The Chaos Communication Congress is an annual hacker conference organized by the Chaos Computer Club. The congress features a variety of lectures and workshops on technical and political issues related to security, cryptography, privacy and online freedom of speech. It has taken place regularly at the end of the year since 1984, with the current date and duration (27–30 December) established in 2005. It is considered one of the largest events of its kind, alongside DEF CON in Las Vegas. == History == The congress is held in Germany. It started in 1984 in Hamburg, moved to Berlin in 1998, and back to Hamburg in 2012, having exceeded the capacity of the Berlin venue with more than 4500 attendees. Since then, it attracts an increasing number of people: around 6600 attendees in 2012, over 13000 in 2015, and more than 15000 in 2017. From 2017 to 2019, it took place at the Trade Fair Grounds in Leipzig, since the Hamburg venue (CCH) was closed for renovation in 2017 and the existing space was not enough for the growing congress. The congress moved back to Hamburg in 2023, after the renovation of CCH was finished. A large range of speakers are featured. The event is organized by volunteers called Chaos Angels. The non-members entry fee for four days was €100 in 2016, and was raised to €120 in 2018 to include a public transport ticket for the Leipzig area. An important part of the congress are the assemblies, semi-open spaces with clusters of tables and internet connections for groups and individuals to collaborate and socialize in projects, workshops and hands-on talks. These assembly spaces, introduced at the 2012 meeting, combine the hack center project space and distributed group spaces of former years. From 1997 to 2004 the congress also hosted the annual German Lockpicking Championships. 2005 was the first year the Congress lasted four days instead of three and lacked the German Lockpicking Championships. 2020 was the first year where the Congress did not take place at a physical location due to the COVID-19 pandemic, giving way to the first Remote Chaos Experience (rC3). The Chaos Computer Club announced to return to the now newly renovated Congress Center Hamburg for the 37th edition of the Chaos Communication Congress. The announcement confirms the usual date of 27-30 December, notably omitting the year it will be held. On 18 October 2022, they confirmed that the congress will indeed not be held in 2022. On 6 October 2023, the CCC announced that 37C3 will take place again on the usual dates in 2023. === Timeline ===
Protocol Builder
Protocol Builder is a tool in programming languages to generate code to build protocols in a fast and reliable way. Network programming for all kinds of protocols (such as TCP, UDP, and SNMP) includes converting data to be transferred to raw bytes in the sending side and parsing these bytes in the receiving side. Protocol builders facilitate this stage, usually by automatically generating the code. Protocol Programming has many components to be developed, these are: server listener, server connection, client connection, packets, and loggers. Most protocol builders implement these components automatically so developers save time and money. Currently, there are two Protocol Builders in the market, one for C++ from UpRedSun which is for TCP and UDP protocols. The second one is for .Net languages which generates the code in C# for TCP Protocols, this tool is called .Net Protocol Builder.
Selmer Bringsjord
Selmer Bringsjord (born November 24, 1958) is a professor of computer science and cognitive science and a former chair of the Department of Cognitive Science at Rensselaer Polytechnic Institute. He also holds an appointment in the Lally School of Management & Technology and teaches artificial Intelligence (AI), formal logic, human and machine reasoning, and philosophy of AI. == Biography == Bringsjord's education includes a B.A. in philosophy from the University of Pennsylvania and a Ph.D. in philosophy from Brown University, where he studied under Roderick Chisholm. He conducts research in AI as the director of the Rensselaer AI & Reasoning (RAIR) Laboratory. He specializes in the logico-mathematical and philosophical foundations of AI and cognitive science, and in collaboratively building AI systems on the basis of computational logic. Bringsjord believes that "the human mind will forever be superior to AI", and that "much of what many humans do for a living will be better done by indefatigable machines who require not a cent in pay". Bringsjord has stated that the "ultimate growth industry will be building smarter and smarter such machines on the one hand, and philosophizing about whether they are truly conscious and free on the other". Bringsjord has an argument for P = NP using digital physics. Other research includes developing a new computational-logic framework allowing the formalization of deliberative multi-agent "mindreading" as applied to the realm of nuclear strategy, with the goal of creating a model and simulation to enable reliable prediction. He has published an opinion piece advocating for counter-terrorism security ensured by pervasive, all-seeing sensors; automated reasoners; and autonomous, lethal robots. Bringsjord received a National Science Foundation award to research Social Robotics and the Covey Award for the advancement of philosophy of computing awarded by the International Association for Computing And Philosophy, among several others prizes. == Books authored == with Yang, Y. Mental Metalogic: A New, Unifying Theory of Human and Machine Reasoning (Mahwah, NJ: Lawrence Erlbaum).(2007) with Zenzen, M. Superminds: People Harness Hypercomputation, and More (Dordrecht, The Netherlands: Kluwer). (2003) ISBN 978-1402010958 with Ferrucci, D. Artificial Intelligence and Literary Creativity: Inside the Mind of Brutus, A Storytelling Machine (Mahwah, NJ: Lawrence Erlbaum).(2000) Abortion: A Dialogue (Indianapolis, IN: Hackett).(1997) What Robots Can and Can’t Be (Dordrecht, The Netherlands: Kluwer).(1992) Soft Wars (New York, NY: Penguin USA). A novel.(1991)
