Reviewerly/exHarmony huggingface dataset:
Scaled Scrum Framework for Cooperative Domain Ontology Evolution

The field of research in ontology engineering appears to be mature, considering the vast number of contemporary methods and instruments for the formalization and application of knowledge representation models. However, the evolutionary aspects of ontologies are still little understood and supported. This is especially important in distributed and collaborative settings like the Semantic web, where ontologies naturally co-operate with their user communities. Various organizations and teams are building common ground in this context. Ontology is instrumental in this process through the formal description of shared knowledge. Such semanticity constitutes a sound basis for defining, sharing (business) objectives and interests and eventually developing useful collaborative services and systems. In this "complex" and dynamic environment, a collaborative model for process change requires more powerful methodologies for engineering, argumentation and negotiation. Software Engineering provides teamwork, team management, feedback management, versioning, merging, and evolving software artifacts with a wealth of techniques and tools. Many of these techniques can be used again in an ontology engineering environment. This paper examines how this problem can be resolved using Scrum and Nexus frameworks, which are among the most robust models for software development.
Image Sensing-Based In-Building Human Demand Estimation for Installation of Automated External Defibrillators

The installation of automated external defibrillators (AEDs) in public places is encouraged, and the determination of the installation locations could be formed into mathematical programming models. In previous studies, data and parameters for those models were generated experimentally or manually. To estimate the parameters in practice for better reliability of the model outcomes, this research aims to design a procedure for the estimation of the real human demand distribution through automatic approaches. Inputs of the proposed work include the image sequences taken inside the building and the network describing the in-building geometry. Images are analyzed via computer vision methods for detection and tracking of people, and also augmented with the extra dimension of image depth estimation through a deep convolutional neural network. The key indicator to project the image back to the real space is identified by the correspondence between the location of humans appear in the image and the location of intersections of camera field of view with the network. Finally, the spatial-temporal human count is recorded to be the output and could serve as input parameter for related AED installation problems. The process is validated in image sequences taken at a corridor in a school building; the performance of each step and the influence of each step is discussed. In conclusion, our research provides a more automatic and robust approach to obtain the real spatial-temporal human distribution in public buildings.
End to end distance measurement algorithms in biology sequences

Biological information inside the DNA allows many characteristics variations. The translation of DNA from cells into sequences, is a very demanding computational process, which over the last decades biologists try to decode and understand. Search and match algorithms, mostly oriented to fuzzy search, are essential to define the structural biological sequences, and finding similarities. This paper revises algorithms used to search and match different types of strings. Our study show the complexity of the algorithms, and experimental results show that Boyer Mores algorithm is good, with 91% accuracy and 61 seconds of processing time. On the other hand, Brute Force algorithm was not so good, with 40% accuracy and 147 seconds of processing time.
Collaborative representation‐based locality preserving projections for image classification

Graph embedding has attracted much more research interests in dimensionality reduction. In this study, based on collaborative representation and graph embedding, the authors propose a new linear dimensionality reduction method called collaborative representation-based locality preserving projection (CRLPP). In the CRLPP, they assume that the similar samples should have similar reconstructions by collaborative representation and the similar reconstructions should also have the similar low-dimensional representations in the projected subspace. CRLPP first reconstructs each training sample using the collaborative representation of the other remaining training samples, and then designs the graph construction of all training samples, finally establishes the objective function of graph embedding using the collaborative reconstructions and the constructed graph. The proposed CRLPP can well preserve the intrinsic geometrical and discriminant structures of high-dimensional data in low-dimensional subspace. The effectiveness of the proposed is verified on several image datasets. The experimental results show that the proposed method outperforms the state-of-art dimensionality reduction.
Sidon sets and statistics of the ElGamal function

In the ElGamal signature and encryption schemes, an element x of the underlying group G=Zp×={1,…,p−1} for a prime p is also considered as an exponent, for example in gx, where g is a generator of G. This ElGamal map x↦gx is poorly understood, and one may wonder whether it has some randomness properties. This work presents two pieces of evidence for randomness. Firstly, experiments with small primes suggest that the map behaves like a uniformly random permutation with respect to two properties that we consider. Secondly, the theory of Sidon sets shows that the graph of this map is equidistributed in a suitable sense. It remains an open question to prove more randomness properties, for example, that the ElGamal map is pseudorandom.
Optimizing the use of smartphones for M-learning as a supplement for magnetic learning with a scientific approach

