3,548 research outputs found
Actor-network procedures: Modeling multi-factor authentication, device pairing, social interactions
As computation spreads from computers to networks of computers, and migrates
into cyberspace, it ceases to be globally programmable, but it remains
programmable indirectly: network computations cannot be controlled, but they
can be steered by local constraints on network nodes. The tasks of
"programming" global behaviors through local constraints belong to the area of
security. The "program particles" that assure that a system of local
interactions leads towards some desired global goals are called security
protocols. As computation spreads beyond cyberspace, into physical and social
spaces, new security tasks and problems arise. As networks are extended by
physical sensors and controllers, including the humans, and interlaced with
social networks, the engineering concepts and techniques of computer security
blend with the social processes of security. These new connectors for
computational and social software require a new "discipline of programming" of
global behaviors through local constraints. Since the new discipline seems to
be emerging from a combination of established models of security protocols with
older methods of procedural programming, we use the name procedures for these
new connectors, that generalize protocols. In the present paper we propose
actor-networks as a formal model of computation in heterogenous networks of
computers, humans and their devices; and we introduce Procedure Derivation
Logic (PDL) as a framework for reasoning about security in actor-networks. On
the way, we survey the guiding ideas of Protocol Derivation Logic (also PDL)
that evolved through our work in security in last 10 years. Both formalisms are
geared towards graphic reasoning and tool support. We illustrate their workings
by analysing a popular form of two-factor authentication, and a multi-channel
device pairing procedure, devised for this occasion.Comment: 32 pages, 12 figures, 3 tables; journal submission; extended
references, added discussio
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
The scale of sense : spatial extent and multimodal urban design
This paper is derived from the work of the UK AHRC/EPSRC 'Designing for the 21st Century' research project Multimodal Representation of Urban Space. This research group seeks to establish a new form of notation for urban design which pays attention to our entire sensory experience of place. This paper addresses one of the most important aspects of this endeavour: scale. Scale is of course a familiar abstraction to all architects and urban designers, allowing for representations tailored to different levels of detail and allowing drawings to be translated into build structures. Scale is also a factor in human experience: the spatial extent of each of our senses is different. Many forms of architectonic representation are founded upon the extension of the visual modality, and designs are accordingly tuned towards this sense. We can all speak from our own experience, however, that urban environments are a feast for all the senses. The visceral quality of walking down a wide tree-lined boulevard differs greatly from the subterranean crowds of the subway, or the meandering pause invited by the city square. Similarly, our experience of hearing and listening is more than just a passive observation by virtue of our own power of voice and the feedback created by our percussive movements across a surface or through a medium. Taste and smell are also excited by the urban environment, the social importance of food preparation and the associations between smell and public health are issues of sensory experience. The tactile experience of space, felt with the entire body as well as our more sensitive hands, allowing for direct manipulation and interactions as well as sensations of mass, heat, proximity and texture. Our project team shall present a series of tools for designers which explore the variety of sensory modalities and their associated scales. This suite of notations and analytical frameworks turn our attention to the sensory experience of places, and offers a method and pattern book for more holistic multi-sensory and multi-modal urban design
A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes
Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de EconomĂa, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize
salient regions. Existing works often adopt attention modules for feature
modeling, with few methods explicitly leveraging fine-grained details to merge
with semantic cues. Thus, despite the auxiliary depth information, it is still
challenging for existing models to distinguish objects with similar appearances
but at distinct camera distances. In this paper, from a new perspective, we
propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D
saliency detection. Our motivation comes from the observation that the
multi-granularity properties of geometric priors correlate well with the neural
network hierarchies. To realize multi-modal and multi-level fusion, we first
use a granularity-based attention scheme to strengthen the discriminatory power
of RGB and depth features separately. Then we introduce a unified cross
dual-attention module for multi-modal and multi-level fusion in a
coarse-to-fine manner. The encoded multi-modal features are gradually
aggregated into a shared decoder. Further, we exploit a multi-scale loss to
take full advantage of the hierarchical information. Extensive experiments on
challenging benchmark datasets demonstrate that our HiDAnet performs favorably
over the state-of-the-art methods by large margins
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