Data Classification Based on the Features Reduction and Piecewise Linear Separation

Deep-learning-based visual data analytics for smart construction management

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Features of technology and its linkages with turnover intention and work exhaustion among IT professionals: A multi-study investigation

We develop a theory for immediately detecting patterns violating linear separability in a training set, as soon as they are presented to the classifier. The theory is based on the computation of the solution cone in the weight space of a single linear threshold unit (LTU). The separability-violating patterns can be skipped and in the end, we obtain a linearly separable subset of the original training set together with the subset's solution cone. We further propose an iterative algorithm for computing the solution cone and we introduce the concept of garbage collection for removing redundancy from the cone representation. The algorithm can be implemented by a novel neural network architecture where the synaptic weights have constant values throughout the life of the synapse. We show that by combining multiple such models we can construct a larger network capable of learning the classification boundaries of convex classes, therefore solving an important class of nonlinearly separable problems.

The role of emerging technologies has gained huge attention in recent years. With the advancements in sensor technologies, internet of things (IoT), and remote sensing, large amounts of data are being generated in agriculture. However, making sense of this data and extracting valuable information offers major/ important challenges. Data visualization techniques offer a solution by providing intuitive and interactive ways to represent complex agricultural data. Furthermore, big data analytics techniques enable the extraction of valuable information and patterns from large datasets, contributing to informed decision-making in smart agriculture. This chapter discusses the importance/benefits of data visualization and big data analytics in smart agriculture. It discusses various data sources in agriculture, including sensor technologies, IoT, and remote sensing. This work also provides an overview of the tools and technologies available for data visualization in smart agriculture, including data visualization software, etc., in detail.

Introduction: Human beings express their emotions through different types of language. The combination of verbal and non-verbal language makes it possible to broaden the capture of the senses and deepen the understanding of a certain social phenomenon. The use of software aims to support the systematization and optimization of the combined data analysis process; Goals: To map scientific production on the use of software for combined analysis of textual and visual data in qualitative research; Methods: Scope review carried out in four international databases (Scopus, EMBASE, MEDLINE and Web of Science), between September 2021 and January 2022. A semi-structured instrument was developed for data collection. The data processing and analysis process was supported by the Rayyan QCR application and the webQDA qualitative analysis software; Results: The final review sample consisted of 11 studies published between 2014 and 2022, mainly in the health area. The publications involved three types of qualitative analysis software: NVivo, Atlas.ti and MaxQda. Photography was the most used source to produce visual data, while focus and discussion groups stood out to produce textual data. Thematic analysis was used in most publications and the software essentially supported the data coding step in a combined way; Conclusions: The combined analysis of textual and visual data carried out with the support of software proved to be a methodological path still on the rise, given that the review identified only 11 studies published in the last eight years.

