Managing license compliance in free and open source software development

License compliance in Free and Open Source Software development is a significant issue today and organizations using free and open source software are predominately focusing on this issue. The non-compliance to licenses in free and open source software development leads to the loss of reputation and the high costs of litigation for organizations. Towards an automated compliance management, we use the Open Digital Rights Language to implement the clauses of open source software licenses in a machine interpretable way and propose a novel algorithm that analyzes compatibility between free and open source software licenses. Also, we describe a framework that inductively manages compliance of license clauses in a free and open source software development. We simulate and evaluate the formalized license compliance management by analyzing a real-time open source software project GRASS.     

A Low-Voltage Fully Differential Constant-Gm Rail-to-Rail CMOS Operational Amplifier

A low-voltage fully differential CMOS operational amplifier withconstant-g_mand rail-to-rail input and output stages ispresented. It is the fully differential version of a previously realizedsingle-ended operational amplifier where a novel circuit to ensure constanttransconductance has been implemented [1]. The input stage is a rail-to-railstructure formed by two symmetrical OTAs in parallel (the input transistorsare operating in weak inversion). The class-AB output stages have also afull voltage swing. A rail-to-rail input common mode feedback structureallows the output voltage control. Measurements in a 0.7 µ standardCMOS process with threshold voltages of about 0.7 V have been done. Theminimum experimental supply voltage is about 1.1 V. The circuit provides a60 dB low frequency voltage gain and about 1.5 MHz unity gain frequency fora total power consumption of about 0.72 mW at a 1.5 V supply voltage.     

Dependability evaluation of hierarchical systems

Hierarchical design represents a natural solution for managing the growing complexity of computing systems. This leads to systems that are hierarchically structured from a logical or physical viewpoint. Because of the inherent complexity of such systems, the incorporation of fault-tolerance features is highly recommended, to achieve the required dependability level. Hence, it is important to develop evaluation tools that allow to analyse the effectiveness of different fault-tolerance mechanisms. We present a systematic procedure for the construction and evaluation of Markov models for transient dependability analysis, which fully exploits the hierarchical structure of the system under evaluation. A thorough time/space complexity analysis of the proposed procedure shows that it allows a considerable time and space saving with respect to a naive approach to the same problem.Work partially supported by CNR, Progetto Finalizzato Sistemi Informatici e Calcolo Parallelo, under grant no. 91.00905 PF96, and by MURST, Progetto 40% Performability di sistemi distributi e paralleli     

Long-term recovery from unawareness, deficient counterregulation and lack of cognitive dysfunction during hypoglycaemia, following institution of rational, intensive insulin therapy in IDDM

Hypoglycaemia unawareness, is a major risk factor for severe hypoglycaemia and a contraindication to the therapeutic goal of near-normoglycaemia in IDDM. We tested two hypotheses, first, that hypoglycaemia unawareness is reversible as long as hypoglycaemia is meticulously prevented by careful intensive insulin therapy in patients with short and long IDDM duration, and that such a result can be maintained long-term. Second, that intensive insulin therapy which strictly prevents hypoglycaemia, can maintain long-term near-normoglycaemia. We studied 21 IDDM patients with hypoglycaemia unawareness and frequent mild/severe hypoglycaemia episodes while on "conventional" insulin therapy, and 20 nondiabetic control subjects. Neuroendocrine and symptom responses, and deterioration in cognitive function were assessed in a stepped hypoglycaemia clamp before, and again after 2 weeks, 3 months and 1 year of either intensive insulin therapy which meticulously prevented hypoglycaemia (based on physiologic insulin replacement and continuous education, experimental group, EXP, n = 16), or maintenance of the original "conventional" therapy (control group, CON, n = 5). At entry to the study, all 21 IDDM-patients had subnormal neuroendocrine and symptom responses, and less deterioration of cognitive function during hypoglycaemia. After intensive insulin therapy in EXP, the frequency of hypoglycaemia decreased from 0.5 +/- 0.05 to 0.045 +/- 0.02 episodes/patient-day; HbA1c increased from 5.83 +/- 0.18 to 6.94 +/- 0.13% (range in non-diabetic subjects 3.8-5.5%) over a 1-year period; all counterregulatory hormone and symptom responses to hypoglycaemia improved between 2 weeks and 3 months with the exception of glucagon which improved at 1 year; and cognitive function deteriorated further as early as 2 weeks (p < 0.05). The improvement in responses was maintained at 1 year. The improvement in plasma adrenaline and symptom responses inversely correlated with IDDM duration. In contrast, in CON, neither frequency of hypoglycaemia, nor neuroendocrine responses to hypoglycaemia improved. Thus, meticulous prevention of hypoglycaemia by intensive insulin therapy reverses hypoglycaemia unawareness even in patients with long-term IDDM, and is compatible with long-term near-normoglycaemia. Because carefully conducted intensive insulin therapy reduces, not increases the frequency of moderate/severe hypoglycaemia, intensive insulin therapy should be extended to the majority of IDDM patients in whom it is desirable to prevent/delay the onset/progression of microvascular complications.     

Organic Light-Emitting Transistors in a Smart-Integrated System for Plasmonic-Based Sensing

The smart integration of multiple devices in a single functional unit is boosting the advent of compact optical sensors for on-site analysis. Nevertheless, the development of miniaturized and cost-effective plasmonic sensors is hampered by the strict angular constraints of the detection scheme, which are fulfilled through bulky optical components. Here, an ultracompact system for plasmonic-sensing is demonstrated by the smart integration of an organic light-emitting transistor (OLET), an organic photodiode (OPD), and a nanostructured plasmonic grating (NPG). The potential of OLETs, as planar multielectrode devices with inherent micrometer-wide emission areas, offers the pioneer incorporation of an OPD onto the source electrode to obtain a monolithic photonic module endowed with light-emitting and light-detection characteristics at unprecedented lateral proximity of them. This approach enables the exploitation of the angle-dependent sensing of the NPG in a miniaturized system based on low-cost components, in which a reflective detection is enabled by the elegant fabrication of the NPG onto the encapsulation glass of the photonic module. The most effective layout of integration is unraveled by an advanced simulation tool, which allows obtaining an optics-less plasmonic system able to perform a quantitative detection up to 10⁻² RIU at a sensor size as low as 0.1 cm³.     

The Role of Morphology in Optically Switchable Transistors Based on a Photochromic Molecule/p‐Type Polymer Semiconductor Blend

The correlation between morphology and optoelectronic performance in organic thin‐film transistors based on blends of photochromic diarylethenes (DAE) and poly(3‐hexylthiophene) (P3HT) is investigated by varying molecular weight (M_w = 20–100 kDa) and regioregularity of the conjugated polymer as well as the temperature of thermal annealing (rt‐160 °C) in thin films. Semicrystalline architectures of P3HT/DAE blends comprise crystalline domains, ensuring efficient charge transport, and less aggregated regions, where DAEs are located as a result of their spontaneous expulsion from the crystalline domains during the self‐assembly. The best compromise between field‐effect mobility (μ) and switching capabilities is observed in blends containing P3HT with M_w = 50 kDa, exhibiting μ as high as 1 × 10^?3 cm² V^?1 s^?1 combined with a >50% photoswitching ratio. Higher or lower M_w than 50 kDa are found to be detrimental for field‐effect mobility and to lead to reduced device current switchability. The microstructure of the regioregular P3HT blend is found to be sensitive to the thermal annealing temperature, with an increase in μ and a decrease in current modulation being observed as a response to the light‐stimulus likely due to an increased P3HT‐DAE segregation, partially hindering DAE photoisomerization. The findings demonstrate the paramount importance of fine tuning the structure and morphology of bicomponent films for leveraging the multifunctional nature of optoelectronic devices.     

Comparison between Different Distributed Methods for Flood Susceptibility Mapping

Flooding is one of the main natural hazards in Northern Europe and several areas of the Northern Boreal Hemisphere, where during intense rainfall events, several river basins are affected by a fast water level rise that may cause severe damage to human lives and properties. For these reasons, the development of flood models to identify susceptible areas is essential for decision-makers. Geographic Information Systems (GIS) are currently accurate and valuable support tools for defining flood susceptibility maps at different spatial scales. In this study, the prediction accuracy of different GIS-based procedures in the identification of flooding susceptibility is tested and compared. These procedures include the frequency ratio, a combination of the frequency ratio and logistic regression, a combination of the frequency ratio and Shannon’s entropy index, and the statistical index. Ten conditioning parameters of flooding susceptibility are considered: elevation, slope, curvature, land use, Topographic Wetness Index, Stream Power Index, hydrogeology, stream distance, flow direction and average annual rainfall. The comparison analysis is carried out by applying these methods to the study area of Devon County in Southwest England. A total of 225 flood events are used to define the models. For model validation, 1000 randomly selected training and testing sub-datasets have been used in the definition of the receiver operating characteristic (ROC) curves. The results show that the procedure based on the statistical index provides the highest accuracy and reliability in flood susceptibility predictions.

     

Can Endogenous Participation Explain Price Volatility? Evidence from an Agent-Based Cobweb Model

This work provides a computational cobweb model with heterogeneous adaptive producers with endogenous market entry and exit. Firms face a borrowing constraint and so can go bankrupt. At the same time when average profits are positive there is an inflow of new firms in the market. Bounded dynamics and endogenous volatility are shown to follow without resorting to nonlinearities.     

Analysing anaphoric ambiguity in natural language requirements

Many requirements documents are written in natural language (NL). However, with the flexibility of NL comes the risk of introducing unwanted ambiguities in the requirements and misunderstandings between stakeholders. In this paper, we describe an automated approach to identify potentially nocuous ambiguity, which occurs when text is interpreted differently by different readers. We concentrate on anaphoric ambiguity, which occurs when readers may disagree on how pronouns should be interpreted. We describe a number of heuristics, each of which captures information that may lead a reader to favor a particular interpretation of the text. We use these heuristics to build a classifier, which in turn predicts the degree to which particular interpretations are preferred. We collected multiple human judgements on the interpretation of requirements exhibiting anaphoric ambiguity and showed how the distribution of these judgements can be used to assess whether a particular instance of ambiguity is nocuous. Given a requirements document written in natural language, our approach can identify sentences that contain anaphoric ambiguity, and use the classifier to alert the requirements writer of text that runs the risk of misinterpretation. We report on a series of experiments that we conducted to evaluate the performance of the automated system we developed to support our approach. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents.