Reviewerly/exHarmony huggingface dataset:
Key-Point Matching Guided Coronary Artery Extraction from CT Coronary Angiography Sequence

Extracting coronary arteries is important in clinical applications of cardiac Computed Tomography Angiography (CTA). However, obtaining an accurate segmentation of coronary artery is challenging because of complex anatomy, variation in image quality, as well as motion induced by heart beating and respiration. This paper presented a semi-automatic framework dedicated to coronary artery segmentation in 3D+t Computed Tomography Angiography (CTA) sequence. The proposed segmentation scheme consists of two parts: vessel centerline extraction and vessel shape recovering. For the starting phase, spherical flux based minimal path method was employed to extract the main centerlines of coronary artery tree. To implement similar centerline extraction scheme automatically in subsequent phases, a novel method key-points detecting method was proposed and an endpoints searching procedure was taken. In this process, the key-points on distal vessels are easily got mismatched. A centerline tracking algorithm constrained by patient-specific vessel length was used to complete the extraction. The the overlap (OV) results of proposed framework are 93.56% (LAD), 90.96% (LCX) and 93.76% (RCA). We also achieved accuracy (AC) r:esult: 0.6508mm (LAD) 0.6141mm (LCX) and 0.6297mm (RCA) which are less than the data voxel size (0.68mm).
Log-sum-exp Optimization based on Continuous Piecewise Linearization Techniques

The log-sum-exp (LSE) function is a class of important non-linear functions that has been widely applied in many fields, such as the signomial geometric optimization, deep neural networks, etc. In this paper, we systematically investigate the log-sum-exp optimization (LSEO) methods. In order to handle the non-linearity and non-convexity of LSEO problems, we propose an iterative algorithm by optimizing a series of continuous piecewise linear (CPWL) problems where LSE functions are approximated using the continuous piecewise linear technique. Since CPWL concave problems have some useful structural properties, we propose an efficient algorithm which solves a series of linear programming problems. To confirm the validity of the proposed method, we test on some toy examples, as well as the multi-class classification based on artificial neural networks with a softmax output layer and the cross-entropy loss function.
Evaluation of Dietary Life Instruction in Middle School Home Economics by Converging Habermas’s Three Systems of Action

This study developed and implemented a teaching · learning process plan for Home Economics in middle school by converging Habermas’s three systems of action. It also examined the effect of the class through the evaluation of students and teachers who participated in the class. This study developed 10 sessions for a teaching and learning process plan by converging three systems of action and reconstructing learning elements related to ‘balanced meal plan’ and ‘food choice’ according to the practical action teaching model. After class, we surveyed the degree of help for students, analyzed the learning activity sheets, and analyzed the reflection journals of teachers to evaluate the effects of the class.&lt;br/&gt;This class was found to be the most helpful in practicing the healthy dietary life of students, expanding their thoughts, understanding learning contents, and helping them change their lives. As a result of analyzing the learning activity sheet, students gained enlightenment by reflecting and evaluating their action through the class; in addition, changes in interest, awareness, and action appeared. Through the convergence of three systems of action, teachers who practiced the class criticized and realized the act that students were unconsciously accepted. In addition, it confirmed the possibility that students could change their lives, family and society by promoting optimal nutrition and health for a good life that pursues the best good.
Bifurcation Analysis of Wilson-Cowan Model with Singular Impulses

The paper concerns with Wilson-Cowan neural model with impulses. The main novelty of the study is that besides the traditional singularity of the model, we consider singular impulses. A new technique of analysis of the phenomenon is suggested. This allows to consider the existence of solutions of the model and bifurcation in ultimate neural behavior is observed through numerical simulations. The bifurcations are reasoned by impulses and singularity in the model and they concern the structure of attractors, which consist of newly introduced sets in the phase space such that medusas and rings.
Post-Processing Summarization for Mining Frequent Dense Subnetworks

Gene expression data for multiple biological and environmental conditions is being collected for multiple species. Functional modules and subnetwork biomarkers discovery have traditionally been based on analyzing a single gene expression dataset. Research has focused on discovering modules from multiple gene expression datasets. Gene coexpression network mining methods have been proposed for mining frequent functional modules. Moreover, biclustering algorithms have been proposed to allow for missing coexpression links. Existing approaches report a large number of edgesets that have high overlap. In this work, we propose an algorithm to mine frequent dense modules from multiple coexpression networks using a post-processing data summarization method. Our algorithm mines a succinct set of representative subgraphs that have little overlap which reduce the downstream analysis of the reported modules. Experiments on human gene expression data show that the reported modules are biologically significant as evident by Gene Ontology molecular functions and KEGG pathways enrichment.
A Simple Depth-Key-Based Image Composition Considering Object Movement in Depth Direction

A simple depth-key-based image composition is proposed, which uses two still images with depth information, background and foreground object. The proposed method can place the object at various locations in the background considering the depth in the 3D world coordinate system. The main feature is that a simple algorithm is provided, which enables us to achieve the depthward movement within the camera plane, without being aware of the 3D world coordinate system. Two algorithms are proposed (P-OMDD and O-OMDD), which are based on the pin-hole camera model. As an advantage, camera calibration is not required before applying the algorithm in these methods. Since a single image is used for the object representation, each of the proposed methods has its limitations in terms of fidelity of the composite image. P-OMDD faithfully reproduces the angle at which the object is seen, but the pixels of the hidden surface are missing. On the contrary, O-OMDD can avoid the hidden surface problem, but the angle of the object is fixed, wherever it moves. It is verified through several experiments that, when using O-OMDD, subjectively natural composite images can be obtained under any object movement, in terms of size and position in the camera plane. Future tasks include improving the change in illumination due to positional changes and the partial loss of objects due to noise in depth images.
Software design as story telling: reflecting on the work of Italo Calvino

Are we really writing software?
A Study on text Recognition and Obstacle Detection Techniques

Blindness is often used to describe some form of visual impairments or vision loss. There is a great dependency for the blind people navigation or walking in any unfamiliar area. They depend on any persons to help them or they use their natural senses such as touch or sound for identification or Navigation. The object detection methodologies can be useful for detecting the object in their navigation path. Object detection is a computer vision technique for locating the objects in images or videos, it leverages on machine learning or deep learning techniques to produce meaningful results. The main aim of this article is to understanding various object detection techniques and analyzing its benefits and drawbacks.
Empathic Flower Companion to Increase Productivity- EFC

Humans nowadays are tending to spend too much time in front of their screens. Direct interaction between humans is falling in numbers and people are losing their empathic behaviour. By integrating empathy and emotions in everyday objects researchers can address this problem. In addition we can have a positive effect on our lives, from physical and mental health through tackling many issues, like productivity, time wasting, stress and other problems. In this article, we tackle the productivity problem by presenting the Empathic Flower Companion (EFC) that will be using the expression of emotions to help the human through their working day. It will be monitoring their time and at the same time analysing the websites they will be surfing. The concept proposed will reduce the time wasted on unproductive websites. The results show an increase of 15% in the productivity of the testers, which shows that EFC was effective in reducing the amount of time wasted.
Leveraging Social Data for Hate Speech Classification

Through the rapid development that takes place on the Internet, hate speech is additionally spreading. We get it and take a see at the ways in which we have been inquired about through online modified strategies to dismiss the speech's substance location. Among these challenges are subtleties of dialect, different definitions of what incorporates upsetting talk, and states of getting to data to plan and test these systems. Other than, a few outstanding strategies test the pathogenic impacts of the issue of decoding capacity - that's, it is exceptionally troublesome to get it why outlines are set to the alternatives that in this paper we examine the innovation to uncover despise discourse on social media. We point to execute supervised classification technologies using a newly released data set described for this purpose. As features, our systems use many models Naive Bayes, Support Vector Machine (SVM), Neural Network. We get results with high accuracy in identifying posts in four categories. The results show that the main challenge lies in random profanity and hate speech from each other. Our proposed approach outperforms all modern methods with a significant increase in accuracy.
ReViz: A Tool for Automatically Generating Citation Graphs and Variants

A systematic literature review provides an overview of multiple scientific publications in an area of research and visualizations of the data of the systematic review enable further in-depth analyses. The creation of such a review and its visualizations is a very time- and labor-intensive process. For this reason, we propose a tool for automatically generating visualizations for systematic reviews. Using this tool, the citations between the included articles can be depicted in a citation graph. However, because the clearness of the information contained in the citation graph is highly dependent on the number of included publications, several strategies are implemented in order to reduce the complexity of the graph without loosing (much) information. The generated graphs and developed strategies are evaluated using different instruments, including an user survey, in which they are rated positively.
Comparison of Procedural Noise-Based Environment Generation Methods

