Cognitive Cities: An Application for Nairobi. Text Message Participation of Slum Inhabitants to Improve Sanitary Facilities

Population growth is always increasing, and thus the concept of smart and cognitive cities is becoming more important. Developed countries are aware of and working towards needed changes in city management. However, emerging countries require the optimization of their own city management. This chapter illustrates, based on a use case, how a city in an emerging country can quickly progress using the concept of smart and cognitive cities. Nairobi, the capital of Kenya, is chosen for the test case. More than half of the population of Nairobi lives in slums with poor sanitation, and many slum inhabitants often share a single toilet, so the proper functioning and reliable maintenance of toilets are crucial. For this purpose, an approach for processing text messages based on cognitive computing (using soft computing methods) is introduced. Slum inhabitants can inform the responsible center via text messages in cases when toilets are not functioning properly. Through cognitive computer systems, the responsible center can fix the problem in a quick and efficient way by sending repair workers to the area. Focusing on the slum of Kibera, an easy-to-handle approach for slum inhabitants is presented, which can make the city more efficient, sustainable and resilient (i.e., cognitive).

Content and Context Aware Strategies for QoS Support in VANETs

The increasing interest in autonomous coordinated driving and in proactive safety services, exploiting the wealth of sensing and computing resources which are gradually permeating the urban and vehicular environments, is making provisioning of high levels of QoS in vehicular networks an urgent issue. At the same time, the spreading model of a smart car, with a wealth of infotainment applications, calls for architectures for vehicular communications capable of supporting traffic with a diverse set of performance requirements. So far efforts focused on enabling a single specific QoS level. But the issues of how to support traffic with tight QoS requirements (no packet loss, and delays inferior to 1ms), and of designing a system capable at the same time of efficiently sustaining such traffic together with traffic from infotainment applications, are still open. In this paper we present the approach taken by the CONTACT project to tackle these issues. The goal of the project is to investigate how a VANET architecture, which integrates content-centric networking, software-defined networking, and context aware floating content schemes, can properly support the very diverse set of applications and services currently envisioned for the vehicular environment.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

The Impact of Human Asset Specificity on the Sourcing of Application Services

The question concerning the circumstances under which it is advantageous for a company to outsource certain information systems functions has been a controversial issue for the last decade. While opponents emphasize the risks of outsourcing based on the loss of strategic potentials and increased transaction costs, proponents emphasize the strategic benefits of outsourcing and high potentials of cost-savings. This paper brings together both views by examining the conditions under which both the strategic potentials as well as savings in production and transaction costs of developing and maintaining software applications can better be achieved in-house as opposed to by an external vendor. We develop a theoretical framework from three complementary theories and test it empirically based on a mail survey of 139 German companies. The results show that insourcing is more cost efficient and advantageous in creating strategic benefits through IS if the provision of application services requires a high amount of firm specific human assets. These relationships, however, are partially moderated by differences in the trustworthiness and intrinsic motivation of internal versus external IS professionals. Moreover, capital shares with an external vendor can lower the risk of high transaction costs as well the risk of loosing the strategic opportunities of an IS.

An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Enhanced Caching Strategies At the Edge of LTE Mobile Networks

With a boom in the usage of mobile devices for traffic-heavy applications, mobile networks struggle to deliver good performance while saving resources to support more users and save on costs. In this paper, we propose enhanced strategies for the preemptive migration of content stored in Information-Centric Networking caches at the edge of LTE mobile networks. With such strategies, the concept of content following the users interested in it becomes a reality and content within caches is more optimized towards the requests of nearby users. Results show that the strategies are feasible, efficient and, when compared to default caching strategies, ensure that content is delivered faster to end users while using bandwidth and storage resources more efficiently at the core of the network.

GTInspector: A Moldable Domain-Aware Object Inspector

Understanding the run-time behaviour of object-oriented applications entails the comprehension of run-time objects. Traditional object inspectors favor generic views that focus on the low-level details of the state of single objects. While universally applicable, this generic approach does not take into account the varying needs of developers that could benefit from tailored views and exploration possibilities. GTInspector is a novel moldable object inspector that provides different high-level ways to visualize and explore objects, adapted to both the object and the current developer need.

Bounded seas

Abstract Imprecise manipulation of source code (semi-parsing) is useful for tasks such as robust parsing, error recovery, lexical analysis, and rapid development of parsers for data extraction. An island grammar precisely defines only a subset of a language syntax (islands), while the rest of the syntax (water) is defined imprecisely. Usually water is defined as the negation of islands. Albeit simple, such a definition of water is naive and impedes composition of islands. When developing an island grammar, sooner or later a language engineer has to create water tailored to each individual island. Such an approach is fragile, because water can change with any change of a grammar. It is time-consuming, because water is defined manually by an engineer and not automatically. Finally, an island surrounded by water cannot be reused because water has to be defined for every grammar individually. In this paper we propose a new technique of island parsing —- bounded seas. Bounded seas are composable, robust, reusable and easy to use because island-specific water is created automatically. Our work focuses on applications of island parsing to data extraction from source code. We have integrated bounded seas into a parser combinator framework as a demonstration of their composability and reusability.

Optimized CUDA-Based PDE Solver for Reaction Diffusion Systems on Arbitrary Surfaces

Partial differential equation (PDE) solvers are commonly employed to study and characterize the parameter space for reaction-diffusion (RD) systems while investigating biological pattern formation. Increasingly, biologists wish to perform such studies with arbitrary surfaces representing ‘real’ 3D geometries for better insights. In this paper, we present a highly optimized CUDA-based solver for RD equations on triangulated meshes in 3D. We demonstrate our solver using a chemotactic model that can be used to study snakeskin pigmentation, for example. We employ a finite element based approach to perform explicit Euler time integrations. We compare our approach to a naive GPU implementation and provide an in-depth performance analysis, demonstrating the significant speedup afforded by our optimizations. The optimization strategies that we exploit could be generalized to other mesh based processing applications with PDE simulations.

Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings.