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  • Benefits to private networks between virtual machines on an ESXi host?

    - by arex1337
    I'm planning this development environment with a few database servers, and originally thought I would have a few private networks. I then thought it might be unnecessary as the ESXi cluster already provides redundancy with 4 NICs (in my case) and should manage the network traffic pretty intelligently, right? Two private networks Zero private networks What are the advantages/disadvantages between the two shown configurations - on an ESXi 4.1 host?

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  • Learning networking fundamentals

    - by bplus
    Not having a CS degree has left large holes in my programming related knowledge. In particular I'd really like to learn some of the computer networking stuff I would have got in a good CS degree. The problem I really have is "not knowing what I don't know". So far I know I don't know anything about the following (as far as computer networks are concearned) -sockets -ports -internet protocol (the whole IP stack I keep hearing about). Can anyone add more to the list? Can anyone suggest a project (writing a toy web server?) Thanks in advance

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  • AI Game Programming : Bayesian Networks, how to make efficient?

    - by Mahbubur R Aaman
    We know that AI is one of the most important part of Game Programming. Bayesian networks is one of the core part of AI at Game Programming. Bayesian networks are graphs that compactly represent the relationship between random variables for a given problem. These graphs aid in performing reasoning or decision making in the face of uncertainty. Here me, utilizing the monte carlo method and genetic algorithms. But tooks much time and sometimes crashes due to memory. Is there any way to implement efficiently?

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  • Reinforcement learning toy project

    - by Betamoo
    My toy project to learn & apply Reinforcement Learning is: - An agent tries to reach a goal state "safely" & "quickly".... - But there are projectiles and rockets that are launched upon the agent in the way. - The agent can determine rockets position -with some noise- only if they are "near" - The agent then must learn to avoid crashing into these rockets.. - The agent has -rechargable with time- fuel which is consumed in agent motion - Continuous Actions: Accelerating forward - Turning with angle I need some hints and names of RL algorithms that suit that case.. - I think it is POMDP , but can I model it as MDP and just ignore noise? - In case POMDP, What is the recommended way for evaluating probability? - Which is better to use in this case: Value functions or Policy Iterations? - Can I use NN to model environment dynamics instead of using explicit equations? - If yes, Is there a specific type/model of NN to be recommended? - I think Actions must be discretized, right? I know it will take time and effort to learn such a topic, but I am eager to.. You may answer some of the questions if you can not answer all... Thanks

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  • Machine Learning Algorithm for Predicting Order of Events?

    - by user213060
    Simple machine learning question. Probably numerous ways to solve this: There is an infinite stream of 4 possible events: 'event_1', 'event_2', 'event_4', 'event_4' The events do not come in in completely random order. We will assume that there are some complex patterns to the order that most events come in, and the rest of the events are just random. We do not know the patterns ahead of time though. After each event is received, I want to predict what the next event will be based on the order that events have come in in the past. The predictor will then be told what the next event actually was: Predictor=new_predictor() prev_event=False while True: event=get_event() if prev_event is not False: Predictor.last_event_was(prev_event) predicted_event=Predictor.predict_next_event(event) The question arises of how long of a history that the predictor should maintain, since maintaining infinite history will not be possible. I'll leave this up to you to answer. The answer can't be infinte though for practicality. So I believe that the predictions will have to be done with some kind of rolling history. Adding a new event and expiring an old event should therefore be rather efficient, and not require rebuilding the entire predictor model, for example. Specific code, instead of research papers, would add for me immense value to your responses. Python or C libraries are nice, but anything will do. Thanks! Update: And what if more than one event can happen simultaneously on each round. Does that change the solution?

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  • Predict Stock Market Values

    - by mrlinx
    I'm building a web semantic project that gathers the maximum ammount of historic data about a certain company and tries to predict its future market stock values. For data I have the historic stock values (not normalized), news (0 to 1 polarity) and subjective content (also a 0 to 1 polarity). What is the best AI system to train and use for this kind of objective? Is a simple NN with back-propagation training the best I can hope for? update: Everyone is concerned about the quality of this system. Although I'm pretty sure the system is as good as a random prediction (or even worse), this is a school project around artificial intelligence and web semantics. Therefore I'm only concerned in picking the best kind of train method for the data I have (NN, RBF, SVM, Bayes, neuro-fuzzy, etc). Its not about making money.