In this paper a comparison of selected algorithms used to procedurally generate terrain for video games is presented. The algorithms’ performance is tested with two implementation environments: Unity and Godot. Results are aggregated and discussed. Conclusions drawn are of two types: intuitive and counter-intuitive.
Improved Strategy for High-Utility Pattern Mining Algorithm

High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification was carried out on nine typical datasets (including two large datasets); results show that our strategy can effectively improve temporal efficiency for mining high-utility patterns.
Online multi-core ranking model for image retrieval

Abstract With the popularity of social media services, efficient online image retrieval urgently needs to meet the diverse needs of network users. How to use the existing semantic information and social label information to establish a content model to bridge the semantic gap is an urgent problem to be solved. In this article, we propose an efficient online multi-core ranking model (OMKR), which is trained by minimizing the triplet loss of hard negative samples based on multiple query dimensions and complementary feature channels. By optimizing the sorting performance of multi-dimensional queries, the semantic consistency between image sorting and text query input is directly maximized without relying on the intermediate semantic annotation process. A large number of experiments on two social media data sets prove the advantages of our method in terms of retrieval performance.
Learning Parameters for Hybrid Bayesian Network

The Hybrid Bayesian network (HBN) is a type of Bayesian network (BN) with continuous and discrete variables. A continuous random variable can have both discrete and continuous parents, whereas a discrete random variable can have only a discrete random variable. Bayesian network (BN) is a graphical representation of the joint distribution of the random variables. BN is used to identify the structure in a large amount of data and captures the conditional independencies among random variables, thereby used for prediction of the unseen or missing data. In real-world scenarios, most of the data is a combination of both discrete and continuous distributions. In this work, parameters are estimated for a continuous random variable in HBN, which is a linear combination of both continuous and discrete parents, also estimating joint multivariate Gaussian distribution for continuous random variable having only continuous parents.
일본의 남성 맞춤복 테일러링 문화에 관한 구술사 연구

This oral history study explores the past and present Japanese menswear custom-tailoring culture. Two master tailors with more than 50 years of experience and two young tailors with less than a year of experience were interviewed about the tailor training process and environment, working conditions, thinking and behavior, and societal and governmental efforts for vitalizing custom-tailoring. The results indicate, first, a 10-year apprenticeship was required in the past, whereas young tailors today must register for three-year professional tailoring classes. Tailors then and now have been trained in a similar sequence of pants, vests, and jackets. Second, regarding working conditions, tailors had to provide gratitude services to their masters for a few years, even after their training ended. In contrast, young tailors today must continue a probationary period after their three-year schooling; however, they experience difficulty with finding tailoring shops for their probation. Third, in terms of thinking and behavior, master tailors learned their trade to earn a living, whereas young tailors today entered the field due to their interests in it. In addition, young tailors want a systematic learning process, whereas master tailors learned their skills while on the job. Last, tailoring academies today are run by tailoring shops and societies. Career exploration programs are offered to elementary and middle school students by the Kobe government in association with the local tailoring society to provide tailoring experiences.
A Similarity Function for HTML Lists

The Web continues to grow day after day, being the largest source of data in several domains of knowledge. Such a data are particularly available into billions of HTML pages. Due to it, Web scraping is an increasing activity with a great focus on structured data, like Web tables and Web lists. While the literature about extraction and matching of Web tables is extensive, this is not the same for Web lists. The main reason for that is the absence of a heading with fixed and explicit attributes into a Web list, which makes difficult to find out similar data for matching purposes. Related work about Web lists similarity do not deal with the comparison of the content of two different lists. In order to fill this gap, this paper proposes a similarity function for HTML-coded lists called Simlist. It innovates by comparing not only the content of the two input lists but also data on the HTML pages that surround the lists and may contribute to infer their contexts. Besides, Simlist may be customized by the user in terms of weights for the internal and surrounding data, as well as auxiliary text similarity measures. Preliminary experimental evaluations show that our similarity function had obtained good precision, recall and f-measure scores even considering the inherent heterogeneity of HTML lists on the Web.
AOPNet: Anchor Offset Prediction Network for Temporal Action Proposal Generation

