Towards In-Transit Analytics for Industry 4.0

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things (iThings-2017), Exeter, UK, 201

Semantic multimedia remote display for mobile thin clients

Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE

Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

e-VLBI observations of Circinus X-1: monitoring of the quiescent and flaring radio emission on AU scales

A recent detection of the peculiar neutron star X-ray binary Circinus X-1 with electronic very long baseline interferometry (e-VLBI) prompted the suggestion that compact, non-variable radio emission persists through the entire 16.6-day orbit of the binary system. We present the results of a high angular resolution monitoring campaign conducted with the Australian Long Baseline Array in real-time e-VLBI mode. e-VLBI observations of Circinus X-1 were made on alternate days over a period of 20 days covering the full binary orbit. A compact radio source associated with Circinus X-1 was clearly detected at orbital phases following periastron passage but no compact radio emission was detected at any other orbital phase, ruling out the presence of a persistent, compact emitting region at our sensitivity levels. The jet was not resolved at any epoch of our 1.4-GHz monitoring campaign, suggesting that the ultrarelativistic flow previously inferred to exist in this source is likely to be dark. We discuss these findings within the context of previous radio monitoring of Circinus X-1.Comment: Accepted for publication in MNRAS. 7 pages, 5 figure

Collaborative Computation in Self-Organizing Particle Systems

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms

The Físchlár digital video recording, analysis, and browsing system

In digital video indexing research area an important technique is called shot boundary detection which automatically segments long video material into camera shots using content-based analysis of video. We have been working on developing various shot boundary detection and representative frame selection techniques to automatically index encoded video stream and provide the end users with video browsing/navigation feature. In this paper we describe a demonstrator digital video system that allows the user to record a TV broadcast programme to MPEG-1 file format and to easily browse and playback the file content online. The system incorporates the shot boundary detection and representative frame selection techniques we have developed and has become a full-featured digital video system that not only demonstrates any further techniques we will develop, but also obtains users’ video browsing behaviour. At the moment the system has a real-user base of about a hundred people and we are closely monitoring how they use the video browsing/navigation feature which the system provides

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness