Reviewerly/exHarmony huggingface dataset:
Automatic airway tree segmentation from pediatric CT images with foreign bodies based on convolutional neural network

Foreign bodies in airway are most common emergency diseases in pediatrics. The peak incidence is for children of 2-3 years old, being one of the main causes of accidental death in children. In most cases, the symptoms of airway foreign body inhalation are non-specific, making early diagnosis difficult and critical. Multi-slice spiral CT examination plays an important role in the initial diagnosis and follow-up evaluation of foreign bodies in the respiratory tract. However, as the foreign body could be very small in size and varies in shape and the physicians might be stressed especially at night, it is extremely prone to missed diagnosis and misdiagnosis, affecting timely treatment and endangering the lives of children. This paper proposed to segment children's airway trees based on a deep fully convolutional neural network to aid the diagnosis. In the proposed method, the atrous convolution with different rates was employed to capture multiscale feature information and receptive fields, and the upsampling model based on global pooling and attention mechanism was explored to integrate the global content information of high-level features into low-level features. The proposed method was validated on 2 datasets. In 20 test cases of the children's thoracic data, the Dice similarity coefficient, volumetric overlap error, relative volume difference and average symmetric surface distance were 90.87%, 15.41%, 18.01% and 0. 57mm, respectively. In 8 test cases from the EXACT09 challenge, the proposed method performed better than existing methods to yield a tree length of 135. 33cm. The proposed airway tree segmentation method could be a potential tool to aid and enhance diagnosis of airway foreign bodies of children.
Unsupervised Monocular Depth Estimation with Scale Unification

Due to the importance of high precision depth data for driving assistance system, related methods to predict scene depth without ground truth have attracted more attention than ever. Therefore, this paper comes up with a novel unsupervised monocular depth estimation model. Based on the structure of Deep3D, the proposed model reduces the influence of the diverse scale of targets by processing input images as pyramid structure. Furthermore, the proposed model improves the `hole' phenomenon in depth maps and solves it by up-sampling the intermediate disparity maps to the initial resolution. The experimental results show that this model achieves convincingly accuracy on classical KITTI dataset and excellent generalization capability on images randomly captured in real driving scene.
Saliency detection based on the fusion of spatial and frequency domain analysis

Saliency detection is a challenging direction in the field of computer vision. To get the more accurate salient target, we propose a visual saliency detection method based on the fusion of spatial and frequency domain analysis. Firstly, the image is smoothed by a Gaussian filter and then segmented by the superpixel segmentation of SLIC. The spatial saliency map is calculated by the color distance. Secondly, the saliency map of frequency domain is obtained by hypercomplex Fourier transform. Finally, the point multiplication operation is adopted to merge the spatial and the frequency domain saliency map. Compared with the others methods, the model we proposed can well retain the complete contour, highlight the target and suppress the background <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup>
On streams and lakes

Abstract This article advances the notion of metaventriloquism by bringing together the concepts of metacommunication and ventriloquism ( Cooren 2010 ). Metaventriloquism is when one makes claims regarding who or what another is speaking behalf of. To explore the implications of metaventriloquism, a public hearing related to a community water controversy is analyzed. The analysis illustrates how metaventriloquism may be used as a form of critique and operates retrospectively in claiming what motivated another, and prospectively in claiming what another should do. The implications of metaventriloquism for the construction of technological risks are also explored.
Web User Clustering Techniques for Recommendation Systems

The Recommender system is a revolutionary technique in E-commerce marketing representing the user preferences and suggestions on items such as books, movies or products. Also, the preferences for any target users are predicted using different recommendation methods. The preferences for any target user can be tailored using different Recommendation methods, such as Content, Collaborative and Hybrid Techniques. There are different challenges that are addressed by the recommendation system: classifying and clustering the user preferences and ratings, finding the users who have common characteristics, suggesting the suitable items to the target user based on past history of users. This chapter provides an elaborative discussion on variety of Recommendation systems techniques, clustering techniques and their merits and demerits.
Fractional Active Contour Model for Edge Detector on Medical Image Segmentation

