Reviewerly/exHarmony huggingface dataset:
Sensitivity Analysis of Evolutionary Algorithm for Software Reusability

Fast and competitive software industry demands rapid development using Component Based Software Development (CBSD). CBSD is dependent on the availability of the high-quality reusable component libraries. Recently, evolutionary multi-objective optimization algorithms have been used to identify sets of reusable software components from the source-code of Object Oriented (OO) software, using different quality indicators (e.g. cohesion, coupling, etc.). Sometimes, these used quality indicators are quite sensitive towards the small variations in their values, although they should not be. Therefore, this paper analyzes the sensitivity of the evolutionary technique for three quality indicators used during the identification: Frequent Usage Pattern (FUP), Semantic and evolutionary coupling. The sensitivity analysis is performed on three widely used open-source OO software. The experimentation is performed by mutating the system to different degrees. Results of the empirical analysis indicate that the semantic parameter is most sensitive and important. Ignoring this feature highly degrades the quality; FUP relation is uniformly sensitive and evolutionary relations's sensitivity is non-uniform.
Knowledge Compilation Languages as Proof Systems

In this paper, we study proof systems in the sense of Cook-Reckhow for problems that are higher in the polynomial hierarchy than coNP, in particular, #SAT and maxSAT. We start by explaining how the notion of Cook-Reckhow proof systems can be apply to these problems and show how one can twist existing languages in knowledge compilation such as decision DNNF so that they can be seen as proof systems for problems such as #SAT and maxSAT.
Radial Basis Function for Solar Irradiance Forecasting in Equatorial Areas

Photovoltaic (PV) solar generation is gaining an increasing attention due to technological advances such as higher efficiency and life of PV cells and cost reduction.Due to its vast territory, Brazil is composed of regions that can explore renewable energy sources for electricity generation, and the solar resource is found satisfactorily in several areas of the country.This article presents a solar irradiance prediction mechanism developed using data collected in Fortaleza-CE, Brazil.Due to the fact of its characteristic of unpredictability for this resource, many researchers look for several methods to take the generation of this type of energy.The predictions were performed using a Radial Basis Function (RBF) a computational model based on the human nervous system, it is a technical and effective for time series forecasting, which is a relatively complex problem, Artificial Neural Network (ANN) with the advancement of 1 hour.In the ANN performance, a total of 34.4% forecasts underestimated solar energy availability, 7% of the forecasts obtained error 0 and 58.6% of forecasts overestimated the solar resource.A total of 62.33% of forecasts was between -10% and 10% of forecast error.The prediction mean error was 5.93% and the Mean Absolute Percentage Error (MAPE) was 11.43%.
Zero-Knowledge Proof-of-Identity: Sybil-Resistant, Anonymous Authentication on Permissionless Blockchains and Incentive Compatible, Strictly Dominant Cryptocurrencies.

Zero-Knowledge Proof-of-Identity from trusted public certificates (e.g., national identity cards and/or ePassports; eSIM) is introduced here to permissionless blockchains in order to remove the inefficiencies of Sybil-resistant mechanisms such as Proof-of-Work (i.e., high energy and environmental costs) and Proof-of-Stake (i.e., capital hoarding and lower transaction volume). The proposed solution effectively limits the number of mining nodes a single individual would be able to run while keeping membership open to everyone, circumventing the impossibility of full decentralization and the blockchain scalability trilemma when instantiated on a blockchain with a consensus protocol based on the cryptographic random selection of nodes. Resistance to collusion is also considered. Solving one of the most pressing problems in blockchains, a zk-PoI cryptocurrency is proved to have the following advantageous properties: - an incentive-compatible protocol for the issuing of cryptocurrency rewards based on a unique Nash equilibrium - strict domination of mining over all other PoW/PoS cryptocurrencies, thus the zk-PoI cryptocurrency becoming the preferred choice by miners is proved to be a Nash equilibrium and the Evolutionarily Stable Strategy - PoW/PoS cryptocurrencies are condemned to pay the Price of Crypto-Anarchy, redeemed by the optimal efficiency of zk-PoI as it implements the social optimum - the circulation of a zk-PoI cryptocurrency Pareto dominates other PoW/PoS cryptocurrencies - the network effects arising from the social networks inherent to national identity cards and ePassports dominate PoW/PoS cryptocurrencies - the lower costs of its infrastructure imply the existence of a unique equilibrium where it dominates other forms of payment
Extending IPOL to New Data Types and Machine-Learning Applications

