Types Of Data Research Articles

A Multivariate Mixture Regression Model for Constrained Responses

Compositional data are vectors typically representing proportions of a whole, that is, those whose elements are strictly positive and subject to a unit-sum constraint. The increasing number of fields where this type of data arises makes the development of proper statistical tools an important issue. From a regression perspective, whenever the multivariate response is a compositional vector, a proper model that accounts for the unit-sum constraint is the well-established Dirichlet regression model. However, there are significant drawbacks mainly due to the limited flexibility of the Dirichlet distribution. The aim of this contribution is to introduce a new multivariate regression model for constrained responses, that is based on the extended flexible Dirichlet distribution (which is a structured mixture with Dirichlet distributed components). The new model is obtained by adopting a novel reparameterization which allows for, among other things, the presence of suitably designed cluster-specific regression patterns. It is shown to provide considerably greater flexibility and better performance than the standard Dirichlet regression model. In particular, from theoretical analysis, intensive simulation studies in many challenging scenarios, as well as from a real data application, it emerges that the new regression model can handle several issues affecting the Dirichlet regression, such as the presence of outliers, latent groups, multi-modality, and positive correlations.

The Effect Of White Sweet Potato On The Quality Of Snow’s Cookies

This research is motivated by the increasing consumption of wheat, where wheat comes from imported wheat. To reduce dependence on wheat, local food ingredients such as white sweet potatoes can be used. Utilizing local food can create new innovations in snow white cookies using white sweet potatoes, as well as increasing public knowledge of the use of local food ingredients. This research aims to analyze the effect of white sweet potato substitution of 15%, 30% and 45% on the quality of Snow’s cookies including shape, color, aroma, texture and taste. This type of research is a pure experiment (true experiment) with a completely randomized design method with one factor. The type of data is primary data sourced from 3 expert panelists who filled in the organoleptic test format. The data obtained were tabulated in table form and analysis of variance (ANOVA) was carried out, if significantly different, continued with the Duncan test. The results of the research showed that there was a significant effect between the substitution of white sweet potato at 15%, 30% and 45% on the texture and taste quality of the Snow’s cookies. Based on statistical tests, the data obtained Fcount (13.60) > Ftable (4.76) on the quality of the crumbly textureand Fcount (12.25) > Ftable (4.76) on the quality of the typical taste of white sweet potatoes, then continued with the test Duncan. The quality test results for the best white sweet potato snow’s cookies were found in the X3 treatment with 45% white sweet potato substitution.

MS-GDA: Improving Heterogeneous Recipe Representation via Multinomial Sampling Graph Data Augmentation

We study the problem of classifying different cooking styles, based on the recipe. The difficulty is that the same food ingredients, seasoning, and the very similar instructions result in different flavors, with different cooking styles. Existing methods have limitations: they mainly focus on homogeneous data (e.g., instruction or image), ignoring heterogeneous data (e.g., flavor compound or ingredient), which certainly hurts the classification performance. This is because collecting enough available heterogeneous data of a recipe is a non-trivial task. In this paper, we present a new heterogeneous data augmentation method to improve classification performance. Specifically, we first construct a heterogeneous recipe graph network to represent heterogeneous data, which includes four main-stream types of heterogeneous data: ingredient, flavor compound, image, and instruction. Then, we draw a sequence of augmented graphs for Semi-Supervised learning through multinomial sampling. The probability distribution of sampling depends on the Cosine distance between the nodes of graph. In this way, we name our approach as Multinomial Sampling Graph Data Augmentation (MS-GDA). Extensive experiments demonstrate that MS-GDA significantly outperforms SOTA baselines on cuisine classification and region prediction with the recipe benchmark dataset. Code is available at https://github.com/LiangzheChen/MS-GDA .

