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Research into second language (L2) reading is an exponentially growing field. Yet, it still has a relatively short supply of comparable, ecologically valid data from readers representing a variety of first languages (L1). This article addresses this need by presenting a new data resource called MECO L2 (Multilingual Eye Movements Corpus), a rich behavioral eye-tracking record of text reading in English as an L2 among 543 university student speakers of 12 different L1s. MECO L2 includes a test battery of component skills of reading and allows for a comparison of the participants’ reading performance in their L1 and L2. This data resource enables innovative large-scale cross-sample analyses of predictors of L2 reading fluency and comprehension. We first introduce the design and structure of the MECO L2 resource, along with reliability estimates and basic descriptive analyses. Then, we illustrate the utility of MECO L2 by quantifying contributions of four sources to variability in L2 reading proficiency proposed in prior literature: reading fluency and comprehension in L1, proficiency in L2 component skills of reading, extralinguistic factors, and the L1 of the readers. Major findings included (a) a fundamental contrast between the determinants of L2 reading fluency versus comprehension accuracy, and (b) high within-participant consistency in the real-time strategy of reading in L1 and L2. We conclude by reviewing the implications of these findings to theories of L2 acquisition and outline further directions in which the new data resource may support L2 reading research.

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An experimental investigation into interaction between language and information graphics in multimodal documents served as the basis for this study. More specifically, our purpose was to investigate the role of linguistic annotations in graph-text documents. Participants were presented with three newspaper articles in the following conditions: one text-only, one text plus non-annotated graph, and one text plus annotated graph. Results of the experiment showed that, on one hand, annotations play a bridging role for integration of information contributed by different representational modalities. On the other hand, linguistic annotations have negative effects on recall, possibly due to attention divided by the different parts of a document.

Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.

Cyber attacks constitute a significant threat to organizations with implications ranging from economic, reputational, and legal consequences. As cybercriminals' techniques get sophisticated, information security professionals face a more significant challenge to protecting information systems. In today's interconnected realm of computer systems, each attack vector has a network dimension. The present study investigates network intrusion attempts with anomaly-based machine learning models to provide better protection than the conventional misuse-based models. Two models, namely an ensemble learning model and a convolutional neural network model, were built and implemented on a data set gathered from a real-life, institutional production environment. To demonstrate the models' reliability and validity, they were applied to the UNSW-NB15 benchmarking data set. The type of attack was limited to probing attacks to keep the scope of the study manageable. The findings revealed high accuracy rates, the CNN model being slightly more accurate.INDEX TERMS Anomaly-based, misuse-based, intrusion detection systems, probing attacks.