Monday, June 17, 2013

Netention Dissolves Boundaries to Help You Survive

Friday, June 14, 2013

Edible Jungle Kit: Mass Spectrum Botanicals

http://MassSpectrumBotanicals.com

"Nature is Resilient."

Wednesday, June 12, 2013

National Survival Agency & Decentralized Intelligence Agency

http://www.itworld.com/internet/98652/narus-develops-a-scary-sleuth-social-media
http://bit.ly/12mVVt2

Binney on The MATRIX/NSANet/PRISM (PRISON) - a BACKUP of EVERYTHING on the NET - EXPRESSLY for DIRT collection against EVERYONE

The excuse for the NSANet PRISM (Prison) MATRIX:

it helps *thwart* attacks and find the bad guys...

The reality:

NARUS devices, in addition to warrants on ALL ISPs, duplicate everything carried on fiber lines - effectively splitting-off every line and storing everything to the NSANet (MATRIX) for the express purpose of COLLECTING DATA (DIRT) on EVERYONE.

What could been done within the Constitution:

NSA could eliminate (filter out) 99.99% of the data collected about the US population (and stop collecting it in the first place w/o probable cause), as well as the rest of the world and STILL thwart attacks and find the bad guys.

William "Bill" Binney explains in his latest interview with RT:

PRISM, ETC use the latest technology, which began with NaurInsight:

The new program, code-named Hone, is designed to give intelligence and law enforcement agencies a leg up on criminals who are now operating anonymously on the Internet.

In many ways, the cyber world is ideal for subversive and terrorist activities, said Antonio Nucci, chief technology officer with Narus. "For bad people, it's an easy place to hide," Nucci said. "They can get lost and very easily hide behind a massive ocean of legal digital transactions."

----

Narus is best known as the creator of NarusInsight, an network monitoring device that can analyze traffic on IP networks. AT&T allegedly used a Narus system to wiretap customer data on behalf of the U.S. National Security Agency as part of a U.S. domestic terrorist surveillance program.

Hone works in tandem with NarusInsight. By Nucci's own admission, however, it can do some pretty "scary" things.

The software's user creates a target profile, and Hone then proceeds to link what Nucci calls "islands of information." Hone can analyze VOIP conversations, biometrically identify someone's voice or photograph and then associate it with different phone numbers.

"I can have a sample of your voice in English, and you can start speaking Mandarin tomorrow. It doesn't matter; I'm going to catch you."

Topic Modeling
It uses artificial intelligence to analyze e-mails and can link mails to different accounts, doing what Nucci calls topical analysis. "It's going to go through a set of documents and automatically it's going to organize them in topics -- I'm not talking about keywords as is done today, I'm talking about topics," he said.

--

Hone will sift through millions of profiles searching for people with similar attributes -- blogger profiles that share the same e-mail address, for example. It can look for statistically likely matches, by studying things like the gender, nationality, age, location, home and work addresses of people.

Another component can trace the location of someone using a mobile device such as a laptop or phone.

Bit by bit, it pieces together the subject's different identities on the Internet.

What can be done?

Paraphrasing Binney, "repeal the totalitarian Patriot Act, NDAA (and the like), fire everyone in the Executive Branch and Congress, then replace them all with citizens who will uphold the Constitution."

so taxpayers are already paying for online backup... of the Internet.

NAURUS.COM

http://www.narus.com/

FUSING TOGETHER HUNDREDS OF DATA DIMENSIONS FOR DEEP, FOCUSED INSIGHT AND CONTROL

CYBERSECURITY TECHNOLOGY

FOR A DYNAMIC WORLD

SCIENTIFIC PUBLICATIONS

Recursive NMF: Efficient Label Tree Learning for Large Multi-Class Problems
Weighted Linear Kernel with Tree Transformed Features For Zero-Day Malware Detection
Combining Supervised and Unsupervised Learning for Zero-Day Malware Detection
Missing or Inapplicable: Treatment of Incomplete Continuous-valued
Features in Supervised Learning
SubFlow: Towards Practical Flow-Level Traffic Classification

