2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第4页

How Do Firms Transact? Guesstimation and Validation of Financial Transaction Networks with Satisfiability 企业如何交易?具有可满足性的金融交易网络的估计与验证

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00017

Christos Tsigkanos, Alessio Arleo, J. Sorger, S. Dustdar

{"title":"How Do Firms Transact? Guesstimation and Validation of Financial Transaction Networks with Satisfiability","authors":"Christos Tsigkanos, Alessio Arleo, J. Sorger, S. Dustdar","doi":"10.1109/IRI.2019.00017","DOIUrl":"https://doi.org/10.1109/IRI.2019.00017","url":null,"abstract":"Knowledge of monetary flow between firms can give a significant advantage both from a profit or research point of view. So-called firm-to-firm transaction networks are valuable in analyzing a market or an economy. However, such detailed and complete data is seldom available. In this work, we aim at supporting economists by reusing available financial information from different sources at different levels of detail and completeness. With our technique, experts' domain knowledge can be fused together with publicly available information to extract a representative, coherent instance of the transaction network. Supporting underspecification is important, as experts may develop partial econometric models. Our technique fills such blanks by systematically guesstimating missing information. Our approach builds upon formal foundations of satisfiability modulo theories and thus obtained transaction networks respect constraints imposed by domain knowledge and input data sources. We outline a taxonomy of general data types in the domain, and we programmatically construct formal predicates describing them. We demonstrate both guestimation of missing information of a transaction network and validation of external, expert-provided models. Finally, we investigate feasibility and performance of the advocated technique over a fragment of the Austrian economy.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126650762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models 基于生成和检索混合对话模型的AI情感会话机器人

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00068

Min-Yuh Day, Chi-Sheng Hung

{"title":"AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models","authors":"Min-Yuh Day, Chi-Sheng Hung","doi":"10.1109/IRI.2019.00068","DOIUrl":"https://doi.org/10.1109/IRI.2019.00068","url":null,"abstract":"ChatBot technology has become a widely used in various application fields. An important topic in the research on conversational robots is the improvement of their temperature during operation for enhanced user interaction. In this study, we propose an artificial intelligence affective conversational robot (AIACR), which is an integration of an artificial intelligence deep learning sentiment analysis model and generative-and retrieval-based dialogue models. The sentiment analysis model developed in this study uses three models, namely, multilayer perceptron (MLP), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM). Moreover, word2vec and semantics are utilized as the basis for similarity ranking models. The deep learning dialogue model, sentiment analysis model, and similarity model were integrated and compared as well. The experimental results show that the sentiment analysis model, similarity model, and dialogue model respectively utilize BiLSTM, word2vec, and the retrieval-based model to achieve the best dialogue performance. The major research contributions of this study are the developed AIACR and the proposed affective conversational robot index (ACR Index) as a criterion for evaluating the effectiveness of emotional dialogue robots.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114336531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

IRI 2019 International Technical Program Committee IRI 2019国际技术计划委员会

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/iri.2019.00008

引用次数: 0

A Trusted Bluetooth Performance Evaluation Model for Brain Computer Interfaces 脑机接口可信蓝牙性能评估模型

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00021

Hassan Karim, D. Rawat

引用次数: 2

Hibiki: A Graph Visualization of Asian Music Hibiki:亚洲音乐的图形可视化

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00053

Ke Wu, M. Rege

{"title":"Hibiki: A Graph Visualization of Asian Music","authors":"Ke Wu, M. Rege","doi":"10.1109/IRI.2019.00053","DOIUrl":"https://doi.org/10.1109/IRI.2019.00053","url":null,"abstract":"Creating a visualization for a specific subdomain is an arduous task since most commercial visualization tools are often written in a way that allows them to be appli-cable to multiple subject domains. These tools are cer-tainly powerful, but inherently weaker since they were not written with a specific subject domain in mind. Thus, many researchers may want to create their own visualization. The goal of this project is to create a Neo4j data-base and an interactive web interface for a dataset that covers the intricacies of the East Asian Music scene, primarily focused on Japanese music. This paper serves as documentation to help other authors understand the processes involved when designing and creating similar tools. We break the project down into 3 separate compo-nents. First, we introduce the fundamentals of a Neo4j Graph Database and data mapping design decisions. Next, we explore what an ETL process looks like and how to implement it using Ruby libraries. Finally, we look at the design of the graph visualization software, it's components, and key design decisions. We end the discussion with some analysis of the visualization's effectiveness to provide information and how to improve computational efficiency of the visualization.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129170857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

IRI 2019 Conference Organizers IRI 2019会议组织者

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/iri.2019.00007

S. Rubin, Shu‐Ching Chen, Elisa Bertino, B. Thuraisingham, M. Shyu, S. Jayarathna, D. Rawat, Nuray Baltaci

引用次数: 0

Title Page iii 第三页标题

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/iri.2019.00002

引用次数: 0

A Conceptual Model for Systems-of-Information Systems 信息系统的概念模型

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00063

J. Fernandes, F. Ferreira, Felipe Cordeiro de Paula, V. V. G. Neto, R. Santos

{"title":"A Conceptual Model for Systems-of-Information Systems","authors":"J. Fernandes, F. Ferreira, Felipe Cordeiro de Paula, V. V. G. Neto, R. Santos","doi":"10.1109/IRI.2019.00063","DOIUrl":"https://doi.org/10.1109/IRI.2019.00063","url":null,"abstract":"The interoperability among multiple Information Systems (IS) have enabled the creation of functionalities that could not be offered by any IS individually. This phenomenon has raised the concept of Systems-of-Information Systems (SoIS). SoIS are dynamic alliances of independent and interoperable IS that share resources to fulfill a set of global common goals. Despite the emergence of studies on this type of complex system, there is a lack of a precise characterization of what exactly is a SoIS and what makes this class of systems different from other similar complex systems. The main contribution of this paper is the establishment of a conceptual model to support researchers and practitioners to recognize a SoIS. We adopted the developed model to show how real cases of SoIS from the environmental management and space domains could be recognized as such. The model also helped us to identify a set of IS that is not considered a SoIS in the financial domain. Results reveal that the proposed conceptual model (i) includes relevant concepts and relations to describe SoIS, (ii) comprehensively characterizes SoIS, and (iii) supports experts to correctly classify a complex system as a SoIS or not.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Variational Encoder-Decoder Recurrent Neural Network (VED-RNN) for Anomaly Prediction in a Host Environment 变分编码器-解码器递归神经网络(ed - rnn)在主机环境中的异常预测

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00025

Lydia Bouzar-Benlabiod, Lila Méziani, S. Rubin, Kahina Belaidi, Nour ElHouda Haddar

{"title":"Variational Encoder-Decoder Recurrent Neural Network (VED-RNN) for Anomaly Prediction in a Host Environment","authors":"Lydia Bouzar-Benlabiod, Lila Méziani, S. Rubin, Kahina Belaidi, Nour ElHouda Haddar","doi":"10.1109/IRI.2019.00025","DOIUrl":"https://doi.org/10.1109/IRI.2019.00025","url":null,"abstract":"Intrusion detection systems (IDS) are important security tools. NIDS monitors network's traffic and HIDS filters local one. HIDS are often based on anomaly detection. Several studies deal with anomaly detection using system-call traces. In this paper, we propose an anomaly detection and prediction approach. System-call traces, invoked by the running programs, are analyzed in real time. For prediction, we use a Sequence to sequence model based on variational encoder-decoder (VED) and variants of Recurrent Neural Networks (RNN), these architectures showed their performance on natural language processing. To make the analogy, we exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to optimize the prediction model input data representation. A one-class classification is done to categorize the sequences into normal or abnormal. Tests are achieved on the ADFA-LD dataset and showed the advantage of the prediction for the intrusion detection/prediction task.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

ADQuaTe: An Automated Data Quality Test Approach for Constraint Discovery and Fault Detection 一种用于约束发现和故障检测的自动化数据质量测试方法

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00023

Hajar Homayouni, Sudipto Ghosh, I. Ray

引用次数: 5