Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics. Deep neural networks are the key driving force behind its recent success, but still seem to be a magic black box lacking interpretability and understanding. This brings up many open safety and security issues with enormous and urgent demands on rigorous methodologies and engineering practice for quality enhancement. A plethora of studies have shown that the state-of-the-art DL systems suffer from defects and vulnerabilities that can lead to severe loss and tragedies, especially when applied to real-world safety-critical applications. In this paper, we perform a large-scale study and construct a paper repository of 223 relevant works to the quality assurance, security, and interpretation of deep learning. We, from a software quality assurance perspective, pinpoint challenges and future opportunities towards universal secure deep learning engineering. We hope this work and the accompanied paper repository can pave the path for the software engineering community towards addressing the pressing industrial demand of secure intelligent applications.

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With the development of Internet of Things (IoT) and machine learning technologies, mobile geographic information systems (GISs) have developed rapidly. Moreover, mobile GIS applications serve all walks of life including remote sensing, geological disaster management, and decision support systems. This article discusses the main development methods of the Android system for mobile GIS, analyzes the characteristics of different development methods, and mainly introduces the technology of developing mobile GIS based on free and open-source software (FOSS) framework. Finally, we present a data collection framework for an Android application development, based on QGIS, QFiled, GeoServer, PostgreSQL, and GeoPackage. The mobile GIS can collect important data. Furthermore, the data collection framework uses a data aggregation technique to filter and remove redundant data. Machine learning approaches are integrated in the GIS to make it intelligent. The application, in the Xishan mining area of Taiyuan, proves that the proposed framework can complete the collection and storage of geological disaster data, which has certain practical significance. Our experimental results show that the data aggregation method is approximately 42.3–44.09 percent (training times) more efficient than the no aggregation approach. Moreover, the attention network may produce an additional overhead in the prediction process, depending on the model. This overhead is observed between 0.58% and 2.83% for the LSTM model.

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The International Workshop on Engineering Safety and Security Systems (ESSS 2013) aims at contributing to the challenge of constructing reliable and secure systems. The workshop covers areas such as formal specification, (extended) type checking, model checking, program analysis/transformation, model-based testing and model-driven software construction. The workshop brings together researchers and industry R&D expertise to exchange their knowledge, discuss their research findings, and explore potential collaborations. The first edition of ESSS was held in Feb 2012 in Singapore, organised by National University of Singapore. The ESSS 2013 workshop is affiliated with the 6th IEEE Conference on Software Testing, Verification and Validation (ICST 2013) in Luxembourg.The main theme of the workshop is methods and techniques for constructing large reliable and secure systems. The goal of the workshop is to establish a platform for the exchange of ideas, discussion, cross-fertilisation, inspiration, co-operation, and dissemination. The topics of the workshop include, but are not limited to:• methods, techniques and tools for system safety and security • methods, techniques and tools for analysis, certification, and debugging of complex safety and security systems • model-based and verification-based testing • emerging application domains such as cloud computing and cyber-physical systems • case studies and experience reports on the use of formal methods for analysing safety and security systemsWe have received a number of 12 submissions this year. Each submission received at least 3 review reports provided by our international Program Committee (PC) and/or external reviewers. The submissions were also discussed in the virtual meeting of the PC and in the end the PC decided to select five papers (four regular papers and one short paper) among the submissions for presentation in the workshop and publication in the IEEE Digital Library in the form of a post-proceedings. In addition to the accepted presentations, we invited two internationally renowned researchers to give an invited talk. The complete workshop program provided a variety of research topics which are of current interest, including methods, techniques and tools to model-based testing, cryptographic verification of programs, code transformation and generation, compliance verification, software-based remote attestation, formal semantics of programs, and formal description of industrial standards.We would like to express our sincere thanks and appreciation to the exceptional work rendered by the program committee members and the external reviewers for providing review reports. We want to thank all the authors for submitting their papers to the workshop, and the participants for attending it. We also would like to thank the program chairs and the organisers of ICST who provided the infrastructure for this event. Their combined efforts have made ESSS 2013 a success.PROGRAM COMMITTEE