Model-Centric, Context-Aware Software Adaptation

Software must be constantly adapted to changing requirements. The time scale, abstraction level and granularity of adaptations may vary from short-term, fine-grained adaptation to long-term, coarse-grained evolution. Fine-grained, dynamic and context-dependent adaptations can be particularly difficult to realize in long-lived, large-scale software systems. We argue that, in order to effectively and efficiently deploy such changes, adaptive applications must be built on an infrastructure that is not just model-driven, but is both model-centric and context-aware. Specifically, this means that high-level, causally-connected models of the application and the software infrastructure itself should be available at run-time, and that changes may need to be scoped to the run-time execution context. We first review the dimensions of software adaptation and evolution, and then we show how model-centric design can address the adaptation needs of a variety of applications that span these dimensions. We demonstrate through concrete examples how model-centric and context-aware designs work at the level of application interface, programming language and runtime. We then propose a research agenda for a model-centric development environment that supports dynamic software adaptation and evolution.

Practical Object-Oriented Back-in-Time Debugging

Back-in-time debuggers are extremely useful tools for identifying the causes of bugs, as they allow us to inspect the past states of objects no longer present in the current execution stack. Unfortunately the "omniscient" approaches that try to remember all previous states are impractical because they either consume too much space or they are far too slow. Several approaches rely on heuristics to limit these penalties, but they ultimately end up throwing out too much relevant information. In this paper we propose a practical approach to back-in-time debugging that attempts to keep track of only the relevant past data. In contrast to other approaches, we keep object history information together with the regular objects in the application memory. Although seemingly counter-intuitive, this approach has the effect that past data that is not reachable from current application objects (and hence, no longer relevant) is automatically garbage collected. In this paper we describe the technical details of our approach, and we present benchmarks that demonstrate that memory consumption stays within practical bounds. Furthermore since our approach works at the virtual machine level, the performance penalty is significantly better than with other approaches.

Back to the future in one week - implementing a Smalltalk VM in PyPy

We report on our experiences with the Spy project, including implementation details and benchmark results. Spy is a re-implementation of the Squeak (i.e., Smalltalk-80) VM using the PyPy toolchain. The PyPy project allows code written in RPython, a subset of Python, to be translated to a multitude of different backends and architectures. During the translation, many aspects of the implementation can be independently tuned, such as the garbage collection algorithm or threading implementation. In this way, a whole host of interpreters can be derived from one abstract interpreter definition. Spy aims to bring these benefits to Squeak, allowing for greater portability and, eventually, improved performance. The current Spy codebase is able to run a small set of benchmarks that demonstrate performance superior to many similar Smalltalk VMs, but which still run slower than in Squeak itself. Spy was built from scratch over the course of a week during a joint Squeak-PyPy Sprint in Bern last autumn.

Quality of Service for Multicasting in Content Addressable Networks

Multicasting is an efficient mechanism for one to many data dissemination. Unfortunately, IP Multicasting is not widely available to end-users today, but Application Layer Multicast (ALM), such as Content Addressable Network, helps to overcome this limitation. Our OM-QoS framework offers Quality of Service support for ALMs. We evaluated OM-QoS applied to CAN and show that we can guarantee that all multicast paths support certain QoS requirements.

Hop-to-Hop Reliability in IP-Based Wireless Sensor Networks - A Cross-Layer Approach

To interconnect a wireless sensor network (WSN) to the Internet, we propose to use TCP/IP as the standard protocol for all network entities. We present a cross layer designed communication architecture, which contains a MAC protocol, IP, a new protocol called Hop-to-Hop Reliability (H2HR) protocol, and the TCP Support for Sensor Nodes (TSS) protocol. The MAC protocol implements the MAC layer of beacon-less personal area networks (PANs) as defined in IEEE 802.15.4. H2HR implements hop-to-hop reliability mechanisms. Two acknowledgment mechanisms, explicit and implicit ACK are supported. TSS optimizes using TCP in WSNs by implementing local retransmission of TCP data packets, local TCP ACK regeneration, aggressive TCP ACK recovery, congestion and flow control algorithms. We show that H2HR increases the performance of UDP, TCP, and RMST in WSNs significantly. The throughput is increased and the packet loss ratio is decreased. As a result, WSNs can be operated and managed using TCP/IP.