GNU WASTE

WASTE is a decentralized chat, instant messaging and file sharing program and protocol. It behaves similarly to avirtual private network by connecting to a group of trusted computers, as determined by the users. This kind of network is commonly referred to as a darknet. It uses strong encryption to ensure that third parties cannot decipher the messages being transferred. The same encryption is used to transmit and receive instant messages, chat, and files, maintain the connection, and browse and search.

trusted hosts.
The distributed nature means that the network isn't dependent on anyone setting up a server to act as a hub. Contrast this with other P2P and chat protocols that require you to connect to a server. This means there is no single point of vulnerability for the network.
Similarly, there is no single group leader; everyone on the network is equal in what they can or cannot do, including inviting other members in to the group, nor can any member kick another from the group, exclude them from public chats, etc.
WASTE can obfuscate its protocol, making it difficult to detect that WASTE is being used.
WASTE has a "Saturate" feature which adds random traffic, making traffic analysis more difficult.
The nodes (each a trusted connection) automatically determine the lowest latency route for traffic and, in doing so,load balance. This also improves privacy, because packets often take different routes.

Tuesday, June 11, 2013

Nomadic Eco-Villages

Exploring New Nomadic Lifestyle and Festival Culture in the Urban Setting

Presentation

https://docs.google.com/presentation/d/19g_XYezo_jHEZINUqZNZUswZaLsuNHpvbsT3vNkW3eU/edit#slide=id.p

"But there have always been those who see hints of the future in our past and for some time a handful of designers have considered the prospect of a modern, sophisticated, nomadic lifestyle. Anticipating imminent paradigm shifts as the un-sustainability of Industrial Age life became apparent, some designers in the mid and late 20th century imagined the emergence of a new nomadic culture in its wake; an Urban Nomad culture of young people re-appropriating technology for open public use, repurposing the obsolete detritus of the passing era, and rediscovering community in the process. Key among these designer-futurists was Ken Isaacs whose exploration of clever DIY structures bridging architecture and furniture drew global interest."

E56 - June 9, 2012 knees and houses and ip tunnelling

clifs wujo June 9, 2013 (45 minutes, .mp3)

grow domes, yurts and round housing, boat housing, warrior nomadism in the mongol empire, housing foundations: cement vs. poles, earthquake-proofing, heating and cooling, anti-fragility, & more

http://www.halfpasthuman.com/essays/hph_dwell.html (pictures below)

Saturday, June 8, 2013

Space-time Tag Planning

A system for suggesting when and where individuals may be involved in similar activities that they have specified that they would like to do.

1.Introduction

Calendar and scheduling software applications are commonly used to plan individual and group activities. This system allows arbitrary tags to be associated with time and space locations to coordinate activity in an arbitrary large population, representing intention vectors. Such tags are generally selected from Wikipedia, serving as a foundation ontology, and can encompass any subject whether it is an activity, physical object, or abstract object.

Populating the vector space with novel opportunities for its participants requires more than one individual user.

2.Collecting Intentions and Displaying Opportunities

Many possible graphical-user-interfaces can be designed to elicit “intention vectors” from a user. The simplest resemble an hour-based calendar starting from the present moment and extending an arbitrary amount of time into the future. The calendar can be arbitrarily subdivided into smaller time-units – the hour is an arbitrary amount of time that seems, to the author, reasonable for allocating intentions.

Each moment can be described in certain aspects:

Want To: what one intends to do or would like to happen, specified as a list of tags
Could Do: recommendations for possible activities (opportunities), specified in terms of:

where (latitude/longitude coordinates)
when
with whom may be involved
what “should” occur as a semantic vector of the tags with the magnitude of its components, in the range of 0..1.0, indicating the relative strength of its presence in this vector

Clicking a cell in the 'Want to' column allows one to select the tags associated with that time slot, perhaps through a pop-up dialog window.

