Reviewerly/exHarmony huggingface dataset:
Sparse Gaussian Process Regression Beyond Variational Inference.

The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the resulting predictive distributions often exhibit substantially underestimated uncertainties. Notably, in the regression case the predictive variance is typically dominated by observation noise, yielding uncertainty estimates that make little use of the input-dependent function uncertainty that makes GP priors attractive. In this work we propose two simple methods for scalable GP regression that address this issue and thus yield substantially improved predictive uncertainties. The first applies variational inference to FITC (Fully Independent Training Conditional; Snelson et.~al.~2006). The second bypasses posterior approximations and instead directly targets the posterior predictive distribution. In an extensive empirical comparison with a number of alternative methods for scalable GP regression, we find that the resulting predictive distributions exhibit significantly better calibrated uncertainties and higher log likelihoods--often by as much as half a nat per datapoint.
Evolving graphs with semantic neutral drift

Abstract We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals’ fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.
PERANCANGAN PENGUJIAN PREFERENCE TEST, UJI HEDONIK DAN MUTU HEDONIK MENGGUNAKAN ALGORITMA RADIAL BASIS FUNCTION NETWORK

Wheat Bread producers are required to produce quality products and are liked by consumers. Increasing the quality of bread will certainly have an impact on sales to be generated. One of the efforts in improving the quality is by doing Hedonic test and Hedonic Quality test. This study aims to develop a system capable of providing an assessment of new products to be released on the market. Hedonic quality is used as a variable for assessing bread products with 4 variables, which include flavor, taste, appearance, and texture. While the hedonic test using six classes is very very like, very like, like, rather like, and do not like, then this result will be used as a class of Knowledge Based (KB). This research uses Radial Basis Function Network (RBFN) algorithm, yielding 98,8% accuracy with 10 fold testing technique. The final goal of the development of this system will create a system capable of providing an assessment of a wheat bread product.
Study of Convolutional Neural Network for Recognition of Baseband Signals

We study the behavior of a functional, pre-defined deep learning convolutional neural network (CNN) used to classify baseband signal modulation types adopted from MathWorks. We examine the behavior of the CNN as the architecture is altered, decreasing and increasing the volume of the hidden layers. We test the viability of the pre-constructed CNN as a solution to real-world communications problems by testing the deep learning networks ability to recognize modulation schemes with a signal-to-noise ratio (SNR) significantly lesser than that used to train the network. We investigate the ability of the pre-constructed CNN to be trained with waveforms of a lower SNR in the interest of more accurate modulation recognition overall. We also demonstrate the efficacy of the CNN model using Universal Software Radio Peripheral (USRP) radios. A long-term goal is to develop the optimized CNN on an SoC-FPGA (System-on-Chip Field Programmable Gate Array) to enable autonomous self-learning as well as self-defense of an intelligent radio system.
Towards increasing reliability of Amazon EC2 spot instances with a fault-tolerant multi-agent architecture

Cloud providers have recently offered their unused resources as transient instances. Amazon sells idle cloud resources as spot instances pricing by an auction-based market mechanism to reduce the cost without any availability guarantee. Thus, to dynamically and autonomously manage cloud resources t o execute user applications ensuring greater reliability with cheaper spot instances is an open problem. In this context, we propose a fault-tolerant multi-agent architecture as middleware of cloud providers and users to mediate access to a wide range of heterogeneous resources providing a resilient application execution environment with a dynamic flexible fault-tolerant mechanism based on adaptive checkpointing. Our architecture combines a case-based reasoning model with a survival analysis model to predict failure events and refine fault-tolerant plans with adequate parameters to increase reliability optimizing total execution time and costs. We evaluated the proposed architecture with real historical data collected from Amazon EC2 price changes including, with approximately 21 million records and generating 1,362,816 scenarios stored in our case knowledge database. The results considering the time to revocation achieved high levels of accuracy (98%) with a gain up to 74.48% to total execution time, reducing total cost when compared to other approaches in the literature.
Erasable Bit Commitment From Temporary Quantum Trust

