Reviewerly/exHarmony huggingface dataset:
Development and Sea Test Results of a Deep-Sea Tsunami Warning Buoy System

Abstract Tsunamis are catastrophic ocean waves that could cause devastating damage to human lives, properties, and coastal infrastructures. This article presents the design and sea test results of a deep-sea tsunami warning buoy system. The system consists of a seafloor tsunami detection subsystem and a surface buoy. The tsunami detection subsystem monitors the tsunami waves through seafloor water pressure measurement. Detection of tsunami waves is achieved by separating the influence of astronomical tidal waves from the seafloor measurements via a cubic polynomial extrapolation method. The deep seafloor measurements and the tsunami detection results are transmitted in real time from the seafloor subsystem to the surface buoy via underwater acoustic communications; the buoy then relays the data to an onshore tsunami warning center via satellite links. The tsunami warning buoy system was evaluated in four deep-sea tests at different locations. In the last sea test, the system achieved an effective working duration of 108 consecutive days. This article analyzes the causes of false alarms and data transmission failures in the sea tests and puts forward effective solutions for system improvement. The system design and the sea test results could serve as references for future development of deep-sea infrastructures.
Novel Classification Method for Short Texts with Few Words

The short text classification methods have emerged in the past few years. However, the short texts with few words like invoice data have the characteristics of extremely short length, nonstandard representation and feature sparsity, which pose challenges for traditional text classification methods. Therefore, in this paper, we proposed a novel classification method for short texts with few words which is based on text Hash vectorization and Random Forest. More specifically, the feature representation of texts is obtained by the Hash vectors of all words based on the text segmentation results, and the Random Forest classifier is adopted on the vector feature space. Extensive experiments demonstrate the effectiveness of our proposed method compared to other competing methods. Meanwhile, further experiments proved that our method has an obvious advantage on accuracy stability and memory consumption.
Improving Agile Software Development Methods by means of User Objectives

This paper aims to combine a user centred agile methodology with Acceptance Test-Driven Development in an efficient manner. Our aim is to ensure usability throughout the Software Life Cycle that allows requirement compliance to be agreed upon by stakeholders (including end users), which is subsequently used to guide the software development process. In addition, we propose to facilitate the software reuse based on User Objectives (that reflect end user needs), which is particularly useful for decreasing development costs. This proposal has been used to develop a new version of an existing application. This approach takes both end user evaluations and correct reuse of previous developments into account. Following the proposed approach, 23 agile iterations and 218 acceptance tests were carried out. Developing and executing the tests required around 34% of the total project time. Using this combined approach has led us to develop a usable application that meets the requirements specified by its end users.
On Visualization of Movements for Monitoring Older Adults

It is a well-known statistics that the percentage of population of older adults will globally surpass the other age categories. It is also observed that a majority of older adults would prefer to stay at their own place of residence for as long as they can. In support of such an independent living life style, various ambient sensor technologies have been designed which can be deployed for long-term monitoring of movements and activities. Depending on the required level of detail associated with such a monitoring system, spatiotemporal data can be collected during various intervals of daily activities for detecting any on-sets of anomalies. The greater granularity, the deeper the level of detail associated with the movement patterns for instances of time and collective durations. This paper we first presents an overview of various visualization techniques which can be employed for monitoring movement patterns. The paper further presents our results of movement visualization experiments using two sample datasets which can be used as a basis for determining movement anomalies. The first dataset is associated with the global movement patterns between various locations in an ambient assisted living environment (AAL) and the other dataset is associated with movement tracking using wearable sensing technologies.
Towards PDE-Based Video Compression with Optimal Masks and Optic Flow

Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high quality results but rely on the computationally expensive task of finding optimal data. For the possible extension to video compression, the data selection is a crucial issue. In this context one could either analyse the video sequence as a whole or perform a frame-by-frame optimisation strategy. Both approaches are prohibitive in terms of memory and run time. In this work we propose to restrict the expensive computation of optimal data to a single frame and to approximate the optimal reconstruction data for the remaining frames by prolongating it by means of an optic flow field. We achieve a notable decrease in the computational complexity. As a proof-of-concept, we evaluate the proposed approach for multiple sequences with different characteristics. We show that the method preserves a reasonable quality in the reconstruction, and is very robust against errors in the flow field.
The Hot Security Topics Analysis for the Announcements in ICANN Website by Using Web Crawler and Association Rule Technology

