Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences. Several recent approaches have attempted to synthesize visible faces from thermal images for cross-modal matching. In this paper, we take a different approach in which rather than focusing only on synthesizing visible faces from thermal faces, we also propose to synthesize thermal faces from visible faces. Our intuition is based on the fact that thermal images also contain some discriminative information about the person for verification. Deep features from a pre-trained Convolutional Neural Network (CNN) are extracted from the original as well as the synthesized images. These features are then fused to generate a template which is then used for verification. The proposed synthesis network is based on the self-attention generative adversarial network (SAGAN) which essentially allows efficient attention-guided image synthesis. Extensive experiments on the ARL polarimetric thermal face dataset demonstrate that the proposed method achieves state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On Biometrics (ICB 2019

Walking the Walk/Talking the Talk: Mission Planning with Speech-Interactive Agents

The application of simulation technology to mission planning and rehearsal has enabled realistic overhead 2-D and immersive 3-D "fly-through" capabilities that can help better prepare tactical teams for conducting missions in unfamiliar locales. For aircrews, detailed terrain data can offer a preview of the relevant landmarks and hazards, and threat models can provide a comprehensive glimpse of potential hot zones and safety corridors. A further extension of the utility of such planning and rehearsal techniques would allow users to perform the radio communications planned for a mission; that is, the air-ground coordination that is critical to the success of missions such as close air support (CAS). Such practice opportunities, while valuable, are limited by the inescapable scarcity of complete mission teams to gather in space and time during planning and rehearsal cycles. Moreoever, using simulated comms with synthetic entities, despite the substantial training and cost benefits, remains an elusive objective. In this paper we report on a solution to this gap that incorporates "synthetic teammates" - intelligent software agents that can role-play entities in a mission scenario and that can communicate in spoken language with users. We employ a fielded mission planning and rehearsal tool so that our focus remains on the experimental objectives of the research rather than on developing a testbed from scratch. Use of this planning tool also helps to validate the approach in an operational system. The result is a demonstration of a mission rehearsal tool that allows aircrew users to not only fly the mission but also practice the verbal communications with air control agencies and tactical controllers on the ground. This work will be presented in a CAS mission planning example but has broad applicability across weapons systems, missions and tactical force compositions

John of Salisbury’s ‘Duel’ with the English Military Class: A Twelfth-Century Cleric’s Lifelong Obsession with Critiquing Martial Ethos and Identity

This thesis uncovers pervasive critiques of milites (soldiers) in the writings of the twelfth-century English cleric, John of Salisbury (c.1110s-1180s). Previous scholarship has proposed John’s descriptions of the social function of milites were detached from his historical context. This study breaks from that theory by examining the development of these ideas across all John's works, revealing an ideology of social reform which responded to the role of milites in the contemporary disputes of the English Church and Crown. This thesis thus broadens understandings of medieval socio-political theories and presents a new legacy for a major figure of the Twelfth-Century Renaissance

Characterisation of Rab-effector complexes at the Golgi apparatus

The rab family of small, ras-like GTPases regulate membrane trafficking events in the secretory and endocytic pathways. They appear to be involved in all stages of vesicular transport, from vesicle budding and cargo selection to motility, docking and membrane fusion. Through a cycle of GTP binding and hydrolysis, rab-effector proteins are recruited to membrane sub-domains in a temporally and spatially specific manner. Several rab proteins localise to the Golgi apparatus, the organelle consisting of stacked, flattened, membrane-bound cisternae through which newly-synthesised proteins are transited and modified, and where proteins and lipids are sorted and packaged for transport to other subcellular destinations. The structure of the Golgi is maintained by a matrix of proteins, many of which have now been shown to be rab-effector proteins. This thesis focuses on Golgi-localised rab proteins and their effector proteins. The cis-Golgi-localised rab protein, rabl, is shown to interact with the Golgi matrix/golgins GM130 and p115 while rab2 binds GM130 as well as a novel tethering factor named golgin-45. siRNA-mediated depletion of these rabs and golgins revealed them to be important for the maintenance of Golgi structure and suggested that p115 is primarily recruited to Golgi membranes by its interaction with rab1 rather than its association with GM130. A search for additional rab1 effectors revealed potential interactions between rab1 and the phosphoinositide- binding/metabolising proteins centaurin?2 and MTMR6. A search for novel effectors of the trans-Golgi-localised rab protein, rab6, was also made, revealing specific interactions with the dynactin subunit p150glued and the dynactin/dynein accessory proteins BicD1 and BicD2. These interactions are proposed to mediate the recruitment of dynactin/dynein to membranous cargo and control minus-end directed, microtubule-dependent vesicle motility. An additional rab6 effector, GOPC, was also identified, which may be responsible for cargo recognition and sorting. Finally, the cis-Golgi rab-effector and matrix protein, GM130, is shown to have a hitherto unsuspected role in the activation of a family of Golgi-localised Ste20 kinases. The implications of all these interactions for Golgi structure and function is discussed

Are Intrusion Detection Studies Evaluated Consistently? A Systematic Literature Review

Cyberinfrastructure is increasingly becoming target of a wide spectrum of attacks from Denial of Service to large-scale defacement of the digital presence of an organization. Intrusion Detection System (IDSs) provide administrators a defensive edge over intruders lodging such malicious attacks. However, with the sheer number of different IDSs available, one has to objectively assess the capabilities of different IDSs to select an IDS that meets specific organizational requirements. A prerequisite to enable such an objective assessment is the implicit comparability of IDS literature. In this study, we review IDS literature to understand the implicit comparability of IDS literature from the perspective of metrics used in the empirical evaluation of the IDS. We identified 22 metrics commonly used in the empirical evaluation of IDS and constructed search terms to retrieve papers that mention the metric. We manually reviewed a sample of 495 papers and found 159 of them to be relevant. We then estimated the number of relevant papers in the entire set of papers retrieved from IEEE. We found that, in the evaluation of IDSs, multiple different metrics are used and the trade-off between metrics is rarely considered. In a retrospective analysis of the IDS literature, we found the the evaluation criteria has been improving over time, albeit marginally. The inconsistencies in the use of evaluation metrics may not enable direct comparison of one IDS to another

Cross-Domain Identification for Thermal-to-Visible Face Recognition

Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e.g., L2 norm) which perform best when images from two different domains (e.g., visible and thermal) are co-registered and temporally synchronized. This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models, which are based on modified network architectures (e.g., VGG16 or Resnet50). This framework is optimized by introducing new cross-domain identity and domain invariance loss functions for thermal-to-visible face recognition, which alleviates the requirement for precisely co-registered and synchronized imagery. We provide extensive analysis of both features and loss functions used, and compare the proposed domain adaptation framework with state-of-the-art feature based domain adaptation models on a difficult dataset containing facial imagery collected at varying ranges, poses, and expressions. Moreover, we analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition