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Listen to the story on Rivet

Product reviews of spatial toys—blocks, puzzles, shape sorters, and building sets—offer a unique window into how people value spatial play in early childhood. These reviews contain millions of unstructured text data points that reflect people's underlying beliefs and priorities. When someone writes that a puzzle "helped with problem-solving skills," states building blocks were "worth every penny," mentions their child "loves it when they label the shape words," or says they purchased a shape sorter to help their child's motor skills improve, they're sharing their beliefs about why it's important to provide children with access to these toys. Conventional laboratory-based studies examining child engagement with puzzles, parental beliefs about spatial toys, or the early development of spatial skills enable researchers to administer tightly-controlled experiments that support causal inferences, but require generalizing findings from a small subset of the population—often assuming observed effects will be invariant over chronological time, stable across different demographic groups, and consistent across distinct types of spatial play. In contrast, analyzing massive amounts of publicly available review data provides less experimental control but offers direct descriptive evidence across years, diverse populations, and variations in real-world spatial play materials without requiring statistical inference or generalization of findings from a small sample to a larger population. In the present study, we curate product reviews for spatial toys marketed to children from 0-5 years using a massive dataset of 890.7K Toys and Games product reviews collected across 8.1 million users from May 1996 to September 2023 (https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023). We use structural topic modeling, an unsupervised machine learning algorithm for identifying themes in natural text data, to uncover how much people prioritize educational benefits, child enjoyment, affordability, and other factors that may drive people’s decisions to purchase spatial toys. This will enable us to examine people’s beliefs across diverse racial, socioeconomic, and educational backgrounds on a scale that would be impractical or impossible in a conventional laboratory studies. Understanding these priorities is crucial because spatial play is an important informal learning opportunity for promoting spatial skills and increasing school readiness and addressing the achievement gap in STEM. By using recent advances in Natural Language Processing to analyze authentic consumer perspectives, we can inform evidence-based policies and interventions that increase meaningful access to spatial toys, especially for families from diverse socioeconomic backgrounds. The overarching goal of this research is to bridge developmental science with the practical realities of how families make decisions about early childhood learning experiences.

It's an EmMajority Report Thursday! Emma hosts Sarah Brayne, professor of sociology at the University of Texas at Austin, to discuss her recent book Predict and Surveil: Data, Discretion, and the Future of Policing. Then, Emma is joined by Donna Murch, associate professor of History at Rutgers University and President of the New Brunswick Chapter of Rutgers AAUP-AFT, to give an update on the teachers strike going on at the university right now. First, Emma runs through updates on the Fifth Circuit's response to the Texas Court ban on the abortion pill, Dianne Feinstein's response to calls for her retirement, the reinstatement of Tennessee's outspoken state house members, Arizona's GOP-led expulsion of an election denier, Biden's labor secretary nomination, ACA and Medicaid expansion for DREAMers, and Biden's border policy, also touching on DeSantis begging his Floridian colleagues not to endorse Trump, Trump's multiple fraud cases, and far-right legislation in Oklahoma and North Dakota, before watching Tom Scott dance around his intense anti-abortion stance. Sarah Brayne then dives right into her fieldwork over the past five years tailing and interviewing myriad members throughout the LAPD, including civilian employees and officers, exploring why the LAPD was the perfect location as one of the most technologically-advanced and well-funded police department, and as a department that has been forced into a more transparent relationship to the public after a few decades of incredible abuse and corruption to end the 20th Century. After briefly walking through her interviews with the LAPD, Brayne dives into the birth of LAPD's dragnet and directed surveillance, with their systems of surveillance stemming from their role in collecting data for the federal government in the wake of 9/11, and the realization that this data can be applied locally in myriad departments, before stepping back to look at military intervention in the Middle East as a further example of US imperialism coming home to roost. Next, Emma and Sarah expand on the relationship between police departments and the corporations involved in surveillance tech and data integration, before they wrap up by tackling tackle the Police's \"nothing to hide, nothing to fear\" ideology when it comes to surveilling other communities in contrast to their complete rejection of any internal surveillance, and why data collection, much like algorithms and AI, can simply never be objective. Donna Murch then walks through the creation of Rutgers' multi-Union coalition in the wake of pandemic cutbacks, their bargaining process, and why NJ labor law is bolstering their fight, before concluding the conversation by walking through union demands, and how best to support them in this endeavor.And in the Fun Half: Emma is joined by Matt Binder and Brandon Sutton as they tackle why you can't spell Bud LiGhT without LGBT, James from Chicago land reveals Sam's mystery role on The Magic School Bus, and the MR Crew dives into the absurd coverage of SF's Tech Bro Stabbing. Bee from Queens dives into the ridiculous trans panic around sports, a GOP State Senator from Missouri votes against banning child brides, and LEGO goes WOKE! Sam from Toronto brings up the antithetical nature of popularism and majoritarian rule to leftist and progressive values, plus, your calls and IMs!Check out Sarah's book here: https://global.oup.com/academic/product/predict-and-surveil-9780190684099?cc=us&lang=en&Get more info on the Rutgers AAUP-AFT here: https://rutgersaaup.org/Become a member at JoinTheMajorityReport.com: https://fans.fm/majority/joinSubscribe to the ESVN YouTube channel here: https://www.youtube.com/esvnshowSubscribe to the AMQuickie newsletter here: https://am-quickie.ghost.io/Join the Majority Report Discord! http://majoritydiscord.com/Get all your MR merch at our store: https://shop.majorityreportradio.com/Get the free Majority Report App!: http://majority.fm/appCheck out today's sponsors:Tushy:The Hello TUSHY bidet washes your bum with fresh water for a WAY better clean than toilet paper. Simply spray and pat dry! It attaches to your existing toilet - requires NO electricity or additional plumbing - and cuts toilet paper use by 80% - so the Hello Tushy bidet pays for itself in a few months. Go to https://hellotushy.com/majority to get 10% off today!Seder's Seeds!: Sam tried to grow some cannabis last year, didn't know what he was doing, but now has some great cannabis seeds! Go to http://www.sedersseeds.com and MajorityReporters will get an automatic 15% off. Enter coupon code \"SEEDS\" for free shipping! Follow the Majority Report crew on Twitter: @SamSeder@EmmaVigeland@MattBinder@MattLech@BF1nn@BradKAlsopCheck out Matt's show, Left Reckoning, on Youtube, and subscribe on Patreon! https://www.patreon.com/leftreckoningSubscribe to Discourse Blog, a newsletter and website for progressive essays and related fun partly run by AM Quickie writer Jack Crosbie. https://discourseblog.com/Check out Ava Raiza's music here! https://avaraiza.bandcamp.com/The Majority Report with Sam Seder - https://majorityreportradio.com/

In this project we will analyze data from an on-line study. It concerns the applicants questionnaire data obtained from the GotPsi project in the period 29-august-2000 till 01-01-2015. The goal is to establish correlations of the questionnaire responses concerning the applicants experience with meditation with their behaviour in a guessing task. Especially the 'dropping out of the study' and 'response bias' will be studies. After the pilot analysis we will update the hypotheses which will result in removal of analyses of correlations that were non significant in the pilot analysis. This is done to prevent over-analyses. The pilot analysis will use the questionnaire data from 29-08-2000 till 06-sep-2004 with N=55985, while the confirmatory analyses will use data from 06-sep-2004 til 01-01-2015 with N=55986.

We hebben besloten nog een jaar door te gaan. De auto moet toch zo af en toe gewassen worden, dan kunnen we dat net zo goed even opnemen. We hebben ook besloten dat het tijd is voor een nieuw design. Vasilis heeft zijn ideeën over content, typografie, grids en layout los gelaten op de Wasstraat. We zijn benieuwd wat je er van vindt. Big data Peet was op een congres over Big Data wat georganiseerd was door Philips. Big data is niet hetzelfde als grote hoeveelheden data. Big data gaat over ENORME hoeveelheden data waarmee er trends ontdekt kunnen worden. Het gaat niet zozeer om individuele data. En toch kan je je afvragen of dat verzamelen van data nu altijd wel nodig is. Kom je er door een slimme energiemeter achter dat mensen in de winter meer stoken? Met logisch redeneren kom je een heel eind, denkt Vasilis. Hij vindt goed design essentieel. Pas als dat in orde is kan je gaan nadenken over analyse. Ik verbaas me altijd over intelligente mensen die ineens veranderen in kritiekloze fanbois Apple Er was wat gerommel bij Apple. De chef software-design, de man die verantwoordelijk was voor gruwelijke kitsch als iCal, is ontslagen. Hij is vervangen door de ontwerper van de Apple hardware. Het zou best eens kunnen dat iOs en OSX er radicaal anders uit gaan zien. Maar Vasilis vreest dat dit in stapjes zal gaan. Dat doen bedrijven als Apple en Adobe altijd: één nieuwe, minimale, feature toevoegen aan hun product en vervolgens van de daken schreeuwen dat het ongelooflijk is. Wat pas echt ongelooflijk is, is dat zo veel intelligente mensen daar telkens weer in trappen. Een hele andere manier van communiceren zie je in de open source wereld. Daar gaat het er niet om om de aandeelhouders tevreden te houden, daar gaat het er om om goede ideeën zo snel mogelijk de wereld in te krijgen zodat er betere dingen gemaakt kunnen worden. Open source software wordt ook wel eens aangeprezen als iets ongelooflijks. Daar is ook niks mis mee, als het maar echt zo is. Het web Hoe zit het web eigenlijk in elkaar? En als je dat weet, zou dat misschien kunnen helpen als je wilt designen voor het web? Vasilis heeft hierover nagedacht en is tot de conclusie gekomen dat er een DTP-programma nodig is voor het web. De huidige design tools werken namelijk precies de verkeerde kant op. De juiste volgorde voor een ontwerp is: content -> typografie -> layout en tot slot verf. Tools als Photoshop stimuleren je om precies andersom te werken. Zou dat de reden zijn waarom zo veel websites nog altijd zo slecht zijn? Er is nog heel veel ruimte voor innovatie op het gebied van web design. Nederland staat digitaal, qua design, nog niet op de kaart. En dat komt door het onderwijs Onderwijs Dutch Design is wereldberoemd. Peet vraagt zich af waar Dutch Digital Design dan toch is. Het bestaat niet. Er wordt wat gerommeld, maar van wereldniveau is het absoluut niet. Zou het misschien aan het onderwijs liggen? Of misschien aan de navelstaarderij van de verschillende disciplines? Of aan het idee dat er geen restricties zijn, met computers kan alles, toch? We concluderen met de oproep om het onderwijs NU te verbeteren. Over vier jaar beginnen we daar namelijk de vruchten pas van te plukken. Reageren Reageren kan natuurlijk via onze twitter-accounts: @vasilis en @peetsneekes.

Raw footage. Edited versions coming this summer.

Raw footage. Edited versions coming this summer.

Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.

Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.

Big Data und Datenästhetik 

Big data visualization technology background Free Vector

Big data visualization technology background Free Vector

A look into how we set up our big data infrastructure for processing, aggregation, and analytics interfaces leveraging the Drupal platform for a UI and Node.js for an API. Involves Document Store databases, traditional RDBMS, and in-memory solutions, as well as a Drupal block-based integration into the Node.js backend for data jobs, pulls, and displays.

A look into how we set up our big data infrastructure for processing, aggregation, and analytics interfaces leveraging the Drupal platform for a UI and Node.js for an API. Involves Document Store databases, traditional RDBMS, and in-memory solutions, as well as a Drupal block-based integration into the Node.js backend for data jobs, pulls, and displays.

A look into how we set up our big data infrastructure for processing, aggregation, and analytics interfaces leveraging the Drupal platform for a UI and Node.js for an API. Involves Document Store databases, traditional RDBMS, and in-memory solutions, as well as a Drupal block-based integration into the Node.js backend for data jobs, pulls, and displays.

We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than current state of the art models. Key features of the proposed framework are: i) it accommodates inconsistent read, meaning that components of the vector variables may be written by some cores while being simultaneously read by others; ii) it covers in a unified way several different specific solution methods, and iii) it accommodates a variety of possible parallel computing architectures. Almost sure convergence to stationary solutions is proved. Numerical results, reported in the companion paper, on both convex and nonconvex problems show our method can consistently outperform existing parallel asynchronous algorithms.

