Dynamic Data Types Research Articles

Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors.

Finding ways to enable seamless communication between deaf and able-bodied individuals has been a challenging and pressing issue. This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition. In this study, four machine learning models-decision tree (DT), support vector machine (SVM), K-nearest neighbor method (KNN), and random forest (RF)-were employed to recognize 20 different types of dynamic sign language data used by deaf individuals. Additionally, a proposed attention-based mechanism of long and short-term memory neural networks (Attention-BiLSTM) was utilized in the process. Furthermore, this study verifies the impact of the number and position of data glove nodes on the accuracy of recognizing complex dynamic sign language. Finally, the proposed method is compared with existing state-of-the-art algorithms using nine public datasets. The results indicate that both the Attention-BiLSTM and RF algorithms have the highest performance in recognizing the twenty dynamic sign language gestures, with an accuracy of 98.85% and 97.58%, respectively. This provides evidence for the feasibility of our proposed data glove and recognition methods. This study may serve as a valuable reference for the development of wearable sign language recognition devices and promote easier communication between deaf and able-bodied individuals.

Аналіз рішень та підходів кластеризації геопросторових даних для оптимізації продуктивності веб-карти та взаємодії користувача

In the contemporary epoch, the management and visualization of geospatial information in web browsers have gained substantial importance. Web maps are indispensable tools across various tourism, goods delivery, and ecology sectors. Furthermore, the extensive support of web browsers on diverse devices enhances the accessibility of geospatial data on the web for various users. However, the incessant increment of geospatial information poses new challenges in efficiently displaying data and navigation through these data on web maps. Therefore, the clustering of geospatial data is crucial in dealing with them. Different clustering methods may affect the performance or visual clarity of web maps. To improve the user experience and optimize the use of computing resources, geodata clustering becomes a necessary tool for processing large volumes of markers on the map. Despite significant progress in the development of geodata clustering solutions in web maps, there are some challenges that developers and users may encounter. In this article, challenges with scaling, dynamic cluster data, and heterogeneity of data are described. Existing problems in geodata clustering in web maps require additional research and development. Understanding these issues will help developers and researchers improve existing solutions and create new methods and approaches for efficient clustering of geodata in web maps. The urgency of solving the problem lies in the search for effective clustering solutions that provide an opportunity to ensure convenient interactivity and fast processing of geodata in web maps. This study provides a comprehensive review of data types and clustering methods. Tools and libraries for geodata clustering in web maps are analyzed. Different types of geodata and approaches to working with them were also studied. Concepts such as semi-static data and their positions alongside static and dynamic data types are elucidated. Through the analysis, optimal scenarios for applying specific clustering methods or the utilization of server-side clustering approaches have been identified. Conclusions have also been drawn on the preferred approach when handling extensive volumes of static or semi-static geospatial data, particularly advocating for the application of server-side clustering with caching. In conclusion, various clustering approaches in web maps, both client-side and server-side, have been scrutinized. The advantages and disadvantages of both approaches, along with recommendations on when to apply each method, have been delineated. A noticeable absence of explicit approaches in clustering vast geospatial data for web map representation underpins the relevance and necessity of research in this direction.

Python- An Appetite for the Software Industry

Python is a scripting language that's high- positioned, interpreted, interactive, and object- oriented. Python is intended to be a veritably accessible programming language. It generally uses English terms rather than punctuation, and it has smaller syntactical structures than other languages. Python is a must-have skill for scholars and working professionals who want to become exceptional software masterminds, especially if they work in the web development field. It's a freshman-friendly scripting language. Some of the crucial features of Python programming language are- It supports OOP as well as functional and structured programming methodologies. It can be used as a scripting language or collected into bytecode for large-scale operations. It allows dynamic type verification and provides veritably high-position dynamic data types. It facilitates scrap collection by itself. Numerous different programming languages have been impacted by Python's design and gospel. Some of those languages are Boo, Cobra, CoffeeScript, Go, Swift, Ruby, etc. Some of the advantages of Python programming language are straightforward, free, simple to use, and largely compatible, object- acquainted, has multitudinous libraries, has erected in data structures, has a wide range of uses, boosts productivity and speed, and simple to understand. One of the most extensively used programming languages is Python. It's an open- source language. Python's demand is growing, and its operations are expanding in virtually every assiduity. It's abundant in every way. It has a wide range of capabilities. Python is a popular programming language. It's also developing a strong request in the IT sector. Python is in high demand across the globe. Python helps you negotiate more in lower time. Python has a large community that supports and meets the requirements of inventors. Python is therefore one of the most popular programming languages. It's a veritably reliable and effective programming language. Python programmers are in high demand because Python is being used in a variety of sectors. Python is an extensively used computer language that was created nearly 25 years ago. Python is useful in a variety of fields, including web development, desktop app development, machine literacy, big data, data analysis, and robotics. Clean syntax, extremely clear law, a wide range of uses, packages that help apply features, and a cool community that helps grow this excellent language are just a many of the reasons why people like this language and why it's well suited for different tasks. The Python programming language has a bright future. The advanced technologies like Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Science, etc and world-notorious companies similar as Amazon, Google, Apple, Deloitte, Microsoft, Netflix, and Accenture have the Python programming language as their backbone which states that Python is in demand and AN APPETITE FOR THE SOFTWARE INDUSTRY! A standard and scientific procedure of an Empirical Exploration Methodology (Survey) was conducted to check the statement stated by the author where 900 repliers from colourful corridors of the globe shared their thoughts. From the check, it was concluded that 99.8% of the respondents feel that Python is one of the in- demand programming languages for the digital assiduity in the present time.