Gravity-based Local Clock Synchronization in Wireless Sensor Networks

Contention-based MAC protocols follow periodic listen/sleep cycles. These protocols face the problem of virtual clustering if different unsynchronized listen/sleep schedules occur in the network, which has been shown to happen in wireless sensor networks. To interconnect these virtual clusters, border nodes maintaining all respective listen/sleep schedules are required. However, this is a waste of energy, if locally a common schedule can be determined. We propose to achieve local synchronization with a mechanism that is similar to gravitation. Clusters represent the mass, whereas synchronization messages sent by each cluster represent the gravitation force of the according cluster. Due to the mutual attraction caused by the clusters, all clusters merge finally. The exchange of synchronization messages itself is not altered by LACAS. Accordingly, LACAS introduces no overhead. Only a not yet used property of synchronization mechanisms is exploited.

The state of in vitro science for use in bioaccumulation assessments for fish

Through the concerted evaluations of thousands of commercial substances for the qualities of persistence, bioaccumulation, and toxicity as a result of the United Nations Environment Program's Stockholm Convention, it has become apparent that fewer empirical data are available on bioaccumulation than other endpoints and that bioaccumulation models were not designed to accommodate all chemical classes. Due to the number of chemicals that may require further assessment, in vivo testing is cost prohibitive and discouraged due to the large number of animals needed. Although in vitro systems are less developed and characterized for fish, multiple high-throughput in vitro assays have been used to explore the dietary uptake and elimination of pharmaceuticals and other xenobiotics by mammals. While similar processes determine bioaccumulation in mammalian species, a review of methods to measure chemical bioavailability in fish screening systems, such as chemical biotransformation or metabolism in tissue slices, perfused tissues, fish embryos, primary and immortalized cell lines, and subcellular fractions, suggest quantitative and qualitative differences between fish and mammals exist. Using in vitro data in assessments for whole organisms or populations requires certain considerations and assumptions to scale data from a test tube to a fish, and across fish species. Also, different models may incorporate the predominant site of metabolism, such as the liver, and significant presystemic metabolism by the gill or gastrointestinal system to help accurately convert in vitro data into representative whole-animal metabolism and subsequent bioaccumulation potential. The development of animal alternative tests for fish bioaccumulation assessment is framed in the context of in vitro data requirements for regulatory assessments in Europe and Canada.

Admissibility in Finitely Generated Quasivarieties

Checking the admissibility of quasiequations in a finitely generated (i.e., generated by a finite set of finite algebras) quasivariety Q amounts to checking validity in a suitable finite free algebra of the quasivariety, and is therefore decidable. However, since free algebras may be large even for small sets of small algebras and very few generators, this naive method for checking admissibility in Q is not computationally feasible. In this paper, algorithms are introduced that generate a minimal (with respect to a multiset well-ordering on their cardinalities) finite set of algebras such that the validity of a quasiequation in this set corresponds to admissibility of the quasiequation in Q. In particular, structural completeness (validity and admissibility coincide) and almost structural completeness (validity and admissibility coincide for quasiequations with unifiable premises) can be checked. The algorithms are illustrated with a selection of well-known finitely generated quasivarieties, and adapted to handle also admissibility of rules in finite-valued logics.

Energy Efficiency Issues in Information-Centric Networking

In this paper we address energy efficiency issues of Information Centric Networking (ICN) architectures. In the proposed framework, we investigate the impact of ICN architectures on energy consumption of networking hardware devices and compare them with the energy consumption of other content dissemination methods. In particular, we investigate the consequences of caching in ICN from the energy efficiency perspective, taking into account the energy consumption of different hardware components in the ICN architectures. Based on the results of the analysis, we address the practical issues regarding the possible deployment and evolution of ICN from an energy-efficiency perspective. Finally, we summarize our findings and discuss the outlook/future perspectives on the energy efficiency of Information-Centric Networks.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.