Reviewerly/exHarmony huggingface dataset:
Towards a Core Ontology for Scientific Research Activities

The increasing volume and complexity of scientific research data associated with its semantic heterogeneity demands strategies to enable data integrated reuse. This is essential to improve global collaborations, in what has been called e-Science. A way to promote data integration is through the use of ontologies. Ontologies can play the role of a shared conceptualization, providing a common semantic background for data interpretation. In the case of scientific research, particularly empirical research, there are many concepts related to research activities that are general, despite any specific domain in which they may occur. Thus, they can be represented by means of a core ontology. In this paper, we propose the design of a core ontology to deal with research activities (e.g., sampling and measurement). As the concepts used are neutral with respect to different application domains, they can be reused to build ontologies for specific research domains, speeding up the development process. To illustrate this, we present an environmental research ontology developed based on this core ontology. The proposed core ontology is grounded in the Unified Foundational Ontology (UFO), which provides a solid basis for its key elements.
3-D Human Body Posture Reconstruction by Computer Vision

Human limb movement sensing is crucial in different areas of science. In this paper, a method for sensing human limb movement and the subsequent reconstruction in a 3-D plane is described. The sensors used in this task are four Microsoft Kinect, which has depth and RGB cameras. Depth images are processed by artificial vision algorithms to delimit an area where the movements will be performed. In the other hand, RGB images are processed by a Convolutional Neural Network to acquire a series of specific points which correspond to the human body’s joints. A comparison of the proposed algorithm performance is also described. The equations that relate the information in two dimensions are obtained by processing the four sensors are used to generate a skeleton in 3-D.
Incentive Contract for Cybersecurity Information Sharing Considering Monitoring Signals

Cyber insurance is a viable method for cyber risk transfer. However, the cyber insurance faces critical challenges, the most important of which is lack of statistical data. In this paper, we proposed an incentive model considering monitoring signals for cybersecurity information haring based on the principal-agent theory. We studied the effect of monitoring signals on increasing the rationality of the incentive contract and reducing moral hazard in the process of cybersecurity information sharing, and analyzed factors influencing the effectiveness of the incentive contract. We show that by introducing monitoring signals, the insurer can collect more information about the effort level of the insured, and encourage the insured to share cybersecurity information based on the information sharing output and monitoring signals of the effort level, which can not only reduce the blindness of incentive to the insured in the process of cybersecurity information sharing, but also reduce moral hazard.
Geometry-aware Relational Exemplar Attention for Dense Captioning

Dense captioning (DC), which provides a comprehensive context understanding of images by describing all salient visual groundings in an image, facilitates multimodal understanding and learning. As an extension of image captioning, DC is developed to discover richer sets of visual contents and to generate captions of wider diversity and increased details. The state-of-the-art models of DC consist of three stages: (1) region proposals, (2) region classification, and (3) caption generation for each proposal. They are typically built upon the following ideas: (a) guiding the caption generation with image-level features as the context cues along with regional features and (b) refining locations of region proposals with caption information. In this work, we propose (a) a joint visual-textual criterion exploited by the region classifier that further improves both region detection and caption accuracy, and (b) a Geometry aware Relational Exemplar attention (GREatt) mechanism to relate region proposals. The former helps the model learn a region classifier by effectively exploiting both visual groundings and caption descriptions. Rather than treating each region proposal in isolation, the latter relates regions in complementary relations, i.e. contextually dependent, visually supported and geometry relations, to enrich context information in regional representations. We conduct an extensive set of experiments and demonstrate that our proposed model improves the state-of-the-art by at least +5.3% in terms of the mean average precision on the Visual Genome dataset.
Vision-based Mobile Robot's Environment Outdoor Perception

Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area.
Eigenedge: A measure of edge centrality for big graph exploration

