Reviewerly/exHarmony huggingface dataset:
Development of methodological foundations for designing a classifier of threats to cyberphysical systems

The emergence of a full-scale quantum computer questions the stability of almost all symmetric and asymmetric cryptography algorithms. At the same time, the rapid growth of computing resources of IT and “G” technologies contributes to an increase in attacks on information and communication (ICS) and cyberphysical systems (CPS). These systems are the core of modern critical cybernetic information systems (CCIS). In such conditions, the primary task of maintaining the required level of security is the classification of modern threats that are integrated with social engineering methods and acquire signs of synergy and hybridity. The paper proposes a synergistic model of threats to ICS/CPS, which takes into account the focus of threats on synergy and hybridity, and the combined impact of security components: information security (IS), cybersecurity (CS), security of information (SI). This approach allows developing methodological foundations for building a unified classifier of threats to cyberphysical systems, forming sets of critical threats, critical points in the ICS/CPS infrastructure elements, based on minimal computing, human and economic costs. The developed methodology for determining the category of an attacker allows systematizing an attacker and, based on the analysis of weighting factors, forming a matrix of correspondence between the capabilities of attackers of various categories and technical means of information security (TMIS). These actions significantly reduce the risk of an attack by certain categories of attackers and allow for planning in the formation of both the IS policy and the corresponding protection profiles.
Software Effort Prediction Using Ensemble Learning Methods

Software Cost Estimation (SCE) is an essential requirement in producing software these days.Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs).Effort is estimated in terms of individual months and length.Overestimation as well as underestimation of efforts can adversely affect software development.Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques.The predominant cost for any product is the expense of figuring effort.Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy.Fortunately, several efforts estimation models are available; however, it is difficult to determine which model is more accurate on what dataset.Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule.We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm.The dataset is based on 499 projects known as China.The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.
Development vectors for research libraries: the review of the key reports at the IFLA World Library and Information Congress 2019

The main theme of the 85th IFLA World Congress – the International Federation of Library Organizations and Institutions, which was held in Athens, Greece in 2019, was "Libraries: A Dialogue for Change". Once again, the congress brought together professionals from many countries of the world to solve the problems facing libraries of various forms. Their solution is achieved by developing common approaches to challenges at the global level (projects “Global Vision”, “Library Map of the World”, etc.), sharing best practices and methods, innovative forms of user service and resources generating. The purpose of this review is to present keynote reports delivered at the IFLA World Congress in 2019 on global trends in the development of research libraries. The selection of the reports was carried out by expert evaluation by the author, as well as some materials published on the Congress website. As a result, it was determined that libraries choose various ways and new methods of work to support researchers: by analyzing user needs; integrating librarians into the research life cycle; implementing the principles of open science and open access; developing discovery services and expanding the functionality of information resources; using artificial intelligence technologies, augmented and virtual reality, etc. This allows the creation of popular research support services, improving the quality of services, expanding the repertoire of information resources / products and services provided, modernize traditional forms and methods of work, and fully satisfy the information needs of users.
Decision Support System for Classification Medullary Thyroid Cancer

Due to the complex, heterogeneous and mimic morphological features of medullary thyroid cancer (MTC).It becomes often difficult to diagnose MTC at early stage.Since histopathological complex patterns of cancerous cells and tissues requires a huge effort to classify.Therefore thyroid cancer classification has become one of the significant research area area(s) of Machine Learning.We propose a decision support system to classify initial variation of morphological appearance of nuclei by using Convolutional Neural Networks (CNNs).The system comprises over three major layers, where image preprocessing techniques are used at top layer along with feature selection techniques.Classification model constructed by using CNNs at the second layer and result visualization described at third layer.Due to the unavailability of datasets for medullary thyroid cancer in literature, this research uses real-world datasets consisting upon 20GB cytological medical images and the approximated classification accuracy is measured about 99.00%.Malignant and non -malignant cells are visualized to assist the doctors in better way.
Does Vote Verification Work: Usage and Impact of Confidence Building Technology in Internet Voting

The paper examines cast-as-intended verification usage in Estonia by looking at who verifies votes, how they do it and what is the effect on perceptions of election integrity. Using anonymized log and survey data a typical use case of verification is established - younger, Linux using male voting late at night - which suggest verification is used by more cyber risk aware users. Vote verifiers, when re-voting, are also more likely to change the voting environment compared to those re-voters who do not verify their vote, indicating verification is not simply used to check one’s own mistakes in candidate selection. The effects of verifying on confidence in the vote being correctly taken into account are substantial - vote verifiers show a stronger belief in the election integrity. Overall, verification technology seems to be building confidence in the system and being used by the more risk aware voting population.
A Robust Image Watermarking Approach Using Cycle Variational Autoencoder

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.
Robust Adaptive Tracking Control of Manipulator Arms with Fuzzy Neural Networks