Image Processing On Line (IPOL) is a journal focused on mathematical descriptions of image processing (IP)/computer vision (CV) algorithms. Since the first article was published in 2010, it has started to become clear that the IP/CV discipline is mainly multi-disciplinary. For example, nowadays images are de-noised using convolutional neural networks (CNN), and fields such as neurophysiology need of the rudiments and techniques of IP/CV, general signal processing and artificial intelligence. IPOL wants to extend the capabilities of its demo system to cope with these needs. Specifically, in this article we review the state of the current demo system and its limitations. It enunciates a detailed project on how to build a more adapted system, and its minimal requirements: new data types, problematic heterogeneous data, the pre-processing and standardization, the possibility to chain different algorithms in a complex chain, and how to compare them.
Computational Linguistics Applications for Multimedia Services

We present Computational Linguistics Applications for Multimedia Services (CLAMS), a platform that provides access to computational content analysis tools for archival multimedia material that appear in different media, such as text, audio, image, and video.The primary goal of CLAMS is: (1) to develop an interchange format between multimodal metadata generation tools to ensure interoperability between tools; (2) to provide users with a portable, user-friendly workflow engine to chain selected tools to extract meaningful analyses; and (3) to create a public software development kit (SDK) for developers that eases deployment of analysis tools within the CLAMS platform.CLAMS is designed to help archives and libraries enrich the metadata associated with their mass-digitized multimedia collections, that would otherwise be largely unsearchable.
Rancang Bangun Sistem Informasi Akreditasi Sekolah

&#x0D; &#x0D; &#x0D; School accreditation is public accountability carried out objectively, fairly, transparently and comprehensively. The instruments used in the school accreditation assessment process refer to the National Education Standards which have eight Standard Points. Since there are many standards and criteria that must be met, the school will difficult to fulfill and measure its performance. This study aims to assist schools in carrying out the performance evaluation process, to meet the standards and criteria that refer to the National Education Standards. In this study a system was developed that can help prepare school accreditation, by providing forms that must be filled in by the school and the system will automatically measure school performance, so that the school can find out its weaknesses to be improved immediately. In its development, the Laravel framework was used with PHP, HTML, and JavaScript as a programming language. The development method used was the waterfall method. System testing in this study uses three testing methods, namely unit testing, black box and MOS (Mean Opinion Score).&#x0D; &#x0D; &#x0D;
Peningkatan Kemampuan Menulis Puisi dengan Metode Paralel Writing melalui Teknik Pengimajinasian Benda Abstrak Siswa Kelas X IPS 3 SMA Negeri 4 Pekanbaru

Learning Indonesian is not only learning languages ​​but also studying literature. Literary learning is also very important with the aim of giving a sense of love to literature and making students have the ability to appreciate and be able to judge the results of literary works. One aspect taught in learning literature is writing poetry. The motive of the importance of the learning process of writing poetry literary works for students is to improve reading and writing skills which can be used as a routine activity to improve achievement. Based on the results of observations on the learning patterns of Literature of Class students at X IPS 3 Pekanbaru of SMAN 4 Pekanbaru, students' poetry writing skills were still low. Students had difficulty in determining ideas, diction, and the first word in writing poetry. The achievement of the indicator editing poetry in accordance with the elements of poetry was not maximal, students were confused in determining diction and language style. It took a solution to these problems through innovation in the application of learning models. Pararrel writing based learning based on the imagination of abstract objects was one solution that can improve the power of writing poetry. So, this study aimed to improve students' poetry writing skills. The method used in this study was class action research with two cycles approach. Based on the results of the research that has been carried out, using the pararrel writing based learning model using imagination of abstract objects can improve poetry writing skills students at X IPS 3 Pekanbaru of SMAN 4 Pekanbarusignificantly.
Obj-GloVe: Scene-Based Contextual Object Embedding

Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference. In this paper, we propose Obj-GloVe, a generic scene-based contextual embedding for common visual objects, where we adopt the word embedding method GloVe to exploit the co-occurrence between entities. We train the embedding on pre-processed Open Images V4 dataset and provide extensive visualization and analysis by dimensionality reduction and projecting the vectors along a specific semantic axis, and showcasing the nearest neighbors of the most common objects. Furthermore, we reveal the potential applications of Obj-GloVe on object detection and text-to-image synthesis, then verify its effectiveness on these two applications respectively.
Pengaruh Persepsi Mengenai Profesi Akuntan Publik, Motivasi, dan Kecerdasan Adversity Terhadap Minat Menjadi Akuntan Publik