In a multi-user system, anyone editing their 'Want to' column would automatically trigger an updating of everyone's 'Could do' column since the clustering results, described in (3), need to be updated.

These updates can be throttled to an arbitrary finite time of maximum frequency. The soonest future time slots can be calculated at a higher priority than later ones allowing near-future plans to be more adaptively and fluidly scheduled.

3.Intention Vectors

The structure of the N-dimensional vector, when clustered, generates recommendations (“could do”) which consist of the following components:

Time (ex: unixtime)
Geographic Latitude
Geographic Longitude
tag0? [0: not present .. 1.0: present]
tag1? [0: not present .. 1.0: present]
…
tagN? [0: not present .. 1.0: present]

4.Clustering

Various clustering methods may be applicable for this system. K-means clustering provides results in the form of centroids in the vector space which can be interpreted as possible meetings between the users who have planned their time with this system.

The distance function between points in the vector space can be biased to favor one set of dimensions or another. For example, to make space, time, or tags proportionally more or less relevant to each other.

5.Improving Semantic Matches with Wikipedia Categories

Many Wikipedia pages are tagged with the “Categories” that it involves. (Categories can also be part of other super-categories.) This forms a taxonomy that can be used to add extra tags to the intention vectors by including all a wikipage's parent categories to a finite amount of iterations. These can be gathered from a page's wikitext or from DBpedia's “skos:broader” property.

These additional “virtual” category tags may be assigned slightly differently strength values (within 0..1.0) than explicitly chosen tags.

6.Predicting an Individual's Future Behavior

Another column for each time slot can show a prediction for an individual's future tags at a given time-slot, according to one's historic entries. This can serve as a reminder system.

When an item is no longer desired to be remembered, one can be explicitly “forget” it via a button in the user-interface.

7.Space-less and Time-less Applications

When the space dimensions are not involved, the system identifies possible on-line meetings, or other situations where space is irrelevant.

When the time dimensions are not involved, the system identifies more-or-less permanent semantic “features” of a geographic region.

When neither space nor time dimensions are involved, the system identifies clusters of “interests” shared by users.

8.Conclusion

The ubiquitous application of this system could have a significant impact in a human society's daily functioning. It can help in all forms of gathering:

education
conferences
business meetings
medical
social
recreational
amongst animals
exotic forms of human interactions yet to be explored

Monday, June 3, 2013

Energy News

from: http://enenews.com/reuters-rising-radioactive-spills-at-fukushima-plant-more-contamination-less-hope

mairs:

"...I try to talk to people now and then, but you're right. They have no idea, and it's really easy for people to think I've gone off the rails and this is just my little pet cause that I've inexplicably latched onto.

I have a theory, like the banks that are too big to fail, the enormity of this is too big for most people to pay attention to. It threatens their world view of life being basically safe, and that everything will go on as it had before this accident, and surely an entire nation-state couldn't possibly be threatened by one little nuclear plant, and surely the rest of us are far away enough, and someone must be taking care of this, right? On and on and on..."

CodeShutdown:

"tell someone Fukushima Equals 3,000 Billion Lethal Doses, and they smile and carry on as if you said vanilla ice cream. Tell them physicists have said Fukushima could destroy the ecology of the northern hemisphere, and its no different than saying a coconut fell on a Tahitian, or chicken little has informed us the sky is falling. Thus you live in an imaginary glass box, cut off from humanity…even friends and family. You could wave your arms, peering at them through the thick glass wall, but everyone has become a stranger"

Optimal Energy Model

DRAFT

Energy cost model for demonstrating possible civilization-scale transitions to safe, clean, sustainable, decentralized, and renewable forms of energy generation and distribution.