Isabelle Guyon
Isabelle Guyon (French pronunciation: [izabɛl ɡɥijɔ̃]; born August 15, 1961) is a French-born researcher in machine learning known for her work on support-vector machines, artificial neural networks and bioinformatics. She is a Chair Professor at the University of Paris-Saclay. Guyon serves as the Director of Research at Google DeepMind since October 2022. She is considered to be a pioneer in the field, with her contribution to the support-vector machines with Vladimir Vapnik and Bernhard Boser. == Biography == After graduating from the French engineering school ESPCI Paris in 1985, she joined the group of Gerard Dreyfus at the Université Pierre-et-Marie-Curie to do a PhD on neural networks architectures and training. Guyon defended her thesis in 1988 and was hired the year after at AT&T Bell Laboratories, first as a post-doc, then as a group leader. She worked at Bell Labs for six years, where she explored several research areas, from neural networks to pattern recognition and computational learning theory, with application to handwriting recognition. She collaborated with Yann LeCun, Léon Bottou, Vladimir Vapnik, Corinna Cortes, Yoshua Bengio, Patrice Simard, and met her future husband, Bernhard Boser. In 1996, Guyon left Bell Labs and raised her children at Berkeley, California. In Berkeley, she created her own machine learning consulting company, Clopinet. She became interested in medical applications, and used her previous work to classify the genes responsible for different types of cancers. Since 2003, Guyon has organized many challenges in data science, in order to stimulate research in this field. She founded ChaLearn in 2011, a non-profit organization aimed at creating machine learning challenges open to everyone. She was Program Chair of NeurIPS 2016 and became General Chair of NeurIPS in 2017. She is also Action Editor for the Journal of Machine Learning Research and Series Editor for Series: Challenges in Machine Learning. She is a member of the European Laboratory for Learning and Intelligent Systems. In 2016, Guyon came back to France to take the Chair Professorship in Big data between the University of Paris-Saclay and INRIA. She works in TAU (TAckling the Underspecified), a research collaboration of the Laboratoire de recherche en informatique. Together with Bernhard Schölkopf and Vladimir Vapnik, she received in 2020 the BBVA Foundation Frontiers of Knowledge Awards for her work in machine learning. == Scientific work == Guyon has worked in many subfields of machine learning, including neural networks, support-vector machines, feature selection and applications of machine learning to biology. === Support-vector machines === Among her most notable contributions, Guyon co-invented support-vector machines (SVM) in 1992, with Bernhard Boser and Vladimir Vapnik. SVM is a supervised machine learning algorithm, comparable to neural networks or decision trees, which has quickly become a classical technique in machine learning. SVMs have especially contributed to the popularization of kernel methods. === Neural networks === During her years at Bell Labs, Guyon took part of numerous projects involving neural networks. In particular, she wrote some of the first papers on the use of neural network for handwriting recognition using the MNIST database. She is also a co-inventor of the siamese neural networks, a neural network architecture used to learn similarities, with applications to signature, face or object recognition. === Machine learning for biology === Guyon is the author of many publications at the intersection of biology (cancer research and genomics) and artificial intelligence. She has notably introduced the use of support-vector machines to detect cancer using genes. === Machine learning challenges === Through her non-profit organization ChaLearn, Guyon has organized and directed challenges open to everyone in order to solve open problems in machine learning, including computer vision, neurosciences, particle physics, feature selection, causality and automated machine learning. Most of the challenges organized by ChaLearn have resulted in publications. Among the most cited ones are: Guyon et al., Result analysis of the NIPS 2003 feature selection challenge, Advances in neural information processing systems, 2005, link Escalera et al., ChaLearn Looking at People Challenge 2014: Dataset and Results, Computer Vision - ECCV 2014 Workshops, Springer International Publishing, 2014, link Guyon et al., A brief Review of the ChaLearn AutoML Challenge, JMLR: Workshop and Conference Proceedings 64:21-30, 2016, link Adam-Bourdario et al., The Higgs boson machine learning challenge, JMLR: Workshop and Conference Proceedings 42:19-55, 2015, link == Private life == She is married to Bernhard Boser, a professor at UC Berkeley. She has twins and one daughter, all three of whom have completed a science degree. Guyon has three citizenships: French by birth, Swiss by marriage and American by naturalization. == Awards and honors == Nomination at the French Academy of technologies (2024) Recipient of the BBVA Foundation Frontiers of Knowledge Awards (2020) American Medical Informatics Association Fellow (2011) == Publications == Bernhard Boser, Isabelle Guyon and Vladmir Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, 1992, doi:10.1145/130385.130401 Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger and Roopak Shah, Signature verification using a" siamese" time delay neural network, Advances in Neural Information Processing Systems, 1994. Isabelle Guyon and André Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, 2003. Isabelle Guyon, Jason Weston, Stephen Barnhill and Vladimir Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, Kluwer Academic Publishers, 2002, doi:10.1023/A:1012487302797
Vera Demberg
Vera Demberg (born 1981) is a German computational linguist and professor of computer science and computational linguistics at Saarland University. Her research interests include cognitive models of human language comprehension, natural language generation, experimental psycholinguistics, multimodal language processing in a dual-task setting, and experimental and computational discourse research and pragmatics. == Career and research == Vera Demberg studied computational linguistics at the Institute for Machine Language Processing at the University of Stuttgart from 2001 to 2006. She then completed a Master's degree in Artificial Intelligence at the University of Edinburgh from 2004 to 2005. She received her Ph.D. from the Department of Computer Science there from 2006 to 2010. Her dissertation paper, titled “Broad-Coverage Model of Prediction in Human Sentence Processing”, was awarded the Cognitive Science Society's “Glushko Dissertation Prize in Cognitive Science” in 2011. In her work, she designed a model of human sentence processing that can be used to predict difficulties in processing at the syntactic level. From 2010 to 2016, Vera Demberg led an independent research group on cognitive models of human language processing and their application to speech dialog systems in the Cluster of Excellence “Multimodal Computing and Interaction” at the University of Saarland. In 2016, she was appointed there to a professorship in computer science and computational linguistics. Demberg's professorship is in the Department of Computer Science (Faculty of Mathematics and Computer Science). She is also a co-opted professor in the Department of Linguistics and Language Technology (Faculty of Philosophy). Since 2020, she has led the ERC Starting Grant “Individualized Interaction in Discourse”. The project conducts research on how to make linguistic interaction with computer systems more natural. She has authored and co-authored numerous papers on the study of computational linguistics and natural language processing. According to Google Scholar, Vera Demberg has an H-index of 30. == Publications == Vera Demberg has authored more than 200 papers; please refer to her scholar page at https://scholar.google.com/citations?user=l2CFSAMAAAAJ == Awards == 2011: Cognitive Science Society Glushko Dissertation Prize in Cognitive Science 2020: ERC Starting Grant “Individualized Interaction in Discourse” 2024: Member of the Academy of Sciences and Literature
Hierarchical control system
A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system. == Overview == A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is a linked node in the tree. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers. Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer. The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. The layers form a hybrid intelligent system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning. A hierarchical task network is a good fit for planning in a hierarchical control system. Besides artificial systems, an animal's control systems are proposed to be organized as a hierarchy. In perceptual control theory, which postulates that an organism's behavior is a means of controlling its perceptions, the organism's control systems are suggested to be organized in a hierarchical pattern as their perceptions are constructed so. == Control system structure == The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. == Applications == === Manufacturing, robotics and vehicles === Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Computer-aided production engineering has been a research focus at NIST since the 1980s. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990s DARPA sponsored research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Real-Time Control System (RCS) and Real-time Control System Software which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle. In November 2007, DARPA held the Urban Challenge. The winning entry, Tartan Racing employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modelling, and mechatronics. === Artificial intelligence === Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture is a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the software agent (i.e. system as a whole), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer. Behavior need not be planned by a superior layer, rather behaviors may be triggered by sensory inputs and so are only active under circumstances where they might be appropriate. Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience. James Albus, while at NIST, developed a theory for intelligent system design named the Reference Model Architecture (RMA), which is a hierarchical control system inspired by RCS. Albus defines each node to contain these components. Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes. Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node. Value judgment is responsible for evaluating the updated situation and evaluating alternative plans. World Model is the local state that provides a model for the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes. At its lowest levels, the RMA can be implemented as a subsumption architecture, in which the world model is mapped directly to the controlled process or real world, avoiding the need for a mathematical abstraction, and in which time-constrained reactive planning can be implemented as a finite-state machine. Higher levels of the RMA however, may have sophisticated mathematical world models and behavior implemented by automated planning and scheduling. Planning is required when certain behaviors cannot be triggered by current sensations, but rather by predicted or anticipated sensations, especially those that come about as result of the node's actions.
