (AUC). Our results have demonstrated that the proposed approach can outperform other existing methods.
Mobile-assisted ESL/EFL vocabulary learning: a systematic review and meta-analysis

Mobile-assisted language learning (MALL) has been a popular research area in recent years in the SLA field, as it has been considered as being able to facilitate language learning by offering an authentic, socially connective, contextually sensitive, and personalized mobile-mediated language learning environment. To investigate the benefits and possible limitations of mobile-assisted L2 vocabulary retention, many researchers continuously conducted relevant experiments worldwide. Among the current mobile-assisted L2 vocabulary learning research, short message services (SMS), multimedia message service (MMS), and mobile applications (apps) are the predominant approaches supported by the situated learning theory, cognitive mechanisms of L2 word retention, behaviorism, and social constructivism. Although language educators have been focusing on this topic, the effectiveness of mobile L2 vocaubulary learning still remained inconclusive. Besides, to date, little research synthesis has been conducted to investigate the effect of mobile-assisted L2 vocabulary learning. The purpose of this meta-analysis is to systematically synthethisize findings from (quaisi-) experimental studies carried out between 2005 and 2018 to examine whether there is a connection between the use of mobile technologies and L2 word retention. The results of 33 primary eligible studies showed a positive and large effect of mobile-assisted L2 word learning interventions. Additionally, SMS/MMS mode was found to be more beneficial for L2 word retention than mobile application mode; however, it is more prudent to interpret this finging as tentative because the number of target words and intervention time were unequal in the two modes. Moreover, the research settings, treatment durations, and task-afforded autonomy are three variables that tend to significantly moderate the effect of mobile-assisted L2 word retention. However, the above results should be viewed as suggestive rather than definitive with a view to the research flaws found in the primary studies and the small sample size of certain categories of moderators when compared.
Bi-LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly Documents

In this paper, we address the keyphrase extraction problem as sequence labeling and propose a model that jointly exploits the complementary strengths of Conditional Random Fields that capture label dependencies through a transition parameter matrix consisting of the transition probabilities from one label to the neighboring label, and Bidirectional Long Short Term Memory networks that capture hidden semantics in text through the long distance dependencies. Our results on three datasets of scholarly documents show that the proposed model substantially outperforms strong baselines and previous approaches for keyphrase extraction.
IWOA: An improved whale optimization algorithm for optimization problems

Abstract The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly.
Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain. In comparison, massive unlabeled 3D CT volumes are usually easily accessible. Current mainstream works to address semi-supervised biomedical image segmentation problem are mostly graph-based. By contrast, deep network based semi-supervised learning methods have not drawn much attention in this field. In this work, we propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be divided into two folds: 1) The deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-labels and thus can help to train better segmentation networks. Experiments are done on our newly collected large dataset with 100 unlabeled cases as well as 210 labeled cases where 16 anatomical structures are manually annotated by four radiologists and confirmed by a senior expert. The results suggest that DMPCT significantly outperforms the fully supervised method by more than 4% especially when only a small set of annotations is used.
Renewable energy prediction: A novel short-term prediction model of photovoltaic output power

Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external factors and the output power is characterized by randomness and indirectness, which poses a great challenge to photovoltaic grid-connection. A hybrid improved multi-verse optimizer algorithm (HIMVO) is proposed to optimize the support vector machine for photovoltaic output prediction. HIMVO algorithm introduces chaotic sequences to initialize the population, which significantly enhances the convergence rate of the algorithm compared with the multi-universe optimizer algorithm. This study applied particle swarm optimization algorithm, dragonfly algorithm, multi-universe optimizer algorithm and HIMVO to testify the availability of the hybrid improved multi-verse optimizer support vector machine model (HIMVO-SVM). The results indicate that HIMVO algorithm has better optimization ability and stability. The four models, HIMVO-SVM, multi-verse optimizer support vector machine, particle swarm optimization support vector machine, back propagation and radical basis function neural network are used to predict output in three different weather types. The results indicate that the model has higher prediction accuracy and stability. The mean square error value of the HIMVO-SVM model decreases by at least 0.0026, 0.0030 and 0.0012, and the mean absolute percentage error value decreases by at least 3.6768%, 1.9772% and 2.7165%, respectively. The proposed method is beneficial to the prediction of output power and conduces to the economic dispatch of the grid and the maintenance of the stability of the power system.
Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders

In this letter, we use deep neural networks for unsupervised clustering of seismic data. We perform the clustering in a feature space that is simultaneously optimized with the clustering assignment, resulting in learned feature representations that are effective for a specific clustering task. To demonstrate the application of this method in seismic signal processing, we design two different neural networks consisting primarily of full convolutional and pooling layers and apply them to: 1) discriminate waveforms recorded at different hypocentral distances and 2) discriminate waveforms with different first-motion polarities. Our method results in precisions that are comparable to those recently achieved by supervised methods, but without the need for labeled data, manual feature engineering, and large training sets. The applications we present here can be used in standard single-site earthquake early warning systems to reduce the false alerts on an individual station level. However, the presented technique is general and suitable for a variety of applications including quality control of the labeling and classification results of other supervised methods.
On Variational Bounds of Mutual Information

Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning.
A Study on Big Knowledge and Its Engineering Issues

After entering the big data era, a new term of `big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed.
SegLink++: Detecting Dense and Arbitrary-shaped Scene Text by Instance-aware Component Grouping

State-of-the-art methods have achieved impressive performances on multi-oriented text detection. Yet, they usually have difficulty in handling curved and dense texts, which are common in commodity images. In this paper, we propose a network for detecting dense and arbitrary-shaped scene text by instance-aware component grouping (ICG), which is a flexible bottom-up method. To address the difficulty in separating dense text instances faced by most bottom-up methods, we propose attractive and repulsive link between text components which forces the network learning to focus more on close text instances, and instance-aware loss that fully exploits context to supervise the network. The final text detection is achieved by a modified minimum spanning tree (MST) algorithm based on the learned attractive and repulsive links. To demonstrate the effectiveness of the proposed method, we introduce a dense and arbitrary-shaped scene text dataset composed of commodity images (DAST1500). Experimental results show that the proposed ICG significantly outperforms state-of-the-art methods on DAST1500 and two curved text datasets: Total-Text and CTW1500, and also achieves very competitive performance on two multi-oriented datasets: ICDAR15 (at 7.1FPS for 1280 × 768 image) and MTWI.
Learning to Detect Human-Object Interactions With Knowledge

The recent advances in instance-level detection tasks lay a strong foundation for automated visual scenes understanding. However, the ability to fully comprehend a social scene still eludes us. In this work, we focus on detecting human-object interactions (HOIs) in images, an essential step towards deeper scene understanding. HOI detection aims to localize human and objects, as well as to identify the complex interactions between them. Innate in practical problems with large label space, HOI categories exhibit a long-tail distribution, i.e., there exist some rare categories with very few training samples. Given the key observation that HOIs contain intrinsic semantic regularities despite they are visually diverse, we tackle the challenge of long-tail HOI categories by modeling the underlying regularities among verbs and objects in HOIs as well as general relationships. In particular, we construct a knowledge graph based on the ground-truth annotations of training dataset and external source. In contrast to direct knowledge incorporation, we address the necessity of dynamic image-specific knowledge retrieval by multi-modal learning, which leads to an enhanced semantic embedding space for HOI comprehension. The proposed method shows improved performance on V-COCO and HICO-DET benchmarks, especially when predicting the rare HOI categories.
Blockchain Technology for Applications in Internet of Things—Mapping From System Design Perspective

Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.
Attentive Single-Tasking of Multiple Tasks

In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as “single-tasking multiple tasks”. The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. This gives the network the ability to accentuate the features that are adapted to a task, while shunning irrelevant ones. We further reduce task interference by forcing the task gradients to be statistically indistinguishable through adversarial training, ensuring that the common backbone architecture serving all tasks is not dominated by any of the task-specific gradients. Results in three multi-task dense labelling problems consistently show: (i) a large reduction in the number of parameters while preserving, or even improving performance and (ii) a smooth trade-off between computation and multi-task accuracy. We provide our system's code and pre-trained models at https://github. com/facebookresearch/astmt.