I'm searching for some good books/tutorials/guides on how to develop distributed applications using Ada.
I already have some books on Ada programming, but all of them don't talk about distribution or they only mention it very briefly.
The ideal thing would be a book/guide that focus on the practical side of things (implementation) but any resource, either free or commercial, is appreciated.
The "Burns & Welling" book covers concurrency in depth, but doesn't have as much to say about distributed systems as I would expect. Nevertheless it is probably essential reading if you're going to be doing a lot of this stuff.
I'm still reading Professor McCormick's book "Building Parallel, Real-time and embedded Applications with Ada" and it does an excellent job of getting a reader started with a wide range of application-oriented aspects of Ada - sadly missing in other books which focus o the base language - and that includes both the DSA (pure Ada) and PolyOrb (for mixed languages) approaches to distributed systems, including very readable code examples.
Start with this latter book (IMO). (and its lead author has been seen around these parts, so this is a good place to ask questions! :-)
Section 8 in the "PolyORB User's Guide" is a small tutorial on how to develop a distributed application in Ada using the Distributed Systems Annex (DSA).
The "PolyORB User's Guide" also contains examples of developing distributed applications using other constructs than the DSA, which might be of interest, but using the DSA is likely to give you the most elegant application if all the components primarily are in Ada.
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I'm very interested in dataflow and concurrency focused languages. I've read up on the subject and repeatedly I see SIGNAL, Esterel, and Lustre mentioned; so I take it they're prominent players in those fields. However, many of their links in the resources I found are dead and they don't seem very accessible. I managed to find a couple compilers I can compile from source (Polychrony Toolset for SIGNAL and the Columbia Compiler for Esterel) but they've both had issues when trying to compile with cmake. Even textbooks teaching these languages have been tough to come by.
With the background of the way, my actual questions are: is anyone really familiar with this field of programming? Are these languages still big deals, or have they "died out" by now? Could it be they're just available to big companies with a hefty price tag, so the average programmer wouldn't really be able to pick those languages up?
I ran into a couple other dataflow/concurrent paradigm languages, such as Oz or E, but they seemed to be mostly for education and not suitable for real world projects. Not to say they aren't impressive languages, but their implementation was limited and it would be unlikely to see them in production contexts. Does anyone know of other languages in this field they can recommend that are actually accessible (have good documentation, tutorials, and an installable compiler to actually code in)? Or can anyone clarify a language such as Oz or E and hopefully show that they indeed are good enough for large real world projects?
All the languages you mentioned are not widespread. This means their compilers and runtime have bugs, the community is narrow and can give little help, and linking with general purpose libraries can be problematic.
I recommend to use an actively supported general purpose language such as Java, Scala, Kotlin or C++. They all have libraries to support asynchronous computations, and dataflow is no more than support of asynchronous procedure call. You even can develop your own dataflow library. This is not that hard: I wrote a dataflow library for Java which is only 40 kilobytes of source code.
Have you tried Céu? It is a recent variant of Esterel, and compiles to C. It is simple to understand, and provides a reactive and concurrent structuring of control flow. Native C calls can be made by just prefixing them with an underscore ("_printf").
http://ceu-lang.org
Also, see the paper "Structured Synchronous Reactive Programming with Céu" for a nice overview.
http://www.ceu-lang.org/chico/ceu_mod15_pre.pdf
These academics languages mostly disappeared as such and are used in industrial tools
Esterel-Lustre are the basis of in Ansys' SCADE
Signal is used in 3DS' ControlBuild
Esterel was used in Synopsys' ConcentricStudio.
Researchers use also Heptagon for synchronous language studies for code generation, formal methods, new concepts.
I am looking for tools (preferably free) to practice various cryptoanalysis and cryptography techniques. Something along the lines of following two online tools but with more techniques.
http://www.cryptool-online.org/index.php?option=com_content&view=article&id=55&Itemid=53&lang=en
http://www.simonsingh.net/The_Black_Chamber/letterfrequencies.html
Any suggestions would be very welcome.
Thanks,
Ambi.
The Matasano Crypto Challenges are an excellent learning resource for cryptography.
We've built a collection of 48 exercises that demonstrate attacks on
real-world crypto.
This is a different way to learn about crypto than taking a class or
reading a book. We give you problems to solve. They're derived from
weaknesses in real-world systems and modern cryptographic
constructions. We give you enough info to learn about the underlying
crypto concepts yourself. When you're finished, you'll not only have
learned a good deal about how cryptosystems are built, but you'll also
understand how they're attacked.
The first couple of sets may seem a bit too easy for someone acquainted with cryptography, but the challenges quickly get more tricky and advanced.
Do functional languages bring anything in the resolution of everyday business problems?
Are there any successful projects that have been implemented using a functional language (ideally with a published test case)?
