Use Of Data Types Research Articles

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

Induced polarization of volcanic rocks. 4. Large-scale induced polarization imaging

SUMMARY Thanks to the emergence of new technologies developed with the goal of performing large-scale galvanometric induced polarization surveys and thanks a better understanding of the underlying physics of induced polarization, this geophysical method can now be applied in the field of volcanology and geothermal resources assessment. A new approach is developed here for directly inverting the primary and secondary electric fields recorded at a set of independent stations when injecting a primary current. The use of independent stations to measure the primary and secondary electrical fields improves the quality of the data by reducing the capacitive coupling effects inherent to systems based on long cables. It avoids issues associated with using the same electrodes for both current injection and voltage measurements and negative apparent resistivity and chargeability values. With such acquisitions, we can perform true 3-D surveys in areas characterized by complex topography such as volcanoes. The numerical scheme we developed returns as output the electrical conductivity and chargeability fields. The implemented methodology presents several advantages. The first is the use of data types at the stations, for example the electric field intensity, that are independent from the local geometrical station parameters such as electrode spacing and dipole orientation. The second advantage lies in the suitability of the proposed approach to perform large-scale applications since we use a matrix-free approach that does not require the assembly of the Jacobian matrices. The third concerns the possibility of performing the inversion on complex geometries through a consistent use of the finite element method on unstructured meshes in combination with algebraic multigrid preconditioning for the regularization and the solution of the forward and adjoint problems. The computation of 3-D sensitivity maps can also be a real asset in survey design. After validating our approach with a benchmark synthetic case study, we test it on a large-scale induced polarization survey that mimic true field conditions on a volcanic environment with rough topography. Our tests demonstrate the high potential of this electric field approach in volcanology especially for deep (3 km) imagining of the internal structure of volcanoes, which in turn could improve our understanding of hydrothermal systems and allow the monitoring of active volcanoes and the potential risk of collapse.

Algebraic Specification for Input-Output in Abstract Data Types

Data Types (ADT) are used when creating software systems, in the systems design. Usually we use algebraic specification to specify the operations in a data type. The use of data types is a methodology or style of working which yields improved design when followed. In this paper we study the addition of input-output operations to the algebraic specification of operations over a data type. The motivation is that input-output operations are used in actual implementations. A specification with input-output is more complete than one without it. We need input-output operations in our programs. This justifies the addition of such operations to the specification. We consider the definition of input-output operations in functional programming in particular in Haskell. Our input-output specifications are not exactly equal to Haskell programs although some of them are likely. We specify input-output operations in a form likely to the specification of the other operations. The result is the algebraic specification of the input-output operations for many frequently used data types. The language considered is sufficiently expressive to model all these operations. The technique is illustrated by means of a variety of examples. We started from sequences, continued with sets and finish with dictionaries. The specifications we present in this paper can be used as specifications of methods of ADT definitions in object oriented programing.

Arithmetical Datatypes, Fracterms, and the Fraction Definition Problem

Datatypes and abstract datatypes are positioned as results of importing aspects of universal algebra into computer science and software engineering. It is suggested that 50 years later, by way of a transfer in the opposite direction, outcomes of research on datatypes can be made available via elementary arithmetic. This idea leads to the notions of an arithmetical signature, an arithmetical datatype and an arithmetical abstract datatype and to algebraic specifications for such entities. The area of fractions in elementary arithmetic is chosen as an application area and while taking a common meadow of rational numbers as the basis, an arithmetical datatype equipped with an absorptive element. The use of datatypes and signatures makes syntax available for giving precise definitions in cases where lack of precision is common place. Fracterm is coined as the name for a fraction when primarily understood as a syntactic entity. The main contribution of the paper is to provide a detailed terminology of fracterms. Subsequently the fraction definition problem is stated, a distinction between explicit definitions of fractions and implicit definitions of fractions is made, and an outline of a survey of both forms of definitions of the notion of a fraction is given.

Forensic epistemology: exploring case-specific research in forensic science

Our inquiry into forensic epistemology explores the use of data types for case-specific research within three pattern interpretation disciplines. It also examines the epistemic status of practitioner case experimentation in forensic science. We developed three cases from different pattern-interpretation disciplines: a friction ridge analysis; a bloodstain pattern analysis; and a footwear impression analysis. For each case, a series of experiments were derived using three different data types: a quantitative approach (using numeric data), a qualitative approach (using image data) and a mixed-method approach (using both numeric and image data). We supplied data analyses that would be common knowledge for any academic researcher. Electronic files were compiled for each case and research method and forwarded by Qualtrics Software to forensic practitioners within the prescribed discipline. Demographic questions on practitioner education level and years of experience were included in the survey, along with open-ended comment areas. The dependent variable is the participants’ percentage confidence in providing an opinion from the data type used. ANOVA analyses indicated that the practitioners were more confident using a mixed-method data approach. No differences were found between the percentage confidence levels and discipline type. Similarly, there was no significant difference between the confidence levels and years of experience or the participants’ education level. The qualitative data analysis validated the quantitative results in that the practitioners were more confident with a mixed-method research approach.