We introduce a new setting for two-party cryptography by introducing the notion of temporarily trusted third parties. These third parties act honest-but-curious during the execution of the protocol. Once the protocol concludes and the trust period expires, these third parties may collaborate with an adversarial party. We implement a variant of the cryptographic primitive of bit commitment in this setting, which we call erasable bit commitment. In this primitive, the sender has the choice of either opening or erasing her commitment after the commit phase. For example, she can ask for an erase before the trust period expires in case the conditions for opening the commitment have not been met. The erasure prevents a future coalition of the trusted party and the receiver from extracting any information about the commitment. However, this option also weakens the cryptographic primitive relative to standard bit commitment. Furthermore, the committed information is not revealed to the trusted node at any stage during the protocol. Our protocol requires a constant number of third parties and can tolerate a small number of corrupt third parties as well as implementation errors.
Infrared Evaluation Classification Method for Deteriorated Insulator Based on Bayesian Algorithm

The insulation state detection of transmission line insulators in power systems is essential to ensure the reliability of transmission power supply. Aiming at the low accuracy of the infrared diagnostic fault diagnosis method for porcelain insulators in transmission lines, this paper proposes a classification method based on Bayesian algorithm for the classification of infrared map degradation degree of insulators. Bayesian algorithm is used to mine the correspondence between the characteristic parameters of the insulator infrared inspection map and the insulator state represented by the map, so as to improve the Bayesian network structure and finally classify the map. The results show that the method can effectively classify the operating state of insulators. The accuracy of the power insulator state classification model based on Bayesian algorithm reaches 94.2%.A new method of intelligent detection and diagnosis for evaluating the deterioration of insulators is proposed. This method has the advantage of high stability and accuracy.
Digital Financial Integration, Investment, Economic Growth, Development and Poverty Reduction

In recent years, the global economy has undergone major transformations involving the liberalization of markets for traditional goods, services, and capital flows. This has led to the emergence of a world financial market underpinned by digital platforms, innovative and the rapid growth of integrated digital platforms, integration, investment, economic growth, development, and the potential for poverty reduction, especially, in the Global South and, in particular in sub-Saharan Africa. The goal of this chapter is to investigate the increasing accessibility and relationship between digital (e-economy) financial integration and poverty alleviation since the era of structural adjustment programs in sub-Sahara Africa with Ghana as a case study. The emphasis is on the New Digital Economy (NDE) relative to new sources of data from mobile and ubiquitous Internet connectivity. The processes of digitalization and financial sector integration and inclusion become increasingly contestable, decomposable, and reconfigurable, and the capacity to innovate will be a key success factor in policies geared toward poverty alleviation. The multiple linear regression model and its estimation using ordinary least squares (OLS) is doubtless the most widely used tool in econometrics. It helps to estimate the relation between a dependent variable and a set of explanatory variables. An OLS model for macro data set relative to a regression model is applied to provide the empirical estimations of the increasing accessibility and the relationship between digital financial integration, investment, economic growth, development, and poverty alleviation.
Mobile robot controller using novel hybrid system

Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Phenotype Inference with Semi-Supervised Mixed Membership Models.

Disease phenotyping algorithms are designed to sift through clinical data stores to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of expert-labeled data, while unsupervised methods may learn spurious or non-disease phenotypes. To address these limitations, we propose the Semi-Supervised Mixed Membership Model (SS3M) - a probabilistic graphical model for learning disease phenotypes from partially labeled clinical data. We show SS3M can generate interpretable, disease-specific phenotypes which capture the clinical features of the disease concepts specified by the labels provided to the model. Furthermore, SS3M phenotypes demonstrate competitive predictive performance relative to commonly used baselines.
Verification of a resetting protocol for an uncontrolled superconducting qubit

