AI Avatar King

AI Avatar King — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Audio mining

  Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. It is most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio. The term audio mining is sometimes used interchangeably with audio indexing, phonetic searching, phonetic indexing, speech indexing, audio analytics, speech analytics, word spotting, and information retrieval. Audio indexing, however, is mostly used to describe the pre-process of audio mining, in which the audio file is broken down into a searchable index of words. == History == Academic research on audio mining began in the late 1970s in schools like Carnegie Mellon University, Columbia University, the Georgia Institute of Technology, and the University of Texas. Audio data indexing and retrieval began to receive attention and demand in the early 1990s, when multimedia content started to develop and the volume of audio content significantly increased. Before audio mining became the mainstream method, written transcripts of audio content were created and manually analyzed. == Process == Audio mining is typically split into four components: audio indexing, speech processing and recognition systems, feature extraction and audio classification. The audio will typically be processed by a speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content. This information may either be used immediately in pre-defined searches for keywords or phrases (a real-time "word spotting" system), or the output of the speech recognizer may be stored in an index file. One or more audio mining index files can then be loaded at a later date in order to run searches for keywords or phrases. The results of a search will normally be in terms of hits, which are regions within files that are good matches for the chosen keywords. The user may then be able to listen to the audio corresponding to these hits in order to verify if a correct match was found. === Audio Indexing === In audio, there is the main problem of information retrieval - there is a need to locate the text documents that contain the search key. Unlike humans, a computer is not able to distinguish between the different types of audios such as speed, mood, noise, music or human speech - an effective searching method is needed. Hence, audio indexing allows efficient search for information by analyzing an entire file using speech recognition. An index of content is then produced, bearing words and their locations done through content-based audio retrieval, focusing on extracted audio features. It is done through mainly two methods: Large Vocabulary Continuous Speech Recognition (LVCSR) and Phonetic-based Indexing. ==== Large Vocabulary Continuous Speech Recognizers (LVCSR) ==== In text-based indexing or large vocabulary continuous speech recognition (LVCSR), the audio file is first broken down into recognizable phonemes. It is then run through a dictionary that can contain several hundred thousand entries and matched with words and phrases to produce a full text transcript. A user can then simply search a desired word term and the relevant portion of the audio content will be returned. If the text or word could not be found in the dictionary, the system will choose the next most similar entry it can find. The system uses a language understanding model to create a confidence level for its matches. If the confidence level be below 100 percent, the system will provide options of all the found matches. ===== Advantages and disadvantages ===== The main draw of LVCSR is its high accuracy and high searching speed. In LVCSR, statistical methods are used to predict the likelihood of different word sequences, hence the accuracy is much higher than the single word lookup of a phonetic search. If the word can be found, the probability of the word spoken is very high. Meanwhile, while initial processing of audio takes a fair bit of time, searching is quick as just a simple test to text matching is needed. On the other hand, LVCSR is susceptible to common issues of speech recognition. The inherent random nature of audio and problems of external noise all affect the accuracies of text-based indexing. Another problem with LVCSR is its over reliance on its dictionary database. LVCSR only recognizes words that are found in their dictionary databases, and these dictionaries and databases are unable to keep up with the constant evolving of new terminology, names and words. Should the dictionary not contain a word, there is no way for the system to identify or predict it. This reduces the accuracy and reliability of the system. This is named the Out-of-vocabulary (OOV) problem. Audio mining systems try to cope with OOV by continuously updating the dictionary and language model used, but the problem still remains significant and has probed a search for alternatives. Additionally, due to the need to constantly update and maintain task-based knowledge and large training databases to cope with the OOV problem, high computational costs are incurred. This makes LVCSR an expensive approach to audio mining. ==== Phonetic-based Indexing ==== Phonetic-based indexing also breaks the audio file into recognizable phonemes, but instead of converting them to a text index, they are kept as they are and analyzed to create a phonetic-based index. The process of phonetic-based indexing can be split into two phases. The first phase is indexing. It begins by converting the input media into a standard audio representation format (PCM). Then, an acoustic model is applied to the speech. This acoustic model represents characteristics of both an acoustic channel (an environment in which the speech was uttered and a transducer through which it was recorded) and a natural language (in which human beings expressed the input speech). This produces a corresponding phonetic search track, or phonetic audio track (PAT), a highly compressed representation of the phonetic content of the input media. The second phase is searching. The user's search query term is parsed into a possible phoneme string using a phonetic dictionary. Then, multiple PAT files can be scanned at high speed during a single search for likely phonetic sequences that closely match corresponding strings of phonemes in the query term. ===== Advantages and disadvantages ===== Phonetic indexing is most attractive as it is largely unaffected by linguistic issues such as unrecognized words and spelling errors. Phonetic preprocessing maintains an open vocabulary that does not require updating. That makes it particularly useful for searching specialized terminology or words in foreign languages that do not commonly appear in dictionaries. It is also more effective for searching audio files with disruptive background noise and/or unclear utterances as it can compile results based on the sounds it can discern, and should the user wish to, they can search through the options until they find the desired item. Furthermore, in contrast to LVCSR, it can process audio files very quickly as there are very few unique phonemes between languages. However, phonemes cannot be effectively indexed like an entire word, thus searching on a phonetic-based system is slow. An issue with phonetic indexing is its low accuracy. Phoneme-based searches result in more false matches than text-based indexing. This is especially prevalent for short search terms, which have a stronger likelihood of sounding similar to other words or being part of bigger words. It could also return irrelevant results from other languages. Unless the system recognizes exactly the entire word, or understands phonetic sequences of languages, it is difficult for phonetic-based indexing to return accurate findings. === Speech processing and recognition system === Deemed as the most critical and complex component of audio mining, speech recognition requires the knowledge of human speech production system and its modeling. To correspond the Human speech production system, the electrical speech production system is developed to consist of: Speech generation Speech perception Voiced & unvoiced speech Model of human speech The electrical speech production system converts acoustic signal into corresponding representation of the spoken through the acoustic models in their software where all phonemes are represented. A statistical language model aids in the process by identifying how likely words are to follow each other in certain languages. Put together with a complex probability analysis, the speech recognition system is capable of taking an unknown speech signal and transcribing it into words based on the program's dictionary. ASR (automatic speech recognition) system includes: Acoustic analysis: input sound waveform is transformed into a feature Acoustic model: establishes relationship between speech signal and phonemes, pronunciation model and lang

