Reviewerly/exHarmony huggingface dataset:
ANALISIS DATA KEJADIAN BENCANA ANGIN PUTING BELIUNG DENGAN METODE ONLINE ANALYTICAL PROCESSING (OLAP)

A whirlwind is a natural disaster with a relatively high incidence. In improving whirlwinddisaster mitigation preparedness, analysis of historical data of events is needed to minimize the possibility of losses. In this study, data analysis was carried out using the Online Analytical Processing (OLAP) method with the Zoho Reports application so that it can be known to the region prone to whirlwind and the time of occurrence to help those who have an importance in decision making. The results of the analysis are in the form of information displayed in graphical form from data on the occurrence of whirlwind in Indonesia in 2011-2014.
Streaming Massive Electric Power Data Analysis Based on Spark Streaming

Electric power user classification is one of the most important methods to realize the optimal allocation of power resources. Through the analysis of users’needs, behavior and habits, Countries and enterprises can offer different incentives for different users. In this way, people are more willing to use green and clean Electric power resources. In the analysis of user clustering, there is a need for real-time processing of massive and high-speed data. In this paper we propose a novel distributed user data stream clustering method based on Spark streaming, improved clusStream algorithm and improved K-means algorithm named “DStreamEPK”. In the final experimental evaluation, we first tested the clustering effectiveness of DStreamEPK on UCI datasets, the results show that the proposed DStreamEPK is better than the traditional K-means clustering algorithm. At the same time, it is found that DStreamEPK can cluster user’s electricity data quickly and efficiently through testing on user’s real data sets.
Adversarial Discriminative Denoising for Distant Supervision Relation Extraction

Distant supervision has been widely used to generate labeled data automatically for relation extraction by aligning knowledge base with text. However, it introduces much noise, which can severely impact the performance of relation extraction. Recent studies have attempted to remove the noise explicitly from the generated data but they suffer from (1) the lack of an effective way of introducing explicit supervision to the denoising process and (2) the difficulty of optimization caused by the sampling action in denoising result evaluation. To solve these issues, we propose an adversarial discriminative denoising framework, which provides an effective way of introducing human supervision and exploiting it along with the potentially useful information underlying the noisy data in a unified framework. Besides, we employ a continuous approximation of sampling action to guarantee the holistic denoising framework to be differentiable. Experimental results show that very little human supervision is sufficient for our approach to outperform the state-of-the-art methods significantly.
HSDS: An Abstractive Model for Automatic Survey Generation

Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.
Performance Prediction in Dynamic Clouds using Transfer Learning

Learning a performance model for a cloud service is challenging since its operational environment changes during execution, which requires re-training of the model in order to maintain prediction accuracy. Training a new model from scratch generally involves extensive new measurements and often generates a data-collection overhead that negatively affects the service performance.In this paper, we investigate an approach for re-training neural-network models, which is based on transfer learning. Under this approach, a limited number of neural-network layers are re-trained while others remain unchanged. We study the accuracy of the re-trained model and the efficiency of the method with respect to the number of re-trained layers and the number of new measurements. The evaluation is performed using traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We study model re-training after changes in load pattern, infrastructure configuration, service configuration, and target metric. We find that our method significantly reduces the number of new measurements required to compute a new model after a change. The reduction exceeds an order of magnitude in most cases.
A Study on Sentiment Analysis on Social Media Data

Sentiment analysis which is otherwise also called sentiment mining or opinion mining is the process of ascertaining and categorizing the positive, negative, or neutral opinion of the speaker or writer about a specific product, service, etc., in essence. The development of the user-generated content in social media has opened new prospects to extract knowledge from the opinions. Sentiment analysis categorizes the opinion of a writer into positive, negative, and neutral. This paper presents a detailed study on sentiment analysis process, various tools used for data collection, importance and the sources of user data. The importance of machine learning in sentiment analysis with various preprocessing steps and the ontology for sentiment is also discussed.
Privacy Preserving Elastic Stream Processing with Clouds Using Homomorphic Encryption

