Virtual communities as narrative processes

By facing the problem to describe the history of a virtual community as the sequence of events generated by its participants, a different perception of the meaning of communitywares emerges. This paper describes a proposal for a virtual community system based on the narrative process that supports the social evolution of the community

Model-based design for self-sustainable sensor nodes

Long-term and maintenance-free operation is a critical feature for large-scale deployed battery-operated sensor nodes. Energy harvesting (EH) is the most promising technology to overcome the energy bottleneck of today’s sensors and to enable the vision of perpetual operation. However, relying on fluctuating environmental energy requires an application-specific analysis of the energy statistics combined with an in-depth characterization of circuits and algorithms, making design and verification complex. This article presents a model-based design (MBD) approach for EH-enabled devices accounting for the dynamic behavior of components in the power generation, conversion, storage, and discharge paths. The extension of existing compact models combined with data-driven statistical modeling of harvesting circuits allows accurate offline analysis, verification, and validation. The presented approach facilitates application-specific optimization during the development phase and reliable long-term evaluation combined with environmental datasets. Experimental results demonstrate the accuracy and flexibility of this approach: the model verification of a solar-powered wireless sensor node shows a determination coefficient () of 0.992, resulting in an energy error of only -1.57 % between measurement and simulation. Compared to state-of-practice methods, the MBD approach attains a reduction of the estimated state-of-charge error of up to 10.2 % in a real-world scenario. MBD offers non-trivial insights on critical design choices: the analysis of the storage element selection reveals a 2–3 times too high self-discharge per capacity ratio for supercapacitors and a peak current constrain for lithium-ion polymer batteries

More than<i> Relata Refero</i>: Representing the Various Roles of Reported Speech in Argumentative Discourse

Reported speech, or relata refero, although not always part of the argumentation tout court, can be an important element of argumentative discourse. It might, for instance, provide information on the position of another party in the discussion or function as part of the premise of an argument from authority. Whereas existing methods of representing argumentative discourse focus on arguments and their interrelations, this paper develops a method that enables the analyst to also include informative elements in the representation, focusing on reported speech. It does so by incorporating the notion of ‘voice’ into the representation framework of Adpositional Argumentation (AdArg). In particular, the paper explains how to formalize the constituents of this notion and illustrates its use in representing (1) an author’s report of the position of another party (including the supporting argumentation); (2) an author’s own position (including the supporting argumentation); and (3) source-based arguments such as the argument from authority, with an indication of the distance of the source from the author

Annotation with adpositional argumentation:Guidelines for building a Gold Standard Corpus of argumentative discourse

This paper explains Adpositional Argumentation (AdArg), a new method for annotating arguments expressed in natural language. In describing this method, it provides the guidelines for designing a Gold Standard Corpus (GSC) of argumentative discourse in terms of so-called argumentative adpositional trees (arg-adtrees). The theoretical starting points of AdArg draw on the combination of the linguistic representation framework of Constructive Adpositional Grammars (CxAdGrams) with the argument categorisation framework of the Periodic Table of Arguments (PTA). After an explanation of these two frameworks, it is shown how AdArg can be used for annotating arguments expressed in natural language. This is done by providing the arg-adtrees of four concrete examples of arguments, which substantiate the four basic argument forms distinguished in the PTA. The present exposition of the fundamental tenets of AdArg enables the building of a GSC of argumentative discourse, that means an annotated corpus of texts and discussions of undisputable high-quality according to argumentation theory experts. Such a GSC should be conveniently annotated in terms of arg-adtrees, which is a time-consuming process, as it needs highly skilled annotators and human supervision. However, its role is crucial for developing instruments for computer-assisted argumentation analysis and eventual application based on machine learning natural language processing algorithms

Hardware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory

Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain's circuits with points of a hyperdimensional space, that is, with hypervectors. Hypervectors are Ddimensional (pseudo)random vectors with independent and identically distributed (i.i.d.) components constituting ultra-wide holographic words: D = 10,000 bits, for instance. At its very core, HD computing manipulates a set of seed hypervectors to build composite hypervectors representing objects of interest. It demands memory optimizations with simple operations for an efficient hardware realization. In this article, we propose hardware techniques for optimizations of HD computing, in a synthesizable open-source VHDL library, to enable co-located implementation of both learning and classification tasks on only a small portion of Xilinx UltraScale FPGAs: (1)We propose simple logical operations to rematerialize the hypervectors on the fly rather than loading them from memory. These operations massively reduce the memory footprint by directly computing the composite hypervectors whose individual seed hypervectors do not need to be stored in memory. (2) Bundling a series of hypervectors over time requires a multibit counter per every hypervector component. We instead propose a binarized back-to-back bundling without requiring any counters. This truly enables onchip learning with minimal resources as every hypervector component remains binary over the course of training to avoid otherwise multibit components. (3) For every classification event, an associative memory is in charge of finding the closest match between a set of learned hypervectors and a query hypervector by using a distance metric. This operator is proportional to hypervector dimension (D), and hence may take O(D) cycles per classification event. Accordingly, we significantly improve the throughput of classification by proposing associative memories that steadily reduce the latency of classification to the extreme of a single cycle. (4) We perform a design space exploration incorporating the proposed techniques on FPGAs for a wearable biosignal processing application as a case study. Our techniques achieve up to 2.39 7 area saving, or 2,337 7 throughput improvement. The Pareto optimal HD architecture is mapped on only 18,340 configurable logic blocks (CLBs) to learn and classify five hand gestures using four electromyography sensors

Hibernus: sustaining computation during intermittent supply for energy-harvesting systems

A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76-100% and 49-79% respectively

Evidence of active subsidence at Basiluzzo island (Aeolian islands, southern Italy) inferred from a Roman age wharf

The Aeolian Arc (Southern Tyrrhenian Sea, Italy) is one of the most active volcanic areas of the Mediterranean basin, affected by volcanic/hydrothermal and seismic activity. Ancient populations settled this region since historical times, building coastal installations which currently are valuable archaeological indicators of relative sea level changes and vertical land movements. In this study we show and discuss data on the relative sea level change estimated from a submerged wharf of Roman age dated between 50 B.C. and 50 A.D., located at Basiluzzo Island. This structure has been studied through marine surveys and archaeological interpretations and is presently located at a corrected depth of 4.10 0.2 m. We explain this submergence by a cumulative effect of the relative sea level change caused by the regional glaciohydro- isostatic signal, active since the end of the last glacial maximum, and the local volcano-tectonic land subsidence. Finally, a total subsidence rate of 2.05 0.1 mm/yr 1, with a volcano-tectonic contribution of 1.43 0.1 mm/yr 1 for the last 2 ka BP, is inferred from the comparison against the latest predicted sea level curve for the Southern Tyrrhenian Sea, suggesting new evaluations of the volcanotectonic hazard for this area of the Aeolian islands