energy technology types

stars, planetary renewable, fossil, nuclear, and those yet undiscovered

form-factors

cosmic
planetary (ex: river or wind)
industrial (ex: nuclear or coal)
community
home (ex: solar panel or wind turbine)
vehicle
device (ex: battery)

physical characteristics (for generation, transmission and consumption)

input/output power

steady, peak/surge

storage energy, energy density
transmission

maximum distance
efficiency

operating conditions (ex: pressure, temperature)
geometry (land area)

cost types

biological health (safety), in spatiotemporal regions
environmental (pollution), in spatiotemporal regions

includes both impacts on-site and in “supporting” regions (ex: places where mining fossil fuels or uranium)

financial

customer: government, population subset, individual

energy (non-ideal or overhead required during generation, storage, transmission, consumption)

cost states

construction
startup
daily maintenance
temporary shutdown

usually followed by a subsequent startup

various accidents

probability variable which can be adjusted as conditions change, or for simulation of different conditions
differentiates between cost for complete (environment + biological) remediation, and for immediate emergency biological triage

deconstruction (permanent shutdown)
transformation to alternate technology
“luddite” virtual cost for a given amount of time (difference in cost per day minus transformation cost) for not being replaced with a less costly energy technology

actual or potential existence (of instances of energy resources and their location in spacetime)
accountable responsible personnel

engineers
regulators
government officials
inspectors
owners
insurers
other stakeholders

This model can simulate the optimal migration of costly energy systems to more efficient and safer (“less costly”) energy technologies.

Resources

http://prst-per.aps.org/abstract/PRSTPER/v8/i2/e020114

The nature of energy is not typically an explicit topic of physics instruction. Nonetheless, verbal and graphical representations of energy articulate models in which energy is conceptualized as a quasimaterial substance, a stimulus, or a vertical location. We argue that a substance ontology for energy is particularly productive in developing understanding of energy transfers and transformations. We analyze classic representations of energy—bar charts, pie charts, and others—to determine the energy ontologies that are implicit in those representations, and thus their affordances for energy learning. We find that while existing representations partially support a substance ontology for energy and thus the learning goal of energy conservation, they have limited utility for tracking the flow of energy among objects.

https://workspace.imperial.ac.uk/urbanenergysystems/Public/2010NGInfra-VanDamKeirstead.pdf

The use of ontologies for the interoperability of software models is widespread, with many applications also in the energy domain. By formulating a shared data structure and a deﬁnition of concepts and their properties, a language is created that can be used between modellers and—formalised in an ontology—between model components. When modelling energy systems, connections between different infrastructures are critical, e.g. the interaction between the gas and electricity markets or the need for various infrastructures including power, heat, water and transport in cities. While a commonly shared ontology of energy systems would be highly desirable, the fact is that different existing models or applications already use dedicated ontologies, and have been demonstrated to work well using them. To beneﬁt from linking data sources and connecting models developed with different ontologies, a translation between concepts can be made. In this paper a model of an urban energy system built upon one ontology is initialised using energy transformation technologies deﬁned in another ontology,

thus illustrating how this common perspective might beneﬁt researchers in the energy domain.

http://www.dehems.eu/cms/wp-content/uploads/2011/04/D8.1-Paper3.pdf

This paper focuses on an approach to build ontology for home energy management domain which is compatible with Suggested Upper Merged Ontology (SUMO). Our starting point in doing so was to study general classifications of home electrical appliances provided by various home appliances vendors and manufacturers. Various vendors and manufacturers use their own arbitrary classification instead of using a single standard classification system for home appliances and there exists no uniformity of appliances specifications among these vendors. Although appliances vendors provide energy efficiency rating of home appliances but they do not provide the detailed specification of the attributes that contributes towards their overall energy consumption. In the absence of these attributes and non existence of a standard ontology it is difficult for reasoning tools to provide a comprehensive comparison of home appliances based on their energy consumption performance and also to provide a comparative analysis of energy consumption of these appliances.

http://sesame-s.ftw.at/

Together with its partners FTW focuses on the development of communication solutions for intelligent energy distribution networks. FTW utilizes its know-how in communication technology for collection and exploitation of (real-time) energy consumption data as well as for the control of distribution networks, energy consumers, and distributed energy generators. The new sector „Intelligent Energy“ and the three new themes „Green-Telco“, „Smart and Energy-Efficient Home“, and „ICT Infrastructure for Electromobility“ open up multiple opportunities for R&D collaboration for industrial players which enable new fields for business along the value chain.