Many analysts have explored the role of informatics in unifying people and personnel in a variety of institutional and noninstitutional settings. The unifying potential of information technology (IT) has only strengthened as a result of the recent hybridization of cellular phones with mobile medical diagnostic technologies, an emerging field known as telemedicine. Tele- or mobile medicine has provided fortuitous innovative industrial conditions that forge cooperative linkages among engineers, communications specialists, biologists, health care providers, and the public. Due to a combination of factors, such as aging demographic cohorts in the United States and the European Union and an increasing demand for real-time biomedical monitoring, analysis, interpretation, and dissemination of diagnostic visual and algorithmic data by patients and providers who are both on the move in geographically fluctuating locations, an elective affinity among creative technological alliances has emerged across medical diagnostics, mobile telecommunications, and informatics industries. With these creative alliances taking place among biomedical and technological communities constructing a new biomedical avant-garde, observers are just beginning to recognize the profound levels of human cooperation that are elicited by these informatics-platform-integrated, telecommunication-dispersed, diagnostic and prophylactic biomedical work groups.KeywordsSocial DistancePower DistanceUncertainty AvoidancePromotional MaterialTrade ShowThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Objectives This paper considers the particular challenges of classification in relation to retired or outdated terms. While reasons for the revision or elimination of terms are varied, the consequences and possible solutions may have much in common. Building on an earlier paper on types of ambiguity in classification (Rajan 2017), the author extends an inquiry into this type of classificatory challenge by looking at examples from the recent and more distant past. While solutions to these challenges emerge from the context of the classification system and the needs of the disciplines which they serve, it may be useful to outline the features of historical ambiguity to inform future conversations and allow cross-disciplinary conversations about how we approach these challenges. Tracing the contours of historical ambiguity may also suggest approaches for multiple stakeholders to engage with term changes when needs emerge. MethodsThis paper extends a vocabulary developed in the course of my research into the nature or contents of otherness. While the literature of classification offers abundant evidence and nuanced discussion of the consequences of othering, there is limited discussion of non-human entities that are othered. In this paper, I apply my earlier taxonomy of ambiguity to particular cases of terms that have fallen out of favor. Extending on Tennis’s work on eugenics in the DDC (2012), I will trace the way in which the “science” of eugenics became ambiguous over time as understanding of the practice and consequences of the discipline changed. While eugenics had profound effects on individuals, the term is not tied to human identity groups. In contrast, the term Asperger’s, used until recently in the DSM as a diagnosis within the autism spectrum, was taken up by many as a term of identity. I will illustrate the strange ambiguity of a term that is officially unrecognized but popularly used, including responses to these changes proposed by members of the affected communities (Zolyomi and Tennis 2017). Finally, I will consider the ambiguity of classification of Asian Americans in the DDC and question how revisions to these terms resolve or fail to resolve this ambiguity (Higgins 2016). In my earlier paper, I suggested that historical ambiguity was a form of what I called conditional ambiguity, all forms of which require some sort of narrative resolution to accurately represent their complex and contextual identities. Revisiting historic terms, I will explore whether there are non-narrative solutions. I will also consider the relationship between historical ambiguity and what I previously termed privacy-related ambiguity, when identities are intentionally obscured or made inaccessible to those who seek to classify them. Areas of overlap may be substantial, for instance, in the realm of indigenous knowledge (Glass 2015). Results Ambiguity is experienced more often than it is described: a confusion antithetical to the aims of classification and other forms of categorization, which are employed to help us make sense of the world. In recent decades, a wide range of scholarship has exposed the consequences of classification. From Bowker and Star’s investigation of classification systems across disciplines (1999, 2007) and Olson’s examination of subject terms in library classifications (2002) to more recent work on representation of race (Furner 2007, Higgins 2016), gender (Adler 2009, Fox 2016b) and neurodiversity (Zolyomi and Tennis 2017), the consequences of othering have been amply illustrated. While the framing of othering has been critical in describing the consequences of classification, the concept did little to illuminate how things became other. concept of ambiguity was proposed as a way to see whether others have any commonality beyond the fact of their misrepresentation (Rajan 2017). Four types of ambiguity were outlined: multiplicity describes cases where the identity of an entity is more extensive or intersectional than the classification system allows; emergence occurs when phenomena are newly observed and identity has not concretized; privacy-related ambiguity is when those classifying are distrusted by the communities subject to representation. Conditional ambiguity describes entities or phenomena that require contextual description for accurate representation, including historical ambiguity, referring to situations where terminology has changed. cases of eugenics and Asian American representation, both in the DDC, and the elimination of the term ‘Aspergers’ from the DSM-V are all illustrative of the concept of historical ambiguity. Terms related to Asian Americans and neurodiversity are both related to human identity groups, while eugenics stands apart as a description of a practice once considered scientific. While the consequences of the othering in each of case is unique to the affected community, from the perspective of those engaged in classification, the examples of eugenics and Aspergers pose the same challenge: how to describe something that was once seen as legitimate and is now considered illegitimate or inaccurate? Must a new accurate representation include some historical context describing the changes in terminology? How would such a description aid the user? In the case of representation of Asian American identity, terminology expanded, yet the question remains how this term relates to historical resources. When and where to apply the term, and to what extent are those classifying responsible for bridging the gap between historical and current terms? Conclusions concept of ambiguity offers a useful lens with which to look at process of classification across systems. In particular, it highlights the qualities that lead to confusion on the part of those who classify and those who look to classification systems for guidance in understanding the world or a domain. Ambiguous resources or entities challenge the efficiency constraints of a system by requiring something closer to a natural-language description to accurately represent. While the concept of historical ambiguity does not offer any specific solutions, it may allow those engaged in the work of classification design and use to anticipate some of the challenges attendant to term change. It may facilitate methods of representation that are both more accurate and accessible to those who seek information about the past and are working to understand the present and future. ReferencesAdler, Melissa. 2009. “Transcending Library Catalogs: A Comparative Study of Controlled Terms in Library of Congress Subject Headings and User-Generated Tags in LibraryThing for Transgender Books.” Journal of Web Librarianship 3: 309–331. https://doi.org/10.1080/19322900903341099. Beghtol, Clare. 1986. “Semantic validity: concepts of warrant in bibliographic classification systems.” Library Resources and Technical Services 30: 109-125.Bowker, Geoffrey, and Susan Leigh Star. 1999. Sorting Things Out: Classification and Its Consequences. Cambridge, Ma: MIT Press. Feinberg, Melanie, Daniel Carter, and Julia Bullard. 2014. “A story without end: writing the residual into descriptive infrastructure.” DIS 2014 - Proceedings of the 2014 ACM SIGCHI Conference on Designing Interactive Systems, Vancouver, Canada, June 21-15, 2014. 385–394. https://doi.org/10.1145/2598510.2598553 Fox, Melodie. 2016a. “Priorities of arrangement or a hierarchy of oppressions?: Perspectives on intersectionality in knowledge organization.” Knowledge Organization, 43: 373-383. Fox, Melodie. 2016b. “Subjects in Doubt: Ontogeny of Intersex in the Dewey Decimal Classification.” Knowledge Organization 43: 581-593. Furner, Jonathan. 2007. “Dewey deracialized: A critical race-theoretic perspective.” Knowledge Organization, 34(3), 144-168. Furner, Jonathan. 2009. “Interrogating ‘Identity’: A Philosophical Approach to an Enduring Issue in Knowledge Organization.” Knowledge Organization 36: 3–16. Giles, David C. 2014. “‘DSM-V Is Taking Away Our Identity’: Reaction of the Online Community to the Proposed Changes in the Diagnosis of Asperger’s Disorder.” Health: 18 (2): 179–195 Glass, Aaron. 2015. “Indigenous Ontologies, Digital Futures: Plural Provenances and the Kwakwaka’wakw collection in Berlin and Beyond.” In Museum as Process: Translating Local and Global Knowledges, ed. Raymond Silverman, 19-44. London: Routledge. Higgins, Molly. 2016. “Totally Invisible: Asian American Representation in the Dewey Decimal Classification, 1876-1996.” Knowledge Organization 43: 609-621. Jacob, Elin K. 2004. “Classification and Categorization: A Difference that Makes a Difference.” Library Trends 52: 515-540. Nissenbaum, Helen. 2004. “Privacy as contextual integrity.” Washington Law Review 79: 119–157. Olson, Hope A. 2002. Power to Name : Locating the Limits of Subject Representation in Libraries. Boston, Ma.: Kluwer Academic Publishers. Rajan, Lilium. 2017. “Ambiguity in Knowledge Organization: Four Proposed Types.” NASKO 6: 239-247. Star, Susan Leigh, and Geoffrey Bowker. 2007. “Enacting silence: Residual categories as a challenge for ethics, information systems, and communication.” Ethics and Information Technology 9: 273–280. https://doi.org/10.1007/s10676-007-9141-7 Tennis, Joseph T. 2012. The Strange Case of Eugenics: A Subject's Ontogeny in a Long‐lived Classification Scheme and the Question of Collocative Integrity. Journal of the American Society for Information Science and Technology 63: 1350-359. Zolyomi, Annuska and Joseph T. Tennis. 2017. “Autism Prism: A domain Analysis Paper Examining Neurodiversity.” NASKO 6: 139-172.

