Java Objects Research Articles

HEDONISTIC LIFESTYLE AND FINANCIAL DISTRESS: THE ROLE OF RELIGIOSITY

A teenager is someone who is in the transition period from child to adult. Many teenagers wrongly attribute it to their lack of knowledge in the field of finance. Teenagers often follow the times and impose lifestyles. A hedonic lifestyle can be defined as a lifestyle that values ​​pleasure. This kind of lifestyle requires a lot to remain a consumer. Therefore, teenagers need many boundaries, one of which is religious teachings or individual religiosity. This research is important because financial distress in teenagers can cause many negative things. This research aims to determine the effect of a hedonistic lifestyle on financial distress through another variable, namely religiosity. This research used a sample of 241 teenagers. The results of this research are that hedonism has a positive effect on financial distress, and religiosity increases the relationship between financial distress and hedonism. This research influences teenagers' lifestyles so they don't make mistakes in socializing. It is hoped that this research can be a guide for teenagers in managing their finances, especially in fulfilling their lifestyle. In future research, it is hoped that researchers will increase the number of samples and expand the research objects, they can choose research objects in Central Java or Indonesia. Researchers are aware of the limitations in data collection, it is hoped that future researchers can expand the research objects and subjects. The conclusion, the hedonistic lifestyle has a positive effect on financial distress and religiosity is able to moderate the influence of the hedonistic lifestyle on financial distress, namely weakening the relationship between the two.

Migrating data from document-oriented database to graph-oriented database

In data migration between different types of NoSQL databases, data may not be directly transferred to the targeted database compared to migration of data between the same types of database. This is due to the heterogeneity of the storage paradigm of the NoSQL databases. For example, when migrating data from a document-oriented database such as MongoDB, which stores data in JSON (Java Object Notation) format to Neo4j, a graph-oriented database stores data in nodes, the differences among these databases’ storage paradigms require different representations of the data model in the targeted graph-oriented database. This paper proposed a sequential approach to migrate data from MongoDB to Neo4j. The approach migrates MongoDB data to Neo4j and verifies the migrated data using a comparative method. The paper discusses the migration algorithm and how complex fields in MongoDB, such as nested documents, are presented in Neo4j.

OATs’inside : Retrieving Object Behaviors From Native-based Obfuscated Android Applications

Analyzing Android applications is essential to review proprietary code and to understand malware behaviors. However, Android applications use obfuscation techniques to slow down this process. These obfuscation techniques are increasingly based on native code. In this article, we propose OATs’inside , a new analysis tool that focuses on high-level behaviors to circumvent native obfuscation techniques transparently. The targeted high-level behaviors are object-level behaviors, i.e., actions performed on Java objects (e.g., field accesses, method calls), regardless of whether these actions are performed using Java or native code. Our system uses a hybrid approach based on dynamic monitoring and trace-based symbolic execution to output control flow graphs (CFGs), 27 pages. for each method of the analyzed application. CFGs are composed of Java-like actions enriched with condition expressions and dataflows between actions, giving an understandable representation of any code, even those fully native. OATs’inside spares users the need to dive into low-level instructions, which are difficult to reverse engineer. We extensively compare OATs’inside functionalities against state-of-the-art tools to highlight the benefit when observing native operations. Our experiments are conducted on a real smartphone: We discuss the performance impact of OATs’inside , and we demonstrate its practical use on applications containing anti-debugging techniques provided by the OWASP foundation. We also evaluate the robustness of OATs’inside using obfuscated unit tests using the Tigress obfuscator.

APPLIED SOFTWARE FOR PERSONAL INFORMATION STORAGE

In today's world, the personal information of any person can be conventionally divided into two large categories: general and special. As you can understand from the name, the first category is responsible for the type of data that can be found in public access, namely: surname, first name, patronymic, signature, place and date of birth, citizenship, marital status, education, bank details, etc. Such a variety of personal data requires different methods and automated means of storage. Today, private and state institutions increasingly refuse to store information in paper form. This is explained by the low level of security of such storage facilities, which can be affected by fire, water, pests, etc. Information that exists in paper form is very easy to lose, for example, misplaced or lost during movement. It is shown that digital storage of information makes it possible to get rid of such problems. Personal data that is stored electronically can often be recovered even if the device from which it was accessed is lost. One of the main types of personal information in modern society is login data: login and password. That is why the algorithmic and software of the automated system for saving the user's personal data was developed. This system should enable the user to generate passwords resistant to standard hacking methods and store them in the database. The development of the automated system took place using the MVC architectural template, which is one of the options for implementing a multi-level architectural model. For detailed design and coding, an object-oriented Java programming language with static strict typing was chosen. It was also decided to develop an automated system in the form of a web application using the Spring framework. During the development process, a local database was designed, which will store personal data in the form of logins and passwords entered by the user or generated by the system itself. An algorithm for generating hack-resistant passwords has been developed. Hash algorithms are also used as an additional protection of the main password of the web application. A graphical interface has been created that allows the user to access the main functions of the automated system.

Integration of the athena vortex lattice aerodynamic analysis software into the multivariate design synthesis of a blended wing body aircraft

This paper describes the process of integrating the Athena Vortex Lattice aerodynamics solver into a conceptual design environment for the multivariate design synthesis and optimisation of a Blended Wing Body (BWB) Aircraft. The work is derived from a PhD study, by one of the authors, which developed a design methodology for the conceptual design synthesis, analysis and optimisation of a BWB aircraft. This research was driven by the desire to utilise proven aerodynamic solvers, written in legacy codes, in modern computational design environments in order to improve the accuracy and fidelity in the design of novel aircraft configurations. The Athena Vortex Lattice code is a legacy code written in the FORTRAN programming language and developed at the Massachusetts Institute of Technology by Mark Drela while the multivariate conceptual design synthesis and optimisation was implemented in JAVA object oriented programming tool. The result from this study is the successful integration of the AVL software into the JAVA programming environment for the multivariate design synthesis optimisation of the BWB without loss of accuracy. The successful integration of these codes permit a more accurate design synthesis, analysis and optimisation of the novel aircraft configuration in the absence of a tailor-made and realistic empirical aerodynamic analysis model for the BWB Aircraft. Additionally, this research enables the automation of the design process and the accomplishment of the multivariate optimisation.

HotFuzz: Discovering Temporal and Spatial Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing

Fuzz testing repeatedly assails software with random inputs in order to trigger unexpected program behaviors, such as crashes or timeouts, and has historically revealed serious security vulnerabilities. In this article, we present HotFuzz, a framework for automatically discovering Algorithmic Complexity (AC) time and space vulnerabilities in Java libraries. HotFuzz uses micro-fuzzing, a genetic algorithm that evolves arbitrary Java objects in order to trigger the worst-case performance for a method under test. We define Small Recursive Instantiation (SRI) as a technique to derive seed inputs represented as Java objects to micro-fuzzing. After micro-fuzzing, HotFuzz synthesizes test cases that triggered AC vulnerabilities into Java programs and monitors their execution in order to reproduce vulnerabilities outside the fuzzing framework. HotFuzz outputs those programs that exhibit high resource utilization as witnesses for AC vulnerabilities in a Java library. We evaluate HotFuzz over the Java Runtime Environment (JRE), the 100 most popular Java libraries on Maven, and challenges contained in the DARPA Space and Time Analysis for Cybersecurity (STAC) program. We evaluate SRI’s effectiveness by comparing the performance of micro-fuzzing with SRI, measured by the number of AC vulnerabilities detected, to simply using empty values as seed inputs. In this evaluation, we verified known AC vulnerabilities, discovered previously unknown AC vulnerabilities that we responsibly reported to vendors, and received confirmation from both IBM and Oracle. Our results demonstrate that micro-fuzzing finds AC vulnerabilities in real-world software, and that micro-fuzzing with SRI-derived seed inputs outperforms using empty values in both the temporal and spatial domains.

Implementasi Transfer Learning Pada Algoritma Convolutional Neural Network untuk Mengklasifikasikan Image Objek Wisata

This study classifies the image of a tourist attraction with 9 labels sky, tree, mountain, water, street, temple, garden, stone and ricefield. The results of multi-label labeling can be used to see the frequency and recommendations of tourist attractions in Central Java, and build a transfer learning model to determine the accuracy value. Classification with multi-label images has its own complexity in the labeling process and few people use it. Testing and evaluating the model uses the equation of accuracy and f-1 score. Several previous researchers also stated that the higher the amount of training data and the number of epochs per step, the higher the accuracy produced. Based on the results of training and evaluation of the four training processes, that 210 data using bs 8, lr 1e-3 and epoch 50 showed an accuracy of 0.8598 with a loss of 0.3245, while 290 data with bs 16, lr 1e-3 and epoch 50 showed an accuracy of 0.8685. with a loss of 0.2903. Then 594 data with bs 32, lr 1e-3 and epoch 50 showed an accuracy of 0.8852 with a loss of 0.2756, and 1000 data with bs 46, lr 1e-3 and epoch 50 showed an accuracy of 0.8833 with a loss of 0.2863. This can answer the statement that the greater the number of datas, the higher the accuracy produced, so that the transfer learning model on the ResNet-50 architecture with multi-label image datas can be applied by showing accuracy results close to the accuracy value on ResNet-50 in the imagenet project. In addition, the contribution of this research is to provide recommendations for potential tourist objects in Central Java, namely tourism objects with the theme of nature, then tourism processed by human hands such as historical places, cultural heritage and family recreation areas.

Impact of Educational Games To Introduction Tourist Destination In Central Java On Elementary School Student

Educational games are learning media in the form of games that contain material in them. Includes questions, guessing pictures or etcetera. Educational games can also become alternative learning media in the covid-19 pandemic. This study aims to determine the effect of educational games on the understanding of elementary school students regarding the introduction of tourist objects in Central Java. The method is pre-experimental research through pretest and posttest to determine the effect of two variables but there is no control group. Students of SDN 3 Purbalingga Lor are the subjects of this study, with a frequency of 14 students. Pretest data was obtained, the lowest score was 10 and the highest was 70, with an average grade as 33. If you look at posttest results, the highest score was obtained with a score of 100 and the lowest score was 80, with an average grade of 95. This proves the educational games have an effect on the ease of students in understanding the material, especially to know tourist attractions in Central Java. 

Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules

Smart contracts constitute the foundation for blockchain distributed applications. These constructs enable transactions in trustless environments using consensus algorithms and software-controlled verification rules. In the current state of the art, there is a shortage of works on the adaptability of smart contracts, and the re-use of their source code is limited mainly to cloning. The paper discusses the pattern of smart contract design and implementation with the overt declaration of verification rules. The author introduces two advantages of the pattern: Firstly, run-time reconfigurability of the list of smart contract verification rules to adjust for various transaction types. Secondly, the re-use of verification rules between different configurations of the smart contract, and among diverse smart contracts. The paper uses blockchain platform-independent stereotypes from a dedicated Unified Modeling Language (UML) profile for designing smart contracts and verification rules. The implementation of the pattern is developed in object-oriented Java language. The pattern exploits polymorphism and controls inheritance by using sealed classes with permission for specialization only for selected final ones. Thus, the pattern ensures two recently highly desired properties in smart contract design and development: re-use and security. Moreover, the declared verification rules list facilitates test automation and reduces test preparation effort due to the re-use of test classes among smart contract configurations. The pattern usage is illustrated in the example of renewable energy exchange within the prosumers community and amid various communities.

Nuclear respiratory factor 1 transcriptomic signatures as prognostic indicators of recurring aggressive mesenchymal glioblastoma and resistance to therapy in White American females.

The mechanisms contributing to recurrence of glioblastoma (GBM), an aggressive neuroepithelial brain tumor, remain unknown. We have recently shown that nuclear respiratory factor 1 (NRF1) is an oncogenic transcription factor and its transcriptional activity is associated with the progression and prognosis of GBM. Herein, we extend our efforts to (1) identify influential NRF1-driven gene and microRNA (miRNA) expression for the aggressiveness of mesenchymal GBM; and (2) understand the molecular basis for its poor response to therapy. Clinical data and RNA-Seq from four independent GBM cohorts were analyzed by Bayesian Network Inference with Java Objects (BANJO) and Markov chain Monte Carlo (MCMC)-based gene order to identify molecular drivers of mesenchymal GBM as well as prognostic indicators of poor response to radiation and chemotherapy. We are the first to report sex-specific NRF1 motif enriched gene signatures showing increased susceptibility to GBM. Risk estimates for GBM were increased by greater than 100-fold with the joint effect of NRF1-driven gene signatures-CDK4, DUSP6, MSH2, NRF1, and PARK7 in female GBM patients and CDK4, CASP2, H6PD, and NRF1 in male GBM patients. NRF1-driven causal Bayesian network genes were predictive of poor survival and resistance to chemoradiation in IDH1 wild-type mesenchymal GBM patients. NRF1-regulatable miRNAs were also associated with poor response to chemoradiation therapy in female IDH1 wild-type mesenchymal GBM. Stable overexpression of NRF1 reprogramed human astrocytes into neural stem cell-like cells expressing SOX2 and nestin. These cells differentiated into neurons and form tumorospheroids. In summary, our novel discovery shows that NRF1-driven causal genes and miRNAs involved in cancer cell stemness and mesenchymal features contribute to cancer aggressiveness and recurrence of aggressive therapy-resistant glioblastoma.

PAMELA: An annotation-based Java modeling framework

• Continuous modeling process. • Strong coupling between model and code with smooth integration. • No code generation for POJO (plain old java objects): less code, less bugs. • Interpretation of models at runtime with standard or specific semantics (meta-programming). This article presents PAMELA, an annotation-based Java modeling framework. PAMELA provides a smooth integration between model and code and enables Java developers to handle software development both at conceptual level and at source-code level, without code transformation and/or generation, avoiding round-trip-related issues.

Validating New Method for Measuring Cohesion in Object-Oriented Projects

Class cohesion, understood as the degree of how tightly are bound or related its internal elements to one another, is often a critically important for quality assurance (QA) of object-oriented software. Typically, a low cohesive class contains disparate and/or isolated members; therefore, cohesion level is useful for detecting of poorly designed classes and ensuring faster and better system reconfiguration. There are over thirty different metrics to measure cohesion, based on class member analysis in terms of number and structure of attributes and methods. Utilizing class cohesion metrics can promote Java code static analysis quality, improve object-oriented programming practices, and suggest advanced and efficient ones. The jPeek tool with its library for inspecting objects in Java was developed by Huawei Technologies Co. Ltd. to achieve these goals [1]. The idea of code quality assessment is well known for a long time; class connectivity metrics were proposed by community several years ago and have not become generally applicable practice in industrial programming. The main goal of this study is to make a methodology for using cohesion metrics to analyze code that allows users to estimate their projects in the most appropriate way. During the study, each of the metrics was studied in detail, the influence of programming patterns on the result of the metrics was evaluated, and as a result a methodology for using the metrics to obtain the most reliable estimate was formulated.

Generation of POJOs and DAOs Classes from Metadata Database

Implementing the persistence layer is a task that the Object Relational Mapping (ORMs) tools have standardized through diverse frameworks and Application Programming Interfaces (APIs). However, these ORMs and APIs have some inconveniences related to their configuration process and the time and effort it takes to learn them, amongst others. For example, every time a database is used, a configuration process is realized to map the database to obtain Plain Old Java Objects (POJOs) and Data Access Objects (DAOs). In order to overcome those disadvantages, this work contributes by proposing a reusable and extensible process that supports the automatic generation of POJOs and DAOs from database scheme metadata, through the specification of an Extended Backus-Naur Form (EBNF) grammar. A tool named GenPOJO was developed in order to test the process. Unlike the existing ORMs, the proposed process does not require the configuration steps for each database. In addition, GenPOJO does not require time to learn since the only input it receives is a file in XML (eXtensible Markup Language) format containing the database scheme metadata.

Characteristics and Response of Tourists in Central Java (Case Study of Flower Garden Development Plans in Semarang)

Semarang has a topography ranges from 500-2,000 above sea level and is surrounded by mountains with a relatively cold climate in several locations. This background is to attract for investors to develop nature-based tourism objects, one of which is a flower garden. It was necessary to conduct a preliminary study of the characteristics and responses of tourists in tourist attractions in Central Java. The research was conducted in 2018 in 3 tourist locations in Central Java, namely Borobudur, Kyai Langgeng Park, and Umbul Sidomukti. The respondents in this study were 100 people. The results showed that majority tourist (56%) wished to tour in groups with a total of 3-16 people in one group and chose to design their trip without the assistance of a travel agency (81%). The ability of tourists to pay for ticket-passs is Rp 36,345 - Rp 99,851 for a trip and tickets for the unit are Rp 9,880 - Rp. 63,052. Tourists also can pay for food prices from the range of IDR 16,240-IDR 46,200 and IDR 7,520-IDR 18,100. The results are relevant as one of the references for investors and the government in managing tourism objects in Central Java.

