Defined Data Types Research Articles

Recommendation system for financial decision-making using Artificial intelligence

The rapid expansion of artificial intelligence (AI) in consumer markets presents challenges, particularly in how cognitive biases influence financial decision-making. These biases can lead to irrational spending, raising ethical concerns about AI’s role in such applications. This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices. The focus is on developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations. The article considers a system for generating personalized financial recommendations based on large language models, which uses transaction history, predicted costs, and anomaly information to generate individual advice. Techniques include using Isolation Forest for identifying atypical financial actions and a combination of ARIMA and LSTM models for budget forecasting. The research also considers integrating these models with large language models (LLMs) to generate personalized recommendations. The methodological part of the work includes an analysis of existing models and their areas of application, defining data types and structures for processing, developing a system that integrates the available models, and testing it. The process of generating recommendations is described, which includes the stages of processing input data, forming context, generating recommendations and evaluating them taking into account user characteristics, such as risk level, financial goals and preferences. The generated recommendations are aimed at optimizing the user's financial behavior and can be adapted to different income levels. Special attention is paid to the ethical aspects of the system, which include ensuring confidentiality, fairness and transparency, as well as the importance of supporting user autonomy in making financial decisions. The system promotes responsible financial behavior by helping to avoid impulsive spending and increasing financial awareness without manipulation or imposing specific decisions.

Multifaceted formal methods and their interdisciplinary role — From the cathedral of ‘components as coalgebras’ to the HCI context and the open source software bazaar

In this article we revisit the history of formal methods with a focus on important aspects that contribute to their interdisciplinary role. We consider: the variability of mathematical representation techniques on which the theoretical foundations of formal methods are based; formal methods multidisciplinarity; their capability to serve at a meta-level in providing the semantics of programming languages, specification and modelling languages as well as higher-level and domain-specific formal notations; and, finally, how some of these higher-level and domain-specific notations may be lifted at an interdisciplinary level.Within this historical review, we are inspired by Luís Barbosa's “components as coalgebras” approach in seeing that the duality data-process is underlying all those aspects of formal methods. We also see that such a duality may not only be expressed in universal terms within category theory, but may also be characterised in practical terms and focused applications by two distinct logic paradigms, equational logic for the data and rewriting logic for the process, by two modelling directions, forward process definitions and backward data-driven process transformations, and by the distinction between syntax, defined by the data structures, and semantics, provided by rewrite rules. We use the Maude modelling language to illustrate the application of the data-process duality. In fact, Maude use equational logic to define data types and rewriting logic to express system evolution. Illustrative examples are from the areas of cognitive science and human-computer interaction (HCI).We then define a data-driven model transformation, which we call elaborative mining, which adopts a backward perspective to recover a behaviour that was observed in real life but was not predicted forward by the original model. Finally, we see how the “open source software bazaar”, which is a metaphor for the apparently chaotic open source development process, offers us a big data context to lift the driving process for model transformation from deterministic to statistical.

Reflexive Event-B: Semantics and Correctness the EB4EB Framework

The Event-B method enables correct by construction modeling of systems. It relies on set theory and first-order logic, to describe a series of refined system models expressed as a set of events modifying state variables. Invariants and theorems are introduced to express system properties submitted to the proof system associated with Event-B. While Event-B has proven its efficiency for the proof of this type of property, it does not offer powerful means allowing the explicit description of properties other than safety and specific forms of reachability. Checking other properties such as deadlock-freeness, liveness, or event scheduling requires ad hoc modeling techniques and external tools such as model checkers or other proof systems. This article presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EB4EB</i> , a new modeling framework offering the capability to introduce formally defined Event-B extensions, in particular new proof obligations corresponding to new properties. It is based on metamodeling techniques. It includes a theory (a metatheory) modeling Event-B and offers means for explicit manipulation of Event-B features and an extension mechanism to explicitly formalize and prove other properties. This reflexive framework relies on a trace-based semantics of Event-B and introduces a set of Event-B theories defining data types, operators, well-defined conditions, theorems, and proof rules to define Event-B constructs and their semantics. Deep and shallow instantiation mechanisms are set up to instantiate the obtained metatheory. The EB4EB framework and its instantiation mechanisms are developed in Event-B using the Rodin platform ensuring correctness and internal consistency of the defined theories. Lamport's clock example, instantiating EB4EB in both shallow and deep mechanisms, is used to evaluate the proposed approach.

Formal domain-driven system development in Event-B: Application to interactive critical systems

The design of complex and/or critical systems requires handling the environment constraints in which these systems evolve. Formal methods allow system developers to design models of such systems. They provide constructs for modelling components and views of these systems. However, these formal methods do not include built-in constructs for modelling the environment, and more broadly, domain knowledge associated with system models. Although ontologies have demonstrated their efficiency in modelling domain-specific features, they are not available as built-in constructs in formal methods.This paper shows how formal ontologies can be used to model domain-specific knowledge, as well as how system models may refer to these ontologies through annotation. We rely on the Event-B refinement and proof state-based method, and the associated theories, to define a framework in which domain-specific knowledge ontologies are formalised as Event-B theories defining data types used to type Event-B system design models. Finally, this framework is deployed for the specific case of interactive critical systems. To illustrate the proposed approach, a case study of the Traffic Collision Avoidance System (TCAS) is developed.

Performing Information Extraction and Ontology Development for Mission Engineering Applications

The development and evaluation of Mission Engineering Threads (METs) require an understanding of the operational context for which a system-of-systems (SoS) will be employed as well as an assessment that the performance of a complex SoS is effective and safe. The information describing the design and performance parameters of the systems within the SoS is distributed in many different physical locations, and is represented in a variety of formats, both structured and unstructured. Because of this dynamic on the structuring of the data to include differing ontology frameworks, it is necessary to develop a framework and toolset to handle the automated extraction of information from disparate information sources. In addition, this extracted information needs to be categorized properly into defined data types as represented in the specific MET to correctly capture the appropriate context of the mission scenario. Semantic technique solutions will be researched, analysed, and applied as a means to infer new facts from existing facts and data. These techniques are particularly powerful when the amount of data and/or the relationships and constraints among data are too cumbersome and complex for human understanding and reasoning. Using the characteristics of wine as an example, we present our framework to show how it enables rapid and contextually relevant extraction, and represents complex information in a user-friendly format.

Pladias platform: Technical description of the database structure.

BackgroundDigitising and aggregating local floristic data is a critical step in the study of biodiversity. The integrative web-based platform Pladias, designed to cover a wide range of data on vascular plants, was recently developed in the Czech Republic. The combination of occurrence data with species characteristics opens many opportunities for data analysis and synthesis.New informationThis article describes the relational structure of the Pladias database service (PladiasDB) and the context of the platform architecture. The structure is relatively complex, as our goal was to cover: (i) species occurrence records, including their management, validation and export of revised species distribution maps, (ii) data on species characteristics with quality control tools using defined data types and (iii) separate user interfaces (UI) for professionals and the general public. We discuss the approaches chosen to model individual elements in PladiasDB and summarise the experience gained during the first five years of operation of the Pladias platform.

Verifiable Model Construction for Business Processes

Business process specified in Business Process Execution Language (BPEL), which integrates existing services to develop composite service for offering more complicated function, is error-prone. Verification and testing are necessary to ensure the correctness of business processes. SPIN, for which the input language is PROcess MEta-LAnguage (Promela), is one of the most popular tools for detecting software defects and can be used both in verification and testing. In this paper, an automatic approach is proposed to construct the verifiable model for BPEL-based business process with Promela language. Business process is translated to an intermediate two-level representation, in which eXtended Control Flow Graph (XCFG) describes the behavior of BPEL process in the first level and Web Service Description Models (WSDM) depict the interface information of composite service and partner services in the second level. With XCFG of BPEL process, XCFGs for partner services are generated to describe their behavior. Promela model is constructed by defining data types based on WSDM and defining channels, variables and processes based on XCFGs. The constructed Promela model is closed, containing not only the BPEL process but also its execution environment. Case study shows that the proposed approach is effective.

Incoming Call Forecasting Framework for a Network Service Provider Call Center during Mobile Expo Events

An accurate forecasting model for call center operations allows a company to optimize the number of staffs needed. However, when a special event is planned, the call volume and pattern during that event can be expected to be different from those reflected in the historical database, and consequently, the accuracy of current forecasting methods may degenerate. Thus, a framework that can help to provide a highly accurate forecasting model in such situations is needed. In this paper, a conceptual framework for forecasting incoming calls during mobile expo events is proposed. The framework comprises four main steps: defining data types, verifying time series forecasting methods based on the defined data types, defining the forecasting model and forecasting the incoming calls. This approach can assist in systematically selecting an appropriate forecasting model when a mobile expo event is arranged. Experimental results show that this framework helps to select the appropriate forecasting model for each mobile expo event organized over one year. For the first and third mobile expo event periods, a mixed-period model with an individual forecasting approach, provides the best result, whereas in the second period, a combined forecasting approach for nonevent and mobile expo event periods, is the most appropriate.