Abstract Quantum resetting protocols allow a quantum system to be sent to a state in the past by making it interact with quantum probes when neither the free evolution of the system nor the interaction is controlled. We experimentally verify the simplest non-trivial case of a quantum resetting protocol, known as the $${{\mathcal{W}}}_{4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>W</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> protocol, with five superconducting qubits, testing it with different types of free evolutions and target–probe interactions. After projection, we obtained a reset state fidelity as high as 0.951, and the process fidelity was found to be 0.792. We also implemented 100 randomly chosen interactions and demonstrated an average success probability of 0.323 for $$\left|1\right\rangle$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfenced> </mml:math> and 0.292 for $$\left|-\right\rangle$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:mrow> <mml:mo>−</mml:mo> </mml:mrow> </mml:mfenced> </mml:math> , and experimentally confirmed the nonzero probability of success for unknown interactions; the numerical simulated values are about 0.3. Our experiment shows that the simplest quantum resetting protocol can be implemented with current technologies, making such protocols a valuable tool in the eternal fight against unwanted evolution in quantum systems.
Comparative study of competences in higher education students

In essence, students do not know the necessary skills, as well as their training needs required for their entry to the higher level. At present, it is essential to have, at least, the basic skills for digital literacy. In this article, we propose a quantitative and qualitative research about the current situation of the digital competences of new students of the Faculty of Accounting and Administration of the Universidad Veracruzana, as well as an important comparison between educational programs to understand the reality of how is their training in the field of digital knowledge at the lower immediate level of education. A methodology is proposed from the conceptual and theoretical part allowing to carry out the conceptualization, under a deductive process, carrying out the process of conceptualization to the operationalization following the phases of theoretical representation of the concept, specification of the concept for the identification of the dimensions and from each dimension its indicators, resulting in an instrument with validity and reliability that was applied to a stratified sample to identify digital competences in the five evaluated dimensions, all so that, based on the diagnosis, strategies can be generated in Universities to identify the competences of new students and then expand their knowledge about this area, in such a way that it allows them to have information for making decisions regarding the training and qualification to improve their learning processes.
Track Finding with Deep Neural Networks

High Energy Physics experiments require fast and efficient methods toreconstruct the tracks of charged particles. Commonly used algorithms aresequential and the CPU required increases rapidly with a number of tracks.Neural networks can speed up the process due to their capability to modelcomplex non-linear data dependencies and finding all tracks in parallel.In this paper we describe the application of the Deep Neural Networkto the reconstruction of straight tracks in a toy two-dimensional model. It isplanned to apply this method to the experimental data taken by the MUonEexperiment at CERN.
Particle Swarm Optimization with Multiple Regression for Optimal Design of Interior Permanent Magnet Synchronous Motor

In this study, we propose to apply multiple regression method to improve the performance of the conventional particle swarm optimization (PSO) algorithm. We can estimate the function formula for the design variables and derive the global solution by using estimated function formula. The convergence speed of the conventional PSO algorithm is improved by using the information of the solution obtained by performing the multiple regression. The proposed algorithm is validated using numerical test function. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the torque ripple and cogging torque. The torque characteristic data of IPMSM required for performing the optimization are obtained using finite element method based on 2-D numerical analysis.
Research on the Energy Distribution and Tendency of Blockchain in the E-commerce Field

In order to understand the hot and forward trend of the research about the blockchain in the Chinese e-commerce field, it takes the literature in e-commerce field with the blockchain of the CNKI database as the sample, and it also uses CiteSpace V, SPSS21.0, Excel2010 and CNKI platform to carry out bibliometrics and visualization analysis. The energy distribution in the area of China's block chain research in e-commerce field is analyzed from the aspects of subject distribution, research level, source journals, author distribution, research institutions, fund projects and so on. And from the high frequency key words, key words social network and other dimensions depth analysis of the research hot spots and frontiers in the e-commerce field of block chain in Chinese academic circles. The study found: The research of China's block chain in the field of publishing has not yet started completely. It needs to be paid attention to by the academic circle, and its research field, scale of cooperation，scale and depth of research need to be strengthened. The research focuses mainly on the application of the agricultural products, the payment of e-commerce, the traceability of the electric business field and the regulatory aspects of the e-commerce. E-commerce segments, e-commerce related areas, e-commerce payment refinement, e-business logistics refinement, e-commerce finance, new technology or ideas related aspects are relatively cutting-edge potential research trends.
‘Paintings for a Crime’: Composed Cognitive Maps for Measuring Crime and Situation