Promoting Literacy with Teacher Knowledge in Analyzing and Using Student Data: A Review of Literature

The concept of data based decision making has been in the forefront of educational reform for decades. Despite the fact that data could better inform the educational decisions teachers make, some educators lack understanding of data and assessment. This could lead to misinterpretation and poor use of the scores. The purpose of this literature review is to learn more about English language arts (ELA) and literacy teachers' current practices and experiences in using assessment data to shape their literacy instruction and to learn more about their perceptions of how this assessment data should be used to shape classroom instruction. This literature review will describe five themes: (a) school leaders and teachers' level of understanding related to interpreting assessment results, (b) use of data types having different applications for school, (c) data collection methods schools employ, (d) the process of utilizing the data, and (e) creating a data culture within school. Finally, possible ideas about the future areas of research are shared.

Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach

Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems.

Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.

In the past decade, genome-wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions. We summarize the contributions to Genetic Analysis Workshop 18 (GAW18) that concern this search using methods for population-based association analysis. Many of the members of our GAW18 working group made use of data types that have only recently become available through the use of next-generation sequencing technologies, with many focusing on the investigation of rare variants instead of or in combination with common variants. Some contributors used a haplotype-based approach, which to date has been used relatively infrequently but may become more important for analyzing rare variant association data. Others analyzed gene-gene or gene-environment interactions, where novel statistical approaches were needed to make the best use of the available information without requiring an excessive computational burden. GAW18 provided participants with the chance to make use of state-of-the-art data, statistical techniques, and technology. We report here some of the experiences and conclusions that were reached by workshop participants who analyzed the GAW18 data as a population-based association study.

A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

Detailed clinical models: a review.

ObjectivesDue to the increasing use of electronic patient records and other health care information technology, we see an increase in requests to utilize these data. A highly level of standardization is required during the gathering of these data in the clinical context in order to use it for analyses. Detailed Clinical Models (DCM) have been created toward this purpose and several initiatives have been implemented in various parts of the world to create standardized models. This paper presents a review of DCM.MethodsTwo types of analyses are presented; one comparing DCM against health care information architectures and a second bottom up approach from concept analysis to representation. In addition core parts of the draft ISO standard 13972 on DCM are used such as clinician involvement, data element specification, modeling, meta information, and repository and governance.ResultsSix initiatives were selected: Intermountain Healthcare, 13606/OpenEHR Archetypes, Clinical Templates, Clinical Contents Models, Health Level 7 templates, and Dutch Detailed Clinical Models. Each model selected was reviewed for their overall development, involvement of clinicians, use of data types, code bindings, expressing semantics, modeling, meta information, use of repository and governance.ConclusionsUsing both a top down and bottom up approach to comparison reveals many commonalties and differences between initiatives. Important differences include the use of or lack of a reference model and expressiveness of models. Applying clinical data element standards facilitates the use of conceptual DCM models in different technical representations.

Mini-Projects Development in Computer Science - Students' Use of Organization Tools

This paper describes a study aimed at identifying different profiles of students' project development processes. Specifically, we assessed the use of abstract data types for the development of knowledge-based projects. The concept of abstract data types was introduced to high school students who took the course Computer Science-Logic Programming. During their studies the students learned and practiced various tools and methods of project development, one of which was based on the use of abstract data types as tools for problem solving and knowledge representation. To this end, a one-day workshop for team development of mini-projects was organized, and the whole development process was audio and video documented, categorized and analyzed. The pro- files of team behavior in the project development process were specified. The analysis of the profiles resulted in identifying four types of project development teams, all of which employed some or- ganizing tool in developing their projects. Two types of the developing teams used abstract data types and two used other methods. The findings indicated that the process of project development of those who used abstract data types was more structured and more organized than others.

High-School Students' Attitudes Regarding Procedural Abstraction

During the last decade a new computer science curriculum has been taught in Israeli high schools. This curriculum introduces CS concepts and problem-solving methods independently of specific computers and programming languages, along with the practical implementation of those concepts and methods encountered in actual programming languages. The advanced study unit of the curriculum, Software Design, introduces the students to various aspects of software systems design through the use of abstract data types (ADTs). One main goal of the unit is to develop abstract thinking skills and problem-solving abilities. More specifically, the unit presents principles such as procedural abstraction, data abstraction, information hiding, modularity, efficiency, and reuse of code. We present the results of a preliminary study whose aim was to assess students' attitudes regarding procedural abstraction. The research population consisted of high-school students who attended the Software Design course (beginners), and a control group of undergraduate CS students (advanced). The results of the study clearly indicated a significant difference between the attitudes of the two groups. We found that the advanced students preferred algorithms that were formulated to some extent with high-level abstraction; however, they encountered a cognitive barrier when dealing with algorithms that they had evaluated as too abstract for them. However, beginners felt more comfortable with algorithms with low-level abstraction. Nevertheless they showed open-mindedness toward some degree of procedural abstraction. The results of the study were used to design scaffolding problem-solving tools for algorithm development, utilizing procedural abstraction techniques that can be adapted to various student populations (Haberman, B. (2002) SIGCSE Bulletin, 34(4), 60–64).

A FORTRAN 90 genetic algorithm module for composite laminate structure design

The design of the stacking sequence for a composite laminate involves a set of discrete variables (ply material and ply orientation), and is thus well-suited to genetic algorithms for design optimization. Such algorithms have typically been custom-designed in FORTRAN 77 to suit specific optimization problems. Fortran 90 is a modern, powerful language with features that support important programming concepts, including those used in object-oriented programming. The Fortran 90 genetic algorithm module is used to define genetic data types, the functions which use these data types, and to provide a general framework for solving composite laminate structure design problems. The language''s support of abstract data types is used to build genetic structures such as populations, subpopulaions, individuals, chromosomes, and genes, and these data types are combined and manipulated by module subroutines. The use of abstract data types and long variable names makes the code useful and easily understood, while dynamic memory allocation makes the module flexible enough to be used in design problems of varying size and specification.

A layered, operational model of data control in programming languages

This paper describes a layered model of the semantics of the data control aspect of programming languages; this aspect of programming language semantics concerns access to the data objects of the program. The model is an information structure model in which the information structures are defined in a relatively precise manner using algebraic specification techniques for abstract data types. The use of abstract data types is also the key to the layering of the description: the outermost layer describes the semantics of the language feature, the middle layer contains definitions for the manipulation of the information structures used within the model, and the innermost layer contains precise descriptions of these information structures.

A software environment for digital signal-processing simulations

A signal-processing software system is described which allows the simulation of systems described by block diagrams or signal-flow graphs. A high-level data-flow language describes the interconnection of the components. All configurations of interconnections are allowed, including those containing feedback. Component systems (blocks) are allowed to be multi-input, multi-output, and to be programmed in any language. Blocks are implemented as separate processes running under a UNIX2 operating system. Input and output signals are transferred between blocks via the UNIXpipe facility. Thedata type of a signal is arbitrary in the context of the compiler; within a component or system, signals and internal variables can be either floating point or fixed point. The compiler enforces strong or weak type checking of signals according to the characteristics of the blocks generating and receiving the signals. The invariance of the program to implementation data type is accomplished by the use of abstract data types. Fixed-point simulations having differing number of bits per signal and per internal variable in a block are supported. Special display software is used to allow any signal in the system to be displayed on any graphics device.

Teaching data abstraction in a beginning Pascal class

Modern programming practice advocates the use of abstract data types to aid in design, coding, and maintenance of programs [1]. Many students who are not computer majors will write programs as part of their jobs. Therefore, it is important to expose those students who will take only one or two programming courses to the idea of abstract data types. The paper below illustrates how this was done in one of our beginning Pascal classes.

An Algebraic Approach to the Standardization and the Certification of Graphics Software

During the last decade most advances in programming languages as well as in programming methodology resulted from a consequent use of sound mathematical tools: the most impressive indicator of such a trend is the use of abstract data types which relate immediately to algebra, model theory and mathematical logic.

Program Development as a Formal Activity

A methodology of program development by transformations is outlined. In particular, ways of representing the transformation rules are discussed, and the relationship between notions of their correctness and the semantic definition of programming languages is studied. How transformation techniques are complemented by the use of abstract data types and assertions is described. In the resulting calculus of transformations, the single rules not only represent design or optimization techniques, but they also incorporate verification principles. To illustrate this approach, the Warshall algorithm is developed by successive applications of transformations.

The use of abstract data types to simplify program modifications

If a program is structured using abstract data types as the basic unit of modularity, then that program is much easier to extend or modify. This thesis is illustrated by the staged development of a program to compute prime numbers based on the sieve of Eratosthenes. This paper includes an extensive introduction to the concept of abstract data types and can be used as a tutorial survey. It includes discussions on the use of abstract data types in connection with recent approaches to data abstractions, heirarchical structure, and program design. Abstract data types are an extension and modification of the traditional concept of data type. An abstract data type defines not only a data representation for objects of the type but also the set of operations that can be performed on objects of the type. Furthermore, the abstract data type can protect the data representation from direct access by other parts of the program.

The use of abstract data types to simplify program modifications

If a program is structured using abstract data types as the basic unit of modularity, then that program is much easier to extend or modify. This thesis is illustrated by the staged development of a program to compute prime numbers based on the sieve of Eratosthenes. This paper includes an extensive introduction to the concept of abstract data types and can be used as a tutorial survey. It includes discussions on the use of abstract data types in connection with recent approaches to data abstractions, heirarchical structure, and program design. Abstract data types are an extension and modification of the traditional concept of data type. An abstract data type defines not only a data representation for objects of the type but also the set of operations that can be performed on objects of the type. Furthermore, the abstract data type can protect the data representation from direct access by other parts of the program.