Reviewerly/exHarmony huggingface dataset:
A Proposed Face Recognition based on Hybrid Algorithm for Features Extraction

This paper offers a suggested method of facial recognition to identify any person. Initially, captured images in the database to apply matching processing, which ease the recognition operation. The most difficult problems in the recognition algorithms are, the difference in face appearance, the lighting effect, and the composite background of the image. This paper presents the fundamental step in the extraction of the properties that depend on the Principal Component Analysis (PCA) also known as Karhunen-Loe (KL) to convert Eigen values to reduce the number of inputs on the network. To identify a person's image, apply Artificial Neural Networks (ANN) using Elman Neural Network (ENN), which is the most necessary and effective applications suitable for biometric systems and image processing. Practically taking photos (10) each person has eight poses (4 to 4 training to test). The experimental results of the ENN classification are calculated as a true acceptable rate (GAR) of 97% while the false acceptable rate FAR is equal to 3%. In addition, the artificial neural network gave a good performance to anyone.
Adversarial Deep Mutual Learning

Recently more attention has been paid to neural network compression due to the increasing requirements about limit memory with high performance. The typical methods based on knowledge distillation have to fix a pre-trained teacher model, and most result-orient methods are limited by the teacher model. In this work, we propose a novel lightweight network training method, which apply adversarial learning to deep mutual learning and train multiple networks in just one framework without pre-trained. We focus on the potential relationships among all networks in our framework to form the ingenious loss rather than these manual designed. Extensive experimental results on several datasets illustrate that the proposed method can significantly outperform other state-of-the-art methods.
Image Authentication Using Tensor Decomposition and Local Features with Geometric Correction

In this paper, we proposed an image hashing using both global and local features. Global features are determined using tensor decomposition and local features are takeout from salient regions. SLIC algorithm are used to find out the salient area. The hash are constructed from global and local features. The test results on large dataset specify that the suggested method is vigorous to content-preserving operations (CPOs) and has good distinction. In addition, the method can also localize the tampering reigns. In this method, there are two phase. In the first phase,” the different image pairs” and “similar and tampered pairs” are segregated using threshold T1. In second phase, tampering localization and separation of. “the similar (authentic) image pairs” and “tampered image pairs” are carried out with another threshold T2. The receiver operating characteristics (ROC) indicate that this technique is superior than others.
Bayesian learning of structures of ordered block graphical models with an application on multistage manufacturing processes

The Ordered Block Model (OBM) is a special form of directed graphical models and is widely used in various fields. In this article, we focus on learning of structures of OBM based on prior knowledge obtained from historical data. The proposed learning method is applied to a multistage car body assembly process to validate the learning efficiency. In this approach, Bayesian score is used to learn the graph structure and a novel informative structure prior distribution is constructed to help the learning process. Specifically, the graphical structure is represented by a categorical random variable and its distribution is treated as the informative prior. In this way, the informative prior distribution construction is equivalent to the parameter estimation of the graph random variable distribution using historical data. Since the historical OBMs may not contain the same nodes as those in the new OBM, the sample space of the graphical structure of the historical OBMs and the new OBM may be inconsistent. We deal with this issue by adding pseudo nodes with probability normalization, then removing extra nodes through marginalization to align the sample space between historical OBMs and the new OBM. The performance of the proposed method is illustrated and compared to conventional methods through numerical studies and a real car assembly process. The results show the proposed informative structure prior can effectively boost the performance of the graph structure learning procedure, especially when the data from the new OBM is small.
GUapp: A Conversational Agent for Job Recommendation for the Italian Public Administration

GUapp is a platform for job-postings search and recommendation for the Italian public administration. The platform offers recommendation services with the aim of matching user skills and requests with job positions available in a given period of time. The recommender system implemented in GUapp, based on Latent Dirichlet Allocation, computes the k-nearest neighbors job positions most similar to the user profile. Furthermore, in order to improve the user experience, GUapp implements a chatbot whose goal is to allow users to interact with the app through natural language. Thanks to that, the search and recommendation process becomes incremental and the user can add new requirements at each stage of the interaction. In this paper we present GUapp, its recommender system, and the chatbot developed for achieving an effective interaction with the user. In the next future, we will carry out in-vivo and in-vitro experiments for evaluating the system and its components in deep.
NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research

