Reviewerly/exHarmony huggingface dataset:
Niche Method Complementing the Nearest-better Clustering

We propose a two-stage niching algorithm that separates local optima areas in the first stage and finds the optimum point of each area using any optimization technique in the second stage. The proposed first stage has complementary characteristics to the shortcoming of Nearest-better Clustering (NBC). We introduce a weighted gradient and distance-based clustering method (WGraD) and two methods for determining its weights to find out niches and overcome NBC. The WGraD creates spanning trees by connecting each search point to other suitable one decided by weighted gradient information and weighted distance information among search points. Since weights influence its clustering result, we propose two weight determination methods 1 and 2. The weight determination method 1 firstly forms one spanning tree and then uses a dynamic pruning method and the Hill-Valley test to cut long edges and repair them. The weight determination method 2 assigns different weights to different search points based on distance information. We combine these methods into WGrad, i.e. WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and compare the characteristics of NBC, WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> using differential evolution (DE) as a baseline search algorithm for obtaining the optimum of each niche after clustering local areas. We design a controlled experiment and run (NBC + DE), (WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> + DE) and (WGraD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> + DE) on 8 benchmark functions from CEC 2015 test suite for single objective multiniche optimization. The experimental results confirmed that the proposed strategy can overcome the shortcoming of NBC and be a complementary niche method of NBC.
Towards the implementation of an ontology based on COBIT framework (CobitOnyology)

for most organizations, understanding the contribution of IT infrastructure to achieve desired objectives is very important. In this context, the IT governance framework COBIT (Control Objective for Information and related Technology) includes a set of essential tools to ensure the monitoring and the control of IT governance. However, COBIT does not provide machine-readable data. Hence, the objective of this paper is to develop an ontology based on COBIT V5 framework called “CobitOntology”. “CobitOntology” is an IT governance solution that will clarify the domain vocabulary and the dependencies between the different elements of the domain. This ontology is realized from text, i. e. from the use of text corpuses, extracting terms and relationships between these terms to make an ontology. We will therefore use a linguistic analysis using a statistical analysis of a corpus (term frequency and inverse document frequency).
Application of Steganography Technique in Cloud Computing

Cloud computing has played a significant role in data and information management through availing computer system resources for all users without direct active management. In return, a considerable population has embarked on cloud computing to share data and information and communicate through the platform hence enjoying the benefit. However, data security is a crucial aspect in cloud computing as a way of assuring users of their confidentiality and safety of data and information stored and transferred through the platform. As a result, the need for data security techniques has facilitated the evolution of different techniques necessary for ensuring the safety of data and information through the platform hence ensuring effective communication channels. Therefore, Steganography techniques have been identified as one of the cloud computing techniques necessary for enhancing cloud data security. In return, this paper analyzes the application of stenography technique in cloud computing as an approach to enhance cloud data security.
Towards an Interactive Visualization Framework for IoT Device Data Flow

The Internet of Things (IoT) has become commonplace in our lives. From smart refrigerators to thermostats, the heterogeneous connectivity it presents has allowed for the automation of tasks and ease of use. However, it has also led to security challenges introducing new attack vectors that are atypical to traditional computing systems. This also complicates tracing a point of system fault in case of an anomalous event or intrusion. In this paper, we propose a visualization framework that can be used to aid the detection of anomalous system events in an IoT ecosystem. This can assist in uncovering valuable insights from data interactions. The proposed framework provides a visual representation of system events in an IoT device. This can be beneficial for use in digital forensic analysis and uncovering system fault or intrusion. Also, it can be used by system administrators or consumers for situational awareness. We discuss our implementation details using a smart home system as a use case and provide future research directions.
Development of Disaster Management and Awareness System Using Twitter Analysis: A Case Study of 2018 Kerala Floods

The twenty-first century is the era of globalization and progressive civilization; our lives have become comfortable, technologically advanced and intellectual. Though humans have proved their proficiencies in every aspect, natural calamities are one of the phenomena that we can not impede. They are destructive, devastating and brutal. Wildfires, floods, earthquakes, tsunamis, etc. all bring about a great deal of economic, social, financial and mental crises not only to the affected people but also to the nation. Kerala floods in India were an extremely grave crisis that occurred in August 2018. The present paper illustrates the development of awareness and disaster management system for such disasters and hence, Kerala floods have been considered as a case study for this development. Further, Twitter is the most popular communication platform identified in the era of social media. In this article, the information has been extracted from Twitter about 2018 Kerala Floods near the affected region and throughout the country. The gathered information helps to select the most accurate scenario produced by Data Crawling. A detailed region-wise classification analysis has been attempted in order to develop an analyzed model for various charitable organizations and NGOs. This analysis provides a better insight into where to administer their relief campaigns.
Feature selection and classification using support vector machine and decision tree

Abstract Breast cancer is one of the human threats which cause morbidity and mortality worldwide. The death rate can be reduced by advanced diagnosis. The objective of this article is to select the reduced number of features the help in diagnosing breast cancer in Wisconsin Diagnostic Breast Cancer (WDBC). This proposed model depicts women who all have no cancer cells or in benign stage later develop into malignant (metastases). Due to the dynamic nature of the big data framework, the proposed method ensures high confidence and low execution time. Moreover, healthcare information growth chases an exponential pattern, and current database systems cannot adequately manage the massive amount of data. So, it is requisite to adopt the “big data” solution for healthcare information.
Searching for Splitting Criteria in Multivariate Decision Tree Using Adapted JADE Optimization Algorithm

Splitting criteria in the decision tree are normally univariate and orthogonal to the parameter axis. This limitation suppresses the decision power of DT and can be overcome by using multivariate splitting criteria. This paper presents an improvement of splitting criteria searching in a decision tree based on swarm intelligence, the global optimization technique. JADE, one of swarm intelligent algorithms, is adapted in this work to search for optimal parameters for the multivariate linear splitting criteria in the multivariate decision tree building. This paper illustrates the splitting criteria in 2D data space and tests the efficiency of the created decision tree. The experimental results show that AJADE-MDT, the proposed algorithm could be used to create the decision tree with higher efficiency compared to the existing algorithms.
Object Detection and Tracking using Zernike Moment

In this paper, the cameras capture lot of information that can be highly used in detecting and tracking objects in motion. Though it is challenging, object detection is used to detect object either in each frame or when it first comes in the video. Various methods like frame difference, background subtraction, optical flow can be used for detection. The highest objective of this is to contribute the human operators which help in detecting and tracking doubtful or uncommon actions in the video sequence. The visual observation system needs fast and strong approaches of detecting and tracking the moving objects. Moving objects were detected using canny edge method positively. Moments of different order were extracted using Zernike moments techniques. Analysis was done on all the Zernike moments from z00 to z88. The results show that the number of unique motion in the video and number of unique motion detected by the system is equal with accuracy of 100%.
AI-Based Online P2P Lending Risk Assessment On Social Network Data With Missing Value

This study explores an important issue of data mining - missing value - by using Neural Network for missing value imputation. We focus on social network data for P2P lending risk assessment. However, social network data usually contain lots of missing values so that predicting accurate credit level is not feasible. We propose a neural network based approach and adapt Generative Adversarial Network concept to generate the missing values. The Generator could output the possible values of the missing values; while the Discriminator could judge if the output values from Generator is appropriate or not for further training. We use real-world P2P data without missing values and the construct datasets with random missing values for evaluation.
PrivGRU: Privacy-preserving GRU inference using additive secret sharing

