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Can algorithm do a better job of matching the right candidates with the right jobs? #RecHangout
 
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Can algorithm do a better job of matching the right candidates with the right jobs? #RecHangout On this episode of the Recruiters Hangout we looked at how semantic, assessment, matching and AI technologies are helping recruiters and recruitment businesses match the right candidates with the right jobs. Guests included: Jakub Zavrel, owner of semantic recruitment technology business, Textkernel Dr Gorkan Ahmetoglu, Lecturer of Business Psychology at University College London and principle at Meta Profiling Ltd Darshana Narayanan, Head of Research at Pymetrics. Supported by: Google+ Host: Louis Welcomme, Marketing Exec at Colleague Software. For almost twenty years we've been driven by customer partnerships built on experience, knowledge and trust. Today Colleague is used by hundreds of specialist recruitment businesses who value software that is adaptable, efficient, relevant and easy to use. - http://www.colleaguesoftware.com Show Host: Alan Whitford, Abtech Partnership. Alan is a strategy consultant for Recruitment Challenges, Talent Acquisition, Candidate Experience, Employment Brand, Recruitment and Social Media. - http://www.abtechpartnership.com/ Supporting Host: Mark Stephens. An established and highly regarded Recruitment Strategist, specialising in improving recruitment efficiencies through technology and process. Mark is the founder of Smart Recruit Online Twitter Host (and writer of the Notes from the #Rechangout): Louise Triance of UK Recruiter. UK Recruiter is the go-to source for information and news on the UK recruitment industry and is the largest online community of recruiters in the UK and organiser of unique Directors Only events. - http://ukrecruiter.co.uk/
Views: 789 Louis Welcomme
Self Adaptive Semantic Focused Crawler For Mining Services Information Discovery
 
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It is well recognized that the Internet has become the largest marketplace in the world, and online advertising is very popular with numerous industries, including the traditional mining service industry where mining service advertisements are effective carriers of mining service information. However, service users may encounter three major issues – heterogeneity, ubiquity, and ambiguity, when searching for mining service information over the Internet. In this paper, we present the framework of a novel self-adaptive semantic focused crawler – SASF crawler, with the purpose of precisely and efficiently discovering, formatting, and indexing mining service information over the Internet, by taking into account the three major issues
Views: 179 Gtek
Kalpa Gunaratna: Semantics-based Summarization of Entities in Knowledge Graphs
 
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Kalpa Gunaratna's Dissertation Defense: "Semantics-based Summarization of Entities in Knowledge Graphs" Wednesday, August 19, 2016 Advisors: Dr. Amit Sheth and Dr. Krishnaprasad Thirunarayan. Dissertation Committee: Dr. Keke Chen, Dr. Gong Cheng, Dr. Edward Curry, and Dr. Hamid R. Motahari-Nezhad. Homepage: http://knoesis.wright.edu/students/kalpa/ Pictures: https://www.facebook.com/media/set/?set=a.1499253150109528.1073741870.199004243467765&type=3 ABSTRACT: The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored. In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance as well as the diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts in these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries. Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals. This makes datatype properties semantically rich compared to having only implementation types. As a result of the enrichment process, we could use both object and datatype properties in the entity summaries, which improves coverage in the entity summaries and can be useful in other applications like dataset profiling and data integration. We evaluate the proposed approaches against the state-of-the-art methods and highlight their capabilities for single and multiple entity summarization.
Views: 240 Knoesis Center
Kriton Speech: Knowledge Acquisition for Ontologies - Depression
 
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Kriton Speech is a general purpose knowledge acquisition tool incorporating a variety of elicitation methods, such as interview techniques, protocol analysis, text mining and machine learning. Psychological interview techniques are used to obtain domain knowledge from an expert, in this case a clinical psychologist. Kriton Speech uses voice as the user interface. The system interviews the user and as a result, builds ontologies and rule-based systems. The output is a Web Ontology Language (OWL) file that can be edited by use of ontology editors such as Protege. Please see http://psychologynetwork.com.au/KritonSpeechWhitePaper.pdf. For more information, email [email protected]
Views: 94 Psychology Network
2004-12-08 CERIAS - Using Statistical Analysis to Locate Spam Web Pages
 
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Recorded: 12/08/2004 CERIAS Security Seminar at Purdue University Using Statistical Analysis to Locate Spam Web Pages Dennis Fetterly, Microsoft Commercial web sites are more dependant than ever on being placed prominently within the result pages returned by a search engine to be successful. "Spam" web pages are web pages that are created for the sole purpose of misleading search engines and misdirecting traffic to target sites. Certain classes of spam pages, in particular those that are machine-generated, diverge in some of their properties from the properties of web pages in general. As a result, these pages can be identified through statistical analysis. We have examined a variety of such properties, including linkage structure, page content, and page evolution, and have found that outliers in the statistical distributions of these properties are predominantly caused by web spam. Joint work with Mark Manasse and Marc Najork. Dennis Fetterly is a Technologist in Microsoft Research\'s Silicon Valley lab, which he joined in May, 2003. His research interests include a wide variety of web related topics including web crawling, the evolution and clustering of pages on the web, and identifying spam web pages. (Visit: www.cerias.purude.edu)
Views: 107 ceriaspurdue
What is ontology? Introduction to the word and the concept
 
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In a philosophical context 0:28 Why ontology is important 1:08 Ontological materialism 1:34 Ontological idealism 1:59 In a non-philosophical context 2:24 Information systems 2:40 Social ontology 3:25 The word ontology comes from two Greek words: "Onto", which means existence, or being real, and "Logia", which means science, or study. The word is used both in a philosophical and non-philosophical context. ONTOLOGY IN A PHILOSOPHICAL CONTEXT In philosophy, ontology is the study of what exists, in general. Examples of philosophical, ontological questions are: What are the fundamental parts of the world? How they are related to each other? Are physical parts more real than immaterial concepts? For example, are physical objects such as shoes more real than the concept of walking? In terms of what exists, what is the relationship between shoes and walking? Why is ontology important in philosophy? Philosophers use the concept of ontology to discuss challenging questions to build theories and models, and to better understand the ontological status of the world. Over time, two major branches of philosophical ontology has developed, namely: Ontological materialism, and ontological idealism. Ontological materialism From a philosophical perspective, ontological materialism is the belief that material things, such as particles, chemical processes, and energy, are more real, for example, than the human mind. The belief is that reality exists regardless of human observers. Ontological idealism Idealism is the belief that immaterial phenomenon, such as the human mind and consciousness, are more real, for example, than material things. The belief is that reality is constructed in the mind of the observer. ONTOLOGY IN A NON-PHILOSOPHICAL CONTEXT Outside philosophy, ontology is used in a different, more narrow meaning. Here, an ontology is the description of what exist specifically within a determined field. For example, every part that exists in a specific information system. This includes the relationship and hierarchy between these parts. Unlike the philosophers, these researchers are not primarily interested in discussing if these things are the true essence, core of the system. Nor are they discussing if the parts within the system are more real compared to the processes that take place within the system. Rather, they are focused on naming parts and processes and grouping similar ones together into categories. Outside philosophy, the word ontology is also use, for example, in social ontology. Here, the idea is to describe society and its different parts and processes. The purpose of this is to understand and describe the underlying structures that affect individuals and groups. Suggested reading You can read more about ontology in some of the many articles available online, for example: http://www.streetarticles.com/science/what-is-ontology Copyright Text and video (including audio) © Kent Löfgren, Sweden
Views: 259654 Kent Löfgren
Learning to Extract Semantic Structure From Documents | Spotlight 3-1A
 
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Xiao Yang; Ersin Yumer; Paul Asente; Mike Kraley; Daniel Kifer; C. Lee Giles We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance.
Concept Map Mining annotation tool
 
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This video shows the Concept Map Mining annotation tool, used to annotate a corpus of essays with their corresponding Concept Maps
Views: 613 Jorge Villalon
A Story of Discrimination and Unfairness (33c3)
 
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https://media.ccc.de/v/33c3-8026-a_story_of_discrimination_and_unfairness Prejudice in Word Embeddings Artificial intelligence and machine learning are in a period of astounding growth. However, there are concerns that these technologies may be used, either with or without intention, to perpetuate the prejudice and unfairness that unfortunately characterizes many human institutions. We show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language—the same sort of language humans are exposed to every day. We replicate a spectrum of standard human biases as exposed by the Implicit Association Test and other well-known psychological studies. We replicate these using a widely used, purely statistical machine-learning model—namely, the GloVe word embedding—trained on a corpus of text from the Web. Our results indicate that language itself contains recoverable and accurate imprints of our historic biases, whether these are morally neutral as towards insects or flowers, problematic as towards race or gender, or even simply veridical, reflecting the status quo for the distribution of gender with respect to careers or first names. These regularities are captured by machine learning along with the rest of semantics. In addition to our empirical findings concerning language, we also contribute new methods for evaluating bias in text, the Word Embedding Association Test (WEAT) and the Word Embedding Factual Association Test (WEFAT). Our results have implications not only for AI and machine learning, but also for the fields of psychology, sociology, and human ethics, since they raise the possibility that mere exposure to everyday language can account for the biases we replicate here. ['Aylin Caliskan']
Views: 1431 media.ccc.de
Application of semantic spaces to sentiment analysis for words. Marcin Tatjewski. Cyberemotions 2013
 
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Marcin Tatjewski, External expert Collective Emotions in Cyberspace Final Conference of EU Project CYBEREMOTIONS 2013 http://www.cyberemotions.eu/ 29-30 January 2013, Faculty of Physics, Warsaw University of Technology Lectures at Final Conference of EU Project CYBEREMOTIONS, Warsaw University of Technology, 29-30 Jan. 2013. 1. Collective emotions in Cyberspace, short review of Cyberemotions Project results, Janusz Hołyst http://www.youtube.com/watch?v=5VOaxNQoZK0 2. The Psychology of (Cyber)Emotions, Arvid Kappas http://www.youtube.com/watch?v=Rewpvvyqqxk 3. The social sharing of emotion, Bernard Rimé http://www.youtube.com/watch?v=x2G-afWwrco 4. How Emotional Are Users Needs? Emotion in Query Logs, Marina Santini http://www.youtube.com/watch?v=CJkyFKL5Y3A 5. Social web sentiment strength detection: methods and issues, Mike Thelwall http://www.youtube.com/watch?v=7ZhijBzLf-4 6. Dynamics of emotions in voice during real-life arguments, Magdalena Igras http://www.youtube.com/watch?v=XWTKRLFeLnQ 7. Application of semantic spaces to sentiment analysis for words, Marcin Tatjewski http://www.youtube.com/watch?v=cv1ICNAhnuw 8. Online Networks and the Diffusion of Protests, Yamir Moreno http://www.youtube.com/watch?v=_iCy0v4nz8Y 9. The impact of cyberspace upon current society, Aaron Ben-Ze'ev http://www.youtube.com/watch?v=o3Uz9EsQjBc 10. Online discussions modelled by an evolving Ising-like dynamics, Julian Sienkiewicz http://www.youtube.com/watch?v=2gCkhOeAMBs 11. A modelling framework for collective emotions in online communities, David Garcia http://www.youtube.com/watch?v=_FoEGCAes_0 12. Patterns of Online Chats with Emotional Bots: Data Analysis and Agent-Based Simulations, Vladimir Gligorijević http://www.youtube.com/watch?v=Ex815-jljkw 13. Transition due to preferential cluster growth of collective emotions in online communities, Anna Chmiel http://www.youtube.com/watch?v=oAVIn1YUBvM 14. Psychological Aspects of Social Communities, Renaud Lambiotte http://www.youtube.com/watch?v=2Z3y58KAgys 15. The Simmel effect and babies names, Krzysztof Kułakowski http://www.youtube.com/watch?v=q3bDg-T90E4 16. A new model of individual opinion dynamics based on information and emotions, Paweł Sobkowicz http://www.youtube.com/watch?v=JUyRfKQITis 17. Human behavior in online social networks, Andrzej Grabowski http://www.youtube.com/watch?v=WfFHJNUz0gU 18. Facial asymmetry and affective communication in 3D Online Virtual Society, Junghyun Ahn http://www.youtube.com/watch?v=WZOI3ou3bFU
Views: 744 fensPW
Kriton Speech: Knowledge Acquisition & Background
 