ICANN is an important non-profit international authority for global domain name management and IP address allocation. Its members come from all over the world, who are committed to ensuring the security, stability and resiliency of the Internet. A web crawler is a kind of the web robot which is a program used to automatically browse and analyze the specific web pages. Therefore, it could conveniently and quickly collect the required data in Internet through the web crawler program for subsequent analyses and researches. As the leader, ICANN is gaining more attention in the increasingly developed Internet. The regular announcements on ICANN’s official website are collected through the semi-automated python-based web crawler which is first designed and proposed in this paper. The hot topics in security issues will be analyzed via both the association rule and statistics technology, which are depended on a list of given keywords. After exploring the upsurges of various issues in the relative security issues or any information, the top 10 hot topics are “Security”, “ICANN”, “SSR”, “Stability”, “Public Comment”, “SSAC,” “DNS”, “DNS Marketplace”, “Technology”, and “DNSSEC”. The highest confidence degree within the association relationship statements regarding as security issues in ICANN website since 2000–2018 is “{Security} ⇨ SSR”. Therefore, the semi-automated python-based web crawler we designed and proposed could not only analyze the hot topic in the regular announcements of ICANN’s official website but also resolve the association relationship among the hot security topics through the association rule.
Some Serious Deliberations and Reflections on the Significance of Non-Functional Requirements for Improving the Quality of Software

So far there in no unanimous consensus over non-functional requirements in Software Engineering community that how to gather, analyze and jot them down in organized manner whereas the significance of these requirements cannot be denied for the complete success of the Project. Indeed, these requirements are the body and soul of the system. For achieving quality attributes both functional and non-functional requirements must be taken into account. In the past, requirement engineers majorly focused on functional requirements whereas they undermined the importance of non-functional requirements which are the quality attributes of the System. In recent days, a great deal of work is being carried on which are the paramount quality attributes that must be dealt with properly for the minimal success of the Software System. In our paper we target two areas of nonfunctional requirements; reliability and scalability to improve the quality of software.
A new method to extract BGA solder balls in complex background X-ray image based on coordinate transformation

In the detection of quality of BGA soldering based on AXI (Automated X-Ray Inspection), it's critical important to extract solder balls accurately. Although the existing methods can extract most of the solder balls in X-ray images, they are sensitive to parameters and could only be used in the case of simple background. In order to solve this problem, a new method based on coordinate transformation is proposed in this paper. In this method, original X-ray images in Cartesian coordinate system are transformed in to polar coordinate firstly, so the edge of solder ball would be transformed from a curve to a straight line. Secondly a nonparametric edge detection could be used to extract the edge of solder balls. Result in Polar coordinate system would be transformed into original Cartesian coordinate system, finally. Through the comparison of two experiments, it is confirmed that solder balls in X-ray image could be extracted by proposed method precisely, while the proposed method has much more adaptability than other traditional methods. When proposed methods combined with other method such as the detection of voids in solder balls, the detection would be effective and improve the performance of AXI system ultimately.
Imputation Strategies for Cold-Start Users in NMF-Based Recommendation Systems

We propose a recommendation system method which is based on NMF (Nonnegative Matrix Factorization) in collaborative filtering to enhance the rating predictions. The proposed method conduct selective imputations that fuses the factored original rating matrix and the factored imputed rating matrix into one system. The outputs of the factorized matrices provide four different ways to calculate the predicted ratings which are called sub-predicted ratings. Our proposed method is capable of predicting the rating by utilizing either the imputed users, or imputed items, or both in order to limit the errors that may be introduced from the imputed ratings. We proposed five strategies to calculate the final predicted rating from the sub-predicted ratings. The prediction results of rating values that are not close to the average of the rating values could be enhanced by utilizing the proposed method. Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach improves the predicted rating especially with Max of value category strategy.
Maximum Range Evaluation of Ammunition Based on Bootstrap Method

Ammunition test is comes up against the condition of lacking sample, because of increasingly long lifetime and restricted reliability test by expenditure and manpower. The evaluation of ammunition index is faced with the problem of insufficient test data. When evaluating small sample subjects faced with the problem of inadequate available prior information or a lack sample size, the commonly used Bayes method is often not ideal on accuracy. Aiming at the problem of insufficient prior information, Bootstrap simulation method is used to simulate the maximum range sample data of ammunition to increase the size of sample, and the expanded sample is used to evaluate the maximum range of ammunition, which greatly reduces the impact of insufficient evaluation sample on the evaluation results. Finally, evaluated the maximum range of a certain type of anti-riot ammunition with the analysis of a specific example, and the final result conforms to the tactical and technical index, which verifies the practicability and effectiveness of the method.
Review on Image Synthesis Techniques

Realistic Image Synthesis is the process of synthesizing images that constitutes the information of a real scene. Although they have strong relevance in multiple fields, realistic image synthesis from a given input is still a challenging problem. By making use of deep learning, more accurate methods were introduced that synthesize images in a more realistic manner. In this paper, a survey on image synthesis based on deep learning is carried out.
Automatic Selection of Sensitive Attributes in PPDP

Privacy-preserving data publishing and data analysis conventional respectable work in past age as encouraging methodologies for sharing data as well as preserving privacy of individual. A remedy to this is data scrambling. Numerous algorithms are formulated to replace lawful information by fictitious but that is practical. Yet, nothing was proposed to automate the detection associated with information scrambling which is very much essential. In this paper, we propose a novel method for automatic selection of sensitive attributes for data scrambling. This is done by ranking the attributes based on attribute evaluation measures. And also, an innovative method for privacy-preserving data publishing with automatic selection of sensitive attributes which provide secure release of information for a data-mining task while preserving sensitive information.
Too Much Information: Can AI Cope with Modern Knowledge Graphs?

Knowledge graphs play an important role in artificial intelligence (AI) applications – especially in personal assistants, question answering, and semantic search – and public knowledge bases like Wikidata are widely used in industry and research. However, modern AI includes many different techniques, including machine learning, data mining, natural language processing, which are often not able to use knowledge graphs in their full size and complexity. Feature engineering, sampling, and simplification are needed, and commonly achieved with custom preprocessing code. In this position paper, we argue that a more principled integrated approach to this task is possible using declarative methods from knowledge representation and reasoning. In particular, we suggest that modern rule-based systems are a promising platform for computing customised views on knowledge graphs, and for integrating the results of other AI methods back into the overall knowledge model.
Methodical Toolkit of Portfolio Management of Services of Professional Education

Abstract The article forms the author’s methodical toolkit for managing the portfolio of the professional education services, as well as the results of its application. To manage the portfolio of the professional education services it is proposed to analyse them in order to identify the most promising areas of activity of the higher education establishments in the market of the educational services. It is proposed to monitor the higher education establishments on the basis of the creation of the n-dimensional matrices, using which the business-units may be compared with each other according to the various criteria. With that end in view, the application of the matrix of the“Boston Consulting Group”is considered, but modified in such a way as, on the one hand, to preserve its main advantages including the simplicity of visual perception and the usual terminology, and on the other hand - to use it for the analysis of the portfolio of the professional education services, taking into account their features. The parameter K (market share) (the horizontal axis of the modified matrix) and the parameter T (demand in the market) (vertical axis of the matrix) serve as a characteristic of each service group. The results of the calculations show that the use of the Boston Consulting Group modified matrix in managing the portfolio of the professional education services will make it possible to allocate the professional education services that are in demand in the market and, consequently, to affect the education establishment competitiveness.
Analysis of filtering algorithms and searching for objects on the video image during the "Morris water maze" and "open field" experiments

The paper discusses the video processing methods needed to partially automate the processing of results and conduct the "Morris Water Maze" and "Open Field" experiments to study the behavior of laboratory mice depending on various external factors. The software part of the work was done in the C ++ programming language in the QtCreator editor using the computer vision library OpenCV 3.2.
Models of organizing research data storage and usage: basic principles, processes and implementation mechanisms