As we are in the age of big data, graph data become bigger. A big graph normally has an overwhelming number of edges. Existing metrics of edge centrality are not quite suitable for dealing with such a large graph. A novel metric for measuring the importance of edges in a graph is introduced in this paper. Compared with the other six popular matrices with respect to a number of real-world graphs, the proposed metric is capable of capturing the structural feature of a graph in a scalable way. The comprehensive experiments have demonstrated the performance of the proposed metric. According to this metric, a filtering approach is presented to reduce the visual clutter of a layout in a way that the hidden patterns can be revealed gradually. As evidenced by real-world examples, our approach allows users to explore graphs in real-time with a high level of details in an interactive way in order to gain insight into graph data.
Opportunities and challenges of machine learning approaches for biomarker signature identification in psychiatry

The identification of reproducible biomarkers is an important step toward personalized medicine of psychiatric disorders. A large repertoire of machine learning tools is available that can aid in identifying such biomarker patterns from high-dimensional biological data. However, in psychiatry, the identification of clinically useful patterns has been challenging, due to the biological complexity and heterogeneity of the disorders, and the low effect sizes of individual biological markers. The incorporation of additional biological knowledge, such as information on biological network structure, or data from diverse modalities, is a promising route to make high-dimensional data more accessible for machine learning, and to identify more meaningful biological illness signatures. Here, we describe opportunities of such integrative analytics approaches and discuss unresolved challenges. We hope that the adaptation and further development of such methodology will aid in identifying clinically useful biological hallmarks of psychiatric disorders.
Differentially Private Recommender System with Autoencoders

In recent years, deep learning has achieved remarkable fruits in a wide variety of domains, such as recommender systems. In addition, privacy preservation is unprecedentedly necessary in today's era. In this paper, we leverage the privacy preservation problem in recommendation with the deep learning model, i.e., autoencoders. In order to predict user preferences in the collaborative filtering way, the work reconstructs user sparse ratings with autoencoders to estimate unobserved user preferences. To further protect user privacy, the Gaussian mechanism is combined in the stochastic gradient descent process to ensure that the training process meets the requirements of approximate differential privacy.
Augmentation-Based Object Detection for Winter Time Applications

Despite the significant advancements in object detection, there is still a number of detection tasks that are challenging and difficult. To eliminate them and to push forward the research in this area a technological "IceVision" Challenge was organized in Russia in 2019. The main objective of this Challenge is to develop new approaches for object detection in severe weather conditions focusing on winter time. The obtained results are supposed to be implemented for autonomous driving. This paper reports the approach that was used by our team in this competition. The approach is based on a Cascade R-CNN model in combination with data augmentation. It allowed us to take fourth place among 187 participants on the preliminary stage of the completion.
Stylometry of literary papyri

In this paper we present the first results of stylometric analysis of literary papyri. Specifically we perform a range of tests for unsupervised clustering of authors. We scrutinise both the best classic distance-based methods as well as the state-of-the-art network community detection techniqes. We report on obstacles concerning highly non-uniform distributions of text size and authorial samples combined with sparse feature space. We also note how clustering performance depends on regularisation of spelling by means of querying relevant annotations.
Image registration with sliding motion

An in-depth introduction and thorough discussion of current approaches for medical image registration with sliding motion is presented in this chapter. Several strategies for locally-adaptive regularization in past, current and future work are described including related research from optical flow in computer vision. In particular recent advances to the Demons framework and discrete optimization strategies are presented that do no require any specific segmentation masks and led to substantial improvements over baseline approaches. A reduction target registration error with respect to expert landmarks and visually plausible sliding in the computed motion fields can be reached using these methods. The great clinical impact of a suitable handling of motion discontinuities is highlighted and future research directions towards advanced graph-based edge priors through supervised learning are presented to the reader.
Improving Basic Natural Language Processing Tools for the Ainu Language

Ainu is a critically endangered language spoken by the native inhabitants of northern Japan. This paper describes our research aimed at the development of technology for automatic processing of text in Ainu. In particular, we improved the existing tools for normalizing old transcriptions, word segmentation, and part-of-speech tagging. In the experiments we applied two Ainu language dictionaries from different domains (literary and colloquial) and created a new data set by combining them. The experiments revealed that expanding the lexicon had a positive impact on the overall performance of our tools, especially with test data unrelated to any of the training sets used.
U-Net Based Architectures for Document Text Detection and Binarization