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A knowledge acquisition dialogue plus a view of the background knowledge on Attention Deficit Hyperactivity disorder. The background knowledge is viewed by use of Protege (Stanford University).
Views: 69 Psychology Network
Symposium on Blockchain for Robotic Systems
 
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Robotic systems are revolutionizing applications from transportation to health care. However, many of the characteristics that make robots ideal for future applications—such as autonomy, self-learning, and knowledge sharing—also raise concerns about the evolution of the technology. Blockchain, an emerging technology that originated in the digital currency field, shows great potential to make robotic operations more secure, autonomous, flexible, and even profitable, thereby bridging the gap between purely scientific domains and real-world applications. This symposium seeks to move beyond the classical view of robotic systems to advance our understanding about the possibilities and limitations of combining state-of-the art robotic systems with blockchain technology. More information at: https://www.media.mit.edu/events/symposium-on-blockchain-for-robotics/ License: CC-BY-4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Views: 1304 MIT Media Lab
Interview about TauChain and Agoras with Ohad Asor
 
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www.idni.org questions are listed in a comment below
Views: 3801 IDNI
Automatic Generation of Semantic Icon Encodings for Visualizations
 
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Full Title: Automatic Generation of Semantic Icon Encodings for Visualizations Authors: Vidya Setlur, Jock D. Mackinlay Abstract: Authors use icon encodings to indicate the semantics of categorical information in visualizations. The default icon libraries found in visualization tools often do not match the semantics of the data. Users often manually search for or create icons that are more semantically meaningful. This process can hinder the flow of visual analysis, especially when the amount of data is large, leading to a suboptimal user experience. We propose a technique for automatically generating semantically relevant icon encodings for categorical dimensions of data points. The algorithm employs natural language processing in order to find relevant imagery from the Internet. We evaluate our approach on Mechanical Turk by generating large libraries of icons using Tableau Public workbooks that represent real analytical effort by people out in the world. Our results show that the automatic algorithm does nearly as well as the manually created icons, and particularly has higher user satisfaction for larger cardinalities of data. DOI:http://doi.acm.org/10.1145/2556288.2557408
Interoperability of Text Corpus Annotations with the Semantic Web
 
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Original version is http://togotv.dbcls.jp/20150321.html Biomedical Linked Annotation Hackathon (BLAH) 2015 was held in The University of Tokyo Kashiwa Campus Station Satellite in Kashiwa, Chiba, Japan. On the last day of the Hackathon (Feb. 27), public symposium of the BLAH 2015 was held. In this talk, Karin Verspoor (University of Melbourne) makes a presentation entitled "Interoperability of Text Corpus Annotations with the Semantic Web". (21:05)
Views: 137 togotv
Introduction to Social Network Analysis
 
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This workshop provides a broad overview of Social Network Analysis. In the first part of the workshop, a concise overview of theoretical concepts is provided, together with examples of data collection methods. The second section discusses network data analysis - network measurements (i.e. density, reciprocity, etc.) and node level measurements (i.e. degree centrality, betweenness centrality, etc.). The last part of the workshop introduces participants to UCINET and NetDraw, software packages used for data management, analysis and visualization.
The Impact Of Website Content Characteristics On Search Engine Rankings
 
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This is a https://seomelbourne.com & https://seosydney.com product “Most users usually examine only the top 10 websites in a search engine results list and only 1% of users check beyond the third page of a search engine results list” – Zhang & Dimitroff, 2005. The characteristics of webpage content are factors that need to be considered when seeking to improve a website’s ranking in search engines. When considering a website’s efficacy, some website moderators may split hairs over font types and colours, while neglecting the fact that there are many more features that extend beyond the visual properties of a website and these are the ones that have a far greater impact on the page’s ranking. With reference to the results of Zhang & Dimitroff’s 2005 study, an analysis of their work can give guidance as to which areas to focus upon in conducting a successful SEO campaign. In their paper they explore content characteristics of webpages (such as keyword position and duplication and layout, and their combinations) and how these factors may impact webpage visibility in a search engine. Each category that was selected was identified as a factor that may affect the return position of a search engine. To understand the results, the details of what these categories represent must be understood. Our next SEO TV video explores in greater depth the importance of keywords and how exactly they can affect your search engine ranking. Having an example of a keyword and highlight its position in the page Keyword optimisation and placement on a website is very important as it is a determining factor when Google ranks your website. As we can see from Travelfish's website - a popular travel site for South-East Asia - we can see that they have optimised the keyword 'travel' in a way that is not distracting for readers. They've included the word travel not onlkeyy in the URL and in their name, but also in their logo, which means that the picture is optimised with the word ‘travelfish’ and ‘travel, but also in a short description and in their body content. By including the word travel many times in different areas of the website, Travelfish is one step closer to ranking on google Highlight a combination of keywords on a page When thinking of your keywords to optimise, also consider various combinations of your keywords both in the title and the body. As we can see from popular website 'Intrepid Travel' they have incorporated their keyword 'travel' in many different ways. While they have done it in the standard way of URL, heading and body content, they’ve also created multiple keyword combinations - including 'small group travel' , 'connected by travel' 'travel deals' 'love travel' and 'travel inspiration . By doing this, they are now even more likely to match with more search queries, making it more likely for them to rank on Google. Highlight visual cues listed below When designing and optimising your website, also factor in the layout of your homepage. While search engines are 'blind' to the visual aspects of your website, it is important that the layout is user friendly. Visual cues like the logo, primary navigation and a hero image are also important aspects to consider. Furthermore, including teasers and links to more content is also important to include - not only does it make it easier for users to find additional content, but it also increases the time spent on your website and more then generates more page views.
Views: 35 SEO TV
Visualizing Data Using t-SNE
 
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Google Tech Talk June 24, 2013 (more info below) Presented by Laurens van der Maaten, Delft University of Technology, The Netherlands ABSTRACT Visualization techniques are essential tools for every data scientist. Unfortunately, the majority of visualization techniques can only be used to inspect a limited number of variables of interest simultaneously. As a result, these techniques are not suitable for big data that is very high-dimensional. An effective way to visualize high-dimensional data is to represent each data object by a two-dimensional point in such a way that similar objects are represented by nearby points, and that dissimilar objects are represented by distant points. The resulting two-dimensional points can be visualized in a scatter plot. This leads to a map of the data that reveals the underlying structure of the objects, such as the presence of clusters. We present a new technique to embed high-dimensional objects in a two-dimensional map, called t-Distributed Stochastic Neighbor Embedding (t-SNE), that produces substantially better results than alternative techniques. We demonstrate the value of t-SNE in domains such as computer vision and bioinformatics. In addition, we show how to scale up t-SNE to big data sets with millions of objects, and we present an approach to visualize objects of which the similarities are non-metric (such as semantic similarities). This talk describes joint work with Geoffrey Hinton.
Views: 115454 GoogleTechTalks
A corpus of psychological diseases and their potential causes
 
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Original version is here: http://togotv.dbcls.jp/ja/20160201.html Biomedical Linked Annotation Hackathon (BLAH) 2 was held in DNA Data Bank of Japan, National Institute of Genetics in Mishima, Shizuoka, Japan. On the first day of the Hackathon (16. Nov.), public symposium of the BLAH 2 was held. In this talk, Fabio Rinaldi makes a presentation entitled "A corpus of psychological diseases and their potential causes". (9:26)
Views: 31 togotv
Social web sentiment strength detection: methods and issues. Mike Thelwall. Cyberemotions 2013.
 
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Mike Thelwall, Cyberemotions Project Member Collective Emotions in Cyberspace Final Conference of EU Project CYBEREMOTIONS 2013 http://www.cyberemotions.eu/ 29-30 January 2013, Faculty of Physics, Warsaw University of Technology Lectures at Final Conference of EU Project CYBEREMOTIONS, Warsaw University of Technology, 29-30 Jan. 2013. 1. Collective emotions in Cyberspace, short review of Cyberemotions Project results, Janusz Hołyst http://www.youtube.com/watch?v=5VOaxNQoZK0 2. The Psychology of (Cyber)Emotions, Arvid Kappas http://www.youtube.com/watch?v=Rewpvvyqqxk 3. The social sharing of emotion, Bernard Rimé http://www.youtube.com/watch?v=x2G-afWwrco 4. How Emotional Are Users Needs? Emotion in Query Logs, Marina Santini http://www.youtube.com/watch?v=CJkyFKL5Y3A 5. Social web sentiment strength detection: methods and issues, Mike Thelwall http://www.youtube.com/watch?v=7ZhijBzLf-4 6. Dynamics of emotions in voice during real-life arguments, Magdalena Igras http://www.youtube.com/watch?v=XWTKRLFeLnQ 7. Application of semantic spaces to sentiment analysis for words, Marcin Tatjewski http://www.youtube.com/watch?v=cv1ICNAhnuw 8. Online Networks and the Diffusion of Protests, Yamir Moreno http://www.youtube.com/watch?v=_iCy0v4nz8Y 9. The impact of cyberspace upon current society, Aaron Ben-Ze'ev http://www.youtube.com/watch?v=o3Uz9EsQjBc 10. Online discussions modelled by an evolving Ising-like dynamics, Julian Sienkiewicz http://www.youtube.com/watch?v=2gCkhOeAMBs 11. A modelling framework for collective emotions in online communities, David Garcia http://www.youtube.com/watch?v=_FoEGCAes_0 12. Patterns of Online Chats with Emotional Bots: Data Analysis and Agent-Based Simulations, Vladimir Gligorijević http://www.youtube.com/watch?v=Ex815-jljkw 13. Transition due to preferential cluster growth of collective emotions in online communities, Anna Chmiel http://www.youtube.com/watch?v=oAVIn1YUBvM 14. Psychological Aspects of Social Communities, Renaud Lambiotte http://www.youtube.com/watch?v=2Z3y58KAgys 15. The Simmel effect and babies names, Krzysztof Kułakowski http://www.youtube.com/watch?v=q3bDg-T90E4 16. A new model of individual opinion dynamics based on information and emotions, Paweł Sobkowicz http://www.youtube.com/watch?v=JUyRfKQITis 17. Human behavior in online social networks, Andrzej Grabowski http://www.youtube.com/watch?v=WfFHJNUz0gU 18. Facial asymmetry and affective communication in 3D Online Virtual Society, Junghyun Ahn http://www.youtube.com/watch?v=WZOI3ou3bFU
Views: 584 fensPW
Moral Math of Robots: Can Life and Death Decisions Be Coded?
 
