143 skills found · Page 2 of 5
Wintellect / ProcMonDebugOutputSee your trace statements in Sysinternals Process Monitor
sanusanth / Python Basic ProgramsWhat is Python? Executive Summary Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective. What is Python? Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: web development (server-side), software development, mathematics, system scripting. What can Python do? Python can be used on a server to create web applications. Python can be used alongside software to create workflows. Python can connect to database systems. It can also read and modify files. Python can be used to handle big data and perform complex mathematics. Python can be used for rapid prototyping, or for production-ready software development. Why Python? Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc). Python has a simple syntax similar to the English language. Python has syntax that allows developers to write programs with fewer lines than some other programming languages. Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick. Python can be treated in a procedural way, an object-oriented way or a functional way. Good to know The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular. In this tutorial Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files. Python Syntax compared to other programming languages Python was designed for readability, and has some similarities to the English language with influence from mathematics. Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses. Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose. Applications for Python Python is used in many application domains. Here's a sampling. The Python Package Index lists thousands of third party modules for Python. Web and Internet Development Python offers many choices for web development: Frameworks such as Django and Pyramid. Micro-frameworks such as Flask and Bottle. Advanced content management systems such as Plone and django CMS. Python's standard library supports many Internet protocols: HTML and XML JSON E-mail processing. Support for FTP, IMAP, and other Internet protocols. Easy-to-use socket interface. And the Package Index has yet more libraries: Requests, a powerful HTTP client library. Beautiful Soup, an HTML parser that can handle all sorts of oddball HTML. Feedparser for parsing RSS/Atom feeds. Paramiko, implementing the SSH2 protocol. Twisted Python, a framework for asynchronous network programming. Scientific and Numeric Python is widely used in scientific and numeric computing: SciPy is a collection of packages for mathematics, science, and engineering. Pandas is a data analysis and modeling library. IPython is a powerful interactive shell that features easy editing and recording of a work session, and supports visualizations and parallel computing. The Software Carpentry Course teaches basic skills for scientific computing, running bootcamps and providing open-access teaching materials. Education Python is a superb language for teaching programming, both at the introductory level and in more advanced courses. Books such as How to Think Like a Computer Scientist, Python Programming: An Introduction to Computer Science, and Practical Programming. The Education Special Interest Group is a good place to discuss teaching issues. Desktop GUIs The Tk GUI library is included with most binary distributions of Python. Some toolkits that are usable on several platforms are available separately: wxWidgets Kivy, for writing multitouch applications. Qt via pyqt or pyside Platform-specific toolkits are also available: GTK+ Microsoft Foundation Classes through the win32 extensions Software Development Python is often used as a support language for software developers, for build control and management, testing, and in many other ways. SCons for build control. Buildbot and Apache Gump for automated continuous compilation and testing. Roundup or Trac for bug tracking and project management. Business Applications Python is also used to build ERP and e-commerce systems: Odoo is an all-in-one management software that offers a range of business applications that form a complete suite of enterprise management applications. Try ton is a three-tier high-level general purpose application platform.
AlessandraGalli / PPGCode to estimate HR from PPG signals using Subspace Decomposition and Kalman filter for the dataset of 22 PPG recordings provided for the 2015 IEEE Signal Processing Cup (SP Cup) competition. The traces are stored in folder 'DATABASE'. Please cite this publication when referencing this material: "Measuring Heart Rate During Physical Exercise by Subspace Decomposition and Kalman Smoothing", IEEE Transaction on Instrumentation & Measurement.
