Reviewerly/exHarmony huggingface dataset:
Generation of Differentially Private Heterogeneous Electronic Health Records

Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advantages that they can be easily distributed and contain many features useful for e.g. classification problems. What makes EHR data sets different from typical machine learning data sets is that they are often very sparse, due to their high dimensionality, and often contain heterogeneous (mixed) data types. Furthermore, the data sets deal with sensitive information, which limits the distribution of any models learned using them, due to privacy concerns. For these reasons, using EHR data in practice presents a real challenge. In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks. We will further explore applying differential privacy (DP) preserving optimization in order to produce DP synthetic EHR data sets, which provide rigorous privacy guarantees, and are therefore shareable and usable in the real world. The performance (measured by AUROC, AUPRC and accuracy) of our model's synthetic, heterogeneous data is very close to the original data set (within 3 - 5% of the baseline) for the non-DP model when tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our model still produces data useful for machine learning tasks, albeit incurring a roughly 17% performance penalty in our tested classification task. We additionally perform a sub-population analysis and find that our model does not introduce any bias into the synthetic EHR data compared to the baseline in either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms of classification performance for either the non-DP or DP variant.
Identifying Impacts of Protocol and Internet Development on the Bitcoin Network

Improving transaction throughput is an important challenge for Bitcoin. However, shortening the block generation interval or increasing the block size to improve throughput makes it sharing blocks within the network slower and increases the number of orphan blocks. Consequently, the security of the blockchain is sacrificed. To mitigate this, it is necessary to reduce the block propagation delay. Because of the contribution of new Bitcoin protocols and the improvements of the Internet, the block propagation delay in the Bitcoin network has been shortened in recent years. In this study, we identify impacts of compact block relay---an up-to-date Bitcoin protocol---and Internet improvement on the block propagation delay and fork rate in the Bitcoin network from 2015 to 2019. Existing measurement studies could not identify them but our simulation enables it. The experimental results reveal that compact block relay contributes to shortening the block propagation delay more than Internet improvements. The block propagation delay is reduced by 64.5% for the 50th percentile and 63.7% for the 90th percentile due to Internet improvements, and by 90.1% for the 50th percentile and by 87.6% for the 90th percentile due to compact block relay.
A Sinhala and Tamil Extension to Generic Environment for Context-aware Correction

There are several types of research available on spell checkers for European languages and Indian languages. However, low resourced languages like Tamil & Sinhala have limited research in this problem space, maybe, because of its highly inflectional and morphologically rich nature. There is no fully functional context-aware spell-checking system, especially as an open source. A Generic Environment for context-aware spell correction approach is extended for resource-scarce languages: Sinhala and Tamil in this paper. Experimental results show that our system detects the error in spelling well and provides the most suitable suggestions for correcting the misspelled words with a minimum of 85% accuracy for Tamil and 70% for the Sinhala Language. This is the first ever context-aware spell corrector for the Sinhala language. Compared to prior Tamil language context-aware spell correctors this leaps in 1) modularized architecture and 2) increased coverage and accuracy. Moreover, this study produced a Tamil and Sinhala spell correction benchmark dataset. Both the dataset and the tools are available for public use.
A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City

Risk management is a key factor for smart city running. There are many risk events in a strict process like transportation management of a smart city or a medical surgery in a smart hospital, and every step may lead to one kind of risk or more. In view of the fact that the occurrence of the flow risks follows the sequence formed by each process step, this paper presents a Bayesian network under strict chain (BN_SC) to model this situation. In this model, the probabilistic reasoning formula is given according to the sequence of process steps, and the probabilities given by the model can do risk factor analysis to support the system to find an effective way to improve the process like machine manufacturing or a medical surgery. Finally, an example is analyzed based on the information given by doctors according to the situation of LC in their hospital located in Sichuan Province of China, which shows the effectiveness and rationality of the proposed BN_SC model.
A Hybrid Approach to Formal Verification of Higher-Order Masked Arithmetic Programs

Side-channel attacks, which are capable of breaking secrecy via side-channel information, pose a growing threat to the implementation of cryptographic algorithms. Masking is an effective countermeasure against side-channel attacks by removing the statistical dependence between secrecy and power consumption via randomization. However, designing efficient and effective masked implementations turns out to be an error-prone task. Current techniques for verifying whether masked programs are secure are limited in their applicability and accuracy, especially when they are applied. To bridge this gap, in this article, we first propose a sound type system, equipped with an efficient type inference algorithm, for verifying masked arithmetic programs against higher-order attacks. We then give novel model-counting based and pattern-matching based methods which are able to precisely determine whether the potential leaky observable sets detected by the type system are genuine or simply spurious. We evaluate our approach on various implementations of arithmetic cryptographicprograms.The experiments confirm that our approach out performs the state-of-the-art base lines in terms of applicability, accuracy and efficiency.
Investigation of Quantum Entanglement through a Trapped Three Level Ion Accompanied with Beyond Lamb-Dicke Regime