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A self-driving car has a split second to decide whether to turn into oncoming traffic or hit a child who has lost control of her bicycle. An autonomous drone needs to decide whether to risk the lives of busload of civilians or lose a long-sought terrorist. How does a machine make an ethical decision? Can it “learn” to choose in situations that would strain human decision making? Can morality be programmed? We will tackle these questions and more as the leading AI experts, roboticists, neuroscientists, and legal experts debate the ethics and morality of thinking machines. Subscribe to our YouTube Channel for all the latest from WSF. Visit our Website: http://www.worldsciencefestival.com/ Like us on Facebook: https://www.facebook.com/worldsciencefestival Follow us on twitter: https://twitter.com/WorldSciFest Original Program Date: June 4, 2016 MODERATOR: Bill Blakemore PARTICIPANTS: Fernando Diaz, Colonel Linell Letendre, Gary Marcus, Matthias Scheutz, Wendell Wallach Can Life and Death Decisions Be Coded? 00:06 Siri... What is the meaning of life? 1:49 Participant introductions 4:01 Asimov's Three Laws of Robotics 6:22 In 1966 ELIZA was one of the first artificial intelligence systems. 10:20 What is ALPHAGO? 15:43 TAY Tweets the first AI twitter bot. 19:25 Can you test learning Systems? 26:31 Robots and automatic reasoning demonstration 30:31 How do driverless cars work? 39:32 What is the trolley problem? 49:00 What is autonomy in military terms? 56:40 Are landmines the first automated weapon? 1:10:30 Defining how artificial intelligence learns 1:16:03 Using Minecraft to teach AI about humans and their interactions 1:22:27 Should we be afraid that AI will take over the world? 1:25:08
Views: 46312 World Science Festival
The Evolution of End User Programming
 
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Google Tech Talk February 1, 2010 ABSTRACT Presented by Allen Cypher, IBM Research Almaden. The popularity of the Web has changed the world of End User Programming. Our research systems can now be built in a web browser that people use in their daily life, semantic information is broadly available, and our users are more experienced and they share their work with others. After twenty-five years of trying to infer the user's intent, Allen will compare early and contemporary end user programming systems to see what progress we have made, and what opportunities we now have for widespread success. Allen Cypher began building systems to automate repetitive activities in 1984. His Eager system was one of the first intelligent agents. In 1993, he edited "Watch What I Do: Programming by Demonstration", which collected the work of earlier pioneers and of the active researchers at the time. In the 90's, he co-developed a visual language called Stagecast Creator that enabled children to create their own games and simulations and publish them on the Web. His current work with CoScripter is aimed at bringing end user programming to the Web.
Views: 15800 GoogleTechTalks
Lecture 1 | Natural Language Processing with Deep Learning
 
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Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Image retrieval using Markovian Semantic Indexing (MSI)
 
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This project is developed in C#.NET for image retrieval using MSI
Views: 60 prem kumar
NIPS 2011 Learning Semantics Workshop: Towards More Human-like Machine Learning of Word Meanings
 
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Learning Semantics Workshop at NIPS 2011 Invited Talk: Towards More Human-like Machine Learning of Word Meanings by Josh Tenenbaum Josh Tenenbaum is a Professor in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology. Him and his colleagues in the Computational Cognitive Science group study one of the most basic and distinctively human aspects of cognition: the ability to learn so much about the world, rapidly and flexibly. Abstract: How can we build machines that learn the meanings of words more like the way that human children do? I will talk about several challenges and how we are beginning to address them using sophisticated probabilistic models. Children can learn words from minimal data, often just one or a few positive examples (one-shot learning). Children learn to learn: they acquire powerful inductive biases for new word meanings in the course of learning their first words. Children can learn words for abstract concepts or types of concepts that have no little or no direct perceptual correlate. Children's language can be highly context-sensitive, with parameters of word meaning that must be computed anew for each context rather than simply stored. Children learn function words: words whose meanings are expressed purely in how they compose with the meanings of other words. Children learn whole systems of words together, in mutually constraining ways, such as color terms, number words, or spatial prepositions. Children learn word meanings that not only describe the world but can be used for reasoning, including causal and counterfactual reasoning. Bayesian learning defined over appropriately structured representations — hierarchical probabilistic models, generative process models, and compositional probabilistic languages — provides a basis for beginning to address these challenges.
Views: 2514 GoogleTechTalks
NIF v4.6 - myNIF Autocomplete & Semantic Query Expansion
 
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Searching for neuroscience literature? Save time by using autocomplete and semantic query expansion features now available in myNIF with the release of v4.6 of the Neuroscience Information Framework
Views: 164 NeuroInfoFramework
Computers versus Common Sense
 
01:15:18
Google TechTalks May 30, 2006 Douglas Lenat Dr. Douglas Lenat is the President and CEO of Cycorp. Since 1984, he and his team have been constructing, experimenting with, and applying a broad real world knowledge base and reasoning engine, collectively "Cyc". Dr. Lenat was a professor of computer science at Carnegie-Mellon University and at Stanford University. His interest and experience in national security has led him to regularly consult for several U.S. agencies and the White House. ABSTRACT It's way past 2001 now, where the heck is HAL? For several decades now we've had high hopes for computers amplifying our mental abilities not just giving us access to relevant stored information, but...
Views: 9631 Google
Apply sentiment analysis successfully to manage marketing campaigns in real time
 