Prevalence of the Infrastructure as a Service (IaaS) clouds has enabled organizations to elastically scale their stream processing applications to public clouds. However, current approaches for elastic stream processing do not consider the potential security vulnerabilities in cloud environments. In this paper we describe the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on Homomorphic Encryption (HomoESM). The HomoESM not only does elastically scale data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real world test setup, which includes an email filter benchmark and a web server access log processor benchmark (EDGAR) we demonstrate the effectiveness of our approach. Multiple experiments on Amazon EC2 indicate that the proposed approach for Homomorphic encryption provides significant results which is 10% to 17% improvement of average latency in the case of email filter benchmark and EDGAR benchmarks respectively. Furthermore, EDGAR add/subtract operations and comparison operations showed 6.13% and 26.17% average latency improvements respectively. These promising results pave the way for real world deployments of privacy preserving elastic stream processing in the cloud.
Insurance, Smart Information Systems and Ethics

This report provides an overview of the current implementation of SIS in the insurance industry, also identifies the positive and negative aspects of using SIS in the insurance industry, including ethical issues which could arise while using SIS in this area. Two companies working in the industry of health insurance are analysed in this report: a German health insurance company (Organisation Y), and a business intelligence centre for healthcare insurers (Organisation X). Further specific ethical issues that arise when using SIS technologies in Organisation Y and Organisation X are critically evaluated. Finally, conclusions are drawn on the case study and areas for improvement are suggested.
Promoting interdisciplinarity in primary school in the framework of science education

The promotion of interdisciplinarity has been gaining prominence all around the world to better prepare students for the real-life challenges of an increasingly demanding society.This study aims to investigate how to promote interdisciplinarity in primary school in the framework of science education.With a qualitative methodology and an interpretative approach, participants are primary school teachers who participated in a collaborative continuing profes-
Online Multimodal Co-indexing and Retrieval of Social Media Data

Effective indexing of socialSocial media|( mediaMedia|( data is key to searching for information on the social Web. However, the characteristics of social media dataData make it a challenging task. The large-scale and streaming nature is the first challenge, which requires the indexing algorithmAlgorithm to be able to efficiently update the indexing structureStructure when receiving data streams. The second challenge is utilizing the rich meta-information of social media data for a better evaluation of the similaritySimilarity between data objects and for a more semantically meaningful indexing of the data, which may allow the users toSearch|( search for them using the different types of queries they like. Existing approachesApproach based on either matrixMatrix operations or hashingHashing usually cannot perform an online update of the indexing base to encode upcoming data streams, and they have difficulty handling noisy data. This chapter presents a study on using theOnline multimodal co-indexing adaptive resonance theory|( OnlineOnline multimodal co-indexing MultimodalMultimodal co-indexing|( Co-indexingCo-indexing Adaptive ResonanceResonance TheoryTheory (OMC-ART)Adaptive resonance theory for an effective and efficient indexing and retrievalRetrieval|( of social media data. More specifically, two types of social media data are considered: (1) the weakly supervised image data, which is associated with captions, tagsTag and descriptions given by the users; and (2) the e-commerceE-commerce product data, which includes product images, titles, descriptions and user comments. These scenarios make this study related to multimodal web image indexing and retrieval. Compared with existing studies, OMC-ARTOMC-ARTonline multimodal co-indexing adaptive resonance theory has several distinct characteristics. First, OMC-ART is able to perform online learningOnline learning|( of sequential data. Second, instead of a plain indexing structure, OMC-ART builds a two-layer one, in which the first layer co-indexes the images by the key visual and textual features based on the generalizedGeneralized distributionsDistribution of the clustersCluster they belong to; while in the second layer, the data objects are co-indexed by their own featureFeature|( distributions. Third, OMC-ART enables flexible multimodal searching by using either visual features, keywords, or a combination of both. Fourth, OMC-ART employs a rankingRanking|( algorithmAlgorithm that does not need to go through the whole indexing systemSystem when only a limited number of images need to be retrieved. Experiments on two publicly accessible image datasetsDataset and a real-world e-commerce dataset demonstrate the efficiency and effectiveness of OMC-ART. The content of this chapter is summarized and extended from [13] ( https://doi.org/10.1145/2671188.2749362 ), and the PythonPython codes ofOnline multimodal co-indexing OMC-ARTAdaptive resonance theory with examples on building an e-commerceE-commerce product search engine are availableAvailable at https://github.com/Lei-Meng/OMC-ART-Build-a-toy-online-search-engine- .
Portrayal and Demonstration of a Novel Procedure for In-Situ Estimation of Gelation Time

Summary Gelation time is among the most important parameters for screening gel formulations. For deep-diversion, a complex and prohibitive setup (that includes long core samples and multiple pressure tabs) would be needed to simulate in-situ gelation. Therefore, the industry typically resorts to bulk tests. In this paper, we present a novel and convenient method to determine in-situ gelation times that capture the gellant flow and interactions within the porous medium at reservoir conditions. We conduct a systematic study of gelation-time using an organically crosslinked polymer gel system. Both bulk and in-situ gelation tests were performed. Bulk tests included conventional bottle tests as well as rheological and NMR measurements. Our novel procedure was used for in-situ tests. A conventional composite core-sample is used. The middle core-plug is saturated with the gellant solution. Both inlet and outlet core-plugs are saturated with the polymer solution. A small predetermined amount of the same solution is injected alternatingly from the inlet and outlet ends (i.e. repeated cycles of isochoric forward and backward injections) where pressure-drops are monitored. Results demonstrated that in-situ gelation time can be conveniently determined by our new measurement method. By monitoring the pressure-drop buildup in-situ gelation time is determined as the point where a dramatic increase is observed (i.e. a shift from slow to rapid increase in pressure-drops). For the studied system, this was determined to occur at 74 hr. Bulk gelation-time observed in bottle tests was 72 hr and hence, surprisingly, corresponded extremely well to this in-situ gelation-time estimate. Yet, we believe this direct correlation to be non-unique and possibly dependent on the gellant/rock combination. Nonetheless, we do embrace simple bottle tests as a valuable technique for initial rough gelation assessment and screening of gellant formulations. On the other hand, more elaborate bulk tests based on rheological and NMR measurements, yielded much shorter gelation-time estimates (58 and 28 hrs, respectively). This is possibly due to such measurements being more sensible to the smallest changes in gellant molecular structures that do not correspond directly to the buildup of significant flow resistance. This study provides an efficient technique for gel formulation screening and evaluation. In situ gelation behavior can be evaluated by this new technique using a conventional coreflooding setup. The technique provides a representative and cost-effective measurement method. The unique fluid injection strategy diminishes viscous fingering and keeps the integrity of gel in core, therefore, it better simulates actual gel flow in reservoir.
Kernel-Coefficient-Based Feature Method for Face Detection

A kernel-coefficient-based feature method is proposed to detect faces. The proposed method uses a mathematical expression and 26 different arrangements of kernel-coefficients of a kernel (testing region). The method manipulates the symmetric appearance of a face with respect to a rigid-kernel (fixed region). The expression, which is used to generate feature values, responds to pixels on edges of the image-objects only. For each distinct arrangement of kernel-coefficients, a feature-value is generated. The objective of the proposed kernel-coefficient-based feature method is to reduce the number of feature values required for face detection.
The Igba-boi apprenticeship approach : arsenal behind growing success of Igbo entrepreneurs in Nigeria

