A management framework for automating network experiments and user behaviour emulation on large scale testbed facilities

Generic test environments such as Emu lab allow to perform large scale tests on different network topologies. While these facilities offer a tool to easily configure the topology, setting up realistic network scenarios afterwards is a manual and time consuming task involving the configuration of dozens of servers, including the installation of software suites and the emulation of subscriber behaviour. Also collecting the evaluation results afterwards can be complex and time consuming. This article discusses a management framework that allows both automating the configuration of networking experiments through a Graphical User Interface and automating the collection of measurements and visualisation of experimental results afterwards

Modeling and predicting the popularity of online news based on temporal and content-related features

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity

Representation learning for very short texts using weighted word embedding aggregation

Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.Comment: 8 pages, 3 figures, 2 tables, appears in Pattern Recognition Letter

Efficiency Evaluation of Character-level RNN Training Schedules

We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectiveness as a function of training time and amount of training data seen. We show that the choice of training and prediction schedule potentially has a considerable impact on the prediction effectiveness for a given training budget.Comment: 3 pages, 3 figure

Monitoring and debugging using an SDK for NFV-powered telecom applications

C

Deploying elastic routing capability in an SDN/NFV-enabled environment

SDN and NFV are two paradigms that introduce unseen flexibility in telecom networks. Where previously telecom services were provided by dedicated hardware and associated (vendor-specific) protocols, SDN enables to control telecom networks through specialized software running on controllers. NFV enables highly optimized packet-processing network functions to run on generic/multi-purpose hardware such as x86 servers. Although the possibilities of SDN and NFV are well-known, concrete control and orchestration architectures are still under design and few prototype validations are available. In this demo we demonstrate the dynamic up-and downscaling of an elastic router supporting NFV-based network management, for example needed in a VPN service. The framework which enables this elasticity is the UNIFY ESCAPE environment, which is a PoC following an ETSI NFV MANO-conform architecture. This demo is one of the first to demonstrate a fully closed control loop for scaling NFs in an SDN/NFV control and orchestration architecture

Impact of Crural Relaxing Incisions, Collis Gastroplasty, and Non–Cross-linked Human Dermal Mesh Crural Reinforcement on Early Hiatal Hernia Recurrence Rates

BackgroundHernia recurrence is the leading form of failure after antireflux surgery and may be secondary to unrecognized tension on the crural repair or from a foreshortened esophagus. Mesh reinforcement has proven beneficial for repair of hernias at other sites, but the use of mesh at the hiatus remains controversial. The aim of this study was to evaluate the outcomes of hiatal hernia repair with human dermal mesh reinforcement of the crural closure in combination with tension reduction techniques when necessary.Study DesignWe retrospectively reviewed the records of all patients who had hiatal hernia repair using AlloMax Surgical Graft (Davol), a human dermal biologic mesh. Objective follow-up was with videoesophagram and/or upper endoscopy at 3 months postoperatively and annually.ResultsThere were 82 patients with a median age of 63 years. The majority of operations (85%) were laparoscopic primary repairs of a paraesophageal hernia with a fundoplication. The crura were closed primarily in all patients and reinforced with an AlloMax Surgical Graft. A crural relaxing incision was used in 12% and a Collis gastroplasty in 28% of patients. There was no mesh-related morbidity and no mortality. Median objective follow-up was 5 months, but 15 patients had follow-up at 1 or more years. A recurrent hernia was found in 3 patients (4%).ConclusionsTension-reducing techniques in combination with human biologic mesh crural reinforcement provide excellent early results with no mesh-related complications. Long-term follow-up will define the role of these techniques and this biologic mesh for hiatal hernia repair