Artificial neural network implementation for masonry compressive strength estimation

An artificial neural network (ANN) implementation for the estimation of masonry compressive strength is presented. A heterogeneous sample is considered, including brick or stone elements, with cementitious or non-cementitious mortar. A multi-layer network was designed with sigmoidal neurons trained using a back-propagation algorithm. An object-oriented Java software program was developed in order to perform the training and the testing processes of the network, using real test data. The mean sum of square errors (SSE) was used as a global performance indicator of the network. The results obtained using the ANN were numerically compared with both real test data and with the results of empirical formulations. The comparisons showed that the ANN approach produced lower SSE than the considered formulations, with good performance on both heterogeneous masonry samples and different masonry systems. The presented approach could be particularly useful when little information is available, avoiding the need for invasive on-site tests and performing only laboratory tests on the brick (or stone) and the mortar. The ANN was able to predict the compressive masonry strength with a very small error, despite the heterogeneity of the considered sample.

Sensitivity to differential NRF1 gene signatures contributes to breast cancer disparities.

Nuclear respiratory factor 1 (NRF1) drives estrogen-dependent breast tumorigenesis. Herein we examined the impact of NRF1 activity on the aggressiveness and disparate molecular signature of breast cancer in Black, White, Asian, and Hispanic women. NRF1 activity by transcription factor target enrichment analysis and causal NRF1-target gene signatures by Bayesian Network Inference with Java Objects (BANJO) and Markov Chain Monte Carlo (MCMC)-based gene order were examined in The Cancer Genome Atlas (TCGA) breast cancer cohorts. We are the first to report increased NRF1 activity based on its differential effects on genome-wide transcription associated with luminal A and B, HER2+ and triple-negative (TN) molecular subtypes of breast cancer in women of different race/ethnicity. We observed disparate NRF1 motif-containing causal gene signatures unique to Black, White, Asian, and Hispanic women for luminal A breast cancer. Further gene order searches showed molecular heterogeneity of each subtype of breast cancer. Six different gene order sequences involving CDK1, HMMR, CCNB2, CCNB1, E2F1, CREB3L4, GTSE1, and LMNB1 with almost equal weight predicted the probability of luminal A breast cancer in whites. Three different gene order sequences consisting of CCNB1 and GTSE1, and CCNB1, LMNB1, CDK1 or CASP3 predicted almost 100% probability of luminal B breast cancer in whites; CCNB1 and LMNB1 or GTSE predicted 100% HER2+ breast cancer in whites. GTSE1 and TUBA1C combined together predicted 100% probability of developing TNBC in whites; NRF1, TUBA1B and BAX with EFNA4, and NRF1 and BTRC predicated 100% TNBC in blacks. High expressor NRF1 TN breast tumors showed unfavorable prognosis with a high risk of breast cancer death in white women. Our findings showed how sensitivity to high NRF1 transcriptional activity coupled with its target gene signatures contribute to racial differences in luminal A and TN breast cancer subtypes. This knowledge may be useful in personalized intervention to prevent and treat this clinically challenging problem.

DCache – Efficient Message Encoding For Inter-Service Communication in dCache: Evaluation of Existing Serialization Protocols as a Replacement for Java Object Serialization

As a well established, large-scale distributed storage system, dCache is used to manage and serve huge amounts of data collected by high energy physics, astrophysics and photon science experiments. Based on a microservices-like architecture, dCache is built as a modular distributed system, where each component provides a different core functionality. These services communicate by passing serialized messages to each other, a core behavior whose performance properties can consequently affect the entire system. This paper compares and evaluates different data serialization protocols in computer science with the objective of replacing and improving upon Java Object Serialization (JOS), which has increasingly presented itself as no longer being sufficiently performant for encoding messages. The criteria for choosing a new framework are collected, analyzed and formalized. The primary motivation for replacing Java serialization for encoding dCache messages is increasing the general speed of message-passing and thereby reducing the round-trip time for user requests. Emphasis is also placed on schema evolution capabilities and framework usability. Approaches for generalizing (de)serialization speed and size measurements based on data structure complexity are introduced, criteria for measuring documentation, learning curve, maintainability and introduction effort are defined. Finally, several selected serialization protocols are evaluated and compared accordingly, concluding with a recommendation for a suitable JOS replacement.

Spreadsheet Model Generator (SMOG): A Lightweight Tool for Object-Spreadsheet Mapping.

In the life science domain, experts are usually familiar with spreadsheet software and often use it in their daily work to collect and structure required domain knowledge. The processing and analysis of spreadsheet data is an important task that must be supported by efficient software solutions. A typical application scenario is for example an integration of spreadsheet data (specified or derived) in an ontology to provide reasoning or search. Different converter tools were developed to support a spreadsheet-to-ontology transformation. Such tools allow often only a relatively simple structure of the spreadsheet template or they require complex mapping processes to map the spreadsheet and ontological entities. In many cases, it is impossible to use the existing converter tools because the spreadsheet data must be processed first before the derived data can be integrated into the ontology. In such cases, an efficient and fast development of customized software solutions is of great importance. In this paper, we present a general spreadsheet processing framework to efficiently read and write spreadsheet data. The Spreadsheet Model Generator (SMOG) provides a simple mechanism to automatically generate the Java object model and mappings between object code and spreadsheet entities. Our solution greatly simplifies the implementation of spreadsheet access methods and enables an efficient development of spreadsheet-based applications. The SMOG has already been used successfully in several biomedical projects under real world conditions.

Mining Reusable Software Components from Object-Oriented Source Code using Discrete PSO and Modeling Them as Java Beans

Object-based software development in the IT-industry during the last decade has focused on reusing the already developed elements. Current software development models and technologies promote software reuse by utilizing component-oriented programming. Component-oriented programming provides more effective reuse as compared to its object-oriented language counterpart. This is because a component provides more abstractness in terms of the functionality in contrast to the object-oriented software element (e.g. class/ interface). The component-based development is highly dependent on the availability of the component library that contains well-defined and already tested functionality in terms of the available components. However, the suitable availability of such component library is still a problem. The lack of a suitable component library is tackled either by designing new components from scratch or by identifying them from the existing software systems. These identified components must be transformed as per component models, such as a Java Bean, in order to increase software reuse and faster software development, and little research has been carried out in this direction. Although many approaches have been proposed to identify components from existing systems and build a component library. But, such approaches lack in transforming the identified components as per specifications of a component model. Further identification of reusable elements also needs to be more accurate and efficient. Accuracy is dependent on the structural and semantic properties of a component. A component can consist of a single or a group of individual elements of the software, and thus identifying components from a set of all elements becomes an exponentially growing problem. Such problems can be more efficiently solved using metaheuristic algorithms. Therefore, this paper targets identifying accurately the components from a singular existing software system and transform those identified components to the Java Bean component model standard. The component identification approach of this paper is based on clustering the underlying software system by utilizing a metaheuristic search-based PSO algorithm. The clusters are identified as a group containing 2/ 3/ 4 software elements. Moreover, the used fitness function is based on the structural (based on usage for member variables) and semantic relations belonging to a software. Since the PSO algorithm is generally applied to continuous problems, whereas our formulated component identification problem is discrete (due to the representation of particles) in nature. Therefore, we have also provided a mechanism through which the PSO algorithm is modified to become applicable to discrete problems. From the obtained clusters (components), the interfaces of the components are identified by identifying independent chains in call-graphs of the underlying components. Finally, these components are transformed as per the Java Bean component model specifications. The proposed approach is validated experimentally on 7 open-source object-oriented Java software systems and results are compared with competitive approaches. The obtained results confirm the correctness of the transformed component and hence their reusability.

Digitalización de la Línea Funcional de la Deglución

La iniciativa busca digitalizar la propuesta de línea funcional de la deglución con líquidos como estrategia de cribado en la atención fonoaudiológica. La planificación metodológica de la propuesta, sustenta el desarrollo de una investigación proyectiva que se estructura de manera digital bajo el concepto de sistema de información en etapas fundamentadas bajo el modelo de desarrollo evolutivo; 1. Requerimiento; 2. Diseño; 3. Prototipo; 4. Testeo; 5. Informe final. Se desarrolla la aplicación bajo un lenguaje de programación orientado a Objetos Java para ser ejecutado en dispositivos móviles con Android; el prototipo permite realizar la calificación de la función valvular y arroja al final de la prueba una impresión diagnóstica, basada en la propuesta diagnóstica que sugiere la iniciativa de línea funcional. La propuesta de digitalización flexibiliza su accionar al pensar en dar respuesta no solo a una concepción valvular de la deglución, sino también a la concepción de las fases deglutorias o las unidades funcionales. Se proyecta como una estrategia evaluativa, pero también como un instrumento de apoyo para fundamentar las acciones de tratamiento basado en el análisis del descenso de la línea.
 Palabras claves: Fonoaudiología; Evaluación; Tecnología.