http://ontolog.cim3.net/file/work/OntologySummit2009/OntologySummit2009_Symposium_20090406-07/Ontology_Across_Building_Emergency_n_Energy_Standards--DeborahMacPherson-MichelleRaymond_20090406.pdf

Ontology Across Building, Emergency, and Energy Standards: The problem is adapting to a rapidly changing world that requires efficient communication. Information about the built environment is central to both emergency and energy needs. There will never be a complete coherent model of everything, but a logical framework for a common core can be developed.

http://oegov.org/

Ontologies for eGovernment enable:

distributed creation and maintenance of information on data, about where it is used, and the government data itself;
standardization of neutral models for data exchange and transformation;
aggregation of data through the use of RDF/OWL formats;
interpretation of data through precise semantics and controlled vocabularies, including geospatial and temporal aspects;
navigation over who is publishing what in what format;
provenance and trust in the sources of data;
correlations and comparisons of data;
accountability of the political process with policy making;
transparency of government efficiencies and effectiveness;
citizen awareness and appreciation of government initiatives.

https://code.google.com/p/oei-ontology/

The Ontology for Energy Investigations (OEI) is a domain ontology for Energy Informatics

http://www.qudt.org/

to provide a unified model of, measurable quantities, units for measuring different kinds of quantities, the numerical values of quantities in different units of measure and the data structures and data types used to store and manipulate these objects in software;
to populate the model with the instance data (quantities, units, quantity values, etc.) required to meet the life-cycle needs of the Constellation Program engineering community.

Thursday, May 30, 2013

Space-time Tag Scheduling

A system for suggesting when and where people may perform similar activities that they have specified they would like to do.

http://en.wikipedia.org/wiki/Cluster_analysis

cluster n-dimensional vectors (ex: with k-means)

time
lat
lon
tag0
tag1
…
tagN

(tags consist of the tags or categories common to a group of people.)

the centroids calculated by clustering indicate potential meeting locations and spaces and what topics may be involved.

the distance of skills can be calculated as the threshold function of a graph-metric across the wikilinks. so if the distance is below a threshold (ex: 4 hops in the upper ontology) then it's considered, but weighted appropriate (1 hop = 100%, 2 hops = 50%, etc..)

wikitag -> dcterms:subject categories

trace further up the ontology with: skos:broader

http://dbpedia.org/data/Machine_learning.json

"http://purl.org/dc/terms/subject" : [ { "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Learning" } ,
     { "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Cybernetics" } ,
     { "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Learning_in_computer_vision" } ,
     { "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Machine_learning" } ]

http://dbpedia.org/data/Category:Machine_learning.json

"http://www.w3.org/2004/02/skos/core#broader" : [ { "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Artificial_intelligence" } ,
{ "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Computational_statistics" } ,
{ "type" : "uri", "value" : "http://dbpedia.org/resource/Category:Learning" } ]

categories(wikitag, ...) - provides a tag vector

ex: { ‘ArtificialIntelligence’: 1, ‘Computers’: 2 }
/wiki/categories/:tag

category similarity = semantic dot product

C(a,b,levelDecay) where a, b are sets of wikitags

returns ( similarity, strengthsOfCommonCategories )

strengthsOfCommonCategories: entries pointing to a Number containing the strength

Clustering algorithm to calculate centroids provides the geographic region where the meeting can occur.