With the increasing popularity of document analysis and recognition systems, text detection (TD) and text binarization (TB) in document images remain challenging tasks. In the paper, we introduced a two-step architecture for the TD task. Firstly, a U-net based model is used to get a text mask in terms of word-level bounding boxes. Secondly, we approximate the mask of the bounding boxes with rectangles using a classic computer vision method. The model achieves state-of-the-art result on document images and outperforms other popular approaches. Moreover, we introduce the Hybrid U-net architecture, which helps to solve the TB and TD problems at the same time. The model demonstrates high results on both problems. The shared convolution encoder allows to reduce the number of parameters and consumed memory compared to separate models without reducing the model performance.
Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator

Custom hardware accelerators of Convolutional Neural Networks (CNN) provide a promising solution to meet real-time constraints for a wide range of applications on low-cost embedded devices. In this work, we aim to lower the dynamic power of a stream-based CNN hardware accelerator by reducing the computational redundancies in the CNN layers. In particular, we investigate the redundancies due to the downsampling effect of max pooling layers which are prevalent in state-of-the-art CNNs, and propose an approximation method to reduce the overall computations. The experimental results show that the proposed method leads to lower dynamic power without sacrificing accuracy.
Virtual Machine Consolidation Framework for Energy and Performance Efficient Cloud Data Centers

Establishing large-scale data center increases demand in computational power of cloud computing.. So cloud datacenters extensively applied a virtualization technology which is realizing energy efficient operations of cloud datacenter. It is a method of an abstraction of the computing resources to make increases the utilization of resources. Virtual machine (VM) consolidation is a method of combining workloads from separate machines or applications into a smaller number of systems. Consolidation takes place to run the maximum number of VMs on a single Physical Machine (PM) to increase the performance of the PM and also to reduce the total power consumption of the data center. While, live migration process PM's ruining virtual machine is transferred to another active PM, so that source PM could be switched off to reduce the energy consumption. Both technologies, developing a data center that provides a good tradeoff between the performance and energy efficiency. There are three contributions in the framework. First, PM overload detection based on Service Level Agreement Violation (SLAV) and under load detection based on minimum number of VM for VM allocation algorithm is established. Second, in VM selection algorithm VM with maximum utilization for migration has been selected. Third, VM placement algorithm using Minimum Power High Available Capacity algorithm for finding new places of VM has been developed. We have evaluated our framework in the CloudSim simulation tool. Then investigated performance metrics of VM consolidation and migration. Experimental results show an effective VM consolidation and migration strategies can improve the energy efficiency.
An Adaptive Threshold Algorithm for Offline Uyghur Handwritten Text Line Segmentation

This paper presents an effective text-line segmentation algorithm and evaluates its performance on Uyghur handwritten text document images. Projection based adaptive threshold selection mechanism is implemented to detect and segment the text lines with different valued thresholds. The robustness of the proposed algorithm is admirable that experiments on 210 Uyghur handwritten document image including 2570 text lines got correct segmentation by 97.70% precision and 99.01% recall rate and outperformed the compared classic text-line segmentation algorithm on same evaluation set.
Video Retargeting

As new display technologies (i.e. foldable phone and modular display) with variable aspect ratios emerge, content-aware video retargeting has attracted much attention from both academia and industry. The content-aware video retargeting aims to adjust the aspect ratio of a video sequence while preserving both, its content and its spatio-temporal consistency. This is a particularly challenging task since these two properties may drastically differ and contradict depending on the video characteristics. In this paper, we explore this conflict in the context of video retargeting, then we propose an appropriate solution to alleviate this issue using a deep recurrent convolutional neural network architecture. First of all, we present a method to generate multiple ground-truth labels under various aspect ratios. Using this dataset, our network is trained to predict various retargeted video candidates from a single input sequence. The resulting candidates present different properties, some of them with more emphasis on the content preservation while the others focus on the spatio-temporal consistency. Among the generated candidates, the final result which satisfy the best compromise is selected. A large set of qualitative and quantitative experiments shows the ability of our method for the content-aware video retargeting.
Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model