In computer terms, segmentation is a process to partition or to divide an image based on the number of objects within the image. The process of segmentation can be easy depending on the quality of the image such as the level of noise, the image contrast and etc. Segmentation on medical images has its own importance such as to extract an importance object like tumor or others, to assist the physician in making decision for surgery purposes. Currently many methods have been developed but to get accuracy in segmenting multi modalities of medical images are still remain unsuccessful. Among all methods, Active Contour Model shows good potential in medical image segmentation. But, accuracy in detecting edges along the object boundary is still remain unsuccessful. The used of fractional calculus that act as the first order integer to extract the missing pixels along the object boundary is seen to have the potential in solving the problem. This paper proposed a method called, Fractional Active Contour (FAC) model. The proposed method tends to highlight the role of fractional as an operator to detect and preserve the missing edges as well as giving the bending capability to the contour of the model. Experiments on several medical images from MRI, CT Scan and X-ray images demonstrates that the proposed FAC with the usage of the powerful fractional calculus as the edge detector model realizes an accurate boundary segmentation although under the constraint of missing edges within the environment of intensity inhomogeneity.
Chapter 3. “Pero… y las caritas esas, ¿cómo se ponen?”

Using a netnographic approach, the present study aims to find out whether age plays a role in the realization of the speech act of complimenting as performed by a Facebook community of Peninsular Spanish users. To that purpose, a corpus of a hundred compliments was collected. Fifty of these compliments were performed by users in the age range of 18–25 and the other fifty by users in the age range of 60–75. To control for the gender variable, only compliments produced by female users were gathered. Results show that age plays a role in the way compliments are produced, especially with regards to the use of non-verbal elements (e.g., emoji). Verbal realization strategies also show interesting differences in relation to age.
Regulatory Element Parallel Mining Algorithm Based on Bit Combination

Regulatory element parallel mining algorithm of frequent itemset based on bit combination is proposed in this paper. The algorithm is represented by converting promoter data filtered by frequent itemset into binary machine bits, and then mining the frequent itemset of the data set in parallel by means of regulatory element. In the whole mining process, if a combination of regulatory elements is frequent, data should be added 1, and if it is not frequent, the data should be pruned. Preprocessing, eliminating and other operations are used to optimize the whole algorithm. Converting the processed raw data and regulatory element into binary bits facilitates the parallel operation of data and saves memory space, breaking through the limitations of traditional methods that cannot be effectively parallelized due to recursive operations in the mining process. In this thesis, OpenMP technology is used to parallelize the algorithm based on regulatory element, which improves the efficiency of the algorithm.
Performance analysis of video streaming service migration using container orchestration

Abstract This paper proposes an implementation of container orchestration to accelerate the migration process from one cloud to another. The use of cloud computing as the infrastructure of a system is in high demand. Cloud computing has been adopted in the use of various applications that are very useful for organizations in establishing a system to reduce effort, cost, and time. The system that runs on the same cloud provider has disadvantages. If the cloud provider goes down, then all of the running services will be affected. Therefore, it is needed the solution to migrate the services to some containers with one controller. In this research, two types of architecture are compared, i.e. container orchestration architecture using Kubernetes and simple container architecture using Docker. We use Kubernetes since it is one of the most popular container orchestrators widely used in big company’s productions. The results show that the container creating time average in the container orchestration architecture using Kubernetes is 26 times faster and the percentage average of resource usage on CPU utilization is 3.6 times smaller compared to simple container architecture using Docker.
A Survey On Image Filtring Algorithms

Image processing, especially filtering has become important because there are many factors that affect the quality and purity of the images. For example, the change in pixel density or illumination will negatively affect the images; therefore noise should be removed using filtering. Image filtering is useful for many applications including smoothing, sharping, removing noise and edges enhancement. This paper analyses the various algorithms used for image filtering which improves image quality and clarity. The first section introduces different algorithms that are used in image filtering and how each algorithm processes the image. Then, a discussion and analysis of the literature review has been done to classify and compare between the algorithms studied.
Autonomous Vehicles and the Law: How Each Field is Shaping the Other

Disciplines can no longer be isolated. Technology has rapidly evolved to the point that driverless vehicles have truly become a reality and are not something out of a futuristic exhibition from the 1950s. However, engineers and researchers working on the development of autonomous vehicles cannot ignore the policy implications and policymakers as well as attorneys cannot ignore the technology. We are at a point where cross-disciplinary collaboration is vital in order to produce a technology that will immensely benefit society. This is the goal of this book: to educate autonomous vehicle developers on legal theory at the most basic level. Both policymakers and lawyers may also find the book helpful in gaining a basic understanding of the technology the developers are working on.
Label Distribution Learning on Imbalanced Data

