Reviewerly/exHarmony huggingface dataset:
Cheetah: A Dynamic Performance Optimization Approach on Heterogeneous Big Data Analytics Cluster

Any MapReduce-based big data analytics clusters systems like Hadoop, Spark face the substantial challenge of the Long Tail problem: a small subset of straggling tasks significantly impede parallel jobs completion. Specially, in heterogeneous environments some tasks become stragglers because of poor performance of some computing nodes, data skew, etc. Therefore, stragglers are well recognized as a major bottleneck in big data processing and hence the early detection and accurate identification of stragglers can have important impacts on big data processing. In past years speculative execution strategies that have been proposed to meet their challenges such as misjudgment or delaying of straggling tasks, improper selection of backup nodes, etc., which result in inaccurate and inefficient performance of the speculative execution. Encouraged by recent successes in applying reinforcement learning (RL) techniques to solve complex online control problems, we study RL can be used for automatic choose right strategy to address stragglers without human-intervention. In this paper, we present Cheetah: a novel dynamic optimal approach for speculative strategy which identifies stragglers by reinforcement learning and automatic chooses the best strategy to launch speculative tasks on heterogeneous cluster. We implement Cheetah with popular reinforcement learning frameworks, and deploy it on a testbed of heterogeneous Spark cluster with 4 nodes. According to the experimental results, Compared to existing approaches, Cheetah reduces the job completion time in different type applications while achieving superior performance. For example, it demonstrates up to almost 16% reduction average job completion time and improve 25% on accuracy over existing solutions on the heterogeneous Spark cluster.
The Basics of Private and Public Data Trusts

The term “data trust” has recently come into circulation to denote some kind of legal governance structure for the management of data, in particular digital databases, but there is much uncertainty and confusion about what a data trust is supposed to be, legally speaking. This paper examines the nature of data as a possible trust asset, and concludes that the traditional trust, the historical creation of English Equity jurisprudence and now found around the world, is a perfectly sensible vehicle for the management of data, in particular the management of combined datasets for both private and charitable purposes, especially educational purposes. The paper also considers the data protection issues that arise in relation to data trusts.
Generative Pseudorehearsal Strategy for Fault Classification Under an Incremental Learning

As fault classification becomes more important in manufacturing industry, the state-of-art machine learning methods have been utilized. However, owing to the problem called catastrophic forgetting, the networks tend to forget the former knowledge. Thus, it is evident that overall classification performance has fallen, when training the existing model with new classes. We propose classification model that retains previous information using generative pseudorehearsal networks. In this method, newly arrived fault classes could be trained on same network which is parameterized by former data. The proposed method shows significant experimental results comparing to non-incremental methods, while achieving memory efficiency and solving the class imbalance problem.
Terminology systematization for Cybersecurity domain in Italian language

This paper aims at presenting the first steps to improve the quality of the first draft of an Italian thesaurus for Cybersecurity terminology that has been realized for a specific project activity in collaboration with CybersecurityLab at Informatics and Telematics Institute (IIT) of the National Council of Research (CNR) in Italy. In particular, the paper will focus, first, on the terminological knowledge base built to retrieve the most representative candidate terms of Cybersecurity domain in Italian language, giving examples of the main gold standard repositories that have been used to build this semantic tool. Attention will be then given to the methodology and software employed to configure a system of NLP rules to get the desired semantic results and to proceed with the enhancement of the candidate terms selection which are meant to be inserted in the controlled vocabulary.
Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.
Security Comparison of Machine Learning Models Facing Different Attack Targets

Machine Learning has exhibited great performance in several practical application domains such as computer vision, natural language processing, automatic pilot and so on. As it becomes more and more widely used in practice, its security issues attracted more and more attentions. Previous research shows that machine learning models are very vulnerable when facing different kinds of adversarial attacks. Therefore, we need to evaluate the security of different machine learning models under different attacks. In this paper, we aim to provide a security comparison method for different machine learning models. We firstly classify the adversarial attacks into three classes by their attack targets, respectively attack on test data, attack on train data and attack on model parameters, and give subclasses under different assumptions. Then we consider support vector machine (SVM), neural networks with one hidden layer (NN), and convolution neural networks (CNN) as examples and launch different kinds of attacks on them for evaluating and comparing model securities. Additionally, our experiments illustrate the effects of concealing actions launched by the adversary.
Politeness strategies as persuasive tools used in Lombok commercial magazines

This research aimed to illuminate the pragmatic function of politeness in the world of advertising. Politeness theory posits that politeness is a key factor for successful communication and persuasion. In this research, a corpus of 91 advertisements, both in English and Indonesian, was collected from Lombok Guide, Lombok Magazine and Majalah Bandara magazine. Each advertisement was then analyzed in terms of category and sub-category of politeness strategy in the two languages. The research found that the use of politeness strategies indicated that English advertisements exploited more “off record”, “bald on-record,” and “positive face strategies,” while Indonesian advertisements tended to use a negative politeness strategy.
An Analysis of Industrial Security Curriculums in Colleges