Abstract The purpose of this study was to optimize smartphone utilization for m-learning as a supplement to magnetic learning with a scientific approach that could be used in learning without limitation of space and time. The research method was based on the ADDIE research and development (R &amp; D) design. The research procedure used was limited product design that had been validated. Data sources at the analysis stage of smartphone utilization needs and student needs for physics learning media are 60 high school students from several provinces. The Validation of the M-learning design was carried out by lecturer who were experts in physics education and physics teacher. Data on needs analysis and m-learning design validation were collected using a questionnaire and analyzed using descriptive quantitative. The results of the questionnaire were obtained 100% of the students who were respondents had an Android smartphone and only 68.3% of students had used an Android smartphone for learning. The results of expert validation and practitioners showed that the m-learning design suitable for magnetic learning supplements was interactive, could be used for independent learning, had learning objectives content, interactive modules, broadcast material, video and animation magnetic phenomena, experimental simulations, material summaries, hyperlinks to other learning sources, as well as interactive quizzes. Learning activities including study of teaching materials, experimental simulations, assignments, quizzes, discussion forums. Synchronous learning activities were carried out for discussion forums, asynchronous for others.
A Comparative Study of Forehead Landmarking Techniques

Lan dmarking on the face provides significant information related to facial structure. Specifically, it is useful to get precise facial measurements that are required in several computer vision applications. The three primary steps for getting facial measurements are face detection, facial landmarking and distance measurement. Several facial landmarking techniques are available in the literature. However, a few of them find landmarks on the forehead area along with the remaining part of the face. On the other hand, locating landmarks on the forehead area is crucial for applications such as face recognition, a criminal investigation, face verification, face beautification, face morphing and age estimation. In this paper, a comprehensive and comparative review on forehead landmarking techniques are presented. Some parameters are discovered based on our thorough study to evaluate them. A parametric comparison of forehead landmarking techniques are also presented. This comprehensive survey will serve as a catalog of available forehead landmarking techniques and will assist researchers to understand technological advancements in the facial landmarking field. A researcher can choose an appropriate facial landmarking technique that includes forehead landmarking too to design an efficient facial measurement approach.
Piracy and its burden on copyright in Nigeria: Challenges and solutions

The Journal of World Intellectual PropertyVolume 23, Issue 3-4 p. 413-429 ORIGINAL ARTICLE Piracy and its burden on copyright in Nigeria: Challenges and solutions Omotayo F. Awomolo-Enujiugha, Corresponding Author Omotayo F. Awomolo-Enujiugha awomoloomotayo@yahoo.com Faculty of Law, University of Abuja, Abuja, Nigeria Correspondence Omotayo F. Awomolo-Enujiugha, Faculty of Law, University of Abuja, KM 23, Mohammed Maccido Road, Airport-Giri Expressway, Abuja, PMB 117, Abuja 902101, Nigeria. Email: awomoloomotayo@yahoo.comSearch for more papers by this author Omotayo F. Awomolo-Enujiugha, Corresponding Author Omotayo F. Awomolo-Enujiugha awomoloomotayo@yahoo.com Faculty of Law, University of Abuja, Abuja, Nigeria Correspondence Omotayo F. Awomolo-Enujiugha, Faculty of Law, University of Abuja, KM 23, Mohammed Maccido Road, Airport-Giri Expressway, Abuja, PMB 117, Abuja 902101, Nigeria. Email: awomoloomotayo@yahoo.comSearch for more papers by this author First published: 15 July 2020 https://doi.org/10.1111/jwip.12158Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume23, Issue3-4July 2020Pages 413-429 RelatedInformation
Effectiveness of the implementation of probing-prompting learning model on student learning outcomes in the discussion of static fluid