Temporal action proposal generation is a hot topic in the field of video understanding, aiming to extract action instances in long untrimmed videos. Although various approaches have been proposed, the challenge of proposal generation with reliable confidence scores and precise boundaries still exists. To solve this challenge, we propose a novel proposal generation framework called Anchor Offset Prediction Network (AOPNet), which utilizes multi-layer Bidirectional RNN (BiRNN) to predict multi-scale action proposals and fine-tune temporal boundaries with the predicted anchor offsets. Experimental results demonstrate that though with the use of anchor boundary offset prediction module, the AOPNet can remarkably improve the classification accuracy of temporal anchors and finally generate temporal proposals with high recall. Ablation studies on the THUMOS-14 dataset demonstrated the effectiveness of the proposed approach.
A new PDE-based resolution enhancement technique for the analysis of low SNR particle displacement images

Speckle images have found multiple applications in image-based measurement techniques, where the objective of measurement is the estimation of speckle displacements between two often consecutive images. Depending on the characteristics of a measurement system and its measurands, the signal-to-noise ratio (SNR) of recorded images may become low and even marginal. One solution to improve the accuracy of speckle displacement estimation in such cases is the use of super-resolution methods. In this article a robust and versatile partial differential equation based resolution enhancement method is presented for the analysis of low SNR speckle displacement signals. The application of the method to synthetic one-dimensional and two-dimensional signals as well as actual particle displacement images indicate that the method is capable of reducing noise effects and partially recovering lost correlation, which in turn makes the selection of smaller interrogation windows and achieving higher resolution and accuracy of displacement estimation possible. The method is applicable to both speckle and particle displacement images.
A Two Step Approach for Joint Detection

Human joints can be used in areas such as activity recognition, animation, augmented reality and robotics. Thus, there is a need to correctly detect these points. This paper proposes a two step method for joint detection. First an initial prediction is performed using the HRNet network. In the second step an improvement is applied on the initial predictions for each joint using a convolutional neural network. Tests were performed using the Unite the People dataset. The new model is able to improve the results of the base model by 1.81%.
Structure Learning of CP-nets Based on Constraint and Scoring Search

CP-nets are one of the powerful tools for learning uncertain relations in the field of artificial intelligence, in which learning problems are the main research content. So far, there are many learning methods about CP-nets, which are widely used in information retrieval, user decision-making, recommendation systems and other fields. This paper attempts to propose a new solution, using an algorithm based on the combination of conditional independence and scoring search to learn the structure of CP-nets, it combines local learning, constraint-based and search scoring techniques, which is principled and effective. Firstly, this paper using the MMPC algorithm to get CPC(candidate parent-child node), and then selecting the appropriate search algorithm as a measurement standard, and perform a scoring search to obtain the optimal network structure. Based on the idea of simulated annealing in the search phase, this paper combines the probability jump feature to perform random search in the solution space to avoid falling into the local optimal solution, obtain better search results, and compare with several search algorithms. In the experimental part, it is compared with other hybrid algorithms such as sparse candidate algorithm, and the agreement is used as a measurement standard to verify the effectiveness of the algorithm.
Analyzing Performance of Virtualization and Green Computation Applying Firefly Approach

The concept of green computing in general innovates the measures to overcome the issues linked towards computation. In fact, now it is a general consideration the greening concept enhances the publication relations as well as minimizes the cost of processing. The green computation not only maximizes the efficiency but also minimizes the computing resources. Deployment of internet of things or cloud service sometimes is complex while linked to its effectiveness as well as cost. In such a case, it is required to ascertain the challenges towards computation in cloud with suitability and design mechanism towards decision making. While analyzing the mechanisms linked to virtualization, the feature of portability with information exchange is obtained in heterogeneous environments. In such cases, the virtualization technology is dynamically linked to the distributed systems rather than remote servers. Accordingly, the utilization of virtualized environment increases boosting the integration of earlier computing technologies. The virtualization of servers in general consolidates the applications to multiple servers.
Parallax-Tolerant Image Stitching for Outdoor Scenes

Abstract Image stitching aims to obtain a natural panorama without ghosts. This paper proposes a panoramic image stitching method for outdoor scenes. This method gets the new homography matrix by analyzing the spatial distribution of the feature points of the outdoor scene image, and then combines with the local registration to achieve precise registration. In order to generate a more natural panorama, this paper also combines the result of registration with local similarity transformation and seam-cutting processing. Finally, the experimental results show that the proposed method obtains better performance than other methods in image stitching of outdoor scenes.