VGGFace2: A dataset for recognising faces across pose and age

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS- Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A, IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin. Datasets and models are publicly available.Comment: This paper has been accepted by IEEE Conference on Automatic Face and Gesture Recognition (F&G), 2018. (Oral

System Support for Bandwidth Management and Content Adaptation in Internet Applications

This paper describes the implementation and evaluation of an operating system module, the Congestion Manager (CM), which provides integrated network flow management and exports a convenient programming interface that allows applications to be notified of, and adapt to, changing network conditions. We describe the API by which applications interface with the CM, and the architectural considerations that factored into the design. To evaluate the architecture and API, we describe our implementations of TCP; a streaming layered audio/video application; and an interactive audio application using the CM, and show that they achieve adaptive behavior without incurring much end-system overhead. All flows including TCP benefit from the sharing of congestion information, and applications are able to incorporate new functionality such as congestion control and adaptive behavior.Comment: 14 pages, appeared in OSDI 200

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks

As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets. Despite being widely used, we find that these datasets suffer from spurious correlations, which are ignored by existing studies and lead to severe overestimation of existing rumor detection performance. The spurious correlations stem from three causes: (1) event-based data collection and labeling schemes assign the same veracity label to multiple highly similar posts from the same underlying event; (2) merging multiple data sources spuriously relates source identities to veracity labels; and (3) labeling bias. In this paper, we closely investigate three of the most popular rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and propose event-separated rumor detection as a solution to eliminate spurious cues. Under the event-separated setting, we observe that the accuracy of existing state-of-the-art models drops significantly by over 40%, becoming only comparable to a simple neural classifier. To better address this task, we propose Publisher Style Aggregation (PSA), a generalizable approach that aggregates publisher posting records to learn writing style and veracity stance. Extensive experiments demonstrate that our method outperforms existing baselines in terms of effectiveness, efficiency and generalizability.Comment: Accepted to ECML-PKDD 202

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Characterisation of self-assembled engineered proteins on gold nanoparticles and their application to biosensing

PhD ThesisThe use of gold nanoparticles (AuNP) has a long and varied history, thought to cover several thousand years. More recently the unique properties of nanoscale materials have stimulated extensive work on nanoparticles and other nanomaterials leading to their use in novel technologies. AuNPs have been of particular interest for bioscience applications due to their biocompatibility and the ease with which biological molecules can be conjugated to their surface. In this study the assembly of engineered proteins, specifically the transmembrane domain of Escherichia coli outer membrane protein A (OmpATM), onto the surface of AuNPs was investigated both in solution and with the particles attached to a SiO2 substrate. AuNPs were adhered to SiO2 surfaces using a novel silane treatment developed by the industrial sponsor and were characterised using spectroscopy, electron and atomic force microscopy. The addition of a single cysteine residue to the OmpATM structure was shown, by UV-Vis and fluorescence spectroscopy, to increase protein binding at equilibrium and form higher stability protein-AuNP complexes in solution. Following this, engineered OmpATM proteins containing tandem antibody-binding domains from Streptococcal protein G were assembled on the AuNP surface and their structure interrogated using neutron and light scattering. This revealed an oriented protein layer where the functional domains extend away from the AuNP surface and are available to bind antibodies. OmpATM-AuNP conjugates were used to develop biosensing assays using both well-established methods, such as lateral flow assays, and novel spectroscopic methods, which use the unique optical properties of AuNPs. Detection of influenza A nucleoprotein, an antigen used to clinically diagnose influenza, was achieved using a bespoke anti-nucleoprotein single-chain antibody domain fused to OmpATM and assembled on 20 nm diameter AuNPs. The results demonstrate that engineered OmpATM proteins conjugated to AuNPs can be used to develop novel diagnostics using a range of read out technologies