In this study, our goal is to obtain the entanglement dynamics of trapped three-level ion interaction two laser beams in beyond Lamb-Dicke parameters.Three values of LDP, 0.09 η = , 0.2 η = and 0.3 η = are given.We used the concurrence and the negativity to measure the amount of quantum entanglement created in the system.The interacting trapped ion led to the formation of phonons as a result of the coupling.In two quantum systems (ion-phonons), analytical formulas describing both these measurements are constructed.These formulas and probability coefficients include first order terms of final state vector.We report that long survival time of entanglement can be provided with two quantum measures.Negativity and concurrence maximum values are obtained N = 0.553 and for LDP = 0.3.As a similar, the other two values of LDP are determined and taken into account throughout this paper.For a more detailed understanding of entanglement measurement results, "contour plot" was preferred in Mathematica 8.
A generic adaptive restart scheme with applications to saddle point algorithms.

We provide a simple and generic adaptive restart scheme for convex optimization that is able to achieve worst-case bounds matching (up to constant multiplicative factors) optimal restart schemes that require knowledge of problem specific constants. The scheme triggers restarts whenever there is sufficient reduction of a distance-based potential function. This potential function is always computable. We apply the scheme to obtain the first adaptive restart algorithm for saddle-point algorithms including primal-dual hybrid gradient (PDHG) and extragradient. The method improves the worst-case bounds of PDHG on bilinear games, and numerical experiments on quadratic assignment problems and matrix games demonstrate dramatic improvements for obtaining high-accuracy solutions. Additionally, for accelerated gradient descent (AGD), this scheme obtains a worst-case bound within 60% of the bound achieved by the (unknown) optimal restart period when high accuracy is desired. In practice, the scheme is competitive with the heuristic of O'Donoghue and Candes (2015).
Automated system for diagnosing the ability to solve computational problems based on structural and mental schemes

Problem and goal. The paper deals with an actual problem - learning to solve computational problems. The components of this problem are highlighted and it is shown that to solve it, it is necessary to automate the learning process, especially in terms of organizing independent work. The purpose of this work is to describe the scientific and technological basis for building an automated diagnostic system for solving computational problems (using the example of physical problems), as well as to present the results of using the developed system in the real pedagogical process. Methodology. The described system is based on a mental approach to learning. The paper introduces the concept of a computational primitive and, based on it, the concept of a structuralmental scheme (SMS) - a graph-like model of the ability to solve problems. An Elo-like rating system was used to provide adaptive, continuous monitoring. In order to increase the strength of mastering the ability to solve problems, we implemented the account of forgetting using a piecewise linear model of forgetting. To assess the formation of the ability to solve problems, a value is introduced - the level of assimilation, which is determined by the structural and mental scheme of the student and reflects its completeness and strength. Results. The results of the application of the described automated diagnostic system for the ability to solve computational problems in the real pedagogical process are presented. The described system proved to be effective, i.e. it positively affects the level of formation of the ability to solve problems. The correlation between the number of tasks solved by the student and the level of assimilation is calculated. The efficiency of the presented system in terms of forming the ability to solve computational problems on the example of physical problems is proved. Conclusion. Automation using a computer system allows to track and store information about the status of each individual SMS connection in memory. This cannot be done using any other non-automated tools and technologies due to the large amount of data and the complexity of calculations.
AI-Aided Core Analysis: Faster and Cheaper SCAL Studies

Summary The main aim of this work is to study the applicability of Machine Learning (ML) techniques for prediction of rock properties, which are commonly defined via special core analysis (SCAL). The mechanism of SCAL prediction on the basis of routine core analysis (RCA) was developed and validated. The possibility of application of ML methods for estimation of some rock characteristics was demonstrated. The comparative analysis of different ML techniques was provided to choose the most stable and accurate forecast methods. It was shown that Gradient Boosting algorithm and Artificial Neural Network allow to create the most robust and accurate models for considered rock properties.
New Design of All-terrain Smart Car Based on Arduino

