A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

Online Payment Module

The aim of this project is to deploy the online payment service in Moodle. All the major debit, credit and international card (transactions) can be accepted for payment. Online payment module prepares a web server that takes all types of transactions. This module can be enabled by the site administrator. If it is enabled, students can pay for their classes through online transactions. Administrator can set an individual price for a course if needed. It allows the user to create their own account and add optional account links. This project is important to resolve the issues for students and administrators to have an easy glance at the course registration like selection of their courses, Fee details. This project makes it easy for students to look for the courses and register, one can check the site as a guest and can create his/her own account and can enroll for subjects. One can see the fee details for each course

Security Issues in mGovernment

E-government is one of the most rapidly evolving service domains in the contemporary information society. Many governments have already developed and provided e-government services to businesses and citizens. Nowadays actors in the government domain attempt to take the next step and exploit the latest wireless technologies in order to provide ubiquitous services for mobile users. However, this approach involves some hidden risks mainly due to the inherent insecurity of the air medium and the vulnerabilities of the wireless systems. Thus, in this paper we investigate the security gaps and considerations which should be taken into account for an m-government system. Finally, we provide a list of security guidelines and policies, which the users of the system should be aware of and follow in order to avoid security attacks

Biologically Inspired Mechanisms for Adversarial Robustness

A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated. The primate visual ventral stream seems to be robust to small perturbations in visual stimuli but the underlying mechanisms that give rise to this robust perception are not understood. In this work, we investigate the role of two biologically plausible mechanisms in adversarial robustness. We demonstrate that the non-uniform sampling performed by the primate retina and the presence of multiple receptive fields with a range of receptive field sizes at each eccentricity improve the robustness of neural networks to small adversarial perturbations. We verify that these two mechanisms do not suffer from gradient obfuscation and study their contribution to adversarial robustness through ablation studies.Comment: 25 pages, 15 figure

Ad-hoc teamwork with behavior-switching agents

As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being able to coordinate in advance. This kind of cooperation, in which agents have to learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be guaranteed in many real-world settings. In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task. This adds complexity to the planning problem as now an agent needs to recognize that a change has occurred in addition to figuring out what is the new type of the teammate it is interacting with. In this paper, we present a novel Convolutional-Neural-Network-based Change Point Detection (CPD) algorithm for ad hoc teamwork. When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms.Electrical and Computer Engineerin