  Read more →
• 

  Visual cryptography

  Visual cryptography is a cryptographic technique which allows visual information (pictures, text, etc.) to be encrypted in such a way that the decrypted information appears as a visual image. One of the best-known techniques has been credited to Moni Naor and Adi Shamir, who developed it in 1994. They demonstrated a visual secret sharing scheme, where a binary image was broken up into n shares so that only someone with all n shares could decrypt the image, while any n − 1 shares revealed no information about the original image. Each share was printed on a separate transparency, and decryption was performed by overlaying the shares. When all n shares were overlaid, the original image would appear. There are several generalizations of the basic scheme including k-out-of-n visual cryptography, and using opaque sheets but illuminating them by multiple sets of identical illumination patterns under the recording of only one single-pixel detector, which exposed the image. Using a similar idea, transparencies can be used to implement a one-time pad encryption, where one transparency is a shared random pad, and another transparency acts as the ciphertext. Normally, there is an expansion of space requirement in visual cryptography. But if one of the two shares is structured recursively, the efficiency of visual cryptography can be increased to 100%. Some antecedents of visual cryptography are in patents from the 1960s. Other antecedents are in the work on perception and secure communication. Visual cryptography can be used to protect biometric templates in which decryption does not require any complex computations. == Example == In this example, the binary image has been split into two component images. Each component image has a pair of pixels for every pixel in the original image. These pixel pairs are shaded black or white according to the following rule: if the original image pixel was black, the pixel pairs in the component images must be complementary; randomly shade one ■□, and the other □■. When these complementary pairs are overlapped, they will appear dark gray. On the other hand, if the original image pixel was white, the pixel pairs in the component images must match: both ■□ or both □■. When these matching pairs are overlapped, they will appear light gray. So, when the two component images are superimposed, the original image appears. However, without the other component, a component image reveals no information about the original image; it is indistinguishable from a random pattern of ■□ / □■ pairs. Moreover, if you have one component image, you can use the shading rules above to produce a counterfeit component image that combines with it to produce any image at all. == (2, n) visual cryptography sharing case == Sharing a secret with an arbitrary number of people, n, such that at least 2 of them are required to decode the secret is one form of the visual secret sharing scheme presented by Moni Naor and Adi Shamir in 1994. In this scheme we have a secret image which is encoded into n shares printed on transparencies. The shares appear random and contain no decipherable information about the underlying secret image, however if any 2 of the shares are stacked on top of one another the secret image becomes decipherable by the human eye. Every pixel from the secret image is encoded into multiple subpixels in each share image using a matrix to determine the color of the pixels. In the (2, n) case, a white pixel in the secret image is encoded using a matrix from the following set, where each row gives the subpixel pattern for one of the components: {all permutations of the columns of} : C 0 = [ 1 0 . . . 0 1 0 . . . 0 . . . 1 0 . . . 0 ] . {\displaystyle \mathbf {C_{0}=} {\begin{bmatrix}1&0&...&0\\1&0&...&0\\...\\1&0&...&0\end{bmatrix}}.} While a black pixel in the secret image is encoded using a matrix from the following set: {all permutations of the columns of} : C 1 = [ 1 0 . . . 0 0 1 . . . 0 . . . 0 0 . . . 1 ] . {\displaystyle \mathbf {C_{1}=} {\begin{bmatrix}1&0&...&0\\0&1&...&0\\...\\0&0&...&1\end{bmatrix}}.} For instance in the (2,2) sharing case (the secret is split into 2 shares and both shares are required to decode the secret) we use complementary matrices to share a black pixel and identical matrices to share a white pixel. Stacking the shares we have all the subpixels associated with the black pixel now black while 50% of the subpixels associated with the white pixel remain white. == Cheating the (2, n) visual secret sharing scheme == Horng et al. proposed a method that allows n − 1 colluding parties to cheat an honest party in visual cryptography. They take advantage of knowing the underlying distribution of the pixels in the shares to create new shares that combine with existing shares to form a new secret message of the cheaters choosing. We know that 2 shares are enough to decode the secret image using the human visual system. But examining two shares also gives some information about the 3rd share. For instance, colluding participants may examine their shares to determine when they both have black pixels and use that information to determine that another participant will also have a black pixel in that location. Knowing where black pixels exist in another party's share allows them to create a new share that will combine with the predicted share to form a new secret message. In this way a set of colluding parties that have enough shares to access the secret code can cheat other honest parties. == Visual steganography == 2×2 subpixels can also encode a binary image in each component image. For example, each white pixel of each component image could be represented by two black subpixels, while each black pixel represented by three black subpixels. When overlaid, each white pixel of the secret image is represented by three black subpixels, while each black pixel is represented by all four subpixels black. Each corresponding pixel in the component images is randomly rotated to avoid orientation leaking information about the secret image. == In popular culture == In "Do Not Forsake Me Oh My Darling", a 1967 episode of TV series The Prisoner, the protagonist uses a visual cryptography overlay of multiple transparencies to reveal a secret message – the location of a scientist friend who had gone into hiding.