Deep Multimodal Data Fusion

Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data (e.g., images, texts, or data collected from different sensors), feature engineering (e.g., extraction, combination/fusion), and decision-making (e.g., majority vote). As architectures become more and more sophisticated, multimodal neural networks can integrate feature extraction, feature fusion, and decision-making processes into one single model. The boundaries between those processes are increasingly blurred. The conventional multimodal data fusion taxonomy (e.g., early/late fusion), based on which the fusion occurs in, is no longer suitable for the modern deep learning era. Therefore, based on the main-stream techniques used, we propose a new fine-grained taxonomy grouping the state-of-the-art (SOTA) models into five classes: Encoder-Decoder methods, Attention Mechanism methods, Graph Neural Network methods, Generative Neural Network methods, and other Constraint-based methods. Most existing surveys on multimodal data fusion are only focused on one specific task with a combination of two specific modalities. Unlike those, this survey covers a broader combination of modalities, including Vision + Language (e.g., videos, texts), Vision + Sensors (e.g., images, LiDAR), and so on, and their corresponding tasks (e.g., video captioning, object detection). Moreover, a comparison among these methods is provided, as well as challenges and future directions in this area.

Comparative Analysis of Dynamic Power Consumption of Parallel Prefix Adder

The Newcomb-Benford law, also known as Benford's law, is the law of anomalous numbers stating that in many real-life numerical datasets, including physical and statistical ones, numbers have a small initial digit. Numbers irregularity observed in nature leads to the question, is the arithmetical-logical unit, responsible for performing calculations in computers, optimal? Are there other architectures, not as regular as commonly used Parallel Prefix Adders, that can perform better, especially when operating on the datasets that are not purely random, but irregular? In this article, structures of a propagate-generate tree are compared including regular and irregular configurations—various structures are examined: regular, irregular, with gray cells only, with both gray and black, and with higher valency cells. Performance is evaluated in terms of energy consumption. The evaluation was performed using the extended power model of static CMOS gates. The model is based on changes of vectors, naturally taking into account spatio-temporal correlations. The energy parameters of the designed cells were calculated on the basis of electrical (Spice) simulation. Designs and simulations were done in the Cadence environment and calculations of the power dissipation were performed in MATLAB. The results clearly show that there are PPA structures that perform much better for a specific type of numerical data. Negligent design can lead to an increase greater than two times of power consumption. The novel architectures of PPA described in this work might find practical applications in specialized adders dealing with numerical datasets, such as, for example, sine functions commonly used in digital signal processing.

SFL-MDrone: Synchronous federated learning enabled multi drones

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.

Quo Vadis Landasan Hukum Pengangkatan Penjabat Kepala Daerah

The Ministry of Home Affairs issued Permendagri No. 4 of 2023 as a legal basis for the appointment of acting regional heads. In fact, viewed from various aspects, the legal basis in the form of Permendagri is not sufficient and inappropriate to accommodate the needs of the mechanism for appointing acting regional heads. The research methods used in this study belong to the normative juridical type of research. The type of data used is secondary data, so the data collection uses literature studies. The data analysis method used is qualitative analysis. This article finds the problem of non-conformity in the formation of appointment regulations in the form of Permendagri, and finds several issues that will be discussed: First, the position of the Ministerial Regulation in the legal system in Indonesia. Second, Permendagri 4/2023 contradicts Article 86 Paragraph (6) of Law No. 23 of 2014 concerning Regional Government. Third, Permendagri 4/2023 ignores the recommendations of Constitutional Court Decision No. 67/PUU-XIX/2021. This article concludes that harmonization is needed in the issuance of technical regulations for the appointment of acting regional heads, so it is recommended to issue laws and regulations in the form of Government Regulations as mandated by the Constitutional Court and laws.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text. Although most of the existing methods unify the semantic feature extraction and hash function learning modules for end-to-end learning, they ignore this issue and do not use information-rich modalities to support information-poor modalities, leading to suboptimal results, although they unify the semantic feature extraction and hash function learning modules for end-to-end learning. Furthermore, previous methods learn hash functions in a relaxed way that causes nontrivial quantization losses. To address these issues, we propose a new method called graph convolutional network (GCN) discrete hashing. This method uses a GCN to bridge the information gap between different types of data. The GCN can represent each label as word embedding, with the embedding regarded as a set of interdependent object classifiers. From these classifiers, we can obtain predicted labels to enhance feature representations across modalities. In addition, we use an efficient discrete optimization strategy to learn the discrete binary codes without relaxation. Extensive experiments conducted on three commonly used datasets demonstrate that our proposed method graph convolutional network-based discrete hashing (GCDH) outperforms the current state-of-the-art cross-modal hashing methods.

Design of storage benchmark kit framework for supporting the file storage retrieval

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.

The accuracy of reliability coefficients: A reanalysis of existing simulations.

Controversy over which reliability estimators should be used persists due to a lack of knowledge about their accuracy. Simulation is an effective tool to obtain an answer, but existing simulation studies yield contradictory results regarding which reliability estimators are the best. The causes of these inconsistent conclusions have yet to be discussed. This study reanalyzes existing studies to understand these contradictions. The most important reason is that previous studies consider only a few reliability estimators. This study examines approximately 30 reliability estimators and finds that there is no single, most accurate reliability estimator across all data types. Instead, several reliability estimators are accurate to comparable levels for unidimensional data (congeneric reliability, Guttman's lambda2, and ten Berge-Zegers's mu). Likewise, multiple reliability estimators perform similarly for multidimensional data (multidimensional parallel reliability, correlated factors reliability, and second-order factor reliability). Whereas many recent studies support factor analysis (FA) reliability estimators, this study shows that not all FA reliability estimators are accurate and that some cause severe overestimation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

Out-of-Sample Tuning for Causal Discovery.

Causal discovery is continually being enriched with new algorithms for learning causal graphical probabilistic models. Each one of them requires a set of hyperparameters, creating a great number of combinations. Given that the true graph is unknown and the learning task is unsupervised, the challenge to a practitioner is how to tune these choices. We propose out-of-sample causal tuning (OCT) that aims to select an optimal combination. The method treats a causal model as a set of predictive models and uses out-of-sample protocols for supervised methods. This approach can handle general settings like latent confounders and nonlinear relationships. The method uses an information-theoretic approach to be able to generalize to mixed data types and a penalty for dense graphs to penalize for complexity. To evaluate OCT, we introduce a causal-based simulation method to create datasets that mimic the properties of real-world problems. We evaluate OCT against two other tuning approaches, based on stability and in-sample fitting. We show that OCT performs well in many experimental settings and it is an effective tuning method for causal discovery.

A Classification Algorithm Inspired by the Chromatographic Separation Mechanism Dedicated to the Classification of Variable-length and Multi-class Vectors

Nowadays, one of the critical problems related to data mining is the processing of large data sets. This article presents an algorithm that may apply to the issues associated with classifying large-volume data sets. The motivation behind defining this type of algorithm was that the methods used to process this data type are subject to several significant limitations. The first considerable limitation of using classical classification methods is ensuring a constant data size. The second type of constraint is related to the data dimension. The last limitation in using classic classification algorithms is associated with the situation in which a given input vector may contain data belonging to many classes simultaneously, in which case we are talking about so-called multi-class vectors. The presented algorithm is inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. As we know, in the case of chromatographic separation, we are dealing with a similar class of problems that occur when processing large data sets, firstly: the molecules of a chemical substance have a different number of molecules - i.e., they have different lengths, which corresponds to the situation that occurs when processing large data sets. In this work, a classification algorithm inspired by the mechanism of resolution chromatography is presented. The article presents the results of calculations for sample data sets. It discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class and multi-class data.

A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices

The simultaneous clustering of observations and features of datasets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive datasets. However, most existing models focus on continuous and dense data in stationary scenarios, where cluster assignments do not evolve over time. This work introduces a novel latent block model for the dynamic co-clustering of data matrices with high sparsity. To properly model this type of data, we assume that the observations follow a time and block dependent mixture of zero-inflated distributions, thus, combining stochastic processes with the time-varying sparsity modeling. To detect abrupt changes in the dynamics of both cluster memberships and data sparsity, the mixing and sparsity proportions are modeled through systems of ordinary differential equations. The inference relies on an original variational procedure whose maximization step trains fully connected neural networks in order to solve the dynamical systems. Numerical experiments on simulated and real world datasets demonstrate the effectiveness of the proposed methodology in the context of count data.

The Impact of QRIS Policy on the Development of Micro Businesses in Medan City, Indonesia

This study analyzes the impact of the QRIS policy in the early stages of implementation on financial performance and expansion of the market share of micro-businesses in Medan City. Financial performance is observed through changes in turnover and net profit; meanwhile, market share is determined based on changes in the number of new customers before and after the implementation of the QRIS policy. This study employs the Difference in Differences (DiD) method. The type of data used is primary data. Purposive and random sampling was utilized with 50 samples of micro businesses for the treatment group and 50 for the control group. The time difference was found between before and after the implementation of the QRIS policy. The results of this study show that the QRIS policy has a positive and significant effect on the increase in new consumers. On the other hand, the QRIS policy has no significant impact on the net profit and turnover of micro businesses. The COVID-19 pandemic has significantly and negatively impacted micro-enterprises, sharply declining revenue and net profit. However, based on the magnitude of the decline, the control group experienced a relatively greater turnover and net profit decline than the treatment group. The implication of this study is the need for efforts to increase micro-entrepreneurs understanding of the importance of faster adaptation to advances in digital technology, one of which is adjustments to a more flexible payment system so that consumers can get a wider choice of payment schemes. In addition, payment system authorities should address various obstacles in using the system and provide more massive socialization of the products offered.

Investigation of the use of topographic data derived from Pléiades imagery for high-resolution hillslope-scale morphometry

Obtaining accurate representations of the Earth surface topography is paramount in many geomorphological investigations. While regional scale studies use Digital Elevation Models (DEM) with resolution of the order of 10–100 m, in many applications resolution <5 m are mandatory to appropriately represent landforms and processes of interest. In particular, hillslopes, which cover the most of continental surfaces, have typical length of the order of 100 m. For that reason, high resolution topographic data are mandatory to correctly represent their morphology. The massive use of higher resolution topographic data has been enabled by a vast array of methodological developments, among which terrestrial and airborne Light Detection And Ranging (LiDAR) or uncrewed aerial vehicle-based photogrammetric approaches are the most commonly used. However, such techniques might be difficult to deploy and not cost-effective, to cover large areas in remote locations.In this study, we present an exploration of the potential of topographic data derived from Pléiades imagery for the characterizing hillslope morphology. We focus on the Gabilan Mesa area in California, where a LiDAR DEM is available and is used as a benchmark. We systematically compare Pléiades-derived topographic data with the DEM constructed from LiDAR data, starting from simple elevation analysis and then using topographic derivatives, such as slope or curvature, which are commonly used in morphometric studies. Finally, we take advantage of the high resolution of the datasets to extract and compare hillslope-scale morphometric parameters, such as length, relief, or hilltop curvature. While some particular situations, such as planar and horizontal surfaces, requires specific attention in terms of the relative differences between the two type of data, the absolute values for the metrics are in good agreement over their relevant range of utilization for hillslope-scale geomorphology.

Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis

Obtaining large-scale radiology reports can be difficult for medical images due to ethical concerns, limiting the effectiveness of contrastive pre-training in the medical image domain and underscoring the need for alternative methods. In this paper, we propose eye-tracking as an alternative to text reports, as it allows for the passive collection of gaze signals without ethical issues. By tracking the gaze of radiologists as they read and diagnose medical images, we can understand their visual attention and clinical reasoning. When a radiologist has similar gazes for two medical images, it may indicate semantic similarity for diagnosis, and these images should be treated as positive pairs when pre-training a computer-assisted diagnosis (CAD) network through contrastive learning. Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks. McGIP uses radiologist gaze to guide contrastive pre-training. We evaluate our method using two representative types of medical images and two common types of gaze data. The experimental results demonstrate the practicality of McGIP, indicating its high potential for various clinical scenarios and applications.

A new exploration tool in the search for native hydrogen and helium

Abstract Native hydrogen and helium have been considered important resources in assisting the energy transition. Hydrogen and helium seeps have been reported worldwide, which may indicate large reserves within the subsurface. However, generation of hydrogen and helium is complex; poorly understood and constrained for both generation processes and migration. One source of native hydrogen is ultramafic rocks, which have experienced serpentinization together with water radiolysis. In contrast, helium generation occurs as the result of the radioactive decay of uranium and thorium present within radiogenically enriched basement. An exploration tool, dedicated to identifying areas with the geological settings and conditions favourable for native hydrogen and helium generation, has been developed and tested. Several databases have been created and integrated as part of this study (geological and geochemical generation models) to support and focus the search for both hydrogen and helium. Machine learning algorithms which extract value from geospatial data types for detecting various accumulations have been implemented. The first machine learning results demonstrate the significant value in integrating data and machine learning for high grading areas more conducive to accumulating hydrogen and helium.

ЭФФЕКТИВНОСТЬ АВТОМАТИЗИРОВАННОГО УПРАВЛЕНИЯ ТРАНСПОРТНЫМИ СРЕДСТВАМИ В ТРАМВАЙНЫХ СИСТЕМАХ

The intellectualization of water transport is accompanied by an expansion of the landscape of threats to transport security, caused by the characteristics and weaknesses of the technologies being introduced, which are the convergence of information and telecommunication technologies, automated and automatic control technologies and artificial intelligence. The peculiarity of these technologies is working with large volumes of information. Violation of the security of information processed in intelligent systems of water transport (illegal access, modification, deletion and similar unauthorized influence) causes a violation of transport security and, as a consequence, the security of critical information infrastructure and the country’s critical infrastructure, national security. Convergent technologies used in intelligent transport systems are characterized by multiple and poorly formalized manifestations of the consequences of threats. The article presents a model for assessing the risks of information security of intelligent water transport systems, based on the methods of the theory of fuzzy sets and fuzzy logic, the use of which makes it possible to take into account the above-mentioned features of the technologies being implemented. The hierarchical structure of the model and the use of fuzzy set theory and fuzzy logic methods make it possible to adapt the model to various risk criteria, types of input data and the level of detail of risk analysis. For the presented model, a methodology for assessing information security risks has been developed and an example of risk calculation is given. The developed model and methodology are intended to build an information security risk management system for autonomous shipping, implementing technologies of hybrid (augmented, extended) intelligence, providing for the use of artificial intelligence controlled by people.

GENETIC ARCHIVES AND LIFELINE: UNRAVELING THE UMBILICAL CORD’S BIOLOGICAL DATABASE

Biological databases are critical components of the field of bioinformatics, which is dedicated to managing, organising, and making sense of the vast and diverse biological data generated by scientific research. These databases play a fundamental role in modern life sciences, allowing researchers, clinicians, and scientists to access, analyse, and interpret a wide range of biological information. These databases serve as invaluable resources, enabling researchers to access, manage, and analyse biological data, accelerating scientific discoveries and enhancing our understanding of life's complexities. There are various types of biological databases that serve as critical resources for researchers and scientists in the field of life sciences. These databases are categorised based on the types of biological data they store and manage. Keywords: Bioinformatics, biological database, genetics, scientific research, genomics, medical advancements, data repositories