Well-defined Semantics Research Articles

Granular computing-based time series anomaly pattern detection with semantic interpretation

Time series analysis may suffer from the “curse of dimensionality” due to its high-dimensionality characteristics. In terms of this issue, information granulation offers an effective vehicle to process time series at a higher level of abstraction. To take this advantage, this study conducts an interpretable time series anomaly pattern detection under the framework of granular computing, where each time series is granulated into a series of semantics according to its patterns and the anomaly ones are identified based on the obtained semantics. First, the time series is partitioned into a predefined number of segments and trend-based information granules are formed for the data points in each time interval. Guided by the principle of justifiable granularity, the granular results maintaining the main features of the original time series realize informative feature extraction and meaningful dimensionality reduction. Then, to realize semantic anomaly pattern detection, the Axiomatic Fuzzy Set (AFS) theory is generalized to construct and compute with semantic time series, and an AFS-based anomaly score is proposed to discover anomaly patterns. In the experiments, the proposed method is conducted on both UCR data sets and real-world time series, where the detected anomaly patterns are equipped with well-defined semantics.

IoT and Digital Twin: a new perspective for Cultural Heritage predictive maintenance

In the construction sector, the maintenance and monitoring of buildings are of fundamental importance, both in civil and cultural heritage buildings, ensuring their preservation, safety, and proper functioning over time. The preservation of the built heritage is one of the main objectives that a nation must pursue, and thanks to the ever-increasing spread of new technologies, it is possible to apply innovative approaches capable of monitoring in real-time the progressive damage of structures or intercepting sudden situations capable of causing extensive damage. Traditional monitoring methodologies, employed so far, are based on direct inspections and manual data collection, and are often expensive and only sometimes effective. In this scenario, a significant contribution has been made by the Internet of Things (IoT) paradigm, which has made it possible to collect real-time data from sensors placed on the structures to be monitored, thus enabling the implementation of predictive maintenance methodologies. A further contribution to the development of these methodologies has come from the emergence of the concept of the Digital Twin (DT), a digital model of an intended or actual real-world product, system or physical process that serves as an effectively indistinguishable digital counterpart of it for practical purposes such as simulation, integration, testing, monitoring and maintenance. In order to make the DT even more effective, it is possible to link it to the real structure through BIM, i.e. a process applied to existing buildings or monuments that aims not only at the mere restitution of the three-dimensional model but above all at the creation of so-called ‘intelligent models’. The latter is rich in geometric information, including the state of conservation of materials, in which all components are parametric objects with well-defined semantics and can contain all the historical information derived from an adequate documentary analysis. Starting from the above, this paper aims to present a methodology for monitoring an existing building, exploiting innovative technologies based on DT and IoT concepts. The case study analyzed is the Scientific Library of the University of Salerno, and the first results of the experiment are more than satisfactory.

CAPNet: Context and Attribute Perception for Pedestrian Detection

With a focus on practical applications in the real world, a number of challenges impede the progress of pedestrian detection. Scale variance, cluttered backgrounds and ambiguous pedestrian features are the main culprits of detection failures. According to existing studies, consistent feature fusion, semantic context mining and inherent pedestrian attributes seem to be feasible solutions. In this paper, to tackle the prevalent problems of pedestrian detection, we propose an anchor-free pedestrian detector, named context and attribute perception (CAPNet). In particular, we first generate features with consistent well-defined semantics and local details by introducing a feature extraction module with a multi-stage and parallel-stream structure. Then, a global feature mining and aggregation (GFMA) network is proposed to implicitly reconfigure, reassign and aggregate features so as to suppress irrelevant features in the background. At last, in order to bring more heuristic rules to the network, we improve the detection head with an attribute-guided multiple receptive field (AMRF) module, leveraging the pedestrian shape as an attribute to guide learning. Experimental results demonstrate that introducing the context and attribute perception greatly facilitates detection. As a result, CAPNet achieves new state-of-the-art performance on Caltech and CityPersons datasets.

