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Listen to the story on Rivet

Listen to the story on Rivet

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/

Raw footage. Edited versions coming this summer.

Raw footage. Edited versions coming this summer.

Big data and artificial intelligence (AI) go hand in hand. Used for tasks like trend prediction, process automation and research, these two technologies can help organisations solve some of the toughest problems. However, the growing volume of data and increasing diversity of data sources make it difficult to use data and AI effectively and at scale. Join us on 23rd of August where we will cover: - Big data and AI solutions: trends and challenges - Overcoming barriers to adoption - Use cases that can benefit from big data and AI - Open source platforms that can kick-start your big data and AI initiative Speakers: - Rob Gibbon, Big Data Product Manager - Michelle Tabirao, NoSQL Product Manager - Andreea Munteanu, MLOps Product Manager

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

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

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 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.

Big Data can generate, through inferences, new knowledge and perspectives. The paradigm that results from using Big Data creates new opportunities. Big Data has great influence at the governmental level, positively affecting society. These systems can be made more efficient by applying transparency and open governance policies, such as Open Data. After developing predictive models for target audience behavior, Big Data can be used to generate early warnings for various situations. There is thus a positive feedback between research and practice, with rapid discoveries taken from practice. DOI: 10.13140/RG.2.2.14677.17120

Jim ( @jcollison ) is joined this week by Christian Johnson (@ TheWizBM ) and VGo’s cofounder and COO Tom Ryden for Home Tech episode #122. In  show 117 , we spoke about the VGo Telepresence robot.  Christian was using one of these devices back in the spring to remote into NASA for his work there.  Tom was good enough to join us to join us for a 45 minute interview. Listen LIVE  every Thursday night at  http://TheAverageGuy.tv/live  @ 6pP/8pC/9pE

This study is devoted to Big Data problems in biometric technologies. Before the traditional database were not capable to handle the large amount of data, so we highlight the concept of using Big Data technology in solution of a wide range of issues faced by the national security, law enforcement system and defence authorities.  The study provides information about Big Data in the field of biometric technology.  by Mr. Manish Vala | Kajal Patel | Harsh Lad "Multi Model Biometrics Data Retrieval Through: Big-Data"  by Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018,  URL: http://www.ijtsrd.com/papers/ijtsrd15933.pdf Direct Link: http://www.ijtsrd.com/computer-science/data-processing/15933/multi-model-biometrics-data-retrieval-through-big-data/mr-manish-vala

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.

¿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]

¿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]

¿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]

¿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]

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.

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.

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.

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.

The management of large events with hundreds of thousands of individuals has remained a challenge over the years. Crushes and stampedes occurring in the events of mass gathering have swallowed many valuable lives around the world. Considering the substantial advancement in positional tracking, wearable technology, and wireless communication, many event organizers are embracing the use of these technologies to get assistance in managing large events. Intelligent monitoring of crowd movement and timely analysis of evolving conditions may aid in early detection of critical situations. The current research aims to propose a big data resource framework to model, simulate, and visualize the crowd conditions for actual venue settings. A distributed framework has been presented to monitor the movement and interaction of individuals in large crowded events through localized sensing and geospatial analysis of massive positional data. The pilgrimage (Hajj) has been considered as a case study for demonstrating the effectiveness of the proposed framework. The proposed framework has been with the help of synthetic data that covered some useful and frequent scenarios based on the case study of pilgrimage (hajj), which is an annual event involving more than a million people.

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, re-searchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in intelligent transportation systems (ITS) that can help al-leviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised ma-chine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study car-ry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individuals has limited the scope of many studies to the development of individual-specific decoders. In this study, we used deep neural network (DNN), a nonlinear hierarchical model, to construct a subject-transfer decoder. Our decoder is the first successful DNN-based subject-transfer decoder. When applied to a large-scale functional magnetic resonance imaging (fMRI) database, our DNN-based decoder achieved higher decoding accuracy than other baseline methods, including support vector machine (SVM). In order to analyze the knowledge acquired by this decoder, we applied principal sensitivity analysis (PSA) to the decoder and visualized the discriminative features that are common to all subjects in the dataset. Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.

Big data integration could involve a large number of sources with unpredictable redundancy information between them. The approach of building a central warehousing to integrate big data from all sources then becomes infeasible because of so large number of sources and continuous updates happening. A practical approach is to apply online query scheduling that inquires data from sources at runtime upon receiving a query. In this paper, we address the Time-Cost Minimization Problem for online query scheduling, and tackle the challenges of source permutation and statistics estimation to minimize the time cost of retrieving answers for the real-time receiving query. We propose the online scheduling strategy that enables the improvement of statistics, the construction of source permutation and the execution of query working in parallel. Experimental results show high efficiency and scalability of our scheduling strategy.

For timely and efficient reactions to disasters, collecting vital and right information is essential. In recent decades, social media platforms such as Twitter, Facebook, Linkedin, Instagram have become valuable sources of information in disaster times. However, the reliability, volume, and velocity of information remain a major concern; this is particular about information issued from disaster locations. This paper proposes an approach for tracking the location of people in danger during times of disaster. The procedure is based on the Twitter application programming interface (API) by using natural language processing (NLP) and big data tools. A number of tweets were analyzed and an accuracy of 86.11% was actualized. 

Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Machine Learning (ML). ML is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, ML is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Various algorithms like Random Forest, Decision Tree, MLP Classifier are applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results.

Abstract Big data technologies have been employed in a variety of fields since their inception, with their use within biomedical and health-care informatics research enhancing at an astonishing speed. At an extraordinary speed and scale, massive amounts of biological and clinical data have indeed been produced and gathered. Big data applications open up new avenues for discovering new knowledge and developing innovative techniques for enhancing health-care quality. The use of big data in health care is a rapidly expanding field, with numerous recent findings and techniques emerging in recent years. The authors will examine and address big data applications in four major biomedical subfields in this paper: (1) bioinformatics, (2) clinical informatics, and (3) imaging informatics, and (4) public health informatics. Link

La digitalización del mundo físico y los algoritmos se han situado en el centro de nuestro estilo de vida. La novedad ya no es que cada persona lleve uno o dos dispositivos conectados, ahora los objetos también se conectan entre sí. Una producción propia de RTVE que muestra cómo los actos cotidianos se convierten en datos digitales cuyo control es una de las grandes batallas de nuestro tiempo. El desarrollo de algoritmos, fórmulas informáticas que predicen nuestro comportamiento, nos acerca a un mundo de decisiones tomadas por la inteligencia artificial.

Unofficial mirror of http://www.documentcloud.org/documents/683510-accenture-building-the-foundation-for-big-data.html

An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student successes and failures. We found that a pattern of declining performance over time is a good predictor of the likelihood of dropping out, and that having dependents or being married or in the military reduces the risk of dropping out. The risk of dropping out was higher for older students, females, and students with previous college education or transfer credits. These results provide a foundation for testing interventions to help students who are at risk and will also help to inform the development of a "research pipeline" that will enable rapid experimental studies of new tools and strategies. (Contains 1 table.) [For the complete proceedings, "Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (Madrid, Spain, Oct 19-21, 2012)," see ED542606.]

An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student successes and failures. We found that a pattern of declining performance over time is a good predictor of the likelihood of dropping out, and that having dependents or being married or in the military reduces the risk of dropping out. The risk of dropping out was higher for older students, females, and students with previous college education or transfer credits. These results provide a foundation for testing interventions to help students who are at risk and will also help to inform the development of a "research pipeline" that will enable rapid experimental studies of new tools and strategies. (Contains 1 table.) [For the complete proceedings, "Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (Madrid, Spain, Oct 19-21, 2012)," see ED542606.]

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.

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.

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.

Discover how AI and machine learning transform big data into actionable insights, driving innovation, automation, and smarter decision-making.

A huge amount of data regarding Terabytes are generated from digital technologies all over the world. Analysis of such massive data termed Bigdata is required to extract essential information from it. Such Bigdata analysis are required in almost every sector such as business intelligence, financial services, consumer services, healthcare services, energy management, industrial process management, IOT, etc. In this paper, we review the works related to big data analytics that is applied in the variety of applicative domains, the techniques involved and the tools and platforms involved.

œBig data' is defined as the collection of large and complex datasets available in structured, semi-structured, and unstructured form which are dif?cult to process using traditional database management tools or data processing applications. Big Data is also defined from 5 Vs which refers to Volume, Variety, Velocity, Veracity and Value .Clinical Research is one of the most important as well as promising part of health research. This article provides an overview on some specific aspects of clinical research when adapted to Big Data Science pathways which could be utilized for transforming millions of data points into predictions & simulation to provide cost effective medicines in reduced timelines. This article also highlights the opportunities & challenges that Big Data brings with it. Dr. Elton Mathias | Dr. Roveena Goveas | Manish Rajak"Clinical Research - A Big Data Science Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9547.pdf  http://www.ijtsrd.com/medicine/other/9547/clinical-research---a-big-data-science-approach/dr-elton-mathias

"Combat supply chain disruptions with wearable technology" -Schmidt (2021) "Computer hardware continues to shrink in size and increase in capability. This trend has allowed the prevailing concept of a computer to evolve from the mainframe to the minicomputer to the desktop. Just as the physical hardware changes, so does the use of the technology, tending towards more interactive and personal systems. Currently, another physical change is underway, placing computational power on the user's body. These wearable machines encourage new applications that were formerly infeasible and, correspondingly, will result in new usage patterns. On-body systems can sense the user's context with little or no assistance from environmental infrastructure. These body-centered systems that "see" as the user sees and "hear" as the user hears, provide a unique "first-person" viewpoint of the user's environment. These tools include a downward-facing, camera-based system for establishing the location of the user; a tag-based object recognition system for augmented reality; and several on-body gesture recognition systems to identify various user tasks in constrained environments. To address the practicality of contextually-aware wearable computers, issues of power recovery, heat dissipation, and weight distribution are examined." -Starner (1999) "In 2014, ProGlove emerged the third-place winner in Intel’s “Make It Wearable Challenge”. The ProGlove team of four who had met at the Technical University of Munich in Bavaria, Germany, had submitted the idea of a “smart glove,” which was a barcode scanner, motion detector, and RFID reader connected to the back of an industrial-grade work glove for use in high volume manufacturing. The idea was based on observations of BMW’s assembly lines, where each item had to be scanned before being assembled. The process involved finding a scanner, picking it up, scanning the component, and then putting away the scanner again. A smart glove could achieve this more quickly and ergonomically, optimising worker productivity and allowing extra steps in quality control to be included in the production." -Wee (2022) ---- Note: "Psychotechnics is a historical term for the application of psychological concepts to optimize "means and ends" in all areas of life and formed a subfield of applied psychology . Psychotechnology was the first psychological discipline to deal with the operational questions of professional suitability, job allocation (suitability and selection) and work performance (monotony and fatigue, performance and work design) or advertising. It can be seen as a classic forerunner of today 's work psychology and was also reflected in business and organizational psychology." ---- [Wearable Computing (MIT)] [DSS] [ProGlove] [Oura Health Oy] [Wearable Data Collection] [Supply Chain Logistics] [Psychotechnik] [Satellite] [Man-Machine Interaction] [Fano / Licklider] [Augmented Reality] [Big Data Analytics (HSI)] [Semiconductors] [NAII] [Cybernetics] [Satellite] [Incident Localization and Assistance System (ILAS)] [IT Service Management (ITSM)] [Cyber-Physical Human System] ---- Bloss, Richard. "Multi-technology sensors are being developed for medical, manufacturing, personal health and other applications not previously possible with historic single-technology sensors." Sensor Review (2017). Deng, Y., Chen, C., Tsow, F., Xian, X., & Forzani, E. (2016). Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2016.7591825 Indruchová, Ivana. "THE IMPACT OF INDUSTRY 4.0 ASPECTS ON LOGISTIC SERVICE PROVIDERS." Perner's Contacts 14.4 (2019): 26-34. Krishna, Sreekar, et al. "A wearable wireless RFID system for accessible shopping environments." 3rd International ICST Conference on Body Area Networks. 2010. Kulyukin, Vladimir, and Aliasgar Kutiyanawala. "From ShopTalk to ShopMobile: vision-based barcode scanning with mobile phones for independent blind grocery shopping." Proceedings of the 2010 Rehabilitation Engineering and Assistive Technology Society of North America Conference (RESNA 2010), Las Vegas, NV. Vol. 703. 2010. Scheuermann, Constantin, et al. "Incident localization and assistance system: A case study of a cyber-physical human system." 2015 IEEE/CIC International Conference on Communications in China-Workshops (CIC/ICCC). IEEE, 2015. Starner, Thad E. "Wearable computers: No longer science fiction." IEEE pervasive computing 1.1 (2002): 86-88. Starner, Thad. Wearable computing and contextual awareness. Diss. Massachusetts Institute of Technology, 1999. Stein, Robert, et al. "Development of a commercially successful wearable data collection system." Digest of Papers. Second International Symposium on Wearable Computers (Cat. No. 98EX215). IEEE, 1998. Thomas, Charu, et al. "RF-pick: comparing order picking using a HUD with wearable RFID verification to traditional pick methods." Proceedings of the 2018 ACM International Symposium on Wearable Computers. 2018. Wee, K.-E., & Iyer, A. (2011). Consolidating or non-consolidating queues: A game theoretic queueing model with holding costs. Operations Research Letters, 39(1), 4–10. doi:10.1016/j.orl.2010.09.011 Wee, Kwan Eng, and Maqbool Dada. "Optimal policies for transshipping inventory in a retail network." Management science 51.10 (2005): 1519-1533. ---- Also see: Licklider, Joseph CR. "Man-computer symbiosis." IRE transactions on human factors in electronics 1 (1960): 4-11. Pask, Gordon. "Man/machine interaction in adaptively controlled experimental conditions." The bulletin of mathematical biophysics 27.1 (1965): 261-273. WEE, Kwan Eng, Holly OTT, and Sin Mei CHEAH. "ProGlove smart gloves: Let’s save four million dollars a day!." (2022): 1-14. ---- Schmidt, Axel. "How companies can combat supply chain disruptions Wearable technology will make the human worker more valuable in the long run." Plant Engineering 75.9 (2021): 10-12. [ "Despite fears that millions of workers will be displaced by technological advances in robots and machine learning (ML), companies are seeing the opposite. There are more than 10 million job openings in the U.S. as of a recent Bureau of Labor Statistics report, making this one of the most prominent labor shortages in the country's history. As witnessed in 2020, technology in the supply chain kept logistic lines functioning despite severe stress on their functionality due to COVID-19 outbreaks and lockdowns. With wearable technologies like barcode scanners and applications that track worker efficiency to help them improve their output, people are seeing a kind of worker revolution within the industrial space. Companies also use more ergonomic solutions, which provide the worker relief from physical stress. Workers connected to the Internet of Things (IoT) are more important than ever to help keep warehouses and supply chains running as smoothly as possible. Companies are nowhere near the point of automating the whole industry. Despite labor shortages, more efficient workers can help companies navigate the waters. With the right suite of technology complementing workers, they can do more with less. There are ways that plant managers and manufacturing leaders can learn from the pandemic, so they are better prepared for the next..." ] Rodríguez Martínez, Marta. Business model, market research and business plan for the establishment of Proglove in Spain. MS thesis. Universitat Politècnica de Catalunya, 2020. [ "Today’s competitive environment of start-ups creates the urgent need to excel from competitors and differ from other companies in order to reach a good position in the current market. The start-up Workaround GmbH (ProGlove) has managed to position itself at the upper end of the global wearables’ industry in Germany. After almost 3 years working at this company, I would like to evaluate if Spain is a good option to establish a new office and contribute further to aim the ProGlove’s vision of being top 1 worldwide in their sector. The purpose of this study is to do a market research and an exhaustive business plan by focusing on increasing the income and visibility of the company mainly in Spain and secondly in France, Portugal and Italy. In order to reach the stated purpose, a deeper understanding of market trends, as well as a detailed business model for the new establishment have been analysed. Afterwards, a realistic Marketing Plan together with a Human Resources and Operational Plan are defined to bring an overview of what and how would be implemented in the new company’s project. Finally, as a centre of discussion of this master thesis, future sales in Spain, France, Portugal and Italy have been estimated. According to these results, the profit and loss account, as well as the funding, the cash-flow forecast, the investment plan and all the economic and financial aspects have been defined in order to have a conclusive response of the profitability and viability of this project." ] ---- Bunker-Ramo Corporation (Defense Systems) https://archive.org/details/bunker_ramo/ Center for the Study of Language and Information (CLIS) https://archive.org/details/csli_sri J.C.R. Licklider (Collection) https://archive.org/details/licklider_jcr/ Situation Theory (CSLI) https://archive.org/details/situation_theory_language_acq/ R. M. Fano (Collection) https://archive.org/details/fano_rm/ Machine Aided Cognition (Project MAC): Roots of AI https://archive.org/details/proj_mac_ai/ Customer Relationship Management (CRM) as Decision Support System (DSS) https://archive.org/details/crm_ai__dss/ Decision Support Systems, (DSS-002-C) https://archive.org/details/dss-000 ---- Agent Based Modeling (ABM) https://archive.org/details/agent_based_models_abm Dedre Gentner (Collection) https://archive.org/details/gentner_dedre/ NATIONAL ARTIFICIAL INTELLIGENCE INITIATIVE (NAII) https://archive.org/details/crpt-116hrpt-617/ Abdul Qadeer Khan (URENCO, Pakistan, and WMD's) https://archive.org/details/abdul_qadeer_khan_urenco/ CDM Smith: Federal Programs Corporation (Enterprise) https://archive.org/details/cdm_smith_enterprise/ Games of Economic Survival: G.L. Thompson https://archive.org/details/gl_thompson_economic_games/ Economic Mobilization: The First Legislative Steps, (Inflation, Supply Chain, Panic Buying, Hoarding) https://archive.org/details/jones-1950 Gordon Pask (Cybernetics & Social Media) https://archive.org/details/pask1975/ Operational Research Society https://archive.org/details/jors.1953.20.fp.png_v03/ Genapp, containers and Abaco (Office of Advanced Cyberinfrastructure OAC) https://archive.org/details/brookes2019 Allen G. Feldt: Pioneer in Urban Gaming (CLUG & ADMATCH) https://archive.org/details/allen_g_feldt_clug/AD1081128/ HSI's (Cybernetics & Department of Homeland Security) https://archive.org/details/hsi-cybernetics/ U.S. White House: Semiconductors, Critical Minerals, Supply Chain https://archive.org/details/bills-116hr-7178ih/ ----

