Reviewerly/exHarmony huggingface dataset:
A Bayesian Network Model for Yellow Rust Forecasting in Winter Wheat

Yellow rust (YR) is one of the most destructive diseases of wheat. We introduced the Bayesian network analysis as a core method and develop a large-scale YR forecasting model based on several important meteorological variables that associate with disease occurrence. To guarantee an effective model calibration and validation, we used multiple years (2010–2012) of meteorological data and the ground survey data in Gansu Province where the YR intimidated most severely in China. The validation results showed that the disease forecasting model is able to produce a reasonable risk map to indicate the disease pressure across the region. In addition, the temporal dispersal of YR can also be delineated by the model. Through a comparison with some classic methods, the Bayesian network outperformed BP neutral network and FLDA in accuracy, which thereby suggested a great potential of Bayesian network in disease forecasting at a regional scale.
Chapter 9. Linguistic inferiority in software localization

Software localization constitutes an important economic and cultural activity. It involves producing a version of a software product in a different language (the target language) than the language of the original version (the source language). The technical challenges associated with software localization seem to receive more attention from field specialists, while linguistic matters are considered secondary in importance. This article aims to highlight linguistic requirements in software localization through a study of the Arabic localized version of Skype. In particular, we will demonstrate that the linguistic approach adopted by the localizer is largely literal, and this has ramifications for product quality.
Determinants on the Number of Children among Married Women in Korea

This study aims to examine what factors affect married women’s childbirth behavior and what causes the gap between their planned and actual number of children by adopting the ‘extended theory of planned behavior’ model. To this end, the main factors used in the model were attitude to childbirth, subjective norms, perceived behavioral control, perceptions on fairness in housework, and sociodemographic characteristics of the subjects. A secondary data analysis was conducted using data extracted from the ‘2015 National Survey on Fertility and Family Health and Welfare’, which was limited to 8,535 married Korean women who responded to items related to their planned number of children. SPSS 24.0 was used to process the data for a multivariate logistic regression analysis as well as a descriptive analysis. Findings showed that married women are likely to give birth to less than their planned number of children when they are young, lack home ownership, have a low monthly average household income, have a progressive attitude to childbirth during cohabitation, and intend to use the work-family reconciliation policy as it applies to them and their spouse. Results also show that married women are likely to give birth to more than their planned number of children when they have a low education level, a low traditional sense of obligation for children, perceive workplace support for childbirth and child care as unnecessary, perceive tax-funded state support for families with multiple children as necessary, and don’t intend to use the work-family reconciliation policy as it applies to their spouse.
Synthesizing Attributes with Unreal Engine for Fine-grained Activity Analysis

We examine the problem of activity recognition in video using simulated data for training. In contrast to the expensive task of obtaining accurate labels from real data, synthetic data creation is not only fast and scalable, but provides ground-truth labels for more than just the activities of interest, including segmentation masks, 3D object keypoints, and more. We aim to successfully transfer a model trained on synthetic data to work on video in the real world. In this work, we provide a method of transferring from synthetic to real at intermediate representations of a video. We wish to perform activity recognition from the low-dimensional latent representation of a scene as a collection of visual attributes. As the ground-truth data does not exist in the ActEV dataset for attributes of interest, specifically orientation of cars in the ground-plane with respect to the camera, we synthesize this data. We show how we can successfully transfer a car orientation classifier, and use its predictions in our defined set of visual attributes to classify actions in video.
Visualization of Data Mining Techniques for the Prediction of Breast Cancer with High Accuracy Rates

Breast cancer is one of the leading causes of death in women worldwide.Around one in 30 women are affected by breast cancer.Mammography has helped in detecting breast cancer in the early stages which have reduced mortality.The diagnosis of breast cancer is dependent on a variety of parameters.In this paper, we aim to create the best model for predicting breast cancer through preprocessing, feature extraction, data visualization and prediction using breast cancer data.Various visualization techniques like violin plot, grid plot, swarm plot and heat plot were utilized for proper feature extraction which has improved the accuracy of our results.For the purpose of prediction, we have used algorithms like the random forest, decision tree with single and multiple predictors, along with the commonly used statistical model, logistic regression model.We have also relied on 5-fold cross-validation methods to measure the unbiasedness of the prediction models for performance reasons.An analysis of the models was carried out and the best model was selected based on its accuracy.The results showcased that the random forest model provided an accuracy rate of 94.724% with decent 5-fold cross-validation, followed by the decision tree model which had an accuracy rate of 100% with poor 5-fold cross-validation.This was followed by the logistic regression model which had an accuracy rate of 88.442% with a low 5-fold cross-validation score.
Methodical approach to the estimation of possible energy production by wind and solar power plants using weather station data

The expediency of using several sources of information on climate factors for estimating the potential of wind and solar energy is substantiated. Specific features of the methodology and developed software for estimating the generation of energy by wind power plants based on weather stations open access data are considered. The possibility of taking into account the results of aerodynamic modeling of the flow of the terrain by the wind flow is realized. The methodology is implemented in the form of a computer program called Wind-MCA. It includes a module for analyzing wind power potential, a module for analyzing wind turbines, an economic analysis module, and a multi-criteria analysis module. Specific features of the methodology and developed software for estimating the generation of energy by solar power plants based on data on the transparency of the atmosphere, temperature and cloudiness are considered. The technique is implemented in the form of a computer program called Sun-MCA. The estimation of the wind energy and solar energy potential of several settlements in the central zone of the Baikal region is carried out taking into account the climate change in the region.
On Privacy-preserving Decentralized Optimization through Alternating Direction Method of Multipliers.

Privacy concerns with sensitive data in machine learning are receiving increasing attention. In this paper, we study privacy-preserving distributed learning under the framework of Alternating Direction Method of Multipliers (ADMM). While secure distributed learning has been previously exploited in cryptographic or non-cryptographic (noise perturbation) approaches, it comes at a cost of either prohibitive computation overhead or a heavy loss of accuracy. Moreover, convergence in noise perturbation is hardly explored in existing privacy-preserving ADMM schemes. In this work, we propose two modified private ADMM schemes in the scenario of peer-to-peer semi-honest agents: First, for bounded colluding agents, we show that with merely linear secret sharing, information-theoretically private distributed optimization can be achieved. Second, using the notion of differential privacy, we propose first-order approximation based ADMM schemes with random parameters. We prove that the proposed private ADMM schemes can be implemented with a linear convergence rate and with a sharpened privacy loss bound in relation to prior work. Finally, we provide experimental results to support the theory.
Smartphones to Learn English: The Use of Android Applications by Non-English Major Students in West Aceh

Smartphones multi-functionality and practicality features are very promising in helping students to learn English language. Numerous applications related to English enables the students to be exposed to English and learn independently and conveniently outside the classroom. This study explored students own use of android applications to help them in learning English. Specifically, it investigated the types of apps the students use, examined their motives in using the apps, and figured out how the apps were perceived to help in acquiring English. The study was conducted as a qualitative study involving 15 non-English majors students recruited from three different campuses in West Aceh. The data were collected through personal interview and focus group discussion that were transcribed and analyzed by coding and concluding the information into a number of themes. It was found that the students had used a number of android apps, particularly dual language dictionary apps to support their informal English language learning. The apps were considered very helpful as the students could access vocabulary and be exposed to English frequently. Easy access and practical use of apps were perceived as the major advantages benefiting the students in gaining vocabulary and other English learning materials to develop their English language skills.
New Hardware Architecture for Self-Organizing Map Used for Color Vector Quantization

In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.
Penerapan Central Event Information Untuk Mencetak Sertifikat dan Verifikasi Dengan QR Code Menggunakan Global Extreme Programming