Structural damage detection based on cross-correlation function with data fusion of various dynamic measurements

To take advantage of various types of dynamic measurement data for structural damage detection, an identification strategy based on cross-correlation function with data fusion of various dynamic measurements was proposed to improve the accuracy of damage identification in the present study. First of all, the cross-correlation functions among the easily acquired strain, acceleration, and displacement responses were theoretically derived when the structure was subjected to ambient excitations. Furthermore, an identification strategy was proposed for structural damage detection with the objective function of minimizing the difference between the measured and computed cross-correlation function under multiple unknown ambient excitations. In the proposed strategy, four optimization methods, namely, gradient search, genetic algorithm, particle swarm optimization, and the hybrid method of particle optimization method and gradient search were applied as the search engine to identify structural unknown damage index. Moreover, the performance of the proposed identification strategy was examined by the numerical studies on a two-dimensional and a three-dimensional truss as well as the experimental study on a cantilever beam. These results showed that the cross-correlation function among different types of vibration measurements could significantly improve the accuracy of the identification results, meanwhile, the proposed strategy exhibited excellent robustness to the measurement noise. In addition, the performance of the proposed strategy with different combinations of vibration data and the influence of reference data on the accuracy of damage identification results were further investigated.

System identification using neuro fuzzy approach for IoT application

The Internet of Things (IoT) has become a popular application in recent years. However, it is the wireless communication mode. In such a scenario, the user would have to send information either nonlinear or dynamic data type in the form of a signal or an image, videos depending on the application. The proposed work is focused on this model identification that tends to nonlinear dynamic system identification for IoT applications. An Autoregressive Moving Average (ARMA) model represents model for IoT application. To verify the model supremacy, an ARMA bench mark system is used. The adaptiveness is proved through variation of weights and can be universally used for the next generation. In the first attempt, the Multilayer Perceptron model (MLP) is considered as the ARMA system and observed. Further, to improve its accuracy, the Adaptive Neuro-Fuzzy system (ANFIS) model is designed for system identification. It is shown in the result section that it identifies better than the MLP as well as traditional system identification techniques.

From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)

Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined map- ping function and unknown nonlinear map-ping between time series and graphs.

Dynamic Data Feeding into BIM for Facility Management: A Prototype Application to a University Building

Building information modelling (BIM) has demonstrated its potential as a solution providing support to a series of operations related to facility management (FM) through building data retrieval, analysis, and processing. However, some challenges to the effective adoption of BIM-centred FM information systems occur in their design and implementation, causing obstacles to usability. Among these challenges are the customization of the information structure for each application case, the dynamic character of data supporting building maintenance, and the range of FM specialities involved, frequently including persons who are not BIM experts. This paper presents a BIM–FM prototype to support operations and access updated environmental data for a university building. The two contributions of the developed prototype are its ability to register two types of dynamic data, namely, the regularly acquired environmental sensor information and the sporadic building intervention records, and the automation of the data feeding, updating, and retrieval processes, allowing a user-friendly environment for both BIM experts and non-BIM users. Exploring the BIM interoperability and the integration of plug-ins, the proposed solution enables the comprehensive registration of dynamic FM-related data in an updated model while being accessible to all the specialities involved in the building management operations, enhancing its usability as an integrated solution for data maintenance and retrieval.

