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In this paper, we propose DEEPRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DEEPRL4FL does so via novel code coverage representation learning (RL) and data dependencies RL for program statements. Those two types of RL on the dynamic information in a code coverage matrix are also combined with the code representation learning on the static information of the usual suspicious source code. This combination is inspired by crime scene investigation in which investigators analyze the crime scene (failed test cases and statements) and related persons (statements with dependencies), and at the same time, examine the usual suspects who have committed a similar crime in the past (similar buggy code in the training data). For the code coverage information, DEEPRL4FL first orders the test cases and marks error-exhibiting code statements, expecting that a model can recognize the patterns discriminating between faulty and non-faulty statements/methods. For dependencies among statements, the suspiciousness of a statement is seen taking into account the data dependencies to other statements in execution and data flows, in addition to the statement by itself. Finally, the vector representations for code coverage matrix, data dependencies among statements, and source code are combined and used as the input of a classifier built from a Convolution Neural Network to detect buggy statements/methods. Our empirical evaluation shows that DEEPRL4FL improves the top-1 results over the state-of-the-art statement-level FL baselines from 173.1% to 491.7%. It also improves the top-1 results over the existing method-level FL baselines from 15.0% to 206.3%. [ABSTRACT FROM AUTHOR]

Digitally enabled two-sided platforms rely on mediating different actors to evoke transactions. Here, the core value-generating mechanisms of these platforms relate to the recommendations that persuade users to make future transactions, thereby driving sales, customer satisfaction, efficiency, and trust. To generate effective recommendations, accurate user profiling is fundamental. Ubiquitous computing provides valuable data to enhance user profiling by uncovering behavioral patterns of individual users. Specifically, through machine learning methods, recommendation systems are able to understand users better by considering both past individual behaviors and their respective contexts. However, state-of-the-art recommendation systems rely either on collaborative or content-based approaches, thereby neglecting a user's time-varying contexts and the dynamics that influence these contexts. We address this shortcoming by developing a context-aware and user-specific hybrid recommendation system using transfer-learning techniques based on (recurrent) neural networks. Evaluating our approach on Expedia's hotel booking data, we demonstrate its enhanced performance compared to common recommendation approaches. [ABSTRACT FROM AUTHOR]

The scientific references in patent (SRPs), one type of non‐patent references (NPRs), is the best representative of scientific knowledge cited in patents (SKCP). However, SKCP is often indicated by NPRs rather than SRPs currently, which may reduce the accuracy of the analysis of science & technology transfer and transformation. In the other hand, the content of SRPs is important for subsequent in‐depth patent analysis, which requires more accurate SRPs' metadata extraction. In order to enhance the accuracy of SRPs identification and SRPs' metadata extraction, this paper uses the representation learning method to learn vectors of the NPRs and the parts separated by delimiters with each SRP (metadata), respectively. These vectors conclude the context information and the same type of metadata may appear in the same position in vector space. Then the problem of SRPs identification and SRPs' metadata extraction are transferred as the automatic categorization with the machine learning method. The experiment is conducted in the field of gene where patents cite lots of SRPs. The result shows that in 1000 patent citations, the accuracy of SRP identification reaches 99.27% and the accuracy of SRP title extraction reaches 90.57% which is 4.51% higher than the pure machine learning method. [ABSTRACT FROM AUTHOR]

Existing human activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose SmartSense, a device-free human activity recognition system based on a novel machine learning algorithm with existing commercial off-the-shelf (COTS) WiFi routers. By exploiting the prevalence of existing WiFi infrastructure in buildings, we developed a novel OpenWrt based firmware for COTS WiFi routers to collect the CSI measurements from regular data frames. To identify different human activities, an automatic kernel representation learning method, namely auto-HSRL, is established to selection informative Hilbert space patterns from time, frequency, wavelet, and shape domains. A new information fusion tool based on multi-view kernel learning is proposed to combine the representations extracted from diverse perspectives and build up a robust and comprehensive activity classifier. Extensive experiments were conducted in an office and the experimental results demonstrate that SmartSense outperforms existing methods and achieves a 98% activity recognition accuracy. [ABSTRACT FROM AUTHOR]

We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations from graph data. These new representations are compared against manually crafted features for defect prediction in real world software projects. [ABSTRACT FROM AUTHOR]