A certificate is a document stating that someone has fulfilled certain requirements and can practice in a field. The certificate verification procedure in universities is still a lot done manually and sometimes does not verify the certificate. This results in many fake certificates. To overcome this problem, a website is created that utilizes the QR Code with the aim of quickly accessing data verification of a certificate. With QR Code scanning technology, paper-based document fraud can be easily carried out without high costs. Therefore, the design of information systems or the creation of web-based applications is a solution to provide convenience to users in the process of data processing, data retrieval to report generation. In making this application the author uses the Agile Software Development model with the Global Extreme Programming process model. While the tools used are UML (Unified Modeling Language) and ERD (Entity Relationship Diagram).
Internet of Things in Industry: A Survey of Technology, Applications and Future Directions

Internet of Things (IoT) is a congregation of interconnected physical objects that are linked with unique identifiers and transfer data in the appropriate network without human intervention. It has the promising opportunity to develop powerful industrial applications. To understand the development in industrial IoT, this paper reviews various business values in industrial application, its architecture, related technical elements, benefits and challenges. Even though it delivers many business values, shop floor visibility, supply chain management, health, safety & environment, predictive maintenance, industrial digit thread, supply chain integration, human machine interface, quality control and big data are discussed in this paper. The major elements related to IoT like Identification, Sensing, communication methodology, computation methods and services are deliberated in this paper. Future research areas are gathered from various literatures and discussed in this paper. The key contribution of this work is to summarize the current contemporary of IoT in industries systematically.
Cheating immune multi-secret sharing without predefined order of secrets

A (t,n)-threshold secret sharing scheme divides a secret into n pieces and distributes them to n players privately, in such a way that any t or more players who collect their pieces can reconstruct the secret correctly, while any t−1 players know no information about the secret. To improve efficiency, a multi-secret sharing scheme is proposed in which multiple secrets are shared at one time. In this paper, we propose a new multi-secret sharing scheme based on hash function. We combine the mathematical ‘shift values’ proposed by He and Dawson in 1994 with asymmetric bivariate polynomials to construct pseudo shares. Our scheme has the following three extended properties: cheaters are detected thanks to the hash function, each share is used many times in secret reconstruction, and the order of recovery of secrets is not predefined. Furthermore, a secure channel between dealer and players is unnecessary, unlike some existing multi-secret sharing schemes.
A novel priority based document image encryption with mixed chaotic systems using machine learning approach

Document images containing different types of information are required to be encrypted with different levels of security. In this paper, the image classification is carried out based on the feature extraction, for color images. The K-Nearest Neighbor (K-NN) method of image classification technique is used for classifying the query Document with trained set of features obtained from the Document database. Optical Character Recognition (OCR) technique is used to check for the presence as well as location of text/numerals in the Documents and to identify the Document type. Priority level is assigned in accordance with the Document type. Document images with different priorities are encrypted with different multi-dimensional chaotic maps. The Documents with different priority levels are diffused with different techniques. Document with highest priority are encrypted with highest level of security but Documents with lower priority levels are encrypted with lesser security levels. The proposed work was experimented for different document types with more number of image features for a large trained database. The results reveals a high speed of encryption for a set of document pages with priorities is more effective in comparison with a uniform method of encryption for all document types. The National Institute of Standards and Technology (NIST) statistical tests are also conducted to check for the randomness of the sequence and achieved good randomness. The proposed work also ensures security against the various statistical and differential attacks.
Online Feature Selection by Adaptive Sub-gradient Methods

The overall goal of online feature selection is to iteratively select, from high-dimensional streaming data, a small, “budgeted” number of features for constructing accurate predictors. In this paper, we address the online feature selection problem using novel truncation techniques for two online sub-gradient methods: Adaptive Regularized Dual Averaging (ARDA) and Adaptive Mirror Descent (AMD). The corresponding truncation-based algorithms are called B-ARDA and B-AMD, respectively. The key aspect of our truncation techniques is to take into account the magnitude of feature values in the current predictor, together with their frequency in the history of predictions. A detailed regret analysis for both algorithms is provided. Experiments on six high-dimensional datasets indicate that both B-ARDA and B-AMD outperform two advanced online feature selection algorithms, OFS and SOFS, especially when the number of selected features is small. Compared to sparse online learning algorithms that use $$\ell _1$$ regularization, B-ARDA is superior to $$\ell _1$$ -ARDA, and B-AMD is superior to Ada-Fobos. Code related to this paper is available at: https://github.com/LUCKY-ting/online-feature-selection .
Quantum Robotics, Neural Networks and the Quantum Force Interpretation

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM’s five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target’s dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target’s dynamics accordingly.
Tanzania Economic Update : The Power of Investing in Girls - Educating Girls and Ending Child Marriage in Tanzania

Economic performance in 2018 has been mixed. The data that are available suggest some areas of softening in the economy.1 Foreign direct investment declined to 2 percent of GDP in 2017, down from about 5 percent in 2014. The current account deficit has increased to 3.8 percent of GDP in the year ending September 2018, from 2.2 in the preceding 12 months. Recent Bank of Tanzania data confirm lower cashew exports and 2017 decline in non-traditional exports has continued into 2018, which raises concerns on prospects for longer term growth. The Tanzania Revenue Authority is reporting that many large tax payers are unable to meet their tax obligations on time. Nonperforming loans have declined recently to 9.7 percent in September 2018 from 12.5 percent in September 2017, but remain almost double the 5 percent statutory threshold. Banks have limited lending to businesses and interest rates are high (18 percent for one-year loans in August 2018), though some banks have lowered benchmark lending rates. On a positive note, credit to the private sector has been edging up, reaching 4.9 percent in the 12 months ending September 2018. The fiscal deficit is still low, not counting payment arrears and delayed refunds of value-added tax. The 2017/18 budget deficit after grants of 1.3 percent of GDP suggests effective spending management but does not factor in payment arrears, with an estimated stock of over 3 percent of GDP. Government is paying down roughly TZS 1 trillion of verified arrears per fiscal year. The low deficit is the result of controlled recurrent expenditures and under execution of the development budget by more than 40 percent. Contributing factors include shortfalls in domestic revenue and external financing for large projects. Public debt is currently sustainable, but there is need for the Government to consider cost-effective financing options and manage associated risks to support public investments. The 2018/19 budget targets public investment to consume 45 percent of total spending, equivalent to 9.1 percent of GDP compared to 5.5 a year prior.
Predicting the Well-functioning of Learning Groups under Privacy Restrictions

Establishing small learning groups in online courses is a possible way to foster collaborative knowledge building in an engaging and effective learning community. To enable group activities it is not enough to design collaborative tasks and to provide collaboration tools for online scenarios. Collaboration in such learning groups is prone to fail or even not to be initiated without explicit guidance. In the target situations, interventions and guiding mechanisms have to scale with a growing number of course participants. To achieve this under privacy constraints, we aim at identifying target indicators for well-functioning group work that do not rely on any kind of information about individual learners.
Electronic healthcare records and prognosis research

Electronic healthcare record (EHR) data, collected during the daily business of patient consultations and treatments, offer huge opportunities to expand the range and scale of prognosis research, in particular because of the real-time and continuous recording of potential prognostic factors and health-related events, and the amount of data and individuals involved. However, with these opportunities come challenges related to the size and complexity of EHR data. This chapter provides an overview of these issues.
Real-Time Global Illumination with Photon Mapping

Indirect lighting, also known as global illumination, is a crucial effect in photorealistic images. While there are a number of effective global illumination techniques based on precomputation that work well with static scenes, including global illumination for scenes with dynamic lighting and dynamic geometry remains a challenging problem. In this chapter, we describe a real-time global illumination algorithm based on photon mapping that evaluates several bounces of indirect lighting without any precomputed data in scenes with both dynamic lighting and fully dynamic geometry. We explain both the pre- and post-processing steps required to achieve dynamic high-quality illumination within the limits of a realtime frame budget.