123 research outputs found
Lessons from Formally Verified Deployed Software Systems (Extended version)
The technology of formal software verification has made spectacular advances,
but how much does it actually benefit the development of practical software?
Considerable disagreement remains about the practicality of building systems
with mechanically-checked proofs of correctness. Is this prospect confined to a
few expensive, life-critical projects, or can the idea be applied to a wide
segment of the software industry?
To help answer this question, the present survey examines a range of
projects, in various application areas, that have produced formally verified
systems and deployed them for actual use. It considers the technologies used,
the form of verification applied, the results obtained, and the lessons that
can be drawn for the software industry at large and its ability to benefit from
formal verification techniques and tools.
Note: a short version of this paper is also available, covering in detail
only a subset of the considered systems. The present version is intended for
full reference.Comment: arXiv admin note: text overlap with arXiv:1211.6186 by other author
Open Information Extraction with Entity Focused Constraints
International audienceOpen Information Extraction (OIE) is the task of extracting tuples of the form (subject, predicate, object), without any knowledge of the type and lexical form of the predicate, the subject, or the object. In this work, we focus on improving OIE quality by exploiting domain knowledge about the subject and object. More precisely, knowing that the subjects and objects in sentences are often named entities, we explore how to inject constraints in the extraction through constrained inference and constraint-aware training. Our work leverages the state-of-the-art OpenIE6 platform, which we adapt to our setting. Through a carefully constructed training dataset and constrained training, we obtain a 29.17% F1-score improvement in the CaRB metric and a 24.37% F1-score improvement in the WIRe57 metric. Our technique has important applications-one of them is investigative journalism, where automatically extracting conflict-of-interest between scientists and funding organizations helps understand the type of relations companies engage with the scientists. Our code and data are available at https: //github.com/prajnaupadhyay/ openie-with-entitie
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
A Survey on Automated Program Repair Techniques
With the rapid development and large-scale popularity of program software,
modern society increasingly relies on software systems. However, the problems
exposed by software have also come to the fore. Software defect has become an
important factor troubling developers. In this context, Automated Program
Repair (APR) techniques have emerged, aiming to automatically fix software
defect problems and reduce manual debugging work. In particular, benefiting
from the advances in deep learning, numerous learning-based APR techniques have
emerged in recent years, which also bring new opportunities for APR research.
To give researchers a quick overview of APR techniques' complete development
and future opportunities, we revisit the evolution of APR techniques and
discuss in depth the latest advances in APR research. In this paper, the
development of APR techniques is introduced in terms of four different patch
generation schemes: search-based, constraint-based, template-based, and
learning-based. Moreover, we propose a uniform set of criteria to review and
compare each APR tool, summarize the advantages and disadvantages of APR
techniques, and discuss the current state of APR development. Furthermore, we
introduce the research on the related technical areas of APR that have also
provided a strong motivation to advance APR development. Finally, we analyze
current challenges and future directions, especially highlighting the critical
opportunities that large language models bring to APR research.Comment: This paper's earlier version was submitted to CSUR in August 202
Visual Deficits in Dyslexia: Examination of stress patterns and the impact of emotions on students’ reading performance
The present study investigates the presence of visual deficits in students with developmental dyslexia as well as their emotions in relation to their reading performance. Dyslexia occurs in approximately 4% of the population (Simmons & Singleton, 2000) and concerns difficulties in reading and spelling for both L1 and L2 learning. One of the most common difficulties of dyslexic individuals has been noticed in stress errors (Paizi, Zoccolotti & Burani, 2011). However, the Greek and English language present differences regarding the visual information that entails stress pattern (such as the diacritic mark in Greek language). Additionally, emotions significantly affect students’ performance whether they are positive or negative (Pekrun et al., 2017). In this mixed-method research, 110 Greek students with dyslexia participated in a training program with pre- and post-phase. The training was assessed through visual and auditory stimuli to observe differences between these two sensory abilities. Moreover, questionnaire, interview and observational data were collected to examine the emotional impact. Results indicated an improvement in the stress pattern assignment of the Greek language after visual training while no improvement was observed in the English language since the stress pattern is not marked. The evidence supports the findings that visual impairments do play a role in the reading performance for both L1 and L2 learning. In addition, both positive and negative emotions were found to play a particular role in students’ performance but the extent of which positive emotions would lead to a positive outcome and negative emotions to a bad outcome was questioned. Nevertheless, anxiety was found to play a crucial role in students’ overall performance
International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts
The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of
Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the
International Conference on Mathematical Analysis and Applications in Science and Engineering –
ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days).
Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering,
industry, and technology, to discuss the development of new mathematical models, theories, and
applications that contribute to the advancement of scientific knowledge and practice. Authors proposed
research in topics including partial and ordinary differential equations, integer and fractional order
equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization,
control, probability, computational mathematics, amongst others.
The conference was designed to maximize the involvement of all participants and will present the state-of-
the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio
Data Science Techniques for Modelling Execution Tracing Quality
This research presents how to handle a research problem when the research variables are still unknown, and
no quantitative study is possible; how to identify the research variables, to be able to perform a quantitative
research, how to collect data by means of the research variables identified, and how to carry out modelling
with the considerations of the specificities of the problem domain. In addition, validation is also encompassed
in the scope of modelling in the current study. Thus, the work presented in this thesis comprises the typical
stages a complex data science problem requires, including qualitative and quantitative research, data collection,
modelling of vagueness and uncertainty, and the leverage of artificial intelligence to gain such insights, which
are impossible with traditional methods.
The problem domain of the research conducted encompasses software product quality modelling, and assessment,
with particular focus on execution tracing quality. The terms execution tracing quality and logging are
used interchangeably throughout the thesis.
The research methods and mathematical tools used allow considering uncertainty and vagueness inherently
associated with the quality measurement and assessment process through which reality can be approximated
more appropriately in comparison to plain statistical modelling techniques. Furthermore, the modelling approach
offers direct insights into the problem domain by the application of linguistic rules, which is an additional
advantage.
The thesis reports (1) an in-depth investigation of all the identified software product quality models, (2) a
unified summary of the identified software product quality models with their terminologies and concepts, (3)
the identification of the variables influencing execution tracing quality, (4) the quality model constructed to
describe execution tracing quality, and (5) the link of the constructed quality model to the quality model of the
ISO/IEC 25010 standard, with the possibility of tailoring to specific project needs.
Further work, outside the frames of this PhD thesis, would also be useful as presented in the study: (1) to define
application-project profiles to assist tailoring the quality model for execution tracing to specific application and
project domains, and (2) to approximate the present quality model for execution tracing, within defined bounds,
by simpler mathematical approaches.
In conclusion, the research contributes to (1) supporting the daily work of software professionals, who need
to analyse execution traces; (2) raising awareness that execution tracing quality has a huge impact on software
development, software maintenance and on the professionals involved in the different stages of the software
development life-cycle; (3) providing a framework in which the present endeavours for log improvements
can be placed, and (4) suggesting an extension of the ISO/IEC 25010 standard by linking the constructed
quality model to that. In addition, in the scope of the qualitative research methodology, the current PhD thesis
contributes to the knowledge of research methods with determining a saturation point in the course of the data
collection process
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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