"Combat supply chain disruptions with wearable technology" -Schmidt (2021) "Computer hardware continues to shrink in size and increase in capability. This trend has allowed the prevailing concept of a computer to evolve from the mainframe to the minicomputer to the desktop. Just as the physical hardware changes, so does the use of the technology, tending towards more interactive and personal systems. Currently, another physical change is underway, placing computational power on the user's body. These wearable machines encourage new applications that were formerly infeasible and, correspondingly, will result in new usage patterns. On-body systems can sense the user's context with little or no assistance from environmental infrastructure. These body-centered systems that "see" as the user sees and "hear" as the user hears, provide a unique "first-person" viewpoint of the user's environment. These tools include a downward-facing, camera-based system for establishing the location of the user; a tag-based object recognition system for augmented reality; and several on-body gesture recognition systems to identify various user tasks in constrained environments. To address the practicality of contextually-aware wearable computers, issues of power recovery, heat dissipation, and weight distribution are examined." -Starner (1999) "In 2014, ProGlove emerged the third-place winner in Intel’s “Make It Wearable Challenge”. The ProGlove team of four who had met at the Technical University of Munich in Bavaria, Germany, had submitted the idea of a “smart glove,” which was a barcode scanner, motion detector, and RFID reader connected to the back of an industrial-grade work glove for use in high volume manufacturing. The idea was based on observations of BMW’s assembly lines, where each item had to be scanned before being assembled. The process involved finding a scanner, picking it up, scanning the component, and then putting away the scanner again. A smart glove could achieve this more quickly and ergonomically, optimising worker productivity and allowing extra steps in quality control to be included in the production." -Wee (2022) ---- Note: "Psychotechnics is a historical term for the application of psychological concepts to optimize "means and ends" in all areas of life and formed a subfield of applied psychology . Psychotechnology was the first psychological discipline to deal with the operational questions of professional suitability, job allocation (suitability and selection) and work performance (monotony and fatigue, performance and work design) or advertising. It can be seen as a classic forerunner of today 's work psychology and was also reflected in business and organizational psychology." ---- [Wearable Computing (MIT)] [DSS] [ProGlove] [Oura Health Oy] [Wearable Data Collection] [Supply Chain Logistics] [Psychotechnik] [Satellite] [Man-Machine Interaction] [Fano / Licklider] [Augmented Reality] [Big Data Analytics (HSI)] [Semiconductors] [NAII] [Cybernetics] [Satellite] [Incident Localization and Assistance System (ILAS)] [IT Service Management (ITSM)] [Cyber-Physical Human System] ---- Bloss, Richard. "Multi-technology sensors are being developed for medical, manufacturing, personal health and other applications not previously possible with historic single-technology sensors." Sensor Review (2017). Deng, Y., Chen, C., Tsow, F., Xian, X., & Forzani, E. (2016). Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2016.7591825 Indruchová, Ivana. "THE IMPACT OF INDUSTRY 4.0 ASPECTS ON LOGISTIC SERVICE PROVIDERS." Perner's Contacts 14.4 (2019): 26-34. Krishna, Sreekar, et al. "A wearable wireless RFID system for accessible shopping environments." 3rd International ICST Conference on Body Area Networks. 2010. Kulyukin, Vladimir, and Aliasgar Kutiyanawala. "From ShopTalk to ShopMobile: vision-based barcode scanning with mobile phones for independent blind grocery shopping." Proceedings of the 2010 Rehabilitation Engineering and Assistive Technology Society of North America Conference (RESNA 2010), Las Vegas, NV. Vol. 703. 2010. Scheuermann, Constantin, et al. "Incident localization and assistance system: A case study of a cyber-physical human system." 2015 IEEE/CIC International Conference on Communications in China-Workshops (CIC/ICCC). IEEE, 2015. Starner, Thad E. "Wearable computers: No longer science fiction." IEEE pervasive computing 1.1 (2002): 86-88. Starner, Thad. Wearable computing and contextual awareness. Diss. Massachusetts Institute of Technology, 1999. Stein, Robert, et al. "Development of a commercially successful wearable data collection system." Digest of Papers. Second International Symposium on Wearable Computers (Cat. No. 98EX215). IEEE, 1998. Thomas, Charu, et al. "RF-pick: comparing order picking using a HUD with wearable RFID verification to traditional pick methods." Proceedings of the 2018 ACM International Symposium on Wearable Computers. 2018. Wee, K.-E., & Iyer, A. (2011). Consolidating or non-consolidating queues: A game theoretic queueing model with holding costs. Operations Research Letters, 39(1), 4–10. doi:10.1016/j.orl.2010.09.011 Wee, Kwan Eng, and Maqbool Dada. "Optimal policies for transshipping inventory in a retail network." Management science 51.10 (2005): 1519-1533. ---- Also see: Licklider, Joseph CR. "Man-computer symbiosis." IRE transactions on human factors in electronics 1 (1960): 4-11. Pask, Gordon. "Man/machine interaction in adaptively controlled experimental conditions." The bulletin of mathematical biophysics 27.1 (1965): 261-273. WEE, Kwan Eng, Holly OTT, and Sin Mei CHEAH. "ProGlove smart gloves: Let’s save four million dollars a day!." (2022): 1-14. ---- Schmidt, Axel. "How companies can combat supply chain disruptions Wearable technology will make the human worker more valuable in the long run." Plant Engineering 75.9 (2021): 10-12. [ "Despite fears that millions of workers will be displaced by technological advances in robots and machine learning (ML), companies are seeing the opposite. There are more than 10 million job openings in the U.S. as of a recent Bureau of Labor Statistics report, making this one of the most prominent labor shortages in the country's history. As witnessed in 2020, technology in the supply chain kept logistic lines functioning despite severe stress on their functionality due to COVID-19 outbreaks and lockdowns. With wearable technologies like barcode scanners and applications that track worker efficiency to help them improve their output, people are seeing a kind of worker revolution within the industrial space. Companies also use more ergonomic solutions, which provide the worker relief from physical stress. Workers connected to the Internet of Things (IoT) are more important than ever to help keep warehouses and supply chains running as smoothly as possible. Companies are nowhere near the point of automating the whole industry. Despite labor shortages, more efficient workers can help companies navigate the waters. With the right suite of technology complementing workers, they can do more with less. There are ways that plant managers and manufacturing leaders can learn from the pandemic, so they are better prepared for the next..." ] Rodríguez Martínez, Marta. Business model, market research and business plan for the establishment of Proglove in Spain. MS thesis. Universitat Politècnica de Catalunya, 2020. [ "Today’s competitive environment of start-ups creates the urgent need to excel from competitors and differ from other companies in order to reach a good position in the current market. The start-up Workaround GmbH (ProGlove) has managed to position itself at the upper end of the global wearables’ industry in Germany. After almost 3 years working at this company, I would like to evaluate if Spain is a good option to establish a new office and contribute further to aim the ProGlove’s vision of being top 1 worldwide in their sector. The purpose of this study is to do a market research and an exhaustive business plan by focusing on increasing the income and visibility of the company mainly in Spain and secondly in France, Portugal and Italy. In order to reach the stated purpose, a deeper understanding of market trends, as well as a detailed business model for the new establishment have been analysed. Afterwards, a realistic Marketing Plan together with a Human Resources and Operational Plan are defined to bring an overview of what and how would be implemented in the new company’s project. Finally, as a centre of discussion of this master thesis, future sales in Spain, France, Portugal and Italy have been estimated. According to these results, the profit and loss account, as well as the funding, the cash-flow forecast, the investment plan and all the economic and financial aspects have been defined in order to have a conclusive response of the profitability and viability of this project." ] ---- Bunker-Ramo Corporation (Defense Systems) https://archive.org/details/bunker_ramo/ Center for the Study of Language and Information (CLIS) https://archive.org/details/csli_sri J.C.R. Licklider (Collection) https://archive.org/details/licklider_jcr/ Situation Theory (CSLI) https://archive.org/details/situation_theory_language_acq/ R. M. Fano (Collection) https://archive.org/details/fano_rm/ Machine Aided Cognition (Project MAC): Roots of AI https://archive.org/details/proj_mac_ai/ Customer Relationship Management (CRM) as Decision Support System (DSS) https://archive.org/details/crm_ai__dss/ Decision Support Systems, (DSS-002-C) https://archive.org/details/dss-000 ---- Agent Based Modeling (ABM) https://archive.org/details/agent_based_models_abm Dedre Gentner (Collection) https://archive.org/details/gentner_dedre/ NATIONAL ARTIFICIAL INTELLIGENCE INITIATIVE (NAII) https://archive.org/details/crpt-116hrpt-617/ Abdul Qadeer Khan (URENCO, Pakistan, and WMD's) https://archive.org/details/abdul_qadeer_khan_urenco/ CDM Smith: Federal Programs Corporation (Enterprise) https://archive.org/details/cdm_smith_enterprise/ Games of Economic Survival: G.L. Thompson https://archive.org/details/gl_thompson_economic_games/ Economic Mobilization: The First Legislative Steps, (Inflation, Supply Chain, Panic Buying, Hoarding) https://archive.org/details/jones-1950 Gordon Pask (Cybernetics & Social Media) https://archive.org/details/pask1975/ Operational Research Society https://archive.org/details/jors.1953.20.fp.png_v03/ Genapp, containers and Abaco (Office of Advanced Cyberinfrastructure OAC) https://archive.org/details/brookes2019 Allen G. Feldt: Pioneer in Urban Gaming (CLUG & ADMATCH) https://archive.org/details/allen_g_feldt_clug/AD1081128/ HSI's (Cybernetics & Department of Homeland Security) https://archive.org/details/hsi-cybernetics/ U.S. White House: Semiconductors, Critical Minerals, Supply Chain https://archive.org/details/bills-116hr-7178ih/ ----

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.

InsideHPC Report Nov 29, 2011 Interview: DARPA's ADAMS Project Taps Big Data to Find the Breaking Bad In this video, Professor David Bader from Georgia Tech discusses his participation in the DARPA ADAMS project. The Anomaly Detection at Multiple Scales (ADAMS) program uses Big Data Analytics to look for cleared personnel that might be on the verge of "Breaking Bad" and becoming internal security threats.

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xxxv, 488 pages ; 24 cm

xxxv, 488 pages ; 24 cm