Deterministic finite automaton
In the theory of computation, a branch of theoretical computer science, a deterministic finite automaton (DFA)—also known as deterministic finite acceptor (DFA), deterministic finite-state machine (DFSM), or deterministic finite-state automaton (DFSA)—is a finite-state machine that accepts or rejects a given string of symbols, by running through a state sequence uniquely determined by the string. Deterministic refers to the uniqueness of the computation run. In search of the simplest models to capture finite-state machines, Warren McCulloch and Walter Pitts were among the first researchers to introduce a concept similar to finite automata in 1943. The figure illustrates a deterministic finite automaton using a state diagram. In this example automaton, there are three states: S0, S1, and S2 (denoted graphically by circles). The automaton takes a finite sequence of 0s and 1s as input. For each state, there is a transition arrow leading out to a next state for both 0 and 1. Upon reading a symbol, a DFA jumps deterministically from one state to another by following the transition arrow. For example, if the automaton is currently in state S0 and the current input symbol is 1, then it deterministically jumps to state S1. A DFA has a start state (denoted graphically by an arrow coming in from nowhere) where computations begin, and a set of accept states (denoted graphically by a double circle) which help define when a computation is successful. A DFA is defined as an abstract mathematical concept, but is often implemented in hardware and software for solving various specific problems such as lexical analysis and pattern matching. For example, a DFA can model software that decides whether or not online user input such as email addresses are syntactically valid. DFAs have been generalized to nondeterministic finite automata (NFA) which may have several arrows of the same label starting from a state. Using the powerset construction method, every NFA can be translated to a DFA that recognizes the same language. DFAs, and NFAs as well, recognize exactly the set of regular languages. == Formal definition == A deterministic finite automaton M is a 5-tuple, (Q, Σ, δ, q0, F), consisting of a finite set of states Q a finite set of input symbols called the alphabet Σ a transition function δ : Q × Σ → Q an initial (or start) state q 0 ∈ Q {\displaystyle q_{0}\in Q} a set of accepting (or final) states F ⊆ Q {\displaystyle F\subseteq Q} Let w = a1a2...an be a string over the alphabet Σ. The automaton M accepts the string w if a sequence of states, r0, r1, ..., rn, exists in Q with the following conditions: r0 = q0 ri+1 = δ(ri, ai+1), for i = 0, ..., n − 1 r n ∈ F {\displaystyle r_{n}\in F} . In words, the first condition says that the machine starts in the start state q0. The second condition says that given each character of string w, the machine will transition from state to state according to the transition function δ. The last condition says that the machine accepts w if the last input of w causes the machine to halt in one of the accepting states. Otherwise, it is said that the automaton rejects the string. The set of strings that M accepts is the language recognized by M and this language is denoted by L(M). A deterministic finite automaton without accept states and without a starting state is known as a transition system or semiautomaton. For more comprehensive introduction of the formal definition see automata theory. == Example == The following example is of a DFA M, with a binary alphabet, which requires that the input contains an even number of 0s. M = (Q, Σ, δ, q0, F) where Q = {S1, S2} Σ = {0, 1} q0 = S1 F = {S1} and δ is defined by the following state transition table: The state S1 represents that there has been an even number of 0s in the input so far, while S2 signifies an odd number. A 1 in the input does not change the state of the automaton. When the input ends, the state will show whether the input contained an even number of 0s or not. If the input did contain an even number of 0s, M will finish in state S1, an accepting state, so the input string will be accepted. The language recognized by M is the regular language given by the regular expression (1) (0 (1) 0 (1)), where is the Kleene star, e.g., 1 denotes any number (possibly zero) of consecutive ones. == Variations == === Complete and incomplete === According to the above definition, deterministic finite automata are always complete: they define from each state a transition for each input symbol. While this is the most common definition, some authors use the term deterministic finite automaton for a slightly different notion: an automaton that defines at most one transition for each state and each input symbol; the transition function is allowed to be partial. When no transition is defined, such an automaton halts. === Local automata === A local automaton is a DFA, not necessarily complete, for which all edges with the same label lead to a single vertex. Local automata accept the class of local languages, those for which membership of a word in the language is determined by a "sliding window" of length two on the word. A Myhill graph over an alphabet A is a directed graph with vertex set A and subsets of vertices labelled "start" and "finish". The language accepted by a Myhill graph is the set of directed paths from a start vertex to a finish vertex: the graph thus acts as an automaton. The class of languages accepted by Myhill graphs is the class of local languages. === Randomness === When the start state and accept states are ignored, a DFA of n states and an alphabet of size k can be seen as a digraph of n vertices in which all vertices have k out-arcs labeled 1, ..., k (a k-out digraph). It is known that when k ≥ 2 is a fixed integer, with high probability, the largest strongly connected component (SCC) in such a k-out digraph chosen uniformly at random is of linear size and it can be reached by all vertices. It has also been proven that if k is allowed to increase as n increases, then the whole digraph has a phase transition for strong connectivity similar to Erdős–Rényi model for connectivity. In a random DFA, the maximum number of vertices reachable from one vertex is very close to the number of vertices in the largest SCC with high probability. This is also true for the largest induced sub-digraph of minimum in-degree one, which can be seen as a directed version of 1-core. == Closure properties == If DFAs recognize the languages that are obtained by applying an operation on the DFA recognizable languages then DFAs are said to be closed under the operation. The DFAs are closed under the following operations. For each operation, an optimal construction with respect to the number of states has been determined in state complexity research. Since DFAs are equivalent to nondeterministic finite automata (NFA), these closures may also be proved using closure properties of NFA. == As a transition monoid == A run of a given DFA can be seen as a sequence of compositions of a very general formulation of the transition function with itself. Here we construct that function. For a given input symbol a ∈ Σ {\displaystyle a\in \Sigma } , one may construct a transition function δ a : Q → Q {\displaystyle \delta _{a}:Q\rightarrow Q} by defining δ a ( q ) = δ ( q , a ) {\displaystyle \delta _{a}(q)=\delta (q,a)} for all q ∈ Q {\displaystyle q\in Q} . (This trick is called currying.) From this perspective, δ a {\displaystyle \delta _{a}} "acts" on a state in Q to yield another state. One may then consider the result of function composition repeatedly applied to the various functions δ a {\displaystyle \delta _{a}} , δ b {\displaystyle \delta _{b}} , and so on. Given a pair of letters a , b ∈ Σ {\displaystyle a,b\in \Sigma } , one may define a new function δ ^ a b = δ a ∘ δ b {\displaystyle {\widehat {\delta }}_{ab}=\delta _{a}\circ \delta _{b}} , where ∘ {\displaystyle \circ } denotes function composition. Clearly, this process may be recursively continued, giving the following recursive definition of δ ^ : Q × Σ ⋆ → Q {\displaystyle {\widehat {\delta }}:Q\times \Sigma ^{\star }\rightarrow Q} : δ ^ ( q , ϵ ) = q {\displaystyle {\widehat {\delta }}(q,\epsilon )=q} , where ϵ {\displaystyle \epsilon } is the empty string and δ ^ ( q , w a ) = δ a ( δ ^ ( q , w ) ) {\displaystyle {\widehat {\delta }}(q,wa)=\delta _{a}({\widehat {\delta }}(q,w))} , where w ∈ Σ ∗ , a ∈ Σ {\displaystyle w\in \Sigma ^{},a\in \Sigma } and q ∈ Q {\displaystyle q\in Q} . δ ^ {\displaystyle {\widehat {\delta }}} is defined for all words w ∈ Σ ∗ {\displaystyle w\in \Sigma ^{}} . A run of the DFA is a sequence of compositions of δ ^ {\displaystyle {\widehat {\delta }}} with itself. Repeated function composition forms a monoid. For the transition functions, this monoid is known as the transition monoid, or sometimes the transformation semigroup. The construction can also be reversed: given a δ ^ {\displaystyle {\wide