Reviewerly/exHarmony huggingface dataset:
On Learning and Learned Data Representation by Capsule Networks

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
BYOD-Insure: A Security Assessment Model for Enterprise BYOD

As organizations continue allowing employees to use their personal mobile devices to access the organizations' networks and the corporate data, a phenomenon called `Bring Your Own Device' or BYOD, proper security controls need to be adopted not only to secure the corporate data but also the organizations themselves. Current literature and research have focused on specific areas or solutions regarding BYOD. This paper proposes a comprehensive security assessment model, BYOD-Insure, that assesses the security of an organization's BYOD posture. The model aims to identify security vulnerabilities in organizations that allow BYODs and answers the question how an organization can mitigate security risks associated with BYOD, in order to protect its corporate data? The paper demonstrates the use of BYOD-Insure by presenting an example where a hypothetical organization's management security posture is assessed. The assessment results are measurable and provide practical recommendations in order to mitigate the inherent risks of BYOD and thus strengthen an organization's security posture. The model is also extendible, adaptable, and flexible. The results are easily visualized using Kiviat's diagrams.
Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.
Asymmetric Cyclegan for Unpaired NIR-to-RGB Face Image Translation

Translating near-infrared (NIR) face into color (RGB) face, is helpful to improve the visual effect of images and the performance of face recognition. The model for unpaired image-to-image translation is suitable for this task due to the high cost of pixel-matched data. Because of the complexity difference between NIR and RGB image domains, the complexity inequality in bidirectional NIR-RGB translations is significant. We analyze the limitation of the original CycleGAN in asymmetric translation tasks, and propose an Asymmetric Cycle-GAN model with U-net-like generators of unequal sizes to adapt to the asymmetric need in NIR-RGB translations. The edge-retain loss between NIR and the generated RGB images is also introduced to enhance face visual quality. The qualitative visual evaluation and quantitative evaluation with face ID and skin color criteria show that our model achieves great improvements compared with state-of-the-art methods on three public datasets and a newly proposed dataset.
Inspection of Aircraft Wing Panels Using Unmanned Aerial Vehicles

In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects' detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects' detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects.
A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model

Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.
An Intelligent Gain based Ant Colony Optimisation Method for Path Planning of Unmanned Ground Vehicles

In many of the military applications, path planning is one of the crucial decision-making strategies in an unmanned autonomous system. Many intelligent approaches to pathfinding and generation have been derived in the past decade. Energy reduction (cost and time) during pathfinding is a herculean task. Optimal path planning not only means the shortest path but also finding one in the minimised cost and time. In this paper, an intelligent gain based ant colony optimisation and gain based green-ant (GG-Ant) have been proposed with an efficient path and least computation time than the recent state-of-the-art intelligent techniques. Simulation has been done under different conditions and results outperform the existing ant colony optimisation (ACO) and green-ant techniques with respect to the computation time and path length.
Computational analysis and comparison of reversible gates for design and test of logic circuits

Quantum computing is one of the most significant anticipation towards the accomplishment of interminable consumer demands of small, high speed, and low-power operable electronics devices. As reversible logic circuits have direct applicability to quantum circuits, design and synthesis of these circuits are finding grounds for emerging nano-technologies of quantum computing. Multiple Controlled Toffoli (MCT) and Multiple Controlled Fredkin (MCF) are the fundamental reversible gates that playing key role in this phase of development. A number of special reversible gates have also been presented so far, which were claimed superior for providing certain purposes like logic development and testing. This paper critically analyses a range of these gates to procure an optimal solution for design, synthesis and testing of reversible circuits. The experimentation is facilitated at three subsequent levels, i.e. gates properties, quantum cost and design & testability. MCT and MCF gates are found up to 50% more cost-effective than special gates at design level and 34.4% at testability level. Maximum reversibility depth (MRD) is included as a new measurement parameter for comparison. Special gates exhibit MRD up to 7 which ideally should be 1 for a system to be physically reversible as that of MCT and MCF gates.
Transformation of education in the digital age

Research article. Global development of the information society requires a corresponding change in the education system. Meanwhile, not only the educational tools used (for example, replacing printed textbooks with electronic copies), but also the concept of building an educational system are subject to change. The purpose of this article is to determine the necessary directions of transformation of the Russian education system in the context of global digitalization. In their research, the authors analyze the situation ofRussiain the global digital space, as well as the main global trends in the digitalization of education. The information base of the research are legislative and other legal acts of the Russian Federation, data of international rating and analytical agencies, materials of the Organization for Economic Co-operation and Development (OECD), as well as data of statistical observations. Based on the assessment of the current state of the Russian educational system and its comparison with global trends, the key directions of the necessary changes have been revealed. In particular, the need for the rapid development of digital infrastructure of educational institutions, training of teachers with modern digital competencies, updating of specialties and areas of training, taking into account trends in the labor market, the formation of educational programs, based on flexible educational trajectories, the development of the concept of continuing education, as well as the introduction of artificial intelligence systems into the educational environment has been shown. During conducting of the research, the authors noted the lack of data necessary for in depth analysis of each of the selected areas. In future, each area should be the subject of independent research, in order to concretize its goals, identify priorities, as well as the formation of a road map for the implementation of measures. The article will be useful to the heads of educational organizations, as well as to all who are engaged in research of modern trends in the market of educational services.
Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics

Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.
Detecting Cybersecurity Events from Noisy Short Text

Semih Yagcioglu, Mehmet Saygin Seyfioglu, Begum Citamak, Batuhan Bardak, Seren Guldamlasioglu, Azmi Yuksel, Emin Islam Tatli. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Live forensics of tools on android devices for email forensics

Email is one communication technology that can be used to exchange information, data, and etc. The development of email technology not only can be opened using a computer but can be opened using an smartphone. The most widely used smartphone in Indonesian society is Android. Within a row, the development technology of higher cybercrime such as email fraud catching cybercrime offenders need evidence to be submitted to a court, for obtain evidence can use tools like Wireshark and Networkminer to analyzing network traffic on live networks. Opportunity, we will do a comparison of the forensic tools it to acquire digital evidence. The subject of this research focused on Android-based email service to get as much digital evidence as possible on both tools. This process uses National Institute of Standards and Technology method. The results of this research that networkminer managed to get the receiving port, while in Wireshark not found.
HeatCraft

Ingestible sensors are pill-like sensors that people swallow mainly for medical purposes. We propose that ingestible sensors also offer unique opportunities to facilitate intriguing bodily experiences in a playful manner. To explore this, we present "HeatCraft", a two-player system that translates the user's body temperature measured by an ingestible sensor to localized thermal stimuli delivered through a waist belt equipped with heating pads. We conducted a study with 16 participants. The study revealed three design themes (Integration of body and technology, Integration of internal body and outside world, and Integration of play and life) along with some open challenges. In summary, this work contributes knowledge to the future design of playful experiences with ingestible sensors.
Enabling Feature Location for API Method Recommendation and Usage Location

Given a new feature request during software evolution, developers are used to employing existing third-party libraries and APIs for implementation. However, it is usually non-trivial to find suitable APIs and to decide where to use these APIs in the original software. In this paper, we develop an approach for recommending API methods and usage locations through mining various software repositories. First, we analyze software repositories and use the feature location technique to localize feature-related files as API usage locations. Then, we utilize the feature-related files and API libraries to identify potential API methods for the implementation of the incoming feature request. We evaluate our approach on 5000 feature requests selected from five Java projects, and the results demonstrate that our approach can achieve 29.7% and 9.6% improvement in terms of Hit@5 and Hit@10 in recommending API methods, compared with the existing approach. For API usage localization, our approach can get MAP, MRR, Hit@1, HIt@5, and Hit@10 values with 0.293, 0.434, 0.303, 0.602, and 0.685, respectively.
Robust point cloud normal estimation via neighborhood reconstruction

This article presents a novel method for estimating normals on unorganized point clouds that preserves sharp features. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a neighborhood reconstruction-based normal estimation method is developed to find a proper neighborhood for points around sharp features. A robust statistics-based method is proposed to identify points that near sharp features and classify the points into two categories: edge points and non-edge points. Two specific neighborhood reconstruction strategies are designed for these two types of points to generate a neighborhood clear of sharp features. The normal of the current point can thus be reliably estimated by principal component analysis using the generated isotropic neighborhood. Numerous case studies have been carried out to compare the reliability and robustness of the proposed method against various existing methods. The experiment results show that the proposed approach performs better than the state-of-the-art methods most of the time and offers an ideal compromise between precision, speed, and robustness.
A practical and effective regularized polynomial smoothing (RPS) method for high-gradient strain field measurement in digital image correlation

Digital image correlation (DIC) is an effective tool for studying the deformation of tested materials. However, high-gradient strain field (HSF) measurement remains a challenging issue in DIC, because multifold factors have a significant effect on the first correlation calculation step of the original displacement and the exact strain reconstruction in the second post-processing step. In this study, we focus on the second step. The formerly proposed local Hermite has been proven as an accurate and robust method, yet substantial programming work is involved in constructing a Hermite element. To solve this issue, a regularized polynomial smoothing (RPS) method is proposed, which replaces the Hermite function of the polynomial. The RPS applies the same kernel function as the point-wise least-squares (PLS) method for easy implementation. Tikhonov regularization and the generalized cross-validation (GCV) function are also used to decrease unavoidable noise and weaken the dependence on window size. The experiments demonstrate that, in the matched/overmatched case, Tikhonov regularization in RPS can greatly decrease the degree of morbidity caused by ill-posed problems in the PLS method, thereby ensuring smoothness and stability of the solution. Moreover, the influences of window size and polynomial order of both the RPS and PLS are also closely studied. In conclusion, the proposed RPS provides a practical, robust and accurate method for HSF measurement.
POS Tag-enhanced Coarse-to-fine Attention for Neural Machine Translation

Although neural machine translation (NMT) has certain capability to implicitly learn semantic information of sentences, we explore and show that Part-of-Speech (POS) tags can be explicitly incorporated into the attention mechanism of NMT effectively to yield further improvements. In this article, we propose an NMT model with tag-enhanced attention mechanism. In our model, NMT and POS tagging are jointly modeled via multi-task learning. Besides following common practice to enrich encoder annotations by introducing predicted source POS tags, we exploit predicted target POS tags to refine attention model in a coarse-to-fine manner. Specifically, we first implement a coarse attention operation solely on source annotations and target hidden state, where the produced context vector is applied to update target hidden state used for target POS tagging. Then, we perform a fine attention operation that extends the coarse one by further exploiting the predicted target POS tags. Finally, we facilitate word prediction by simultaneously utilizing the context vector from fine attention and the predicted target POS tags. Experimental results and further analyses on Chinese-English and Japanese-English translation tasks demonstrate the superiority of our proposed model over the conventional NMT models. We release our code at https://github.com/middlekisser/PEA-NMT.git.
Impact of DNI nowcasting on annual revenues of CSP plants for a time of delivery based feed in tariff

The impact of combined direct normal irradiation nowcasts on revenues of two different concentrating solar power plant technologies under a time-of-delivery tariff is investigated.The applied tariff scheme offers considerably increased remuneration during evening hours with maximum energy demand and is similar to that used in South Africa for some concentrating solar power (CSP) plants already.The ideal forecast derived from ground-based observations was used to quantify the maximum economic advantage of using any forecasting scheme -and is estimated around 4 % for parabolic trough power plants and as around 8 % for solar tower power plants.This implies a maximum impact of up to 2.2 Mio and 5.3 Mio EUR of additional revenues per year for a typical 110 MW solar trough or solar tower power plant, respectively.The investigated nowcast strategy merges several satellite and numerical weather prediction based nowcasts together with a smart persistence approach in order to generate a best-of nowcast for now-and forecast time horizons up to 9 hours and in the 15 min temporal resolution required for the electricity market participation.The results for the combined nowcast are evaluated with respect to daily power plant operating principles and focusing on a power plant specific forecast verification strategy.The combined nowcast is therefore compared with ECMWF IFS based forecasts and with an optimized ground observation driven persistence approach in two representative locations around the Mediterranean area.The study investigates the impact both with respect to annual energy yield and economic annual revenues of CSP plants.It is found that in our study period 2010 and 2013 to 2015 the merged nowcast strategy adds 0.8 to 4.4 % in additional revenues per year compared to the ECMWF IFS day ahead forecast, which is a typical example of nowadays routinely available forecasts in the power plant's control room.This implies additional revenues of about 450 to 2900 kEUR per year when adding a nowcasting scheme to the solar production forecast tool already in operation at some power plants.
ICT COMPETENCE FOR SECONDARY SCHOOL TEACHERS AND STUDENTS IN THE CONTEXT OF EDUCATION INFORMATIZATION: GLOBAL EXPERIENCE AND CHALLENGES FOR UKRAINE

The paper is focused on the role of ICT education as a key component in the education systems in EU, USA and Ukraine. The research results show that the use of ICT in education and training has been a priority in most European countries during the last decade. It has been determined that the possession of ICT-competency has been proclaimed an important component of successful schooling and lifelong learning, further professional development, effective management of the learning process, and the use of ICT in learning activities in a modern digital society. It has been revealed that in the European countries, the standards of ICT-competency are developed and implemented at all levels of education, the systems of mandatory monitoring and certification of ICT-competency of students, teachers and heads of general educational institutions are in operation. Results show that teachers mainly focus on the development of technical ICT skills, whereas the ICT curriculum centres on the integrated use of ICT within the learning and teaching process. Analysis of foreign experience on modern approaches to the assessment has shown that the assessment of the pupils’ ICT competency in Ukraine is extremely important in the context of modern reforms, since in Ukraine standards for assessing ICT-competency are valid only within the subject “Informatics” at the level of secondary school, while the worldwide tendency is the end-to-end using of assessment tools for students' ICT-competency. Inference should be drawn that building digital competence by embedding and learning ICT should start as early as possible, i.e. in primary education, by learning to use digital tools critically, confidently and creatively, with attention paid to security, safety, and privacy. Teachers need to be equipped with the digital competence themselves, in order to support this process. The authors of the paper made conclusion about the necessity for Ukraine both to study the progressive European experience and to synchronize the education policy in ICT-education with the strategies of the EU and the member states.