The learning space for executing general motions of a flexible joint manipulator is quite large and the dynamics are, in general, nonlinear, time-varying, and complex. The objective of this paper is to design a nonlinear system based on the fuzzy neural network control using supervised training, into executing reference trajectories by a flexible joint manipulator. The structure identifications of controller networks are performed by using the Adaptive Neural Fuzzy Inference System (ANFIS), with new parameters and weight coefficients automatically adapted and adjusted, in order to decrease position tracking errors. In order to adapt and reduce the number of undefined parameters in the network, a new technique is used. Reported research works use the Euler method for the resolution of the arm's dynamic function, in this paper, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK4) method. A comparative study has been carried out between these two methods in order to prove the effectiveness of the later. Finally, in order to test the robustness of the proposed approach, it was also investigated considering parameter variations. The tracking speed of the model on the system control accuracy was also analyzed. The simulation results show that the proposed approach has a good tracking effect.
A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture

Speech synthesis is the artificial production of human speech. A typical text-to-speech system converts a language text into a waveform. There exist many English TTS systems that produce mature, natural, and human-like speech synthesizers. In contrast, other languages, including Arabic, have not been considered until recently. Existing Arabic speech synthesis solutions are slow, of low quality, and the naturalness of synthesized speech is inferior to the English synthesizers. They also lack essential speech key factors such as intonation, stress, and rhythm. Different works were proposed to solve those issues, including the use of concatenative methods such as unit selection or parametric methods. However, they required a lot of laborious work and domain expertise. Another reason for such poor performance of Arabic speech synthesizers is the lack of speech corpora, unlike English that has many publicly available corpora (LjSpeech, https://keithito.com/LJ-Speech-Dataset/ ., Blizzard 2012, http://www.cstr.ed.ac.uk/projects/blizzard/2012/phase_one/ .) and audiobooks. This work describes how to generate high quality, natural, and human-like Arabic speech using an end-to-end neural deep network architecture. This work uses just $$\langle $$ text, audio $$\rangle $$ pairs with a relatively small amount of recorded audio samples with a total of 2.41 h. It illustrates how to use English character embedding despite using diacritic Arabic characters as input and how to preprocess these audio samples to achieve the best results.
The Sensory Interactive Table

Eating is in many ways a social activity. Yet, little is known about the social dimension of eating influencing individual eating habits. Nor do we know much about how to purposefully design for interactions in the social space of eating. This paper presents (1) the journey of exploring the social space of eating by designing an artifact, and (2) the actual artifact designed for the purpose of exploring the interaction dynamics of social eating. The result of this Research through Design journey is the Sensory Interactive Table: an interactive dining table based on explorations of the social space of eating, and a probe to explore the social space of eating further.
Enhancing phase sensitivity with number state filtered coherent states

Number state filtered coherent states are a class of nonclassical states obtained by removing one or more number states from a coherent state. Phase sensitivity of an interferometer is enhanced if these nonclassical states are used as input states. The optimal phase sensitivity, which is related to the quantum Cramer–Rao bound for the input state, improves beyond the standard quantum limit. It is argued that the removal of more than one suitable number state leads to better phase sensitivity. As an important limiting case in this context, the even and odd coherent states, where the odd and even number states are filtered from coherent state, respectively, are considered. The optimal phase sensitivity for these limiting cases equals that of the squeezed vacuum. It is observed that the improvement in phase sensitivity is not in direct proportion to the nonclassicality of the input states.
Pushing Purcell enhancement beyond its limits

Purcell-enhanced emission from a coupled emitter-cavity system is a fundamental manifestation of cavity quantum electrodynamics. Starting from a theoretical description we derive a scheme for photon emission from an emitter coupled to a birefringent cavity that exceeds hitherto anticipated limitations. Based on a recent study and experimental investigation of the intra-cavity coupling of orthogonal polarisation modes in birefringent cavities, we now decouple the emitter and the photon prior to emission from the cavity mode. Effectively, this is "hiding" the emitter from the photon in the cavity to suppress re-excitation, increasing the overall emission through the cavity mirrors. In doing so we show that tailored cavity birefringence can offer significant advantages and that these are practically achievable within the bounds of present-day technology. It is found that birefringence can mitigate the tradeoff between stronger emitter-cavity coupling and efficient photon extraction. This allows for longer cavities to be constructed without a loss of performance -- a significant result for applications where dielectric mirrors interfere with any trapping fields confining the emitter. We then generalise our model to consider a variety of equivalent schemes. For instance, detuning a pair of ground states in a three-level emitter coupled to a cavity in a Lambda-system is shown to provide the same enhancement, and it can be combined with a birefringent cavity to further increase performance. Additionally, it is found that when directly connecting multiple ground states of the emitter to form a chain of coupled states, the extraction efficiency approaches its fundamental upper limit. The principles proposed in this work can be applied in multiple ways to any emitter-cavity system, paving the way to surpassing the traditional limits of such systems with technologies that exist today.
Minimal blowing pressure allowing periodic oscillations in a simplified reed musical instrument model: Bouasse-Benade prescription assessed through numerical continuation