  Read more →
• 

  MDS matrix

  An MDS matrix (maximum distance separable) is a matrix representing a function with certain diffusion properties that have useful applications in cryptography. Technically, an m × n {\displaystyle m\times n} matrix A {\displaystyle A} over a finite field K {\displaystyle K} is an MDS matrix if it is the transformation matrix of a linear transformation f ( x ) = A x {\displaystyle f(x)=Ax} from K n {\displaystyle K^{n}} to K m {\displaystyle K^{m}} such that no two different ( m + n ) {\displaystyle (m+n)} -tuples of the form ( x , f ( x ) ) {\displaystyle (x,f(x))} coincide in n {\displaystyle n} or more components. Equivalently, the set of all ( m + n ) {\displaystyle (m+n)} -tuples ( x , f ( x ) ) {\displaystyle (x,f(x))} is an MDS code, i.e., a linear code that reaches the Singleton bound. Let A ~ = ( I n A ) {\displaystyle {\tilde {A}}={\begin{pmatrix}\mathrm {I} _{n}\\\hline \mathrm {A} \end{pmatrix}}} be the matrix obtained by joining the identity matrix I n {\displaystyle \mathrm {I} _{n}} to A {\displaystyle A} . Then a necessary and sufficient condition for a matrix A {\displaystyle A} to be MDS is that every possible n × n {\displaystyle n\times n} submatrix obtained by removing m {\displaystyle m} rows from A ~ {\displaystyle {\tilde {A}}} is non-singular. This is also equivalent to the following: all the sub-determinants of the matrix A {\displaystyle A} are non-zero. Then a binary matrix A {\displaystyle A} (namely over the field with two elements) is never MDS unless it has only one row or only one column with all components 1 {\displaystyle 1} . Reed–Solomon codes have the MDS property and are frequently used to obtain the MDS matrices used in cryptographic algorithms. Serge Vaudenay suggested using MDS matrices in cryptographic primitives to produce what he called multipermutations, not-necessarily linear functions with this same property. These functions have what he called perfect diffusion: changing t {\displaystyle t} of the inputs changes at least m − t + 1 {\displaystyle m-t+1} of the outputs. He showed how to exploit imperfect diffusion to cryptanalyze functions that are not multipermutations. MDS matrices are used for diffusion in such block ciphers as AES, SHARK, Square, Twofish, Anubis, KHAZAD, Manta, Hierocrypt, Kalyna, Camellia and HADESMiMC, and in the stream cipher MUGI and the cryptographic hash function Whirlpool, Poseidon.

  Read more →
• 

  CANaerospace

  CANaerospace is a higher layer protocol based on Controller Area Network (CAN) which has been developed by Stock Flight Systems in 1998 for aeronautical applications. == Background == CANaerospace supports airborne systems employing the Line-replaceable unit (LRU) concept to share data across CAN and ensures interoperability between CAN LRUs by defining CAN physical layer characteristics, network layers, communication mechanisms, data types and aeronautical axis systems. CANaerospace is an open source project, was initiated to standardize the interface between CAN LRUs on the system level. CANaerospace is continuously being developed further and has also been published by NASA as the Advanced General Aviation Transport Experiments Databus Standard in 2001. It found widespread use in aeronautical research worldwide. A major research aircraft that employs several CANaerospace networks for real-time computer interconnection is the Stratospheric Observatory for Infrared Astronomy (SOFIA), a Boeing 747SP with a 2.5m astronomic telescope. CANaerospace is also frequently used in flight simulation and connects entire aircraft cockpits (i.e. in Eurofighter Typhoon simulators) to the simulation host computers. In Italy CANaerospace is used as UAV data bus technology. Furthermore, CANaerospace serves as communication network in several general aviation avionics systems. The CANaerospace interface definition closes the gap between the ISO/OSI layer 1 and 2 CAN protocol (which is implemented in the CAN controller itself) and the specific requirements of distributed systems in aircraft. It may be used as a primary or ancillary avionics network and was designed to meet the following requirements: Democratic network: CANaerospace does not require any master/slave relationships between LRUs or a "bus controller", thereby avoiding a potential single source of failure. Every node in the network has the same rights for participation in the bus traffic. Self-identifying message format: Each CANaerospace message contains information about the type of the data and the transmitting node. This allows the data to be unambiguously recognized at each receiving node. Continuous Message Numbering: Each CANaerospace message contains a continuously incremented number which allows coherent processing of messages in the receiving stations. Message Status Code: Each CANaerospace message contains information about the integrity of the data is conveying. This allows receiving stations to evaluate the quality of the received data and to react accordingly. Emergency Event Signaling: CANaerospace defines a mechanism that allows each node to transmit information about exception or error situations. This information can be used by other stations to determine the network health. Node Service Interface: As an enhancement to CAN, CANaerospace provides a means for individual stations on the network to communicate with each other using connection-oriented and connectionless services. Predefined CAN Identifier Assignment: CANaerospace offers a predefined identifier assignment list for normal operation data. In addition to the predefined list, user-defined identifier assignment lists may be used. Ease of Implementation: The amount of code to implement CANaerospace is very little by design in order to minimize the effort for testing and certification of flight safety critical systems. Openness to Extensions: All CANaerospace definitions are extendable to provide flexibility for future enhancements and to allow adaptions to the requirements of specific applications. Free Availability: No cost whatsoever apply for the use of CANaerospace. The specification can be downloaded from the Internet == Physical interface == To ensure interoperability and reliable communication, CANaerospace specifies the electrical characteristics, bus transceiver requirements and data rates with the corresponding tolerances based on ISO 11898. The bit timing calculation (baud rate accuracy, sample point definition) and robustness to electromagnetic interference are given special emphasis. Also addressed are CAN connector, wiring considerations and design guidelines to maximize electromagnetic compatibility. == Communication layers == The Bosch CAN specification itself allows messages being transmitted both periodically and aperiodically but does not cover issues like data representation, node addressing or connection-oriented protocols. CAN is entirely based on Anyone-to-Many (ATM) communication which means that CAN messages are always received by all stations in the network. The advantage of the CAN concept is inherent data consistency between all stations, the drawback is that it does not allow node addressing which is the basis for Peer-to-Peer (PTP) communication. Using CAN networks in aeronautical applications, however, demands a standard targeted to the specific requirements of airborne systems which implies that communication between individual stations in the network must be possible to enable the required degree of system monitoring. Consequently, CANaerospace defines additional ISO/OSI layer 3, 4 and 6 functions to support node addressing and unified ATM/PTP communication mechanisms. PTP communication allows to set up client/server interactions between individual stations in the network either temporarily or permanently. More than one of these interactions may be in effect at any given time and each node may be client for one operation and server for another at the same time. This CANaerospace mechanism is called "Node Service Concept" and allows i.e. to distribute system functions over several stations in the network or to control dynamic system reconfiguration in case of failure. The Node Service concept supports both connection-oriented and connectionless interactions like with TCP/IP and UDP/IP for Ethernet. Enabling both ATM and PTP communication for CAN requires the introduction of independent network layers to isolate the different types of communication. This is realized for CANaerospace by forming CAN identifier groups as shown in Figure 1. The resulting structure creates Logical Communication Channels (LCCs) and assigns a specific communication type (ATM, PTP) to each of the LCCs. User-defined LCCs provide the necessary freedom for designers and allow the implementation of CANaerospace according to the needs of specific applications. Figure 1: Logical Communication Channels for CANaerospace As a side effect, the CAN identifier groups in Figure 1 affect the priority of the message transmission in case of bus arbitration. The communication channels are therefore arranged according to their relative importance: Emergency Event Data Channel (EED): This communication channel is used for messages which require immediate action (i.e. system degradation or reconfiguration) and have to be transmitted with very high priority. Emergency Event Data uses ATM communication exclusively. High/Low Priority Node Service Data Channel (NSH/NSL): These communication channels are used for client/server interactions using PTP communication. The corresponding services may be of the connection-oriented as well as the connectionless type. NSH/NSL may also be used to support test and maintenance functions. Normal Operation Data Channel (NOD): This communication channel is used for the transmission of the data which is generated during normal system operation and described in the CANaerospace identifier assignment list. These messages may be transmitted periodically or aperiodically as well as synchronously or asynchronously. All messages which cannot be assigned to other communication channels shall use this channel. High/Low Priority User-Defined Data Channel (UDH/UDL): This channel is dedicated to communication which cannot, due to their specific characteristics, be assigned other channels without violating the CANaerospace specification. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. To ensure interoperability it is highly recommended that the use of these channels is minimized. Debug Service Data Channel (DSD): This channel is dedicated to messages which are used temporarily for development and test purposes only and are not transmitted during normal operation. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. == Data representation == The majority of the real-time control systems used in aeronautics employ "big endian" processor architectures. This data representation was therefore specified for CANaerospace as well. With big endian data representation, the most significant bit of any datum is arranged leftmost and transmitted first on CANaerospace as shown in Figure 2. Figure 2: "Big Endian" Data Representation for CANaerospace CANaerospace uses a self-identifying message