Public accountants play a role in improving the quality and credibility of financial information. The interest of the people to become a public accountant, is still low. This study aims to determine the interest of non-regular accounting students to become public accountants as measured by student perceptions, self-motivation, and adversity intelligence. This research was conducted on 2015 non-regular accounting students at the Faculty of Economics and Business at Udayana University. Determination of samples using non-probability sampling method with saturated sampling technique. The number of respondents used in this study was 158 people. Data collection method is carried out by a survey method with questionnaire techniques that are measured using a Likert scale. The results of this study indicate that the variables of student perception, self motivation, and adversity intelligence have a positive influence on the interest of non-regular accounting students at Udayana University to become public accountants.&#x0D; Keywords: student perception, self motivation, adversity intelligence, interest in becoming a public accountant.&#x0D; &#x0D;
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data. Many dimensionality reduction methods have been widely adopted to extract low-dimensional, smooth and time-evolving latent trajectories. However, simple state transition structures, linear embedding assumptions, or inflexible inference networks impede the accurate recovery of dynamic portraits. In this paper, we propose a novel latent dynamic model that is capable of capturing nonlinear, non-Markovian, long short-term time-dependent dynamics via recurrent neural networks and tackling complex nonlinear embedding via non-parametric Gaussian process. Due to the complexity and intractability of the model and its inference, we also provide a powerful inference network with bi-directional long short-term memory networks that encode both past and future information into posterior distributions. In the experiment, we show that our model outperforms other state-of-the-art methods in reconstructing insightful latent dynamics from both simulated and experimental neural datasets with either Gaussian or Poisson observations, especially in the low-sample scenario. Our codes and additional materials are available at https://github.com/sheqi/GP-RNN_UAI2019.
Towards the Ontology Development for Smart Transportation Infrastructure Planning via Topic Modeling

Towards the Ontology Development for Smart Transportation Infrastructure Planning via Topic Modeling Jin Zhu and Sudipta Chowdhury Pages 507-514 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Media age has seen a huge amount of data flowing in from all directions, be it online news sources, social media, technical documents, and many more. There is a huge scope of these data sources for utilization in the transportation sector that can potentially improve the current practice of transportation infrastructure planning. In order to effectively capture, analyze, and utilize the information from various sources, ontologies are useful tools as they can provide clear and structured knowledge in the transportation domain. Majority of the existing transportation-related ontologies focus on traffic management and route planning. The objective of this paper is to initiate the development of an integrated ontology that can help with long-term planning and decision-making of transportation infrastructure by proposing a preliminary taxonomy in this domain. To this end, 20 transportation planning visionary documents published by government agencies were collected and analyzed using topic modelling techniques. Specifically, two topic modeling methods: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) models were used to extract important and emerging concepts related to transportation infrastructure planning. Leveraging the important and emerging concepts, a preliminary taxonomy of transportation infrastructure planning was then developed and presented. Keywords: Smart Transportation infrastructure planning; Ontology; Taxonomy; LDA; NMF DOI: https://doi.org/10.22260/ISARC2019/0068 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Voronoi boundary classification : A high-dimensional geometric approach via weighted monte carlo integration

Voronoi cell decompositions provide a classical avenue to classification. Typical approaches however only utilize point-wise cell-membership information by means of nearest neighbor queries and do ...
WITHDRAWN: Approximation properties of fully connected deep neural networks

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
Robust unsupervised discriminative dependency parsing

Discriminative approaches have shown their effectiveness in unsupervised dependency parsing. However, due to their strong representational power, discriminative approaches tend to quickly converge to poor local optima during unsupervised training. In this paper, we tackle this problem by drawing inspiration from robust deep learning techniques. Specifically, we propose robust unsupervised discriminative dependency parsing, a framework that integrates the concepts of denoising autoencoders and conditional random field autoencoders. Within this framework, we propose two types of sentence corruption mechanisms as well as a posterior regularization method for robust training. We tested our methods on eight languages and the results show that our methods lead to significant improvements over previous work.
Privacy-Enabled Recommendations for Software Vulnerabilities