Of all the 55 security departments in colleges, the information security departments occupies the majority of 45. Though existing security focused too much on information security, a new research stream on industrial security from a perspective of convergence has emerged. Since the first department of industrial security was established in a college in 2014, the total of four departments have been established until 2018. A new discipline to solve the existing problems and an opportunity to cultivate industrial security professionals have been greatly appreciated; however, the definition of industrial security is not yet specific, and more discussions on education categories and subjects with a focus on the characteristics of convergence are needed. The demand for industrial security professionals has gradually increasing and the departments of industrial security have been established to meet the demand. It is necessary, in this circumstance, to find out how the industrial security departments are teaching students. To this end, the purpose of this article is to deliver an overview of the educational curriculums of the four industrial security departments. This study analyzed 127 subjects in the curriculums, categorized educational areas, and employed Social Network Analysis to understand the relationships between subjects of each departments.
PERFORMANCE COMPARISON BETWEEN REACT NATIVE AND FLUTTER

The global mobile OS market share is mainly dominated by two operative systems. One of them is Android, and the other one called iOS. These two together had more than 99% of the market share in 2 ...
Prediksi Tingkat Kriminalitas Menggunakan Fuzzy Logic

Crime is an act of violation of the law that damages morals and social life in society. The purpose of this study is to predict the level of crime so that the results can be considered by related parties. The decisive factors in predicting crime rates in Batam City are unemployment, age and population. Sources of data were obtained from the statistical center agency and the Batam City Barelang Police. To do the crime rate process, use fuzzy logic with the Mamdani method. The input variable consists of unemployment, age and population, while the output is the crime rate. The stages in the processing are done by the Mamdani fuzzy logic that is determining the formation of the fuzzy set. This study produces prediction of crime in Batam City at position 49.5 which is in the medium range, with a population of 50 as inputted, 60 unemployment, and 50 years of age.
Feature Selection using Genetic Programming

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.
Fuzzy Neural Networks

In the previous chapters, you saw neural networks based on crisp inputs, weights, parameters, etc. But in real-life applications, it’s not necessary that you always get the same kind of inputs. Fuzziness in neural networks results in networks having Fuzzy Signals, Fuzzy Weights, etc., in which case you are dealing with Fuzzy Neural Networks. This chapter looks at the different architectures of Fuzzy Neural Networks and the components that define them. You will later learn about the Adaptive Neuro Fuzzy Architecture and its different versions.
Evaluation of image dehazing techniques based on a realistic benchmark

Image dehazing is a complex problem that aims to restore the Visibility of hazy scenes. In recent years, there has been a remarkable progress of single image dehazing techniques, but an important problem is the lack of realistic dehazing benchmarks. In this paper we introduce a new realistic dehazing dataset that consists of 22 pairs of hazy images and haze-free images (ground truth). The sets of images are recorded indoor and are characterized by dense and homogeneous hazy images that have been recorded by introducing real haze, generated by professional haze machines. The scenes have been captured under the same illumination conditions for both hazy and haze-free images. In addition, we perform a comprehensive evaluation of the state-of-the-art single image dehazing techniques providing also a quantitative validation of the existing dehaing methods using the PSNR, SSIM and CIEDE2000 objective measures.
Pengelompokan topik dokumen berbasis text mining dengan algoritme K-means : Studi kasus pada dokumen Kedutaan Besar Australia Jakarta

The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents. K-Means algorithm was used as a non- hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016. This research concluded that text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.
Fault-Tolerant Visual Secret Sharing Scheme Using Meaningful Shares

Security problem is becoming increasingly crucial in many fields. In order to ensure that secret images are not being stolen during information transmission, a preferred way is to encrypt them before transmission. Visual secret sharing scheme (VSSS) is a scheme of secure communication technology in which a secret image is encrypted into some shares and then restored through human vision by stacking shares directly. A fault-tolerant VSSS is a VSSS which is not necessary to align the shares precisely when recovering the secret image. In the other side, encrypting a secret image into two meaningless shares might invite the attacker's attention. Therefore, this paper proposed two fault- tolerant VSSS which encrypt one secret image into two meaningful shares, and with the fault-tolerant mechanism.
Using Improved Glowworm Swarm Optimization Algorithm for Clustering Analysis

High-quality clustering algorithms play an essential role in the data analysis. Traditional clustering algorithms are susceptible to initial cluster centers, which leads to the degradation of clustering quality. Clustering analysis based on Swarm Intelligence (SI) optimization algorithms can solve the problem of traditional clustering algorithms by the better ability to find the optimal solutions, such as Glowworm Swarm Optimization (GSO) algorithm. The GSO-based clustering analysis can use the multi-modal optimization ability to search for the optimal cluster centers. This paper proposes two clustering techniques based on the GSO algorithm: one is to realize self-organizing clustering of data through improved GSO algorithm, and the other is to hybrid the improved GSO self-organizing clustering algorithm with the k-means algorithm. The proposed algorithms test on the Iris data set. The experimental results show that the enhanced GSO algorithm proposed in this paper can efficiently realize self-organization clustering of data without initializing cluster centers and cluster number, and the proposed hybrid algorithm has better clustering results over four clustering algorithms.
Visual Saliency Prediction in Dynamic Virtual Reality Environments Experienced with Head-Mounted Displays: An Exploratory Study