Abstract Due to bad working environment, the flexibility and steering stability of all-terrain vehicles have become the main factors affecting the performance. Designing a new all-terrain electric vehicle is aimed at improving the stability of driving on low-attach roads and maneuverability at low speeds. Compared with traditional internal combustion engines, four-wheel-drive all-terrain vehicles have obvious advantages in vehicle motion control and optimal allocation of driving force, thus making it play an important role in all-terrain vehicle technology in the future. Within the scope of the requirements of the “Explorer” modular component equipment, by means of prototype design, assembly, commissioning, operation, the smart vehicles that can automatically and smoothly pass through the obstacles of ten different characteristics and different difficulty without any intervention are designed and completed.
On the Cover: Violin Plot

Violin plots are less known than box plots, although they are cousins in that they show similar information. In addition to median and interquartile range, a violin plot shows the distribution of the data with a rotated smoothing curve. The distribution reveals additional details on the data (e.g., the number and location of peaks). A violin plot can be either elegant like a real violin or rough like the nose of a sawfish––it depends on the shape of the data and the smoothing function. Used properly, violin plots are appealing and insightful. This issue's cover features The Musical Relative of the Boxplot– The Violin Plot by Aron Fink from Goethe-University Frankfurt am Main in Germany. “The graphic shows a violin plot of the bias of the ability estimates for high ability students resulting from a computerized adaptive test using four different algorithms,” Fink described. “The middle part of each violin shows a classical box-whisker-plot,” the German researcher continued. “The shape of the violin displays the distribution of the data.” Fink created the graphic using the ggplot2 package in R. This package can be used to create many elegant data visualizations, violin plots included. Interested readers may refer to https://cran.r-project.org/web/packages/ggplot2/index.html for details about the ggplot2 package. To sum up, violin plots provide an advanced visualization of the distributions in a compact and attractive format that has several advantages over common approaches like classical box or density plots. They provide practitioners and educational researchers with a novel graphical tool which can easily be created using standard software like ggplot2. If you are interested in learning more about this informative data visualization, please contact Aron Fink (a.fink@psych.uni-frankfurt.de). If you have any questions or suggestions on the cover in general, please email Zhongmin Cui (zhongmin.cui@act.org).
Using Analogy in Teaching Mathematics in Primary School

The article discusses the possibilities of using analogy as a method of cognition by younger schoolchildren of mathematical phenomena and&#x0D; objects and at the same time a learning method. The educational essence of analogy, its structure and types are described: explanatory, search, systematizing analogy. The didactic potential of analogy is revealed, the conditions for the successful implementation of didactic goals using this method are given. The methodological materials presented in the article can be used by teachers in the preparation of students of pedagogical specialties and teachers in the course of teaching younger students in mathematics.
Reactive control scheme based on fuzzy behaviors for mobile robot

This paper presents the design and implementation of a behavior-based control scheme. The construction of the set of behaviors is based on the use of fuzzy logic as a means for materializing the designer’s knowledge into the behaviors. The behavior set was established by left and right wall following and obstacle avoidance. These three behaviors were programmed and coordinated by a subsumption architecture or behavioral inhibition. Behavior simulations and coordination scheme design were tested by means of real experiments using a mobile robotic platform. Finally, the results are presented, where the control actions are executed by the robotic system achieving a secure navigation.
A tame sequence of transitive Boolean functions

Given a sequence of Boolean functions $ (f_{n})_{n \geq 1} $, $ f_{n} \colon \{ 0,1 \}^{n} \to \{ 0,1 \}$, and a sequence $ (X^{(n)})_{n\geq 1} $ of continuous time $ p_{n} $-biased random walks $ X^{(n)} = (X_{t}^{(n)})_{t \geq 0}$ on $ \{ 0,1 \}^{n} $, let $ C_{n} $ be the (random) number of times in $ (0,1) $ at which the process $ (f_{n}(X_{t}))_{t \geq 0} $ changes its value. In [7], the authors conjectured that if $ (f_{n})_{n \geq 1} $ is non-degenerate, transitive and satisfies $ \lim _{n \to \infty } \mathbb {E}[C_{n}] = \infty $, then $ (C_{n})_{n \geq 1} $ is not tight. We give an explicit example of a sequence of Boolean functions which disproves this conjecture.
INTERNET OF THINGS: PERSPECTIVES OF ADAPTIVE SYSTEMS

Research subject. Adaptive systems in the concept of the Internet of things. Objective. To analyze and present the capabilities of adaptive systems in relation to the eco-system of the Internet of things. Method. In addition to the scientific approach, graph (network) and algebraic methods are used in the work, as well as methods of complex analysis. Core results. The use of adaptive systems will make it possible to predict the values of environmental parameters and perform a series of actions without user intervention. As a result of this adjustment, the system becomes adaptive, learning and adapting to changes in the environment, or to the modes of the commands given. Practical relevance. The article presents an analysis of systems and provides examples of the use of adaptive systems.
International Education Quality Assurance System and Status Use of TOPIK : Focusing on Four-Year College for International Students Admission plan.