New software vulnerabilities are published daily. Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process. While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data. As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations. To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity. To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities. The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
Generating Calligraphy Works Based on DiscoGAN

Abstract Copying the works of a good calligrapher is a necessary step for many beginners. However, due to the large number of Chinese characters, it is a challenge to automatically generate Chinese characters in the style of calligraphers. With the development of deep learning, the recognition of Chinese characters, style migration and other research have achieved remarkable results. However, there are few studies on the generation of calligraphy works, especially on the lack of paired training data. In this work, we propose a method for generating calligraphy works. This method is generally applicable to the generation of calligraphy works of various styles. And the validity of our method is verified by experiments on data sets.
Optimistic Proximal Policy Optimization.

Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains. However, it is known that learning a good policy is difficult in a domain where rewards are rare. We propose a method, optimistic proximal policy optimization (OPPO) to alleviate this difficulty. OPPO considers the uncertainty of the estimated total return and optimistically evaluates the policy based on that amount. We show that OPPO outperforms the existing methods in a tabular task.
Estimation of Malaria Symptom Data Set using Hidden Markov Model

Clinical study of malaria presents a modeling challenge as patients disease status and progress is partially observed and assessed at discrete clinic visit times. Since patients initiate visits based on symptoms, intense research has focused on identication of reliable prediction for exposure, susceptibility to infection and development of severe malaria complications. Despite detailed literature on malaria infection and transmission, very little has been documented in the existing literature on malaria symptoms modeling, yet these symptoms are common. Furthermore, imperfect diagnostic tests may yield misclassication of observed symptoms. Place and Duration of Study: The main objective of this study is to develop a Bayesian Hidden Markov Model of Malaria symptoms in Masinde Muliro University of Science and Technology student population. An expression of Hidden Markov Model is developed and the parameters estimated through the forward-backward algorithm.
School Bus Route Optimization Based on Improved Ant Colony Algorithm

In order to optimize the school bus route reasonably, this paper proposes a method of joint operation of multiple schools, multi-point concentration and multi-point distribution based on the single-center school bus route optimization problem. Taking shenyang as an example, the improved ant colony algorithm is used to solve the model, and the influence of factors such as actual road traffic and service quality is fully considered. Finally, the model is compared with the results of network analysis module in GIS. The results show that the model can significantly reduce the operating cost of the school bus company, improve the service quality of the school bus, and verify the validity of the model.
Pengaruh Kinerja Lingkungan Dan Kinerja Keuangan Teradap Pengungkapan Informasi Lingkungan

This research is using quantitive study aimed to see whether there are influence of environmental performance and financial performance on environmental disclosure. This study uses non-financial companies listed on the Indonesia Stock Exchange as a population. Sample selected by purposive sampling and collected 20 non-financial companies, with a research period of 2015-2017, but for the measurement of financial performance, the data taken was data for 2014-2016, because in this study, tested is the effect of financial performance in the previous year on disclosure of environmental information in the current year. Testing the hypothesis in this study uses Multiple Linear Regression Analysis with SPSS version 25 and a significance level of 5% (0.05). The results of this study indicate that: (1) environmental performance has a significant positive effect on environmental disclosure, (2) financial performance in the previous year has a significant negative effect on environmental disclosure in the current year. The variables of environmental performance and financial performance can explain the environmental disclosure variable of 26,4%.Keywords : Environmental Disclosure, Environmental Performance, Financial Performance
Data domain cloud tier: backup here, backup there, deduplicated everywhere!

Data Domain has added a cloud tier capability to its on-premises storage appliance, allowing clients to achieve the cost benefits of deduplication in the cloud. While there were many architectural changes necessary to support a cloud tier in a mature storage product, in this paper, we focus on innovations needed to support key functionality for customers. Consider typical customer interactions: First, a customer determines which files to migrate to the cloud by estimating how much space will be freed on the on-premises Data Domain appliance. Second, a customer transfers selected files to the cloud and later restores files back. Finally, a customer deletes a file in the cloud when its retention period has expired. Each of these operations requires significant architectural changes and new algorithms to address both the impact of deduplicated storage and the latency and expense of cloud object storage. We also present analysis from deployed cloud tier systems. As an example, some customers have moved more than 20PB of logical data to the cloud tier and achieved a total compression factor (deduplication * local compression) of 40× or more, resulting in millions of dollars of cost savings.