01:03:10
In this age of the Internet and social media, customers want to communicate directly and instantly with the financial institutions they are banking with, with feedback on a bank's activity occurring almost instantaneously. Banks face increasing pressure to analyse customer reactions in campaigns and product launches real time so that they can adjust their actions to market behaviour. Yet traditional method of analysing customer sentiment through surveys and focus groups are expensive and time-consuming. SUBSCRIBE to our channel! Visit our website http://www.theasianbanker.com/ Like us on Facebook https://www.facebook.com/TheAsianBanker Follow us on: Twitter https://twitter.com/theasianbanker LinkedIn https://www.linkedin.com/company/the-asian-banker Instagram https://www.instagram.com/theasianbankerofficial/
Views: 160 theasianbanker
Mod-01 Lec-23 CLIA; IR Basics
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 1890 nptelhrd
Qiang Yang: When Transfer Learning Meets Deep Learning
 
01:01:13
Abstract: Deep learning has achieved great success as evidenced by many challenging applications. However, deep learning developed so far has some inherent limitations. In particular, deep learning is not yet adaptable to different domains and cannot handle small data. In this talk, I will give an overview of how transfer learning can help alleviate these problems. In particular, I will survey some recent progress on integrating deep learning and transfer learning together and show some interesting applications in sentiment analysis, image processing and dialog systems. Bio: Qiang Yang is the head of Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST), where he is a New Bright Endowed Chair Professor of Engineering and the founding director of HKUST’s Big Data Institute. His research interest is artificial intelligence, including machine learning, data mining and planning. He is a fellow of AAAI, IEEE, IAPR and AAAS. He received his PhD from the Department of Computer Science at the University of Maryland, College Park in 1989 and had been a faculty member at the University of Waterloo between 1989 and 1995. He was a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001. He had been the founding director of the Huawei's Noah's Ark Research Lab between 2012 and 2015. He was the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and the founding Editor in Chief of IEEE Transactions on Big Data (IEEE TBD). He has served as a PC Chair or General Chair of several international conferences, including ACM KDD, IJCAI, RecSys, IUI and ICCBR. In 2017, he received the ACM SIGKDD Distinguished Service Award. He is currently the President of IJCAI and a council member of AAAI.
Views: 388 UMD CS
Analyzing the Privacy of Android Apps
 
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Google Tech Talk June 17, 2015 (click "show more" for more info) Presented by Jason Hong, Carnegie Mellon University ABSTRACT: Many smartphone apps collect sensitive data about people, in a manner that many users find very surprising. How can we help everyday people in understanding the behaviors of their apps? In this talk, Jason Hong presents three things. The first is results of interviews and surveys of app developers, probing their attitudes and behaviors towards privacy. The second is PrivacyGrade.org, a site that combines crowdsourcing and static analysis to analyze the behavior of 1M Android apps. The third is Gort, a tool that combines heuristics, crowdsourcing, and dynamic analysis to help analysts understand the behavior of a given app. Since the original presentation, Android M launched a new permission model that Hong described as "offer[ing] a lot more privacy protection for people, primarily by making it easier to see what data is being requested as it is being used." ABOUT THE SPEAKER: Jason Hong is an associate professor in the Human Computer Interaction Institute at Carnegie Mellon University. He works in the areas of ubiquitous computing and usable privacy and security, and his research has been featured in the New York Times, MIT Tech Review, CBS Morning Show, CNN, Slate, and more. Jason is also a co-founder of Wombat Security Technologies, and has participated on DARPA's Computer Science Study Panel (CS2P), is an Alfred P. Sloan Research Fellow, a Kavli Fellow, a PopTech Science fellow, and currently holds the HCII Career Development fellowship.
Views: 3365 GoogleTechTalks
Alexey Potapov - Extending Universal Intelligence Models with Formal Notion of Representation
 
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Winter Intelligence Oxford - AGI12 - http://agi-conference.org/2012 ==Extending Universal Intelligence Models with Formal Notion of Representation== Abstract. Solomonoff induction is known to be universal, but incomputable. Its approximations, namely, the Minimum Description (or Message) Length (MDL) principles, are adopted in practice in the efficient, but non-universal form. Recent attempts to bridge this gap leaded to development of the Representational MDL principle that originates from formal decomposition of the task of induction. In this paper, possible extension of the RMDL principle in the context of universal intelligence agents is considered, for which introduction of representations is shown to be an unavoidable meta-heuristic and a step toward efficient general intelligence. Hierarchical representations and model optimization with the use of information-theoretic interpretation of the adaptive resonance are also discussed. Key words: Universal Agents, Kolmogorov Complexity, Minimum Description Length Principle, Representations Paper: http://agi-conference.org/2012/wp-content/uploads/2012/12/paper_10.pdf Alexey Potapov, Sergey Rodionov AIDEUS, Russia {potapov,rodionov}@aideus.com http://winterintelligence.org
Panelist: Numenta
 
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NumentaThe Numenta Platform for Intelligent Computing is now available. The first release of the Numenta Platform for Intelligent Computing (NuPIC) is a research release targeted at sophisticated developers for the purpose of education and experimentation. NuPIC implements a hierarchical temporal memory system (HTM) patterned after the human neocortex. We expect NuPIC to be used on problems that, generally speaking, involve identifying patterns in complex data. The ultimate applications likely will include vision systems, robotics, data mining and analysis, and failure analysis and prediction. Numenta is committed to creating and supporting an open, collaborative community of companies and individuals interested in working on HTM systems. Concurrent with the Numenta Platform release, Numenta also has launched developer community tools and training materials. For an overview of HTM, see Technology. For more information on Numenta, see About Numenta. For more detailed information on HTM and NuPIC, see Education. To download the research release, see Software. http://www.numenta.com/
Views: 3284 CITRIS
#bbuzz: Dominik Benz "Bug bites Elephant: Test-driven Quality Assurance..."
 