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  • What algorithms are suitable for this simple machine learning problem?

    - by user213060
    I have a what I think is a simple machine learning question. Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that had similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object. a Here's the pseudo-code: Classifier=new_classifier() while True: new_object,new_object_descriptions = get_new_object_and_descriptions() past_similar_objects = Classifier.classify(new_object,new_object_descriptions) correct_objects = calc_successful_matches(new_object,past_similar_objects) Classifier.train_successful_matches(object,correct_objects) But, there are some stipulations that may limit what classifier can be used: There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.) Again, I prefer speed when millions of objects are being classified, over accuracy. What are decent, fast machine learning algorithms for this purpose?

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  • Reinforcement learning And POMDP

    - by Betamoo
    I am trying to use Multi-Layer NN to implement probability function in Partially Observable Markov Process.. I thought inputs to the NN would be: current state, selected action, result state; The output is a probability in [0,1] (prob. that performing selected action on current state will lead to result state) In training, I fed the inputs stated before, into the NN, and I taught it the output=1.0 for each case that already occurred. The problem : For nearly all test case the output probability is near 0.95.. no output was under 0.9 ! Even for nearly impossible results, it gave that high prob. PS:I think this is because I taught it happened cases only, but not un-happened ones.. But I can not at each step in the episode teach it the output=0.0 for every un-happened action! Any suggestions how to over come this problem? Or may be another way to use NN or to implement prob function? Thanks

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  • Entropy using Decision Tree's

    - by Matt Clements
    Train a decision tree on the data represented by attributes A1, A2, A3 and outcome C described below: A1 A2 A3 C 1 0 1 0 0 1 1 1 0 0 1 0 For log2(1/3) = 1.6 and log2(2/3) = 0.6, answer the following questions: a) What is the value of entropy H for the given set of training example? b) What is the portion of the positive samples split by attribute A2? c) What is the value of information gain, G(A2), of attribute A2? d) What is IFTHEN rule(s) for the decision tree?

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  • Word characteristics tags

    - by theBlinker
    I want to do a riddle AI chatbot for my AI class. So i figgured the input to the chatbot would be : Something like : "It is blue, and it is up, but it is not the ceiling" Translation : <Object X> <blue> <up> <!ceiling> </Object X> (Answer : sky?) So Input is a set of characteristics (existing \ not existing in the object), output is a matched, most likely object. The domain will be limited to a number of objects, i could input all attributes myself, but i was thinking : How could I programatically build a database of characteristics for a word? Is there such a database available? How could i tag a word, how could i programatically find all it's attributes? I was thinking on crawling wikipedia, or some forum, but i can't see it build any reliable word tag database. Any ideas on how i could achieve such a thing? Any ideas on some literature on the subject? Thank you

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  • Using a Cyc Image in Windows

    - by nrhine1
    Hi, I am trying to use a Microtheory for a research project I am working on, and am having trouble getting my saved Image of constants I create to work correctly. I save the image after creating the constants using (write-image "world\MyImage") and then going to the \server\run\ directory and using run-cyc-32bit.bat -w "world\MyImage" It loads the server correctly, but not with my constants. I found the above command at the reference page here. Thank you for any help

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  • Hebbian learning

    - by Bane
    I have asked another question on Hebbian learning before, and I guess I got a good answer which I accepted, but, the problem is that I now realize that I've mistaken about Hebbian learning completely, and that I'm a bit confused. So, could you please explain how it can be useful, and what for? Because the way Wikipedia and some other pages describe it - it doesn't make sense! Why would we want to keep increasing the weight between the input and the output neuron if the fire together? What kind of problems can it be used to solve, because when I simulate it in my head, it certainly can't do the basic AND, OR, and other operations (say you initialize the weights at zero, the output neurons never fire, and the weights are never increased!)