Rule-based models via the axiomatic fuzzy set clustering and their granular aggregation

Rule-based models have become a popular way to represent and analyze the main knowledge residing in data because of the increasing complexity and uncertainty. For extracting semantically sound rules in a multi-view perspective, a novel rule-based model combined with the axiomatic fuzzy set (AFS) algorithm is developed in distributed systems. Using the idea of the AFS algorithm, several clusters and accompanying fuzzy descriptions are formed in terms of data distribution; thereby, the input of rules exhibits well-defined semantics by the logic compound of the predefined linguistic terms. The output of the rule is approximated by the Takagi–Sugeno (T–S) model, in which an extended weighted least squares method is designed to optimize the parameter vectors simultaneously. In virtue of the diversity of these individual results, a granular aggregation procedure incorporates the weighted principle of justifiable granularity to summarize all the local results into compact and meaningful descriptors (information granules). Five experiments considering synthetic and publicly available datasets are carried out to demonstrate the performance of the proposed approach. In addition, the proposed approach is also shown effective in an application involving the credit dataset.

Cameleer A deductive verification tool for OCaml

OCaml is particularly well-fitted for formal verification. On one hand, it is a multi-paradigm language with a well-defined semantics, allowing one to write clean, concise, type-safe, and efficient code. On the other hand, it is a language of choice for the implementation of sensible software, e.g., industrial compilers, proof assistants, and automated solvers. Yet, with the notable exception of some interactive tools, formal verification has been seldom applied to OCaml-written programs. In this paper, we present the ongoing project Cameleer, aiming for the development of a deductive verification tool for OCaml, with a clear focus on proof automation. We leverage on the recently proposed GOSPEL, Generic OCaml SPE cification Language, to attach rigorous, yet readable, behavioral specification to OCaml code. The formally-specified program is fed to our toolchain, which translates it into an equivalent program in WhyML, the programming and specification language of the Why3 verification framework. Finally, Why3 is used to compute verification conditions for the generated program, which can be discharged by off-the-shelf SMT solvers. We present successful applications of the Cameleer tool to prove functional correctness of several significant case studies, like FIFO queues (ephemeral and applicative implementations) and leftist heaps, issued from existing OCaml libraries.

Infinite Probabilistic Databases

Probabilistic databases (PDBs) model uncertainty in data in a quantitative way. In the established formal framework, probabilistic (relational) databases are finite probability spaces over relational database instances. This finiteness can clash with intuitive query behavior (Ceylan et al., KR 2016), and with application scenarios that are better modeled by continuous probability distributions (Dalvi et al., CACM 2009). We formally introduced infinite PDBs in (Grohe and Lindner, PODS 2019) with a primary focus on countably infinite spaces. However, an extension beyond countable probability spaces raises nontrivial foundational issues concerned with the measurability of events and queries and ultimately with the question whether queries have a well-defined semantics. We argue that finite point processes are an appropriate model from probability theory for dealing with general probabilistic databases. This allows us to construct suitable (uncountable) probability spaces of database instances in a systematic way. Our main technical results are measurability statements for relational algebra queries as well as aggregate queries and Datalog queries.

A new phylogenetic data standard for computable clade definitions: the Phyloreference Exchange Format (Phyx).

To be computationally reproducible and efficient, integration of disparate data depends on shared entities whose matching meaning (semantics) can be computationally assessed. For biodiversity data one of the most prevalent shared entities for linking data records is the associated taxon concept. Unlike Linnaean taxon names, the traditional way in which taxon concepts are provided, phylogenetic definitions are native to phylogenetic trees and offer well-defined semantics that can be transformed into formal, computationally evaluable logic expressions. These attributes make them highly suitable for phylogeny-driven comparative biology by allowing computationally verifiable and reproducible integration of taxon-linked data against Tree of Life-scale phylogenies. To achieve this, the first step is transforming phylogenetic definitions from the natural language text in which they are published to a structured interoperable data format that maintains strong ties to semantics and lends itself well to sharing, reuse, and long-term archival. To this end, we developed the Phyloreference Exchange Format (Phyx), a JSON-LD-based text format encompassing rich metadata for all elements of a phylogenetic definition, and we created a supporting software library, phyx.js, to streamline computational management of such files. Together they form a foundation layer for digitizing and computing with phylogenetic definitions of clades.

Computability of the Behaviour of Impacting Systems Beyond Zeno Times

Mathematical models of hybrid systems may exhibit Zeno behaviour, in which infinitely many discrete events occur in a finite time interval. Although this may be considered physically unrealistic, since Zeno behaviour is useful in modelling real-world systems, it is important for computational tools to be able to handle it. In particular, verification tools should use a well-defined semantics under which the Zeno behaviour is effectively and rigorously computable. In this paper, we give a semantics for systems with events similar to mechanical impacts and show that computation of the behaviour is possible, even beyond the Zeno time. If we allow perturbations outside this class of impacting systems, then it may not be possible to handle Zeno behaviour in a realistic way.

A Formal Framework for Complex Event Recognition

Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees.In this article, we embark on the task of giving a rigorous and efficient framework to CER. We propose a formal language for specifying complex events, calledcomplex event logic(CEL), that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. We give insight into the language design trade-offs regarding the strict sequencing operators of CEL and selection strategies.With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by introducing a formal computational model for CER, calledcomplex event automata(CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by output-linear delay enumeration of the results.

I♥LA

Communicating linear algebra in written form is challenging: mathematicians must choose between writing in languages that produce well-formatted but semantically-underdefined representations such as LaTeX; or languages with well-defined semantics but notation unlike conventional math, such as C++/Eigen. In both cases, the underlying linear algebra is obfuscated by the requirements of esoteric language syntax (as in LaTeX) or awkward APIs due to language semantics (as in C++). The gap between representations results in communication challenges, including underspecified and irrepro-ducible research results, difficulty teaching math concepts underlying complex numerical code, as well as repeated, redundant, and error-prone translations from communicated linear algebra to executable code. We introduce I♥LA, a language with syntax designed to closely mimic conventionally-written linear algebra, while still ensuring an unambiguous, compilable interpretation. Inspired by Markdown, a language for writing naturally-structured plain text files that translate into valid HTML, I♥LA allows users to write linear algebra in text form and compile the same source into LaTeX, C++/Eigen, Python/NumPy/SciPy, and MATLAB, with easy extension to further math programming environments. We outline the principles of our language design and highlight design decisions that balance between readability and precise semantics, and demonstrate through case studies the ability for I♥LA to bridge the semantic gap between conventionally-written linear algebra and unambiguous interpretation in math programming environments.

SENSE: A Flow-Down Semantics-Based Requirements Engineering Framework

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or improper functioning of the system that would be hard to fix later. In this context, this article proposes SENSE, a framework based on standardized expressions of natural language with well-defined semantics, called boilerplates, that support a flow-down procedure for requirement management. This framework integrates sets of boilerplates and proposes the most appropriate of them, depending, among other considerations, on the type of requirement and the developing system, while providing validity and completeness verification checks using the minimum consistent set of formalities and languages. SENSE is a consistent and easily understood framework that allows engineers to use formal languages and semantics rather than the traditional natural languages and machine learning techniques, optimizing the requirement development. The main aim of SENSE is to provide a complete process of the production and standardization of the requirements by using semantics, ontologies, and appropriate NLP techniques. Furthermore, SENSE performs the necessary verifications by using SPARQL (SPIN) queries to support requirement management.

Probabilistic Data with Continuous Distributions

Statistical models of real world data typically involve continuous probability distributions such as normal, Laplace, or exponential distributions. Such distributions are supported by many probabilistic modelling formalisms, including probabilistic database systems. Yet, the traditional theoretical framework of probabilistic databases focuses entirely on finite probabilistic databases. Only recently, we set out to develop the mathematical theory of infinite probabilistic databases. The present paper is an exposition of two recent papers which are cornerstones of this theory. In (Grohe, Lindner; ICDT 2020) we propose a very general framework for probabilistic databases, possibly involving continuous probability distributions, and show that queries have a well-defined semantics in this framework. In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (B´ar´any et al. 2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases.

