Reviewerly/exHarmony huggingface dataset:
Analysis and parsing of unstructured cyber-security incident data

The latest threat intelligence platforms use structured protocols to share and analyze cyber-security data. However, most of this data is reported to the platform in the form of unstructured text such as social media posts, emails, and news articles, which then require manual conversion to structured form. In order to bridge the gap between unstructured and structured data, we propose to implement a natural-language-processing-(NLP)-based information extraction (IE) system that takes texts within the cyber-security domain and parses them into structured format. Our approach targets the VERIS format and makes use of the VERIS Community Database as a source of unstructured texts---primarily consisting of news articles-and their structured counterparts (VERIS reports). We propose first to use a supervised machine learning (ML) classifier to discriminate between cyber-related and non-cyber-related texts, and then to use ML classifiers decide which VERIS parameters are relevant in a given text. Then, we propose to use NLP and IE techniques to extract tuples of grammatically co-dependent words. Finally, these tuples will be passed to a domain- and field-specific IE components to fill in different fields of an output VERIS report.
Wide-field 3D imaging with an LED pattern projector for accurate skin feature measurements via Fourier transform profilometry

Accurate 3D imaging of human skin features with structured light methods is hindered by subsurface scattering, the presence of hairs and patient movement. In this work, we propose a wide-field 3D imaging system capable of reconstructing large areas, e.g. the whole surface of the forearm, with an axial accuracy in the order of 10 microns for measuring scattered skin features, like lesions. By pushing the limits of grating projection we obtain high-quality fringes within a limited depth of field. We use a second projector for accurate positioning of the object. With two or more cameras we achieve independent 3D reconstructions automatically merged in a global coordinate system. With the positioning strategy, we acquire two consecutive images for absolute phase retrieval using Fourier Transform Profilometry to ensure accurate phase-to-height mapping. Encouraging experimental results show that the system is able to measure precisely skin features scattered in a large area.
Analyzing online schema extraction approaches for linked data knowledge bases

In order to help data curators, data scientists, and other users in the domain of Linked Data to identify potentially new data sources, it is important to understand the corresponding data schema. The schema can help to determine the domain of the data (e.g. bibliographic data, geospatial data, etc.), its structure and more. We analyzed several strategies and systems which extract schemas in an online approach. Established systems using SPARQL endpoints for online schema extraction are limited when knowledge bases are very large or complex. Due to the growth of Linked Data, the knowledge bases has become larger and their structure more complex. Therefore, this paper will discuss some limitations of current strategies.
HPC enabled immersive and non-immersive visualization of large scale scientific data

Advancement in the areas of high performance computing and computational sciences have facilitated the generation of an enormous amount of research data by computational scientists - the volume, velocity and variability of Big 'Research' Data has increased across all disciplines. An immersive and non-immersive analytics platform capable of handling extreme-scale scientific data will enable scientists to visualize unwieldy simulation data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain usable information. Our immersive and non-immersive visualization work is an attempt to provide computational scientists with the ability to analyze the extreme-scale data generated. The main purpose of this paper is to identify different characteristics of a scientific data analysis process to provide a general outline for the scientists to select the appropriate visualization systems to perform their data analytics. In addition, we will include some of the details on how to how the immersive and non-immersive visualization hardware and software are setup. We are confident that the findings in our paper will provide scientists with a streamlined and optimal visual analytics workflow.
The Role of The Library as a Public Space in Facilitating The Social Activities of Coastal Communities

Libraries are part of people's lives that reflect changes that occur in society. The concept of a library that is open to the public and all walks of life is similar to the concept of Jurgen Harbermas public space. The purpose of this study was to describe the role of libraries that had been carried out by library managers in taking a role in social activities in coastal communities. This research has used a social approach through the reality that exists in society. Qualitative research design of community activities in utilizing libraries. The study was conducted in the coastal area of Semarang city. Data are collected through observation and in-depth interviews with several parties involved. To examine the validity of the data, this study applies a triangulation technique of sources focusing on key informants living in the community and policy makers about libraries. The results of this study library not only as a place to collect, manage, and serve sources of information, but the library has become a place for people to gather. The library becomes a public space that is in demand by all levels of society, especially for children. This community of people gathered has a positive meaning, because currently the community lacks open space and can accommodate a large number of people. Program socialization activities from the village government, as well as community social activities in the form of regular meetings of PKK mothers, health posts, religious posts can use the library. Library is an added value because it is not a mere space, but on the sidelines of activities they can read books that can be useful.
A Productivity Framework for Software Development Literature Review

