Useful Knowledge

2007 ◽  
Author(s):  
MARILYN STRATHERN
Keyword(s):  
Useful Knowledge

This chapter discusses useful knowledge, yet it also acknowledges the fact that there may be something gained from uselessness. Three kinds of uselessness are encountered during this chapter: extraneous detail, irrelevant parallels, and modelling for its own sake. The chapter covers topics such as plagiarism and making knowledge relevant to one's life. One conclusion drawn from this chapter is that knowledge has to be more than information in order for it to be accumulated or managed.

Hutton as an Author

2018 ◽  
Author(s):  
Susan E. Whyman
Keyword(s):  
Personal Experience ◽  
Literary Culture ◽  
Life Writing ◽  
Reading And Writing ◽  
Useful Knowledge ◽  
Industrial Town

Reading and writing were cornerstones of the lives of self-educated rough diamonds like Hutton. He is a perfect example of the dreaded rising author, who wrote for money, without education or status. His writings reveal new modes of authorship and the literary culture of an industrial town. Chapter 6 appraises his work by examining 70 periodical reviews of Hutton’s 15 books. Based on personal experience, they mixed history, travelogues, and life writing. Though they suited the nation’s thirst for entertainment and useful knowledge, Hutton has not been recognized as a new kind of writer, who produced unlearned books for a commercial age. His blunt style and breach of polite norms horrified the literary establishment. But his accessible prose satisfied new audiences and led to alternative yardsticks of literary taste. Hutton thus had an impact on two contrasting groups of readers, and helped put the English Midlands on the national literary map.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals The Correlations between Horizontal and Vertical Peripheral Refractions and Human Eye Shape Using Magnetic Resonance Imaging in Highly Myopic Eyes

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 966
Author(s):  
Hui-Ying Kuo ◽  
John Ching-Jen Hsiao ◽  
Jing-Jie Chen ◽  
Chi-Hung Lee ◽  
Chun-Chao Chuang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Shape Deformation ◽  
Spherical Equivalent ◽  
Horizontal Meridian ◽  
Resonance Imaging ◽  
Vertical Meridian ◽  
Useful Knowledge ◽  
Human Eye ◽  
The Relationship

The aim of this study was to determine the relationship between relative peripheral refraction and retinal shape by 2-D magnetic resonance imaging in high myopes. Thirty-five young adults aged 20 to 30 years participated in this study with 16 high myopes (spherical equivalent < −6.00 D) and 19 emmetropes (+0.50 to −0.50 D). An open field autorefractor was used to measure refractions from the center out to 60° in the horizontal meridian and out to around 20° in the vertical meridian, with a step of 3 degrees. Axial length was measured by using A-scan ultrasonography. In addition, images of axial, sagittal, and tangential sections were obtained using 2-D magnetic resonance imaging. The highly myopic group had a significantly relative peripheral hyperopic refraction and showed a prolate ocular shape compared to the emmetropic group. The highly myopic group had relative peripheral hyperopic refraction and showed a prolate ocular form. Significant differences in the ratios of height/axial (1.01 ± 0.02 vs. 0.94 ± 0.03) and width/axial (0.99 ± 0.17 vs. 0.93 ± 0.04) were found from the MRI images between the emmetropic and the highly myopic eyes (p < 0.001). There was a negative correlation between the retina’s curvature and relative peripheral refraction for both temporal (Pearson r = −0.459; p < 0.01) and nasal (Pearson r = −0.277; p = 0.011) retina. For the highly myopic eyes, the amount of peripheral hyperopic defocus is correlated to its ocular shape deformation. This could be the first study investigating the relationship between peripheral refraction and ocular dimension in high myopes, and it is hoped to provide useful knowledge of how the development of myopia changes human eye shape.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Yan Liu ◽  
Bin Guo ◽  
Daqing Zhang ◽  
Djamal Zeghlache ◽  
Jingmin Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Start ◽  
Weighting Scheme ◽  
Training Data ◽  
Chain Store ◽  
Useful Knowledge ◽  
Adversarial Network ◽  
Brick And Mortar ◽  
New City ◽  
Learning Machine

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

Slow scholarship for social work: A praxis of resistance and creativity

2021 ◽  
pp. 147332502199086
Author(s):  
Stéphanie Wahab ◽  
Gita R Mehrotra ◽  
Kelly E Myers
Keyword(s):  
Domestic Violence ◽  
Social Work ◽  
Knowledge Production ◽  
Relational Ontology ◽  
Research Project ◽  
Useful Knowledge ◽  
Time Space ◽  
Rapid Production ◽  
Time Frames

Expediency, efficiency, and rapid production within compressed time frames represent markers for research and scholarship within the neoliberal academe. Scholars who wish to resist these practices of knowledge production have articulated the need for Slow scholarship—a slower pace to make room for thinking, creativity, and useful knowledge. While these calls are important for drawing attention to the costs and problems of the neoliberal academy, many scholars have moved beyond “slow” as being uniquely referencing pace and duration, by calling for the different conceptualizations of time, space, and knowing. Guided by post-structural feminisms, we engaged in a research project that moved at the pace of trust in the integrity of our ideas and relationships. Our case study aimed to better understand the ways macro forces such as neoliberalism, criminalization and professionalization shape domestic violence work. This article discusses our praxis of Slow scholarship by showcasing four specific key markers of Slow scholarship in our research; time reimagined, a relational ontology, moving inside and towards complexity, and embodiment. We discuss how Slow scholarship complicates how we understand constructs of productivity and knowledge production, as well as map the ways Slow scholarship offers a praxis of resistance for generating power from the epistemic margins within social work and the neoliberal academy.

Schools of Empiricism

2021 ◽  
Vol 62 (1) ◽  
pp. 261-289
Author(s):  
Andreas Friedolin Lingg
Keyword(s):  
Eighteenth Century ◽  
Sixteenth Century ◽  
Personal Experience ◽  
Economic Transformation ◽  
Mining District ◽  
Late Medieval ◽  
Scientific Observation ◽  
Useful Knowledge ◽  
Mining Town ◽  
Forms Of Knowledge

Abstract Recent research emphasizes that empiricist approaches already emerged long before the seventeenth and eighteenth century. While many of these contributions focus on specific professions, it is the aim of this article to supplement this discourse by describing certain social spaces that fostered empiricist attitudes. A particularly interesting example in this respect is the mining region of the Erzgebirge (Saxony) in the fifteenth and sixteenth century. The following article will use this mining district as a kind of historical laboratory, as a space not only for scientific observation but also as a structure within which specific forms of knowledge were socially tested, to show how the economic transformation of this region supported the rise of characteristic elements of empiricist thinking. It is common practice to link the appraisal of useful knowledge, (personal) experience and the distrust towards (scholastic) authorities in those days with only small minorities. By addressing not only the struggles of the commercial elites but also the challenges faced by the average resident of a mining town, this paper tries to add to this view by demonstrating how entire masses of people inhabiting the late medieval Erzgebirge were affected by and schooled to think in empiricist ways.

Psychological behavior signal analysis for sports person during their activity session

2021 ◽  
pp. 1-10
Author(s):  
Guochun Liu ◽  
Jian Zheng ◽  
Lin Jiang ◽  
Karthik Chandran ◽  
Beenu Mago
Keyword(s):  
Heart Rate ◽  
Signal Detection ◽  
Signal Analysis ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Psychological Skills ◽  
Social Dimensions ◽  
Useful Knowledge ◽  
Structural Problems ◽  
Psychological Behavior

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

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