The operations of the robots are governed by a set of rules limiting the weight of robots, and their cargo, to ensure safe operations. Two key impediments for the commercial success of model-based diagnosis MBD include a a failure to integrate diagnostics modeling within the requirements and design phase, and b a high degree of diagnostic ambiguity during run-time.
This article addresses both of these impediments by providing a formal framework that integrates requirements-based design with MBD modeling. The proposed framework extends the consistency-based theory of MBD with a requirements-based design theory based on contracts. Architectural bad smells are design decisions found in software architectures that degrade the ability of systems to evolve. This paper presents an approach to verify that a software architecture is smell-free using the Archery architectural description language. The language provides a core for modelling software architectures and an extension for specifying constraints.
The approach consists in precisely specifying architectural smells as constraints, and then verifying that software architectures do not satisfy any of them. The constraint language is based on a propositional modal logic with recursion that includes: a converse operator for relations among architectural concepts, graded modalities for describing the cardinality in such relations, and nominals referencing architectural elements. Four architectural smells illustrate the approach. In these settings, many occurring activities can be documented and have established them as learning environments.
As knowledge exchange is proved to occur in FLOSS, the dynamic and free nature of participation poses a great challenge in understanding activities pertaining to Learning Processes. In this paper we raise this question and propose an ontology called OntoLiFLOSS in order to define terms and concepts that can explain learning activities taking place in these communities. The objective of this endeavor is to define in the simplest possible way a common definition of concepts and activities that can guide the identification of learning processes taking place among FLOSS members in any of the standard repositories such as mailing list, SVN, bug trackers and even discussion forums.
At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data. In this paper, we explore a different perspective called process mining.
Process mining has been proved to be successful in terms of tracing and reconstructing process models from data logs event logs. The chief objective of our analysis is threefold.
We aim to achieve: 1 conformance to predefined models; 2 discovery of new model patterns; and, finally, 3 extension to predefined models. The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but most of the existing approaches are based only on text, disregarding information that comes from the relationships among users and posts.
In this paper we consider microblogs as heterogeneous networks and we use an approach based on latent representation of nodes to infer, given a specific topic, the sentiment polarity of posts and users at the same time.
The experimental investigation show that our approach, by taking into account both content and relationship information, outperforms supervised classifiers based only on textual content. Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows.
The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes.
One successful way of doing this is through specification methods. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions. The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations produced after the treatment with monoclonal antibodies mAb. KRAS screening tests done before the start of the treatment are not very sensitive to identify minute quantity of the mutated cells, which can produce resistance to the therapy after the beginning of the treatment.
Here we present a mathematical model for the analysis of KRAS mutations behavior in colorectal cancer with respect to mAb treatments. To evaluate the drug performance we have developed equations for two types of tumors cells, i. Both tumor cell populations were treated with a combination of mAb and chemotherapy drugs. It was observed that even the minimal initial concentration of KRAS mutation before the treatment has the ability to make the tumor refractory to the treatment.
creatoranswers.com/modules/springs/hombres-solteros-de-40-a.php Finally, Cetuximab mAb and Irinotecan chemotherapy drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment is only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them.
DISPAS is an agent-based simulator for fish stock assessment developed as a decision making support for the sustainable management of fishery. We retain the currently available spatial scale, able to represent a limited average region of the sea, and we introduce a new spatial macro-scale, able to represent the whole sea.
At the macro-scale a single agent represents an area of five square nautical miles and manages groups of fish in different age classes. The interactions among the macro agents permit the exchange of individuals of each class among neighbor areas.
A case study regarding the Solea solea Linnaeus, ; Soleidae stock of the northern Adriatic Sea is used to show the intended approach, taking into account the available data, coming from fishery independent scientific surveys. Ecosystems and their biodiversity have to be protected and preserved as sources of services and goods.
The human population controls and modifies ecosystems to improve its health conditions and welfare. The consequences of human activities should be carefully monitored and ecosystems should be managed to protect all of the species and preserve their functioning. The development of strategies for ecosystem management benefits from the use of computational techniques to model the dynamics of species that interact with their abiotic and biotic environment.
Life scientists and computer scientists need to work together to define and analyse ecosystem models. However, there is a multifaceted gap between the approaches used in life science and those used in computer science. Such gap is both cultural and technical, and results in a number of challenges. In this paper we identify these challenges and provide technical and cultural proposals for solving them. Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior.
In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data.
The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions. This paper presents a combination of symbolic execution and partial order reduction to achieve path-sensitive race detection.
The presented approach limits the complexity of symbolic execution of multi-threaded code by applying it with a fixed scheduling algorithm only. Alternative thread interleavings are generated from fixed-scheduling ones with ample set partial order reduction on an abstraction level of thread interactions. Races are detected on the abstraction level.
The proposed algorithm is implemented as plug-in extension of Eclipse CDT and evaluated by running it on the race condition test cases from the Juliet suite. Non-Markovian systems are usually difficult to represent and analyse using currently available stochastic process calculi. By relying on a combination between the newly introduced process algebra PHASE and the probabilistic model checker PRISM, we examine the dynamics of one such system, which involves a collaborative text review performed by two manuscript editors, and focus on the derivation of quantitative performance measures.
We find that approximating non-Markovian transitions through single Markovian transitions is fast, but inaccurate, while employing more complex phase-type approximations is somewhat slow, but considerably more precise. In an election, it is imperative that the vote of the single voters remain anonymous and undisclosed. Information theory is applied to quantify this leak and ascertain that it remains below an acceptable threshold.
Therefore, the ability to pro-duce low cost, high quality software is crucial to technological and social progress. An intrinsic characteristic of real-world application software is the need to evolve in order to adjust to new or changing requirements. Maintaining quality while embracing change is one of the main challenges of software engineering. Software engineers may take advantage of theories, languages, methods, and tools that derive from both the system-atic research of the academic community and the experience of real-world practitioners. An important role of software engineering as a scientific discipline is to create a feedback cycle between academia and industry by proposing new solutions and identifying those that "work" in practical contexts.
FASE accepts papers on both academic research and industrial experiences, but they must clearly identify the problem and the envisioned solutions. Specifically, contributions are encouraged that combine concep-tual and methodological aspects with their formal foundation and tool support.
The ETAPS conferences accept two types of contributions: research papers and tool demonstration papers. Both types will appear in the proceedings. Submitted papers must be in English and present original re-search. All submitted papers must be unpublished and not submitted for publication elsewhere. In particular, simultaneous submission of the same contribution to multiple ETAPS conferences is not permitted.
In this book, Hussmann builds a bridge between the pragmatic methods for the design of information systems and the formal, mathematical background. Firstly. This well-written book encourages scientists and software engineers to apply formal methods to practical software development problems.
One author of each accepted paper must attend the conference to present that paper.