Drawing on qualitative methodologies that integrate verbal and non-verbal texts, this study investigated novice teachers’ attributions of their experiences of internship, as conveyed through a visual text. Novices were invited to design a visual text that represented their experience during internship, as part of a national call entitled ‘Novices in Poster.’ The posters submitted yielded the publication of 127 published posters, which constituted the data for our study. The visual data were analyzed using a multilayered approach to the analysis of visual texts, drawing on Lemke’s analysis of visual and verbal data as complementary channels for obtaining an integrative interpretation of the expressed experience. Findings indicate that novices expose critical stances in relation to activism, collegiality, and leverage, making public their unique potential to improve the educational system. The visual texts underscored novices’ strong social–professional stances towards teaching, positioning them as delegates and agents striving for professional justice.

Firstly, the concepts and classification about visualized data mining technology have been elaborated in this paper, then the technology of data mining, data visualization and regression analysis are discussed, Finally, combined with the example of sliding bearing wear coefficient test, using data mining, data visualization and regression analysis technology, this paper design a system to implement visualized data mining technology under the web environment.

The extensive use of Information and Communication Technologies and the consequent unprecedented generation of data have radically transformed the way we understand cities today. The vision of smart cities considers technology as an enabling force for the emergence of new forms of intelligence and collaboration, enhancing, thus, the problem-solving capacity of the city. Despite the wide range of applications aiming to improve urban systems and city governance, urban planning processes are rarely informed by online platforms and data generated by them lack comprehensive data visualization approaches. This research introduces an exploratory approach to exploit urban data through interactive visualization and game design, as a way to facilitate the access and understanding of such data. A novel methodology is proposed, leveraging on spatial data as an input source which drives the generation of three-dimensional environments and interactive applications supported by game engines. More specifically, this research builds upon existing tools and methods for geoprocessing and spatial analysis and embeds them in a 3D environment that employs game design elements. Three indicative visualization scenarios are designed, developed, and implemented to showcase the dynamics and flexibility of the proposed methodology, based on the registry of an urban reporting application in Thessaloniki.

Data visualization is associated with Business Intelligence and acts as the main base of data understanding for the decision making.The techniques and application used to apply the data visualization can be expensive or difficult to understand in accordance with the business targets.In this context, the present paper has the objective to demonstrate the possibility of applying an open source software (Python 3) for several types of data visualization.The method uses different data representation techniques, such as histograms, scatters, correlations, 3D surfaces, etc. Analyzed data refers to human resources evaluation, as the management can analyze and understand how data such as: satisfaction level of the employee, the average monthly hours worked, project number that the employee participated in, and time spent by company for employee care.The results delivered several types of representations that offer conclusions about the positioning of evaluated human resources in conjunction with other data.

It aims at exploring the effect of coordinated relative poverty governance in rural areas based on blockchain and back propagation (BP) neural network, so that the living standard of rural areas can be significantly improved. In view of the shortcomings and defects existing in the rural relative poverty governance, artificial intelligence technologies such as blockchain and neural network are innovatively introduced to conduct intelligent storage and data visualization analysis on the rural relative poverty governance process. A visualized governance and mechanism innovation model of rural relative poverty data based on blockchain and BP neural network is constructed. Finally, its performance is analyzed through experiments. The results show that the accuracy of data visualization prediction of the proposed algorithm model reaches 94.31%. When the amount of data is 5.5 Mb, the calculation consumption of the proposed algorithm is 0.08 s, and the average leakage rate finally stabilizes below 10%. Therefore, the constructed poverty data visualization governance model can achieve good data transmission security performance while ensuring high classification accuracy. It can provide experimental basis and contribution for the intelligent development and mechanism innovation of rural relative poverty governance.

Author(s): Bethel, E. Wes; Childs, Hank; Mascarenhas, Ajith; Pascucci, Valerio; Prabhat | Abstract: Visualization is a highly data intensive science: visualization algorithms take as input vast amounts of data produced by simulations or experiments, and then transform that data into imagery. It turns out, as we shall explore in this chapter, that visualization reveals a somewhat different view of scientific data management challenges than are examined elsewhere in this book. For example, a data ordering and storage layout that works well for saving data from memory to disk may not be the best thing for subsequent visual data analysis algorithms. This chapter will present four broad topic areas under this general rubric: (1) a view of SDM-related issues from the perspective of implementing a production-quality, parallel capable visual data analysis infrastructure; (2) novel data storage formats for multi-resolution, streaming data movement, access and use by post-processing tools; (3) data models, formats and APIs for performing efficient I/O for both simulations and post-processing tools, discussion of issues and previous work in this space; (4) how combining state-of- the-art techniques from scientific data management and visualization enables visual data analysis of truly massive datasets.

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.