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  • Is it theoretically possible to emulate a human brain on a computer?

    - by JoelK
    Our brain consists of billions of neurons which basically work with all the incoming data from our senses, handle our consciousness, emotions and creativity as well as our hormone system, etc. So I'm completely new to this topic but doesn't each neuron have a fixed function? E.g.: If a signal of strength x enters, if the last signal was x ms ago, redirect it. From what I've learned in biology about our nerves system which includes our brain because both consist of simple neurons, it seems to me as our brain is one big, complicated computer. Maybe so complicated that things such as intelligence and cognition become possible? As the most complicated things about a neuron pretty much are the chemical aspects on generating an electric singal, keeping itself alive, and eventually segmenting itself, it should be pretty easy emulating some on a computer, or? You won't have to worry about keeping your virtual neuron alive, or? If you can emulate a single neuron on a computer, which shouldn't be too hard, could you theoretically emulate more than 1000 billions of them, recreating intelligence, cognition and maybe even creativity? In my question I'm leaving out the following aspects: Speed of our current (super) computers Actually writing a program for emulating neurons I don't know much about this topic, please tell me if I got anything wrong :) (My secret goal: Make a copy of my brain and store it on some 10 million TB HDD and make someone start it up in the future)

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  • Information Modeling

    - by Betamoo
    The sensor module in my project consists of a rotating camera, that collects noisy information about moving objects in the surrounding environment. The information consists of distance, angle and relative change of the moving objects.. The limiting view range of the camera makes it essential to rotate the camera periodically to update environment information... I was looking for algorithms / ways to model these information, in order to be able to guess / predict / learn motion properties of these object.. My current proposed idea is to store last n snapshots of each object in a queue. I take weighted average of positions and velocities of moving object, but I think it is a poor method... Can you state some titles that suit this case? Thanks

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  • Summary of usage policies for website integration of various social media networks?

    - by Dallas
    To cut to the chase... I look at Twitter's usage policy and see limitations on what can and can't be done with their logo. I also see examples of websites that use icons that have been integrated with the look and feel of their own site. Given Twitter's policy, for example, it would appear that legal conversations/agreements would need to take place to do this, especially on a commercial site. I believe it is perfectly acceptable to have a plain text button that simply has the word "Tweet" on it, that has the same functionality. My question is if anyone can provide online (or other) references that attempt to summarize what can and can't be done when integrating various social networks into your own work? The answer I will mark as the correct one will be the one which provides the best resource(s) giving the best summaries of what can and can't be done with specific logos/icons, with a secondary factor being that a variety of social networking sites are addressed in your answer. Before people point to specific questions, I am looking for a well-rounded approach that considers a breadth of networks and considerations. Background: I would like to incorporate social media icons and functionality, but would like to consider what type of modifications can be done without needing to involve lawyers. For example, can I bring in a standard Facebook logo, but incorporate my site color into the logo? Would the answer differ if I maintained their color, but add in a few pixels of another color to transition? I am not saying I want to do this, but rather using it as an example.

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  • Combinations into pairs

    - by Will
    I'm working on a directed network problem and trying to compute all valid paths between two points. I need a way to look at paths up to 30 "trips" (represented by an [origin, destination] pair) in length. The full route is then composed of a series of these pairs: route = [[start, city2], [city2, city3], [city3, city4], [city4, city5], [city5, city6], [city6, city7], [city7, city8], [city8, stop]] So far my best solution is as follows: def numRoutes(graph, start, stop, minStops, maxStops): routes = [] route = [[start, stop]] if distance(graph, route) != "NO SUCH ROUTE" and len(route) >= minStops and len(route) <= maxStops: routes.append(route) if maxStops >= 2: for city2 in routesFromCity(graph, start): route = [[start, city2],[city2, stop]] if distance(graph, route) != "NO SUCH ROUTE" and len(route) >= minStops and len(route) <= maxStops: routes.append(route) if maxStops >= 3: for city2 in routesFromCity(graph, start): for city3 in routesFromCity(graph, city2): route = [[start, city2], [city2, city3], [city3, stop]] if distance(graph, route) != "NO SUCH ROUTE" and len(route) >= minStops and len(route) <= maxStops: routes.append(route) if maxStops >= 4: for city2 in routesFromCity(graph, start): for city3 in routesFromCity(graph, city2): for city4 in routesFromCity(graph, city3): route = [[start, city2], [city2, city3], [city3, city4], [city4, stop]] if distance(graph, route) != "NO SUCH ROUTE" and len(route) >= minStops and len(route) <= maxStops: routes.append(route) if maxStops >= 5: for city2 in routesFromCity(graph, start): for city3 in routesFromCity(graph, city2): for city4 in routesFromCity(graph, city3): for city5 in routesFromCity(graph, city4): route = [[start, city2], [city2, city3], [city3, city4], [city4, city5], [city5, stop]] if distance(graph, route) != "NO SUCH ROUTE" and len(route) >= minStops and len(route) <= maxStops: routes.append(route) return routes Where numRoutes is fed my network graph where numbers represent distances: [[0, 5, 0, 5, 7], [0, 0, 4, 0, 0], [0, 0, 0, 8, 2], [0, 0, 8, 0, 6], [0, 3, 0, 0, 0]] a start city, an end city and the parameters for the length of the routes. distance checks if a route is viable and routesFromCity returns the attached nodes to each fed in city. I have a feeling there's a far more efficient way to generate all of the routes especially as I move toward many more steps, but I can't seem to get anything else to work.

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  • ifconfig networking telnet

    - by jhon
    Hi guys, I'm newbie around networking, I have a question: what I want is to telnet a specific IP/server, let us say 192.168.128.1 then, I try $telnet 192.168.128.1 Trying... and that's all.. I never get connected one of my friends made some script that "fixes" it, AFTER running it I was able to connect to the server using $telnet 192.168.128.1 $ user: unfortunately I lost that script, so I'm here requesting your help. Reading my old notes, I remember that the script performed some modification to the entries listed by ifconfig -a, I also have the ifconfig's output (copy & paste) $ ifconfig -a adapter0: flags=5e080863,c0<UP,BROADCAST,NOTRAILERS,RUNNING,SIMPLEX,MULTICAST,GROUPRT,64BIT,CHECKSUM_OFFLOAD(ACTIVE),PSEG,LARGESEND,CHAIN> inet 192.168.128.150 netmask 0xffffff00 broadcast 192.168.128.255 tcp_sendspace 131072 tcp_recvspace 65536 rfc1323 0 adapter1: flags=5e080863,c0<UP,BROADCAST,NOTRAILERS,RUNNING,SIMPLEX,MULTICAST,GROUPRT,64BIT,CHECKSUM_OFFLOAD(ACTIVE),PSEG,LARGESEND,CHAIN> inet 192.168.251.150 netmask 0xffffff00 broadcast 192.168.251.255 tcp_sendspace 131072 tcp_recvspace 65536 rfc1323 0 adapter2: flags=5e080863,c0<UP,BROADCAST,NOTRAILERS,RUNNING,SIMPLEX,MULTICAST,GROUPRT,64BIT,CHECKSUM_OFFLOAD(ACTIVE),PSEG,LARGESEND,CHAIN> inet 192.168.250.150 netmask 0xffffff00 broadcast 192.168.250.255 tcp_sendspace 131072 tcp_recvspace 65536 rfc1323 0 lo0: flags=e08084b<UP,BROADCAST,LOOPBACK,RUNNING,SIMPLEX,MULTICAST,GROUPRT,64BIT> inet 127.0.0.1 netmask 0xff000000 broadcast 127.255.255.255 inet6 ::1/0 tcp_sendspace 131072 tcp_recvspace 131072 rfc1323 1 more than commands, I'm looking for some explanation why does "adding/changing" those entries enables me to connect to the server. I do not see the server ip (i.e 192.168.128.1) listed above. thanks

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  • Internet service providers

    - by gautam kumar
    I am unclear about the differences between international, national, regional and local ISPs. Please explain the differences and their importance, with examples. I am new to this site, so please forgive me if my question is not up to your expectations.

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  • Variable sized packet structs with vectors

    - by Rev316
    Lately I've been diving into network programming, and I'm having some difficulty constructing a packet with a variable "data" property. Several prior questions have helped tremendously, but I'm still lacking some implementation details. I'm trying to avoid using variable sized arrays, and just use a vector. But I can't get it to be transmitted correctly, and I believe it's somewhere during serialization. Now for some code. Packet Header class Packet { public: void* Serialize(); bool Deserialize(void *message); unsigned int sender_id; unsigned int sequence_number; std::vector<char> data; }; Packet ImpL typedef struct { unsigned int sender_id; unsigned int sequence_number; std::vector<char> data; } Packet; void* Packet::Serialize(int size) { Packet* p = (Packet *) malloc(8 + 30); p->sender_id = htonl(this->sender_id); p->sequence_number = htonl(this->sequence_number); p->data.assign(size,'&'); //just for testing purposes } bool Packet::Deserialize(void *message) { Packet *s = (Packet*)message; this->sender_id = ntohl(s->sender_id); this->sequence_number = ntohl(s->sequence_number); this->data = s->data; } During execution, I simply create a packet, assign it's members, and send/receive accordingly. The above methods are only responsible for serialization. Unfortunately, the data never gets transferred. Couple of things to point out here. I'm guessing the malloc is wrong, but I'm not sure how else to compute it (i.e. what other value it would be). Other than that, I'm unsure of the proper way to use a vector in this fashion, and would love for someone to show me how (code examples please!) :) Edit: I've awarded the question to the most comprehensive answer regarding the implementation with a vector data property. Appreciate all the responses!

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  • Confusion Matrix of Bayesian Network

    - by iva123
    Hi, I'm trying to understand bayesian network. I have a data file which has 10 attributes, I want to acquire the confusion table of this data table ,I thought I need to calculate tp,fp, fn, tn of all fields. Is it true ? if it's then what i need to do for bayesian network. Really need some guidance, I'm lost.

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  • What is the difference between causal models and directed graphical models?

    - by Neil G
    What is the difference between causal models and directed graphical models? or: What is the difference between causal relationships and directed probabilistic relationships? or, even better: What would you put in the interface of a DirectedProbabilisticModel class, and what in a CausalModel class? Would one inherit from the other? Collaborative solution: interface DirectedModel { map<Node, double> InferredProbabilities(map<Node, double> observed_probabilities, set<Node> nodes_of_interest) } interface CausalModel: DirectedModel { bool NodesDependent(set<Node> nodes, map<Node, double> context) map<Node, double> InferredProbabilities(map<Node, double> observed_probabilities, map<Node, double> externally_forced_probabilities, set<Node> nodes_of_interest) }

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  • Using c#,c/c++ or java to improve BBN with GA

    - by madicemickael
    I have a little problem in my little project , I wish that someone here could help me! I am planning to use a bayesian network as a decision factor in my game AI and I want to improve the decision making every step of the way , anyone knows how to do that ? Any tutorials / existing implementations will be very good,I hope some of you could help me. I heard that a programmer in this community did a good implementation of this put together for poker game AI.I am planning to use it like him ,but in another poker(Texas) or maybe Rentz. Looking for C/c++ or c# or java code. Thanks , Mike

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  • Efficient way to store a graph for calculation in Hadoop

    - by user337499
    I am currently trying to perform calculations like clustering coefficient on huge graphs with the help of Hadoop. Therefore I need an efficient way to store the graph in a way that I can easily access nodes, their neighbors and the neighbors' neighbors. The graph is quite sparse and stored in a huge tab separated file where the first field is the node from which an edge goes to the second node in field two. Thanks in advance!

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