ODM Clinical Data Generator: Syntactically Correct Clinical Data Based on Metadata Definition.

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.

Python-based preprocessing for applying machine learning in breast cancer metastasis prediction.

e13558 Background: Artificial intelligence (AI) and machine learning (ML) have outstanding contributions in oncology. One of the applications is the early detection of breast cancer. Recently, several ML and data mining techniques have been used for both detection and classification of breast cancer cases. It is found that about 25% of breast cancer cases have an aggressive cancer at diagnosis time, with metastatic spread. The absence or presence of metastatic spread largely determines the patient’s survival. Hence, early detection is very important for reducing cancer mortality rates Methods: This study aims at applying ML and data mining, using AI techniques, for exploring and preprocessing breast cancer dataset, before building the ML classification Model for breast cancer metastasis prediction. The model will be implemented for mass screening, to prioritize patients who are more likely to develop metastases. A dataset of breast cancer cases was provided by the Oncology and Nuclear Medicine Department, Faculty of Medicine, Alexandria University. It contains clinical records of 5236 patients, diagnosed with breast cancer. ML libraries in Python programming language was used to explore the dataset and determine ratio of missing data, define data types, redundant data, and specify class label and predictors that to be used for the classification model. Results: In this work, the results showed that missing data ratio in some columns exceeds 90%, there are redundant features to be eliminated, data type conversion and feature reduction should be applied to prepare the data. Conclusions: Based on the previous findings, it is recommended to use ML preprocessing python libraries to prepare the dataset before building ML classification model of breast cancer metastasis prediction.

Challenges in the Development of Health System Performance Dashboard for Malaysia

Introduction: Traditional static reports of performance monitoring in healthcare sector is inconsistent, incomparable and time-consuming. Incapable of reflecting the whole-of-system or effectively support decision making, that a digitalized interactive tool for measuring, monitoring, and managing performance is mandatory. Our aims are: (1) Enculturation of integrative healthcare performance; (2) Addressing challenges to development of the Health System Performance Dashboard (HSPD) for Malaysia; and (3) Invitation for smart partnership.Methods: There are six steps for a dashboard implementation and development from Dashboard Development and Deployment: A Methodology for Success by Noetix Corporation. This proof of concept project has approaching the end of second stage; building a prototype. We discussed on the challenges that we encountered until the development of the prototype and the proposed recommendations on each challenges.Results: The first step in developing a dashboard is planning. This involves team member’s identification and roles, determining project objectives and scope, setting a timeline and budget, and defining strategic Key Performance Indicators (KPIs). Second step is the requirements gathering by interviewing stakeholders, setting expectations, explore functionality, define data types and build a prototype.Conclusion: Digitalization and HSPD are warranted to assist decision-makers to prioritize on a larger scale. A key success factor to realization of HSPD is an anchored data scientist team. This calls for an urgent and important smart partnership with the experts. A.I. expertise is needed for continuous diagnostic, predictive and prescriptive analytics of the massive healthcare data.Acknowledgement: Director General of Health, Deputy Director General of Health (Research and Technical Support), Director of Research Policy and Planning Division, National Institute of HealthInternational Journal of Human and Health Sciences Supplementary Issue: 2019 Page: 54

Biostatistical Analysis: A Primer for Clinical Exercise Physiology, Part II

ABSTRACTThis paper is the second in a two-part series intended to provide a brief overview of some of the important concepts in the field of biostatistics. In this paper, basic analysis methods are reviewed and issues important to the conduct and interpretation of research studies are discussed. Statistical methods of analysis are dependent on the type of data to be analyzed. Five common types of data are briefly explained: nominal, binary, ordinal, discrete, and continuous. Basic analysis methods are presented in the context of these defined data types. The interpretation of a study's results is the final critical step upon completion of a research study. The issues of critical thinking, bias, confounding, validity and the potential for over-interpretation of research results are discussed. Understanding biostatistical concepts and appropriately employing them over the course of the study is an essential part of quality research.

