The Graphcore Intelligence Processing Unit (IPU) is a newly developed processor type whose architecture does not rely on the traditional caching hierarchies. Developed to meet the need for more and more data-centric applications, such as machine learning, IPUs combine a dedicated portion of SRAM with each of its numerous cores, resulting in high memory bandwidth at the price of capacity. The proximity of processor cores and memory makes the IPU a promising field of experimentation for graph algorithms since it is the unpredictable, irregular memory accesses that lead to performance losses in traditional processors with pre-caching.

This paper aims to test the IPU’s suitability for algorithms with hard-to-predict memory accesses by implementing a breadth-first search (BFS) that complies with the Graph500 specifications. Precisely because of its apparent simplicity, BFS is an established benchmark that is not only subroutine for a variety of more complex graph algorithms, but also allows comparability across a wide range of architectures.

We benchmark our IPU code on a wide range of instances and compare its performance to state-of-the-art CPU and GPU codes. The results indicate that the IPU delivers speedups of up to 4×4× over the fastest competing result on an NVIDIA V100 GPU, with typical speedups of about 1.5×1.5× on most test instances.

Online social networks such as Facebook and Twitter are part of the everyday life of millions of people. They are not only used for interaction but play an essential role when it comes to information acquisition and knowledge gain. The abundance and detail of the accumulated data in these online social networks open up new possibilities for social researchers and psychologists, allowing them to study behavior in a large test population. However, complex application programming interfaces (API) and data scraping restrictions are, in many cases, a limiting factor when accessing this data. Furthermore, research projects are typically granted restricted access based on quotas. Thus, research tools such as scrapers that access social network data through an API must manage these quotas. While this is generally feasible, it becomes a problem when more than one tool, or multiple instances of the same tool, is being used in the same research group. Since different tools typically cannot balance access to a shared quota on their own, additional software is needed to prevent the individual tools from overusing the shared quota. In this paper, we present a proxy server that manages several researchers' data contingents in a cooperative research environment and thus enables a transparent view of a subset of Twitter's API. Our proxy scales linearly with the number of clients in use and incurs almost no performance penalties or implementation overhead to further layer or applications that need to work with the Twitter API. Thus, it allows seamless integration of multiple API accessing programs within the same research group.

In the recent years, online social networks have become an important source of news and the primary place for political debates for a growing part of the population. At the same time, the spread of fake news and digital wildfires (fast- spreading and harmful misinformation) has become a growing concern worldwide, and online social networks the problem is most prevalent. Thus, the study of social networks is an essential component in the understanding of the fake news phenomenon. Of particular interest is the network connectivity between participants, since it makes communication patterns visible. These patterns are hidden in the offline world, but they have a profound impact on the spread of ideas, opinions and news. Among the major social networks, Twitter is of special interest. Because of its public nature, Twitter offers the possibility to perform research without the risk of breaching the expectation of privacy. However, obtaining sufficient amounts of data from Twitter is a fundamental challenge for many researchers. Thus, in this paper, we present a scalable framework for gathering the graph structure of follower networks, posts and profiles. We also show how to use the collected data for high-performance social network analysis.
 

360 panorama video displayed through Virtual reality (VR) glasses or large screens o®ers immersive user experiences, but as such technology becomes commonplace, the need for e±cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di®erent trade-o®s in order to save bandwidth while preserving the video quality in the user's ̄eld-of-view (FoV). Using 360 VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e±cient end-to-end design and real-world implementation of such a 360 streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-o® between bandwidth usage and the video quality in the user's FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o®s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.

This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events. In the first challenge, we mainly rely on object and scene level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are combined using early, late and double fusion techniques achieving an average F1-score of 65.03%, 60.59% and 63.58%, respectively. For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30% and 61.02% for run 1 and run 2, respectively.

With 360◦ panorama video technology becoming commonplace, the need for efficient streaming methods for such videos arises. We go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user’s field-of-view. We have created an architecture that combines RTP and DASH to deliver 360◦ VR content to a Huawei set-top-box and a Samsung Galaxy S7. Our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the field-of- view changes. 

The interest in neural networks has increased sig- nificantly, and the application of this type of machine learning is vast, ranging from natural image classification to medical image segmentation. However, many users of neural networks tend to use them as a black box tool. They do not access all of the possible variations, nor take into account the respective classification accuracies and costs. In our work, we focus on multiclass image classification, and in this research, we shed light on the trade-offs between systems using a single multiclass classification and multiple binary classifiers, respectively. We have tested the these classifiers on several modern neural network architectures, including DenseNet, Inception v3, Inception ResNet v2, Xception, NASNet and MobileNet. We have compared several aspects of the performance of these architectures during training and testing using both classification styles. We have compared classification speed and several classification accuracy metrics. Here, we present the results from experiments on a total of 99 networks: 11 multiclass and 88 individual binary networks, for an 8-class classification of medical images. In short, using multiple binary classification networks resulted in a 7% increase in the average F1 score, a 1% increase in average accuracy, a 1% increase in precision, and a 4% increase in average recall. However, on average, such a multi-network style performed the classification 7.6 times slower compared to a single network multiclass implementation. These collective findings show that both approaches can be applied to modern neural network structures. Several binary networks will often give increased classification accuracy, but at the cost of classification speed and resource consumption. 

Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance. 

In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.

Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.