There are quite a few listed on Functional Programming in the Real World. From the site:
The main criterion for being real-world is that the program was written primarily to perform some task, not primarily to experiment with functional programming.
The Xen hypervisor is at base, implemented in OCAML; and Erlang is deployed in ultra-high reliability telephony systems (the ones that have zero down-time over periods of years).
One implementation of Perl 6, Pugs, is written in Haskell, but it has largely given way to the standard Rakudo Perl implementation.
Friends of mine use Haskell every day to implement financial algorithms.
There was a talk at the Lang.NET conference about how they'd used F# to improve the performance of an insurance application, which is about as everyday as you can get. Silverlight video, WMV video. That said, most of the focus of that talk is on F#'s concurrency support, less on the idiomatically functional aspects of the language.
Xmonad is a dynamically tiling X11 window manager that is written and configured in Haskell.
Facebook's chat feature makes heavy use of Erlang. http://www.facebook.com/note.php?note_id=14218138919&id=9445547199&index=0
Have you heard of Lisp machines before? The emacs editor also makes extensive use of Lisp.
I would argue that the Lotus Notes formula language is an example of a widely used real world functional programming language.
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Assume you know a student who wants to study Machine Learning and Natural Language Processing.
What specific computer science subjects should they focus on and which programming languages are specifically designed to solve these types of problems?
I am not looking for your favorite subjects and tools, but rather industry standards.
Example: I'm guessing that knowing Prolog and Matlab might help them. They also might want to study Discrete Structures*, Calculus, and Statistics.
*Graphs and trees. Functions: properties, recursive definitions, solving recurrences. Relations: properties, equivalence, partial order. Proof techniques, inductive proof. Counting techniques and discrete probability. Logic: propositional calculus, first-order predicate calculus. Formal reasoning: natural deduction, resolution. Applications to program correctness and automatic reasoning. Introduction to algebraic structures in computing.
This related stackoverflow question has some nice answers: What are good starting points for someone interested in natural language processing?
This is a very big field. The prerequisites mostly consist of probability/statistics, linear algebra, and basic computer science, although Natural Language Processing requires a more intensive computer science background to start with (frequently covering some basic AI). Regarding specific langauges: Lisp was created "as an afterthought" for doing AI research, while Prolog (with it's roots in formal logic) is especially aimed at Natural Language Processing, and many courses will use Prolog, Scheme, Matlab, R, or another functional language (e.g. OCaml is used for this course at Cornell) as they are very suited to this kind of analysis.
Here are some more specific pointers:
For Machine Learning, Stanford CS 229: Machine Learning is great: it includes everything, including full videos of the lectures (also up on iTunes), course notes, problem sets, etc., and it was very well taught by Andrew Ng.
Note the prerequisites:
Students are expected to have the following background: Knowledge of
basic computer science principles and skills, at a level sufficient to write
a reasonably non-trivial computer program. Familiarity with the basic probability theory.
Familiarity with the basic linear algebra.
The course uses Matlab and/or Octave. It also recommends the following readings (although the course notes themselves are very complete):
Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998
For Natural Language Processing, the NLP group at Stanford provides many good resources. The introductory course Stanford CS 224: Natural Language Processing includes all the lectures online and has the following prerequisites:
Adequate experience with programming
and formal structures. Programming
projects will be written in Java 1.5,
so knowledge of Java (or a willingness
to learn on your own) is required.
Knowledge of standard concepts in
artificial intelligence and/or
computational linguistics. Basic
familiarity with logic, vector spaces,
and probability.
Some recommended texts are:
Daniel Jurafsky and James H. Martin. 2008. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Second Edition. Prentice Hall.
Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.
James Allen. 1995. Natural Language Understanding. Benjamin/Cummings, 2ed.
Gerald Gazdar and Chris Mellish. 1989. Natural Language Processing in Prolog. Addison-Wesley. (this is available online for free)
Frederick Jelinek. 1998. Statistical Methods for Speech Recognition. MIT Press.
The prerequisite computational linguistics course requires basic computer programming and data structures knowledge, and uses the same text books. The required articificial intelligence course is also available online along with all the lecture notes and uses:
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Second Edition
This is the standard Artificial Intelligence text and is also worth reading.
I use R for machine learning myself and really recommend it. For this, I would suggest looking at The Elements of Statistical Learning, for which the full text is available online for free. You may want to refer to the Machine Learning and Natural Language Processing views on CRAN for specific functionality.
My recommendation would be either or all (depending on his amount and area of interest) of these:
The Oxford Handbook of Computational Linguistics:
(source: oup.com)
Foundations of Statistical Natural Language Processing:
Introduction to Information Retrieval:
String algorithms, including suffix trees. Calculus and linear algebra. Varying varieties of statistics. Artificial intelligence optimization algorithms. Data clustering techniques... and a million other things. This is a very active field right now, depending on what you intend to do.
It doesn't really matter what language you choose to operate in. Python, for instance has the NLTK, which is a pretty nice free package for tinkering with computational linguistics.
I would say probabily & statistics is the most important prerequisite. Especially Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) are very important both in machine learning and natural language processing (of course these subjects may be part of the course if it is introductory).
Then, I would say basic CS knowledge is also helpful, for example Algorithms, Formal Languages and basic Complexity theory.
Stanford CS 224: Natural Language Processing course that was mentioned already includes also videos online (in addition to other course materials). The videos aren't linked to on the course website, so many people may not notice them.
Jurafsky and Martin's Speech and Language Processing http://www.amazon.com/Speech-Language-Processing-Daniel-Jurafsky/dp/0131873210/ is very good. Unfortunately the draft second edition chapters are no longer free online now that it's been published :(
Also, if you're a decent programmer it's never too early to toy around with NLP programs. NLTK comes to mind (Python). It has a book you can read free online that was published (by OReilly I think).
How about Markdown and an Introduction to Parsing Expression Grammars (PEG) posted by cletus on his site cforcoding?
ANTLR seems like a good place to start for natural language processing. I'm no expert though.
Broad question, but I certainly think that a knowledge of finite state automata and hidden Markov models would be useful. That requires knowledge of statistical learning, Bayesian parameter estimation, and entropy.
Latent semantic indexing is a commonly yet recently used tool in many machine learning problems. Some of the methods are rather easy to understand. There are a bunch of potential basic projects.
Find co-occurrences in text corpora for document/paragraph/sentence clustering.
Classify the mood of a text corpus.
Automatically annotate or summarize a document.
Find relationships among separate documents to automatically generate a "graph" among the documents.
EDIT: Nonnegative matrix factorization (NMF) is a tool that has grown considerably in popularity due to its simplicity and effectiveness. It's easy to understand. I currently research the use of NMF for music information retrieval; NMF has shown to be useful for latent semantic indexing of text corpora, as well. Here is one paper. PDF
Prolog will only help them academically it is also limited for logic constraints and semantic NLP based work. Prolog is not yet an industry friendly language so not yet practical in real-world. And, matlab also is an academic based tool unless they are doing a lot of scientific or quants based work they wouldn't really have much need for it. To start of they might want to pick up the 'Norvig' book and enter the world of AI get a grounding in all the areas. Understand some basic probability, statistics, databases, os, datastructures, and most likely an understanding and experience with a programming language. They need to be able to prove to themselves why AI techniques work and where they don't. Then look to specific areas like machine learning and NLP in further detail. In fact, the norvig book sources references after every chapter so they already have a lot of further reading available. There are a lot of reference material available for them over internet, books, journal papers for guidance. Don't just read the book try to build tools in a programming language then extrapolate 'meaningful' results. Did the learning algorithm actually learn as expected, if it didn't why was this the case, how could it be fixed.
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Who is winning in the "Low vs High fidelity prototyping" debate?
Should prototype-zero (P0) be the first version of the final product? Or should be P-0 always a throwaway? What approach is the industry favoring?
Excelent article from wikipedia: Software prototyping
A prototype should always be a throwaway - a prototype is used to quickly prove a concept and influence the design of the real product. As such, a lot of things which are important for a real product (a thought-out architecture and design, reliability, security, maintainability, etc.) fall by the wayside. If you do take these things into account when building your prototype, you're not really building a prototype anymore.
My experience with prototypes where the code directly evolved into an actual product shows that the end-result suffers because of it - the lack of a real architecture resulted in a lot of cobbled-together code that had to be constantly hacked to add new features. I've even seen a case the original technology chosen for rapid development of the prototype was not the best choice for the actual product, and a complete re-write was necessary for V2.
I think we, the pedants, have lost this particular battle -- alleged "prototypes" (which by definition should be rewritten from scratch!!!-) are in fact being "evolved" into (often half-baked "betas"), etc.
Even today, I've applauded at the smart attempt by a colleague of mine to recapture the concept, even if the term is a lost battle: he's setting up a way for proofs of concept small projects to be developed (and, if the concept does get proven, transferred to software engineers for real prototyping, then development).
The idea is that, in our department, we have many people who aren't (and aren't in fact supposed to be!-) software developers, but are very smart, computer savvy, and in daily contact with the reality "in the trenches" -- they are the ones who are most likely to smell an opportunity for some potential innovation which could have real impact once implemented as a "production-ready" software project. Salespeople, account managers, business analysts, technology managers -- at our company, they all often fit this description.
But they're NOT going to program in C++, hardly at all in Java, maybe in Python but miles away from "productionized" -- indeed they're far more likely to whip up a smart proof of concept in php, javascript, perl, bash, Excel+VBA, and sundry other "quick and dirty" technologies we don't even want to dream about productionizing and supporting forevermore!-)
So by calling their prototypes "proofs of concept", we hope to encourage them to embody their daring concepts in concrete form (vague natural-language blabberings and much waving of hands being least useful, and alien to the company's culture anyway;-) and yet sharply indicate that such projects, if promoted to exist among the software engineers' goals and priorities, DO have to be programmed from scratch -- the proof-of-concept serves, at best, as a good draft/sketch spec for what the engineers are aiming for, definitely NOT to be incrementally enriched, but redone from the root up!-).
It's early to say how well this idea works -- ask me in three months, when we evaluate the quarter's endeavors (right now, we're just providing a blueprint for them, hot on the heels of evaluating last quarter's department- and company-wise undertakings!-).
Write the prototype, then keep refactoring it until it becomes the product.
The key is to not hesitate to refactor when necessary.
It helps to have few people working on it initially. With too many people working on something, refactoring becomes more difficult.
Response from BUNDALLAH, HAMISI
A prototype typically simulates only a few aspects of the features of the eventual program, and may be completely different from the eventual implementation.
Contrary to what my other colleagues have suggested above, I would NOT advise my boss to opt for the throw away prototype model. I am with Anita on this. Given the two prototype models and the circumstances provided, I would strongly advise the management (my boss) to opt for the evolutionary prototype model. The company being large with all the other variables given such as the complexity of the code, the newness of the programming language to be used, I would not use throw away prototype model. The throw away prototype model becomes the starting point from which users can re-examine their expectations and clarify their requirements. When this has been achieved, the prototype model is 'thrown away', and the system is formally developed based on the identified requirements (Crinnion, 1991). But with this situation, the users may not know all the requirements at once due to the complexity of the factors given in this particular situation. Evolutionary prototyping is the process of developing a computer system by a process of gradual refinement. Each refinement of the system contains a system specification and software development phase. In contrast to both the traditional waterfall approach and incremental prototyping, which required everyone to get everything right the first time this approach allows participants to reflect on lessons learned from the previous cycle(s). It is usual to go through three such cycles of gradual refinement. However there is nothing stopping a process of continual evolution which is often the case in many systems. According to Davis (1992), an evolutionary prototyping acknowledges that we do not understand all the requirements (as we have been told above that the system is complex, the company is large, the code will be complex, and the language is fairly new to the programming team). The main goal when using Evolutionary Prototyping is to build a very robust prototype in a structured manner and constantly refine it. The reason for this is that the Evolutionary prototype, when built, forms the heart of the new system, and the improvements and further requirements will be built. This technique allows the development team to add features, or make changes that couldn't be conceived during the requirements and design phase. For a system to be useful, it must evolve through use in its intended operational environment. A product is never "done;" it is always maturing as the usage environment change. Developers often try to define a system using their most familiar frame of reference--where they are currently (or rather, the current system status). They make assumptions about the way business will be conducted and the technology base on which the business will be implemented. A plan is enacted to develop the capability, and, sooner or later, something resembling the envisioned system is delivered. (SPC, 1997).
Evolutionary Prototypes have an advantage over Throwaway Prototypes in that they are functional systems. Although they may not have all the features the users have planned, they may be used on an interim basis until the final system is delivered.
In Evolutionary Prototyping, developers can focus themselves to develop parts of the system that they understand instead of working on developing a whole system. To minimize risk, the developer does not implement poorly understood features. The partial system is sent to customer sites. As users work with the system, they detect opportunities for new features and give requests for these features to developers. Developers then take these enhancement requests along with their own and use sound configuration-management practices to change the software-requirements specification, update the design, recode and retest. (Bersoff and Davis, 1991).
However, the main problems with evolutionary prototyping are due to poor management: Lack of defined milestones, lack of achievement - always putting off what would be in the present prototype until the next one, lack of proper evaluation, lack of clarity between a prototype and an implemented system, lack of continued commitment from users. This process requires a greater degree of sustained commitment from users for a longer time span than traditionally required. Users must be constantly informed as to what is going on and be completely aware of the expectations of the 'prototypes'.
References
Bersoff, E., Davis, A. (1991). Impacts of Life Cycle Models of Software Configuration Management. Comm. ACM.
Crinnion, J.(1991). Evolutionary Systems Development, a practical guide to the use of prototyping within a structured systems methodology. Plenum Press, New York.
Davis, A. (1992). Operational Prototyping: A new Development Approach. IEEE Software.
Software Productivity Consortium (SPC). (1997). Evolutionary Rapid Development. SPC document SPC-97057-CMC, version 01.00.04.