Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks - Gburrek, Tobias; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (Proc. Asilomar Conference on Signals, Systems, and Computers)
We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.
Keywords: Diarization; time difference of arrival; ad-hoc acoustic sensor network; meeting transcription

mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks - Schneider, Stefan Balthasar; Werner, Stefan; Khalili, Ramin; Hecker, Artur; Karl, Holger (IEEE/IFIP Network Operations and Management Symposium (NOMS))
Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.
Keywords: wireless mobile networks; network management; continuous control; cognitive networks; autonomous coordination; reinforcement learning; gym environment; simulation; open source

AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development - Maalouly, Jad; Hemker, Dennis; Hedayat, Christian; Rückert, Christian; Kaufmann, Ivan; Olbrich, Marcel; Lange, Sven; Mathis, Harald (2022 Kleinheubach Conference)
In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.
Keywords: emc; pcb; electronic system development; machine learning; neural network

Use What You Know: Network and Service Coordination Beyond Certainty - Werner, Stefan; Schneider, Stefan Balthasar; Karl, Holger (IEEE/IFIP Network Operations and Management Symposium (NOMS))
Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.
Keywords: network management; service management; AI; Monte Carlo Tree Search; model-based; QoS

Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal - Kucklick, Jan-Peter; Müller, Oliver (ACM Transactions on Management Information Systems)
Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.
Keywords: Interpretability; Convolutional Neural Network; Accuracy-Interpretability Trade-Of; Real Estate Appraisal; Hedonic Pricing; Grad-Ram

Formalizing cognitive biases in medical diagnostic reasoning - Battefeld, Dominik; Kopp, Stefan (Presented at the 8th Workshop on Formal and Cognitive Reasoning (FCR), Trier.)
This paper presents preliminary work on the formalization of three prominent cognitive biases in the diagnostic reasoning process over epileptic seizures, psychogenic seizures and syncopes. Diagnostic reasoning is understood as iterative exploration of medical evidence. This exploration is represented as a partially observable Markov decision process where the state (i.e., the correct diagnosis) is uncertain. Observation likelihoods and belief updates are computed using a Bayesian network which defines the interrelation between medical risk factors, diagnoses and potential findings. The decision problem is solved via partially observable upper confidence bounds for trees in Monte-Carlo planning. We compute a biased diagnostic exploration policy by altering the generated state transition, observation and reward during look ahead simulations. The resulting diagnostic policies reproduce reasoning errors which have only been described informally in the medical literature. We plan to use this formal representation in the future to inversely detect and classify biased reasoning in actual diagnostic trajectories obtained from physicians.
Keywords: Diagnostic reasoning; Cognitive bias; Cognitive model; POMDP; Bayesian network; Epilepsy; CDSS

Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities - Afifi, Haitham; Guenther, Michael; Brendel, Andreas; Karl, Holger; Kellermann, Walter (14. ITG Conference on Speech Communication (ITG 2021))
Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons
Keywords: microphone utility; microphone selection; wireless acoustic sensor network; network delay; reinforcement learning

Distributed Online Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Qarawlus, Haydar; Karl, Holger (IEEE International Conference on Distributed Computing Systems (ICDCS))
Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).
Keywords: network management; service management; coordination; reinforcement learning; distributed

Divide and Conquer: Hierarchical Network and Service Coordination - Schneider, Stefan Balthasar; Jürgens, Mirko; Karl, Holger (IFIP/IEEE International Symposium on Integrated Network Management (IM))
In practical, large-scale networks, services are requested by users across the globe, e.g., for video streaming. Services consist of multiple interconnected components such as microservices in a service mesh. Coordinating these services requires scaling them according to continuously changing user demand, deploying instances at the edge close to their users, and routing traffic efficiently between users and connected instances. Network and service coordination is commonly addressed through centralized approaches, where a single coordinator knows everything and coordinates the entire network globally. While such centralized approaches can reach global optima, they do not scale to large, realistic networks. In contrast, distributed approaches scale well, but sacrifice solution quality due to their limited scope of knowledge and coordination decisions. To this end, we propose a hierarchical coordination approach that combines the good solution quality of centralized approaches with the scalability of distributed approaches. In doing so, we divide the network into multiple hierarchical domains and optimize coordination in a top-down manner. We compare our hierarchical with a centralized approach in an extensive evaluation on a real-world network topology. Our results indicate that hierarchical coordination can find close-to-optimal solutions in a fraction of the runtime of centralized approaches.
Keywords: network management; service management; coordination; hierarchical; scalability; nfv

Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Khalili, Ramin; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Hecker, Artur (Transactions on Network and Service Management)
Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.
Keywords: network management; service management; coordination; reinforcement learning; self-learning; self-adaptation; multi-objective

Every Node for Itself: Fully Distributed Service Coordination - Schneider, Stefan Balthasar; Klenner, Lars Dietrich; Karl, Holger (IEEE International Conference on Network and Service Management (CNSM))
Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).
Keywords: distributed management; service coordination; network coordination; nfv; softwarization; orchestration

Self-Driving Network and Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Khalili, Ramin; Hecker, Artur (IEEE International Conference on Network and Service Management (CNSM))
Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.
Keywords: self-driving networks; self-learning; network coordination; service coordination; reinforcement learning; deep learning; nfv

5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services - Camps-Mur, Daniel; Canellas, Ferran; Machwe, Azahar; Paracuellos, Jorge; Choumas, Kostas; Giatsios, Dimitris; Korakis, Thanasis; Razzaghi Kouchaksaraei, Hadi (the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020))
Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper, we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.
Keywords: Orchestration; multi-domain; cellular network virtualization; SDN; 5G

Network formation with NIMBY constraints - Block, Lukas
We study the structure of power networks in consideration of local protests against certain power lines (’not-in-my-backyard’). An application of a network formation game is used to determine whether or not such protests arise. We examine the existence of stable networks and their characteristics, when no player wants to make an alteration. Stability within this game is only reached if each player is sufficiently connected to a power source but is not linked to more players than necessary. In addition we introduce an algorithm that creates a stable network.
Keywords: Network formation; NIMBY; Power networks; Nash stability

Demystifying TasNet: A Dissecting Approach - Heitkaemper, Jens; Jakobeit, Darius; Boeddeker, Christoph; Drude, Lukas; Haeb-Umbach, Reinhold (ICASSP 2020 Virtual Barcelona Spain)
In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invariant training (u-PIT) based separation system in the frequency domain until the TasNet system is reached, thus blending components of frequency domain approaches with those of time domain approaches. Some of the intermediate variants achieve comparable signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of frequency domain processing: compatibility with classic signal processing tools such as frequency-domain beamforming and the human interpretability of the masks. Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR) criterion used as loss function in TasNet is related to a logarithmic mean square error criterion and that it is this criterion which contributes most reliable to the performance advantage of TasNet. Finally, we critically assess which gains in a noise-free single channel environment generalize to more realistic reverberant conditions.
Keywords: voice activity detection; speech activity detection; neural network; statistical speech processing

Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments - Heitkaemper, Jens; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (INTERSPEECH 2020 Virtual Shanghai China)
Speech activity detection (SAD), which often rests on the fact that the noise is "more'' stationary than speech, is particularly challenging in non-stationary environments, because the time variance of the acoustic scene makes it difficult to discriminate speech from noise. We propose two approaches to SAD, where one is based on statistical signal processing, while the other utilizes neural networks. The former employs sophisticated signal processing to track the noise and speech energies and is meant to support the case for a resource efficient, unsupervised signal processing approach. The latter introduces a recurrent network layer that operates on short segments of the input speech to do temporal smoothing in the presence of non-stationary noise. The systems are tested on the Fearless Steps challenge database, which consists of the transmission data from the Apollo-11 space mission. The statistical SAD achieves comparable detection performance to earlier proposed neural network based SADs, while the neural network based approach leads to a decision cost function of 1.07% on the evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of the art.
Keywords: voice activity detection; speech activity detection; neural network; statistical speech processing

Approaches for Collaborative Security Defences in Multi Network Environments - Koning, Ralph; Polevoy, Gleb; Meijer, Lydia; de Laat, Cees; Grosso, Paola (2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom))
Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.
Keywords: computer network security; multinetwork environments; multidomain defensive action; task execution order; timing influence defense efficiency; distributed attacks; collaborative security defence approach; minimize propagation approach; minimize countermeasure approach; counteract everywhere approach; Conferences; Cloud computing; Computer crime; Edge computing; Security; Defense Approaches; Multi-Domain Defense; Collaborative Defense; Defense Algorithms; Computer Networks

In Reviews We Trust: But Should We? Experiences with Physician Review Websites - Kersting, Joschka; Bäumer, Frederik Simon; Geierhos, Michaela (Proceedings of the 4th International Conference on Internet of Things, Big Data and Security)
The ability to openly evaluate products, locations and services is an achievement of the Web 2.0. It has never been easier to inform oneself about the quality of products or services and possible alternatives. Forming one’s own opinion based on the impressions of other people can lead to better experiences. However, this presupposes trust in one’s fellows as well as in the quality of the review platforms. In previous work on physician reviews and the corresponding websites, it was observed that there occurs faulty behavior by some reviewers and there were noteworthy differences in the technical implementation of the portals and in the efforts of site operators to maintain high quality reviews. These experiences raise new questions regarding what trust means on review platforms, how trust arises and how easily it can be destroyed.
Keywords: Trust; Physician Reviews; Network Analysis

Service Function Chaining Across OpenStack and Kubernetes Domains - Razzaghi Kouchaksaraei, Hadi; Karl, Holger (13th ACM International Conference on Distributed and Event-based Systems)
Remarkable advantages of Containers (CNs) over Virtual Machines (VMs) such as lower overhead and faster startup has gained the attention of Communication Service Providers (CSPs) as using CNs for providing Virtual Network Functions (VNFs) can save costs while increasing the service agility. However, as it is not feasible to realise all types of VNFs in CNs, the coexistence of VMs and CNs is proposed. To put VMs and CNs together, an orchestration framework that can chain services across distributed and heterogeneous domains is required. To this end, we implemented a framework by extending and consolidating state-of-the-art tools and technologies originated from Network Function Virtualization (NFV), Software-defined Networking (SDN) and cloud computing environments. This framework chains services provisioned across Kubernetes and OpenStack domains. During the demo, we deploy a service consist of CN- and VM-based VNFs to demonstrate different features provided by our framework.
Keywords: Network Function Virtualization; Software-defined Networking; Cloud Computing; service orchestration; OpenStack; Kubernetes

SLA-controlled Proxy Service Through Customisable MANO Supporting Operator Policies - Soenen, Thomas; Vicens, Felipe; Bonnet, José; Parada, Carlos; Kapassa, Evgenia; Touloupou, Marious; Fotopulou, Eleni; Zafeiropoulos, Anastasios; Pol, Ana; Kolometsos, Stavros; Xilouris, George; Alemany, Pol; et al. (2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM))
Service Level Agreements are essential tools enabling clients and telco operators to specify required quality of service. The 5GTANGO NFV platform enables SLAs through policies and custom service lifecycle management components. This allows the operator to trigger certain lifecycle management events for a service, and the network service developer to define how to execute such events (e.g., how to scale). In this demo we will demonstrate this unique 5GTANGO concept using an elastic proxy service supported by a high availability SLA enforced through a range of traffic regimes.
Keywords: 5G mobile communication; contracts; quality of service; telecommunication traffic; virtualisation; custom service lifecycle management components; lifecycle management events; network service developer; elastic proxy service; SLA-controlled proxy service; customisable MANO; operator policies; Service Level Agreements; unique 5G TANGO concept; 5G TANGO NFV platform; quality of service; traffic regimes; high availability SLA; Monitoring; Probes; Portals; Quality of service; Tools; Servers; Graphical user interfaces

Measuring the efficiency of SDN mitigations against attacks on computer infrastructures - Koning, R.; de Graaff, B.; Polevoy, Gleb; Meijer, R.; de Laat, C.; Grosso, P. (Future Generation Computer Systems)
Software Defined Networks (SDN) and Network Function Virtualisation (NFV) provide the basis for autonomous response and mitigation against attacks on networked computer infrastructures. We propose a new framework that uses SDNs and NFV to achieve this goal: Secure Autonomous Response Network (SARNET). In a SARNET, an agent running a control loop constantly assesses the security state of the network by means of observables. The agent reacts to and resolves security problems, while learning from its previous decisions. Two main metrics govern the decision process in a SARNET: impact and efficiency; these metrics can be used to compare and evaluate countermeasures and are the building blocks for self-learning SARNETs that exhibit autonomous response. In this paper we present the software implementation of the SARNET framework, evaluate it in a real-life network and discuss the tradeoffs between parameters used by the SARNET agent and the efficiency of its actions.
Keywords: Software defined networks; Network function virtualization; Cyber attacks; Cyber security; Defense efficiency; Overlay networks

Internet of Things Architecture for Handling Stream Air Pollution Data - Kersting, Joschka; Geierhos, Michaela; Jung, Hanmin; Kim, Taehong (Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security)
In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.
Keywords: Wireless Sensor Network; Internet of Things; Stream Data; Air Pollution; DSMS; Real-time Data Processing

Formulation of reliability-related objective functions for design of intelligent mechatronic systems - Kaul, Thorben; Meyer, Tobias; Sextro, Walter (SAGE Journals)
State-of-the-art mechatronic systems offer inherent intelligence that enables them to autonomously adapt their behavior to current environmental conditions and to their own system state. This autonomous behavior adaptation is made possible by software in combination with complex sensor and actuator systems and by sophisticated information processing, all of which make these systems increasingly complex. This increasing complexity makes the design process a challenging task and brings new complex possibilities for operation and maintenance. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability, which in turn increases reliability. The development of such an adaption strategy requires appropriate methods to evaluate reliability based on currently selected system behavior. A common approach to implement such adaptivity is to base system behavior on different working points that are obtained using multiobjective optimization. During operation, selection among these allows a changed operating strategy. To allow for multiobjective optimization, an accurate system model including system reliability is required. This model is repeatedly evaluated by the optimization algorithm. At present, modeling of system reliability and synchronization of the models of behavior and reliability is a laborious manual task and thus very error-prone. Since system behavior is crucial for system reliability, an integrated model is introduced that integrates system behavior and system reliability. The proposed approach is used to formulate reliability-related objective functions for a clutch test rig that are used to compute feasible working points using multiobjective optimization.
Keywords: Integrated model; reliability; system behavior; Bayesian network; multiobjective optimization

I’ll Have What S/he’s Having: A Case Study of a Social Trading Network - Pelster, Matthias (Proceedings of the International Conference on Information Systems)
Opinion leaders of an investment network can have a significant impact on capital mar-kets because their investment decisions are adopted by their peers and trigger large trad-ing cascades, increasing herding behavior and comovement among stock returns. This paper analyzes the interaction-based relations of traders from a large social trading plat-form and identifies the driving forces and the opinion leaders within a large online trading network as the nodes with the highest centrality and the highest force of infection, respec-tively. Relying on recent insights from epidemiological research, I maintain that central-ity identifies the most central traders in the network, while the expected force quantifies the most influential traders and their spreading power. I study the behavior and charac-teristics that set central and influential traders apart from other traders. The ability to identify focal points and their trading behavior within a trading network is important for investors, investment advisers, and policy makers.
Keywords: Online trading; investment advice; network modeling; Expected Force; herding.

