Reviewerly/exHarmony huggingface dataset:
Analysis of the Randomness Performance of the Proposed Stream Cipher Based Cryptographic Algorithm

In the cryptographic field, it is a cryptosystem practice to use a secret key together with a cryptographic algorithm parameter. These cryptographic algorithms should produce pseudorandom data to understand better the cryptographic performance of such ciphers. To understand the performance of a cryptographic algorithm a statistical analysis should be performed. In this paper, a randomness analysis of the proposed cryptographic algorithm using combined multilevel substitution and encryption techniques were executed. Statistical test results were presented using NIST STS to measure the randomness behavior of the keystream output. The statistical analysis utilized the non-parameterized test selection, such as cumulative sums, runs test, longest runs of ones, and frequency. The performed experiment used 100 samples with 100 bits in length. The algorithm achieved an average success rate of 99.50% in four statistical tests used. Therefore, it is assumed that the proposed cryptographic algorithm is effective in producing a random ciphertext sequence. For future works, it is then suggested to implement it in software application to boost electronic medical records protection.
Josephson Effect based Superconducting Electronics

Josephson parametric amplifiers (JPAs) operated in the coherent quantum regime exhibit ultimate sensitivity. We review physical principles, properties and applications of JPAs in quantum information processing.
Finding Solutions to Cybersecurity Challenges in the Digital Economy

New technological innovation has incorporated frontier technologies in transforming the way we access and use existing products and services. The economy is becoming increasingly disrupted by revolutionary enterprises using technologies such as cloud computing, extensive use of artificial intelligence and data analytics, integration of interoperable internet of things (IoT) devices and the blockchained decentralization, including advanced materials. At the same time, regulatory and legal systems need to be strengthened in order for the ecosystem be protected from cyber risks and threats to allow for the market actors to flourish. In order to enjoy the benefits brought on by digital technologies, there are regulatory issues that come with technological adoption along with financial stability implications and consumer protection, with suggestions that regulators themselves adopt advanced technologies (RegTech) to embark on the new era of market supervision and monitoring to enhance cybersecurity.
GPU Parallel Computing for Detection and Classification Object in Robot Soccer ERSOW

ERSOW robot soccer that participated in the Indonesian Wheeled Robot Soccer Contest, has many abilities such as object detection and classification, control and navigation system, self-localization and mapping, and also real-time communication between each other. This research focusing on object detection and classification on the robots as one of important processes to provide main data sources for all further actions. Therefore, this process takes longer computation time to detect and classify multiple objects, to improve detection and classification performance speed without sacrifice the accuracy value, we proposed GPU parallel computing on offline training phase and online inference phase. In the offline training phase, the neural network model can be trained in parallel processes using selected GPU hardware. As a result of training, we can transfer learning the model knowledge to another host. The experiments on the NVIDIA Jetson AGX Xavier board show that the custom model as the result of the offline training phase achieves more than 30 fps and pre-trained model SSD-MobileNet-v2 achieve more than 99 fps.
Efficient Statistical Learning Framework with Applications to Human Activity and Facial Expression Recognition

In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. The multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model's parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.
Conformance testing of ontologies through ontology requirements

In recent years, several standard ontologies have been developed to maximise semantic interoperability in different domains; such standard ontologies ensure quality and integrity when describing a domain. Therefore, mechanisms to guarantee that developers build ontologies that conform to such standards are needed. However, while in fields such as Software Engineering or industry, conformance testing plays an essential role during product development, in the Ontology Engineering field there is a lack of techniques for this type of testing. This work introduces an ontology conformance testing method to analyse conformance between an ontology and a standard based on the standard requirements. Grounded on this method, the work also presents a minimum common knowledge identification method for analysing how a group of standards covers a particular domain and for identifying whether there are conflicts between them. This work has been validated by analysing the conformance between an ontology network and a set of standards on the Internet of Things domain, and by analysing the minimum common knowledge between such standards. This analysis shows that the conformance between ontologies and standards is mostly related to definition of classes. Furthermore, the analysis shows that although the analysed standards are related to the same domain, they are created to describe different areas of concern and, thus, there is a minimum overlap between them. Finally, it was concluded that the quality of the conformance analysis depends on the quality of the requirements specification: the more precise the requirements, the more precise the analysis between ontologies and standards.
Blockchain: A Survey on Healthcare Perspective and Its Challenges

An impeccable and flawless exchange of digital information in a healthcare domain can revolutionize the treatment process. It can be computed by encompassing and leveraging digital progress in infrastructure for securely storing Electronic Medical Records (EMRs). If the security of Electronic Medical Records is left unaddressed, or failed to notice at a crucial stage of care, it could hamper the effectiveness of the course of treatment. Therefore, researchers have proposed blockchain-based solutions that will not only defend data from being damaged but will also assure that unethical data exposure is avoided. Hence, to make this critical information more secure, application of blockchain technology has become indispensable. The significant contribution of this paper is to introduce the existing state of blockchain-based medical healthcare solutions. This paper also discusses the industrial, administrative and business challenges in enactment of blockchain in the healthcare industry. The intent of this survey paper is to discuss the literature review on real-time usability of blockchain application in healthcare systems.
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images

In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revisit rate. This work proposes to bridge the gap between image quality and the price by improving the image quality via super-resolution (SR). Recently, a number of deep SR techniques have been proposed to enhance satellite images. However, none of these methods utilize the region-level context information, giving equal importance to each region in the image. This, along with the fact that most state-of-the-art SR methods are complex and cumbersome deep models, the time taken to process very large satellite images can be impractically high. We, propose to handle this challenge by designing an SR framework that analyzes the regional information content on each patch of the low-resolution image and judiciously chooses to use more computationally complex deep models to super-resolve more structure-rich regions on the image, while using less resource-intensive non-deep methods on non-salient regions. Through extensive experiments on a large satellite image, we show substantial decrease in inference time while achieving similar performance to that of existing deep SR methods over several evaluation measures like PSNR, MSE and SSIM.
Design and Implementation of Comprehensive Test Automation Method for API Adapter in C-Plane and U-Plane

The Business-to-Business-to-X (B2B2X) model has increased the importance of orchestrators that build and operate services that consists of various wholesale services. To develop RESTful API-based services, service providers need to catch up on the specifications of new wholesale services and specification changes of existing services quickly and inexpensively. Therefore, it is important to develop software called an API adapter that absorbs API differences from various services quickly and inexpensively. In this paper, we propose a method for comprehensively automating testing of not only C-Plane signals such as service provisioning/change/abolishment but also U-Plane signals such as communication between user devices and servers. We implemented our proposal using open source software and found that it improved test efficiency in an actual software development project.
A Comparative Study on Three-mode Fuzzy Co-clustering Based on Co-occurrence Aggregation Criteria

Three-mode fuzzy co-clustering is a promising technique for analyzing relational co-occurrence information among three mode elements. This paper proposes a modified version of the three-mode fuzzy clustering for categorical multivariate data (3FCCM) algorithm, which was constructed with the aggregation criterion of three elements based on the fuzzy c-means (FCM) concept and often suffers from careful tuning of three independent fuzzification parameters. In the modified algorithm, a novel clustering criterion is proposed based on a probabilistic concept, where we can easily tune the fuzziness degree of three-mode fuzzy partition by comparing with the probabilistic standard. The characteristics of the two algorithms are discussed through comparative experiments such that the modified version is more useful in tuning the influences of the fuzziness parameters and is more promising in real applications than the conventional one.
Implementation of MediCare Social Media System

Smart technologies such as computers, smartphones, smart bracelets, smart medical devices can be used by medicinal organizations because of their enhanced usability, multiple uses, low costing, flexibility, mobility, reliability, and new capabilities. This encouraged health organizations to boost their demand for new healthcare technologies and enable users to multiple access medical services and improve the user experience. The current paper presents the implementation of an integrated system - MediCare system, which has the role of a medical social media system. To improve the health services in Iraq, we developed the MediCare system to take advantage of techniques like social-oriented networks, smartphone, wearable devices, medical devices. The MediCare system can be used on tablets and smartphones operating on the Android system through its Android App Interface. In addition, the MediCare web application can be used by devices like computers, smartphones, and tablets.
Atmospheric dispersion in oblique imaging from an airborne platform

In a stand-off imaging scenario from a high-altitude airborne platform, the camera line-of-sight (LOS) passes through atmosphere layers of variable density at an oblique angle. Dispersion blur arises because of wavelength-dependent refraction in the atmosphere. In visible-and-near-infrared (VNIR) spectral band the atmospheric dispersion is significant and has some effect onto resolution performance in LOS elevation dimension. Historically the atmospheric refraction and dispersion issues were studied and analyzed in astronomy, considering an infinitely distant celestial body imaged by a ground-based telescope. The calculation involves an atmosphere properties model as well as an expression for air refractive index as function of pressure, temperature and humidity. However, a straightforward application of the star–to-ground telescope refraction formulae to the ground target–toairborne camera case leads to incorrect results. In the latter case boundary conditions for light ray refraction are different, since the ground target-to-camera distance is finite rather than infinite. There are dispersion-related ray angle differences both at departure of light from the target and on arrival to the camera. Only the latter part of the overall atmospheric dispersion is perceived at the camera and causes image blur. An iterative method of the camera-perceived dispersion calculation is presented. Parametric study results illustrate the dispersion dependence on camera altitude and LOS zenith angle. The model results are validated by comparison to vertical-to-horizontal sharpness ratio statistics calculated from images taken by Condor2 cameras at various ranges. The dispersion-related NIIRS grade loss is relatively small, and in most practical cases no optical compensation of the atmospheric dispersion is necessary.
Privacy-Cheating Discouragement: A New Homomorphic Encryption Scheme for Cloud Data Security

Right from the point when the customers are trading their own private / confidential information to any outsider or to the cloud, by then there is a lot of obligation with respect to both securities as well as consistency. The re-appropriating process is computationally secure on the off chance that it is majorly carried-out without divulging to the next outside operator or cloud server, either first information or the real answer for the calculations. Secure multiparty computation processes a specific capacity without uncovering their private data. Homomorphic encryption technology is another arrangement which can function well with this circumstance. In homomorphic encryption strategy, ciphertext ought to be sent-over to the cloud, the scientific calculations are carried out on the ciphertext itself. In the event that the resultant of the calculation is decrypted, the right plaintext outcome must be acquired. In this paper, an extensive state of the art review is presented to analyse the existing protocols in this domain. We given the comprehensive procedure of proposed Homomorphic Encryption protocol for cloud data security. Security analysis along with the experimental simulation results for our proposed procedure are also discussed in the paper.
Semi-Supervised Boosting Using Similarity Learning Based on Modular Sparse Representation With Marginal Representation Learning of Graph Structure Self-Adaptive

The purpose of semi-supervised boosting strategy is to improve the classification performance of one given classifier for a large number of unlabeled data.In the semi-supervised boosting strategy, the unlabeled samples are assigned for pseudo labels according to similarities between the labeled samples and the unlabeled samples, and the unlabeled samples with high confidences of pseudo labels are selected as labeled samples at the same time.Good similarities help to assign more appropriate pseudo labels to the unlabeled samples.These selected samples with pseudo labels will be used as the labeled samples to train the new ensemble classifier.Therefore, good and distinguishable similarities learning between unlabeled samples and labeled samples has shown remarkable importance due to its promising performance for semi-supervised boosting strategy.This article presents semi-supervised boosting framework using similarity learning based on modular sparse representation by employing a marginal regression function with probabilistic graphical structure adaptation.In this article, distinguishable regression targets analysis, graph structure adaptation, robust modular sparse representation and semi-supervised boosting learning are seamlessly incorporated into a joint framework.This framework learns marginal regression targets from data rather than exploiting the conventional zero-one matrix that greatly hinders the freedom of regression fitness and degrades the performance of regression results to improve the interclass separation of the learned representation.Meanwhile, a regularization term based on probabilistic connection knowledge is used to construct a graph regularization with adaptive optimization, which improves the intra-class compactness of the learned representation.Additionally, modular sparse representation learning is used to improve the robustness of the learned representation.The experimental results on four datasets including face and object show that the recognition rates of the proposed method are significantly better than other state-of-the-art methods.
Self Localization Based On Neighborhood Probability Mapping for Humanoid Robot

The humanoid robot competition is an autonomous robot with a human-like body platform with a single camera as a vision sensor and balancing sensor to support them to play soccer in the specific field. The technical challenges in this competition such following the ball, running during search the ball, dynamic walking, kicking while maintaining the balance body condition, decision making with other robot, localization and mapping as research issues investigated in the Humanoid competition. Localization and mapping still big challenges in humanoid competition, it was only single camera is used in competition rule and no others sensor to support the position and orientation during playing the game. The proposed system was developed is neighborhood probability mapping. The long-term goal of this research is to realize an ideal system to accelerate the redesign field condition and implementation process in a humanoid robot that can be monitored in real-time. The aim of this research is to take the opportunities: (a) increasing the robot's performance of vision and intelligence on the humanoid robot; (b) with this SLAM method the robot can distinguish between the balls that are in the field and outside the field; (c) able to distinguish the enemy goal from the goal itself based on goal detection and line detection; (d) the goal keeper robot capable of acting as an attacker and scanning the kick towards the enemy goal. The testing condition was implemented between simulation testing and real testing in same times. Based on the data experimental result, the robot can estimate their position and orientation during searching the ball position, goal position and obstacle coordinate with high real time accuracy. The result shows that the proposed system can be applied to the humanoid soccer robot in the real time directly and it worked with less error.
Redundancy-Aware Pruning of Convolutional Neural Networks

Pruning is an effective way to slim and speed up convolutional neural networks. Generally previous work directly pruned neural networks in the original feature space without considering the correlation of neurons. We argue that such a way of pruning still keeps some redundancy in the pruned networks. In this letter, we proposed to prune in the intermediate space in which the correlation of neurons is eliminated. To achieve this goal, the input and output of a convolutional layer are first mapped to an intermediate space by orthogonal transformation. Then neurons are evaluated and pruned in the intermediate space. Extensive experiments have shown that our redundancy-aware pruning method surpasses state-of-the-art pruning methods on both efficiency and accuracy. Notably, using our redundancy-aware pruning method, ResNet models with three times the speed-up could achieve competitive performance with fewer floating point operations per second even compared to DenseNet.
Optical Characterization of a Pendular Impact

We present a simple but accurate correlation between an experimental characterization and a simple analytical prediction of a wave propagation induced by the impact of a pendulum in a box filled with fine sand; this research is based on optical measurements and was carried out using the digital image correlation technique to determine the motion on the surface of the box visually observed by the reflection of the laser. We suggest that the reaction to the impact in the body will be similar to an equivalent damped mass-spring system, obtaining an average error of $3.67208$ x$10^{-4}$.
Experimental, ad hoc, online, inter-university student e-contest during the pandemic – Lessons learned

Purpose: We analyze the effect of the current pandemic on students e-contests. Method: We compare the e-contest with the 10 previous editions of the same but face-to-face contest. Statistical and NLP analysis are applied. Conclusions: Apparently the competition did not suffer because of being a virtual one; the basic interconnectivity means arise no serious technical issue. Disadvantages found: the interconnectivity is more limited than for the face-to-face case; online jury-competitors interactivity is poorer than face-to-face interactivity; human factors, higher uncertainties in the organization process, and less time to spend in the process for the local organizers are major limiting factors; concerns on the participation and evaluation fairness are higher; involuntary gender discrimination seems lower, but persists; there are serious concerns related to privacy, including differential privacy; peculiarities of the presented topics and of the evaluation process emerged, but it is unclear if they are related to the online nature of the competition, to the extra stress on the participants during the pandemic, to other factors, or are random. Some conclusions may be valid for the analyzed case only, yet some are general enough for being worth reporting. A preprint was published in ArXiv.
Real-Time Hand Gesture Replication System using 3D Modelling Software

This article proposes a method to replicate the realtime hand gesture movements on a 3-D simulation software called Blender 3D. It is a gaming engine simulation software that is also used for simulation of 3D models. A special glove is designed for this purpose, and each finger on the glove is fitted with a flex sensor. Flex sensors (or bend sensors) are two-terminal sensors and work just like a variable resistor in which resistance changes upon bending. The voltage drop across the flex sensor is passed through a microcontroller (Arduino used here) into the Blender-3D. A 3D model of a human hand is also needed to be designed in the Blender- 3D. This model will be used to observe our hand movements on the software. Python code and Arduino code is to be written to guide the hand movement based on the flex sensor values. The project can be used in numerous ways, like helping especially abled people replace their hands with bionic hands which can be suited for their personal hand structure or designing games that can be played with gesture movements of the finger.