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Does ad-context matter on the effectiveness of online advertising? 30

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Does ad-context matter on the effectiveness of online advert...

30th International Conference on Software engineering and knowledge engineering, SEKE 2018

作者： Sun, Caihong Zhang, Meina Zuo, Meiyun Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

ISBN: (纸本)1891706446

In this paper, we focus on examining the effects of Ad-context on the click-Through rate (CTR) for the online advertising. Many researches have shown that ad-context congruity is a key factor to CTR, but the features of ad-context are rarely introduced in CTR prediction algorithms. By leveraging data from various sources, and using text mining and sentiment analysis techniques, our proposed approach extracts three types of features (i.e. users, advertisements and ad-context features) to predict CTR. User features describe "Who" is browsing the webpage, advertisement features depict "How" an ad serving is, and ad-context features include "What" the product is, "Where" an ad displays, as well as the "Mood" of the context. Experiment results show that our proposed approach outperforms the benchmark models by introducing ad-context features. Novel ad-context features we proposed make good contributions to the prediction of CTR. The research highlights the power of ad-context in online advertising CTR prediction. Moreover it gives an insight into supporting effective advertising and improving users' satisfaction. © 2018 Universitat zu Koln. All rights reserved.

关键词： Sentiment analysis

Incremental processing of noisy user utterances in the spoken language understanding task

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arXiv 2019年

作者： Constantin, Stefan Niehues, Jan Waibel, Alex Karlsruhe Institute of Technology Institute for Anthropomatics and Robotic Maastricht University Department of Data Science and Knowledge Engineering

The state-of-The-Art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times. If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered. In this work, we present a model-Agnostic method to achieve high quality in processing incrementally produced partial utterances. Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components. We get improvements of up to 47.91 absolute percentage points in the metric F1-score. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

Edge-minimum saturated k-planar drawings

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arXiv 2020年

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Netherlands Utrecht University Netherlands TU Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D ∈ D is saturated when the addition of any edge to D results in D0 ∈/ D—this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdös, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2/k−(k mod 2) (n − 1) edges for any k ≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k + 1)/k (k-1) (n − 1) edges for any k ≥ 6. Copyright © 2020, The Authors. All rights reserved.

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Records systems and information systems: Connecting in organizations 8th

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Records systems and information systems: Connecting in organ...

8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International Research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

Trustworthy preference completion in social choice

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arXiv 2020年

作者： Li, Lei Xue, Minghe H., Chen X., Wu The School of Computer Science and Technology University of Science and Technology of China China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents’ rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the trustworthy preference completion. Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to find k-nearest trustworthy neighbors of the agent with trust-oriented Kendall-Tau distances, which will handle the cases when an agent exhibits irrational behaviors or provides only noisy rankings. Then, for alternative pairs, a bijection can be built from the ranking space to the preference space, and its certainty and conflict can be evaluated based on a well-built statistical measurement Probability-Certainty Density Function. Therefore, a certain common voting rule for the first k trustworthy neighboring agents based on certainty and conflict can be taken to conduct the trustworthy preference completion. The properties of the proposed certainty and conflict have been studied empirically, and the proposed approach has been experimentally validated compared to state-of-arts approaches with several data sets. © 2020, CC BY.

关键词： Nearest neighbor search

Structure-aware random fourier kernel for graphs 21

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Structure-aware random fourier kernel for graphs

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jinyuan Fang Qiang Zhang Zaiqiao Meng Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hangzhou Innovation Center Zhejiang University China and College of Computer Science and Technology Zhejiang University China and AZFT Knowledge Engine Lab China School of Computing Science University of Glasgow United Kingdom and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

ISBN: (纸本)9781713845393

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels. In this paper, we propose a novel structure-aware random Fourier (SRF) kernel for GPs that brings several benefits when modeling graph-structured data. First, SRF kernel is defined with a spectral distribution based on the Fourier duality given by the Bochner's theorem, transforming the kernel learning problem to a distribution inference problem. Second, SRF kernel admits a random Fourier feature formulation that makes the kernel scalable for optimization. Third, SRF kernel enables to leverage geometric structures by taking subgraphs as inputs. To effectively optimize GPs with SRF kernel, we develop a variational EM algorithm, which alternates between an inference procedure (E-step) and a learning procedure (M-step). Experimental results on five real-world datasets show that our model can achieve state-of-the-art performance in two typical graph learning tasks, i.e., object classification and link prediction.

