scholarly journals On the data set’s ruins

AI & Society ◽  
2020 ◽  
Author(s):  
Nicolas Malevé
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
High Speed ◽  
Large Population ◽  
Active Role ◽  
The Internet ◽  
Data Sets ◽  
Specific Character ◽  
Unlimited Access

Abstract Computer vision aims to produce an understanding of digital image’s content and the generation or transformation of images through software. Today, a significant amount of computer vision algorithms rely on techniques of machine learning which require large amounts of data assembled in collections, or named data sets. To build these data sets a large population of precarious workers label and classify photographs around the clock at high speed. For computers to learn how to see, a scale articulates macro and micro dimensions: the millions of images culled from the internet with the few milliseconds given to the workers to perform a task for which they are paid a few cents. This paper engages in details with the production of this scale and the labour it relies on: its elaboration. This elaboration does not only require hands and retinas, it also crucially zes mobilises the photographic apparatus. To understand the specific character of the scale created by computer vision scientists, the paper compares it with a previous enterprise of scaling, Malraux’s Le Musée Imaginaire, where photography was used as a device to undo the boundaries of the museum’s collection and open it to an unlimited access to the world’s visual production. Drawing on Douglas Crimp’s argument that the “musée imaginaire”, a hyperbole of the museum, relied simultaneously on the active role of the photographic apparatus for its existence and on its negation, the paper identifies a similar problem in computer vision’s understanding of photography. The double dismissal of the role played by the workers and the agency of the photographic apparatus in the elaboration of computer vision foreground the inherent fragility of the edifice of machine vision and a necessary rethinking of its scale.

Participant recruitment and data collection through Facebook: the role of personality factors

2017 ◽  
Author(s):  
Sean Chandler Rife ◽  
Kelly L. Cate ◽  
Michal Kosinski ◽  
David Stillwell
Keyword(s):  
Data Collection ◽  
Social Research ◽  
Personality Factors ◽  
The Internet ◽  
Data Sets ◽  
Social Networking Service ◽  
Participant Recruitment ◽  
Conducting Research ◽  
Promising Avenue

As participant recruitment and data collection over the Internet have become more common, numerous observers have expressed concern regarding the validity of research conducted in this fashion. One growing method of conducting research over the Internet involves recruiting participants and administering questionnaires over Facebook, the world’s largest social networking service. If Facebook is to be considered a viable platform for social research, it is necessary to demonstrate that Facebook users are sufficiently heterogeneous and that research conducted through Facebook is likely to produce results that can be generalized to a larger population. The present study examines these questions by comparing demographic and personality data collected over Facebook with data collected through a standalone website, and data collected from college undergraduates at two universities. Results indicate that statistically significant differences exist between Facebook data and the comparison data-sets, but since 80% of analyses exhibited partial η2 < .05, such differences are small or practically nonsignificant in magnitude. We conclude that Facebook is a viable research platform, and that recruiting Facebook users for research purposes is a promising avenue that offers numerous advantages over traditional samples.

A Digital Diagnostic Aide for Skincare: The Role of Computer Vision and Machine Learning in Revealing Skin Texture Changes

2020 ◽  
pp. 898-915
Author(s):  
Jaya Shankar Vuppalapati ◽  
Santosh Kedari ◽  
Anitha Ilapakurti ◽  
Chandrasekar Vuppalapati ◽  
Sharat Kedari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Skin Texture

ATTRIBUTION OF SOCIAL DOMINANCE AND MALENESS TO SCHEMATIC FACES

1999 ◽  
Vol 27 (4) ◽  
pp. 331-337 ◽  
Author(s):  
C. Senior ◽  
J. Barnes ◽  
R. Jenkins ◽  
S. Landau ◽  
M.L. Phillips ◽  
...  
Keyword(s):  
Social Dominance ◽  
Large Population ◽  
Higher Order ◽  
Male Gender ◽  
The Internet ◽  
Data Sets ◽  
Strong Indicator ◽  
Traditional Manner

We report findings which suggest perception of ‘higher order’ attributes such as gender and social dominance are perceived from a schematic face. To investigate a large population, the first two experiments were carried out in both the traditional manner and on the Internet. Results obtained from both were not significantly different so the data sets were combined. Lowered eyebrow position was a strong indicator of both social dominance and the male gender. A schematic face with a sad mouth resulted in the face's being viewed as less dominant and less male. Eyegaze direction also was investigated and discussed in terms of dyadic influence. Evidence supported the assumption that both social dominance and the male gender are perceived through similar facial configurations on a schematic face. Limitations include the use of schematic face pairs, and the presentation of single faces in research is discussed.

scholarly journals The Active Role of the Internet and Social Media Use in Nonpharmaceutical and Pharmaceutical Preventive Measures against COVID-19

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 113
Author(s):  
Tian Xie ◽  
Meihui Tang ◽  
Robert Jiqi Zhang ◽  
James H. Liu
Keyword(s):  
Social Media ◽  
Preventive Measures ◽  
Media Use ◽  
Mainland China ◽  
Active Role ◽  
The Internet ◽  
Social Media Use ◽  
Mediating Role ◽  
Internet And Social Media

During the COVID-19 pandemic, does more internet and social media use lead to taking more- or less-effective preventive measures against the disease? A two-wave longitudinal survey with the general population in mainland China in mid-2020 found that during the COVID-19 pandemic, internet and social media use intensity promoted the adoption of nonpharmaceutical and pharmaceutical antipandemic measures. The first wave of data (n = 1014) showed that the more intensively people used the internet/social media, the more they perceived the threat of the pandemic, and took more nonpharmaceutical preventive measures (e.g., wearing masks, maintaining social distance, and washing hands) as a result. The second wave (n = 220) showed firstly the predicted relationship between internet/social media use intensity and the perceived threat of the pandemic and the adoption of nonpharmaceutical preventive measures by cross-lagged analysis; secondly, the predictive effect of internet/social media use on the adoption of pharmacological measures (i.e., willingness to vaccinate against COVID-19) and the mediating role of perceived pandemic threat were verified. The article concludes with a discussion of the role of the internet and social media use in the fight against COVID-19 in specific macrosocial contexts.

scholarly journals Role of Artificial Intelligence Machine Learning in Deepening the Internet Plus Social Work Service

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hexiao Yin
Keyword(s):  
Machine Learning ◽  
Social Work ◽  
Social Services ◽  
Volunteer Management ◽  
The Internet ◽  
Improvement Effect ◽  
Good Improvement ◽  
Service Mode ◽  
Information Circulation

The traditional social work services are mainly visits which have some problems such as inconvenient information circulation, unreasonable resource allocation, and low service efficiency. To improve these problems, Internet plus is used to reform social work services and form an Internet plus social work service mode. Although this model has a very good improvement effect on social work service, with the rapid increase of the number of social work services and the rapid growth of the number of volunteers, this model has limitations in the arrangement of social work services and volunteer management. Therefore, based on this model, with the help of machine learning, the Internet plus social work service mode can be deepened by using machine learning to manage social services and volunteers. Internet plus social work service is the main problem in this paper. The Internet plus social work service mode is formed. Then, the deepening role of machine learning in Internet + social work service is discussed, and some problems in Internet plus social work service mode are improved. Internet plus social work service mode can better improve the problems in traditional social work service. The paper also uses machine learning to further optimize the mode of Internet plus social work service, which has a good application in social work service prospects.

scholarly journals Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

2021 ◽  
Vol 1 (3) ◽  
pp. 138-165
Author(s):  
Thomas Krause ◽  
Jyotsna Talreja Wassan ◽  
Paul Mc Kevitt ◽  
Haiying Wang ◽  
Huiru Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Data Sets ◽  
Raw Data ◽  
Public And Private ◽  
Rumen Microbiome

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

scholarly journals Sequencing Enabling Design and Learning in Synthetic Biology

2020 ◽  
Author(s):  
Pierre-Aurelien Gilliot ◽  
Thomas E. Gorochowski
Keyword(s):  
Machine Learning ◽  
Synthetic Biology ◽  
De Novo ◽  
Data Sets ◽  
New Wave ◽  
Design Rules ◽  
Biological Design ◽  
Sequencing Technologies ◽  
Dna And Rna

The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design. Here, we show how sequencing is supporting the creation of a new wave of biological parts and systems, as well as providing the vast data sets needed for the machine learning of design rules for predictive bioengineering. However, we believe this is only the tip of the iceberg and end by providing an outlook on recent advances that will likely broaden the role of sequencing in synthetic biology and its deployment in real-world environments.

A high-speed world with fake news: brand managers take warning

2019 ◽  
Vol 29 (2) ◽  
pp. 234-245 ◽  
Author(s):  
Mark Peterson
Keyword(s):  
Scientific Method ◽  
High Speed ◽  
Time Frame ◽  
The Internet ◽  
Activity Levels ◽  
Fake News ◽  
Content Type ◽  
Sudden Rise ◽  
Practical Implications

Purpose In an increasingly dangerous era for brands because of the emergence of fake news on the internet, brand managers need to know what is happening with fake news. This study aims to present perspectives on how to cope in an era of fake news. Design/methodology/approach The author provides a general review of fake news and what its sudden rise means for brand managers. Findings The study highlights the importance of context for news and the role of institutions, such as businesses and governments. The study calls brand managers to slow down in the high-speed world of the infosphere to preserve the integrity of their brands. Research limitations/implications The study is limited by its time frame as the internet continues to evolve. However, for times when fake news presents a threat to brands and other institutions, the study is relevant. Practical implications Brand managers need to slow down their activity levels just as savvy readers need to slow down their own reading on the internet. By doing this, brand managers will be better able to defend their brands in an era characterized by volatility, uncertainty, complexity and ambiguity (VUCA). Social implications The study suggests that resistance to fake news and its pernicious effects can be improved by taking an approach to processing content on the internet characterized by the scientific method. In this way, a context for news can be derived and fake news can be identified. In this way, societal trust can be improved. Originality/value This study is original because it analyzes the implications of fake news for brand managers and presents the most workable steps for identifying fake news.

scholarly journals A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

2021 ◽  
Vol 7 ◽  
pp. e414
Author(s):  
Shilan S. Hameed ◽  
Wan Haslina Hassan ◽  
Liza Abdul Latiff ◽  
Fahad Ghabban
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Learning Approaches ◽  
Systems Quality ◽  
Internet Of Medical Things ◽  
Privacy Issues

Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

scholarly journals Learning Deep Attention Network from Incremental and Decremental Features for Evolving Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chuxin Wang ◽  
Haoran Mo
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real World ◽  
Learning Problems ◽  
Data Sets ◽  
Attention Network ◽  
Benchmark Data ◽  
Multiple Cross ◽  
Feature Attention ◽  
The Cross

In many real-world machine learning problems, the features are changing along the time, with some old features vanishing and some other new features augmented, while the remaining features survived. In this paper, we propose the cross-feature attention network to handle the incremental and decremental features. This network is composed of multiple cross-feature attention encoding-decoding layers. In each layer, the data samples are firstly encoded by the combination of other samples with vanished/augmented features and weighted by the attention weights calculated by the survived features. Then, the samples are encoded by the combination of samples with the survived features weighted by the attention weights calculated from the encoded vanished/augmented feature data. The encoded vanished/augmented/survived features are then decoded and fed to the next cross-feature attention layer. In this way, the incremental and decremental features are bridged by paying attention to each other, and the gap between data samples with a different set of features are filled by the attention mechanism. The outputs of the cross-feature attention network are further concatenated and fed to the class-specific attention and global attention network for the purpose of classification. We evaluate the proposed network with benchmark data sets of computer vision, IoT, and bio-informatics, with incremental and decremental features. Encouraging experimental results show the effectiveness of our algorithm.

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