The Ecuadorian State implemented policies to improve the quality and reliability of statistical material. Among these materials, composed cognitive maps were innovated and applied. These materials consist of drawings made by witnesses and victims of perceived hostile and dangerous territories that were analyzed through ethnographic and Grounded Theory Method. This process allowed for a deeper understanding of hotspots, associated criminal situations and large victimization patterns throughout the national territory, than traditional ones. The composed cognitive maps and resulting spatial dynamics of crime helped to overcome the gap between the micro and macro level problem in criminology and gave important insights to design crime policies. We briefly describe the experience, compare the method to the traditional ones and give an example of its diagnostic potential compared to regular information managed to day by the police.
A Lightweight Neural Network for Monocular View Generation With Occlusion Handling

In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single image. The method is built upon the idea of blending two predictions: a prediction based on disparity estimation, and a prediction based on direct minimization in occluded regions. The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps. At test time, the approach can thus generate a left-side and a right-side view from one input image, as well as a depth map and a pixelwise confidence measure in the prediction. The work outperforms visually and metric-wise state-of-the-art approaches on the challenging KITTI dataset, all while reducing by a very significant order of magnitude (5 or 10 times) the required number of parameters (6.5 M).
On Selection of Optimal Classifiers

The current advances of computational power and storage allow more models to be created and stored from significant data resources. This progress opens the opportunity to re-cycle and re-use such models in similar exercises. The evaluation of the machine learning algorithms and selection of an appropriate classifier from an existing collection of classifiers are still challenging tasks. In most cases, the decision of selecting the classifier is left to the user. When the selection is not performed accurately, the outcomes can have unexpected performance results. Classification algorithms aim to optimise some of the distinct objectives such as minimising misclassification error, maximising the accuracy, or maximising the model quality. The right choice for each of these objectives is critical to the quality of the classifier selected. This work aims to study the use of a multi-objective method that can be undertaken to find a set of suitable classifiers for a problem at hand. In this study, we applied seven classifiers on mental health data sets for classifier selection in terms of correctness and reliability. The experimental results suggest that this approach is useful in finding the best trade-off among the objectives of selecting a suitable classifier framework.
Design of Digital Video Watermarking Technique Based on Motion Frames

The availability of high internet bandwidth instantaneously transfer the digital video multimedia object between two or work workstations. Additionally, the advent of digital technology can be mistreating by malicious user to replicate multiple identical copies of the original video illegally. These capabilities introduce the issue of developing the methods for protecting the copyright of video multimedia objects. The proposed technique suggested detecting the motion part of the video by applying heuristic method of histogram difference between two consecutive frames. Then powerful transformations, Singular Value Decomposition (SVD), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are used to insert the different segment of the scrambled watermarks into different motion frames of the video. The experimental results show that the scheme is robust to unintentional and intentional video specific attacks which considered being frequently occurring attacks. The proposed algorithm has been contrasted with an existing DWT, DCT or hybrid-based approaches and is found to exhibit better robustness.
Risk Management Approach

The previous chapter discussed how to conduct a risk assessment exercise and give a risk assessment report presentation. This chapter discusses the approach to be followed for managing the risks identified during the risk assessment exercise. This chapter will also focus on identifying assets and applying security controls.
VIRTUAL LABS IN EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7277-7281ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1732Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
The Holographic Academic: Rethinking Telepresence in Higher Education

Using holographic projection technologies at an event titled ‘Women in Tech’, Imperial College Business School launched in November 2018 what it claimed to be the ‘world’s first holographic event at a university’. This form of teaching via telepresence has the potential for a significant disruption of the lecture format and also raises profound questions around the pedagogy of giving lectures in this way. This chapter asks some of those questions as well as attempting possible answers to them and is therefore intended as a set of practical considerations for teachers, technologists, and policy makers that might wish to investigate holographic delivery for their own institutions.