  Read more →
• 

  Model-based clustering

  In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group. == Model-based clustering == Suppose that for each of n {\displaystyle n} observations we have data on d {\displaystyle d} variables, denoted by y i = ( y i , 1 , … , y i , d ) {\displaystyle y_{i}=(y_{i,1},\ldots ,y_{i,d})} for observation i {\displaystyle i} . Then model-based clustering expresses the probability density function of y i {\displaystyle y_{i}} as a finite mixture, or weighted average of G {\displaystyle G} component probability density functions: p ( y i ) = ∑ g = 1 G τ g f g ( y i ∣ θ g ) , {\displaystyle p(y_{i})=\sum _{g=1}^{G}\tau _{g}f_{g}(y_{i}\mid \theta _{g}),} where f g {\displaystyle f_{g}} is a probability density function with parameter θ g {\displaystyle \theta _{g}} , τ g {\displaystyle \tau _{g}} is the corresponding mixture probability where ∑ g = 1 G τ g = 1 {\displaystyle \sum _{g=1}^{G}\tau _{g}=1} . Then in its simplest form, model-based clustering views each component of the mixture model as a cluster, estimates the model parameters, and assigns each observation to cluster corresponding to its most likely mixture component. === Gaussian mixture model === The most common model for continuous data is that f g {\displaystyle f_{g}} is a multivariate normal distribution with mean vector μ g {\displaystyle \mu _{g}} and covariance matrix Σ g {\displaystyle \Sigma _{g}} , so that θ g = ( μ g , Σ g ) {\displaystyle \theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle \theta _{g}} for g = 1 , … , G {\displaystyle g=1,\ldots ,G} , are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. The Bayesian approach also allows for the case where the number of components, G {\displaystyle G} , is infinite, using a Dirichlet process prior, yielding a Dirichlet process mixture model for clustering. === Choosing the number of clusters === An advantage of model-based clustering is that it provides statistically principled ways to choose the number of clusters. Each different choice of the number of groups G {\displaystyle G} corresponds to a different mixture model. Then standard statistical model selection criteria such as the Bayesian information criterion (BIC) can be used to choose G {\displaystyle G} . The integrated completed likelihood (ICL) is a different criterion designed to choose the number of clusters rather than the number of mixture components in the model; these will often be different if highly non-Gaussian clusters are present. === Parsimonious Gaussian mixture model === For data with high dimension, d {\displaystyle d} , using a full covariance matrix for each mixture component requires estimation of many parameters, which can result in a loss of precision, generalizabity and interpretability. Thus it is common to use more parsimonious component covariance matrices exploiting their geometric interpretation. Gaussian clusters are ellipsoidal, with their volume, shape and orientation determined by the covariance matrix. Consider the eigendecomposition of a matrix Σ g = λ g D g A g D g T , {\displaystyle \Sigma _{g}=\lambda _{g}D_{g}A_{g}D_{g}^{T},} where D g {\displaystyle D_{g}} is the matrix of eigenvectors of Σ g {\displaystyle \Sigma _{g}} , A g = diag { A 1 , g , … , A d , g } {\displaystyle A_{g}={\mbox{diag}}\{A_{1,g},\ldots ,A_{d,g}\}} is a diagonal matrix whose elements are proportional to the eigenvalues of Σ g {\displaystyle \Sigma _{g}} in descending order, and λ g {\displaystyle \lambda _{g}} is the associated constant of proportionality. Then λ g {\displaystyle \lambda _{g}} controls the volume of the ellipsoid, A g {\displaystyle A_{g}} its shape, and D g {\displaystyle D_{g}} its orientation. Each of the volume, shape and orientation of the clusters can be constrained to be equal (E) or allowed to vary (V); the orientation can also be spherical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: It can be seen that many of these models are more parsimonious, with far fewer parameters than the unconstrained model that has 90 parameters when G = 4 {\displaystyle G=4} and d = 9 {\displaystyle d=9} . Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also be used as the basis for a method to choose the variables in the clustering model, eliminating variables that are not useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. The mixture-of-experts framework extends model-based clustering to include covariates. == Example == We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but we use this information only for evaluating clustering methods, not for classifying subjects. The BIC plot shows the BIC values for each combination of the number of clusters, G {\displaystyle G} , and the clustering model from the Table. Each curve corresponds to a different clustering model. The BIC favors 3 groups, which corresponds to the clinical assessment. It also favors the unconstrained covariance model, VVV. This fits the data well, because the normal patients have low values of both sspg and insulin, while the distributions of the chemical and overt diabetes groups are elongated, but in different directions. Thus the volumes, shapes and orientations of the three groups are clearly different, and so the unconstrained model is appropriate, as selected by the model-based clustering method. The classification plot shows the classification of the subjects by model-based clustering. The classification was quite accurate, with a 12% error rate as defined by the clinical classification. Other well-known clustering methods performed worse with higher error rates, such as single-linkage clustering with 46%, average link clustering with 30%, complete-linkage clustering also with 30%, and k-means clustering with 28%. == Outliers in clustering == An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivariate normal densities by t {\displaystyle t} -distributions, with the idea that the long tails of the t {\displaystyle t} -distribution would ensure robustness to outliers. However, this is not breakdown-robust. A third approach is the "tclust" or data trimming approach which excludes observations identified as outliers when estimating the model parameters. == Non-Gaussian clusters and merging == Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. == Non-continuous data == === Categorical data === Clustering multivariate categorical data is most often done using the latent class model. This assumes that the data arise from a finite mixture model, where within each cluster the variables are independent. === Mixed data === These arise when variables are of different types, such as continuous, categorical or ordinal data. A latent class model for mixed data assumes local independence between the variable. The location model relaxes the local independence assumption. The clustMD approach assumes that the observed variables are manifestations of underlying continuous Gaussian latent