The Game of Reciprocation Habits - Polevoy, Gleb; de Weerdt, M.M.; Jonker, C.M. (Proceedings of the 2016 European Conference on Artificial Intelligence)
Keywords: agents; action; repeated reciprocation; fixed; floating; network; Nash equilibrium; social welfare; price of anarchy; price of stability; convex combination

Improving Human Interaction in Crowdsensing - Polevoy, Gleb; de Weerdt, Mathijs M. (Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems)
Keywords: dynamics; emotion modeling; negotiation; network interaction; shared effort game

Influence and Dissemination Of Sentiments in Social Network Communication Patterns - Hillmann, Robert; Trier, Matthias (ECIS 2013 Proceedings)
Previous research suggests the existence of sentiments in online social networks. In comparison to real life human interaction, in which sentiments have been shown to have an influence on human behaviour, it is not yet completely understood which mechanisms explain how sentiments influence users in online environments. We develop a theoretical framework that tries to bridge the gap between social influence theories that focus on offline interactions on one hand and online interaction in social networks on the other hand. We then test our hypothesis about the influence and dissemination of sentiments in a quantitative analysis that is based on retrieved textual messages of communication patterns in over 12000 online social networks. Our empirical results suggest a general influence of sentiments on node communication patterns that is evidenced by increased occurrences of subsequent messages that express the same sentiment polarization. We interpret these findings and suggest future research to advance our currently limited theories that assume perceived and generalized social influence to path-dependent social influence models that consider actual behaviour.
Keywords: Social Network Analysis; Sentiment Analysis; Communication Patterns

Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model - Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (21th European Signal Processing Conference (EUSIPCO 2013))
In this paper we present a combined hardware/software approach for synchronizing the sampling clocks of an acoustic sensor network. A first clock frequency offset estimate is obtained by a time stamp exchange protocol with a low data rate and computational requirements. The estimate is then postprocessed by a Kalman filter which exploits the specific properties of the statistics of the frequency offset estimation error. In long term experiments the deviation between the sampling oscillators of two sensor nodes never exceeded half a sample with a wired and with a wireless link between the nodes. The achieved precision enables the estimation of time difference of arrival values across different hardware devices without sharing a common sampling hardware.
Keywords: synchronization; acoustic sensor network

Sentiment Polarization and Balance among Users in Online Social Networks - Hillmann, Robert; Trier, Matthias (AMCIS 2012 Proceedings)
Communication within online social network applications enables users to express and share sentiments electronically. Existing studies examined the existence or distribution of sentiments in online communication at a general level or in small-observed groups. Our paper extends this research by analyzing sentiment exchange within social networks from an ego-network perspective. We draw from research on social influence and social attachment to develop theories of node polarization, balance effects and sentiment mirroring within communication dyads. Our empirical analysis covers a multitude of social networks in which the sentiment valence of all messages was determined. Subsequently we studied ego-networks of focal actors (ego) and their immediate contacts. Results support our theories and indicate that actors develop polarized sentiments towards individual peers but keep sentiment in balance on the ego-network level. Further, pairs of nodes tend to establish similar attitudes towards each other leading to stable and polarized positive or negative relationships
Keywords: Social Network Analysis; Ego-Network Analysis; Node Polarization; Sentiment Dissemination

Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks - Hillmann, Robert; Trier, Matthias (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patternsand possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance ofhierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.
Keywords: Social Network Analysis; Dynamic Network Motif Analysis; Sentiment Dissemination; Networking Effects; Triads

Development of Automotive Communication Based Real-Time Systems - A Steer-by-Wire Case Study - Klobedanz, Kay; Kuznik, Christoph; Elfeky, Ahmed; Müller, Wolfgang (Proceedings of IESS09)
Safety-critical automotive systems must fulfill hard real-time constraints to guarantee their reliability and safety requirements. In the context of network-based electronics systems, high-level timing requirements have to be carefully mastered and traced throughout the whole development process. In this paper, we outline the management of scheduling-specific timing information by the application of a steer-by-wire design example. We apply the principles of the AUTOSAR-compliant Timing Augmented Description Language (TADL) following the methodology introduced by the TIMMO project[2]. Focus of the example will be the identification of end-to-end timing constraints and their refinement by means of stimuli-response event chains.
Keywords: Abstraction Level Controller Area Network High Abstraction Level Event Chain Automotive System

A hierarchical approach to unsupervised shape calibration of microphone array networks - Hennecke, Marius; Ploetz, Thomas; Fink, Gernot A.; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009))
Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments.
Keywords: acoustic sensing tasks; array geometry; calibration; coherence analysis; hierarchical procedure; local shape calibration; microphone array networks; microphone arrays; network calibration method; sensor arrays; SRP-PHAT; unsupervised shape calibration

A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we derive an uncertainty decoding rule for automatic speech recognition (ASR), which accounts for both corrupted observations and inter-frame correlation. The conditional independence assumption, prevalent in hidden Markov model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned on the complete observed feature vector sequence. This is a more informative posterior than one conditioned only on the current observation. The novel decoding is used to obtain a transmission-error robust remote ASR system, where the speech capturing unit is connected to the decoder via an error-prone communication network. We show how the clean speech posterior can be computed for communication links being characterized by either bit errors or packet loss. Recognition results are presented for both distributed and network speech recognition, where in the latter case common voice-over-IP codecs are employed.
Keywords: automatic speech recognition; bit errors; codecs; communication links; corrupted observations; decoding; distributed speech recognition; error-prone communication network; feature vector sequence; hidden Markov model-based ASR; hidden Markov models; inter-frame correlation; Internet telephony; network speech recognition; packet loss; speech posterior; speech recognition; transmission error robust speech recognition; uncertainty decoding; voice-over-IP codecs

Credible Threats as an Instrument of Regulation for Network Industries - Haucap, Justus; Heimeshoff, Ulrich; Uhde, André (REGULATORY CHANGES, INNOVATIONS AND INVESTMENT DYNAMICS IN THE DIGITAL WORLD ECONOMY)
In order to avoid over-regulation, some countries have experimented with what might be called threat-based regulation. The idea is - similarly to the contestable market model - that actual regulation may not be necessary as long as the threat of regulation is credible. In that case, so the idea, even monopolies will abstain from setting monopolistic prices in order to avoid being regulated. As experience from Germany and New Zealand has demonstrated, there are quite a number of problems associated with threat-based regulatory systems. The most significant problem has turned out to be the likely under-regulation and according welfare losses from market power, and in this context, a critical issue that has been identified is the credibility of the regulatory threat. If the threat of regulation is not credible, a light-handed regulatory regime (LHRR) amounts to leaving monopolistic firms unregulated, clearly an undesirable situation from a welfare point of view. This failure of threat-based regulatory systems results from potential free-rider problems associated with good corporate behaviour (as in the case of Germany's electricity market) as well as the lack of credibility of the threat either because of the weakness of the enforcement authorities or due to political constraints. Furthermore, a problem emerges if exercising the threat of regulation implies an abandonment of the LHRR and a corresponding substitution with heavy-handed ex ante regulation. An innovative way out of this dilemma has been the quasi-automatic threshold system originally designed for New Zealand's electricity distribution networks, which aims at simulating the pressures that companies would face in competitive markets.
Keywords: light-handed regulation; network industries; electricity; telecommunications

Peer-To-Peer Technology for Interconnecting Web Services in Heterogeneous Networks - Schattkowsky, Tim; Loeser, Chris; Müller, Wolfgang (Proceedings of AINA2004)
The interconnection of software components is a key to enabling collaborative work. However, technology differences and security barriers like firewalls often hinder establishing collaborative infrastructures between enterprises or even within a single enterprise. We introduce a peer-to-peer based network infrastructure that transparently overcomes these problems using relay and routing mechanisms as well as different underlying transport protocols. We discuss the application of this technology to interconnect Web services.
Keywords: Peer to peer computing; Web services; Intelligent networks; Routing; Relays; Simple object access protocol; Collaborative work; Transport protocols; Network address translation; Java