KernelErr / FoxearFox Ear is a Linux process behavior trace tool powered by eBPF.
gimelfarb / ProductionStackTraceWithout deploying PDBs, generate a .NET exception stack trace that can be processed to retrieve source file and line number info
hase-project / HaseTimeless debugging with symbolic execution and processor trace
aryoda / TryCatchLogR package for better error handling incl. logging with a full (!) stack trace incl. line numbers, post-mortem analysis and support for parallel processing
mafintosh / Block TraceFigure out if your node process is blocked because the CPU is spinning and exit the program with a stack trace if that is the case
recursivelabsai / Self TracingBuilding on Anthropic's Circuit Tracer, Neuronpedia, Ameisen et al. (2025) and Lindsey et al. (2025), we attempt to extend the paradigm with adaptive context engineering to enable recursive self-interpretation, where models continuously monitor, trace, and explain their own decision processes
SOYJUN / FTP Implement Based On UDPThe aim of this assignment is to have you do UDP socket client / server programming with a focus on two broad aspects : Setting up the exchange between the client and server in a secure way despite the lack of a formal connection (as in TCP) between the two, so that ‘outsider’ UDP datagrams (broadcast, multicast, unicast - fortuitously or maliciously) cannot intrude on the communication. Introducing application-layer protocol data-transmission reliability, flow control and congestion control in the client and server using TCP-like ARQ sliding window mechanisms. The second item above is much more of a challenge to implement than the first, though neither is particularly trivial. But they are not tightly interdependent; each can be worked on separately at first and then integrated together at a later stage. Apart from the material in Chapters 8, 14 & 22 (especially Sections 22.5 - 22.7), and the experience you gained from the preceding assignment, you will also need to refer to the following : ioctl function (Chapter 17). get_ifi_info function (Section 17.6, Chapter 17). This function will be used by the server code to discover its node’s network interfaces so that it can bind all its interface IP addresses (see Section 22.6). ‘Race’ conditions (Section 20.5, Chapter 20) You also need a thorough understanding of how the TCP protocol implements reliable data transfer, flow control and congestion control. Chapters 17- 24 of TCP/IP Illustrated, Volume 1 by W. Richard Stevens gives a good overview of TCP. Though somewhat dated for some things (it was published in 1994), it remains, overall, a good basic reference. Overview This assignment asks you to implement a primitive file transfer protocol for Unix platforms, based on UDP, and with TCP-like reliability added to the transfer operation using timeouts and sliding-window mechanisms, and implementing flow and congestion control. The server is a concurrent server which can handle multiple clients simultaneously. A client gives the server the name of a file. The server forks off a child which reads directly from the file and transfers the contents over to the client using UDP datagrams. The client prints out the file contents as they come in, in order, with nothing missing and with no duplication of content, directly on to stdout (via the receiver sliding window, of course, but with no other intermediate buffering). The file to be transferred can be of arbitrary length, but its contents are always straightforward ascii text. As an aside let me mention that assuming the file contents ascii is not as restrictive as it sounds. We can always pretend, for example, that binary files are base64 encoded (“ASCII armor”). A real file transfer protocol would, of course, have to worry about transferring files between heterogeneous platforms with different file structure conventions and semantics. The sender would first have to transform the file into a platform-independent, protocol-defined, format (using, say, ASN.1, or some such standard), and the receiver would have to transform the received file into its platform’s native file format. This kind of thing can be fairly time consuming, and is certainly very tedious, to implement, with little educational value - it is not part of this assignment. Arguments for the server You should provide the server with an input file server.in from which it reads the following information, in the order shown, one item per line : Well-known port number for server. Maximum sending sliding-window size (in datagram units). You will not be handing in your server.in file. We shall create our own when we come to test your code. So it is important that you stick strictly to the file name and content conventions specified above. The same applies to the client.in input file below. Arguments for the client The client is to be provided with an input file client.in from which it reads the following information, in the order shown, one item per line : IP address of server (not the hostname). Well-known port number of server. filename to be transferred. Receiving sliding-window size (in datagram units). Random generator seed value. Probability p of datagram loss. This should be a real number in the range [ 0.0 , 1.0 ] (value 0.0 means no loss occurs; value 1.0 means all datagrams all lost). The mean µ, in milliseconds, for an exponential distribution controlling the rate at which the client reads received datagram payloads from its receive buffer. Operation Server starts up and reads its arguments from file server.in. As we shall see, when a client communicates with the server, the server will want to know what IP address that client is using to identify the server (i.e. , the destination IP address in the incoming datagram). Normally, this can be done relatively straightforwardly using the IP_RECVDESTADDR socket option, and picking up the information using the ancillary data (‘control information’) capability of the recvmsg function. Unfortunately, Solaris 2.10 does not support the IP_RECVDESTADDR option (nor, incidentally, does it support the msg_flags option in msghdr - see p.390). This considerably complicates things. In the absence of IP_RECVDESTADDR, what the server has to do as part of its initialization phase is to bind each IP address it has (and, simultaneously, its well-known port number, which it has read in from server.in) to a separate UDP socket. The code in Section 22.6, which uses the get_ifi_info function, shows you how to do that. However, there are important differences between that code and the version you want to implement. The code of Section 22.6 binds the IP addresses and forks off a child for each address that is bound to. We do not want to do that. Instead you should have an array of socket descriptors. For each IP address, create a new socket and bind the address (and well-known port number) to the socket without forking off child processes. Creating child processes comes later, when clients arrive. The code of Section 22.6 also attempts to bind broadcast addresses. We do not want to do this. It binds a wildcard IP address, which we certainly do not want to do either. We should bind strictly only unicast addresses (including the loopback address). The get_ifi_info function (which the code in Section 22.6 uses) has to be modified so that it also gets the network masks for the IP addresses of the node, and adds these to the information stored in the linked list of ifi_info structures (see Figure 17.5, p.471) it produces. As you go binding each IP address to a distinct socket, it will be useful for later processing to build your own array of structures, where a structure element records the following information for each socket : sockfd IP address bound to the socket network mask for the IP address subnet address (obtained by doing a bit-wise and between the IP address and its network mask) Report, in a ReadMe file which you hand in with your code, on the modifications you had to introduce to ensure that only unicast addresses are bound, and on your implementation of the array of structures described above. You should print out on stdout, with an appropriate message and appropriately formatted in dotted decimal notation, the IP address, network mask, and subnet address for each socket in your array of structures (you do not need to print the sockfd). The server now uses select to monitor the sockets it has created for incoming datagrams. When it returns from select, it must use recvfrom or recvmsg to read the incoming datagram (see 6. below). When a client starts, it first reads its arguments from the file client.in. The client checks if the server host is ‘local’ to its (extended) Ethernet. If so, all its communication to the server is to occur as MSG_DONTROUTE (or SO_DONTROUTE socket option). It determines if the server host is ‘local’ as follows. The first thing the client should do is to use the modified get_ifi_info function to obtain all of its IP addresses and associated network masks. Print out on stdout, in dotted decimal notation and with an appropriate message, the IP addresses and network masks obtained. In the following, IPserver designates the IP address the client will use to identify the server, and IPclient designates the IP address the client will choose to identify itself. The client checks whether the server is on the same host. If so, it should use the loopback address 127.0.0.1 for the server (i.e. , IPserver = 127.0.0.1). IPclient should also be set to the loopback address. Otherwise it proceeds as follows: IPserver is set to the IP address for the server in the client.in file. Given IPserver and the (unicast) IP addresses and network masks for the client returned by get_ifi_info in the linked list of ifi_info structures, you should be able to figure out if the server node is ‘local’ or not. This will be discussed in class; but let me just remind you here that you should use ‘longest prefix matching’ where applicable. If there are multiple client addresses, and the server host is ‘local’, the client chooses an IP address for itself, IPclient, which matches up as ‘local’ according to your examination above. If the server host is not ‘local’, then IPclient can be chosen arbitrarily. Print out on stdout the results of your examination, as to whether the server host is ‘local’ or not, as well as the IPclient and IPserver addresses selected. Note that this manner of determining whether the server is local or not is somewhat clumsy and ‘over-engineered’, and, as such, should be viewed more in the nature of a pedagogical exercise. Ideally, we would like to look up the server IP address(es) in the routing table (see Section 18.3). This requires that a routing socket be created, for which we need superuser privilege. Alternatively, we might want to dump out the routing table, using the sysctl function for example (see Section 18.4), and examine it directly. Unfortunately, Solaris 2.10 does not support sysctl. Furthermore, note that there is a slight problem with the address 130.245.1.123/24 assigned to compserv3 (see rightmost column of file hosts, and note that this particular compserv3 address “overlaps” with the 130.245.1.x/28 addresses in that same column assigned to compserv1, compserv2 & comserv4). In particular, if the client is running on compserv3 and the server on any of the other three compservs, and if that server node is also being identified to the client by its /28 (rather than its /24) address, then the client will get a “false positive” when it tests as to whether the server node is local or not. In other words, the client will deem the server node to be local, whereas in fact it should not be considered local. Because of this, it is perhaps best simply not to use compserv3 to run the client (but it is o.k. to use it to run the server). Finally, using MSG_DONTROUTE where possible would seem to gain us efficiency, in as much as the kernel does not need to consult the routing table for every datagram sent. But, in fact, that is not so. Recall that one effect of connect with UDP sockets is that routing information is obtained by the kernel at the time the connect is issued. That information is cached and used for subsequent sends from the connected socket (see p.255). The client now creates a UDP socket and calls bind on IPclient, with 0 as the port number. This will cause the kernel to bind an ephemeral port to the socket. After the bind, use the getsockname function (Section 4.10) to obtain IPclient and the ephemeral port number that has been assigned to the socket, and print that information out on stdout, with an appropriate message and appropriately formatted. The client connects its socket to IPserver and the well-known port number of the server. After the connect, use the getpeername function (Section 4.10) to obtain IPserver and the well-known port number of the server, and print that information out on stdout, with an appropriate message and appropriately formatted. The client sends a datagram to the server giving the filename for the transfer. This send needs to be backed up by a timeout in case the datagram is lost. Note that the incoming datagram from the client will be delivered to the server at the socket to which the destination IP address that the datagram is carrying has been bound. Thus, the server can obtain that address (it is, of course, IPserver) and thereby achieve what IP_RECVDESTADDR would have given us had it been available. Furthermore, the server process can obtain the IP address (this will, of course, be IPclient) and ephemeral port number of the client through the recvfrom or recvmsg functions. The server forks off a child process to handle the client. The server parent process goes back to the select to listen for new clients. Hereafter, and unless otherwise stated, whenever we refer to the ‘server’, we mean the server child process handling the client’s file transfer, not the server parent process. Typically, the first thing the server child would be expected to do is to close all sockets it ‘inherits’ from its parent. However, this is not the case with us. The server child does indeed close the sockets it inherited, but not the socket on which the client request arrived. It leaves that socket open for now. Call this socket the ‘listening’ socket. The server (child) then checks if the client host is local to its (extended) Ethernet. If so, all its communication to the client is to occur as MSG_DONTROUTE (or SO_DONTROUTE socket option). If IPserver (obtained in 5. above) is the loopback address, then we are done. Otherwise, the server has to proceed with the following step. Use the array of structures you built in 1. above, together with the addresses IPserver and IPclient to determine if the client is ‘local’. Print out on stdout the results of your examination, as to whether the client host is ‘local’ or not. The server (child) creates a UDP socket to handle file transfer to the client. Call this socket the ‘connection’ socket. It binds the socket to IPserver, with port number 0 so that its kernel assigns an ephemeral port. After the bind, use the getsockname function (Section 4.10) to obtain IPserver and the ephemeral port number that has been assigned to the socket, and print that information out on stdout, with an appropriate message and appropriately formatted. The server then connects this ‘connection’ socket to the client’s IPclient and ephemeral port number. The server now sends the client a datagram, in which it passes it the ephemeral port number of its ‘connection’ socket as the data payload of the datagram. This datagram is sent using the ‘listening’ socket inherited from its parent, otherwise the client (whose socket is connected to the server’s ‘listening’ socket at the latter’s well-known port number) will reject it. This datagram must be backed up by the ARQ mechanism, and retransmitted in the event of loss. Note that if this datagram is indeed lost, the client might well time out and retransmit its original request message (the one carrying the file name). In this event, you must somehow ensure that the parent server does not mistake this retransmitted request for a new client coming in, and spawn off yet another child to handle it. How do you do that? It is potentially more involved than it might seem. I will be discussing this in class, as well as ‘race’ conditions that could potentially arise, depending on how you code the mechanisms I present. When the client receives the datagram carrying the ephemeral port number of the server’s ‘connection’ socket, it reconnects its socket to the server’s ‘connection’ socket, using IPserver and the ephemeral port number received in the datagram (see p.254). It now uses this reconnected socket to send the server an acknowledgment. Note that this implies that, in the event of the server timing out, it should retransmit two copies of its ‘ephemeral port number’ message, one on its ‘listening’ socket and the other on its ‘connection’ socket (why?). When the server receives the acknowledgment, it closes the ‘listening’ socket it inherited from its parent. The server can now commence the file transfer through its ‘connection’ socket. The net effect of all these binds and connects at server and client is that no ‘outsider’ UDP datagram (broadcast, multicast, unicast - fortuitously or maliciously) can now intrude on the communication between server and client. Starting with the first datagram sent out, the client behaves as follows. Whenever a datagram arrives, or an ACK is about to be sent out (or, indeed, the initial datagram to the server giving the filename for the transfer), the client uses some random number generator function random() (initialized by the client.in argument value seed) to decide with probability p (another client.in argument value) if the datagram or ACK should be discarded by way of simulating transmission loss across the network. (I will briefly discuss in class how you do this.) Adding reliability to UDP The mechanisms you are to implement are based on TCP Reno. These include : Reliable data transmission using ARQ sliding-windows, with Fast Retransmit. Flow control via receiver window advertisements. Congestion control that implements : SlowStart Congestion Avoidance (‘Additive-Increase/Multiplicative Decrease’ – AIMD) Fast Recovery (but without the window-inflation aspect of Fast Recovery) Only some, and by no means all, of the details for these are covered below. The rest will be presented in class, especially those concerning flow control and TCP Reno’s congestion control mechanisms in general : Slow Start, Congestion Avoidance, Fast Retransmit and Fast Recovery. Implement a timeout mechanism on the sender (server) side. This is available to you from Stevens, Section 22.5 . Note, however, that you will need to modify the basic driving mechanism of Figure 22.7 appropriately since the situation at the sender side is not a repetitive cycle of send-receive, but rather a straightforward progression of send-send-send-send- . . . . . . . . . . . Also, modify the RTT and RTO mechanisms of Section 22.5 as specified below. I will be discussing the details of these modifications and the reasons for them in class. Modify function rtt_stop (Fig. 22.13) so that it uses integer arithmetic rather than floating point. This will entail your also having to modify some of the variable and function parameter declarations throughout Section 22.5 from float to int, as appropriate. In the unprrt.h header file (Fig. 22.10) set : RTT_RXTMIN to 1000 msec. (1 sec. instead of the current value 3 sec.) RTT_RXTMAX to 3000 msec. (3 sec. instead of the current value 60 sec.) RTT_MAXNREXMT to 12 (instead of the current value 3) In function rtt_timeout (Fig. 22.14), after doubling the RTO in line 86, pass its value through the function rtt_minmax of Fig. 22.11 (somewhat along the lines of what is done in line 77 of rtt_stop, Fig. 22.13). Finally, note that with the modification to integer calculation of the smoothed RTT and its variation, and given the small RTT values you will experience on the cs / sbpub network, these calculations should probably now be done on a millisecond or even microsecond scale (rather than in seconds, as is the case with Stevens’ code). Otherwise, small measured RTTs could show up as 0 on a scale of seconds, yielding a negative result when we subtract the smoothed RTT from the measured RTT (line 72 of rtt_stop, Fig. 22.13). Report the details of your modifications to the code of Section 22.5 in the ReadMe file which you hand in with your code. We need to have a sender sliding window mechanism for the retransmission of lost datagrams; and a receiver sliding window in order to ensure correct sequencing of received file contents, and some measure of flow control. You should implement something based on TCP Reno’s mechanisms, with cumulative acknowledgments, receiver window advertisements, and a congestion control mechanism I will explain in detail in class. For a reference on TCP’s mechanisms generally, see W. Richard Stevens, TCP/IP Illustrated, Volume 1 , especially Sections 20.2 - 20.4 of Chapter 20 , and Sections 21.1 - 21.8 of Chapter 21 . Bear in mind that our sequence numbers should count datagrams, not bytes as in TCP. Remember that the sender and receiver window sizes have to be set according to the argument values in client.in and server.in, respectively. Whenever the sender window becomes full and so ‘locks’, the server should print out a message to that effect on stdout. Similarly, whenever the receiver window ‘locks’, the client should print out a message on stdout. Be aware of the potential for deadlock when the receiver window ‘locks’. This situation is handled by having the receiver process send a duplicate ACK which acts as a window update when its window opens again (see Figure 20.3 and the discussion about it in TCP/IP Illustrated). However, this is not enough, because ACKs are not backed up by a timeout mechanism in the event they are lost. So we will also need to implement a persist timer driving window probes in the sender process (see Sections 22.1 & 22.2 in Chapter 22 of TCP/IP Illustrated). Note that you do not have to worry about the Silly Window Syndrome discussed in Section 22.3 of TCP/IP Illustrated since the receiver process consumes ‘full sized’ 512-byte messages from the receiver buffer (see 3. below). Report on the details of the ARQ mechanism you implemented in the ReadMe file you hand in. Indeed, you should report on all the TCP mechanisms you implemented in the ReadMe file, both the ones discussed here, and the ones I will be discussing in class. Make your datagram payload a fixed 512 bytes, inclusive of the file transfer protocol header (which must, at the very least, carry: the sequence number of the datagram; ACKs; and advertised window notifications). The client reads the file contents in its receive buffer and prints them out on stdout using a separate thread. This thread sits in a repetitive loop till all the file contents have been printed out, doing the following. It samples from an exponential distribution with mean µ milliseconds (read from the client.in file), sleeps for that number of milliseconds; wakes up to read and print all in-order file contents available in the receive buffer at that point; samples again from the exponential distribution; sleeps; and so on. The formula -1 × µ × ln( random( ) ) , where ln is the natural logarithm, yields variates from an exponential distribution with mean µ, based on the uniformly-distributed variates over ( 0 , 1 ) returned by random(). Note that you will need to implement some sort of mutual exclusion/semaphore mechanism on the client side so that the thread that sleeps and wakes up to consume from the receive buffer is not updating the state variables of the buffer at the same time as the main thread reading from the socket and depositing into the buffer is doing the same. Furthermore, we need to ensure that the main thread does not effectively monopolize the semaphore (and thus lock out for prolonged periods of time) the sleeping thread when the latter wakes up. See the textbook, Section 26.7, ‘Mutexes: Mutual Exclusion’, pp.697-701. You might also find Section 26.8, ‘Condition Variables’, pp.701-705, useful. You will need to devise some way by which the sender can notify the receiver when it has sent the last datagram of the file transfer, without the receiver mistaking that EOF marker as part of the file contents. (Also, note that the last data segment could be a “short” segment of less than 512 bytes – your client needs to be able to handle this correctly somehow.) When the sender receives an ACK for the last datagram of the transfer, the (child) server terminates. The parent server has to take care of cleaning up zombie children. Note that if we want a clean closing, the client process cannot simply terminate when the receiver ACKs the last datagram. This ACK could be lost, which would leave the (child) server process ‘hanging’, timing out, and retransmitting the last datagram. TCP attempts to deal with this problem by means of the TIME_WAIT state. You should have your receiver process behave similarly, sticking around in something akin to a TIME_WAIT state in case in case it needs to retransmit the ACK. In the ReadMe file you hand in, report on how you dealt with the issues raised here: sender notifying receiver of the last datagram, clean closing, and so on. Output Some of the output required from your program has been described in the section Operation above. I expect you to provide further output – clear, well-structured, well-laid-out, concise but sufficient and helpful – in the client and server windows by means of which we can trace the correct evolution of your TCP’s behaviour in all its intricacies : information (e.g., sequence number) on datagrams and acks sent and dropped, window advertisements, datagram retransmissions (and why : dup acks or RTO); entering/exiting Slow Start and Congestion Avoidance, ssthresh and cwnd values; sender and receiver windows locking/unlocking; etc., etc. . . . . The onus is on you to convince us that the TCP mechanisms you implemented are working correctly. Too many students do not put sufficient thought, creative imagination, time or effort into this. It is not the TA’s nor my responsibility to sit staring at an essentially blank screen, trying to summon up our paranormal psychology skills to figure out if your TCP implementation is really working correctly in all its very intricate aspects, simply because the transferred file seems to be printing o.k. in the client window. Nor is it our responsibility to strain our eyes and our patience wading through a mountain of obscure, ill-structured, hyper-messy, debugging-style output because, for example, your effort-conserving concept of what is ‘suitable’ is to dump your debugging output on us, relevant, irrelevant, and everything in between.
stephenfewer / OllySocketTraceOllySocketTrace is a plugin for OllyDbg to trace the socket operations being performed by a process.
caiannello / JsVoronoiPCBJavascript app to process a PCB image and output G-code suitable for laser engraver or similar machine. To reduce tool time, by default, the board traces are Voronoi Tesselated, though this is optional.
Hackingzone / Winpirateto grab or steal the password of systemAutomated sticky keys hack. Post exploitation it grabs browser passwords, history, and network passwords Download git clone https://github.com/pourquoibenoit/WinPirate.git This project is still in progress Estimated Completion Date: soon…ish Here’s the plan. We create a way to automate doing the sticky keys windows hack from a bootable USB. Then, we automate getting as many saved passwords as possible, drop a listener, and delete all traces that we were there. All without being detected by antivirus we should add a mimikittenz option if the computer was found running and unlocked, otherwise we can just run it later remotely How to Use Requirements: a linux bootable USB, this repo on the USB (not in the OS, just put it in the root directory) chromepasswords.py requires PyWin32 If the computer is locked: 1) shutdown windows (make sure not hibernating by holding shift while pressing shut down) 2) hit F12 and select USB 3) sudo -i 4) fdisk -l (note: if you’re on Kali Linux, run parted -l) 5) mkdir /media/windows 6) mount /dev/WHATEVERTHEWINDOWSPARTITIONWASCALLED /media/windows -t ntfs 7) run Stickykeys.sh 8) restart and boot to Windows 9) hit Shift 5 times fast, a command prompt will appear 10) cd to the USB and run WinPirate.bat If the computer isn’t locked: cd to the USB and run Run.bat (this will run WinPirate.bat silently in the background, it should be done in < 10 seconds Current Issues 1) The chrome passwords grabber that I made is still a .py For it to work, I need to convert it to exe so it doesn’t require python to be installed on the system. You can run it with python chromepasswords.py -csv and it will decrypt the Chrome saved passwords database and export it as a CSV 2) The sticky keys automation doesn’t speed the process up as much as I previously thought, as evident by the lengthy “How to Use” section 3) I haven’t been able to write any tools that grab passwords for IE or Firefox Source: https://github.com/pourquoibenoit/WinPirate
Aryia-Behroziuan / ReferencesPoole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". 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Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. 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The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. 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Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently
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