Although multi-label learning can handle problems involving label ambiguity, it is not suitable for applications where the overall distribution of the importance of the labels matters. Imbalanced data also poses challenges to classification applications. In this paper, we propose a novel learning paradigm named Label Distribution Learning on Imbalanced Data (LDLID) for such applications, which is based on the improved Kullback-Leibler Divergence and uses the adaptive step size to update the gradient. The experimental results show that LDLID achieves better performance in comparison with four widely-used algorithms on four imbalanced datasets.
Class Prediction Performance Boost Using Gaussian Mixture Models

Fine-grained image classification remains a challenging task due to subtle visual differences between subordinate categories. Most existing approaches focus on improving key parts detection ability and feature extraction ability of neural networks, while our approach focuses on improving class prediction ability with Gaussian mixture models. Gaussian mixture layers (GML), which learn Gaussian mixture feature distribution and predict classes, are designed to replace fully connection layer, or placed right after them. Experiment results indicate that classification performance can be improved significantly by using GML on top of VGG model. In addition, GML are compatible with any neural networks that contains fully connection layers, which enables them to further boost the performance of cutting-edge classification networks.
Automatic Analysis of Brain Tumor from Magnetic Resonance Images based on Geometric Median Shift

In this paper, we propose an automated approach based on the geometric median shift algorithm over Riemannian manifolds, for the brain tumor detection and segmentation in magnetic resonance images (MRI). This approach is based on the geometric median, geodesic distance. We propose the median shift to overcome the limitation of mean which is not necessary a point in a set. The geodesic distance can describe data points distributed on a manifold, compared to the Euclidean distance, and produce efficient results for image analysis. Coupled with k-means algorithm, the proposed framework can cluster the brain image into tree regions (gray matter, white matter and cerebrospinal fluid) and abnormalities regions. We applied this approach to clustering the brain tissues and brain tumor segmentation, which is validated on a synthetic brain MRI. The obtained results using two datasets show the efficiency of the used algorithm validated qualitatively by the measurement of Dice Similarity Coefficient.
SAR Images Co-registration Based on Phase Congruency Algorithm

As the first step in image processing operations, corner detection is a vital procedure where it can be applied to many applications such as matching features, image registration, image mosaicking, change detection.... Registration of images can be described as the process of getting the pixel location misaligned between two or more images. A modified corner detector is proposed in this paper based on a combination of both phase congruence, later named PC, and Harris corner detector where PC image can provide fundamental and meaningful features despite complex changes in intensity. The performance was similar to detectors for the Shi-Tomasi, FAST, and Harris corner. Experiments are carried out using simulated images. As metric metrics, MSE (mean square error) and PSNR (peak signal-to-noise ratio) are used. The experimental results verify the effectiveness where the advantages of image constitutional representation are utilized, allowing the extraction of the powerful key points since it preserves the robustness of the coregistration process using image frequency properties that are not variant to illumination. It also has the ability to produce reasonable results as opposed to state-of-the-art such as Shi-Tomasi, FAST, and Harris algorithms.
Interpreting Collectively Compressed Multi-Layered Neural Networks

This paper proposes a new information-theoretic method to interpret the main characteristics of neural networks after learning. The method does not try to interpret networks from one viewpoint among many, but the method considers all possible representations from the neural networks. The new method tries to compress multi-layered neural networks and to average all possible weights and activations, which is close to the ensemble method, popular in improving generalization performance. The method was applied preliminarily to the symmetric data set for showing the easily interpretable final results. The result shows that the method could improve generalization performance by using the information augmentation. In addition, compressed weights from multi-layered networks could contribute to improved generalization performance for two-layered networks without hidden layers. Then, the final collective weights showed perfectly the symmetric property of the data set. In addition, the connection weights of the two-layered networks showed also the symmetric property by using the collective or compressed weights as initial weights.
Study on a New Method of Rare Pattern Mining Based on QAR Data

Most pattern mining methods concentrate on finding frequent pattern. However, rare pattern mining in some cases is more interesting than frequent pattern mining since rare pattern represents unexpected or unknown information. In this paper, we propose a new method of rare pattern mining based on QAR data to mine rare patterns which is related to pilot unsafe manipulation. The proposed method takes advantage of the correlation between QAR parameter and pilot unsafe manipulation to define the item order in item set and makes use of the concept of global support degree. At the same time, 4 types of focused overrun events are used as entry points to explore pilot unsafe manipulation scenarios in the experiments. The experimental results show that the proposed method in this paper reduces execution time. Finally, pilots and regulatory experts are invited to confirm the mining results. The feedback verify method's feasible and effective.
SafeComp: Protocol for Certifying Cloud Computations Integrity