  Read more →
• 

  Data exhaust

  Data exhaust (also exhaust data) is the trail of data generated as a by-product of users' online activity, behaviour, and transactions, rather than data they deliberately create or submit. It forms part of a broader category of unconventional data that also includes geospatial, network, and time-series data, and may be useful for predictive analytics. Data exhaust can take the form of cookies, temporary files, log files, clickstream records and stored preferences. Actions such as visiting a web page, following a link, or dwelling on an element may all generate exhaust data that is recorded without the user's active awareness. Unlike primary content — which the user intentionally creates — exhaust data is a passive side effect of interaction. A bank, for example, might treat the amounts and parties involved in a transaction as primary data, while secondary data could include whether the transaction was carried out at a cash machine rather than a branch. == Uses == Data exhaust collected by companies is often information that is not immediately useful in isolation, but can be aggregated and analysed to improve products, personalise content, identify trends, and support quality control. Companies may also store exhaust data for future analysis or sell it to third parties. Shoshana Zuboff has described this practice as a core mechanism of what she terms surveillance capitalism, in which behavioural data generated by users is converted into predictive products. Kosciejew notes that large quantities of often raw data are collected in this way, much of which is never analysed. == Medical exhaust data == Many medical devices — including pacemakers, dialysis machines and surgical cameras — generate exhaust data as a by-product of their operation. The majority of this data is never captured or analysed, and is typically discarded once a procedure ends or a device completes its routine monitoring cycle. The potential use of data generated by implanted devices such as pacemakers raises additional legal and ethical questions around ownership and consent. Using electronic health records for research also creates challenges because of the volume of data involved, creating a need for automated algorithms to process it. == Privacy and regulation == The collection and distribution of data exhaust is not in itself illegal in most jurisdictions, but its use raises questions of privacy and informed consent. Steps commonly taken to address these concerns include data anonymisation, offering users an opt-out from the sale of their data, and publishing explicit privacy policies that disclose what data is collected and how it is used.

  Read more →
• 

  Data thinking

  Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, with a focus on user experience and data analytics, including data collection and interpretation The framework aims to apply data literacy and inform decision-making through data-driven insights. == Major components == According to "Computational thinking in the era of data science": Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules. Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary. The framework highlights the importance of preserving domain-specific meaning during data analysis. Data thinking incorporates statistical and logical analysis to identify patterns and irregularities. Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data. The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations. == Major phases == === Strategic context and risk analysis === Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. === Ideation and exploration === In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. === Prototyping === A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. === Implementation and monitoring === Solutions are tested and monitored for performance and continual improvement. == Implementing Data Thinking == The following resources explain more about data thinking and its applications: "Data Thinking: Framework for data-based solutions" by StackFuel "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group "Data Science Thinking" by SpringerLink These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.

  Read more →
• 

  Strong cryptography

  Strong cryptography or cryptographically strong are general terms used to designate the cryptographic algorithms that, when used correctly, provide a very high (usually insurmountable) level of protection against any eavesdropper, including the government agencies. There is no precise definition of the boundary line between the strong cryptography and (breakable) weak cryptography, as this border constantly shifts due to improvements in hardware and cryptanalysis techniques. These improvements eventually place the capabilities once available only to the NSA within the reach of a skilled individual, so in practice there are only two levels of cryptographic security, "cryptography that will stop your kid sister from reading your files, and cryptography that will stop major governments from reading your files" (Bruce Schneier). The strong cryptography algorithms have high security strength, for practical purposes usually defined as a number of bits in the key. For example, the United States government, when dealing with export control of encryption, considered as of 1999 any implementation of the symmetric encryption algorithm with the key length above 56 bits or its public key equivalent to be strong and thus potentially a subject to the export licensing. To be strong, an algorithm needs to have a sufficiently long key and be free of known mathematical weaknesses, as exploitation of these effectively reduces the key size. At the beginning of the 21st century, the typical security strength of the strong symmetrical encryption algorithms is 128 bits (slightly lower values still can be strong, but usually there is little technical gain in using smaller key sizes). Demonstrating the resistance of any cryptographic scheme to attack is a complex matter, requiring extensive testing and reviews, preferably in a public forum. Good algorithms and protocols are required (similarly, good materials are required to construct a strong building), but good system design and implementation is needed as well: "it is possible to build a cryptographically weak system using strong algorithms and protocols" (just like the use of good materials in construction does not guarantee a solid structure). Many real-life systems turn out to be weak when the strong cryptography is not used properly, for example, random nonces are reused A successful attack might not even involve algorithm at all, for example, if the key is generated from a password, guessing a weak password is easy and does not depend on the strength of the cryptographic primitives. A user can become the weakest link in the overall picture, for example, by sharing passwords and hardware tokens with the colleagues. == Background == The level of expense required for strong cryptography originally restricted its use to the government and military agencies, until the middle of the 20th century the process of encryption required a lot of human labor and errors (preventing the decryption) were very common, so only a small share of written information could have been encrypted. US government, in particular, was able to keep a monopoly on the development and use of cryptography in the US into the 1960s. In the 1970, the increased availability of powerful computers and unclassified research breakthroughs (Data Encryption Standard, the Diffie-Hellman and RSA algorithms) made strong cryptography available for civilian use. Mid-1990s saw the worldwide proliferation of knowledge and tools for strong cryptography. By the 21st century the technical limitations were gone, although the majority of the communication were still unencrypted. At the same the cost of building and running systems with strong cryptography became roughly the same as the one for the weak cryptography. The use of computers changed the process of cryptanalysis, famously with Bletchley Park's Colossus. But just as the development of digital computers and electronics helped in cryptanalysis, it also made possible much more complex ciphers. It is typically the case that use of a quality cipher is very efficient, while breaking it requires an effort many orders of magnitude larger - making cryptanalysis so inefficient and impractical as to be effectively impossible. == Cryptographically strong algorithms == This term "cryptographically strong" is often used to describe an encryption algorithm, and implies, in comparison to some other algorithm (which is thus cryptographically weak), greater resistance to attack. But it can also be used to describe hashing and unique identifier and filename creation algorithms. See for example the description of the Microsoft .NET runtime library function Path.GetRandomFileName. In this usage, the term means "difficult to guess". An encryption algorithm is intended to be unbreakable (in which case it is as strong as it can ever be), but might be breakable (in which case it is as weak as it can ever be) so there is not, in principle, a continuum of strength as the idiom would seem to imply: Algorithm A is stronger than Algorithm B which is stronger than Algorithm C, and so on. The situation is made more complex, and less subsumable into a single strength metric, by the fact that there are many types of cryptanalytic attack and that any given algorithm is likely to force the attacker to do more work to break it when using one attack than another. There is only one known unbreakable cryptographic system, the one-time pad, which is not generally possible to use because of the difficulties involved in exchanging one-time pads without them being compromised. So any encryption algorithm can be compared to the perfect algorithm, the one-time pad. The usual sense in which this term is (loosely) used, is in reference to a particular attack, brute force key search — especially in explanations for newcomers to the field. Indeed, with this attack (always assuming keys to have been randomly chosen), there is a continuum of resistance depending on the length of the key used. But even so there are two major problems: many algorithms allow use of different length keys at different times, and any algorithm can forgo use of the full key length possible. Thus, Blowfish and RC5 are block cipher algorithms whose design specifically allowed for several key lengths, and who cannot therefore be said to have any particular strength with respect to brute force key search. Furthermore, US export regulations restrict key length for exportable cryptographic products and in several cases in the 1980s and 1990s (e.g., famously in the case of Lotus Notes' export approval) only partial keys were used, decreasing 'strength' against brute force attack for those (export) versions. More or less the same thing happened outside the US as well, as for example in the case of more than one of the cryptographic algorithms in the GSM cellular telephone standard. The term is commonly used to convey that some algorithm is suitable for some task in cryptography or information security, but also resists cryptanalysis and has no, or fewer, security weaknesses. Tasks are varied, and might include: generating randomness encrypting data providing a method to ensure data integrity Cryptographically strong would seem to mean that the described method has some kind of maturity, perhaps even approved for use against different kinds of systematic attacks in theory and/or practice. Indeed, that the method may resist those attacks long enough to protect the information carried (and what stands behind the information) for a useful length of time. But due to the complexity and subtlety of the field, neither is almost ever the case. Since such assurances are not actually available in real practice, sleight of hand in language which implies that they are will generally be misleading. There will always be uncertainty as advances (e.g., in cryptanalytic theory or merely affordable computer capacity) may reduce the effort needed to successfully use some attack method against an algorithm. In addition, actual use of cryptographic algorithms requires their encapsulation in a cryptosystem, and doing so often introduces vulnerabilities which are not due to faults in an algorithm. For example, essentially all algorithms require random choice of keys, and any cryptosystem which does not provide such keys will be subject to attack regardless of any attack resistant qualities of the encryption algorithm(s) used. == Legal issues == Widespread use of encryption increases the costs of surveillance, so the government policies aim to regulate the use of the strong cryptography. In the 2000s, the effect of encryption on the surveillance capabilities was limited by the ever-increasing share of communications going through the global social media platforms, that did not use the strong encryption and provided governments with the requested data. Murphy talks about a legislative balance that needs to be struck between the power of the government that are broad enough to be able to follow the qui

  Read more →
• 

  Web Intents

  Web Intents was an experimental framework for web-based inter-application communication and service discovery. Web Intents consists of a discovery mechanism and a very light-weight RPC system between web applications, modelled after the Intents system in Android. In the context of the framework an Intent equals an action to be performed by a provider. Web Intents allow two web applications to communicate with each other, without either of them having to actually know what the other one is. == Support == === Client === Google Chrome versions 18 to 23 natively supported Web Intents. This support was disabled in version 24, citing the existence of a "number of areas for development in both the API and specific user experience in Chrome". There is a JavaScript shim with support for IE 8, IE 9, Opera, Safari, Firefox 3+ and Chrome 3+. === Server === There are some Web Intents proxy pages that make available some real services that don't yet support intents. AddThis supports Web Intents by their sharing tools regardless of browser support. == History == Paul Kinlan of Google announced the Web Intents project in December 2010. He soon released a prototype API to GitHub. In August 2011 Google announced that Chrome would support Web Intents. Google and Mozilla have started co-operating to unify Web Intents and Mozilla's Web Activities (which tries to solve the same problem) into one proposal. In November 2012, Greg Billock of Google announced that experimental support of Web Intents had been removed from Chrome.

  Read more →
• 

  Peñabot

  Peñabot is the nickname for automated social media accounts allegedly used by the Mexican government of Enrique Peña Nieto and the PRI political party to keep unfavorable news from reaching the Mexican public. Peñabot accusations are related to the broader issue of fake news in the 21st century. == History of disinformation in Mexican politics == The PRI political party has been reported to use fake news since before Peña Nieto. The main tactic originally was to spread such propaganda through open radio and television networks. Such tactic was effective in Mexico, because newspaper readership is low and cable TV is largely limited to the middle classes; consequently, the country's two major television networks – Televisa and TV Azteca – exert a significant influence in national politics. Televisa itself, not only owns around two-thirds of the programming on Mexico's TV channels, making it not only Mexico's largest television network, but also is the largest media network in the Spanish-speaking world. == Peñabots == Analysts have given the name Peñabots to a suspected network of automated accounts on social media used by the Mexican government to spread pro-government propaganda and to marginalize dissenting opinions in social media. The bots were first noticed in the 2012 elections when they were used to disseminate opinions in support of Enrique Peña Nieto on social networks such as Twitter and Facebook. According to Aristegui Noticias, their usage went against articles 6 and 134 of the Mexican Constitution. Those used by Peña Nieto's government cost an estimated 80 million pesos monthly, which news outlets argued only helped the government spread fake support towards the president, but did not have a benefit towards Mexican people (with whom EPN was highly unpopular). Facebook held approximately 640,321 Peñabots, while Twitter had less. As of July 2017, Oxford Internet Institute's Computational Propaganda Research Project claimed many western democracies, Mexico included, perform social media manipulation, thus saying the manipulation comes directly from the Mexican government itself. During Peña Nieto's subsequent presidency, analysts noted that Peñabots were used to overpower trending topics that critiqued government, to flood trending government critical hashtags with spam, to create fake trends by pushing alternative hashtags, and to push smear campaigns and threats against government-critical activists and journalists. Peñabots were distinguished as their pattern of activity was distinct from that of ordinary interaction on social networks. === Meadebots === On Twitter it was reported that about 94% of the followers of 2018 presidential candidate from the PRI Jose Antonio Meade were bots. When Antonio Meade presented himself as a candidate for the 2018 presidential election, his social media accounts such as "@MovimientoMEADE" (created by the PRI's official account @PRI_Nacional), obtained a huge quantity of followers in a short span of time. Some users noticed and brought it to attention, and after investigation it was reported 94% of such followers were bots (702,000 out of 747,000), and the account was eliminated from Twitter after 20 hours. The fake accounts used the hashtags #YoConMeade and #Meade18. It was further revealed was that Meade's official account on Twitter, @JoseAMeadeK had 25% bots (216,000 fake followers out of the 981,000). == Manipulation of news media in Mexico, through television == The Mexican government of Peña Nieto has been accused of using various means to keep unfavorable news from reaching the Mexican people. Many Mexicans have protested this practice as it clearly goes against the freedom of speech. The PRI has been reported to use fake news since before Peña Nieto. The main tactic has been to spread such propaganda through radio and television. This tactic is perceived as effective in Mexico, because newspaper readership is low and research on the Internet and cable TV is largely limited to the middle classes; consequently, the country's two major television networks – Televisa and TV Azteca – exert a significant influence in national politics. Televisa itself, owns around two-thirds of the programming on Mexico's TV channels, making it not only Mexico's largest television network, but also is the largest media network in the Spanish-speaking world. In June 2012, before the 2012 Mexican presidential elections, the British newspaper The Guardian published a series of allegations claiming Televisa, sold favorable coverage to top politicians in its news and entertainment shows, this scandal became known as the Televisa controversy. The documents published by 'The Guardian alleged that a secretive circle within Televisa manipulated news coverage to favor PRI presidential candidate Enrique Peña Nieto, who was poised as favorite to win. Televisa's secret circle supposedly commissioned videos to promote Peña Nieto and lash out his political rivals in 2009. The Guardian documents suggest that Televisa's secret team distributed such videos through e-mail, posting them posted them on Facebook and YouTube, some can still be seen there. Another document was a PowerPoint presentation, with a slide explicitly aimed at rival leftist candidate of the Party of the Democratic Revolution (PRD), Andrés Manuel López Obrador. Supposedly given to The Guardian by a Televisa employee. The document's authenticity was never possible to confirm– however dates, names, and events largely coincide. Televisa refused to talk the documents, and denied a relationship with the PRI or its presidential candidate, saying that they had provided equal media coverage to all parties. Televisa published an article supposedly showing discrepancies in The Guardian documents and denying accusations. Mexican citizens complained about the perceived favoritism towards Enrique Peña Nieto and the PRI, protesting through the Yo Soy 132 movement which Televisa covered in detail. However, Televisa's news media coverage is perceived to have been biased, by using a media coverage tactic Mexican citizens call cortinas de humo (smoke screens). These introduce a news scandal giving extensive coverage to distract citizens from a potential conflict-of-interest or controversy that could damage the image of the politician favored by the network. An example of a perceived smoke screen would be the news media coverage of "Caso Michoacán" and "Caso Paolette" distracting all the attention from the parallel "Yo soy 132" movement. A few years later, on the day of September 11, 2016; factual evidence of Televisa's performing media manipulation emerged, when a Televisa news anchor while live-on air reading a teleprompter, mistakenly read out loud that "try that Jaime "Ël Bronco" Rodríguez Calderón (Nuevo Leon's governor) is mentioned as little as possible". Newspaper El Universal caught it on video and published it social media. Televisa didn't mention the story and declined to comment. Lack of news coverage concerning Nuevo León's Governor Jaime Rodriguez, is perceived due to him being the first elected governor to not be part of any political party (Independent Governor), and because unlike the governors from the PRI preceding him, the independent governor "El Bronco" doesn't spend money on publicity at all, preferring to communicate all news by using social media such as Twitter and Facebook. While the incident may have proven Televisa's bias, there wasn't anything to incriminate the PRI political party or Enrique Peña Nieto, though it did further suspicion of Televisa manipulating news media. In contrast, a December 2017 article of The New York Times, reported Enrique Peña Nieto spending about 2000 million dollars on publicity, during his first 5 years as president, the largest publicity budget ever spent by a Mexican President. Additionally, 68 percent of news journalists admitted to not believe to have enough freedom of speech, and award-winning news reporter Carmen Aristegui was controversially fired shortly after revealing the Mexican White House scandals. == Violence and spying towards news journalists and civil rights activists == Far for only being receiving accusations of spreading fake news, the Mexican government of EPN (Enrique Peña Nieto) has also been accused of violence towards news journalists, and of spying on them, and also towards civil right leaders and their families. During his tenure as president, Peña Nieto has been accused of failing to protect news journalists, whose deaths are speculated to be politically triggered, by politicians attempting to prevent them from covering political scandals. The New York Times published a news report on the matter titled, "In Mexico it's easy to kill a journalist", on it mentioning how during EPN's government, Mexico became one of the worst countries on which to be a journalist. The assassination of journalist Javier Valdez on May 23, 2017, received national coverage, with multiple news journalists