The paper presents the derivation of Yule-Walker equations for the nonlinear autoregressive model NAR(p) of a time-series. The proposed method allows for easy calculation of parameters of the model. In the determined equations, higher order statistics are used, instead of autocovariances. For the linear autoregressive model AR(p), the standard Yule-Walker equations are directly based on autocovariances of time-series, or equivalently autocorrelations if the equations are rescaled. Unfortunately, it does not apply for nonlinear model. The authors show a compact matrix notation of Yule-Walker equations for the nonlinear autoregressive model with the nonlinearity of polynomial type of degree 2, with the use of higher order statistics up to 4th order, and numerical examples for electromyography signals for different hand movements.
Word Recognition for the Balinese Palm Leaf Manuscripts

Most of the ancient Balinese manuscript collections on palm leaves are now in danger from the natural physical degradation caused by the aging period of the leaves as the writing media and the unprotected storage from dust, fungus, and insects. The digitization projects for Balinese palm leaf manuscripts, which already collected many digitized manuscripts, were halted without the next step to create a complete transliterated text of the manuscripts. To overcome this problem, an automatic word recognition system is urgently needed to be integrated into the manuscript digitization project. This paper presents an experimental study on word recognition for the Balinese manuscripts on palm leaves. In this research, some image-based feature extraction methods are used and are tested to recognize the isolated word segment images without doing any “character-by-character” recognition step and without doing any OCR-transliteration method. The recognition rate for three different features, with six different schemes and parameters for the classifiers are calculated. The proposed feature combination of HoG with the NPW-Kirsch features improve the recognition rate, and the K-NN classifier performs better compare to the NN classifier in the condition of the imbalance number of data training for each class in the dataset. The effect of the length of the word on the recognition rate are also analyzed. We found that longer words have better recognition accuracy, both in terms of Recall and Precision values. The results of this research serve as the initial analysis for the future word spotting system for the Balinese palm leaf manuscripts.
Implementation of a Databaseless Web REST API for the Unstructured Texts of Migne's Patrologia Graeca with Searching capabilities and additional Semantic and Syntactic expandability

The search through large corpora of unstructured text on the Web Domain is not an easy task and such services are not offered for the common user. One such corpus is the published works of east Christian fathers by Jacques Paul Migne, known as Patrologia Graeca (PG). In this paper, an application of a Databaseless model is presented for extracting information from the unstructured patristic works of PG on the Web Domain. The user queries terms that may exist in PG and retrieves all the paragraphs-fragments that contain these terms. The time for retrieving the information is faster than implementing this querying system in a common Relational Database Management System (RDMS). Our proposed system is portable, secure and can be easily maintained by institutions or organizations. The system auto-transforms the PG corpus into a Representational State Transfer Access Point Interface (REST API) for retrieving and processing the information, using the JavaScript Object Notation (JSON) format. The User Interface (UI) is completely distinguished from the backbone of the system while the system, on the other hand, can be easily extended for more complicated queries as well as to be applied to other corpora. Two kinds of user interfaces are described: The first one is completely static, useful for the average user using the Web browser. The second one illustrates the use of simple Shell Scripting, for searching and extracting statistical, syntactical and semantic information in real time. In both cases we try to strip down the complexity in order to accomplish the corpus transformation and searching, in a simple, secure and manageable way. Difficulties and key problems are also discussed.
Semantic Processing Method to Improve a Query-Based Approach for Mining Concept Maps

Concept maps are powerful visual tools for organizing and representing knowledge. The development of computational systems for building and managing concept maps has facilitated the creation of concept maps repositories, which can be considered valuable knowledge models. In this paper, a semantic processing method to improve a query-based approach for mining concept maps repositories is proposed. The method is based on a mechanism of semantic extension of the represented concepts in the concept maps and a set of rules for guiding the semantic integration of the conceptual structures and retrieval information processes. WordNet is used for the semantic extension of concepts, which is supported in a disambiguation algorithm. Results of experimental evaluations of the disambiguation algorithm applied to several concept maps are presented and compared with the state of the art. The application of the proposed method is illustrated through a study case with promising results.