Reviewerly/exHarmony huggingface dataset:
A Process for Building a Consistent Ontology for Family

In accordance to Berners Lee, semantic web is an extension of the current web in which the existing information on the web are organized and encoded meaningfully using ontology language, thus enabling effective communication between machines and human. Ontology is the backbone of the semantic web that contributes to the knowledge sharing among intended parties over distributed systems. Ontology development is a domain-oriented process. In this work, we developed a family ontology using Protégé, an open source tool to represent family structure with minimal asserted family relationships and maximal inference. We provide dictionary table, binary relation, logical axioms, and SWRL rules tables to show the development process. We also check its consistency by running a series of tests, executed by heterogeneous reasoners available in Protégé.
Graph-Based Denormalization for Migrating Big Data from SQL Database to NoSQL Database

In this big data era, the data storing methods are vary based upon the data type and the technologies upgradation. Due to the increase of voluminous data, the traditional Relational Database Management Systems (RDBMS) are immature to handle the unstructured data. To overcome this issue, NoSQL databases are used to store and process the unstructured data. The big data migration from SQL to NoSQL database is more complex. The SQL databases are well-normalized database. Denormalization plays a major role in retrieving the data more efficiently. This work is carried on migrating the big data from SQL to NoSQL database using the Graph-based Denormalization method. The proposed method is more efficient for big data migration and post-migration process.
Single image super-resolution based on gradient profile prior and nonlocal self-similarity feature

Image super-resolution has received great attention in recent years. In order to produce a high-quality HR image with minimal artifacts, we propose a new image super-resolution method. We propose a diffusion function to refine the gradient directions along the edges. Based on the neighboring gradient profiles, a GPS optimization function is devised to make the estimated sharpness more accurate. In order to break the limitations of traditional non-local self-similarity method, we propose a new non-fixed search method to search for non-local self-similarity image patches. Besides, gradient profile prior is used for suppressing the ringing artifacts effectively. A new image reconstruction framework is designed by combining gradient profile prior and non-local self-similarity prior. Finally, we propose a high-pass filter function to get the high-frequency components, which then enhance the image quality and edge details by shock filter. The experimental results demonstrate that the new algorithm surpasses the previous state-of-the-art methods, in both visual quality and PSNR /SSIM/IFC performance.
Fraudster Detection Based on Modularity Optimization Algorithm

With the advancement and popularization of healthcare informatization, healthcare fraud has happened frequently. Doctors, patients and pharmacies are involved, and these various subjects cooperate with each other to form a complicated fraud relationship. Healthcare fraud has caused great damage to the safety of the health insurance fund. However, fraudster detection problem has not been solved properly at present. Therefore, Fraudster Detection Based on Modularity Optimization Algorithm (FDMOA) approach is developed to find doctor fraudsters in a good accuracy, which models the medical insurance records as a heterogeneous weighted network of doctors and medicines, and uses a modularity optimization algorithm to divide doctors and drugs into corresponding communities on a heterogeneous network. Finally, anomaly detection strategy was used in the community to find an abnormal doctor. Experiments show that the proposed method outperforms distance-based, clustering-based, and LOF algorithms.
Guiding the Functional Change Decisions in Agile Project: An Empirical Evaluation

Agile methods are becoming increasingly used in software industry as a response to the challenges of managing the frequent changes during the software life-cycle. However, an important number of agile projects yield unsatisfactory results and end up with failure. This is due mainly to a lack of structured change control process. A well-defined change control process gives software industry a significant competitive advantage. This paper describes an evaluation of functional changes affecting either an ongoing sprint or an implemented sprint. This evaluation can greatly assist the development teams in making appropriate decisions. We quantitatively and qualitatively evaluate 15 software development projects using agile (scrum) method. We also investigate the use of COSMIC Functional Size Measurement method for a rapid quantification and evaluation of a change request.
Investigation of Inverse kinematics Solution for a Human-like Aerial Manipulator Based on The Metaheuristic Algorithms