Coming to terms with quantified reasoning

The theory of finite term algebras provides a natural framework to describe the semantics of functional languages. The ability to efficiently reason about term algebras is essential to automate program analysis and verification for functional or imperative programs over inductively defined data types such as lists and trees. However, as the theory of finite term algebras is not finitely axiomatizable, reasoning about quantified properties over term algebras is challenging. In this paper we address full first-order reasoning about properties of programs manipulating term algebras, and describe two approaches for doing so by using first-order theorem proving. Our first method is a conservative extension of the theory of term alge- bras using a finite number of statements, while our second method relies on extending the superposition calculus of first-order theorem provers with additional inference rules. We implemented our work in the first-order theorem prover Vampire and evaluated it on a large number of inductive datatype benchmarks, as well as game theory constraints. Our experimental results show that our methods are able to find proofs for many hard problems previously unsolved by state-of-the-art methods. We also show that Vampire implementing our methods outperforms existing SMT solvers able to deal with inductive data types.

Spatiotemporal social (STS) data model: correlating social networks and spatiotemporal data

A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users.

A type system for Continuation Calculus

Continuation Calculus (CC), introduced by Geron and Geuvers, is a simple foundational model for functional computation. It is closely related to lambda calculus and term rewriting, but it has no variable binding and no pattern matching. It is Turing complete and evaluation is deterministic. Notions like "call-by-value" and "call-by-name" computation are available by choosing appropriate function definitions: e.g. there is a call-by-value and a call-by-name addition function. In the present paper we extend CC with types, to be able to define data types in a canonical way, and functions over these data types, defined by iteration. Data type definitions follow the so-called "Scott encoding" of data, as opposed to the more familiar "Church encoding". The iteration scheme comes in two flavors: a call-by-value and a call-by-name iteration scheme. The call-by-value variant is a double negation variant of call-by-name iteration. The double negation translation allows to move between call-by-name and call-by-value.

A Project Based CS/IS-1 Course with an Active Learning Environment

High level programming languages use system defined data types and the user defined data types in computations. We have developed a project-based CS/IS-1 course to substitute the traditional lecture based classroom to help students design and use at least two user defined data types in their computations to solve real world problems. Abstract data types and basic programming constructs are introduced efficiently to the students in an active learning environment using games. To assess and evaluate the changes made we distributed the course module among our students and other instructors. This paper describes our experience in developing the project based active learning environment.

Signal Processing Analysis of Temperature Fluctuations for a Fuel Subassembly using SCILAB

This paper investigates the simulation study of signal processing of temperature fluctuations and their use in determining malfunctions in coolant fluid flow in a fuel subassembly, by the use of SCILAB software. SCILAB is free open source software with a GNU general public license (GPL) compatible license. Its main features includes a large number of mathematical functions, high level programming language, 2-D and 3-D graphics, advanced data structures and user defined data types. With respect to our purpose, this tool acts as an excellent setup for performing data analysis, signal processing and statistics. The variation of RMS fluctuation for different values of time with temperature increase has been investigated for aiding in program development for FPG A for use in real time monitoring.

Efficient and Type-Safe Generic Data Storage

In this paper we present an elegant method for sequentializing arbitrary data using the generic language extension of the functional programming language Clean. We show how the proposed operations can be used to store values of any concrete data type in several kinds of IO containers (such as files or arrays of characters), and how to manipulate stored data efficiently. Moreover, by extending stored data with encoded type information, data manipulation will be type-safe. Defining these operations generically has the advantage that specific instances for user defined data types can be generated fully automatically. Compared to traditional sequentialization methods (or to common data manipulation, using relational data bases) our operations are an order of magnitude faster.

Special Issue: Advanced Strategies in Grid Environments—Models and Techniques for Scheduling and Programming

Special Issue: Advanced Strategies in Grid Environments—Models and Techniques for Scheduling and Programming

SaIL: A Spatial Index Library for Efficient Application Integration

With the proliferation of spatial and spatio-temporal data that are produced everyday by a wide range of applications, Geographic Information Systems (GIS) have to cope with millions of objects with diverse spatial characteristics. Clearly, under these circumstances, substantial performance speed up can be achieved with the use of spatial, spatio-temporal and other multi-dimensional indexing techniques. Due to the increasing research effort on developing new indexing methods, the number of available alternatives is becoming overwhelming, making the task of selecting the most appropriate method for indexing the data according to application needs rather challenging. Therefore, developing a library that can combine a variety of indexing techniques under a common application programming interface can prove to be a valuable tool. In this paper we present SaIL (SpAtial Index Library), an extensible framework that enables easy integration of spatial and spatio-temporal index structures into existing applications. We focus on design issues and elaborate on techniques for making the framework generic enough, so that it can support user defined data types, customizable spatial queries, and a broad range of spatial (and spatio-temporal) index structures, in a way that does not compromise functionality, extensibility and, primarily, ease of use. SaIL is publicly available and has already been successfully utilized for research and commercial applications.