Data Sorting Research Articles

FPGA Implementation of Efficient CFAR Algorithm for Radar Systems.

The constant false-alarm rate (CFAR) algorithm is essential for detecting targets during radar signal processing. It has been improved to accurately detect targets, especially in nonhomogeneous environments, such as multitarget or clutter edge environments. For example, there are sort-based and variable index-based algorithms. However, these algorithms require large amounts of computation, making them difficult to apply in radar applications that require real-time target detection. We propose a new CFAR algorithm that determines the environment of a received signal through a new decision criterion and applies the optimal CFAR algorithms such as the modified variable index (MVI) and automatic censored cell averaging-based ordered data variability (ACCA-ODV). The Monte Carlo simulation results of the proposed CFAR algorithm showed a high detection probability of 93.8% in homogeneous and nonhomogeneous environments based on an SNR of 25 dB. In addition, this paper presents the hardware design, field-programmable gate array (FPGA)-based implementation, and verification results for the practical application of the proposed algorithm. We reduced the hardware complexity by time-sharing sum and square operations and by replacing division operations with multiplication operations when calculating decision parameters. We also developed a low-complexity and high-speed sorter architecture that performs sorting for the partial data in leading and lagging windows. As a result, the implementation used 8260 LUTs and 3823 registers and took 0.6 μs to operate. Compared with the previously proposed FPGA implementation results, it is confirmed that the complexity and operation speed of the proposed CFAR processor are very suitable for real-time implementation.

Improving efficiency of fitting Cox proportional hazards models for time-to-event outcomes in genome-wide association studies (GWAS).

Technologies identifying single nucleotide polymorphisms (SNPs) in DNA sequencing yield an avalanche of data requiring analysis and interpretation. Standard methods may require many weeks of processing time. The use of statistical methods requiring data sorting, matrix inversions of a high-dimension and replication in subsets of the data on multiple outcomes exacerbate these times.A method which reduces the computational time in problems with time-to-event outcomes and hundreds of thousands/millions of SNPs using Cox-Snell residuals after fitting the Cox proportional hazards model (PH) to a fixed set of concomitant variables is proposed. This yields coefficients for SNP effect from a Cox-Snell adjusted Poisson model and shows a high concordance to the adjusted PH model.The method is illustrated with a sample of 10000 SNPs from a genome-wide association study in a diabetic population. The gain in processing efficiency using the proposed method based on Poisson modelling can be as high as 62%. This could result in saving of over three weeks processing time if 5 million SNPs require analysis. The method involves only a single predictor variable (SNP), offering a simpler, computationally more stable approach to examining and identifying SNP patterns associated with the outcome(s) allowing for a faster development of genetic signatures. Use of deviance residuals from the PH model to screen SNPs demonstrates a large discordance rate at a 0.2% threshold of concordance. This rate is 15 times larger than that based on the Cox-Snell residuals from the Cox-Snell adjusted Poisson model. The method is simple to implement as the procedures are available in most statistical packges. The approach involves obtaining Cox-Snell residuals from a PH model, to a binary time-to-event outcome, for factors which need to be common when assessing each SNP. Each SNP is then fitted as a predictor to the outcome of interest using a Poisson model with the Cox-Snell as the exposure variable.

Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

Encoder-Decoder Architecture for Image Steganography using Skip Connections.

Data security has become a basic need in today's world as it shifts more and more toward an all-online mode of operation. All sorts of data and information are now passed from one entity to another over a variety of transmission channels as well as stored on virtual online repositories, and a large chunk of that information is highly private. Over this stream of technological advancements, various data security techniques like encryption, data hiding, etc. have been developed to provide security while transmitting or storing data. This paper proposes an approach to hiding one image inside another image by using CNN in for feature extraction and processing. The proposed model works on the principles of the auto-encoder architecture and is made up of two modules one of which is responsible for the task of processing the secret image and extracting n features and the incorporating those features in the cover image ion such a manner that the cover image retains its original visual qualities and also the secret image gets hidden in it, that module is the Hiding network/Encoder. The other part of this network is the one responsible to then extract the hidden secret image and them create back to its original, this paper is the Reveal network/Decoder. The proposed model is implemented on ImageNet and Pascal-VOC datasets with images of different sizes. In order to judge the models ability to hide the image and to extract is back, PSNR and SSIM are used as performance evaluation Metrics.

Blockchain-Enabled Technique for Privacy- Preserved Medical Recommender System

With the proliferation of privacy issues surrounding the Internet of Medical (IoMT) recommender system data, this study presents a Secure Recommendation and Training Technique (SERTT) which is contingent on a combination of both federated learning and blockchain approaches. Firstly, the study presents a new framework for recording, sorting, and transmission of IoMT data while incorporating blockchain to ensure that the IoMT data transmitted to cloud servers is not made vulnerable by the sharing of the original data. Secondly, by utilizing medical data, the study designs a Recommender Data Management Neural Architecture (REDMANA) which is based on federated learning and model searching training framework. The proposed technique guarantees that the model gradients which are trained by each node are not disclosed all through the universal training and modeling procedure. This makes the raw data inaccessible to either the IoMT data provider or the user. Considering that the model ensures that users can only obtain their necessary inquiries, neither medical data suppliers nor users can obtain access to raw data. Thus, it reduces the issues of safeguarding medical data sets to the issues of securing data processing. Using numerical analysis and experiments the proposed technique is compared with other existing techniques, the result shows that the proposed SERTT system is efficient and secures recommender data management training and modeling technique and that it performs previously designed techniques as compared.

Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo.

A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to haveoccurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data.

Research on Enterprise Marketing Management Innovation Under the Background of Big Data——Taking Harbin Sanxing Culture Communication Co., Ltd. as an Example

In the development of network technology in recent years, the application of big data has gradually become an important development trend in all walks of life, bringing new challenges and opportunities to the development of the cultural industry. As a popular cultural communication company at present, it should be clear how to do a good job in the marketing management innovation strategy of the enterprise in the context of the development of big data. Actively explore to achieve the stable development of the enterprise, such as mass marketing, multi-marketing, etc. that are common in the market today, through data collection and sorting, do a good job in talent training, do a good job in technical research, and further improve the future development of the company every day. This is also in line with the current innovative road of cultural industry marketing development.

EDITORIAL SPECIAL ISSUE: PART IV-III-II-I SERIES

EDITORIAL SPECIAL ISSUE: PART IV-III-II-I SERIES

Replication Strategy with Comprehensive Data Center Selection Method in燙loud Environments

As the amount of data continues to grow rapidly, the variety of data produced by applications is becoming more affluent than ever. Cloud computing is the best technology evolving today to provide multi-services for the mass and variety of data. The cloud computing features are capable of processing, managing, and storing all sorts of data. Although data is stored in many high-end nodes, either in the same data centers or across many data centers in cloud, performance issues are still inevitable. The cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud environment. The real challenge here is developing the right data replication strategy with minimal data movement that guarantees efficient network usage, low fault tolerance, and minimal replication frequency. The key problem discussed in this research is inefficient network usage discovered during selecting a suitable data center to store replica copies induced by inadequate data center selection criteria. Hence, to mitigate the issue, we proposed Replication Strategy with a comprehensive Data Center Selection Method (RS-DCSM), which can determine the appropriate data center to place replicas by considering three key factors: Popularity, space availability, and centrality. The proposed RS-DCSM was simulated using CloudSim and the results proved that data movement between data centers is significantly reduced by 14% reduction in overall replication frequency and 20% decrement in network usage, which outperformed the current replication strategy, known as Dynamic Popularity aware Replication Strategy (DPRS) algorithm.

A PERFORMANCE COMPARISON OF SORTING ALGORITHMS IN DIFFERENT PROGRAMMING LANGUAGES

Sorting, as one of the basic algorithms, has a wide range of applications in software development. As the amount of processed data grows, the need for fast and efficient data sorting increases significantly. There are many sorting algorithms and their extensions. However, choosing the best and most versatile among them is impossible. All these algorithms have their specifics, which determine the scope of their effective use. Therefore, the problem of deciding the optimal algorithm for certain specific conditions is relevant. This choice is often a non-trivial task, and an unsuccessful choice of algorithm can cause difficulties with data processing performance. To determine which algorithm will be the best in a particular situation, you need to analyse all the factors that affect the operation of algorithms: the size and structure of the data set, the range of element values, the form of access (random or sequential), the orderliness, the amount of additional memory required to execute the algorithm, etc. In addition, different algorithms have different performance in different programming languages. The study analyses the advantages and disadvantages of nine popular sorting algorithms (Bubble, Insertion, Selection, Shell, Merge, Quick, Counting, Radix, and Heap) due to their specifics and limitations on their possible use. The performance of these algorithms implemented in four popular programming languages (C++, C#, Java and JavaScript) is tested. We experimentally discovered that the performance of sorting algorithms differs depending on the programming language. The applied aspect of the study is that its conclusions and results will allow developers to choose the best algorithm for a particular programming language, depending on the size, range, structure, etc. of the data set to be sorted. Considering this is significant when we have to sort large amounts of data in search engines, scientific and engineering applications. After all, the sorting algorithm's efficiency significantly affects the system's overall performance.

A Multi-Criteria Optimisation Algorithm for the Evaluation of Scientific Activity

The article discusses a multi-criteria optimization algorithm, developed for the estimation of the quality of scientific activity. The presented solution is based on a modified knapsack algorithm, the novelty of the approach relies on extended data sorting – with splitting apparently equivalent data blocks. The criteria of the algorithm are based on the regulations in force in Poland, but it is possible to easily adapt the methodology to the requirements for scientists in other countries and for other tasks requiring multi-criteria optimization

STRENGTHENING THE DIMENSIONS OF PANCASILA STUDENT PROFILE IN SCHOOL MOVER IN THE ERA OF INDUSTRIAL REVOLUTION 4.0

The progressive era impacted the decline in the Pancasila values implementation in everyday life. The young generation behaves not according to the values and norms that apply in society. Based on this, it is necessary to strengthen character education in the era of the industrial revolution 4.0. In this era, science and technology developed rapidly. School mover is the school model of Kurikulum Merdeka implementation. One of the messages in the curriculum is strengthening character education. The character education implementation in the Kurikulum Merdeka is based on Pancasila values, called the Pancasila student profile.
 This research used qualitative methods, especially case studies. The researcher collected data by observation, in-depth interviews, and documentation; he tested the validity of the data by source triangulation and method triangulation. Data analysis included the stages of collecting data, identifying, sorting data, and concluding.
 Based on the research results, school movers, especially the Persatuan Umat Islam Elementary School, implemented strengthening character education by developing a religious basis, namely Islamic values. Educators trained students to implement the dimensions of the Pancasila student profile with a humanist approach and innovative strategies. They also used technology in various school activities because they educated the millennial generation; this generation is interested in technological developments.

A dataset of UAV multispectral images for the grassland-livestock balance

The grassland-livestock balance system plays an important role in reconciling the protection of grassland resources with the sustainable development of animal husbandry. The accurate estimation of the grassland-livestock balance is the basis for the implementation of the grassland-livestock balance system. In order to explore the accurate calculation method of the important parameter, i.e. livestock grass demand in the calculation system, in this study, we set up an experimental area in the natural grassland of Hulunbuir, Inner Mongolia, to simulate a real grazing scene, and conducted 4 grazing experiments controlling the sheep number and grazing time. We obtained multispectral images of the experimental area before and after grazing were with the help of the UAV remote sensing, and carried out a quadrat survey. After data sorting and preprocessing, we produced this dataset of high spatial resolution image data and accurate quadrat investigation data. It can be applied to the research on the daily grass demand of grazing sheep on Hulunbuir meadow steppe in autumn. This dataset provides data support for the accurate estimation of local grassland-livestock balance, and is of great significance for the adaptive dynamic management of grass and livestock.

Conceptualising adherence to exercise for musculoskeletal pain: A concept mapping study.

The concept of adherence to exercise for musculoskeletal (MSK) pain is poorly defined and inadequately measured. This study aimed to, (1) conceptualise adherence to exercise therapy for MSK pain, and (2) identify statements most representative of the new conceptualisation that could be developed into items for a new measurement tool. Concept mapping methodology was used, which is an integrated mixed methods approach. Focus groups with stakeholders generated statements describing adherence to exercise for MSK pain. Statements were grouped according to themes and rated for importance. Data analysis via multidimensional scaling and hierarchical cluster analysis produced a series of concept maps, which were refined during a further stakeholder workshop to produce the final conceptualisation of exercise adherence. Mean importance ratings established statements most suitable for future development. Twenty-eight participants produced 100 unique statements concerning adherence, which were sorted and rated. Analysis of the sort data with further participant refinement concluded that adherence to exercise consists of six domains: communication with experts; targets; how exercise is prescribed; patient knowledge and understanding; motivation and support; and psychological approach and attitudes. Fifty-six statements were rated with above average importance for inclusion in a new measure of adherence to exercise for MSK pain. Adherence to exercise for MSK pain is a complex and multi-dimensional construct represented by six distinct domains. Statements that best represent these domains have been identified by key stakeholders and will inform the development of a new measure of adherence to exercise for MSK pain.

SISTEM SIMBOL DALAM LABUHAN SARANGAN DI KELURAHAN SARANGAN KECAMATAN PLAOSAN KABUPATEN MAGETAN

This study aims to describe and explain the symbol system contained in the Labuhan Sarangan. This research is a type of qualitative research based on an eclectic approach as an outcome of the collaboration of semiotics and hermeneutics theory. The researcher acts as a human instrument. The data of this research are verbal and nonverbal symbols. Verbal symbol data came from the speeches of the actors in the Labuhan Sarangan. Nonverbal symbol data came from the actions of the actors, equipment, and tools that support the implementation of the Labuhan Sarangan. Data collection was done by observing and interviewing Labuhan Sarangan. Data analysis is done in four stages, namely sorting data according to the range of problems, organizing data in formation and categorization, interpreting data based on empirical understanding, and evaluating data units to produce conclusions. The result of the study showed the findings on the shape, meaning, and function of the symbol. Findings in the form of symbols include verbal and nonverbal symbols. The verbal symbols findings include words or mantras or prayers and gamelan that is used as an accompaniment to the Labuhan Sarangan, which is spoken during the labuhan procession. Findings nonverbal symbols include the actions of the actors, equipment, and tools that support the implementation of the Labuhan Sarangan and the offerings used at the time of the Labuhan Sarangan. The findings on the meaning of symbols include religion, ethics, and philosophy. The function of symbols findings includes the purpose of knowledge, outlook on life, group solidarity, mediation, communication, and participation. Keyword: symbol of offerings, form, meaning, function Keyword: symbol of offerings, form, meaning, function

A Methodology for the Design and Engineering of Smart Product Service Systems: An Application in the Manufacturing Sector

The combination of servitization and digitalization is increasingly changing the economy and society at the global level towards sustainability goals. Companies are shifting their business models, typically oriented to selling products, towards providing bundles of products and services and integrating them with technologies enabling data collection and analysis, resulting in the so-called smart Product Service Systems (PSS). Different approaches and techniques have been put forth to design PSS and, more recently, smart PSS, but they continue to primarily concentrate on establishing value propositions and ignore the question of what sort of operational data can be gathered and used to deliver the PSS solution. Therefore, manufacturing companies willing to expand their portfolio with new advanced services nowadays still face multiple challenges. To address this gap, this study proposes the Service Engineering Methodology for the engineering of smart PSS (SEEM-Smart), which takes into account the trade-off between customer satisfaction and internal efficiency with a focus on data gathering and information flow. The methodology is then applied in a real-world setting. The case study shows the application of the SEEM-Smart for engineering a new data-driven service offering enabled by a cloud-based platform supporting the service provision.

“When people see me, they know me; they trust what I say”: characterizing the role of trusted sources for smoke risk communication in the Okanogan River Airshed Emphasis Area

IntroductionAs wildfire smoke events increase in intensity and frequency in the Pacific Northwest, there is a growing need for effective communication on the health risks of smoke exposure. Delivery through a trusted source or intermediary has been shown to improve reception of risk communication messages. This is especially salient in rural and tribal communities who may be hesitant to trust information from state and federal agency sources. This study aims to identify and characterize trusted sources for smoke risk information in the Okanogan River Airshed Emphasis Area (ORAEA), a rural region of North Central Washington state that is heavily impacted by smoke from wildfires and prescribed fire.MethodsThe research team conducted a qualitative study using data collected through key informant interviews and focus groups to assess the role of various sources and intermediaries in disseminating smoke risk information. We used a consensual coding approach in NVivo Qualitative Analysis Software to sort data into preliminary categories, which were grouped into themes using a thematic analysis approach. We used member checking and iterative feedback processes with local project partners throughout the project to ensure credibility of results.ResultsThrough the analysis, we identified three themes characterizing trusted sources for smoke risk communication in the ORAEA. These themes were: (1) local and tribal sources of information are perceived as more trustworthy than state and federal government sources, (2) trustworthiness is determined by an evaluation of multiple factors, in particular, perceived credibility, quality of information, and relationship with the source, and (3) conservative political ideology and perceived parallels with COVID-19 communication influence perception of trust. Within each theme, we identified several sub-themes, which contributed additional nuance to our analysis.ConclusionThis study provides insights into which sources of information are trusted by rural and tribal community members in the ORAEA and why. Results from our study emphasize the importance of relationships and collaboration with local and tribal partners in smoke risk communication. In this paper, we discuss implications for state and federal agency practitioners and present recommendations for how to work with local and tribal partners on smoke risk communication.

User experience and information architecture of the National Digital Library and archives of Iran: a usability investigation and card sorting

PurposeThis study aims to evaluate the usability and information architecture of the digital library (DL) website of the National Library and Archives of Iran (NLAI).Design/methodology/approachThis applied study used an exploratory mixed method, card sorting. Data were collected by interviewing, observation, usability test and card sorting. By interviewing users about problems of the DL, eight tasks were predefined and users’ problems in the path were identified. Their satisfaction of the tasks and the usability rates were measured via a questionnaire. Card sorting was done to inform on the information organization of the website elements. The study population included all users of the DL of the NLAI in two groups: ten initiator users (public users) and ten expert users (librarians). SPSS was used for analysing the usability test quantitative data. MaxQda was applied for analysing interview-driven qualitative data. Qualitative content analysis, categorization (data organization and grouping) and determining main and secondary codes were applied as well. The sort optimal application was used for analysing card sorting data in the form of similarity matrix and dendrogram. For validating qualitative findings, triangulation was used. The internal reliability of the used questionnaire amounted to a = 0.87FindingsRegarding the assigned tasks, new initiator users by consuming 367.67 s for registering and new expert users by consuming 403 s spent the most time. Task 2 ranked first in being incomplete among 40% of initiator users and Task 3 ranked first by 30% of incompleteness. Expert users had more unsuccessful attempts. Task 5 with the mean rate of 3.35 and Task 8 with the mean rate of 2.25 were the most difficult and the easiest tasks, respectively. Some usability components were rated lower than the moderate point. Only 30% of initiator users and 10% of expert users were satisfied with the website. A total of 12 categories and 452 codes were identified as main problems of the DL. The problems related to a vague perception of concepts and labels with 90 repetitions and digital source display with six repetitions ranked as the first and the last problems in working with the DL, respectively. The sort optimal package produced the card sorting results as a matrix similarity and a dendrogram. Card sorting reflected some changes in organizing information items. Interviews after card sorting emerged some new groups to be included, such as links to other digital libraries, shared databases in the organization and frequently asked questions.Originality/valueThe library’s website should be designed in a manner so that it can satisfy users with different traits. As the information technologies are increasingly developing, the importance of such a design increases for better service provision and effective competition.

Indexing with Excel, Part 2. Checking indexing data

The first article in this series showed how a Microsoft Excel table can be converted into a database. Excel’s database capabilities are a prerequisite for the program to be used meaningfully in indexing. Furthermore, it was shown that in order to sort data professionally, additional fields have to be created in Excel, which are used to prepare the sorting according to internationally recognised rules. After Excel has been set up in this way, checks can be made on indexes, among other things. In order to be able to carry out such checks, further fields must be included in Excel, which are filled with special check formulae. The current article in the series focuses on the topic of checks and how sensible formulae can be developed. Through detailed examples and illustrations it shows that Excel can be used for checks similar to those in DIPs (dedicated indexing programs), but that much more is also possible.

Identifying level and category shift in Legenda Pulau Senua bilingual storybook

to the TL. Adding occurs because TL has grammatical category which SL lacks. Omitting occurs because the TL has a grammatical category which exists in the SL.
 Purpose: This study is conducted to analyze translation shifts in Bilingual book story.
 Design and methods: This study used qualitative research method in the form of content analysis. Data analysis techniques is the process of organizing and sorting data into the patterns, categories and units of the basic description so that it can be determined the theme and can be formulated working hypothesis as based on the data.
 Results: there were 15 data that indicated translation shift in this book, and the category shift were dominantly used. Any shift in the process of translation is normal, as long as it does not change the meaning or the message of the text.