Smart robotic wheelchairs, as well as other assistive robotic devices, can provide an effective form of independent mobility for those who suffer with motor disabilities. Although many control interfaces exist to operate these devices, brain computer interfaces (BCI) offer a control modality for those who have little to no motor function, as well as being able to re-associate movement with brain functionality. Although BCIs have been designed for robotic wheelchairs, more research and development is required before they can be adopted for use in the ‘real world’. One key challenge on that journey is the user training required to achieve an acceptable accuracy of the control. In this paper, we aim to identify the best training method by comparing users trained on a simple task, in a simulated environment on a 2D display (VR-2DD) and in a virtual environment using a virtual reality headset (VR-HMD). We trained 15 participants in mix of high and low noise virtual environments or on a simple training task, and found a significant improvement in the classification accuracies of the participants who trained using the VR-2DD task compared with those who were trained with the simple task. We also carried out active (online) tests across all participants in the same virtual training environment, with a varying level of external stimuli, and found a significant improvement in the performance of participants in both VR groups compared to participants in the simple task group.

Hardware enclaves rely on a disjoint memory model, which maps each physical address to an enclave to achieve strong memory isolation. However, this severely limits the performance and programmability of enclave programs. While some prior work proposes enclave memory sharing, it does not provide a formal model or verification of their designs. This paper presents Cerberus, a formal approach to secure and efficient enclave memory sharing. To reduce the burden of formal verification, we compare different sharing models and choose a simple yet powerful sharing model. Based on the sharing model, Cerberus extends an enclave platform such that enclave memory can be made immutable and shareable across multiple enclaves via additional operations. We use incremental verification starting with an existing formal model called the Trusted Abstract Platform (TAP). Using our extended TAP model, we formally verify that Cerberus does not break or weaken the security guarantees of the enclaves despite allowing memory sharing. More specifically, we prove the Secure Remote Execution (SRE) property on our formal model. Finally, the paper shows the feasibility of Cerberus by implementing it in an existing enclave platform, RISC-V Keystone.

Universities struggle to provide both the quantity and diversity of compute resources that their researchers need when their researchers need them. Purchasing resources to meet peak demand for all resource types is cost prohibitive for all but a few institutions. Renting capacity on commercial clouds is seen as an alternative to owning. Commercial clouds though expect to be paid. The Campus Compute Cooperative (CCC) provides an alternative to purchasing capacity from commercial providers that provides increased value to member institutions at reduced cost. Member institutions trade their resources with one another to meet both local peak demand as well as provide access to resource types not available on the local campus that are available elsewhere. Participating institutions have dual roles. First as consumers of resources when their researchers use CCC machines, and second as producers of resources when CCC users from other institutions use their resources. In order to avoid the tragedy of the commons in which everyone only wants to use resources, the resource providers will receive credit when their resources are used by others. The consumer is charged based on the quality of service (high, medium, low) and the particulars of the resource provided (speed, interconnection network, memory, etc.). Account balances are cleared monthly. This paper describes solutions to both the technical and sociopolitical challenges of federating university resources and early results with the CCC. Technical issues include the security model, accounting, job specification/management and user interfaces. Socio-political issues include institutional risk management, how to manage market forces and incentives to avoid sub-optimal outcomes, and budget predictability.

In the last years, aside from fine-grained reconfigurable architectures such as FPGAs, coarse-grained reconfigurable architectures (CGRAs), which typically have building blocks of a fixed bit-width (8 bit, 16 bit, etc.), have gained in importance in academia as well as in industry. CGRAs are usually used for domain-specific computations and have advantages over traditional FPGAs in terms of area and power cost, performance, and reconfiguration time. Thus, architectures with coarse-grained reconfiguration features have also been studied in projects (Sec. 1, 2, 4) within the priority program Reconfigurable Computing Systems and the project CoMap (Sec. 3), which are all sponsored by the German science foundation.

Thanks to recent progress in category-level object recognition, we have now come to a point where these techniques have gained sufficient maturity and accuracy to succesfully feed back their output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer meta-data such as depth cues, 3D points, or object decomposition in images of previously unseen object instances. Our approach builds on the implicit shape model of Leibe and Schiele, and extends it to transfer annotations from training images to test images. Experimental results validate the viability of our approach.

We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors

Currently the HoneyComb architecture is already implemented in VHDL and ready for tests. At the moment, testing, synthesis and layouting work of the architecture on TSMC 90 nm standard cell technology is ongoing. For the presentation I am planning to introduce the HoneyComb architecture in detail and giving an outlook on planned software tools as well as first application results. Furthermore the definition of the HoneyComb-language will be introduced which is defined for abstract programming purposes to ease the usability of the architecture.