Productivity research has for the most part examined approaches that are focused on deriving a single measure for Productivity = Output / Input. This can work under specific scenarios and context but cannot be applied universally to a broad range of software development projects. This paper makes the assertion that it is possible to measure productivity in a manner that will help identify opportunities to improve productivity.
Search relevant retrieval on indonesian translation hadith document using query expansion and smoothing probabilistic model

Hadith are words, deeds, decrees and approvals of the Prophet Muhammad SAW which are used as the basis for Islamic Shari'a law after the Qur'an. Currently there are many websites that provide information about hadith to facilitate users in the process of hadith learning or what we usually know as Information Retrievel (IR), such as the Lidwa Pustaka website. Basically, IR provides a search box for users to enter queries that reflect the user's information needs. The hadith search process on Lidwa Pustaka uses exact string matching method, which in the process of searching the hadith to the user's query must be the same as the hadith document in order per word (term), so that for partial matching search (matching the query in each word without sequential) can't be done yet. In addition, the writing of synonyms or variant strings that differ in Indonesian hadith translations, such as (al-khomru) are written as "khamar", "khamer", "khamr" or "minuman keras", making the process of the hadith search less precise. Therefore, this study aims to improve the search system for the hadith, using the approach of query expansion and smoothing probability models, namely Jelinerk-Mercer Smoothing, Dirichlet Smoothing and Absolute Discounting Smoothing. The use of query expansion and smoothing probability models in this study resulted in Mean Average Precision ALL (MAP ALL) values in all hadith documents, Mean Average Precision@30 and the highest recall value of 30 compared to other methods, such as exact matching method for Lidwa Pustaka, Latent Semantic Indexing, Probability Model and Cosine Similarity.
Simple linear regression model based data clustering

KMeans is one of most popular algorithms in data mining (ranking number 2) and has be widely used in many fields. KMeans uses Euclidean distance to compare two data. However Euclidean distance is sensitive to linear transform in data collection process. Due to these linear transforms, the distance between two data points for the same class (intra-class distance) may larger than those for different classes (inter-class distance) that may cause low clustering performance for KMeans algorithm. In this paper, we propose simple linear regression approach for data clustering. Instead of using Euclidean distance to measure the difference, we recommend using the goodness of fitting (or normalized cross correlation) to measure the similarity and compare two data points. Using this new data comparison technique, we introduce linear regression approach for data clustering and demonstrate that the proposed method has higher performance and low computational cost than KMeans methods.
Spatial Drawing Framework Design for VR-Based Painting Application

A spatial painting system generates a line and curved surface in real time using a virtual brush and palette as controllers. In this study, a spatial drawing framework is designed and efficiently configured with various kinds of modules. By designing the spatial drawing framework with several modules and defining the interfaces, researchers can develop the necessary modules themselves by updating algorithms. Specifically, the base curve generation for creating curved surface and mesh and surface generation comprise various textures and particles. A virtual reality (VR)-based spatial painting application based on this framework was developed and is currently on display at exhibitions.
Imbalanced Datasets Resampling Through Self Organizing Maps and Genetic Algorithms

The paper presents a novel approach for the resampling of imbalanced datasets aiming at the improvement of classifiers performance. The method exploits two self–organizing–maps for the determinations of the clusters of majority and minority data. Clusters centroids are used to select the samples whose under–sampling or over–sampling is more convenient while the optimal resampling rates are determined through a genetic algorithm that maximizes the classifier performance. The algorithm is tested on several datasets coming from both the UCI repository and real industrial applications and compared to other widely used resampling methods.
Bio-mechanical fuzzy analysis on the technical action of windmill coupling swipe in hip-hop movement

The research is carried out the kinematics analysis of combined technical actions in hip-hop movements by the methods of three-dimensional analysis of motion biomechanics. Based on three aspects of the movement track, motion characteristics and core technical action analysis, the results show that : the facet joint play the role of driving action; The movement can be changed by changing the direction of the force when connected; And the body should be reduced the buffer in the way the fingertips or toes touching the ground first when doing actions.
Optimal close‐up views for precise 3D manipulation

Abstract Direct manipulation can be used as an intuitive interface for spatial interaction with multiple objects in virtual environments. However, users often have difficulties in precisely manipulating objects in the 3D space; this is mainly because the user is not provided with enough visual information to sufficiently recognize the spatial relationships among objects. We present a new method for improving the preciseness of object manipulation in the 3D space by providing the user with an auxiliary view that is consistently retargeted to closely highlight the spatial relationship between the manipulated object and the object nearest to it. Both the target and the position of the auxiliary camera are computed efficiently using the bisector surface of the two involved objects. The viewing target is set to the center of the closest area between the two objects, and the camera position is optimized under an objective function primarily measuring the visual clarity of the auxiliary view. We demonstrate the usefulness of our method through various experiments based on practical usage scenarios.
Improvement of Criminal Identification by Smart Optimization Method

Data-mining methods, which can be optimized via different methods, are applied in crime detection. This work, the decision tree algorithm is used for classifying and optimizing its structure with the smart method. This method is applied to two datasets: Iraq and India criminals. The goal of the proposed method is to identify criminals using a mining method based on smart search. This contribution helps in the acquisition of better results than those provided by traditional mining methods via controlling the size of the tree through decreasing leaf size.
A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

We propose a new lightweight BCH code corrector of the random number generator such that the bitwise dependence of the output value is controllable. The proposed corrector is applicable to a lightweight environment and the degree of dependence among the output bits of the corrector is adjustable depending on the bias of the input bits. Hitherto, most correctors using a linear code are studied on the direction of reducing the bias among the output bits, where the biased input bits are independent. On the other hand, the output bits of a linear code corrector are inherently not independent even though the input bits are independent. However, there are no results dealing with the independence of the output bits. The well-known von Neumann corrector has an inefficient compression rate and the length of output bits is nondeterministic. Since the heavy cryptographic algorithms are used in the NIST’s conditioning component to reduce the bias of input bits, it is not appropriate in a lightweight environment. Thus we have concentrated on the linear code corrector and obtained the lightweight BCH code corrector with measurable dependence among the output bits as well as the bias. Moreover, we provide some simulations to examine our results.
A Survey on Parallel Join Algorithms Using MapReduce on Hadoop

In this paper, we will present a recent survey on the improvements over parallel join algorithms using the popular MapReduce framework on the distributed file system Hadoop. We will talk briefly about MapReduce and Hadoop frameworks, and we will discuss the general main steps to install, configure and start using Hadoop. Then we will talk about parallel join algorithms where we are going to divide join algorithms into categories, and we will discuss in each category the main works of improvements from the beginning to the date in chronological way. After that, we will organize these works into an easy-to-learn table, and we will present an analysis of these works in term of advantages and disadvantages. This survey will help researchers to study the improvements over parallel join algorithms in a chronological manner in one place to simplify the process of improving and proposing new approaches to improve parallel join algorithms using MapReduce on Hadoop.
Digital Repositories and Linked Data: Lessons Learned and Challenges

Digital repositories have been used by Universities and Libraries to store their bibliographic, scientific, and/or institutional contents, and then make their corresponding metadata publicly available to the web and through the OAI-PMH protocol. However, such metadata is not descriptive enough for a document to be easily discoverable. Even though the emergence of Semantic Web technologies have produced the interest of Digital Repository providers to publish and enrich their content using Linked Data (LD) technologies, those institutions have used different generation approaches, and in certain cases ad-hoc solutions to solve particular use cases, but none of them has performed a comparison between existing approaches in order to demonstrate which one is the best solution prior to its application. In order to address this question, we have performed a benchmark study that compares two commonly used generation approaches, and also describes our experience, lessons learned and challenges found during the process of publishing a DSpace digital repository as LD. Results show that the straightforward method for extracting data from a digital repository is through the standard OAI-PMH protocol, whose performance in terms of execution time is much shorter than the database approach, while additional data cleaning tasks are minimal.
Factors Influencing Blockchain Diffusion in the Supply Chain

The literature concerning supply chain management (SCM) and blockchain integration is scarce, and organizations and decision makers still have a limited knowledge about blockchain solution diffusion in SCM. Under these circumstances, studies aimed to improving the understanding of the main blockchain diffusion factors are fundamental. This chapter is a contribution to bridging such gaps, as it aims to unlock the driving factors of blockchain diffusion in the SCM environment. Based on the emerging literature on blockchain, supply chain management, expectancy theory, and diffusion of innovations, a model was developed and validated, considering data from India's supply chain management professionals. The data was analyzed using partial least square structural equation modeling (PLS-SEM). The results indicated that IT deployment capability, compatibility, and trading partner pressure are factors that affect blockchain diffusion significantly. Also, the results bring essential managerial and theoretical implications regarding the blockchain diffusion in the SCM.
Text Clustering Using PSO Based Dynamic Adaptive SOM for Detecting Emergent Trends

Detection and realization of new trends from corpus are achieved through Emergent Trend Detection (ETD) methods, which is a principal application of text mining. This article discusses the influence of the Particle Swarm Optimization (PSO) on Dynamic Adaptive Self Organizing Maps (DASOM) in the design of an efficient ETD scheme by optimizing the neural parameters of the network. This hybrid machine learning scheme is designed to accomplish maximum accuracy with minimum computational time. The efficiency and scalability of the proposed scheme is analyzed and compared with standard algorithms such as SOM, DASOM and Linear Regression analysis. The system is trained and tested on DBLP database, University of Trier, Germany. The superiority of hybrid DASOM algorithm over the well-known algorithms in handling high dimensional large-scale data to detect emergent trends from the corpus is established in this article.
Quantitative evaluation of Bayer chromatic imaging on the accuracy of photogrammetric measurements

Bayer chromatic cameras are increasingly being used in photogrammetry for high-accuracy deformation measurements. However, studies on the quantitative accuracy evaluation of different photogrammetric methods using Bayer chromatic images are lacking, as the influence mechanism of chromatic images on measurement accuracy is unclear. This work performed substantial tests to investigate the influence of chromatic images on the photogrammetric measurement accuracy. Firstly, the spatial resolution and signal-to-noise ratio (SNR) of chromatic images were quantitatively evaluated. The performance of Bayer chromatic images was found to be systematically worse than the monochromatic counterparts with the same imaging configurations. Different photogrammetric measurements were then carried out using monochromatic and chromatic images. The measurement accuracy from chromatic images was significantly lower than that from monochromatic images, except for the grid method with square-profile fringes. Therefore, it is recommended to use monochromatic cameras for carrying out deformation measurements, unless there is a requirement of colour identification.
Blind recognition of alpha‐stable random carrier signals by an eavesdropper in random communication systems

Invisibility of alpha-stable (α-stable) noise as carrier signals, in the presence of additive white Gaussian noise (AWGN) as channel noise, is a key factor to ensure covert transmission by employing random communication systems (RCSs). This study introduces a novel blind recognition method for an eavesdropper to detect the presence of real-valued symmetric and skewed α-stable random carrier signals in the presence of AWGN. The introduced method is based on the proposed random carrier signal recogniser (RCSR), which consists of fractional lower-order auto-covariance block, threshold control block and the random carrier signal recognition indicator. The proposed RCSR first detects the possible presence of α-stable random carrier signals and then recognises the impulsiveness and skewness parameters, exploited by the transmitter and the intended receiver, to extract covertly conveyed binary information. However, the determined covert range can be adopted to perform secure transmission by RCSs. The simulation results reflect the simplicity of the proposed method as it is capable of performing effectively in real time by utilising extremely less number of observed samples.
Training Sample Generation Software

This paper proposes new software for images annotations in order to make training samples. It can be applicable for artificial intelligent systems in areas such as human behavior analysis, identifying nonstandard patterns in human behavior, or situations leading to accidents, while driving a vehicle, operating a train, piloting an aircraft, etc. Also, the main requirements for newly developed applications for image annotation are formulated.
Correspondent Sensitive Encryption Standard (CSES) Algorithm in Insecure Communication Channel

The purpose of the research was to implement the correspondent sensitive encryption algorithm for encryption of files and sending them through insecure communication channel. Currently, in practical applications correspondents are using either one known to them pair of keys (what has negative influence on security), use Public Key Infrastructure or as the simplest solution they are exchanging key through another channel. Additional benefits of our proposed solution are integrity control and ability to limit decryption capability to known recipients. The solution we propose allows correspondents to pair their devices fast and easily, so they could securely and without passwords transfer file through insecure channel.
Research on the load balancing strategy for original pages based on cloud storage

Load balance plays an important role in the information acquisition system’s performance. Especially in the state of cloud storage, the load balance is well done, which is conducive to the full utilization of computing resources and reduces the response time of distributed operations. The internal mechanism of original page load balance is given based on the analysis of the two recent commonly used dynamic load balance methods. Five original page oriented load balancing strategies are compared from the experimental and theoretical perspectives on the premise of calculating load index. Finally, the conclusion is drawn that date channel storage calculation sensitive partition is the most optimal load partition strategy.