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Dominik Benz http://berlinbuzzwords.de/sessions/bug-bites-elephant-test-driven-quality-assurance-big-data-application-development Around the currently available large piles of Big Data, there's happening quite a mixed gathering: Business Engineers define which insightswould be precious, Analysts build models, Hadoop programmers tame the flood of data, and Operations people setup machines and networks. It's exactly the interplay of all participants which is central to project success. This setup together with the distributed nature of processing poses new challenges to well-established models of assuring software artifact quality: How can non-programmers define acceptance criteria? How can functionalities be tested which depend on cluster execution, orchestration of, e.g., different hadoop jobs without delaying the development process? Which data selection is suited best for simulating the live environment? How can intermediate results in arbitrary serialization formats be inspected? In this talk, experiences and best practices from approaching these problems in a large-scale log data analysis project will be presented. At 1&1, our team develops hadoop applications which process roughly 1 billion log events (~1 TB) per day. We will give an overview of the hard- and software setup of our quality assurance environment, which includes FitNesse as a wiki-style acceptance testing framework.Starting from a comparison with existing test frameworks like MRUnit, we will explain how we automate the parameterized deployment of our applications, choose test data sampling strategies, perform workflow management and orchestration of jobs / applications, and use Pig for inspection of intermediate results and definition of final acceptance criteria. Our conclusion is that test-driven development in the field of Big Data requires adaption of existing paradigms, but is crucial for maintaining high quality standards for the resulting applications. About the speaker: Dr. Dominik Benz studied Computer Science with minor Psychology at the University of Freiburg, Germany. In his PhD at the Knowledge and Data Engineering Group (University of Kassel) he applied Data Mining and Knowledge Discovery methods to large datasets of Social Web Systems in order to discover emergent semantic structures. Since November 2012 he is working as a Big Data Engineer at Inovex GmbH, focussing on quality-driven development of Hadoop Applications in Business Intelligence contexts.
Mod-01 Lec-22 Natural Language Processing and Informational Retrieval
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 3137 nptelhrd
Snow Tha Product - “Nights" (feat. W. Darling)
 
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Snow Tha Product - “Nights" (feat. W. Darling) Download: http://smarturl.it/DownloadNights Stream: http://smarturl.it/StreamNights Connect with Snow https://twitter.com/SnowThaProduct https://www.facebook.com/SnowThaProduct https://www.instagram.com/snowthaproduct https://soundcloud.com/snowthaproduct http://www.snowthaproduct.com/
Views: 7369000 SNOWTHAPRODUCT
Using Personalization to Improve XML Retrieval
 
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As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, re-ranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.
Bebe Rexha - I Can't Stop Drinking About You [Official Music Video]
 
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Check out the official music video for Bebe Rexha's "I Can't Stop Drinking About You"! Bebe Rexha's "I Don't Wanna Grow Up" EP is available now on iTunes! Download it here: smarturl.it/IDontWannaGrowUpEP LISTEN Available on iTunes: http://bit.ly/1ouIvWw Available on Spotify: http://smarturl.it/ICSDAYSpotify CONNECT WITH BEBE Offical Website: http://www.beberexha.com Facebook: https://www.facebook.com/Beberexha Twitter: http://www.twitter.com/BEBEREXHA Instagram: http://instagram.com/beberexha Youtube: http://www.youtube.com/BEBEREXHA Soundcloud: https://soundcloud.com/beberexha LYRICS No ones gonna love you like I do. No ones gonna care like I do. And I can feel it in the way that you breathe. I know you dream of her while you sleep next to me. I can't stop drinking about you. I gotta numb the pain. I can't stop drinking about you. Without you I ain't the same. So pour a shot in my glass and I'll forget forever! So pour a shot in my glass cause it makes everything better! Darlin tell me what more can I do? Don't you know that I was meant for you? You say I feel like heaven on earth, But You'd never know what heaven was if it wasn't for... her. I can't stop drinking about you. I gotta numb the pain. I can't stop drinking about you. Without you I ain't the same. So pour a shot in my glass and I'll forget forever! So pour a shot in my glass cause it makes everything better! I can't stop drinking about you. I can't stop drinking about you. No ones gonna love you like I do. I can't stop drinking about you. I can't stop drinking about you. So pour a shot in my glass and I'll forget forever! So pour a shot in my glass cause it makes everything better! No ones gonna love you like I do.
Views: 19608834 Bebe Rexha
Yale Day of Data 2015: Chaitan Baru, “Data Science R&D: Current Activities, Future Directions"
 
01:13:06
Chaitan Baru, Senior Advisor for Data Science, CISE Directorate, National Science Foundation, gives a keynote presentation for the Yale Day of Data 2015, with an introduction by Yale University President, Peter Salovey, on September 18, 2015. For more information about the Day of Data, visit http://elischolar.library.yale.edu/dayofdata/
Views: 509 YaleUniversity
Maryann Martone - Where do we go from here? (databases and ontologies)
 
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Maryann Martone, University of California, San Diego, USA INCF Short Course "Introduction to Neuroinformatics", September 2012. Munich, Germany http://www.incf.org/programs/training-committee/courses/ni-2012 Talk title: Where do we go from here? (databases and ontologies) Neuroanatomist Maryann Martone talks about experiences in data integration and the neurosciences.
Views: 418 INCF
CHI 2015 Plenary: Susan Dumais - Large-Scale Behavioral Data
 
