diff --git a/cache/a1a153948582a0c0528e5e032afccc740ccd4f8643d6606e4d6ac62eac245c448c2801ec194bcd47a207761ec094cc05d7ba222262912af5e2298fec31d9fd86 b/cache/a1a153948582a0c0528e5e032afccc740ccd4f8643d6606e4d6ac62eac245c448c2801ec194bcd47a207761ec094cc05d7ba222262912af5e2298fec31d9fd86 new file mode 100644 index 0000000..5f67a99 Binary files /dev/null and b/cache/a1a153948582a0c0528e5e032afccc740ccd4f8643d6606e4d6ac62eac245c448c2801ec194bcd47a207761ec094cc05d7ba222262912af5e2298fec31d9fd86 differ diff --git a/cache/c70d7a9791aec11552f7b8b33159e86f2eecfcf1d286810b0393cabbe324119b25d2d6a73469fb8b3e1654909677506e6f0d4150fc58f3b27120fb5a5cb394e4 b/cache/c70d7a9791aec11552f7b8b33159e86f2eecfcf1d286810b0393cabbe324119b25d2d6a73469fb8b3e1654909677506e6f0d4150fc58f3b27120fb5a5cb394e4 new file mode 100644 index 0000000..23a00be Binary files /dev/null and b/cache/c70d7a9791aec11552f7b8b33159e86f2eecfcf1d286810b0393cabbe324119b25d2d6a73469fb8b3e1654909677506e6f0d4150fc58f3b27120fb5a5cb394e4 differ diff --git a/cache/e40185ffb8512b408070b8760fd0559e4bf25b7ce7f867a27a75641e3d535389ba562d5748340167acfa1c346a83cc34c1d70c7159275528bfcda69d30deaa9a b/cache/e40185ffb8512b408070b8760fd0559e4bf25b7ce7f867a27a75641e3d535389ba562d5748340167acfa1c346a83cc34c1d70c7159275528bfcda69d30deaa9a new file mode 100644 index 0000000..45f7453 Binary files /dev/null and b/cache/e40185ffb8512b408070b8760fd0559e4bf25b7ce7f867a27a75641e3d535389ba562d5748340167acfa1c346a83cc34c1d70c7159275528bfcda69d30deaa9a differ diff --git a/citing/cite.go b/citing/cite.go new file mode 100644 index 0000000..cb2637a --- /dev/null +++ b/citing/cite.go @@ -0,0 +1 @@ +package citing diff --git a/data/aggregation.kdb b/data/aggregation.kdb new file mode 100644 index 0000000..e77ba31 --- /dev/null +++ b/data/aggregation.kdb @@ -0,0 +1,19 @@ + +Usually modelled through chemotaxis + +There are a few parameters we can control in the slimemold model. The main ones are: + +1. Density of Slimes +2. How much pheromone they drop +3. Diffusion/Evaporation Rate of the pheromone +4. Pheromone Detection + +These all result in different outcomes. + +For example, if you increase the density, you end up with world filled with pheromones very quickly. But the model eventually starts to converge to aggregation. + +Aggregation is however very sensitive to the density of pheromone. At half strength, aggregation still happens, but the clusters are much smaller. + +Any lower and aggregation stops. + +typeof:consensus \ No newline at end of file diff --git a/data/byzantine-generals.kdb b/data/byzantine-generals.kdb new file mode 100644 index 0000000..3fe11fb --- /dev/null +++ b/data/byzantine-generals.kdb @@ -0,0 +1,6 @@ +consensus + + +Impossibility Result + +No solution for the 3 generals problem (in which messages are oral/unsigned) exists cite;-f:3:-y:650;https://people.eecs.berkeley.edu/~luca/cs174/byzantine.pdf cite;-f:4;https://people.eecs.berkeley.edu/~luca/cs174/byzantine.pdf as it is impossible to for an honest general/commander to determine who the traitor is (the ostensible originator or the message courier). \ No newline at end of file diff --git a/data/chemotaxis.kdb b/data/chemotaxis.kdb new file mode 100644 index 0000000..05d582e --- /dev/null +++ b/data/chemotaxis.kdb @@ -0,0 +1,4 @@ +movement of a motile cell or organism, or part of one, in a direction corresponding to a gradient of increasing or decreasing concentration of a particular substance. + +* slimemold +* aggregation \ No newline at end of file diff --git a/data/consensus.kdb b/data/consensus.kdb index 77dfcf5..7b19c6c 100644 --- a/data/consensus.kdb +++ b/data/consensus.kdb @@ -1,5 +1,12 @@ +source: https://people.eecs.berkeley.edu/~luca/cs174/byzantine.pdf + +* instance:snowflake +* sybil-attack +* byzantine-generals + +See also: subtype:synchronization subtype:aggregation + + Observations: the 2/3m threshold doesn't seem to hold in particle models because of local isolation -See also: subtype:synchronization - -instance:snowflake 