Deep Learning-Based User Feedback Classification in Mobile App Reviews

As online users are interacting with many mobile apps under different usage contexts, user needs in an app design process have become a critical issue. Existing studies indicate timely and constructive online reviews from users become extremely crucial for developers to understand user needs and create innovation opportunities. However, discovering and quantifying potential user needs from large amounts of unstructured text is a nontrivial task. In this paper, we propose a domain-oriented deep learning approach that can discover the most critical user needs such as app product new features and bug reports from a large volume of online product reviews. We conduct comprehensive evaluations including quantitative evaluations like F-measure a, and qualitative evaluations such as a case study to ensure the quality of discovered information, specifically, including the number of bug reports and feature requests. Experimental results demonstrate that our proposed supervised model outperforms the baseline models and could find more valuable information such as more important keywords and more coherent topics. Our research has significant managerial implications for app developers, app customers, and app platform providers

GeoLinter: A Linting Framework for Choropleth Maps

Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.Comment: to appear in IEEE Transactions on Visualization and Computer Graphic

Resolution and sensitivity of a Fabry-Perot interferometer with a photon-number-resolving detector

With photon-number resolving detectors, we show compression of interference fringes with increasing photon numbers for a Fabry-Perot interferometer. This feature provides a higher precision in determining the position of the interference maxima compared to a classical detection strategy. We also theoretically show supersensitivity if N-photon states are sent into the interferometer and a photon-number resolving measurement is performed.Comment: 8 pages, 12 figures, 1 table, minor extensions, title changed, new figures added, reference correcte

Nonenzymatic catalytic signal amplification for nucleic acid hybridization assays

Devices, methods, and kits for amplifying the signal from hybridization reactions between nucleic acid probes and their cognate targets are presented. The devices provide partially-duplexed, immobilized probe complexes, spatially separate from and separately addressable from immobilized docking strands. Cognate target acts catalytically to transfer probe from the site of probe complex immobilization to the site of immobilized docking strand, generating a detectable signal. The methods and kits of the present invention may be used to identify the presence of cognate target in a fluid sample

Firm Actions Toward Data Breach Incidents and Firm Equity Value: An Empirical Study

Managing information resources including protecting the privacy of customer data plays a critical role in most firms. Data breach incidents may be extremely costly for firms. In the face of a data breach event, some firms are reluctant to disclose information to the public. Firm may be concerned with the potential drop in the market value following the revelation of a data breach. This paper examines the impact of data breach incidents to the firm’s market value/equity value, and explores the possibility that certain firm behaviors may reduce the cost of the incidents. We use regression analysis to identify the factors that affect cumulative abnormal stock return (CAR). Our results indicate that when data breach happens, firms not only should notify customers or the public timely, but also try to control the amount of information disclosed. These findings should provide corporate executives with guidance on managing public disclosure of data breach incidents