Pragmatic reuse for DSML development

By bridging the semantic gap, domain-specific language (DSLs) serve an important role in the conquest to allow domain experts to model their systems themselves. In this publication we present a case study of the development of the Continuous REactive SysTems language (CREST), a DSL for hybrid systems modeling. The language focuses on the representation of continuous resource flows such as water, electricity, light or heat. Our methodology follows a very pragmatic approach, combining the syntactic and semantic principles of well-known modeling means such as hybrid automata, data-flow languages and architecture description languages into a coherent language. The borrowed aspects have been carefully combined and formalised in a well-defined operational semantics. The DSL provides two concrete syntaxes: CREST diagrams, a graphical language that is easily understandable and serves as a model basis, and crestdsl, an internal DSL implementation that supports rapid prototyping—both are geared towards usability and clarity. We present the DSL’s semantics, which thoroughly connect the various language concerns into an executable formalism that enables sound simulation and formal verification in crestdsl, and discuss the lessons learned throughout the project.

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).

A Linked Open Data Service and Portal for Pre-modern Manuscript Research

This paper presents a Linked Open Data publishing model for aggregating data from heterogeneous, distributed pre-modern manuscript databases into a global, harmonized data model and service. Our research hypothesis is that on top of the global data service based on ontologies and well-defined semantics, tools and applications can be created for solving novel research problems in manuscript studies using Digital Humanities methods. First results in implementing such a system in the international Mapping Manuscript Migrations project are described with lessons learned discussed in dealing with complex and imperfect historical data.

All the Clades in the World: Building a Semantically-Rich and Testable Ontology of Phylogenetic Clade Definitions

Taxonomic names are ambiguous as identifiers of biodiversity data, as they refer to a particular concept of a taxon in an expert’s mind (Kennedy et al. 2005). This ambiguity is particularly problematic when attempting to reconcile taxonomic names from disparate sources with clades on a phylogeny. Currently, such reconciliation requires expert interpretation, which is necessarily subjective, difficult to reproduce, and refractory to scaling. In contrast, phylogenetic clade definitions are a well-developed method for unambiguously defining the semantics of a clade concept in terms of shared evolutionary ancestry (Queiroz and Gauthier 1990, Queiroz and Gauthier 1994), and these semantics allow locating clades on any phylogeny. Although a few software tools have been created for resolving clade definitions, including for definitions expressed in the Mathematical Markup Language (e.g. Names on Nodes in Keesey 2007) and as lists of GenBank accession numbers (e.g. mor in Hibbett et al. 2005), these are application-specific representations that do not provide formal definitions with well-defined semantics for every component of a clade definition. Being able to create such machine-interpretable definitions would allow computers to store, compare, distribute and resolve semantically-rich clade definitions. To this end, the Phyloreferencing project (http://phyloref.org, Cellinese and Lapp 2015) is working on a specification for encoding phylogenetic clade definitions as ontologies using the Web Ontology Language (OWL in W3C OWL Working Group 2012). Our specification allows the semantics of these definitions, which we call phyloreferences, to be described in terms of shared ancestor and excluded lineage properties. The aim of this effort is to allow any OWL-DL reasoner to resolve phyloreferences on a phylogeny that has itself been translated into a compatible OWL representation. We have developed a workflow that allows us to curate phyloreferences from phylogenetic clade definitions published in natural language, and to resolve the curated phyloreference against the phylogeny upon which the definition was originally created, allowing us to validate that the phyloreference reflects the authors’ original intent. We have started work on curating dozens of phyloreferences from publications and the clade definition database RegNum (http://phyloregnum.org), which will provide an online catalog of all clade definitions that are part of the Phylonym Volume, to be published together with the PhyloCode (https://www.ohio.edu/phylocode/). We will comprehensively curate these definitions into a reusable and fully computable ontology of phyloreferences. In our presentation, we will provide an overview of phyloreferencing and will describe the model and workflow we use to encode clade definitions in OWL, based on concepts and terms taken from the Comparative Data Analysis Ontology (Prosdocimi et al. 2009), Darwin-SW (Baskauf and Webb 2016) and Darwin Core (Wieczorek et al. 2012). We will demonstrate how phyloreferences can be visualized, resolved and tested on the phylogeny that they were originally described on, and how they resolve on one of the largest synthetic phylogenies available, the Open Tree of Life (Hinchliff et al. 2015). We will conclude with a discussion of the problems we faced in referring to taxonomic units in phylogenies, which is one of the key challenges in enabling better integration of phylogenetic information into biodiversity analyses.

Modeling Self-Adaptive Software Systems by Fuzzy Rules and Petri Nets

A self-adaptive software system is one that can autonomously modify its behavior at runtime in response to changes in the system and its environment. It is a challenge to model such a kind of systems since it is hard to predict runtime environmental changes at the design phase. In this paper, a formal model called intelligent Petri net (I-PN) is proposed to model a self-adaptive software system. I-PN is formed by incorporating fuzzy rules to a regular Petri net. The proposed net has the following advantages. 1) Since fuzzy rules can express the behavior of a system in an interpretable way and their variables can be reconfigured by the runtime data, the proposed model can model runtime environment and system behavior. 2) Since a fuzzy inference system with well-defined semantics can be used in a complementary way with other model languages for the analysis, thus the proposed model can be analyzed, even though it is described in two different languages: component behaviors in Petri nets while logic control in fuzzy rules. 3) The proposed model has self-adaption ability and can make adaptive decisions at runtime with the help of fuzzy inference reasoning. We adopt a manufacturing system to show the feasibility of the proposed model.

Fuzzy time series forecasting based on axiomatic fuzzy set theory

In fuzzy time series, a way of representing their original numeric data through a collection of fuzzy sets plays a pivotal role and impacts the prediction performance of the constructed forecasting models. An evident shortcoming of most existing models is that fuzzy sets (their membership functions) are developed in an intuitive manner so that not all aspects of the time series could be fully captured. In this study, using an idea of axiomatic fuzzy set clustering we take the distribution of data into account to position time series in the framework of fuzzy sets. The obtained clusters exhibit well-defined semantics. To produce numeric results of forecasting, we develop a method to determine the prototypes based on the corresponding fuzzy description of the clusters. The commonly used enrollment time series is applied to demonstrate how the proposed method works. The experimental results exploiting the Taiwan Stock Exchange Capitalization Weighted Stock Index demonstrate that the proposed method can effectively improve forecasting accuracy. Furthermore, the proposed approach is of a general form and as such can be easily integrated with various fuzzy time series models.

Canonical decomposition of belief functions based on Teugels’ representation of the multivariate Bernoulli distribution

A canonical decomposition of belief functions is a unique decomposition of belief functions into elementary pieces of evidence. Smets found an equivalent representation of belief functions, which he interpreted as a canonical decomposition. However, his proposal is not entirely satisfactory as it involves elementary pieces of evidence, corresponding to a generalisation of belief function axioms, whose semantics lacks formal justifications. In this paper, a new canonical decomposition relying only on well-defined concepts is proposed. In particular, it is based on a means to induce belief functions from the multivariate Bernoulli distribution and on Teugels’ representation of this distribution, which consists of the means and the central moments of the underlying Bernoulli random variables. According to our decomposition, a belief function results from as many crisp pieces of information as there are elements in its domain, and from simple probabilistic knowledge concerning their marginal reliability and the dependencies between their reliability. In addition, we show that instead of interpreting with some difficulty Smets’ representation of belief functions as a canonical decomposition, it is possible to give it a different and well-defined semantics in terms of measures of information associated with the reliability of the pieces of information in our decomposition.

A Retargetable Compilation Framework for Heterogeneous Reconfigurable Computing

The future trend in microprocessors for the more advanced embedded systems is focusing on massively parallel reconfigurable architectures, consisting of heterogeneous ensembles of hundreds of processing elements communicating over a reconfigurable interconnection network. However, the mastering of low-level microarchitectural details involved in the programming of such massively parallel platforms becomes too cumbersome, which limits their adoption in many applications. Thus, there is a dire need for an approach to produce high-performance scalable implementations that harness the computational resources of the emerging reconfigurable platforms. This article addresses the grand challenge of accessibility of these diverse reconfigurable platforms by suggesting the use of a high-level language, occam-pi, and developing a complete design flow for building, compiling, and generating machine code for heterogeneous coarse-grained hardware. We have evaluated the approach by implementing complex industrial case studies and three common signal processing algorithms. The results of the implemented case studies suggest that the occam-pi language-based approach, because of its well-defined semantics for expressing concurrency and reconfigurability, simplifies the development of applications employing runtime reconfigurable devices. The associated compiler framework ensures portability as well as the performance benefits across heterogeneous platforms.