An Object-Oriented Model for Extensible Concurrent Systems: the Composition-Filters Approach

Applying the object-oriented paradigm for the development of large and complex software systems offers several advantages, of which increased extensibility and reusability are the most prominent ones. The object-oriented model is also quite suitable for modeling concurrent systems. However, it appears that extensibility and reusability of concurrent applications is far from trivial. The problems that arise, the so-called inheritance anomalies are analyzed and presented in this paper. A set of requirements for extensible concurrent languages is formulated. As a solution to the identified problems, an extension to the object-oriented model is presented; composition filters. Composition filters capture messages and can express certain constraints and operations on these messages, for example buffering. In this paper we explain the composition filters approach, demonstrate its expressive power through a number of examples and show that composition filters do not suffer from the inheritance anomalies and fulfill the requirements that were established

A framework for community detection in heterogeneous multi-relational networks

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure

Abstracting object interactions using composition filters

It is generally claimed that object-based models are very suitable for building distributed system architectures since object interactions follow the client-server model. To cope with the complexity of today's distributed systems, however, we think that high-level linguistic mechanisms are needed to effectively structure, abstract and reuse object interactions. For example, the conventional object-oriented model does not provide high-level language mechanisms to model layered system architectures. Moreover, we consider the message passing model of the conventional object-oriented model as being too low-level because it can only specify object interactions that involve two partner objects at a time and its semantics cannot be extended easily. This paper introduces Abstract Communication Types (ACTs), which are objects that abstract interactions among objects. ACTs make it easier to model layered communication architectures, to enforce the invariant behavior among objects, to reduce the complexity of programs by hiding the interaction details in separate modules and to improve reusability through the application of object-oriented principles to ACT classes. We illustrate the concept of ACTs using the composition filters model

A Programming Environment for Visual Block-Based Domain-Specific Languages

AbstractVisual block-based programming is useful for various users such as novice programmers because it provides easy operations and improves the readability of programs. Also, in programming education, it is known to be effective to initially present basic language features and then gradually make more advanced features available. However, the cost of implementing such visual block-based languages remains a challenge. In this paper, we present a programming environment for providing visual block-based domain- specific languages (visual DSLs) that are translatable into various programming languages. In our environment, programs are built by combining visual blocks expressed in a natural language. Blocks represent program elements such as operations and variables. Tips represent snippets, and macro blocks represent procedures. Using Tips and macros make code more abstract, and reduce the number of blocks in code. Visual DSLs can be a front-end for various languages. It can be easily restricted and extended by adding and deleting blocks. We applied our programming environment to Processing, an educational programming language for media art. We show that the environment is useful for novice programmers who learn basic concepts of programming and the features of Processing

PirB regulates asymmetries in hippocampal circuitry

Left-right asymmetry is a fundamental feature of higher-order brain structure; however, the molecular basis of brain asymmetry remains unclear. We recently identified structural and functional asymmetries in mouse hippocampal circuitry that result from the asymmetrical distribution of two distinct populations of pyramidal cell synapses that differ in the density of the NMDA receptor subunit GluRε2 (also known as NR2B, GRIN2B or GluN2B). By examining the synaptic distribution of ε2 subunits, we previously found that β2-microglobulin-deficient mice, which lack cell surface expression of the vast majority of major histocompatibility complex class I (MHCI) proteins, do not exhibit circuit asymmetry. In the present study, we conducted electrophysiological and anatomical analyses on the hippocampal circuitry of mice with a knockout of the paired immunoglobulin-like receptor B (PirB), an MHCI receptor. As in β2-microglobulin-deficient mice, the PirB-deficient hippocampus lacked circuit asymmetries. This finding that MHCI loss-of-function mice and PirB knockout mice have identical phenotypes suggests that MHCI signals that produce hippocampal asymmetries are transduced through PirB. Our results provide evidence for a critical role of the MHCI/PirB signaling system in the generation of asymmetries in hippocampal circuitry

Sense-making strategies in explorative intelligence analysis of network evolutions

Visualising how social networks evolve is important in intelligence analysis in order to detect and monitor issues, such as emerging crime patterns or rapidly growing groups of offenders. It remains an open research question how this type of information should be presented for visual exploration. To get a sense of how users work with different types of visualisations, we evaluate a matrix and a node-link diagram in a controlled thinking aloud study. We describe the sense-making strategies that users adopted during explorative and realistic tasks. Thereby, we focus on the user behaviour in switching between the two visualisations and propose a set of nine strategies. Based on a qualitative and quantitative content analysis we show which visualisation supports which strategy better. We find that the two visualisations clearly support intelligence tasks and that for some tasks the combined use is more advantageous than the use of an individual visualisation