Malware have been used as a means for conducting cyber attacks for decades. Wide adoption of smartphones, which store lots of private and confidential information, made them an important target for malware developers. Android as the dominant mobile operating system has always been an interesting platform for malware developers and lots of Android malware species are infecting vulnerable users every day which make manual malware investigation an impossible mission. Leveraging machine learning techniques for malware forensics would assist cyber forensic investigators in their fight against malicious programs. In this paper, we present two machine learning aided approaches for static analysis of the mobile applications: one based on permissions , while the other based on source code analysis that utilizes a bag of words representation model. Our source code based classification achieved F-score of 95.1%, while the approach that used permission names only performed with F-measure of 89%. Our approach provides a method for automated static code analysis and malware detection with high accuracy and reduces smartphone malware analysis time.
Abstract. Within the scientific literature, tables are commonly used to present factual and statistical information in a compact way, which is easy to digest by readers. The ability to "understand" the structure of tables is key for information extraction in many domains. However, the complexity and variety of presentation layouts and value formats makes it difficult to automatically extract roles and relationships of table cells. In this paper, we present a model that structures tables in a machine readable way and a methodology to automatically disentangle and transform tables into the modelled data structure. The method was tested in the domain of clinical trials: it achieved an F-score of 94.26% for cell function identification and 94.84% for identification of inter-cell relationships.
The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications. Text mining provided in the past methods to retrieve and extract information from text; however, most of these approaches ignored tables and figures. The research done in mining table data still does not have an integrated approach for mining that would consider all complexities and challenges of a table. Our research is examining the methods for extracting numerical (number of patients, age, gender distribution) and textual (adverse reactions) information from tables in the clinical literature. We present a requirement analysis template and an integral methodology for information extraction from tables in clinical domain that contains 7 steps: (1) table detection, (2) functional processing, (3) structural processing, (4) semantic tagging, (5) pragmatic processing, (6) cell selection and (7) syntactic processing and extraction. Our approach performed with the F-measure ranged between 82 and 92%, depending on the variable, task and its complexity.
Current biomedical text mining efforts are mostly focused on extracting information from the body of research articles. However, tables contain important information such as key characteristics of clinical trials. Here, we examine the feasibility of information extraction from tables. We focus on extracting data about clinical trial participants. We propose a rule-based method that decomposes tables into cell level structures and then extracts information from these structures. Our method performed with a F-measure of 83.3% for extraction of number of patients, 83.7% for extraction of patient's body mass index and 57.75% for patient's weight. These results are promising and show that information extraction from tables in biomedical literature is feasible.
In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner. A database of social innovation is an important source of information that can expand collaboration between social innovators, drive policy and serve as an important resource for research. Such a database needs to have projects described and summarized. In this paper, we propose and compare several methods (e.g. SVM-based, recurrent neural network based, ensambled) for describing projects based on the text that is available on project websites. We also address and propose a new metric for automated evaluation of summaries based on topic modelling.
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