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 \ No newline at end of file diff --git a/data/lightning-bugs.kdb b/data/lightning-bugs.kdb index 597739b..45b5d2d 100644 --- a/data/lightning-bugs.kdb +++ b/data/lightning-bugs.kdb @@ -1,22 +1,31 @@ Lightning Bug category: typeof:synchronization -- +There are multiple approaches to the sync, from sutble adjustments to complete resets. The very simplified model involves having each node maintain an excitement level which builds over time, and once it reaches a given threshold causes the node to flash (and reset the level). If a node is nearby a neighbour that flashes, the flash causes the node to also fire (and reset the excitement level). This slowly moves the nodes closer together over time. 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GdYkrArYTbJLsrCje4+ebehMnEnIT7ht3JZ8th+/9xq5cNXEuITirVWvfUtxQJxeB1t7umVJJyHEGF+q00kpChKX5tsxp3v1deU38NnISEooRP9/tPA436lGgnnIJTr2RAQs3ckVCD8P5utZ8Y4/EipAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE5LTExLTEwVDE2OjM3OjI4LTA4OjAwxcKsaQAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxOS0xMS0xMFQxNjozNzoyOC0wODowMLSfFNUAAAAASUVORK5CYII= +Original simplified model proposed by Richmod in 1930 - I needed to use k-nearest-neighbours / quadtree based approach to get this to work. +source: Richmond, Carl A. "Fireflies flashing in unison." Science 71.1847 (1930): 537-538. cite;-f:1;https://zero.sci-hub.se/2227/40b764e0b2b3a5c769160823430c5ca4/richmond1930.pdf -There are multiple approaches to the sync, from sutble adjustments to complete resets. -source: Original Model: https://sci-hub.se/https://science.sciencemag.org/content/71/1847/537.2 -Richmond, Carl A. "Fireflies flashing in unison." Science 71.1847 (1930): 537-538. +Although there was a hnt on this approach in 1915 -source: Almost there: https://sci-hub.se/https://www.nature.com/articles/096411a0 +source: https://sci-hub.se/https://www.nature.com/articles/096411a0 + +There were many incorrect theories involving random happenstance (gusts of wind) or leader-based protocols. These persisted until well into the 1930s: +http://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/buck_synchronous_rhythmic_flashing_of_fireflies.pdf + +Implementation Details + +While I believe this approach could work strictly with pheromones, I have only implement this using a k-nearest-neighbours / quadtree based approach to find the neighbours within a given range (therefore providing a more top-down approach than would be nice in the model). + + +Further Reading source: http://fiumsa.edu.bo/docentes/mramirez/RAMIREZ18%20%282%29.pdf -source: https://sci-hub.se/https://dl.acm.org/citation.cfm?id=965421&dl=ACM&coll=DL (Most useful, but doesn't cite things properly) +source: https://sci-hub.se/https://dl.acm.org/citation.cfm?id=965421&dl=ACM&coll=DL source: https://sci-hub.se/https://www.jstor.org/stable/24950352 -Wrong Explanations: -http://people.math.gatech.edu/~weiss/uploads/5/8/6/1/58618765/buck_synchronous_rhythmic_flashing_of_fireflies.pdf + + 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diff --git a/data/lotka-volterra.kdb b/data/lotka-volterra.kdb index ca19bc8..7a23417 100644 --- a/data/lotka-volterra.kdb +++ b/data/lotka-volterra.kdb @@ -1,3 +1,3 @@ -The Lotka–Volterra equations, also known as the predator–prey equations, are a pair of first-order nonlinear differential equations, frequently used to describe the dynamics of biological systems in which two species interact, one as a predator and the other as +The Lotka–Volterra equations, also known as the instance:predator-prey equations, are a pair of first-order nonlinear differential equations, frequently used to describe the dynamics of biological systems in which two species interact, one as a predator and the other as prey + -See Also: instance:predator-prey \ No newline at end of file diff --git a/data/predator-prey.kdb b/data/predator-prey.kdb index 3d272ce..7a4af88 100644 --- a/data/predator-prey.kdb +++ b/data/predator-prey.kdb @@ -1,3 +1,11 @@ typeof:synchronization -See also: typeof:lotka-volterra \ No newline at end of file +Models interactions between prey who eat grass, and predators who eat prey. + +These interactions produce oscillations (is stable parameters are chosen, they should resemble typeof:lotka-volterra ) - The ringing of the prey graph is caused by competition & availability of grass. + +Implementation Details + +While I have been able to obtain stable oscillations, the numbers involved a a little extreme - as the number of prey will range from ~45,000 to 30, and similary predators from 1 or 2 to 16,000. Such extremes are accounted for by the model, and it is likely that these numbers are related to the size of the environment (300x300 = 90,000 pixels) - and thus the relative density of the prey and predators, and could therefore be reduced if the environment was made smaller (this wouldn't actually decrease the disk of population extinction through random chance - as the lows would still be low, the highs just wouldn't be as high) + 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\ No newline at end of file diff --git a/data/references.kdb b/data/references.kdb new file mode 100644 index 0000000..817451f --- /dev/null +++ b/data/references.kdb @@ -0,0 +1,21 @@ +@inproceedings{douceur2002sybil, + title={The sybil attack}, + author={Douceur, John R}, + booktitle={International workshop on peer-to-peer systems}, + pages={251--260}, + year={2002}, + organization={Springer}, + url={https://www.freehaven.net/anonbib/cache/sybil.pdf} +} + +@article{lamport1982byzantine, + title={The Byzantine generals problem}, + author={Lamport, Leslie and Shostak, Robert and Pease, Marshall}, + journal={ACM Transactions on Programming Languages and Systems (TOPLAS)}, + volume={4}, + number={3}, + pages={382--401}, + year={1982}, + publisher={ACM}, + url={https://people.eecs.berkeley.edu/~luca/cs174/byzantine.pdf} +} diff --git a/data/slimemold.kdb b/data/slimemold.kdb new file mode 100644 index 0000000..951e512 --- /dev/null +++ b/data/slimemold.kdb @@ -0,0 +1,4 @@ +instance:aggregation + + +Movement by chemotaxis \ No newline at end of file diff --git a/data/sybil-attack.kdb b/data/sybil-attack.kdb new file mode 100644 index 0000000..6a8ff49 --- /dev/null +++ b/data/sybil-attack.kdb @@ -0,0 +1,5 @@ +Relating to malicious actors in peer to peer networks + +see also: consensus + +Naming: cite;-f:1;https://www.freehaven.net/anonbib/cache/sybil.pdf \ No newline at end of file diff --git a/data/synchronization.kdb b/data/synchronization.kdb index 7db1a3a..165c623 100644 --- a/data/synchronization.kdb +++ b/data/synchronization.kdb @@ -1,4 +1,5 @@ -Adjustment of a clock or watch to show the same time as another. + +Models * instance:lightning-bugs * instance:predator-prey diff --git a/data/test.kdb b/data/test.kdb deleted file mode 100644 index f81d684..0000000 --- a/data/test.kdb +++ /dev/null @@ -1 +0,0 @@ -dfgdf \ No newline at end of file diff --git a/data/threshold-consensus.kdb b/data/threshold-consensus.kdb new file mode 100644 index 0000000..86f5b0c --- /dev/null +++ b/data/threshold-consensus.kdb @@ -0,0 +1,37 @@ +A trivial intuition that one can derive from the construction of PBFT algorithms is that if number of peers (n) in a system is known/can be fixed, you can solve a lot of problems by defining models to require input from a threshold of n before taking an action. + +We assume that such a system is immune to sybil attacks because n is fixed and known - new identities cannot be created, or the validation for new entities is outside the scope of consideration. + +If you can combine such a threshold mechanism with a level of cryptographically verifiable pseudonyms ( and perhaps historical peer trust ratings), you can construct protocols that can make determinations about events within a group without necessarily reaching for consensus. + +(By consensus I refer to traditional PBFT algorithms where more than two thirds of peers need to agree that