The Igbo ethnic group in Nigeria is known for success in business and entrepreneurship. Through an apprenticeship scheme known as Igba-boi, people from this ethnic group have dominated and continued to excel above their contemporaries from other ethnic groups in the country and beyond. This article profiles the Igba-boi scheme and comes up with a model on how to improve it. A survey research design grounded on a qualitative method was used. Purposive sampling technique was used to identify individuals with in-depth knowledge about this approach for one-on-one interview and focus group discussion in selected regions of Nigeria. The data analyses were performed using Atlas. ti v8, through thematic approach. Findings reveal the approach involves three main stages: the talent identification, the learning stage and the settlement/start-off stage. The parents/guidance helps identify their children‘s entrepreneurship ambition and provide capacity building such as sourcing a mentor. The child indicates interest of entrepreneurship and accepts to pursue the career at early stage. Potential mentor/entrepreneur takes the child through the training process and in the end provides support to establish and individually innovation. In light of discrepancies during settlement of apprentices after a successful completion of training, the study recommends legal involvement in the process. It will be appropriate for the government to use the approach as a resource for entrepreneurship capacity building. The approach should be improved as pedagogy in institutions of learning to spur entrepreneurship spirit amongst the youths.
Planting Cedar: An Open Source Linked Data Vocabulary Manager at the University of Houston Libraries

This article describes the rationale for creating and maintaining Cedar, a linked data controlled vocabulary management system for the University of Houston (UH) Libraries, including the systems that support and extend its functionality. It also discusses the design process for Cedar, its interfaces, the vocabulary generation and management process, and how Cedar is utilized by UH Libraries systems and workflows. The article concludes with a discussion of issues encountered during application development, questions remaining to be answered around establishing policies for local term creation, lessons learned, and future plans for Cedar use and development.
Information search by applying VDL-based iconic tags: an experimental study

Purpose Visual Distinctive Language (VDL)-based iconic tags are structured visual information annotation. They explicate the content and organization of tagged information by graphical and symbolic features in order to improve the vocabulary problems of textual tags. The purpose of this paper is to investigate how these special icons help in tagged-based user information searching. Design/methodology/approach A two-stage experiment was designed and conducted so as to follow and quantify the searching process in specific searching target case and no specific searching target case when using VDL-based iconic tags. Findings The experimental results manifested that VDL-based iconic tags enhanced the role of tag in information searching. They could make user better understand tag clusters, which, in turn, provide global structure of involved topics. Also, VDL-based iconic tags helped user to find out searching target more quickly with higher accuracy by taking advantages of visual representation of tag categories and symbolic signification of tag content. Originality/value This study is one of the first to verify how structured icons work in information searching and how user’s graphical cognition impacts on tag-based information searching process. The research findings are dedicated to the theory of VDL-based iconic tags, as well as to a new visualization method for search user interface design.
Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic

A new intelligent method of behaviors fusion for mobile robot applications is implemented. The developed fuzzy architecture control can activate multiple behaviors at the same time. The degree of activation (DA) of each behavior is handled intelligently to ensure the autonomous navigation of a mobile robot in a dynamic and uncertain environment. An odometric system adapted with all unicycle platforms is elaborated too, allowing the determination of the position and orientation in real time of the robot. The whole architecture is tested and implemented via an FPGA using the VHDL hardware programming language. The calculations are done in fixed point to save the consumption of FPGA blocks, and to keep the accuracy to perform all tasks during navigation. Waveform and experimental results indicate the relevance and rapidity of the system, and show the possibility to embark it in different robotic platform such as two-wheeled wheelchairs, and within various navigation environments.
A Mixture Model Approach for Clustering Bipartite Networks

This chapter investigates the latent structure of bipartite networks via a model-based clustering approach which is able to capture both latent groups of sending nodes and latent variability of the propensity of sending nodes to create links with receiving nodes within each group. This modelling approach is very flexible and can be estimated by using fast inferential approaches such as variational inference. We apply this model to the analysis of a terrorist network in order to identify the main latent groups of terrorists and their latent trait scores based on their attendance to some events.
A Bayesian Extension of the Description Logic $$\mathcal {ALC}$$

Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension $$\mathcal {BALC}$$ of the DL $$\mathcal {ALC}$$ . We present a tableau-based procedure for deciding consistency, and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical $$\mathcal {ALC}$$ .
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, external covariates, and non-linear interactions between the two. In this paper, we propose to achieve this through a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We demonstrate the utility of our model on simulated examples and applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes. In each setting we show how the c-GPLVM can extract low-dimensional structures from high-dimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches.