Harnessing Collective Intelligence Through Pattern Mining in Social Computational Systems

2018 ◽  
pp. 91-110
Author(s):  
Gaganmeet Kaur Awal ◽  
K. K. Bharadwaj
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Pattern Mining ◽  
Collective Intelligence ◽  
Social Systems ◽  
Big Data Analytics ◽  
Diverse Range ◽  
Business Applications ◽  
Computational Systems ◽  
The Web

Due to the digital nature of the web, the social web mimics the real-world social dynamics that manifest themselves as data and can be easily mined as patterns, making the web a fertile ground for business and research-oriented analytical applications. Collective intelligence (CI) is a multifaceted field with roots in sociology, biology, and many other disciplines. Various manifestations of CI support the successful existence of large-scale social systems. This chapter gives an overview of the principles of CI and the concept of “wisdom of crowds” and highlights how to maximize the potential of big data analytics for CI. Also, various techniques and approaches have been described that leverage these CI concepts across a diverse range of ever-evolving social systems for commercial business applications like influence mining, expertise discovery, etc.

Mapping global water projects: improving access to donor investment information on the web

Water Policy ◽  
2003 ◽  
Vol 5 (3) ◽  
pp. 203-212
Author(s):  
J. Lisa Jorgensona
Keyword(s):  
Water Resources ◽  
Web Sites ◽  
Large Scale ◽  
Technical Assistance ◽  
Water Withdrawal ◽  
Small Scale ◽  
Water Projects ◽  
Essential Information ◽  
Water Project ◽  
The Web

This paper discusses a series of discusses how web sites now report international water project information, and maps the combined donor investment in more than 6000 water projects, active since 1995. The maps show donor investment:  • has addressed water scarcity,  • has improved access to improvised water resources,  • correlates with growth in GDP,  • appears to show a correlation with growth in net private capital flow,  • does NOT appear to correlate with growth in GNI. Evaluation indicates problems in the combined water project portfolios for major donor organizations: •difficulties in grouping projects over differing Sector classifications, food security, or agriculture/irrigation is the most difficult.  • inability to map donor projects at the country or river basin level because 60% of the donor projects include no location data (town, province, watershed) in the title or abstracts available on the web sites.  • no means to identify donor projects with utilization of water resources from training or technical assistance.  • no information of the source of water (river, aquifer, rainwater catchment).  • an identifiable quantity of water (withdrawal amounts, or increased water efficiency) is not provided.  • differentiation between large scale verses small scale projects. Recommendation: Major donors need to look at how the web harvests and combines their information, and look at ways to agree on a standard template for project titles to include more essential information. The Japanese (JICA) and the Asian Development Bank provide good models.

Building a Culture of Ethical, Comparable, Authentic Assessment

2019 ◽  
pp. 463-481
Author(s):  
Andrew Reid ◽  
Julie Ballantyne
Keyword(s):  
Teaching And Learning ◽  
Large Scale ◽  
Authentic Assessment ◽  
Subject Area ◽  
Music Educators ◽  
System Level ◽  
Diverse Range ◽  
Effective Learning ◽  
School Based ◽  
The Subject

In an ideal world, assessment should be synonymous with effective learning and reflect the intricacies of the subject area. It should also be aligned with the ideals of education: to provide equitable opportunities for all students to achieve and to allow both appropriate differentiation for varied contexts and students and comparability across various contexts and students. This challenge is made more difficult in circumstances in which the contexts are highly heterogeneous, for example in the state of Queensland, Australia. Assessment in music challenges schooling systems in unique ways because teaching and learning in music are often naturally differentiated and diverse, yet assessment often calls for standardization. While each student and teacher has individual, evolving musical pathways in life, the syllabus and the system require consistency and uniformity. The challenge, then, is to provide diverse, equitable, and quality opportunities for all children to learn and achieve to the best of their abilities. This chapter discusses the designing and implementation of large-scale curriculum as experienced in secondary schools in Queensland, Australia. The experiences detailed explore the possibilities offered through externally moderated school-based assessment. Also discussed is the centrality of system-level clarity of purpose, principles and processes, and the provision of supportive networks and mechanisms to foster autonomy for a diverse range of music educators and contexts. Implications for education systems that desire diversity, equity, and quality are discussed, and the conclusion provokes further conceptualization and action on behalf of students, teachers, and the subject area of music.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

A widespread pathway for substitution of adenine by diaminopurine in phage genomes

Science ◽  
2021 ◽  
Vol 372 (6541) ◽  
pp. 512-516
Author(s):  
Yan Zhou ◽  
Xuexia Xu ◽  
Yifeng Wei ◽  
Yu Cheng ◽  
Yu Guo ◽  
...  
Keyword(s):  
Large Scale ◽  
Restriction Enzymes ◽  
Diverse Range ◽  
Host Restriction ◽  
Form And Function ◽  
Multienzyme System ◽  
Dna Modifications ◽  
Dna Production ◽  
And Function

DNA modifications vary in form and function but generally do not alter Watson-Crick base pairing. Diaminopurine (Z) is an exception because it completely replaces adenine and forms three hydrogen bonds with thymine in cyanophage S-2L genomic DNA. However, the biosynthesis, prevalence, and importance of Z genomes remain unexplored. Here, we report a multienzyme system that supports Z-genome synthesis. We identified dozens of globally widespread phages harboring such enzymes, and we further verified the Z genome in one of these phages, Acinetobacter phage SH-Ab 15497, by using liquid chromatography with ultraviolet and mass spectrometry. The Z genome endows phages with evolutionary advantages for evading the attack of host restriction enzymes, and the characterization of its biosynthetic pathway enables Z-DNA production on a large scale for a diverse range of applications.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals Evaluation of dietary intake assessed by the Dutch self-administered web-based dietary 24-h recall tool (Compl-eat™) against interviewer-administered telephone-based 24-h recalls