Abstract This study aims to determine the effectiveness of the Probing-Prompting learning model on student learning outcomes. To see student learning outcomes, researchers use a test in the form of multiple-choice questions. This research is to know the effectiveness of the learning model Probing Prompting towards the Learning Outcomes of students in the static fluid material class XI IPA SMAN 1 Sukadana 2018/2019 school year. This research is a quasi-experimental research. This study uses two classes as an experimental class and one class as a control class. The sampling technique used was purposive sampling. The research sample is by choosing a class that is contained in class XI MIA, which has low learning outcomes. Then the researchers gave treatment to class XI MIA 2 as an experimental class by applying the learning model Probing Prompting in learning, and class XI MIA 1 as a control class applied to the learning model in Discovery Learning accordance with the habits of educators. To find out the differences in learning outcomes in the control class and the experimental class a t-test was conducted using the Polled Variance formula. The results of the analysis of learning outcomes show t count = 9.51 while t table = 2.01 so that t count &gt;t table , and H1 received. The learning model is Probing-Prompting more effective in improving student learning outcomes, and effectiveness can be seen with the effect size test. The results of the test Effect size are, therefore, included in the medium category (d = 1,021 &lt;0.8). Based on these results, it can be concluded that the learning model is Probing Prompting effective in improving student learning outcomes.
Human pose estimation with spatial context relationships based on graph convolutional network

Based on the statistical verification that there is a monotonic relationship between the probability of correctly estimating human joints and their confidence values, this paper proposed a human pose estimation model with spatial context relationships based on graph convolution. The model firstly selects credible joints according to the confidence threshold, and then uses human joints as nodes and the spatial context relationships between the joints as edges to construct a human pose graph, and achieves the spatial inference of credible joints to incredible joints by graph convolution. The model can be embedded in any existing pose estimator to achieve end-to-end training. And the experimental results on the MPII dataset show that the model can realize the propagation of position information between joints, and the spatial context relationships between joints are beneficial to correct the location of incredible joints.
A New Text Sentiment Analysis Method Based on Chinese Morphological Features and HowNet

Abstract Traditional deep learning methods have two problems when using vectorized words as input. One is that they only consider the overall semantic information of the vocabulary, but ignore the morphological features of the Chinese vocabulary and the prior knowledge of the Chinese external knowledge base. Second, the word vector corresponding to each word will be limited to a single word vector training model. Aiming at these problems, we propose a double-channel convolutional neural network model based on Chinese morphological features and HowNet. First, the cw2vec model and the SAT model (Sememe Attention over Target Model) are used to train the word vectors. Second, the two different word vectors are used as the input of the two channels of the model. Finally, the convolutional neural network are used to extract the characteristics of the two channels to complete the sentiment analysis task. The comparative experimental results on the two data sets show that the proposed model achieves significantly better classification performance than traditional sentiment analysis methods.
Implementing Cloud Qualified Electronic Signatures for Documents using Available Cryptographic Libraries: A Survey

According to European law (that also has equivalent national regulations across the globe), qualified electronic signatures have the same legal value as handwritten ones, so paper documents are replaced by electronic versions. As a consequence, electronic document flows are reshaping the way people interact with governments, banks and other companies, enabling them to sign any kind of documents from the comfort of their office or home, without the need to visit a physical branch. The overall costs are also reduced, as physical paper management is no longer needed. Remote digital signatures have the potential of adding extra convenience, as no more special cryptographic devices are needed to be own, managed, and use by the signers. This paper presents a survey of the methods one can use to integrate remote digital signatures into existing signing applications using the most popular and widely used cryptographic APIs and low-level and high-level cryptographic SDKs.
Tracing and detection of ICS Anomalies Based on Causality Mutations

The algorithm of causal anomaly detection in industrial control physics is proposed to determine the normal cloud line of industrial control system so as to accurately detect the anomaly. In this paper, The causal modeling algorithm combining Maximum Information Coefficient and Transfer Entropy was used to construct the causal network among nodes in the system. Then, the abnormal nodes and the propagation path of the anomaly are deduced from the structural changes of the causal network before and after the attack. Finally, an anomaly detection algorithm based on hybrid differential cumulative is used to identify the specific anomaly data in the anomaly node. The stability of causality mining algorithm and the validity of locating causality anomalies are verified by using the data of classical chemical process. Experimental results show that the anomaly detection algorithm is better than the comparison algorithm in accuracy, false negative rate and recall rate, and the anomaly location strategy makes the anomaly source traceable.
SAR Image Denoising Based on WB-Filter

