Application of Adversarial Attacks on Malware Detection Models

Malware detection is vital as it ensures that a computer is safe from any kind of malicious software that puts users at risk. Too many variants of these malicious software are being introduced everyday at increased speed. Thus, to guarantee security of computer systems, huge advancements in the field of malware detection are made and one such approach is to use machine learning for malware detection. Even though machine learning is very powerful, it is prone to adversarial attacks. In this project, we will try to apply adversarial attacks on malware detection models. To perform these attacks, fake samples that are generated using Generative Adversarial Networks (GAN) algorithm are used and these fake malware data along with the actual data is given to a machine learning model for malware detection. Here, we will also be experimenting with the percentage of fake malware samples to be considered and observe the behavior of the model according to the given input. The novelty of this project is given by the use of adversarial samples that are generated by the implementation of word embeddings produced by our generative algorithms

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Mobilizing the Past for a Digital Future : The Potential of Digital Archaeology

Mobilizing the Past is a collection of 20 articles that explore the use and impact of mobile digital technology in archaeological field practice. The detailed case studies present in this volume range from drones in the Andes to iPads at Pompeii, digital workflows in the American Southwest, and examples of how bespoke, DIY, and commercial software provide solutions and craft novel challenges for field archaeologists. The range of projects and contexts ensures that Mobilizing the Past for a Digital Future is far more than a state-of-the-field manual or technical handbook. Instead, the contributors embrace the growing spirit of critique present in digital archaeology. This critical edge, backed by real projects, systems, and experiences, gives the book lasting value as both a glimpse into present practices as well as the anxieties and enthusiasm associated with the most recent generation of mobile digital tools. This book emerged from a workshop funded by the National Endowment for the Humanities held in 2015 at Wentworth Institute of Technology in Boston. The workshop brought together over 20 leading practitioners of digital archaeology in the U.S. for a weekend of conversation. The papers in this volume reflect the discussions at this workshop with significant additional content. Starting with an expansive introduction and concluding with a series of reflective papers, this volume illustrates how tablets, connectivity, sophisticated software, and powerful computers have transformed field practices and offer potential for a radically transformed discipline.https://dc.uwm.edu/arthist_mobilizingthepast/1000/thumbnail.jp

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality