ForBackBench

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use this knowledge to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA) that provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework of temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.

As Crash Imminent Braking (CIB) systems have been introduced to the passenger vehicle market segment, there is an immediate need to develop a standardized performance scoring system which enables an objective comparison of similar CIB systems implemented across various vehicle platforms. It is desirable that this standard is adopted by the entire automotive industry to further advance CIB technologies. It is also a valuable reference for consumers to compare the safety of different vehicles. This paper describes an approach for the establishment of such a standard to evaluate and compare the performance of CIB systems. A set of testing scenarios is proposed for the performance evaluation of CIB systems. Subjective and arguable test scenarios are avoided as much as possible. The applicability of the proposed scoring system is demonstrated with the data collected from some proposed testing scenarios in the evaluation of a 2011 model-year passenger vehicle equipped with a CIB system.

Efficient SPARQL-to-SQL with R2RML mappings

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear conceptual architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema. In this paper, we present a comprehensive approach to, and a complete system for, ontology-based data integration. In this system, the global schema is expressed in terms of a TBox of the tractable Description Logics ${\textit{DL-Lite}_{\mathcal A}}$, the sources are relations, and the mapping language allows for expressing GAV sound mappings between the sources and the global schema. The mapping language has specific mechanisms for addressing the so-called impedance mismatch problem, arising from the fact that, while the data sources store values, the instances of concepts in the ontology are objects. By virtue of the careful design of the various languages used in our system, answering unions of conjunctive queries can be done through a very efficient technique ( LogSpace with respect to data complexity) which reduces this task to standard SQL query evaluation. We also show that even very slight extensions of the expressive abilities of our system lead beyond this complexity bound.

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear conceptual architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema. In this paper, we present a comprehensive approach to, and a complete system for, ontology-based data inte- gration. In this system, the global schema is expressed in terms of a TBox of the tractable Description Logics DL-LiteA, the sources are relations, and the map- ping language allows for expressing GAV sound mappings between the sources and the global schema. The mapping language has specific mechanisms for ad- dressing the so-called impedance mismatch problem, arising from the fact that, while the data sources store values, the instances of concepts in the ontology are objects. By virtue of the careful design of the various languages used in our sys- tem, answering unions of conjunctive queries can be done through a very efficient technique (LOGSPACE with respect to data complexity) which reduces this task to standard SQL query evaluation. We also show that even very slight extensions of the expressive abilities of our system lead beyond this complexity bound.

BackgroundThe COVID-19 pandemic has profoundly affected the health and care of older adults, with particularly negative consequences for those residing in long-term care homes (LTCH) and retirement homes (RH). To inform the implementation of interventions with the most potential for impact, Healthcare Excellence Canada identified six promising practices and policy options that can be introduced to ensure that LTCH and RH are better prepared for potential future outbreaks. A total of 22 implementation science teams (ISTs) were funded to support LTCH and RH across Canada in their implementation of these practices. This study aims to identify the enablers and barriers to the successful implementation of evidence-based practices and the impact of intervention in LTCH and RH across Canada.MethodsA survey-based longitudinal correlational design will be used. The Organizational Readiness for Knowledge Translation (OR4KT) tool will be used to assess the readiness of LTCH and RH to implement the selected practice. The OR4KT includes 59 questions and takes about 15 min to complete. Five to ten respondents per organization, holding different job positions, will be invited by the ISTs to complete the OR4KT in 91 LTCH or RH across Canada at the beginning of the project (T1) and 6 months after the first measurement (T2).DiscussionThe study will provide a benchmark for assessing the readiness of LTCH and RH to implement evidence-based practices. It will also inform decision-makers about barriers and facilitators that influence the integration of promising practices in these organizations.

Adapting automated test generation to GUI testing of industry applications

In this paper, we present methods for automatic test generation from formal specifications. These are used to create intelligent test benches that are able to cover corner case behaviors in much less time. We have developed a prototype tool for intelligent test generation within the layered test bench architecture proposed in RVM. We present results on verification IPs of standard bus protocols to show the effectiveness of our approach.

To ensure proper implementation, the DFT process has to be supported by a comprehensive set of tools for system specification, design verification, testability analysis, test generation and software validation. Any DFT/DFD strategy has to be based on a thorough understanding of the cost of test structure over the life of the automotive products. Without a viable cost of test model it will be impossible to justify the test features added to the module that usually impact the cost of design and are of benefit only for the later stages ofthe product life

Service-Oriented Architectures (SOA) play an important role in systems/devices interoperability in the Industrial Internet of Things (IIoT), allowing the management of the interaction between consumers and producers of a given service. Despite this, when the systems cannot understand each other, it is necessary to create translators that allow their communication. The TAG-Tool is an example of an automatic generation tool for semantic data translators using annotated metadata with concepts from a reference ontology. A management approach is proposed for the use of tools for automatic generation of translators, such as the TAG-Tool, which makes this management more efficient, namely by managing the systems that can be related, thus allowing control over the translators who can be created. This way, it is possible to store created translators, avoid creating them whenever they are requested, as well as use downtime to generate non-existent and obsolete translators. This management is a relevant addition in automating the generation of translators and integration with IoT management systems. A tool was developed to validate the proposed approach.

1 - As a consequence of their location between land and open sea, coastal lagoons are subject to strong anthropogenic pressures due to tourism and/or heavy shellfish/fish farming. Furthermore, they receive fresh water from their catchment areas loaded with urban, agricultural and/or industrial effluents and domestic sewages. 2 - These pressures are responsible for important ecosystem alterations i.e. eutrophication, bacterial contamination, algal blooms (toxic or not), anoxia and fish kills. Further, additional problems arise from costal erosion, subsidence and effects related to extreme meteorological events, typical from Mediterranean areas. 3 - The development of management tools for coastal lagoons is a complex task requiring interdisciplinary research and active interaction with the end-users. By developing and implementing a set of information tools (Environmental databases, GIS, watershed and lagoon hydrodynamic coupled with biological models), the DITTY project aimed at the development of the scientific and operational bases for a sustained and rational utilisation of the available resources in Southern European lagoons in five case studies: Ria Formosa (Portugal), Mar Menor (Spain), Thau Lagoon (France), Sacca di Goro (Italy) and gulf of Gera (Greece). 4 - During this project, a set of scenarios was defined with end-users and stakeholders, addressing issues such as population growth, change in land use or resource exploitation, dredging operations and water reuse. whenever appropriate. Effect of climate change is also considered, taking 2015 as time horizon.

Mobile applications have become a ubiquitous part of our daily life, providing users with access to various services and utilities. Text input, as an important interaction channel between users and applications, plays an important role in core functionality such as search queries, authentication, messaging, etc. However, certain special text (e.g., -18 for Font Size) can cause the app to crash, and generating diversified unusual inputs for fully testing the app is highly demanded. Nevertheless, this is also challenging due to the combination of explosion dilemma, high context sensitivity, and complex constraint relations. This paper proposes InputBlaster which leverages the LLM to automatically generate unusual text inputs for mobile app crash detection. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. In detail, InputBlaster leverages LLM to produce the test generators together with the mutation rules serving as the reasoning chain, and utilizes the in-context learning schema to demonstrate the LLM with examples for boosting the performance. InputBlaster is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline. Besides, we integrate it with the automated GUI testing tool and detect 37 unseen crashes in real-world apps.

Software development industry is a key factor in the worldwide economy. In Mexico, this sector consists mostly of small and medium enterprises (SMEs) and they represent the 99% of the business software industry. This highlights the importance of implementing best practices in software engineering SMEs to ensure continuous improvement. In this context, there are several models, methodologies and standards that contain best practices focused on improving software development processes such as CMMI-DEV v1.3, ISO 12207, ISO 15504, etc. However, to implement it in SMEs they should be tailored according to the organization size and type of business. Therefore, SMEs require the use of techniques and tools that help them implementing the best practices. This paper aims to present a proposal of a method for creating catalogs of tools and techniques related to software development project management, specifically in risk management, that will provides a set of techniques and tools than an SME can select according to its specific needs in order of facilitate the implementation of best practices.

We present a novel selection technique for VR called LenSelect. The main idea is to decrease the Index of Difficulty (ID) according to Fitts’ Law by dynamically increasing the size of the potentially selectable objects. This facilitates the selection process especially in cases of small, distant or partly occluded objects, but also for moving targets. In order to evaluate our method, we have defined a set of test scenarios that covers a broad range of use cases, in contrast to often used simpler scenes. Our test scenarios include practically relevant scenarios with realistic objects but also synthetic scenes, all of which are available for download. We have evaluated our method in a user study and compared the results to two state-of-the-art selection techniques and the standard ray-based selection. Our results show that LenSelect performs similar to the fastest method, which is ray-based selection, while significantly reducing the error rate by 44%.

MuFA (Multi-type Fourier Analyzer): A tool for batch generation of MuMax3 input scripts and multi-type Fourier analysis from micromagnetic simulation output data