A reed instrument model with N acoustical modes can be described as a 2N dimensional autonomous nonlinear dynamical system. Here, a simplified model of a reed-like instrument having two quasi-harmonic resonances, represented by a four dimensional dynamical system, is studied using the continuation and bifurcation software AUTO. Bifurcation diagrams of equilibria and periodic solutions are explored with respect to the blowing mouth pressure, with focus on amplitude and frequency evolutions along the different solution branches. Equilibria and periodic regimes are connected through Hopf bifurcations, which are found to be direct or inverse depending on the physical parameters values. Emerging periodic regimes mainly supported by either the first acoustic resonance (first register) or the second acoustic resonance (second register) are successfully identified by the model. An additional periodic branch is also found to emerge from the branch of the second register through a period-doubling bifurcation. The evolution of the oscillation frequency along each branch of the periodic regimes is also predicted by the continuation method. Stability along each branch is computed as well. Some of the results are interpreted in terms of the ease of playing of the reed instrument. The effect of the inharmonicity between the first two impedance peaks is observed both when the amplitude of the first is greater than the second, as well as the inverse case. In both cases, the blowing pressure that results in periodic oscillations has a lowest value when the two resonances are harmonic, a theoretical illustration of the Bouasse-Benade prescription.
Deep artifact-free residual network for single-image super-resolution

Recently, convolutional neural networks have shown promising performance for single-image super-resolution. In this paper, we propose Deep Artifact-Free Residual (DAFR) network which uses the merits of both residual learning and usage of ground-truth image as target. Our framework uses a deep model to extract the high-frequency information which is necessary for high-quality image reconstruction. We use a skip-connection to feed the low-resolution image to the network before the image reconstruction. In this way, we are able to use the ground-truth images as target and avoid misleading the network due to artifacts in difference image. In order to extract clean high-frequency information, we train the network in two steps. The first step is a traditional residual learning which uses the difference image as target. Then, the trained parameters of this step are transferred to the main training in the second step. Our experimental results show that the proposed method achieves better quantitative and qualitative image quality compared to the existing methods.
Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol

The key point of this paper is to assess and look over the top of the line network layer-based VPN (Virtual Private Network) protocols because data link layer is hardly ever found to be in use in organizations, the reason is because of its exceedingly high charge. Virtual Private Network (VPN) is commonly used in business situations to provide secure communication channels over public infrastructure such as Internet. Virtual Private Networks (VPNs) provide a secure encrypted communication between remote networks worldwide by using Internet Protocol (IP) tunnels and a shared medium like the Internet. The paper follows and sets standards for different types of protocols and techniques, the VPN (Virtual Private Network) architectural feature is made to deliver dependable and safe network that is not in line with regular networks that provide a higher trust and a higher secure channel between user and organization. The current study took place to summaries the usage of existing VPNs protocol and to show the strength of every VPN. Through different studies that have been made by other researchers as well as an extra focus on the state of art protocol, Wire guard. It is also worthy to mention that wire guard compared with other protocols such as IPsec and GRE. The studies show the WireGuard being a better choice in terms of other well-known procedures to inaugurate a secure and trusted VPN.
GCOMB: Learning budget-constrained combinatorial algorithms over billion-sized graphs

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on obtaining high-quality solutions, scalability to billion-sized graphs has not been adequately addressed. In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. In this paper, we propose a framework called GCOMB to bridge these gaps. GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
The machine learning model as a guide

The labeling of datasets is an important task for supervised and semi-supervised machine learning that can be addressed with visual analytics. With model-based active learning and user-based interactive labeling, there are two complementary strategies for this task. We present an approach that combines the strengths of both areas and aims to guide users through model-based recommendations and highlighting in one interface when selecting and labeling instances. For this purpose, an active learning strategy is used to recommend useful instances in addition to the user-based selection of instances. We have implemented the approach and conducted a user survey to research the effects guidance by visual cues has on the users' selection strategies. The proposed approach combines both perspectives in a single interactive visualization to support the user with different degrees of guidance in the selection of instances. Our results of the user survey suggest that user guidance has a positive influence on the users' perceived confidence and difficulty in selecting instances, on their orientation, and on their perceived impression of the models' performance. A video of the approach is available: https://youtu.be/TYPWG85Akn0.
Philippine Basic Education Learning Continuity Plan: Creating Space for Indigenous Peoples toward Inclusive post-COVID-19 Education

The continuity of education after the current crisis is a major national concern. A vulnerable group that may be further marginalized in the process consists of learners in indigenous communities. Through a review of the Philippine Basic Education Learning Continuity Plan (BE-LCP), this article seeks to create space for the indigenous peoples (IP) learners toward an inclusive post-coronavirus disease 2019 (COVID-19) education. Considering the pre-COVID-19 background of resources and the current epidemiological status of IP communities, the researcher presents strategies on how post-COVID-19 education can be delivered. For example, in an IP community with electricity access and low technology access but no internet and has high to moderate risk of virus infection, a combination of print modular learning and television- and radio- based learning can be adopted but not the synchronous and asynchronous learning and stagger in-person learning. This suggested option and others are offered in the paper.
Log‐ratio analysis of microbiome data with many zeroes is library size dependent

Abstract Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artefact of the sequencing platform, and as a result, such data are compositional. To avoid library size dependency, one common way of analysing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centred log‐ratio, hereafter called a log‐ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this study, we show on real data and by simulation that, applied to data that combine these two aspects, log‐ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log‐ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.