In this paper, an investigation of robust and effective real-time kinematics solution for a human-like aerial manipulator attached to an aerial vehicle was presented, the vehicle's motion wasn't considered in this study. The kinematics of the human-like aerial manipulator using the Denavit-Hartenberg model was introduced. The fundamental scope of this paper is to present a new design of a human-like aerial manipulator, in addition, to get a global online inverse kinematics solution of configurations with a weighted specific objective function and fulfilling the imposed constraints. Acknowledging the forward kinematics equations of the human-like aerial manipulator, the trajectory planning issue is consequently handled as an optimization task. Several well-known types of computing methods are documented in the literature for solving complicated nonlinear problems. Accordingly, this study suggests two artificial intelligent techniques considered as search algorithms, the differential evolution (DE) algorithm and modified shuffled frog-leaping algorithm (MSFLA). These algorithms are related to metaheuristic and population-based approaches. Moreover, the DE and MSFLA are able to solve the inverse kinematics problem taking into account the mobile platform, in addition to, avoiding singularities, since it doesn't demand the inversion of a Jacobian matrix. Simulation and experiments for the inverse kinematics solution were carried out for trajectory planning of a six-degree-of-freedom (DOF) human-like aerial manipulator in order to confirm the feasibility and effectiveness of the supposed methods: DE and MSFLA. in addition, the experimental tests were conducted based on FPGA using an embedded system board sbRIO-9636.
POLAr: Geographic Placement Optimization for Latency Sensitive Applications

To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.
ANALYSIS OF THE STATE OF PEDAGOGICAL WORKERS TRAINING TO USE SMART TECHNOLOGIES IN THE EDUCATIONAL PROCESS

The article presents the content of the notion "SMART-complex" is defined as an interconnected set of normative and educational-methodical materials existing in the educational and educational environment of an educational institution and necessary for the effective formation of competencies as a programmed result of mastering the discipline. It is substantiated that this educational complex should be constructed on the basis of SMART-management. Based on the analysis, it is suggested: S (specification) – in accordance with the educational-professional program, clearly identify the professional and general competencies that need to be formed within the discipline and specify the program learning outcomes; M (measurability) – measure the result of the educational process on the basis of the existing knowledge and skills, the work performed (the creation of a material or ideal object); A (approachability) – to create a content library for educational discipline, a system of tasks for students in order to form individual trajectories for studying discipline, introduce elements of distance learning; R (relevance) – to reduce the time for the mastering of «secondary» information, to strengthen practical training in laboratories and workshops, to specify the content of training on the basis of a practice-oriented approach; T (time constraints) – set up intermediate stages of performance and protection of works, change time characteristics taking into account individual aspirations of students.
Enhance Engine Room Diagnostics Through Audio-Focused VR Simulation

A marine engineer’s task is to maintain all systems in an operational state, to diagnose and rectify problems arising, and to understand what maintenance will be required to keep the vessel appropriately operational and safe. This capability is built upon the training and experience of the engineering crew, the information that can be gained by reading and interpreting engine room instrumentation, and the familiarity with the vessel and an in-situ intuitive feel for normal operation. In this paper, we examine how audio can enhance remote interaction and feedback information. We gathered real-world data from an engine room that allow us to create a realistic virtual engine room for testing. We carried out usability test on simulated failure scenarios where we look at how VR technology might enable engineers to experience immersive information from a remote location and allow them to diagnose and give feedback on the system. Our findings suggest that sound plays a vital role in identifying failures and could potentially be used in the operation of unmanned and autonomous vessels.
A Knowledge Discovery of Relationships among Dataset Entities Using Optimum Hierarchical Clustering by DE Algorithm

In recent years, discovering relationships among entities and their features in a dataset has been received a great attention in data analytics. This study aims to reveal the relationships among entities in a dataset according to a specific sequence of features which are guided according to the accuracy of the hierarchical clustering made up by the features. In this paper, a new metric, called Discriminating Features based Cohesion (DFC) factor, is defined as pair-wise stickiness measure among entities which indicates their degree of attachment (i.e., cohesive force). In this direction, a new framework is proposed; which utilizes an evolutionary algorithm (i.e., DE) for the optimal discriminating feature selection and also a hierarchical clustering method for computing DFC factors. DE algorithm is employed to identify features which their clustering hierarchical tree has the maximum accuracy, then the intermediate and final DFC factors' matrices are computed by using a hierarchical clustering of the most discriminating features. The intermediate and final DFC factors' matrices have been utilized to discovery the knowledge among Dataset Entities including answering crucial data mining queries which cannot be answered by using a standalone clustering method. In order to conduct a case study, a real-world dataset is utilized; which contains 17 entities (i.e., countries) presented by corresponding 24 continuous features. The DE algorithm finds the most discriminating features in each step, which are eliminated for the next step to calculate a matrix of DFC factors. In the final step, the proposed method ranks the entities in terms of their DFC factor and features based on their elimination order (i.e., discrimination power).
Keyword and keyphrase based ranking of documents with NoSQL databases in business context