Gated Recurrent Unit (GRU) has wide application fields, such as sentiment analysis, speech recognition, and other sequential data processing. For efficient prediction, a growing number of model owners choose to deploy the trained GRU models through the machine-learning-as-a-service method (MLaaS). However, deploying a GRU model in cloud generates privacy issues for both model owners and prediction clients. This paper presents the architecture of PrivGRU and designs the privacy-preserving protocols to complete the secure inference. The protocols include base protocols and principal protocols. Base protocols define basic linear and non-linear computations, while principal protocols construct the gating mechanisms of GRUs. The main benefit of PrivGRU is to address privacy problems while enjoying the efficiency and convenience of MLaaS. The overall secure inference is performed on shares, which retain two properties of security: correctness and privacy. To prove the security, this work adopts Universal Composability (UC) framework with the honest-but-curious corruption model. As each protocol is proved to UC-realize the ideal functionality, it can be arbitrarily composed in any manner. This strong security feature makes PrivGRU more flexible and practical in future implementation.
Application of Cloud Computing in Electric Power Utility Systems

The concept of “Cloud computing” became very interesting in recent years because it enables optimization of resources used and costs paid for it. Considering all advantages, this approach is applied widely in business systems of general purpose. In recent years, in literature it is possible to find considerations related to application of this approach in corporate systems as electric power utilities. Having in mind that such types of systems represent infrastructure ones that have great impact to the security of people and utilities, a very important question related to information security should be seriously considered. This chapter discusses advantages and disadvantages of application of cloud computing in electric power utility systems.
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines

The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a Self-Learning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism.
RAMP: Real-Time Anomaly Detection in Scientific Workflows

Research integrity is crucial to ensuring the trustworthiness of scientific discoveries. This work is aimed at detecting misbehaviors targeting scientific workflows, which are computing paradigms widely used to facilitate scientific collaborations across multiple geographically distributed research sites. We develop a new system called RAMP(Real-Time Aggregated Matrix Profile) for real-time anomaly detection in scientific workflow systems. RAMP builds upon an existing time series data analysis technique called Matrix Profile to detect anomalous distances among subsequences of event streams collected from scientific workflows in an online manner. Using an adaptive uncertainty function, the anomaly detection model is dynamically adjusted to prevent high false alarm rates. RAMP can incorporate user feedback on reported anomalies and modify model parameters to improve anomaly detection accuracy. Our experimental results from applying RAMP to the logs generated by DATAVIEW, a scientific workflow platform, show that RAMP is able to identify a varied range of anomalies with high accuracy for both interleaved and non-interleaved workflow executions in real time.
Morbig: A Static parser for POSIX shell

The POSIX shell language defies conventional wisdom of compiler construction on several levels: The shell language was not designed for static parsing, but with an intertwining of syntactic analysis and execution by expansion in mind. Token recognition cannot be specified by regular expressions and lexical analysis depends on the parsing context and the evaluation context. Besides, the unorthodox design choices of the shell language fit badly in the usual specification languages used to describe other programming languages. This makes the standard usage of Lex and Yacc as a pipeline inadequate for the implementation of a parser for POSIX shell. The existing implementations of shell parsers are complex and use low-level character-level parsing code that is difficult to relate to the POSIX specification. We find it hard to trust such parsers, especially when using them for writing automatic verification tools for shell scripts. This paper offers an overview of the technical difficulties related to the syntactic analysis of the POSIX shell language. It also describes how we have resolved these difficulties using advanced parsing techniques (namely speculative parsing, parser state introspection, context-dependent lexical analysis and longest-prefix parsing) while keeping the implementation at a sufficiently high level of abstraction so that experts can check that the POSIX standard is respected. The resulting tool, called Morbig, is an open-source static parser for a well-defined and realistic subset of the POSIX shell language. Its implementation crucially relies on the purity and incrementality of LR(1) parsers generated by Menhir, a parser generator for OCaml.
Data preprocessing in knowledge discovery in breast cancer: systematic mapping study

ABSTRACT Data Mining (DM) is a set of techniques that allow to analyse data from different perspectives and summarising it into useful information. Data mining has been increasingly used in medicine, especially in oncology. Data preprocessing is the most important step of knowledge extraction process and allows to improve the performance of the DM models. Breast cancer (BC) becomes the most common cancer among females worldwide and the leading cause of women's death. This paper aims to perform a systematic mapping study to analyse and synthesise studies on the application of preprocessing techniques for a DM task in breast cancer.Therefore, 66 relevant articles published between 2000 and October 2018 were selected and analysed according to five criteria: year/channel of publication, research type, medical task, empirical type and preprocessing task. The results show that Conferences and journals are the most targeted publication sources, researchers were more interested in applying preprocessing techniques for the diagnosis of BC, historical-based evaluation was the most used empirical type in the evaluation of preprocessing techniques in BC, and data reduction was the most investigated task of preprocessing in BC. However, A low number of papers discussed treatment which encourages researchers to devote more efforts to this task.
Sparse Super-Regular Networks

It has been argued by Thom and Palm that sparsely-connected neural networks (SCNs) show improved performance over fully-connected networks (FCNs). Super-regular networks (SRNs) are neural networks composed of a set of stacked sparse layers of (epsilon, delta)-super-regular pairs, and randomly permuted node order. Using the Blow-up Lemma, we prove that as a result of the individual super-regularity of each pair of layers, SRNs guarantee a number of properties that make them suitable replacements for FCNs for many tasks. These guarantees include edge uniformity across all large-enough subsets, minimum node in-and out-degree, input-output sensitivity, and the ability to embed pre-trained constructs. Indeed, SRNs have the capacity to act like FCNs, and eliminate the need for costly regularization schemes like Dropout. We show that SRNs perform similarly to X-Nets via readily reproducible experiments, and offer far greater guarantees and control over network structure.
Local Differential Privacy with K-anonymous for Frequency Estimation

Data release, such as statistics of data distribution, in many data analysis and machine learning tasks is needed, which poses significant risks of user's privacy. Usually, to preserve privacy of every individual, frequency estimation based on LDP (Local Differential Privacy) is used to replace the real distribution of data. Unfortunately, when an individual sends values multiple times, privacy leakage, i.e., same value problems may occur, along with other performance problems such as memory usage problem. To narrow these gaps, SAnonLDP (Sample Anonymous Local Differential Privacy) is proposed in this paper. We build the SAnonLDP framework by integrating k-anonymous into LDP, which includes four blocks: random grouping; anonymous and Walsh-Fourier transforms; random response; singular value decomposition (SVD). Among them, the second block 'Anonymous and Walsh-Fourier transforms' significantly decreases the communication cost and the memory requirements. The left blocks make up for the loss of information to achieve an acceptable frequency estimation. More important, we verify that this estimation is unbiased by the strict mathematical reasoning. Finally, the numerical experiments demonstrate that SAnonLAP achieves better KL-divergence and estimation error compared to another known privacy model: RAPPOR.
A demonstration of B-EagleV Visualizing massive point cloud directly from HDFS

The advent of Hadoop has inspired many researchers to conduct studies on big data. These studies have covered a wide range of aspects of big spatial data. However, they still face challenges in visualizing big spatial data on a distributed storage model since loading multi-resolution data is inefficient. For this reason, multi-resolution data are usually excluded from the distributed storage model to speed up the loading process. This limitation prompted the introduction of B-EagleV, a novel Hadoop-based solution that enables users to manage and visualize massive point cloud data on Hadoop Distributed File System (HDFS) without moving the multi-resolution data to a local server. This paper presents the achievements of B-EagleV in efforts to discover the values of Hadoop in visualizing massive point cloud data.
Performance Analysis of Machine Learning Algorithms for Fall Detection

Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. Application of machine learning in areas of AALS such as fall detection has the potential to have huge social impact. There has been active research in the application of machine learning in fall detection, using data generated by various means such as wearable devices, environment sensors and vision based systems. The main challenge is to create a model that detects falls accurately, while keeping the design of the fall detection system minimal and non-intrusive. Wearable devices equipped with inertial motion unit (IMU) sensors and vital signs sensors are commonly used to enable analysis around performance of machine learning (ML) models. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We present details on the data set we have generated for this purpose, and compare the performance of various ML algorithms on the collected dataset, with features from IMU sensors vis-à-vis those from IMU sensors in combination with vital signs sensors. We also apply machine learning algorithms on two public datasets, one with only IMU sensor parameter values and the second with only vital signs parameter values, and summarize their performance.