Experiment 1

Inputs

points.push( [ 0, 0, hoursFromNow(0), ['a', 'b'] ] );

points.push( [ 0.25, 0, hoursFromNow(0.2), ['a', 'b'] ] );

points.push( [ 0.5, 0, hoursFromNow(1), [ 'b'] ] );

points.push( [ 0.5, 0.25, hoursFromNow(5.0), [ 'd'] ] );

points.push( [ 1.0, 0.5, hoursFromNow(2), ['c'] ] );

points.push( [ 1.1, 0.6, hoursFromNow(2.2), ['c'] ] );

points.push( [ 0.9, 0.55, hoursFromNow(1.9), ['c'] ] );

Outputs (k=3 centroids)

{ location: [ 0.25, 0 ],

time: Wed May 29 2013 17:12:55 GMT-0400 (EDT),

a: 1,

b: 1.3333333333333333 }

{ location: [ 1, 0.5375000000000001 ],

time: Wed May 29 2013 20:50:25 GMT-0400 (EDT),

d: 0.5,

c: 0.75 }

{ location: [ 0.3610355449374765, 0.08459244179539382 ],

time: Wed May 29 2013 18:21:31 GMT-0400 (EDT),

a: 0.7926491668913513,

b: 0.3531777923926711,

d: 0.373177990084514,

c: 0.8343242050614208 }

Outputs (k=2 centroids)

{ location: [ 1, 0.5 ],

time: Thu May 30 2013 02:49:11 GMT-0400 (EDT),

d: 2 }

{ location: [ 0.625, 0.275 ],

time: Wed May 29 2013 18:02:11 GMT-0400 (EDT),

a: 0.5,

b: 0.6666666666666666,

c: 0.5 }

Source Code

var _ = require('underscore');

var kmeans = require('kmeans');

function hoursFromNow(n) {

return Date.now() + 60.0 * 60.0 * 1000.0 * n;

}

function getUniqueTags(t) {

var tags = [];

for (var i = 0; i < t.length; i++) {

var tt = t[i];

tags = tags.concat(tt[3]);

}

tags = _.unique(tags);

return tags;

}

function getObservations(t, tags) {

var obs = [];

for (var i = 0; i < t.length; i++) {

var tt = t[i];

var l = [ tt[0], tt[1], tt[2] ];

var totalContained = 0;

for (var k = 0; k < tags.length; k++) {

if (_.contains(tt[3], tags[k]))

totalContained++;

}

if (totalContained > 0) {

for (var k = 0; k < tags.length; k++) {

l.push(_.contains(tt[3], tags[k]) ? (1.0/totalContained) : 0.0)

}

obs.push(l);

}

return obs;

}

function normalize(points, index) {

var min, max;

min = max = points[0][index];

for (var i = 0; i < points.length; i++) {

var pp = points[i][index];

if (pp < min) min = pp;

if (pp > max) max = pp;

}

for (var i = 0; i < points.length; i++) {

var pp = points[i][index];

pp = (pp-min) / (max-min);

points[i][index] = pp;

}

return [points, min, max];

}

var centroids = 2;

var points = [];

points.push( [ 0, 0, hoursFromNow(0), ['a', 'b'] ] );

points.push( [ 0.25, 0, hoursFromNow(0.2), ['a', 'b'] ] );

points.push( [ 0.5, 0, hoursFromNow(1), [ 'b'] ] );

points.push( [ 0.5, 0.25, hoursFromNow(5.0), [ 'd'] ] );

points.push( [ 1.0, 0.5, hoursFromNow(2), ['c'] ] );

points.push( [ 1.1, 0.6, hoursFromNow(2.2), ['c'] ] );

points.push( [ 0.9, 0.55, hoursFromNow(1.9), ['c'] ] );

var tags = getUniqueTags(points);

var obs = getObservations(points, tags);

var timeNorm = normalize(obs, 2);

obs = timeNorm[0];

console.log(obs);

var km = kmeans.create(obs, centroids);

var result = km.process();

console.log('RESULTS');

var m = result.means;

for (var i = 0; i < m.length; i++) {

var mm = m[i];

var res = {

location: [mm[0], mm[1]], time: new Date((mm[2]*(timeNorm[2]-timeNorm[1]) + timeNorm[1]))

};

if (mm.length > 3) {

for (var k = 3; k < mm.length; k++) {

var t = tags[k-3];

if (mm[k] > 0)

res[t] = mm[k];

}

console.log(res);

}

//console.log(result.clusters);

//console.log(result.variances);