The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly similar, we lack initiatives to categorize the data, methods, and findings systematically. In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research. We additionally use NTARC to perform a critical review of the field of research conducted in the last two decades. The collection of descriptive research summaries enables the easy retrieval of relevant information and a better reuse of past studies by the application of quantitative analysis. Among others benefits, it enables the critical review of methodologies, the detection of common flaws, the obtaining of baselines, and the consolidation of best practices. Furthermore, it provides a basis to achieve reproducibility, a key requirement that has long been undervalued in the area of traffic analysis. Thus, besides reading hard copies of papers, with NTARC, researchers can make use of a digital environment that facilitates queries and reviews over a comprehensive field corpus.
Cooperative Collision Avoidance Method for Multi-UAV Based on Kalman Filter and Model Predictive Control

Collision avoidance is the primary problem to be solved in formation flight of multiple Unmanned aerial vehicles(UAVs). Firstly, a cooperative collision avoidance architecture of multiple UAVs is designed according to the requirement of autonomous collision avoidance of single UAV. Then a new cooperative collision avoidance method of multiple UAVs based on Kalman filter and model predictive control(MPC) is proposed. In this method, extended Kalman filter(EKF) is used to estimate the state of obstacles and target points in uncertain environment space, and to predict the trajectory of obstacles and target points. At the same time, relevant performance index functions and constraints are set up. On the basis of sharing environmental information, model predictive control strategy is used to guide and make cooperative collision avoidance decisions for multiple UAVs. The simulation results show that the proposed method is effective in uncertain environment perception and UAV collision avoidance, and the cooperative mechanism has obvious advantages.
From Spiritual to Virtual: An Interactive Digital Art Creation of Virtual Reality Borobudur

This research is triggered by the lack of studies of spiritual meaning as content for digital games. In addition, the idea of art creation is also initiated by the visual sensation of the Borobudur temple that felt by the researcher in his childhood until now. This visual sensation could be deemed as dynamics sublimation of the Borobudur. According to egotistic reflection, these dynamics sublimation were influenced by researcher's visual perception due to his experiences of having life full of struggle. And then got a vision of life enlightenment as related as philosophy of Buddhism in the visualization of the Borobudur temple. Basically, this spiritual experience will be developed as main concept of the game design with virtual reality technology. The whole idea of this study is investigating the contribution of spiritual element of the Borobudur to the aesthetics of interaction. However, this paper focuses on preliminary of the practice-led research methodology of VR Borobudur art creation. Exploratory hypothesis is the spiritual of life enlightenment could be employed as contents of digital game of VR Borobudur. The end of the project will use the VR Borobudur for play test observation as one of method to investigating the contribution of the aesthetics of interaction in the game with virtual reality technology. In initial summary, the researcher estimates the design method of interactive digital arts creation based on religious substance need more depth understanding in synthesis of spiritual philosophy and cultural studies.
Comparative analysis on integrated digital forensic tools for digital forensic investigation

Abstract As a variety of digital devices have recently become widely commercialized, digital forensics, a process where electronic information stored in digital devices is accurately identified, collected, preserved and analysed and the information is submitted to the court as evidence, is gaining enormous popularity. This paper analyses characteristics, applications, limitations of digital forensic tools and compare the tool with others in terms of ease of use, thereby helping investigators to adopt integrated digital forensic tools in their investigation.
Guidelines and Tool Support for Building a Cybersecurity Awareness Program for SMEs

Nowadays companies have become highly dependent on digital technology for running their business, regardless their size or domain. Smaller organisations require a specific attention because of their lower level of protection, capability of reaction and recovery while they are increasingly being targeted by cyberattacks. In order to improve their level of cybersecurity and resilience, a first step is to raise awareness. It is however not an easy task because it is highly dependent on human factors, spread across the whole organisation, including managers, business users and IT staff. This paper aims at supporting the development of a cybersecurity awareness program for small and medium enterprises. In order to build the program on strong foundations, the current state of awareness of such companies is presented and a SWOT analysis carried out. Different instruments for efficiently supporting the deployment of the program are then presented. A practical experience carried out in Belgium to implement some of the proposed instruments is also presented and some lessons learned are discussed.
Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions

Model-based clustering approaches generally assume that the observations to be clustered are generated from a mixture of distributions, each component of the mixture corresponding to a particular parametric distribution. Most commonly, the underlying distribution is assumed to be normal, which is inadequate for many situations, for example when skewness or multimodality is present within the components. The problem is intensified when the data dimension increases, leading to inaccurate groupings and incorrect inference. A new Bayesian model-based clustering approach is proposed, that can handle a variety of complexities in the data, based on a recently introduced family of geometric skew normal distributions. The performance of this methodology is illustrated through a number of simulation studies and applications to a number of datasets from genomics and medicine.
Developing of a New Hybrid Clustering Algorithm Based on Density

Clustering is one of the fundamental techniques of data mining that is used for dataset analysis. Clustering algorithms group available data based on similarity or distance measures. Two important clustering methods used in the literature are hierarchical and density based methods. A lot of algorithms have been developed based on these two concepts separately. Birch and its extensions are samples of hierarchical based methods. DBSCAN and its extensions are samples of density based methods. In this paper, a new algorithm is proposed to use both concepts together to achieve an acceptable speed and results, simultaneously. At first, it tries to make clusters using a hierarchical method. If it decides to make a new cluster, then the algorithm checks for density. In this manner, it tries to postpone splitting the clusters. To show the effect of the proposed algorithm, some evaluations are performed on some synthetic and real datasets which show some improvements over related works.
A novel evolutionary optimization algorithm inspired in the intelligent behaviour of the hunter spider

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. Evolutionary intelligence is a research field that models the behaviour of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complicated optimizable systems. In this paper, a novel evolutionary technique called Artificial Spider Algorithm (ASA) for solving optimization tasks in unconstrained problems with high nonlinearity is proposed. The ASA is based on the simulation of spider behaviour. For this purpose, a new metaphysical method according to spinning web and hunting insects via spider is inspired in nature. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known evolutionary methods. The comparison investigates several test functions that are commonly considered within the literature of evolutionary algorithms. The result shows a high performance and effectiveness of this method for searching a global optimum, as well as the cost reduction noticeably for various benchmark functions.
Research on crack expansion characteristics of semi-disc polymethyl methacrylate specimen with weak prefabricated double-layer surfaces under dynamic load

In this study, based on an experimental digital laser dynamic caustic system, a dynamic fracture experiment on a polymethyl methacrylate (PMMA) semi-disc specimen with a weak precast double-layer surface under an impact load was carried out, the cracking, expansion, and wearing patterns of the cracks under different tilt angles (60°, 45°, and 30°) were studied, and the changing characteristics of the trajectory, velocity, and dynamic stress intensity factors of a crack expansion were compared. In addition, a newly developed discrete lattice spring method was adopted to simulate the expansion of dynamic cracks in a PMMA specimen with weak double-layer surfaces, and it was found that this numerical model can reproduce experimental phenomena for analyzing the effects of a weak surface spacing on the crack expansion. Combined with the test and numerical analysis results, it can be concluded that the tilt angle and spacing of a weak double-layer surface will have a significant influence on the crack expansion characteristics.
In-memory parallelization of join queries over large ontological hierarchies

The Resource Description Framework (RDF) data model enables the construction of knowledge graphs over various domains, using ontologies in order to encode information about the domain, and simple statements in the form of subject-predicate-object triples for data representation, facilitating the interlinking and exchange of Web data. However, this simplicity comes with the cost of having to execute a large number of joins in order to get the desirable query results, while at the same time large ontological hierarchies complicate the query answering process even more, for systems that provide complete answers with respect to such ontological axioms. In this work we present PARJ, an in-memory RDF store which takes into consideration ontological hierarchies during join processing with very low performance overhead, avoiding expensive preprocessing and materialization of implications, and is also amenable to straightforward parallelization. Specifically, we present a join implementation that allows to achieve any desired degree of parallelism on arbitrary join queries and RDF graphs stored in memory using compact vertical partitioning. We use an adaptive join processing approach, such that we take advantage of complete or even partial ordering of RDF data, which is compactly stored in order to increase spatial locality and keep memory consumption low, coupled with an ID-to-Position vector index used when ordering does not allow for efficient scanning of the input relation. Finally, we experimentally show the efficiency and scalability of our proposal.
Simultaneous Estimation of Neutron Flux and Reactivity in Nuclear Reactors

In this article, we propose a methodology based on cubature Kalman filtering (CKF) for simultaneous estimation of two important variables of nuclear reactors, viz. the neutron flux and the total core reactivity, from signals of delayed response self-powered neutron detectors (SPNDs). Moreover, by using the linearity of our model, the Rao-Blackwellized CKF (RBCKF) algorithm is developed for our estimation problem, which offers benefits in terms of smaller computational load in comparison to standard CKF. Furthermore, a scheme which is termed as adaptive-RBCKF (A-RBCKF) has been designed for adaptation of process noise covariance. A comparative study of the two state estimators, that is, RBCKF and A-RBCKF, through simulations under different transient scenarios demonstrates the efficacy of A-RBCKF over RBCKF.
Non-Technical skills needed by cyber security graduates

Technical cyber-defence is often insufficient to protect a system as most cyber security incidences involve a human who takes an erroneous action or regrettable decision. To address the human factors, cyber security professionals need a range of technical and non-technical skills to enable them to effectively protect cyber-assets. There is research on the non-technical skills needed in ICT and engineering, but little specific to cyber security, and nothing in an Australian context. To support the job readiness of cyber security graduates it is necessary to identify the non-technical skills that are needed to be competitive in the modern cyber security market.To identify the non-technical skills needed or looked for by the Australian cyber security industry we ran an anonymous online survey. A range of demographic data was collected including gender, location, and role. Survey participants were asked about their use of non-technical skills, and the non-technical skills they seek when hiring junior staff. This paper reports on the most frequently sought after non-technical skills. A brief analysis indicates that some of the most sought after non-technical skills are neither explicitly taught nor explicitly assessed in most Master of Cyber Security degrees in Australia.With more and more Australian universities offering cyber security degrees, the findings of the survey can be used to inform cyber security curriculum design.
Deep Learning and Its Applications in Medical Imaging

Big data are information assets characterized by high volume, velocity, variety, and veracity. Large, multilevel, and integrated datasets offer the promise of unlocking novel insights and accelerating breakthroughs. According to an increasing interest in artificial intelligence around the world, deep learning has attracted a great deal of public attention. Deep learning techniques increase learning capacity and provide a decision support system at scales that are transforming the future of health care. Every day, deep learning algorithms are used broadly across different industries. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from intricate data. This chapter explains what deep learning is and why it is so important. We provide several examples where deep learning techniques find applications and what kind of problems can be solved using deep learning in medical imaging. This chapter also introduces a particular type of deep learning algorithm, including autoencoders, restricted Boltzmann machines, deep belief network, recurrent neural networks, and convolutional neural networks with practical variant case studies, which are at the basis of deep learning.
Managing Patient Medical Record using Blockchain in Developing Countries: Challenges and Security Issues

The world was invaded by the COVID-19 pandemic, which shook the whole world and only technology has made it possible to preserve the minimum subsistence of many areas, including the healthcare field. In developing countries, many have applied the technology through centralized health systems. Unfortunately, this solution cannot be generalized everywhere in countries where a well- defined health organization is lacking. So in countries where the patient has difficulty using the technology. Not to mention issues such as attacks that can endanger critical patient records and their privacy by revealing their medical secrets. Several challenges concern the management of medical record, and among the solutions that promise to solve many problems concerning this critical point is the blockchain, through its decentralized form and its security mechanisms. This article examines the benefits and opportunities of applying blockchain in healthcare, specifically in developing countries, focusing on security and privacy issues. It presents use cases via blockchain-based solutions for managing medical records. Finally, it presents a discussion of the different challenges and problems to be solved for the implementation of this solution in developing countries.