Reviewerly/exHarmony huggingface dataset:
Ontologies and ontological methods in linguistics

Abstract In the last decade, linguists have started to develop and make use of ontologies, encouraged by the progress made in areas such as Artificial Intelligence and the Semantic Web. This paper gives an overview of notions and dimensions of “ontology” and of ontologies for and in linguistics. It discusses building blocks, design aspects, and capabilities of formal ontologies and provides some implementation pointers. The focus of this paper, however, is on linguistic research and what a modelling framework based on ontologies has to offer. Accordingly, the paper does not aim at providing an overview of specific models for computational processing. To illustrate the issues at hand, an example scenario from linguistic typology is selected instead, where the aim of describing the world's languages is approached through ontologies.
RecDNNing: a recommender system using deep neural network with user and item embeddings

The success of applying deep learning to many domains has gained strong interest in developing new revolutionary recommender systems. However, there are little works studying these systems that employ deep learning; additionally, there is no study showing how to combine the users and items embedding with deep learning to enhance the effectiveness of the recommender systems. Therefore, this paper proposes a novel approach called RecDNNing with a combination of embedded users and items combined with deep neural network. The proposed recommendation approach consists of two phases. In the first phase, we create a dens numeric representation for each user and item, called user embedding and item embedding, respectively. Following that, the items and users embedding are averaged and then concatenated before being fed into the deep neural network. In the second phase, we use the model of the deep neural network to take the concatenated users and items embedding as the inputs in order to predict the scores of rating by applying the forward propagation algorithm. The experimental results on MovieLens show that the proposed RecDNNing outperforms state-of-the-art algorithms.
Application of Pontryagin's minimum principle to Grover's quantum search problem

Grover's algorithm is one of the most famous algorithms which explicitly demonstrates how the quantum nature can be utilized to accelerate the searching process. In this work, Grover's quantum search problem is mapped to a time-optimal control problem. Resorting to Pontryagin's Minimum Principle we find that the time-optimal solution has the bang-singular-bang structure. This structure can be derived naturally, without integrating the differential equations, using the geometric control technique where Hamiltonians in the Schr\"odinger's equation are represented as vector fields. In view of optimal control, Grover's algorithm uses the bang-bang protocol to approximate the optimal protocol with a minimized number of bang-to-bang switchings to reduce the query complexity. Our work provides a concrete example how Pontryagin's Minimum Principle is connected to quantum computation, and offers insight into how a quantum algorithm can be designed.
Research on Intrusion Detection Based on Improved DBN-ELM

To leverage the feature extraction of DBN and the fast classification and good generalization of ELM, an improved method of DBN-ELM is proposed for intrusion detection. The improved model uses deep belief network (DBN) to train NSL-KDD dataset and feed them back to the extreme learning machine (ELM) for classification. A classifier is connected at each intermediate level of the DBN-ELM. By majority voting on the output of classifier and ELM, the final output is calculated by integration. Experiments show that the improved model increases the classification confidence and accuracy of the classifier. The model has been benchmarked on the NSL-KDD dataset, and the accuracy of the model has been improved to 97.82%, while the false alarm rate has been reduced to 1.81%. Proposed improved model has been also compared with DBN, ELM, DBN-ELM and achieves competitive accuracy.
Information Theoretic Multi-Target Feature Selection via Output Space Quantization

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms—the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents an algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classification and multivariate regression. We achieve this by combining two ideas—deriving low-order information theoretic approximations for the input space and using quantization algorithms for deriving low-dimensional approximations of the output space. Under the above framework we derive a novel criterion, Group-JMI-Rand, which captures various high-order target interactions. In an extensive experimental study we showed that our suggested criterion achieves competing performance against various other information theoretic feature selection criteria suggested in the literature.
Factored Convolutional Neural Network for Amharic Character Image Recognition