The majority of data kept in organizations are in unstructured arrangement including text, sound, video etc. The scope of the keywords is in recovering required documents in a web look or to brief documents for ranking purposes. Keywords are the minutest segments which is used to represent a textual document and are frequently used to point to the most pertinent data contained in texts. Extracting keywords and key phrases is mainly used for identifying content of a document. In this research, we introduce a keyword and key phrase-based extraction approach using MongoDB which is a NoSQL database. By using this new method, documents are queried and ranked with the help of the keyword or keyphrases which is decided and specified by the user in a query. This result can be considered as a measure to decide the Key Performance Indicator of any business organization.
A Study on Image Based Gender Classification Using Convolutional Neural Network

Frontal face image based gender detection is an important module for many computer vision applications. In this paper, we presented a convolutional neural network (CNN) based gender classification model. The faces in image were first detected and located based on the histogram of oriented gradient (HOG) features. Then the 68 facial landmarks were extracted using the ensemble of regression tree (ERT) method. Based on the landmarks, the face was aligned and scaled to fixed size. The aligned and scaled face image was feed to the RestNet. Finally, the fully connected layers were built to classify the gender. The area under curve (AUC) on testing dataset achieved 0.95. In addition, the misidentified cases were analyzed to further understand the classification model.
Probabilistic Graphical Models and Their Inferences (Tutorial)

Probabilistic graphical models are useful for modelling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc. And they can be extended to capture multi-stage decision contexts. Fundamentally these models capture (in) dependence structure of the context, but model learning is hard in practice. There are methods to do this, from simple independence test-based ones to more advanced score-based methods. When these models are used as classifiers, model learning can be done discriminatively, thus resulting higher classification accuracies in them.
Application of Perceptual Features for User Authentication in Distributed Systems

This paper will present selected approaches based on cognitive solutions for creation security procedures and cryptographic protocols. In particular will be described the ways of using perceptual and cognitive features in creation of secure protocols, oriented on user authentication and data management. The new areas of cognitive cryptography will be also described, which offer a new possibilities in security areas oriented on application of perceptual features and personal characteristics.
Investigation of image registration method for the multi-directional image fusion of woven fabrics

Traditional single-side scanning or single-vision image acquisition methods have the limitation of incomplete information caused by the existence of blind spots. To collect the complete texture information of fabric images, a new multi-vision image acquisition and the related fusion method is developed to solve this problem. However, linear addition of image sequences acquired from multiple directions cannot achieve a good result of image fusion, it is necessary to conduct the image fusion based on the image registration between images at pixel level. Therefore, a new multi-directional digital image acquisition system for woven fabrics is established in this article; one set of image fusion algorithm based on image registration is proposed for the image enhancement of fabric. Fabric texture images are digitized by means of multi-directional vision imaging instead of unidirectional imaging, the structural information of fabric texture could be enhanced using image registration and fusion technology and the indexing and localization of texture corresponding points could be controlled using matching points or control points. Our experimental results show that the proposed method could be used to merge the effective information from the multi-directional vision images completely, it has the potential application for the rendering of woven fabrics using image driven virtual reality enhancement.
Research on Robust Algorithm of Color Holographic Watermark Based on DCT-DWT

Abstract Based on the DCT-DWT algorithm, a new method for embedding color holographic watermarks into color carrier images is proposed. The color watermark image and the color carrier image are divided into three channels: R, G, and B. The hologram is generated for each channel’s watermark image by a four-step phase shift method, and the hologram is subjected to Arnold transformation. The carrier image is decomposed by first-order wavelet, the watermark embedding space is changed from spatial domain to transform domain, low-frequency components are extracted and segmented, the scrambled holographic watermark images are respectively sub-channel embedded in the carrier image by discrete cosine transform. The experimental results show that the algorithm is robust and can resist some conventional attacks. In this algorithm, the watermark image is transformed into a hologram, which increases the difficulty of decryption.
Evolution of quantum states via Weyl expansion in dissipative channel*