A Modular Remote Sensing Big Data Framework

Today, remote sensing (RS) data are already regarded as “big data.” Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

Mixed data-driven sequential three-way decision via subjective–objective dynamic fusion

In the context of granular computing, sequential three-way decision is a useful tool to triadic thinking, triadic computing and triadic processing from coarser to finer under multilevel and multiview granularity space. In this paper, we mainly explore a novel framework of sequential three-way decision for the fusion of mixed data from the subjective and objective dynamic perspectives. The former focuses on the decision maker’s dynamic behavior without considering the time-evolving data, and the latter emphasizes on dealing with dynamic mixed data over time by multi-stage decision-making. We firstly utilize four T-norm operators and kernel-based similarity relations to integrate different types of dynamic data. Then the subjective and objective models of sequential three-way decision are investigated based on decision thresholds, attribute importance and cost reduction. Finally, the comparative experiments are reported to verify that our proposed models can achieve the lower decision cost and the acceptable accuracy.

What We Need to Consider During and After the SARS-CoV-2 Pandemic.

Even though extreme containment and mitigation strategies were implemented by numerous governments around the world to slow down the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the number of critically ill patients and fatalities keeps rising. This crisis has highlighted the socioeconomic disparities of health care systems within and among countries. As new CoVID policies and responses are implemented to lessen the impact of the virus, it is imperative (1) to consider additional mitigation strategies critical for the development of effective countermeasures, (2) to promote long-term policies and strict regulations of the trade of wildlife and live animal markets, and (3) to advocate for necessary funding and investments in global health, specifically for the prevention of and response to natural and manmade pandemics. This document considers some of these challenges.

Platforms as service ecosystems: Lessons from social media

The growing business expansion of social media platforms is changing their identity and transforming the practices of networking, data and content sharing with which social media have been commonly associated. We empirically investigate these shifts in the context of TripAdvisor and its evolution since its very establishment. We trace the mutations of the platform along three stages we identify as search engine, social media platform and end-to-end service ecosystem. Our findings reveal the underlying patterns of data types, technological functionalities and actor configurations that punctuate the business expansion of TripAdvisor and lead to the formation of its service ecosystem. We contribute to the understanding of the current trajectory in which social media find themselves as well as to the literature on platforms and ecosystems. We point out the importance of services that develop as commercially viable and constantly updatable data bundles out of diverse and dynamic data types. Such services are essential to the making of the complementarities that are claimed to underlie ecosystem formation.

A real-time magnetic resonance imaging study of cross-speaker variability in the production of /ɹ/

There is a longstanding interest in understanding the production mechanisms of the American English (AE) rhotic, /ɹ/ especially given both the wide intraspeaker variability and interspeaker heterogeneity in its articulation, and the resultant acoustics. A diversity of tongue shapes, from those creating a more anterior tongue-tip (“retroflex”) constriction to those with a more posterior bunched tongue dorsum constriction, has been observed with notable intraspeaker variation as a function of context; more specifically, a tendency for retroflexion preceding low and/or back vowels. The present study aims to analyze AE /ɹ/ production using real-time MRI videos (83 frames/s) where the target sound was produced in several contexts by 32 American English speakers (16 F/16 M). Initial findings in symmetric VCV production reveal that some of these speakers exclusively use a “retroflex” strategy, some a “bunched” strategy, and others both strategies, consistent with the previously observed tendencies. This observed variability, and the fact that multiple types of static and dynamic imaging data are available from the same speakers, makes this dataset ideal for further probing /ɹ/ production strategies in conjunction with speaker-specific characteristics of vocal tract structure, such as palate shape, and function, such as inter-articulator coordination. [Work supported by NIH and NSF.]

Advanced Cartographic Techniques for Making Sense of Big Dynamic Data

Abstract. We have demonstrated application of advanced cartographic approaches to visualization of the U.S. electric grid. To address the challenges of high cognitive load of animated maps, we have applied an approach to combine data preprocessing, event detection, and several advanced cartographic techniques. In the future, the proposed methodology can be generalized to other types of dynamic big data.

Dynamic tuneable G protein-coupled receptor monomer-dimer populations

G protein-coupled receptors (GPCRs) are the largest class of membrane receptors, playing a key role in the regulation of processes as varied as neurotransmission and immune response. Evidence for GPCR oligomerisation has been accumulating that challenges the idea that GPCRs function solely as monomeric receptors; however, GPCR oligomerisation remains controversial primarily due to the difficulties in comparing evidence from very different types of structural and dynamic data. Using a combination of single-molecule and ensemble FRET, double electron–electron resonance spectroscopy, and simulations, we show that dimerisation of the GPCR neurotensin receptor 1 is regulated by receptor density and is dynamically tuneable over the physiological range. We propose a “rolling dimer” interface model in which multiple dimer conformations co-exist and interconvert. These findings unite previous seemingly conflicting observations, provide a compelling mechanism for regulating receptor signalling, and act as a guide for future physiological studies.