In this paper we propose a novel CNN based approach for Amharic character image recognition. The proposed method is designed by leveraging the structure of Amharic graphemes. Amharic characters could be decomposed in to a consonant and a vowel. As a result of this consonant-vowel combination structure, Amharic characters lie within a matrix structure called 'Fidel Gebeta'. The rows and columns of 'Fidel Gebeta' correspond to a character's consonant and the vowel components, respectively. The proposed method has a CNN architecture with two classifiers that detect the row/consonant and column/vowel components of a character. The two classifiers share a common feature space before they fork-out at their last layers. The method achieves state-of-the-art result on a synthetically generated dataset. The proposed method achieves 94.97% overall character recognition accuracy.
Densely connected deep U-Net for abdominal multi-organ segmentation

The U-Net is the well-known architecture for semantic segmentation and has achieved remarkable successes in many medical image segmentation applications. However, the features learned by standard convolution layers are not distinctive. To address this problem, we propose a novel architecture, called densely connected deep U-Net(DC U-Net). Specifically, the HU values in CT slices were first windowed in a range to exclude irrelevant organs, and then put it into DC U-Net for training. The DC U-Net consists of three blocks with dense connections, selective deconvlution layers with upsample and transconvlution filling function. To further improve the accuracy of small regin of interest segmentation with limited dataset, we proposed a novel loss function. With respect to the ground truth, average Dice overlap ratios for the liver and spleen are 94.9% and 92.1% respectively. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach

Text emotion distribution learning (EDL) aims to develop models that can predict the intensity values of a sentence across a set of emotion categories.Existing methods based on supervised learning require a large amount of well-labelled training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity.In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample.Specifically, we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters.We then train a meta-learner that can adapt to new data with only a few training samples on the clusters, and further fit the meta-learner on KNNs of a testing sample for EDL.In this way, we effectively augment the learning ability of a model on the small sample.To demonstrate the performance, we compare the proposed approach with state-ofthe-art EDL methods on a widely used EDL dataset: SemEval 2007 Task 14 (Strapparava and Mihalcea, 2007).Results show the superiority of our method on small-sample emotion distribution learning.
Recurrent Reconstructive Network for Sequential Anomaly Detection

Anomaly detection identifies anomaly samples that deviate significantly from normal patterns. Usually, the number of anomaly samples is extremely small compared to the normal samples. To handle such imbalanced sample distribution, one-class classification has been widely used in identifying the anomaly by modeling the features of normal data using only normal data. Recently, recurrent autoencoder (RAE) has shown outstanding performance in the sequential anomaly detection compared to the other conventional methods. However, RAE, which has a long-term dependency problem, is optimized only to handle the fixed-length inputs. To overcome the limitations of RAE, we propose recurrent reconstructive network (RRN) as a novel RAE, with three functionalities for anomaly detection of streaming data: 1) a self-attention mechanism; 2) hidden state forcing; and 3) skip transition. The designed self-attention mechanism and the hidden state forcing between the encoder and decoder effectively manage the input sequences of varying length. The skip transition with the attention gate improves the reconstruction performance. We conduct a series of comprehensive experiments on four datasets and verify the superior performance of the proposed RRN in the sequential anomaly detection tasks.
Migrating Java-Based Apo-Games into a Composition-Based Software Product Line

A software product line enables an organization to systematically reuse software features that allow to derive customized variants from a common platform, promising reduced development and maintenance costs. In practice, however, most organizations start to clone existing systems and only extract a software product line from such clones when the maintenance and coordination costs increase. Despite the importance of extractive software-product-line adoption, we still have only limited knowledge on what practices work best and miss datasets for evaluating automated techniques. To improve this situation, we performed an extractive adoption of the Apo-Games, resulting in a systematic analysis of five Java games and the migration of three games into a composition-based software product line. In this paper, we report our analysis and migration process, discuss our lessons learned, and contribute a feature model as well as the implementation of the extracted software product line. Overall, the results help to gain a better understanding of problems that can appear during such migrations, indicating research opportunities and hints for practitioners. Moreover, our artifacts can serve as dataset to test automated techniques and developers may improve or extent them in the future.
Research on Image Generation and Style Transfer Algorithm Based on Deep Learning

Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image.This paper presented a solution based on a deep learning framework to realize image generation and style transfer.The method first used the conditional generation to resist the network, optimizes the loss function of the training mapping relationship, and generated the actual image from the input sketch.Then, by defining and optimizing the perceptual loss function of the style transfer model, the style features are extracted from the image, thereby forming the actual The conversion between images and stylized art images.Experiments show that this method can greatly reduce the work of coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick figures into actual object images.
Improved Optimization of Motion Primitives for Motion Planning in State Lattices

In this paper, we propose a framework for generating motion primitives for lattice-based motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the end-point boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic reoptimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.
A novel visual approach for enhanced attribute analysis and selection

As a consequence of the current capabilities of collecting and storing data, a data set of many attributes frequently reflects more than one phenomenon. Understanding the role of attribute subsets and their impact on the organization and structure of a data set under study is paramount to many exploratory and analytical tasks. Example applications range from medicine to financial markets, whereby one wishes to locate subsets of variables that impact the prediction of target categorical attributes. The user is essential in this context since automated techniques are not currently capable of embedding user knowledge in attribute selections. In this work, we propose an approach to deal with the analysis and selection of attributes in a data set based on three principles: firstly, we center the analysis of the relationships on categorical attributes or labels, because they usually summarize important state variables in the application; secondly, we express the relationship between target attributes and all others in the data set within a single visualization, providing understanding of a large number of correlations in the same visual frame; thirdly, we propose an interactive dual-visual approach whereby changes and selections in attribute space reflect visually on the configuration of data layouts, conceived to support immediate analysis of the impact of selected subsets of attributes in the organization of the data set. We validate our approach by means of a number of case studies, illustrating distinct scenarios of knowledge acquisition and feature selection.
Walsh–Hadamard-Kernel-Based Features in Particle Filter Framework for Underwater Object Tracking

One of the well-established research domains among computer vision scientists is object tracking. However, not much work has been done in underwater scenarios. This article addresses the problem of visual tracking in the underwater environment with the stationary and nonstationary camera setups. In order to deal with the underwater optical dynamics, a dominant color component-based scene representation is employed in the YCbCr color space. An adaptive approach is devised to select the Walsh-Hadamard (WH) kernels for the efficient extraction of color, edge, and texture strengths, whereas a new feature called range strength is proposed to extract the variation of intensity from underwater sequences in the local neighborhood using the WH kernel. The likelihood of these feature strengths is integrated in a particle filter framework to track the object of interest in underwater sequences. The reference feature strengths used in assigning weights to the particles are updated based on the S$\phi$rensen distance. The coefficients of feature strengths are calculated in such a way that if one feature fails, then its coefficient become insignificant, whereas the more suitable features get higher feature coefficients. The effectiveness of the proposed scheme is evaluated using the underwater video datasets: reefVid, fish4knowledge (F4K), underwaterchangedetection (UWCD), and National Oceanic and Atmospheric Administration (NOAA). The performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes. The quantitative analysis of the proposed scheme is carried out using three evaluation measures: overall intersection over union, centroid location error, and average tracking error. The performance of the proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges.
GRU Based Deep Learning Model for Prognosis Prediction of Disease Progression

Recently different Deep Learning (DL) models have been emerging to predict the disease amelioration. Generally deep learning is the state of art for learning the multiple representation of neuron. Discriminant DL model includes different architecture and initial architecture is Recurrent Neural Network (RNN) which learn the data labeled in the form of sequence and it has long term dependency and vanishing gradient problem. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) is an improved version of RNN which deal with problems in RNN. Deep care LSTM is one of the model in LSTM developed by [Trang Pham et.al] for predicting diabetes disease and compared the results with markovian and support vector machine model. In deep LSTM model some problems are reported like short term trajectories, less accuracy. To overcome the problem Deep Care GRU has been proposed to identify the diabetes disease amelioration.
Tensorpose: Real-time pose estimation for interactive applications

The state of the art has outstanding results for 2D multi-person pose estimation using multi-stage Deep Neural Networks in images with high accuracy. However, the use of these models on real-time applications may be impractical not just because they are computationally intensive, but also because they suffer from flicking, from the inability for capturing temporal correlations among video frames, as well as from image degradation. To tackle these problems, we expand the use of pose estimation to motion capture in interactive applications. To do so, we propose a novel deep neural network with streamlined architecture and tensor decomposition for pose estimation with improved processing time, named TensorPose. We introduce an architecture for markerless motion capture using Convolutional Neural Networks combined with sparse optical flow and Kalman Filters. We also apply this architecture in a multi-user environment, based on the Holojam framework, where it is possible to create simultaneous collaborative experiences.
Blockchain-Based Outsourced Storage Schema in Untrusted Environment

Outsourced data, as significant service offered by the cloud service provider (CSP), can effectively facilitate the data owner (DO) overcoming the storage limitations on massive data. To ensure the availability of data, DO usually outsources the data replications to multiple CSPs (multi-CSPs) and utilizes a third party metadata management (TPMM) to dominate the metadata of the corresponding replications. However, during the outsourced procedures, DO can hardly confirm the confidence of the TPMM who may take some malicious behaviors to affect the reliability of data. Thus, DO inevitably faces data security issues caused by the over-reliance on the semi-trusted TPMM to manage the metadata of replications. In this paper, we focus on the problem of reliable outsourced data service among multi-CSPs in untrusted environment, that is, how to reliably store and verify the metadata of the data replications in untrusted multi-CSPs environment. To address the problem, we use the novel blockchain technology as a medium to build a trusted outsourced service platform. Moreover, we fully consider the innovative characteristics of blockchain including decentralized architecture, redundancy storage, collective maintenance, and tamper resistant to ensure the data cannot be changed maliciously. We first design a blockchain-based outsourced service framework for storing data replications in untrusted environment, which contains three key layers, that is, storage layer, verification layer, and blockchain layer. Then, we devise a novel concept of verification peer (VP) for maintaining metadata stored by a form of blockchain, and each of which holds the entire blockchain locally to prevent metadata from being maliciously tampered with. Finally, based on the proposed model, we introduce a collaborative algorithm invoked by VPs to store and verify the metadata of replications. We present a completed analysis and conduct extensive experiments on multi-CSPs scenario. The evaluation results demonstrate that our proposed approach achieves superior performance.
An Evaluation of Deep Learning Techniques for Qr Code Detection

In this work, we employ deep learning models for detecting QR Codes in natural scenes. A series of different model configurations are evaluated in terms of Average Precision, and an architecture modification that allows detection aided by object subparts annotations is proposed. This modification is implemented in our best scoring model, which is compared to a traditional technique, achieving a substantial improvement in the considered metrics. The dataset used in our evaluation, with bounding box annotations for both QR Codes and their Finder Patterns (FIPs), will be made publicly available. This dataset is significantly bigger than known available options at the moment, so we expect it to provide a common benchmark tool for QR Code detection in natural scenes.
Lung 4D CT Image Registration Based on High-Order Markov Random Field

To solve the problem that traditional image registration methods based on continuous optimization for large motion lung 4D CT image sequences are easy to fall into local optimal solutions and lead to serious misregistration, a novel image registration method based on high-order Markov Random Field (MRF) is proposed. By analyzing the effect of the deformation field constraint of the potential functions with different order cliques in MRF model, energy functions with high-order cliques form are designed separately for 2D and 3D images to preserve topology of the deformation field. In order to preserve the topology of the deformation field more effectively, it is necessary to apply a smooth term and a topology preservation term simultaneously in the energy function and use logarithmic function to impose a penalty on the Jacobian matrix with high-order cliques in the topology preservation term. For the complexity of the designed energy function with high-order cliques form, Markov Chain Monte Carlo (MCMC) algorithm is used to solve the optimization problem of the designed energy function. To address the high computational requirements in lung 4D CT image registration, a multi-level processing strategy is adopted to reduce the space complexity of the proposed registration method and promotes the computational efficiency. In the DIR-lab dataset with 4D CT images and the COPD (Chronic Obstructive Pulmonary Disease) dataset with 3D CT images, the average target registration error (TRE) of our proposed method can reach 0.95 mm respectively.