Abstract
Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, presenting both empirical and theoretical evidence of its advantages.

Abstract
We propose a programming model for distributed concurrent systems with mobile objects in the context of a process calculus. Code mobility is induced by lexical scoping on names. Objects and messages migrate towards the site where their prefixes are lexically bound. Class definitions, on the other hand, are downloaded from the site where they are defined and are instantiated locally upon arrival. We provide several programming examples to demonstrate the expressiveness of the model. Finally, based on this model we describe an. architecture for a run-time system supporting concurrent, distributed computations and code mobility.

Abstract
We describe a programming language for distributed computations that supports mobile resources and is based on a process calculus. The syntax, semantics and implementation of the language are presented with a focus on the novel model of computation.

Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Abstract
The reading of the T650 glyph has been a puzzle for decades. Here, we analyze the semantic contexts in which the glyph appears together with available phonetic evidence to arrive at a phonetic reading of JOM. We provide grammatical reconstructions of the lexical contexts and discuss the rebuses involved in non semantic contexts.