Software metadata: Systematic characterization of the memory behaviour of dynamic applications

Development of new embedded systems requires tuning of the software applications to specific hardware blocks and platforms as well as to the relevant input data instances. The behaviour of these applications heavily relies on the nature of the input data samples, thus making them strongly data-dependent. For this reason, it is necessary to extensively profile them with representative samples of the actual input data. An important aspect of this profiling is done at the dynamic data type level, which actually steers the designers choice of implementation of these data types. The behaviour of the applications is then characterized, through an analysis phase, as a collection of software metadata that can be used to optimize the system as a whole. In this paper we propose to represent the behaviour of data-dependent applications to enable optimizations, rather than to analyze their structure or to define the engineering process behind them. Moreover, we specifically limit ourselves to the scope of applications dominated by dynamically allocated data types running on embedded systems. We characterize the software metadata that these optimizations require, and we present a methodology, as well as appropriate techniques, to obtain this information from the original application. The optimizations performed on a complete case study, utilizing the extracted software metadata, achieve overall improvements of up to 42% in the number of cycles spent accessing memory when compared to code optimized only with the static techniques applied by GNU G++.

Direct memory access usage optimization in network applications for reduced memory latency and energy consumption

Today, wireless networks are becoming increasingly ubiquitous. Usually several complex multi-threaded applications are mapped on a single embedded system and each of them is triggered by a different input stream (in accordance with the run-time behaviours of the user and the environment). This dynamicity renders the task of fully analyzing at design-time these systems very complex, if not impossible. Therefore, run-time information has to be used in order to produce an efficient design. This introduces new challenges, especially for embedded system designers using a Direct Memory Access (DMA) module, who have to know in advance the memory transfer behaviour of the whole system, in order to design and program their DMA efficiently. This is especially important in embedded systems with DRAM memories as the concurrent accesses from different processing elements can adversely affect the page-based architecture of these memory elements. Even more, the increasingly common usage of dynamic data types further complicates the problem because the exact location of data instances in the memory is unknown at design-time. In this paper we propose a system-level optimization methodology to adapt the DMA usage parameters automatically at run-time, according to online information. With our proposed optimization approach we manage to reduce the mean latency of the memory transfers by more than 18%, thus reducing the average number of cycles that processing elements or DMAs have to waste waiting for data from the main memory, while optimizing energy consumption and system responsiveness. We evaluate our approach using a set of real-life applications and real wireless dynamic streams.

A parallel evolutionary algorithm to optimize dynamic data types in embedded systems

New multimedia embedded applications are increasingly dynamic, and rely on dynamically-allocated data types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of possible DDTs implementations. That implies the minimization of embedded design variables (memory accesses, power consumption and memory usage). Up to know, some very effective heuristic algorithms have been developed in order to solve this problem, but it is unknown how good the selected DDTs are since the problem is NP-complete and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. This paper describes several parallel evolutionary algorithms for DDTs optimization in Embedded Systems, where parallelism improves the solutions found by the corresponding sequential algorithm, which indeed is quite effective compared with other previously proposed procedures. Experimental results show how a novel parallel multi-objective genetic algorithm, which combines NSGA-II and SPEA2, allows designers to reach a larger number of solutions than previous approximations.

Exploration methodology of dynamic data structures in multimedia and network applications for embedded platforms

In the last years, there is a trend towards network and multimedia applications to be implemented in portable devices. These applications usually contain complex dynamic data structures. The appropriate selection of the dynamic data type (DDT) combination of an application affects the performance and the energy consumption of the whole system. Thus, DDT exploration methodology is used to perform trade-offs between design factors, such as performance and energy consumption. In this paper we provide a new approach to the DDT exploration procedure, based on a new library of DDTs which remedies the limitations of an existing solution and allows the DDT optimization of a wider range of application domains. Using the new library, we performed DDT exploration in network and multimedia benchmarks and achieved performance and energy consumption improvements up to 22% and 5.8%, respectively.

Systematic methodology for exploration of performance – Energy trade-offs in network applications using Dynamic Data Type refinement

Modern network applications require high performance and consume a lot of energy. Their inherent dynamic nature makes the dynamic memory subsystem a critical contributing factor to the overall energy consumption and to the execution time performance. This paper presents a novel, systematic methodology for generating performance-energy trade-offs by implementing optimal Dynamic Data Types, finely tuned and refined for network applications. Our systematic methodology is supported by a new, fully automated tool. We assess the effectiveness of the proposed approach in four representative, real-life case studies and provide significant energy savings and performance improvements compared to the original implementations.

The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using two-step Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature works well in finite sample for a number of evaluation points as small as two. Incorrectly ignoring the individual effects, or the dependence between the initial conditions and the individual effects results in an overestimation of the coefficients of the lagged dependent variables. An application to incremental and radical product innovations by Dutch business firms illustrates the method.