Reviewerly/exHarmony huggingface dataset:
Application of Method Threshold Secret Sharing in Securing Data

Abstract Threshold secret sharing is one of the cryptographic techniques to secure a confidential data by dividing or distributing the data into several parts called shares , each part of the data does not provide any information about the secret in question if it is not combined with other parts. In general, thescheme is secret sharing divided into three namely Threshold, Prevention and Disenrollment schemes. The scheme applied to this application is the threshold secret sharing. Threshold secret sharing , has a concept that allows n people participated to hold fractional(share) different generated from s. Meanwhile, to reconstruct the data, it is necessary to have different pieces ofshare , each of which is held by a different participant. The security system that will be created using one of the platforms commonly used today is Visual Basic.NET
Stone Duality for Relations

We show how Stone duality can be extended from maps to relations. This is achieved by working order-enriched and defining a relation from A to B as both an order-preserving function $$A^{\textrm{op}}\times B\rightarrow \mathbbm {2}$$ and as a subobject of $$A\times B$$ . We show that dual adjunctions and equivalences between regular categories, taken in a suitably order-enriched sense, extend to (framed bi)categories of relations.
Does Access to International Capital Markets Affect Investment Dynamics in Sub-Saharan Africa?

This study investigates the influence of government borrowing through international capital markets on investment dynamics in Sub-Saharan Africa (SSA). We apply the synthetic control method to Gabon, Ghana and Senegal to assess whether this kind of government borrowing affects public and private investment, and FDI in these countries using annual data for the period 1995–2017. Our results suggest that government borrowings through international capital markets have not affected public and private investment, but that the move may have boosted these countries’ capacity to attract FDI. These results lend support to the hypothesis that these countries’ exposure to international capital markets constitutes an opportunity to register on the investors’ radar.
An anomaly detection data recognition algorithm of portable gas sensor for calibration in mine IoT based on sliding time window

Due to the increasing deployment of Internet of Things in the mining industry, portable gas monitoring devices have been widely used. According to the character of time series of gas stream, the paper studies on mathematics analysis method of time series similarity based on pattern beam and pattern set. Combining with short-time stationary of gas data, the feature selection method of short-time gas data based on sliding time window is proposed. On the basis of the fuzzy C-Means clustering algorithm, short-time gas stream in the fuzzy C-Means clustering algorithm is put forward to analyze the convergence effects of the data of gas stream based on binary statistic and multivariate statistic, which provides qualified data that available for analysis and calculation for data correction of gas sensors afterward.
Background Information Detection in Substations Based on Anomaly Detection

The substations are important parts of modern electrical grids. In this sense, it is necessary to detect the anomaly and problems in it. In this paper, we study on the information detection in substations based on traditional anomaly detection algorithms. The data flow from the background information is represented by feature vectors. And those from the historical data are used to build the background references. Afterward, the feature vector of the input data flow is examined using the anomaly detection algorithm. Based on the results, the anomaly in the background information in the substations can be found and located. Then, some high-precision identification algorithms can be further employed to recognition the type of the problems. In this way, the problems occurred in the substations can be found and solved in time.
Unsupervised anomaly detection in time series exploiting local and global information

We introduce a novel machine learning ensemble architecture for anomaly detection, that exploits global and local information from a 1d time series. A double step validation is performed to decide if a time period is anomalous: from one side a Long Short-Term Memory is trained to be reliable at forecasting, hence a parametric test on the forecasting’s error is used spot the anomalies. Concurrently, a Variational Autoencoder is trained to compress both global and local information from the series to a low-dimensional normal distribution, raising an anomaly if a time step’s likelihood is below a threshold. While anomaly detection with deep learning techniques often comes with the assumption that forecasting error is gaussian, we prove that this is in general a wrong assumption: we show that error function is better approximated by a distribution chosen dynamically. We validate our work on some public physical datasets, outperforming the current deep learning methods in terms of precision and recall.
Research on Virtual Teaching Technology Based on Android System

Aiming at the unpredictability and inconvenient operation of traditional teaching methods of industrial robots, the virtual graphics technology is used to achieve the teaching of robots in Android system. Through the programming method of OpenGL and java, the virtual graphics structure of six-degree-of-freedom manipulator and working scene is designed. At the same time, the method of combining rough detection with fine detection is used to detect the collision, so as to realize the obstacle avoidance process of the robot in the virtual teaching. The hardware and software platform of the robot is built to verify the feasibility of portable virtual teaching technology in Android system.
PND98 PATTERNS AND PREDICTION FOR COGNITIVE DECLINE IN ALZHEIMER'S PATIENTS AS ASSESSED BY THE MINI-MENTAL STATUS EXAM IN AN AMBULATORY ELECTRONIC MEDICAL RECORD