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Applications of machine learning methods for boiling modeling and prediction: A comprehensive review

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Chemical Thermodynamics and Thermal Analysis 2022年 8卷

作者： Rashidi, M.M. Alhuyi Nazari, M. Harley, C. Momoniat, E. Mahariq, I. Ali, N. Faculty of Science University of Johannesburg PO Box 524 Auckland Park 2006 South Africa Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Sichuan Chengdu 610054 China Faculty of New Sciences and Technologies University of Tehran Tehran Iran Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Johannesburg P.O. Box 524 Auckland Park 2006 South Africa College of Engineering and Technology American University of the Middle East Kuwait

Boiling refers to the heat transfer mechanism that occurs due to the phase transition from liquid to vapor. In comparison with single phase heat transfer, this mechanism has several advantages such as much higher rate at lower temperature differences. Regarding the complexities of two phase heat transfer simulation, due to the involvement of various parameters in this phenomenon, applying intelligent methods such as artificial neural networks could be useful for modeling this type of heat transfer mechanism. In the present article, studies on the modeling of pool boiling heat transfer utilizing machine learning methods have been reviewed, and their findings are reflected. According to the outcomes of the reviewed works, it can be concluded that using intelligent methods can provide accurate predictions of pool boiling heat transfer with a R² of around 0.99 in some cases. In addition, by applying these methods it would be possible to predict the heat transfer in cases of utilizing nanofluids or porous media. The exactness and applicability range of these models is influenced by several elements such as the considered inputs, applied methods and employed functions. Using an appropriate method with optimal parameter values for the relevant intelligent method would lead to higher precision in modeling. © 2022

关键词： Artificial neural network Machine learning Nanofluid Pool boiling

RAKA: Co-training of relationships and attributes for cross-lingual knowledge alignment

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arXiv 2019年

作者： Chen, Bo Zhang, Jing Tang, Xiaobin Chen, Hong Li, Cuiping Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education Renmin University of China Information School Renmin University of China

Cross-lingual knowledge alignment suffers from the attribute heterogeneity when leveraging the attributes and also suffers from the conflicts when combing the results inferred from attributes and relationships. This paper proposes an interaction-based attribute model to capture the attribute-level interactions for estimating entity similarities, eliminating the negative impact of the dissimilar attributes. A matrix-based strategy is adopted in the model to accelerate the similarity estimation. We further propose a co-training framework together with three merge strategies to combine the alignments inferred by the attribute model and the relationship model. The whole framework can effectively and efficiently infer the aligned entities, relationships, attributes, and values simultaneously. Experimental results on several cross-lingual knowledge datasets show that our model significantly outperforms state-of-the-art comparison methods (improving 2.35-51.57% in terms of Hit Ratio@1). Copyright © 2019, The Authors. All rights reserved.

关键词： Alignment

Model development and model selection for simultaneous optimization of corrosion inhibitor dosages for a cooling water system

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Electrochimica Acta 2025年

作者： Chamanthi Denisha Jayaweera Ivaylo Hitsov Maxime Van Haeverbeke Kimberly Solon Cristian Camilo Gomez Cortes Tom Depover Thomas Diekow Arne Verliefde Ingmar Nopens CAPTURE - Centre for advanced process technology for urban resource recovery Frieda Saeysstraat 1 9052 Gent Belgium BIOMATH - Model-based analysis and optimization of bioprocesses Department of data analysis and mathematical modeling Faculty of Bioscience Engineering Ghent University Coupure Links 653 9000 Gent Belgium PAINT – Particle and interfacial technology Department of green chemistry and technology Faculty of Bioscience Engineering Ghent University Coupure Links 653 9000 Gent Belgium KERMIT – Knowledge-based systems Department of data analysis and mathematical modeling Faculty of Bioscience Engineering Ghent University Coupure Links 653 9000 Gent Belgium Dow Olefinverbund GmbH. Werk Böhlen Olefinstraße 1 04564 Böhlen Germany

The present study examines the utility of a hybrid model based on the Butler-Volmer equation for simultaneously optimizing dosages of multiple corrosion inhibitors. The model is trained to predict corrosion rates measured by linear polarization resistance (LPR). It offers a cheaper and quicker alternative to predicting corrosion inhibition efficiency, which is generally measured by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. For the first time, the current study demonstrates how a hybrid model can be used to capture the relationship between corrosion inhibitors and the corrosion rate measured by linear polarization resistance. The hybrid model facilitates separating the benefit of co-occurrence and direct linear correlations from the impact inhibitors have on the parameters of the Butler-Volmer equation. Purely data-driven models such as support vector regression (SVR) rely on co-occurrence and direct correlations to make predictions. Therefore, the Sobol sensitivities of inhibitors in purely data-driven models are diminished in the presence of more correlated variables. This study demonstrates how an SVR model fails to predict the corrosion rate when variables less or similarly correlated as the inhibitors are used. An interrupted analysis illustrates how the response of the corrosion rate to each inhibitor varies according to the Sobol sensitivity of the inhibitors in the model. Optimization of inhibitor dosages was carried out using the NSGA II algorithm, facilitating the simultaneous minimization of chemical usage and the corrosion rate. The hybrid model where the surrogate cathodic component (C bc ) was modeled with an SVR and trained with variables selected by magnitude-based partial mutual information, was deemed most suitable for optimizing inhibitor dosages. Thus, the hybrid model is the best candidate for the optimization of multiple corrosion inhibitors added to a cooling water system, enabling the effective mitigation o

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