2017 ◽  
Vol 6 ◽  
Author(s):  
Saskia Meijboom ◽  
Martinette T. van Houts-Streppel ◽  
Corine Perenboom ◽  
Els Siebelink ◽  
Anne M. van de Wiel ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Large Scale ◽  
Alcohol Intake ◽  
Lower Number ◽  
Vitamin B ◽  
Food Groups ◽  
Web Based ◽  
The Web

AbstractSelf-administered web-based 24-h dietary recalls (24 hR) may save a lot of time and money as compared with interviewer-administered telephone-based 24 hR interviews and may therefore be useful in large-scale studies. Within the Nutrition Questionnaires plus (NQplus) study, the web-based 24 hR tool Compl-eat™ was developed to assess Dutch participants’ dietary intake. The aim of the present study was to evaluate the performance of this tool against the interviewer-administered telephone-based 24 hR method. A subgroup of participants of the NQplus study (20–70 years, n 514) completed three self-administered web-based 24 hR and three telephone 24 hR interviews administered by a dietitian over a 1-year period. Compl-eat™ as well as the dietitians guided the participants to report all foods consumed the previous day. Compl-eat™ on average underestimated the intake of energy by 8 %, of macronutrients by 10 % and of micronutrients by 13 % as compared with telephone recalls. The agreement between both methods, estimated using Lin's concordance coefficients (LCC), ranged from 0·15 for vitamin B1 to 0·70 for alcohol intake (mean LCC 0·38). The lower estimations by Compl-eat™ can be explained by a lower number of total reported foods and lower estimated intakes of the food groups, fats, oils and savoury sauces, sugar and confectionery, dairy and cheese. The performance of the tool may be improved by, for example, adding an option to automatically select frequently used foods and including more recall cues. We conclude that Compl-eat™ may be a useful tool in large-scale Dutch studies after suggested improvements have been implemented and evaluated.

Situating and Abandoning Geoengineering: A Typology of Five Responses to Dangerous Climate Change

2013 ◽  
Vol 46 (01) ◽  
pp. 23-27 ◽  
Author(s):  
Clare Heyward
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Solar Radiation Management ◽  
Diverse Range ◽  
Political Issues ◽  
Government Organizations ◽  
Mitigation And Adaptation ◽  
Potential Strategy ◽  
Dangerous Climate Change ◽  
Radiation Management

Geoengineering, the “deliberate, large-scale manipulation of the planetary environment in order to counteract anthropogenic climate change” (Shepherd et al. 2009, 1), is attracting increasing interest. As well as the Royal Society, various scientific and government organizations have produced reports on the potential and challenge of geoengineering as a potential strategy, alongside mitigation and adaptation, to avoid the vast human and environmental costs that climate change is thought to bring (Blackstock et al. 2009; GAO 2010; Long et al. 2011; Rickels et al. 2011). “Geoengineering” covers a diverse range of proposals conventionally divided into carbon dioxide removal (CDR) proposals and solar radiation management (SRM) proposals. This article argues that “geoengineering” should not be regarded as a third category of response to climate change, but should be disaggregated. Technically, CDR and SRM are quite different and discussing them together under the rubric of geoengineering can give the impression that all the technologies in the two categories of response always raise similar challenges and political issues when this is not necessarily the case. However, CDR and SRM should not be completely subsumed into the preexisting categories of mitigation and adaptation. Instead, they can be regarded as two parts of a five-part continuum of responses to climate change. To make this case, the first section of this article discusses whether geoengineering is distinctive, and the second situates CDR and SRM in relation to other responses to climate change.

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

scholarly journals Reciprocity and behavioral heterogeneity govern the stability of social networks

2020 ◽  
Vol 117 (6) ◽  
pp. 2993-2999 ◽  
Author(s):  
Roslyn Dakin ◽  
T. Brandt Ryder
Keyword(s):  
Social Networks ◽  
Social Connectedness ◽  
Social Dynamics ◽  
Social Systems ◽  
Bird Species ◽  
Network Stability ◽  
Apparent Paradox ◽  
Trade Off ◽  
Behavioral Heterogeneity ◽  
The Stability

The dynamics of social networks can determine the transmission of information, the spread of diseases, and the evolution of behavior. Despite this broad importance, a general framework for predicting social network stability has not been proposed. Here we present longitudinal data on the social dynamics of a cooperative bird species, the wire-tailed manakin, to evaluate the potential causes of temporal network stability. We find that when partners interact less frequently and when social connectedness increases, the network is subsequently less stable. Social connectivity was also negatively associated with the temporal persistence of coalition partnerships on an annual timescale. This negative association between connectivity and stability was surprising, especially given that individual manakins who were more connected also had more stable partnerships. This apparent paradox arises from a within-individual behavioral trade-off between partnership quantity and quality. Crucially, this trade-off is easily masked by behavioral variation among individuals. Using a simulation, we show that these results are explained by a simple model that combines among-individual behavioral heterogeneity and reciprocity within the network. As social networks become more connected, individuals face a trade-off between partnership quantity and maintenance. This model also demonstrates how among-individual behavioral heterogeneity, a ubiquitous feature of natural societies, can improve social stability. Together, these findings provide unifying principles that are expected to govern diverse social systems.

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