more than two thirds of peers agree on a particular event) + +I think that there probably exists a set of problems that can be solved in a metadata resistant system without an independent observer and without consensus i.e. problems that require knowing some state, but not every peer needs to know the same state (or agree on which state). + +(see metadata resistant consensus for a definition of independent observer and the inspiration for this note) + +To construct a trivial example, borrowing from CFDT, we can imagine an alarm flag represented by a single boolean value. The flag is false is the alarm has never been tripped, and true if it has been tripped. Once tripped the flag cannot be untripped. Any single peer within a group can sign a message and trip the alarm. Consensus is not needed. Which peer trips the alarm is also unimportant. + +Above would be an example of a protocol requiring a threshold of 1/n. At 2⁄3 of peers you reach problems that require PBFT consensus. I think there are some useful solutions sitting in between. + +Those problems have certain attributes: + + They cannot require strict ordering of events - we are interested in systems without independent observers. + They cannot require consensus between nodes - we are interested in thresholds below 2/3n, see the literature for impossibility results of achieving consensus with less than that threshold of honest nodes. + They should not rely on peer correctness - while we assume sybils are not a problem, we do not assume that any single peer can be trusted, without additional information. + +Our tripwire example might be seen as failing on point 3, a malicious peer could trip an alarm when no such alarm should have been tripped. As such the nature of the alarm matters. We can get around this for now by stating that our alarm is to indicate that there is a malicious actor within the group. + +By introducing asymmetric trust into our protocols we can start to build more complex operations without relying on group consensus. If a peer trusts another peer, then they can weight their input higher than that of another peer, and thus reduce the threshold necessarily needed to reach a decision. + +At the extreme end of the trust spectrum, if a peer trusts every other peer, then they will believe everything they are told, if they trust no peers, then they must weight for group consensus. + +In between exists a world of chaotic trust whereby individual peers may make distinct determinations about the world. This is obviously not a desirable property for many kinds of actions within a decentralized group, but might be acceptable for some kinds of protocols. + +My instincts tell me that there are ways of driving such protocols towards eventual consistency, but that eventual consistency is not necessarily a goal of such protocols. + +As I write this I am drawn to make comparisons to alarm pheromones, indeed I think that there might be a strong analogy here, and that one example of a threshold consistency protocol that might have realistic application would be file storage among a group. + +Bad actors within the group might attempt to gain an advantage of file access without contributing storage or bandwidth. Peers that do take on that burden might use a threshold consistency protocol / alarm pheromone to alert other peers to that untrustworthiness or to signify that they need to offload storage burden. + +Such a protocol has not been well thought out and is but the seed of an idea, that I might be tempted to explore further in the future. diff --git a/fetch/fetch.go b/fetch/fetch.go new file mode 100644 index 0000000..96ccdab --- /dev/null +++ b/fetch/fetch.go @@ -0,0 +1 @@ +package fetch diff --git a/cmd/kdb.go b/kdb.go similarity index 81% rename from cmd/kdb.go rename to kdb.go index de974fb..da92dbc 100644 --- a/cmd/kdb.go +++ b/kdb.go @@ -66,15 +66,19 @@ func loadPage(title string) (*Page, error) { concept := template.HTML(construct_graph(title)) + linked := make(map[string]bool) for _, word := range strings.Fields(body) { - if hasPage(word) { - body = strings.ReplaceAll(body, word, fmt.Sprintf("%v", normalize(word), normalize(word))) - } - if strings.HasPrefix(word, "http") { - body = strings.ReplaceAll(body, word, fmt.Sprintf("%v", word, word)) - } - if strings.HasPrefix(word, "data:image/png;base64,") { - body = strings.ReplaceAll(body, word, fmt.Sprintf("", word)) + if !linked[normalize(word)] { + if hasPage(word) { + body = strings.ReplaceAll(body, word, fmt.Sprintf("%v", normalize(word), normalize(word))) + } + if strings.HasPrefix(word, "http") { + body = strings.ReplaceAll(body, word, fmt.Sprintf("%v", word, word)) + } + if strings.HasPrefix(word, "data:image/png;base64,") { + body = strings.ReplaceAll(body, word, fmt.Sprintf("", word)) + } + linked[normalize(word)] = true } } @@ -100,23 +104,12 @@ func viewHandler(w http.ResponseWriter, r *http.Request) { } p, err := loadPage(title) if err != nil { - http.Redirect(w, r, "/edit/"+title, http.StatusFound) - return + p = &Page{} + p.Title = title } renderTemplate(w, "view", p) } -func editHandler(w http.ResponseWriter, r *http.Request) { - title, err := getTitle(w, r) - if err != nil { - return - } - p, err := loadPage(title) - if err != nil { - p = &Page{Title: title} - } - renderTemplate(w, "edit", p) -} func saveHandler(w http.ResponseWriter, r *http.Request) { title, err := getTitle(w, r) @@ -150,27 +143,32 @@ func get_links(name string) (links []string) { func construct_graph(word string) string { seen := make(map[string]bool) - graph := "digraph {\n\""+word+"\";\n" + graph := "digraph {\n ratio=\"compress\";size=\"8,4!\";splines=true;\""+word+"\";\n" graph = construct_graph_sub(graph, word, seen, 1) graph += "}" ioutil.WriteFile("tmp.dot", []byte(graph), 0640) - subProcess := exec.Command("dot", "-Tsvg", "-otmp.svg", "tmp.dot") + subProcess := exec.Command("dot", "-Tsvg", "-otmp.svg","-Nfontname=Chilanka", "tmp.dot") subProcess.Run() subProcess.Wait() data, _ := ioutil.ReadFile("tmp.svg") return "
"+strings.ReplaceAll(string(data), ` `, "")+"" + "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">`, "") } func construct_graph_sub(graph string, word string, seen map[string]bool, depth int) string { if depth >= 0 { } else { return graph + //4b3557 } links := get_links(normalize(word)) if !seen[normalize(word)] { - graph += fmt.Sprintf("\"%v\" [href=\"/view/%v\"];\n", normalize(word), normalize(word)) + col := "fillcolor=\"white\"" + if depth == 1 { + col = "fillcolor=\"#b09cbc\"" + } + graph += fmt.Sprintf("\"%v\" [href=\"/view/%v\",%v,style=filled];\n", normalize(word), normalize(word),col) } seen[normalize(word)] = true @@ -187,7 +185,7 @@ func construct_graph_sub(graph string, word string, seen map[string]bool, depth } - if !seen[normalize(word)+"++"+normalize(link)] || !seen[normalize(link)+"++"+normalize(word)] { + if normalize(word) != normalize(link) && (!seen[normalize(word)+"++"+normalize(link)] || !seen[normalize(link)+"++"+normalize(word)]) { if strings.HasPrefix(link, "typeof:") { graph += fmt.Sprintf("\"%v\" -> \"%v\" [style=dotted]\n", link[7:], word) seen[word+"++"+link[7:]] = true @@ -201,7 +199,7 @@ func construct_graph_sub(graph string, word string, seen map[string]bool, depth seen[word+"++"+link[9:]] = true seen[link[9:]+"++"+word] = true } else { - graph += fmt.Sprintf("\"%v\" -> \"%v\"\n", word, link) + graph += fmt.Sprintf("\"%v\" -> \"%v\" []\n", word, link) } seen[normalize(word)+"++"+normalize(link)] = true @@ -241,7 +239,6 @@ func indexHandler(w http.ResponseWriter, r *http.Request) { func main() { http.HandleFunc("/", indexHandler) http.HandleFunc("/view/", viewHandler) - http.HandleFunc("/edit/", editHandler) http.HandleFunc("/save/", saveHandler) log.Fatal(http.ListenAndServe(":8080", nil)) } diff --git a/templates/edit.html b/templates/edit.html deleted file mode 100644 index 61fc30b..0000000 --- a/templates/edit.html +++ /dev/null @@ -1,6 +0,0 @@ -

Editing {{.Title}}

- -
-
-
-
\ No newline at end of file