This work explores a set of well-studied visual saliency features through seven saliency prediction methods with the aim of assessing how applicable they are for estimating visual saliency in dynamic virtual reality (VR) environments that are experienced with head-mounted displays. An in-depth analysis of how the saliency methods that make use of depth cues compare to ones that are based on purely image-based (2D) features is presented. To this end, a user study was conducted to collect gaze data from participants as they were shown the same set of three dynamic scenes in 2D desktop viewing and 3D VR viewing using a head-mounted display. The scenes convey varying visual experiences in terms of contents and range of depth-of-field so that an extensive analysis encompassing a comprehensive array of viewing behaviors could be provided. The results indicate that 2D features matter as much as depth for both viewing conditions, yet depth cue is slightly more important for 3D VR viewing. Furthermore, including depth as an additional cue to the 2D saliency methods improves prediction for both viewing conditions, and the benefit margin is greater in 3D VR viewing.
Unsupervised Learning: Neural Network Toolkits

The next unsupervised learning technique take the unsupervised process one step further.
Simulation of artificial intelligence in a computer game

Abstract Artificial intelligence, implemented in the space of a computer game, will allow performing a variety of practice-oriented tasks and time-consuming algorithms: finding ways, avoiding obstacles, conducting combat, choosing the appropriate strategy, simulating emotions, dialogue, etc. The main problem that should be solved when developing an appropriate gaming application is to simulate the mechanism for controlling all possible functions of the game object. The research methodology includes: analysis of scientific works of Russian and foreign authors; the method of system information analysis, the method of mathematical and information modeling; computer experiment method, as a kind of computational; decision making algorithms. The paper presents a solution to the problem, including the informed choice of a decision-making algorithm, and the design of a model of a fuzzy finite automaton that combines the advantages of a model of a generating finite automaton and fuzzy logic. Based on the results of testing the implemented model, a conclusion was formulated about the main advantage of the artificial intelligence model for the gaming application - its ability to make an unusual decision in the arisen game situation.
Fuzzy Rules and Reasoning

The previous chapter discussed different kinds of sets as well as their properties and operations that can be performed on them. You also looked at some of the operations and their applications in Python. That chapter concluded with a small introduction to the different types of membership functions and their Python applications.
An Improved Lightning Attachment Procedure Optimization Algorithm for Function Optimization

The hybridization of different meta-heuristic algorithms is for expanding the synergies of a single optimization method used alone and achieving a better optimum search performance. In this work, we proposed a hybrid optimization method combining lightning attachment procedure optimization algorithm (LAPO) and the gravitational search algorithm (GSA), and applied to the function optimization. In order to integrate the excellent exploitation performance of LAPO with the great exploration capability of the GSA to synthesize the strength of each algorithm, we utilized series hybrid mode and some benchmark test functions were employed for evaluating and comparing the performance with the standard mode. Meanwhile, the most commonly used algorithm, particle swarm optimization algorithm is also used for contrast. The experiment results show that the hybrid algorithm obtains better time efficiency and convergence capacity, also have a great ability to avoid local optimums.
Polish Language Modelling Based on Deep Learning Methods and Techniques

The article presents an example of deep learning methods application for language modelling in Polish. Language modelling helps to predict a sequence of recognized words or characters, and it can be used for improving speech processing and speech recognition. However, currently the field of language modelling is shifting from statistical language modelling methods to neural networks and deep learning methods. There are still many difficult problems to solve in natural language modelling. Nevertheless, deep learning methods achieve the most modern results for some specific language modelling problems. In this paper are presented the most interesting natural language modelling tasks, such as word-based and character-based language modelling, in which deep learning methods achieve some progress. New research results presented in this paper, in reference to previous articles, are focused on how to develop a character-based language model using a recurrent neural network and deep machine learning techniques. The use of both language modelling methods at the same time allow to develop hybrid language models that are characterized by even better properties and can greatly improve speech recognition. The presented results relate to the modelling of the Polish language but the achieved research results and conclusions can also be applied to language modelling application for other languages.
Neural Networks and Deep Learning with TensorFlow

This chapter focuses on neural networks and how we can build them to perform machine learning, by closely mimicking the human brain. We will start by determining what neural networks are and how similarly they are structured to the neural network in humans. Then, we will deep dive into the architecture of neural networks, exploring the different layers within. We will explain how a simple neural network is built and delve into the concepts of forward and backward propagation. Later, we will build a simple neural network, using TensorFlow and Keras. In the final sections of this chapter, we will discuss deep neural networks, how they differ from simple neural networks, and how to implement deep neural networks with TensorFlow and Keras, again with performance comparisons to simple neural networks.