This study aims to analyze Ministry of Education’s International Education Quality Assurance System and Status Use of TOPIK in Admission plan for the top 20 colleges of domestic International Students. Research results, the college Admission system have to operate within the framework only differences by International Student policy and infrastructure in college, Ministry of Education’s <Standard operation processing guide for International students and language trainees> and Ministry of Justice’s <Guidelines for visa issuance and stay management for International students>. Due to a sharp decline in the school-age population, attracting International students has become a requirement, not an option, as a way to overcome the financial problems of universities in the recent difficulty in meeting the college entrance quota. However, it is necessary not only to focus on attracting International students but also to improve their academic ability and Korean language proficiencies for their graduation and employment, using TOPIK as the indicator. It is expected that the results of this paper will be utilized as basic data for research on the Internationalization of college and a policy of attracting International students
Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System

Skin cancer is considered as the most type of cancer that happens in humans. Three basic types of cancer occur which are basal cell carcinoma (BCC), Squamous cell carcinoma (SCC). Skin cancer leads to death if it is not diagnosed in an early stage. Fortunately, early diagnosis of skin cancer raises the survival rate of victims. Computer-aided has a great role to detect skin cancer which leads to saving human life. Based on that, this study proposes a computer-aided diagnosis (CAD) system that detects skin cancer using digital images, techniques of image processing, by using the case-based reasoning and expert system. The main goal for designing this system is to create a cheap, easy-to-use, and relatively accurate system for detecting skin cancer in an early stage to save human life, raises the survival rate, and decreases the cost of the dermoscopy test.
Special Issue “Semiconductor Laser Dynamics: Fundamentals and Applications”

With the advent of integrated photonics, a crucial role is played by semiconductor diode lasers (SDLs) as coherent light sources. Old paradigms of semiconductor laser dynamics, like optical injection, external feedback and the coupling of lasers, regained relevance when SDLs were integrated on photonic chips. This Special Issue presents a collection of seven invited feature papers and 11 contributed papers reporting on recent advances in semiconductor laser dynamics.
A Narrow Deep Learning Assisted Visual Tracking with Joint Features

A robust tracking method is proposed for complex visual sequences. Different from time-consuming offline training in current deep tracking, we design a simple two-layer online learning network which fuses local convolution features and global handcrafted features together to give the robust representation for visual tracking. The target state estimation is modeled by an adaptive Gaussian mixture. The motion information is used to direct the distribution of the candidate samples effectively. And meanwhile, an adaptive scale selection is addressed to avoid bringing extra background information. A corresponding object template model updating procedure is developed to account for possible occlusion and minor change. Our tracking method has a light structure and performs favorably against several state-of-the-art methods in tracking challenging scenarios on the recent tracking benchmark data set.
Proposed Design for Single Axis Photovoltaic Solar Tracker

Our energy needs increase day by day, so the search for a renewable source of energy has become a necessity of life. Photovoltaic solar energy (PVSE) is one of the most important sources in our community because it is clean and renewable. This paper discusses improvement the production energy from this source by using practical design of single axis solar tracking system. The designed solar tracking system used microcontroller Arduino Mega, real time clock (RTC), limit switches and servo motor. The mechanism of the designed solar tracker is applied by moving the solar structure to track the sun on better angle to be perpendicular to the sun to get the most energy from it using the RTC and limit switches.  Comparison between solar tracking system and fixed system reveal that the system is more economic and larger power production. This tracking system is designed and tested in real environmental in Qena governorate in Egypt. The 10 kW solar power plants are used as a case study for this system. Comparison between the proposed solar tracking system and the solar fixed system for the same demand is performed using PVsyst software as theoretically. Also, economic viability between the two practical systems is produced.
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation

Explaining how overparametrized neural networks simultaneously achieve low risk and zero empirical risk on benchmark datasets is an open problem. PAC-Bayes bounds optimized using variational inference (VI) have been recently proposed as a promising direction in obtaining non-vacuous bounds. We show empirically that this approach gives negligible gains when modeling the posterior as a Gaussian with diagonal covariance--known as the mean-field approximation. We investigate common explanations, such as the failure of VI due to problems in optimization or choosing a suboptimal prior. Our results suggest that investigating richer posteriors is the most promising direction forward.