Scalable Accurate Consolidation of Passively Measured Statistical Data

Passive probes continuously collect a significant amount of traffic vol- ume, and autonomously generate statistics on a large number of metrics. A common statistical output of passive probe is represented by probability mass functions (pmf). The need for consolidation of several pmfs arises in two contexts, namely: (i) whenever a central point collects and aggregates measurement of multiple disjoint vantage points, and (ii) whenever a local measurement processed at a single vantage point needs to be distributed over multiple cores of the same physical probe, in order to cope with growing link capacity. Taking an experimental approach, we study both cases assessing the impact of different consolidation strategies, obtaining general design and tuning guidelines

Could neurolecturing address the limitations of live and recorded lectures?

Lectures are a common teaching method in higher education. However, they have many serious limitations, including boredom, attendance, short attention span, low knowledge transmission and the passivity of students. This paper suggests how a combination of electroencephalography (EEG) and eye-tracking technology could address some of these limitations – an approach that I have called neurolecturing. Neurolecturing could measure students’ attention, learning and cognitive load and provide real time feedback to students and lecturers. It could also play a role in the flipped classroom and artificial intelligence tutoring

Could neurolecturing address the limitations of live and recorded lectures?

Lectures are a common teaching method in higher education. However, they have many serious limitations, including boredom, attendance, short attention span, low knowledge transmission and the passivity of students. This paper suggests how a combination of electroencephalography (EEG) and eye-tracking technology could address some of these limitations – an approach that I have called neurolecturing. Neurolecturing could measure students’ attention, learning and cognitive load and provide real time feedback to students and lecturers. It could also play a role in the flipped classroom and artificial intelligence tutoring

Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focused on training decoders on specific, stereotyped tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in natural settings requires adaptive strategies and scalable algorithms that require minimal supervision. Here we propose an unsupervised approach to decoding neural states from human brain recordings acquired in a naturalistic context. We demonstrate our approach on continuous long-term electrocorticographic (ECoG) data recorded over many days from the brain surface of subjects in a hospital room, with simultaneous audio and video recordings. We first discovered clusters in high-dimensional ECoG recordings and then annotated coherent clusters using speech and movement labels extracted automatically from audio and video recordings. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Our results show that our unsupervised approach can discover distinct behaviors from ECoG data, including moving, speaking and resting. We verify the accuracy of our approach by comparing to manual annotations. Projecting the discovered cluster centers back onto the brain, this technique opens the door to automated functional brain mapping in natural settings

Accelerating Reconfigurable Financial Computing

This thesis proposes novel approaches to the design, optimisation, and management of reconfigurable computer accelerators for financial computing. There are three contributions. First, we propose novel reconfigurable designs for derivative pricing using both Monte-Carlo and quadrature methods. Such designs involve exploring techniques such as control variate optimisation for Monte-Carlo, and multi-dimensional analysis for quadrature methods. Significant speedups and energy savings are achieved using our Field-Programmable Gate Array (FPGA) designs over both Central Processing Unit (CPU) and Graphical Processing Unit (GPU) designs. Second, we propose a framework for distributing computing tasks on multi-accelerator heterogeneous clusters. In this framework, different computational devices including FPGAs, GPUs and CPUs work collaboratively on the same financial problem based on a dynamic scheduling policy. The trade-off in speed and in energy consumption of different accelerator allocations is investigated. Third, we propose a mixed precision methodology for optimising Monte-Carlo designs, and a reduced precision methodology for optimising quadrature designs. These methodologies enable us to optimise throughput of reconfigurable designs by using datapaths with minimised precision, while maintaining the same accuracy of the results as in the original designs

Automatic Configuration of Programmable Logic Controller Emulators

Programmable logic controllers (PLCs), which are used to control much of the world\u27s critical infrastructures, are highly vulnerable and exposed to the Internet. Many efforts have been undertaken to develop decoys, or honeypots, of these devices in order to characterize, attribute, or prevent attacks against Industrial Control Systems (ICS) networks. Unfortunately, since ICS devices typically are proprietary and unique, one emulation solution for a particular vendor\u27s model will not likely work on other devices. Many previous efforts have manually developed ICS honeypots, but it is a very time intensive process. Thus, a scalable solution is needed in order to automatically configure PLC emulators. The ScriptGenE Framework presented in this thesis leverages several techniques used in reverse engineering protocols in order to automatically configure PLC emulators using network traces. The accuracy, flexibility, and efficiency of the ScriptGenE Framework is tested in three fully automated experiments

BAMBI: BLUETOOTH ACCESS MANAGEMENT & BEACON IDENTIFICATION

Cybersecurity is a constantly developing field. Patches that secured yesterday’s technology do not safeguard against occurring threats, necessitating continuous research in the field. The outbreak of Bluetooth Low Energy (BLE) devices dramatically expands the attack surface. BLE is one of the most widely applicable low-power connectivity standards. The low cost, low power consumption, and ready availability of BLE modules have made them a popular wireless technology for Internet of Things (IoT) devices and power constrained applications. However, the deployment of BLE-enabled devices enlarges the network attack surface. In spite of that, access management is insufficient for Bluetooth Low Energy devices. To elucidate, understanding the difference between known and unknown, malicious and non-malicious devices within a perimeter can be crucial in today’s cyberspace. This research proposes an approach called BAMBI - Beacon Access Management and Beacon Identification, which sought to develop an efficient, accurate, and easy-to implement solution for device/beacon identification and access management. The proposed solution, BAMBI, addresses these areas for the Bluetooth Low Energy Protocol. There are a few components to BAMBI that make up this solution. Device Identification, Device Classification, and Access Management are components that make BAMBI the first of its kind for the BLE protocol. Although this research is limited to the BLE protocol, it does introduce avenues for other connectivity standards such as Zig-bee and Bluetooth to adapt without much overhead

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number