Synthetic aperture radar (SAR) imaging is an important tool in providing images of the earth's surface for military and civil applications. SAR image is highly affected by speckle noise which is a multiplicative type of noise formed by interference echo of resolving units. In this article, a new method is proposed for denoising the SAR image with preserving its quality to a good extent. The proposed technique is called WB-Filtering which is combining the effect of both Wiener and bilateral filter in a new filtering approach which is WB filter. The filter designed in the current study can remove both Speckle noise and Gaussian noise. The experiment results revealed the superior performance of current methods on both Wiener and bilateral filter regarding PSNR, MSE, SSIM and EPI parameters. The PSNR of a despeckling test-1 image after has been corrupted by speckle noise with variance of 0.03, using WB-filter was about 80% compared to that using Wiener and bilateral filter which is around (25% and 30% respectively).
Using Word2vec to Match Knowledge Points and Test Questions: A Case Study

The development of artificial intelligence and machine learning has strengthened the deep integration of information technology and education. Test questions are important for students' daily practice as online learning resources. Therefore, studying the matching method between knowledge points and test questions has great meaning both in theory and reality. In this paper, the cosine distance is used to calculate the similarity between the knowledge points text and the test questions text. The Word2vec model is used to distill the character words to obtain word vectors. The similarity in vector space is used to represent the semantic similarity of text. On the similarity computing, we compared the method based on Word2vec with TF-IDF word frequency statistics. It is verified that the method based on Word2vec can quickly find the relationship between knowledge points and test questions, so as to provide technical support for education and teaching.
Prototype of Integrated Livestock Recording Application with Animal Identification and Certification System in Kebumen

Abstract The Department of Agriculture and Food of Kebumen has utilized information technology to register livestock and provide identity cards to livestock with the Livestock Card Database system. Unfortunately, between the Livestock Card Database System and the Recording and Certification System was not integrated, so there is one job carried out by two officers. Data on making livestock cards comes from forms filled out by data officers in the village area from livestock, while data recording comes from recording officers from members of herding group / designated officers. The output of this research is in the form of an Android-based recording application prototype design, which is integrated with the livestock card database system as an identification system, and the recording and certification system as a system for animalcertification.
Necklace: a novel long text detection model

This study proposes a new text detection model and a novel tag generation method for long text detection in natural scenes. The background suppression module is added to the network architecture to integrate feature mapping of each channel, weaken background features, enhance text features, and improve detection accuracy. In the design of the regression frame, the use of elliptical geometric regression, through the formation of long text through a plurality of elliptical connections, better fit the shape of the text line to solve the problem of long text detection. The proposed method has achieved the performance of state-of-art in the popular datasets.
Exploring Personalized University Ranking and Recommendation

Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non# personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and considerations in mind, when choosing the university to study.
Super-resolution Image Reconstruction from Projections Using Non-local and Local Regularizations

Abstract This article presents a super-resolution (SR) method dedicated to tomographic imaging, where an image is reconstructed from projections obtained with low-resolution detectors. In this work, upscaling the image resolution is performed by backprojecting the projection measurements into the high-resolution image space modeled on a finer grid. Since this upscaling process often creates irregular pixels, it is important to employ regularizers that can reduce the irregular pixels while preserving fine details. Here we consider two different types of regularizers, non-local and local regularizers, each of which has been independently used for image reconstruction and is known to have its own advantages and disadvantages depending on the edge structures in the underlying image. To achieve a good compromise between the two types of regularizers, we selectively combine them using a space-variant weighting factor, which is systematically determined by our own criterion to classify edges. The experimental results show that our proposed SR method improves the reconstruction accuracy in various image quality assessments and has the potential to be useful in a wide range of imaging applications.
Zeroconf Network Retail Kiosk for Fish Products Traceability

Being driven by increased product quality challenges and customers' awareness about social and environmental aspects, food traceability information must reach customers where they need to choose which products to buy. The best way of providing customers with traceability information is by putting traceability kiosks in the points of sale. This paper describes the implementation of a Fish products' traceability kiosks networking system, in the scope of ValorMar R&D project, that allows all kiosk devices in a shop or supermarket to communicate with each other using a Zero Configuration Network (Zeroconf), for keeping their internal database synchronized.