Business Analytics: The Art and Science of Big Data Ventana Research - The Tech Museum A new wave of business analytics is upon us, and it's blowing the lid of old paradigms. With the explosion of information assets resulting in Big Data and the exponential appetite for metrics and forward looking key indicators such as predictive analytics, companies need to take a different approach. Will analysts step to the foreground as critical leaders of analytics for business or will the much-discussed data scientists come to the rescue? Learn more about critical new research on the next generation of business analytics and how real time data will impact the operational analytics of the future. This session will examine the reality of business and IT now and look ahead to 2013. Gain exclusive insight into the research agenda that will shape the way organizations assess and invest into these two essential business technologies. Tony Cosentino, VP & Research Director, Business Analytics, Ventana Research

We present complexity and numerical results for a new asynchronous parallel algorithmic method for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed method hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than state-of-the-art models. In the companion paper we provided a detailed description on the probabilistic model and gave convergence results for a diminishing stepsize version of our method. Here, we provide theoretical complexity results for a fixed stepsize version of the method and report extensive numerical comparisons on both convex and nonconvex problems demonstrating the efficiency of our approach.

This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep learning approaches (tuned parameters) which are intended for distributed computing, and the approaches that focused on the designed parameters but often limited by sequential computing and cannot scale up. In the evaluation of our approach, it is shown that DDRL is able to achieve state-of-art classification accuracy efficiently on both medium and large datasets. The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches. More specifically, DDRL contains two main components, i.e., feature extraction and selection. A hierarchical distributed deep representation learning algorithm is designed to extract image statistics and a nonlinear mapping algorithm is used to map the inherent statistics into abstract features. Both algorithms are carefully designed to avoid millions of parameters tuning. This leads to a more compact solution for image classification of big data. We note that the proposed approach is designed to be friendly with parallel computing. It is generic and easy to be deployed to different distributed computing resources. In the experiments, the largescale image datasets are classified with a DDRM implementation on Hadoop MapReduce, which shows high scalability and resilience.

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. More specifically, we scale up the novel Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) to handle millions of features with tens of thousand samples. The proposed method is guaranteed to find an optimal subset of maximally predictive features with minimal redundancy, yielding higher predictive power and improved interpretability. Its effectiveness is demonstrated through applications to classify phenotypes based on module expression in human prostate cancer patients and to detect enzymes among protein structures. We achieve high accuracy with as few as 20 out of one million features --- a dimensionality reduction of 99.998%. Our algorithm can be implemented on commodity cloud computing platforms. The dramatic reduction of features may lead to the ubiquitous deployment of sophisticated prediction models in mobile health care applications.

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

En esta edición de CZ nos visitó Gianni Hanawa, de Level 3, para hablar de Big Data y cómo procesar grandes volúmenes de datos. También comentamos el hackeo de la cuenta de Twitter de Tinelli, el alcance de la nueva medida que controla el gasto en el exterior, la detención de uno de los fundadores de Pirate Bay, una victoria para Roja Directa, la lucha Apple-Samsung que no cesa (y que tiene de fondo la de Google) y la tele del futuro (4K y 8K).

Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for {\em real-time} processing of streaming data pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the nuclear norm. After recasting the non-separable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the {\em batch} nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algorithm is developed to obtain multi-way decompositions of \emph{low-rank tensors} with missing entries, and perform imputation as a byproduct. Simulated tests with both synthetic as well as real Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives.

Extracting useful information from large data sets can be a daunting task. Topological methods for analyzing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying such topological features -- connected components, holes, or voids -- and for determining how such features persist as the data is viewed at different scales. This paper provides quantum algorithms for calculating Betti numbers in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speedup over classical algorithms for topological data analysis.

Public Web Experiments have noise but ure useful to find weak effects or effects shared by a minority of subjects because of the large number of trials and subjects. In this project we have split the data randomly in two subsets and use one subset to search for effects that will be confirmed in the other subset. We have decided ast the time of preregistration to formally test a diurnal effect in psi-performance that we found for a subset of subjects.

We live in an interconnected world of volatility and disruption. Systems are linked in a web of dizzying and only partially visible complexity, and change in any one domain has a swift impact on many others. Join Andrew Zolli in a walking tour of emerging tools – such as next-generation satellite imagery, social media, and advanced analytics – that allow us to make sense of this complexity and render the underlying relationships between people and the planet more visible, accessible, and actionable. Learn how the move from “Big Data” to “Big Indicators” enables new approaches to planetary stewardship, planetary health, and social wellbeing.

We analyze a compression scheme for large data sets that randomly keeps a small percentage of the components of each data sample. The benefit is that the output is a sparse matrix and therefore subsequent processing, such as PCA or K-means, is significantly faster, especially in a distributed-data setting. Furthermore, the sampling is single-pass and applicable to streaming data. The sampling mechanism is a variant of previous methods proposed in the literature combined with a randomized preconditioning to smooth the data. We provide guarantees for PCA in terms of the covariance matrix, and guarantees for K-means in terms of the error in the center estimators at a given step. We present numerical evidence to show both that our bounds are nearly tight and that our algorithms provide a real benefit when applied to standard test data sets, as well as providing certain benefits over related sampling approaches.

L’UTC organisait du 19 au 22 janvier un séminaire sur le thème "Big data, open data et sciences sociales".

L’UTC organisait du 19 au 22 janvier un séminaire sur le thème "Big data, open data et sciences sociales".

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.

Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

workers in Kenya

With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'etat. The study validates and quantifies the common intuition that data on social media (beyond mainstream news sources) are able to predict major events.

With computing and sensing woven into the fabric of everyday life, we live in an era where we are awash in a flood of data from which we can gain rich insights. Augmented reality (AR) is able to collect and help analyze the growing torrent of data about user engagement metrics within our personal mobile and wearable devices. This enables us to blend information from our senses and the digitalized world in a myriad of ways that was not possible before. AR and big data have a logical maturity that inevitably converge them. The tread of harnessing AR and big data to breed new interesting applications is starting to have a tangible presence. In this paper, we explore the potential to capture value from the marriage between AR and big data technologies, following with several challenges that must be addressed to fully realize this potential.

🇫🇷Probablement une des vidéos les plus importantes que vous verrez de toute votre vie. Je n'ai même pas besoin de vous dire de partager car je sais que vous le ferez 🇺🇸Probably one of the most important videos you'll ever see. I don't even need to tell you to share because I know you will

¿Por qué un Podcast?, sobre Transformación Digital e Industria 4.0 Nuestro objetivo, es Educar a las nuevas generaciones de profesionales que deben de afrontar la transformación Digital en su industria o negocio, por medio de experiencias, entrevistas y charlas amenas. Mas que un podcast es una hoja de ruta para tu desarrollo profesional. Twitter: @Tdi40podcast Linkedin: linkedin.com/in/tdi-podcast-b45628200 Youtube: TDI4.0 Podcast web: https://www.tdi40.com Mail: [email protected]

For more information, including links to the scientific evidence base for the statements in this video, as well as further resources: http://medicatingnormal.com/ Robert Whitaker on Wikipedia: https://bit.ly/33tgN4n Robert Whitaker’s books provide much more detail about this subject, including citations to the scientific research. His book “Anatomy of an Epidemic: Magic Bullets, Psychiatric Drugs, and the Astonishing Rise of Mental Illness in America” is available here: https://amzn.to/2OO45tI A link to summaries and the original scientific articles of the Xanax study discussed here: https://medicatingnormal.com/benzodiazepines-anti-anxiety-meds/ Xanax is the brand name of Alprazolam. Medicating Normal on Facebook: https://www.facebook.com/pg/medicatingnormalfilm/ Medicating Normal on Twitter: https://twitter.com/medicatingnorm1?lang=en Donate: https://medicatingnormal.com/donate/ Note: This video does not constitute medical advice. Stopping psychiatric drugs, especially abruptly, can be dangerous, as withdrawal effects may be severe, disabling or even life-threatening. Music: http://www.bensound.com/royalty-free-music Video edited by Daniel Mackler

The processes of generating innovative solutions mostly rely on skilled experts who are usually unavailable and their outcomes have uncertainty. Computer science and information technology are changing the innovation environment and accumulating Big Data from which a lot of knowledge is to be discovered.

The processes of generating innovative solutions mostly rely on skilled experts who are usually unavailable and their outcomes have uncertainty. Computer science and information technology are changing the innovation environment and accumulating Big Data from which a lot of knowledge is to be discovered.

This article assumes acquired the skills and expertise of a statistician in unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART, random forest). What skills and knowledge do a statistician must acquire to reach the "Volume" scale of big data? After a quick overview of the different strategies available and especially of those imposed by Hadoop, the algorithms of some available learning methods are outlined in order to understand how they are adapted to the strong stresses of the Map-Reduce functionalities

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse-grained to different levels, we show that agents having access to more and better data can perform worse than others in certain situations. The relation between information asymmetry and individual payoffs is seen to be complex, depending on the composition of the population of competing agents.

This document is issued on date 2016-05-27.

Along with the advent of MOOCs and other online learning platforms such as Khan Academy, the role of online education has continued to grow in relation to that of traditional on-campus instruction. Rather than tackle the problem of evaluating large educational units such as entire online courses, this paper approaches a smaller problem: exploring a framework for evaluating more granular educational units, in this case, short educational videos. We have chosen to leverage an adaptation of traditional Bayesian Knowledge Tracing (BKT), intended to incorporate the usage of video content in addition to assessment activity. By exploring the change in predictive error when alternately including or omitting video activity, we suggest a metric for determining the relevance of videos to associated assessments. To validate our hypothesis and demonstrate the application of our proposed methods we use data obtained from both the popular Khan Academy website and two MOOCs offered by Stanford University in the summer of 2014. [For complete proceedings, see ED560503.]

Along with the advent of MOOCs and other online learning platforms such as Khan Academy, the role of online education has continued to grow in relation to that of traditional on-campus instruction. Rather than tackle the problem of evaluating large educational units such as entire online courses, this paper approaches a smaller problem: exploring a framework for evaluating more granular educational units, in this case, short educational videos. We have chosen to leverage an adaptation of traditional Bayesian Knowledge Tracing (BKT), intended to incorporate the usage of video content in addition to assessment activity. By exploring the change in predictive error when alternately including or omitting video activity, we suggest a metric for determining the relevance of videos to associated assessments. To validate our hypothesis and demonstrate the application of our proposed methods we use data obtained from both the popular Khan Academy website and two MOOCs offered by Stanford University in the summer of 2014. [For complete proceedings, see ED560503.]

CCTV Center for Media and Democracy presents a public talk and call to action with Jeff Chester, Center for Digital Democracy. Thursday, November 9th at Burlington, Vermont City Hall Reception at 5:30 pm - Public Talk at 6 pm Register for this event at www.bit.ly/data2020 $10+ Donation Gratefully Accepted Students Free

Terrorism has virtually invaded our day to day lives. We can’t imagine of passing a day without a terrorist attack in any part of the country that has brought in irreparable loss to mankind and also invaluable material destruction. The knowledge and information we collect about the terrorists’ operations are highly voluminous and is increasingly becoming multidimensional, thereby pushing the analysis of Big Data into new frontiers. This data when combined with counter-intelligence inputs brings in a new perspective on the efforts to combat terrorism. As new terror outfits spring up consistently, applying suitable data mining techniques on such Big Data has a great impact on the counter terrorism measures and understanding the pattern of attacks. In this research we have analyzed the performance of classifiers like decision tree and ensemble classifier on the Global Terrorism Database and the results have shown that the ensemble method outperforms for the given dataset. This Conference ICIDRET (International Conference on Inter Disciplinary Research in Engineering and Technology 2015) is a part of Association of Scientists, Developers and Faculties - International lead by President Anbuoli Parthasarathy and Secretary Kokula Krishna Hari Kunasekaran. Editor-in-Chief : Kokula Krishna Hari K. For information visit www.icidret.in

In the information age, one of the most influential institutions is education. The recent emergence of MOOCS [Massively Open Online Courses] is a sample of the new expectations that are offered to university students. Basing decisions on data and evidence seems obvious, and indeed, research indicates that data-driven decision-making improves organizational productivity. The most dramatic factor shaping the future of higher education is Big Data and analytics. Big Data emphasizes that the data itself is a path to value generation in organizations and it is, also, a critical value for higher education institutions. The emerging practice of academic analytics is likely to become a new useful tool for a new era. Analytics and big data have a significant role to play in the future of higher education. This paper attempts an analytical practice about the use of e-learning technological tools to generate relevant information, for the teacher and the students who try to optimize their learning process.This combination of data-processing and analytical learning is an aid to improve significantly higher education and mark the path to follow in the new educational era. [For the full proceedings, see ED562127.]

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