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Susan Dumais ACM-W Athena Lecture: Large-Scale Behavioral Data: Potential and Pitfalls Over the last decade, the rise of web services has made it possible to gather traces of human behavior in situ at a scale and fidelity previously unimaginable. Large-scale behavioral data enables researchers and practitioners to detect adverse drug reactions and interactions, to understand how information diffuses through social networks, how people browse and search for information, how individual learning strategies are related to educational outcome, etc. Using examples from search, I will highlight how observational logs provide a rich new lens onto the diversity of searchers, tasks, and interactivity that characterize information systems today, and how experimental logs have revolutionized the way in which web-based systems are designed and evaluated. Although logs provide a great deal of information about what people are doing, they provide little insight about why they are doing so or whether they are satisfied. Complementary methods from observations, laboratory studies and panels are necessary to provide a more complete understanding of and support for search which is increasingly a core fabric of people’s everyday lives. The CHI community should lead the way in shaping best practices and policy in behavioral log studies. Biography: Susan Dumais a Distinguished Scientist at Microsoft and Deputy Managing Director of the Microsoft Research Lab in Redmond. Prior to joining Microsoft Research, she was at Bell Labs and Bellcore, where she worked on Latent Semantic Analysis, techniques for combining search and navigation, and organizational impacts of new technology. Her current research focuses on user modeling and personalization, context and search and temporal dynamics of information. She has worked closely with several Microsoft groups (Bing, Windows Desktop Search, SharePoint, and Office Online Help) on search-related innovations. Susan has published widely in the fields of information science, human-computer interaction and cognitive science, and holds several patents on novel retrieval algorithms and interfaces. Susan is also an adjunct professor in the Information School at the University of Washington. She is Past-Chair of ACM's Special Interest Group in Information Retrieval (SIGIR), and serves on several editorial boards, technical program committees, and government panels. She was elected to the CHI Academy in 2005, an ACM Fellow in 2006, received the SIGIR Gerard Salton Award for Lifetime Achievement 2009, was elected to the National Academy of Engineering (NAE) in 2011, and received the ACM Athena Lecturer Award, and Tony Kent Strix Award in 2014. ACM DL::http://dl.acm.org/citation.cfm?id=2702613 Slides::https://chi2015.acm.org/files/CHI2015-Athena-Dumais_ShareBuilds.pdf WEB::https://chi2015.acm.org Recorded on April 23, 2015 at the CHI Conference on Human Factors in Computing Systems, Seoul, South Korea
Views: 156 ACM SIGCHI
TLT Symposium 2013 Lunch Keynote (edited)
 
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Interest in big data, data mining, and analytics is strong and growing in business and government. Recent reports by McKinsey, HBR, and Deloitte indicate that big data and analytics are just beginning beginning to make their impact in many sectors. The tools and methods of analytics are developing rapidly and are increasingly easier to use. In education, the adoption of analytics has been slow, and when initiated, often focused on improving organizational processes or identifying at-risk-learners. Analytics hold significant value in improving the spectrum of the teaching and learning process, not only for targeting a particular variable. This presentation will review the context that's driving popularity of analytics, provide cases and examples of use in education, and argue for the use of proactive models that emphasizes improving the learning experience, instead of only reacting to warning signs.
Views: 3861 Penn State TLT
Sabi feat. Tyga - Cali Love [OFFICIAL MUSIC VIDEO]
 
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Cali Love by Sabi - Feat. Tyga Get "Cali Love on iTunes:" http://SmartUrl.it/CaliLove Links: http://OfficialSabi.com Http://Facebook.com/SabiOfficial Http://Twitter.com/SabiSoundz
Views: 1957923 Sabi
Search engine indexing
 
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Search engine indexing collects, parses, and stores data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process in the context of search engines designed to find web pages on the Internet is web indexing. Popular engines focus on the full-text indexing of online, natural language documents. Media types such as video and audio and graphics are also searchable. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1447 Audiopedia
Structured Probabilistic Models for Computational Social Science
 
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With today’s abundance of data, probabilistic models have an opportunity to answer fundamental questions about human behavior and interactions. However, unlike standard inference tasks, socio-behavioral outcomes are frequently interdependent, rely on heterogenous observations, and require complex reasoning. To effectively capture this structure, we need richer modeling frameworks. In this talk, I present my research on developing probabilistic methods that address the needs of computational social science by: 1) making interrelated inferences 2) discovering patterns and causal relationships, and 3) fusing noisy domain knowledge with statistical signals. First, I introduce modeling templates that exploit useful structural patterns to make collective, consistent predictions. Next, I present algorithms that bolster domain knowledge by learning causal relationships and structural patterns directly from data. These approaches have shed light on identifying stances in online debates, detecting social media indicators of alcoholism relapse, and inferring causal links between factors that affect mood. Finally, I outline a research agenda that unifies structured methods with promising embedding and latent variable models to address questions in causality and social science. See more at https://www.microsoft.com/en-us/research/video/structured-probabilistic-models-for-computational-social-science/
Views: 1143 Microsoft Research
Artificial intelligence | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Artificial intelligence Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
Views: 32 wikipedia tts
Fairness in Machine Learning
 
01:20:55
Machine learning is increasingly being adopted by various domains: governments, credit, recruiting, advertising, and many others. Fairness and equality are critical aspects, especially in light of anti-discriminatory laws in these domains. Opaque machine learning models: Awareness and mitigation of biases (inherent and perpetuated) is essential. See more on this video at https://www.microsoft.com/en-us/research/video/fairness-machine-learning/
Views: 1073 Microsoft Research
Data Science in Social Spaces: Personalization vs. Privacy by Elena Zheleva
 
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Elena Zheleva from the University of Illinois at Chicago presents her talk for the DIMACS/Northeast Big Data Hub Workshop on Privacy and Security for Big Data April 24 - 25, 2017 DIMACS Center, CoRE Building, Rutgers University Organizing Committee: René Bastón, Columbia University Joseph Lorenzo Hall, The Center for Democracy and Technology Adam Smith, Pennsylvania State University Sean Smith, Dartmouth College Rebecca Wright, Rutgers University Moti Yung, Snapchat Presented under the auspices of the DIMACS Big Data Initiative on Privacy and Security, the DIMACS Special Focus on Cybersecurity and in collaboration with the Northeast Big Data Innovation Hub. http://dimacs.rutgers.edu/Workshops/BigDataHub/program.html
Views: 250 Rutgers University
Lecture - 40 Natural Language Processing II
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 20123 nptelhrd

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