Introduction: Storage and usage of research data become more sophisticated as their quantity and diversity grow. Research data have a number of features which do not allow you to copy the approaches and tools used in commercial or governmental data-processing facilities. Providing researchers with specialized tools for working with data is an urgent task in research management. Purpose: Identifying and describing the basic principles for working with research data, the processes and stages of this work, the mechanisms for implementing the principles and solving the problems of organizing the storage and usage of research data. Results: We review and discuss the principles on which the storage and usage of research data can be based, including the FAIR Data Principles. The main goal of organizing the work with research data and the central focus of its principles is the effective use and reuse of this data. We present a hierarchy of mechanisms which can be applied when working with research data for solving scientific and organizational problems. The main processes and lifecycle stages of scientific data and research processes based on them are listed in the article. A number of well-known models of such lifecycles are considered. It is proposed, instead of trying to build a universal model, to use or create models based on the presented list of stages for specific cases or classes of data-driven research. Practical relevance: The hierarchy of concept classes developed in the work for the field “Organizing the storage and usage of scientific data” will be used as an ontology core, and for the development of regulatory documents, recommendations and information systems supporting data-driven research.
The Impact of Organizational Silence Causal Factors on Self-Efficacy of Health Center Employees in the Jordanian Capital City (Amman)

The research aims at exploring the impact of the causes of organizational silence in the self-efficiency of health centers’ employees in Amman. Research population consisted of 1374 employees of health centers in the Jordanian capital city (Amman), while the sample which was selected according to the statistic methods and procedures consisted of 200 male and female employees. The descriptive- analytic approach was adopted to achieve the research objectives. Results revealed the causes of the employees’ organizational silence achieved the high level with (81.00%) and the employee’s self-efficiency got the high level with (83.33%) and managerial and organizational factors have been to found the most comprehensible Ones, averaging (84%). While the factors of anxiety and fear were the least comprehensible ones, averaging (78.33%). Additionally, there was statistical significance to the impact of causes of the organizational silence in the health centers’ employees’ self-efficiency. In light of the results, the most significant recommendations were that the officials need to deal with the causes of the organizational silence in a way that help in achieving the employees’ and the work’s interest; to enhance their self-efficiency accordingly.
Classification of human body parts using histogram of oriented gradients

In computer vision, classification of human body parts faces challenges such as partial occlusion of body parts, viewpoint variation, lightning and appearance changes, etc. In this paper in an outdoor scene human is detected using Histogram of Oriented Gradients (HOG) and Cascade adaboost classifier. Further the detected human is parsed into regions like head, torso, leg and further subdivided into neck, left-right elbow, left-right shoulder, left-right wrist, left-right hip, left-right ankle and left-right knee. Each distinct part was trained and classified using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) classifier. The proposed method was evaluated using Leeds Sports Pose (LSP) dataset.
Preprocessing Techniques for High Quality Text Extraction from Text Images

In this age of digitization, there is a growing need to preserve physical copies of documents such as historical text. It is important in digitization to capture every aspect of the document which is infeasible due to challenges such as fading, creases, and shadows. Various approaches have been put forth to improve upon text extraction by means of preprocessing. This paper analyses the effect of applying some general preprocessing methods such as Thresholding, Morphology, and Blurring and enhancements of quality in the output obtained. Experimental results show that preprocessing improves the visual and structural quality of the document to a certain extent.
Teaching Material Imitating the Advanced Driver-Assistance System for Measurement and Control Education

The rapid growth of the information technology industry in recent decades requires an increasing number of programmers and engineers, as well as an excellent training environment for relevant human resources. Currently, school curricula promote information education and, since 2012, “measurement and control by programs” have become compulsory topics of study in the technology and home economics (technological field) class in junior high schools. In addition, it has been determined that programming education will be made a compulsory topic in elementary schools from 2020 onward. Thus, programming education is included in the mandatory education program and, accordingly, various associated teaching materials have been developed and distributed to date. However, adequate steps have not been taken to improve the information education skill of the teachers who use the aforementioned teaching materials. In particular, only very few teachers understand the essence of programming control, such as the role played by a program in measurement and control. Hence, there is an urgent need to train teachers to make them capable of providing programming education in school settings. Hence, in this study, we develop a teaching material that helps teachers understand the flow of measurement and control by program using an optical distance sensor and an actuator and design a class practice for the students in the faculty of education. Finally, based on this education practice, we discuss an educational effect of the aforementioned teaching material and its applicability in junior high school settings.