Based on the Weyl expansion representation of Wigner operator and its invariant property under similar transformation, we derived the relationship between input state and output state after a unitary transformation including Wigner function and density operator. It is shown that they can be related by a transformation matrix corresponding to the unitary evolution. In addition, for any density operator going through a dissipative channel, the evolution formula of the Wigner function is also derived. As applications, we considered further the two-mode squeezed vacuum as inputs, and obtained the resulted Wigner function and density operator within normal ordering form. Our method is clear and concise, and can be easily extended to deal with other problems involved in quantum metrology, steering, and quantum information with continuous variable.
A Multimodal Lossless Coding Method for Skeletons in Videos

Nowadays, skeleton information in videos plays an important role in human-centric video analysis but effective coding such massive skeleton information has never been addressed in previous work. In this paper, we make the first attempt to solve this problem by proposing a multi-modal skeleton coding tool containing three different coding schemes, namely, spatial differential-coding scheme, motion-vector-based differential-coding scheme and inter prediction scheme, thus utilizing both spatial and temporal redundancy to losslessly compress skeleton data. More importantly, these schemes are switched properly for different types of skeletons in video frames, hence achieving further improvement of compression rate. Experimental results show that our approach leads to 74.4% and 54.7% size reduction on our surveillance sequences and overall test sequences respectively, which demonstrates the effectiveness of our skeleton coding tool.
Understanding Home Inactivity for Human Behavior Anomaly Detection

The importance of Ambient Assisted Living (AAL) systems as a new paradigm to provide assistance to independent older adults at home is constantly growing. In this context, one of the most demanded feature is the possibility to promptly alert caregivers if an anomaly -e.g., a fall or a domestic accident - occurs. However, it is particularly difficult to monitor situations where the subject performs activities that do not involve motion (e.g., "sleeping", "watching TV"). In such cases, there is the need to understand whether the behavior of the person at home has a normal flow or if, for example, it is due to an illness that the monitoring system might not distinguish from other activities.
User Identification and Authentication Through Voice Samples

Voice authentication is a fundamentalRakshit, Soubhik topic of research in today’s technology. Reliable speech recognition is hard to achieve, but many approaches have been proposed in recent years to achieve such with an improved degree of accuracy. The following paper presents a novel approach through which users can be authenticated with reasonable accuracy using a small voice sample. The proposed method uses MFCCs, a well-known methodology for extracting features from the voice sample and finally uses Gaussian Mixture Models (GMM) for classification. An advantage of using MFCCs as the speech features is that the model is language independent. A model trained in one language can work equally well for a model trained in a different language.
Information Security Policy Compliance Model at Indonesian Government Institutions: A Conceptual Framework

The security breaches of confidential information have remained difficult to solve due to increased external and internal threats to organization. The internal threat is predominantly the result of poor employee behavior towards organization’s information security policy. If users do not comply with information security policy, security solutions lose their efficacy. The information security policy serves as a tool to provide direction on how to manage and secure all organizational operations including critical assets, infrastructure, people, and process in organizations. A major challenge for organizations is encouraging employees to comply with information security policy. The objective of this paper is to develop a model for investigating the critical factors that influence employee compliance with information security policy based on Technology Acceptance Model (TAM). Some researchers have extended TAM to include additional factors that influence behavior. In order to develop the model, we conducted a literature review and a discussion with the information security experts from the Government and Higher Education Institutions. The factors that affect employee compliance with organization’s information security policy have been identified. Through this study, we find that the TAM can be used to develop a model for investigating employee compliance with Information Security Policy by extended it with organizational and national cultures.
Advanced Behavioral Analyses Using Inferred Social Networks: A Vision

The success of many businesses is based on a thorough knowledge of their clients. There exists a number of supervised as well as unsupervised data mining or other approaches that allow to analyze data about clients, their behavior or environment. In our ongoing project focusing primarily on bank clients, we propose an innovative strategy that will overcome shortcomings of the existing methods. From a given set of user activities, we infer their social network in order to analyze user relationships and behavior. For this purpose, not just the traditional direct facts are incorporated, but also relationships inferred using similarity measures and statistical approaches, with both possibly limited measures of reliability and validity in time. Such networks would enable analyses of client characteristics from a new perspective and could provide otherwise impossible insights. However, there are several research and technical challenges making the outlined pursuit novel, complex and challenging as we outline in this vision paper.