Patient-reported outcomes (PRO) measures, when used in routine clinical care, have been shown to benefit physician satisfaction and patient outcomes1. Implementation into clinical work flows, however, can be challenging. We examine here Mini-Mental Status Exam (MMSE) usage in an ambulatory Electronic Medical Record (EMR) in Alzheimer’s patients, including evaluating predictors of cognitive decline2. This study was conducted a nationally representative ambulatory EMR dataset from Jan 1, 2014 to Dec 31, 2018, in OMOP Common Data Model, v5. Analyses were conducted in SHYFT Quantum v6.7.0. Patients had either 2 diagnosis codes for Alzheimer’s disease, at least 1 Alzheimer’s treatment prescription, and at least 180 days continuous activity pre-index. Descriptive statistics were summarized for baseline demographics, treatments and MMSE scores. Changes in MMSE over time, as well as Kaplan-Meier time-to-decline were calculated. Prediction of MMSE-decline was compared using multiple models, including Linear Regression, Gaussian Naïve Bayes, Multi-level Perceptron, K-Nearest Neighbor, LDA, SVM, Decision Tree, Random Forest, Gradient Boosted, and XG Boosted Regressions. Model performance was assessed by standard metrics such as RMSE or MAE. A 3:1 training/testing split was employed using cross-validation scoring with a K-fold of 6. MMSE Score change was captured in less than a fifth of available patients. Few showed continued assessment after demonstrated decline, hampering ability to assess decline after diagnosis or treatment initiation. Among patients with at least two MMSE scores, patient demographics, pre-existing dementia and the earliest MMSE score were statistically significant predictors of MMSE decline. Common Alzheimer’s comorbidities3, such as major depressive disorder and bipolar disorder, were not statistically significant predictors of MMSE score change. Best model performance was achieved with XG Boosted Regressor. Despite the sparseness of PRO assessment, data capture was sufficient to drive predictive modeling. Together, these findings highlight both the difficulties and opportunities of PRO integration into real-world clinical care.
Application of machine learning in software engineering: an overview

Today, software is one of the main technologies contributing to the development of society. Therefore, its quality is a major requirement for both the global software industry and software engineering, which deals with all aspects of improving the quality and reliability of software products at all stages of their life cycle. To solve software engineering problems, the use of artificial intelligence methods is becoming increasingly relevant. The article presents a brief description of machine learning methods such as artificial neural networks, support vector machine, decision trees, inductive logic programming and others. Also, examples of the application of these methods to solve some problems of forecasting and quality assessment in software engineering are presented, recommendations for applying machine learning algorithms to solving problems of software engineering are given. The review will be useful by researchers and practitioners as a starting point, because it identifies important and promising areas of research. This will ultimately lead to more effective solving of software engineering problems, providing better, more reliable and cost effective software products. Problems in programming 2019; 4: 92-110
Optimization Iteration Min-Max Cross Over Genetic Algorithm To Generate Fuzzy membership Function Automatically

Abstract Generating membership function fuzzy for Fuzzy system is important to representative of the problem. Some research generating a membership function automatic had done and still have a problem to generate procces in max iterasi, so that this research will generating membership function fuzzy otomatic used genetic algorithm by analysing the cross over point.
Towards phonetic interpretability in deep learning applied to voice comparison

A deep convolutional neural network was trained to classify 45 speakers based on spectrograms of their productions of the French vowel /ɑ/ Although the model achieved fairly high accuracy – over 85 % – our primary focus here was phonetic interpretability rather than sheer performance. In order to better understand what kind of representations were learned by the model, i) several versions of the model were trained and tested with low-pass filtered spectrograms with a varying cut-off frequency and ii) classification was also performed with masked frequency bands. The resulting decline in accuracy was utilized to spot relevant frequencies for speaker classification and voice comparison, and to produce phonetically interpretable visualizations.
Fault diagnosis model of deep belief networks based on adaptive genetic algorithm optimization

Aiming at the problem that the application of deep neural network in fault diagnosis is limited due to its long running time and lack of theoretical basis for initial parameter setting, this paper proposes a fault diagnosis model combining Adaptive Genetic Algorithm (AGA) and Deep Belief Networks (DBN). By defining the error function and initial parameters of DBN as the fitness function and initial chromosome population of AGA, respectively, using the global optimization ability of AGA, iteratively obtains the initial parameters that minimize the DBN error function and brings them into Training in DBN. The AGADBN fault diagnosis model proposed in this paper is verified by the MNIST data set. Compared with DBN and kNN, the convergence speed and recognition accuracy are significantly improved, which is of positive significance for improving the application of deep neural network in fault diagnosis field.
Set Aggregation Network as a Trainable Pooling Layer

Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data. Based on the recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an alternative global pooling layer. In contrast to typical pooling operators, SAN allows to embed a given set of features to a vector representation of arbitrary size. We show that by adjusting the size of embedding, SAN is capable of preserving the whole information from the input. In experiments, we demonstrate that replacing global pooling layer by SAN leads to the improvement of classification accuracy. Moreover, it is less prone to overfitting and can be used as a regularizer.
Designing Image Correction Software with the Wavelet Method

Abstract Images can be produced by tools such as cameras, satellites or scanners. The process of transferring images from these devices can cause interference in the form of noise. Damage to photos or images is something that cannot be avoided, where this damage is usually caused by a time factor, a factor in someone’s accidental handling of a photo, or other external factors. This will cause the photo to be cured until information loss or artistic value occurs. Image processing is a solution to handle or manipulate an image, in other words in image processing is also offered a technique to deal with the problem above, because handling an image or photo that is damaged is also a process of manipulation of that image. One method of image repair due to noise is through a filtering process by dividing the image into four parts of the actual size, namely the filtered image and the other part are the details of the image where this process reflects the performance of the wavelet transformation method. The results of the trial using the software show the number of filter coefficients affecting the results of filtering with wavelet transformations, where the more the number of filter coefficients daubechies the smaller the visualization of contrast. Repeated decomposition of the image on the wavelet transformation will erode the original image, this is caused by the more pixel values transformed by both the low-pass-filter and high-pass-filter processes.
Internet of Service

With the rise of technologies such as artificial intelligence, cloud computing and big data, crowd science has achieved vigorous development. The research of the crowd system requires the mutual interaction between the mass underlying units to support, that is, the support of the business operation environment. Every unit is a single service unit, so at this time the business operating environment can be called the service Internet. Therefore, some related researches on the architecture, form, norms and nature of the service Internet have also been taken as important parts of the research of the crowd system. And they have gained extensive attention and in-depth exploration in the academic community. This paper analyzes the development and connection of crowd system and service Internet, and discusses the overall framework, ideas, key technologies and solutions for researching the service Internet architecture in the current complex environment.
Empirical Evidence: Application in a Specific Case Study

In this chapter, a step-by-step description of the methodological postulates is developed, taking into account a specific case of validation (supported by a Master’s degree in engineering, active in a higher education institution). This is done because there is a need to validate the proposal contained here in a specific scenario, so that the model presented undergoes a rigorous process leading to its certification.
Analogical News Angles from Text Similarity

The paper presents an algorithm providing creativity support to journalists. It suggests analogical transfer of news angles from reports written about different events than the one the journalist is working on. The problem is formulated as a matching problem, where news reports with similar wordings from two events are matched, and unmatched reports from previous cases are selected as candidates for a news angle transfer. The approach is based on document similarity measures for matching and selection of transferable candidates. The algorithm has been tested on a small data set and show that the concept may be viable, but needs more exploration and evaluation in journalistic practice.
Understanding Structure of Concurrent Actions

Whereas most work in reinforcement learning (RL) ignores the structure or relationships between actions, in this paper we show that exploiting structure in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a combinatorial explosion of the action space. This paper proposes two methods: a first approach uses implicit structure to perform high-level action elimination using task-invariant actions; a second approach looks for more explicit structure in the form of action clusters. Both methods are context-free, focusing only on an analysis of the action space and show a significant improvement in policy convergence times.
Libraries’ potential for developing readers’ identities (the example of Ufa)