We define a problem of certifying computation integrity performed by some remote party we do not necessarily trust. We present a multi-party interactive protocol called SafeComp that solves this problem under specified constraints. Comparing to the nearest related work, our protocol reduces a proof construction complexity from $$O(n\log n)$$ to O(n), turning a communication complexity to exactly one round using a certificate of a comparable length.
Damarjati Application for Development of Javanese Language Learning in High Schools

Abstract The background of this study is the difficulty of Javanese high school language teachers in implementing the 2013 Local Content Java Language Curriculum. The problem discussed is how the application of the Damarjati application in the development of HOTS-based Javanese language learning in high school. The objective to be achieved is to describe the standard operating procedures of the Damarjati application in the development of HOTS-based Javanese language learning in high school. The object of this research is the application of the Damarjati application in the development of HOTS-based Javanese language learning with a population of Javanese language teachers and high school students in Central Java. The phenomenological approach applied in this research is carried out by developing products in the learning process with the Content Management System (CMS) format. Based on in-depth studies it was concluded that: 1) the Damarjati application was opened through damarjati.upgris.ac.id.; choose classes and semesters; choose themes and sub-themes, and provide corrections and comments; and 2) the application of the Damarjati application can develop HOTS-based Javanese language learning in high schools through the nggatekake menu, tetakon , naliti , nyoba , ngatonake , and gladhen . Our gratitude goes to the In the 7th International Conference on DV-X melalui Method Committee, through Ms. Mega Novita for her willingness to give our team the opportunity to join this highly qualified international conference.
Automatic Recognition of Gender and Genre in a Corpus of Microtexts

In this paper, we focus on author’s gender and writing genre recognition solely on books titles. We analyse data extracted from the bibliography resources of the National Library of Poland. Within a paper, we compare different methods of text (title) representation and classification. It includes word embedding models such as word2vec, ELMo and classification algorithms such as linear models, multilayer perceptron and bidirectional LSTM. It is shown, that the writing genre (for defined 28 classes) could be automatically recognized based only on the book title with accuracy equal to 0.74. The best results were achieved by fastText methods with word n-grams.
Intelligent Inference Agent for Safety Systems Events

The paper describes the idea of the intelligent agent based on the ontogenic neural networks. The agent is created for inference problem needed in some safety systems. The main goal of the agent is to find if the aggregated data collected from the different sensors should point actual state of the system as alarm. The neural approach to the agent structure allows to combine the diverse nature of the sensors output into single clear answer. The ontogenic type of the neural network is the key to tune the topology to real needs driven by the scale and the safety systems features. The size of the training vectors can be limited as well as the number of the training epochs. Better results of alarm state prediction we can expect when we use the hybrid combination of agents focused on some specific types of events. This way the idea of the Complex Event Processing (CEP) safety systems seems to be sensible where Event Processing Agents (EPA) are also intelligent tools created based on already data sets collected previously.
Review selection based on content quality

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing methods is far from ideal. One reason is because of lacking effective criteria to assess the quality of reviews. In this paper, we propose two novel measures, i.e. feature relevance and feature comprehensiveness, to assess the quality of reviews in terms of review content. A review selection approach is presented to select a set of reviews with high quality based on the two measures. Experiments on real-world review datasets show that our proposed method can assess the review quality effectively to improve the performance of review selection.
Block Chain Technology: An Outline

In the digital world security and privacy preservation is a most challenging task. Lots of transaction happens digitally which is highly secured. Digitally secured money transaction is a major problem. Block chain technology helps to do highly secure digital money transaction with high returns. This paper discusses about various features of block chain and also lots of applications where the block chain plays a major role.
NewsScatter: Topic Similarity in Social Media

News organisations that use social media sites (such as Twitter) rapidly generate a large volume of data every day. Data visualisation is an effective way to represent microblogging data graphically in order to increase understanding of what news organisations are reporting over a given time period. This paper focuses on visualising the tweets (posts on Twitter) of a broad cross-section of English-language news outlets over time, with the goal of developing an interactive web-based visualisation system that summarises the output of news outlets' tweets as a scatter plot where each point refers to a news outlet. Such a visualisation could enable interested parties to explore similarities and differences among what news outlets report based on the frequency with which specific words are used.