Events held by libraries under the guise of state national policy bear special meaning for the republic of Bashkortostan. Libraries are gradually transforming into centers where one can not only read literature, but also receive complementary services and spend their free time productively. The extent to which this idea is implemented in all of Bashkortostan’s libraries depends on funding. This article attempts to understand the influence of libraries on forming the identities of readers. The central Zeki Velidi Togan National Library, as well as district libraries numbers 23 and 4 served as the object for our research. The design of each of these libraries primarily serves national (Bashkortostani) identity, while possessing certain foreign elements (Ufa, Russian, Soviet, Bashkir). A common trend appears to be the stable abundance of books about Salavat Yulaev – one of the symbols of the republic. Compared to the others, the central library holds considerably more events than any district library. Most events held by the central library are aimed at developing and sustaining Bashkir identity. Bashkir language is used during many gatherings, while the primary audience of thematic evenings is comprised of Bashkirs (there tend to be less Tatars Потенциал библиотек в формировании идентичностей читателей (на примере Уфы) 53 № 4, Том 10, 2019 and Russians present at these events). Ufa, Tatar and religious identities are almost completely absent from the events conducted by the National library. District libraries work in several directions simultaneously: working with troubled youth, moral education, local history, fields such as aesthetics, ecology etc. Evens held by district libraries are more diverse and correlate with the holiday calendar. Both district libraries evaluated provide books in several languages, however, the bulk of those books are written in Russian. Meanwhile books in Bashkir and Tatar are in somewhat less demand, due to their irrelevance (they are mostly read by national language students and teachers, of which there are not too many), and a lack of popular literature written in national languages.
Post Profiles Research Based on Electric Power Major

In the era of big data, how to effectively use information resources under the condition of information overload has been the focus of academia and industry. As an important data analysis method, user profile technology is widely used in the field of big data, including the field of recommendation system. Based on the background of electric power post training, this paper constructs the post knowledge thesaurus of electric power industry, uses the word segmentation tool-Jieba to segment the job description, and processes the text after the word segmentation combined with Term Frequency–Inverse Document Frequency (TF-IDF) algorithm. Then, the post profiles at all levels are displayed by Wordcloud visualization tool. Finally, the effectiveness of our method is proved by experiments. This work provides a basis for the intelligent recommendation of the best learning materials in various positions and auxiliary online examination technology for employees in the future.
Project Method in Teaching Russian as a Foreign Language to Iranian University Students

The article features project method in teaching Russian as a foreign language to students of Iranian universities. The research objective was to study the opinions of teaching staff (13 interviewees). The research was based on the project theory and personality-oriented approach. The survey showed that most teachers consider this method an indicator of professional skill and argue that project activities affect the motivation of teachers and students. Most of them implement project method in their work. However, they also point out the following problems: a lack of time and effort to create and apply pedagogical innovations, as well as a lack of material incentives and support from colleagues; problems associated with the transition from the role of a mentor to the role of a consultant; difficulties in applying research and creative methods. The results can be used to teach Russian to Iranian university students.
Siamese Visual Tracking with Robust Adaptive Learning

Correlation filters and deep learning methods are the two mainly directions in the research of visual tracking. However, these trackers do not balance accuracy and speed very well at the same time. The application of the Siamese networks brings great improvement in accuracy and speed, and an increasing number of researchers are paying attention to this aspect. In the paper, based on the Siamese networks model, we propose a robust adaptive learning visual tracking algorithm. HOG features, CN features and deep convolution features are extracted from the template frame and search region frame respectively, and we analyze the merits of each feature and perform feature adaptive fusion to improve the validity of feature representation. Then, we update the two branch models with two learning change factors and realize a more similar match to locate the target. Besides, we propose a model update strategy that employs the average peak-to-correlation energy (APCE) to determinate whether to update the learning change factors to improve the accuracy of tracking model and reduce the tracking drift in the case of tracking failure, deformation or background blur etc. Extensive experiments on the benchmark datasets (OTB-50, OTB-100) demonstrate that our visual tracking algorithm performs better than several state-of-the-art trackers for accuracy and robustness.
On transmission efficiency of quantum modulations

In quantum information theory, it is important to find modulations with low information loss for noisy channels. In this paper, using the quantum dynamical entropy and the quantum dynamical mutual entropy, we investigate the transmission efficiency of two quantum modulators through attenuation channels.
Procedural Editing of Virtual Terrains Using 3D Bézier Curves

The process of manually shaping and adding features to virtual terrains is a time-consuming task prevalent during the development process of many applications, such as games and virtual simulators. We present a technique to shape virtual terrains by creating excavations and embankments to flatten and smooth the terrain along paths defined by composite cubic 3D Bézier curves. The curves are procedurally generated based on an input set of vertices. Our approach is capable of carving smooth paths on terrains for placement or rendering of roads, railways, rivers, or any other feature that follows a path. We employ spatial hashing and data structures to optimize execution on the GPU.