  Read more →
• 

  Yao's test

  In cryptography and the theory of computation, Yao's test is a test defined by Andrew Chi-Chih Yao in 1982, against pseudo-random sequences. A sequence of words passes Yao's test if an attacker with reasonable computational power cannot distinguish it from a sequence generated uniformly at random. == Formal statement == === Boolean circuits === Let P {\displaystyle P} be a polynomial, and S = { S k } k {\displaystyle S=\{S_{k}\}_{k}} be a collection of sets S k {\displaystyle S_{k}} of P ( k ) {\displaystyle P(k)} -bit long sequences, and for each k {\displaystyle k} , let μ k {\displaystyle \mu _{k}} be a probability distribution on S k {\displaystyle S_{k}} , and P C {\displaystyle P_{C}} be a polynomial. A predicting collection C = { C k } {\displaystyle C=\{C_{k}\}} is a collection of boolean circuits of size less than P C ( k ) {\displaystyle P_{C}(k)} . Let p k , S C {\displaystyle p_{k,S}^{C}} be the probability that on input s {\displaystyle s} , a string randomly selected in S k {\displaystyle S_{k}} with probability μ ( s ) {\displaystyle \mu (s)} , C k ( s ) = 1 {\displaystyle C_{k}(s)=1} , i.e. Moreover, let p k , U C {\displaystyle p_{k,U}^{C}} be the probability that C k ( s ) = 1 {\displaystyle C_{k}(s)=1} on input s {\displaystyle s} a P ( k ) {\displaystyle P(k)} -bit long sequence selected uniformly at random in { 0 , 1 } P ( k ) {\displaystyle \{0,1\}^{P(k)}} . We say that S {\displaystyle S} passes Yao's test if for all predicting collection C {\displaystyle C} , for all but finitely many k {\displaystyle k} , for all polynomial Q {\displaystyle Q} : === Probabilistic formulation === As in the case of the next-bit test, the predicting collection used in the above definition can be replaced by a probabilistic Turing machine, working in polynomial time. This also yields a strictly stronger definition of Yao's test (see Adleman's theorem). Indeed, one could decide undecidable properties of the pseudo-random sequence with the non-uniform circuits described above, whereas BPP machines can always be simulated by exponential-time deterministic Turing machines.

  Read more →
• 

  Trusted Computing

  Trusted Computing (TC) is a technology developed and promoted by the Trusted Computing Group. The term is taken from the field of trusted systems and has a specialized meaning that is distinct from the field of confidential computing. With Trusted Computing, the computer will consistently behave in expected ways, and those behaviors will be enforced by computer hardware and software. Enforcing this behavior is achieved by loading the hardware with a unique encryption key that is inaccessible to the rest of the system and the owner. TC is controversial as the hardware is not only secured for its owner, but also against its owner, leading opponents of the technology like free software activist Richard Stallman to deride it as "treacherous computing", and certain scholarly articles to use scare quotes when referring to the technology. Trusted Computing proponents such as International Data Corporation, the Enterprise Strategy Group and Endpoint Technologies Associates state that the technology will make computers safer, less prone to viruses and malware, and thus more reliable from an end-user perspective. They also state that Trusted Computing will allow computers and servers to offer improved computer security over that which is currently available. Opponents often state that this technology will be used primarily to enforce digital rights management policies (imposed restrictions to the owner) and not to increase computer security. Chip manufacturers Intel and AMD, hardware manufacturers such as HP and Dell, and operating system providers such as Microsoft include Trusted Computing in their products if enabled. The U.S. Army requires that every new PC it purchases comes with a Trusted Platform Module (TPM). As of July 3, 2007, so does virtually the entire United States Department of Defense. == Key concepts == Trusted Computing encompasses six key technology concepts, of which all are required for a fully Trusted system, that is, a system compliant to the TCG specifications: Endorsement key Secure input and output Memory curtaining / protected execution Sealed storage Remote attestation Trusted Third Party (TTP) === Endorsement key === The endorsement key is a 2048-bit RSA public and private key pair that is created randomly on the chip at manufacture time and cannot be changed. The private key never leaves the chip, while the public key is used for attestation and for encryption of sensitive data sent to the chip, as occurs during the TPM_TakeOwnership command. This key is used to allow the execution of secure transactions: every Trusted Platform Module (TPM) is required to be able to sign a random number (in order to allow the owner to show that he has a genuine trusted computer), using a particular protocol created by the Trusted Computing Group (the direct anonymous attestation protocol) in order to ensure its compliance of the TCG standard and to prove its identity; this makes it impossible for a software TPM emulator with an untrusted endorsement key (for example, a self-generated one) to start a secure transaction with a trusted entity. The TPM should be designed to make the extraction of this key by hardware analysis hard, but tamper resistance is not a strong requirement. === Memory curtaining === Memory curtaining extends common memory protection techniques to provide full isolation of sensitive areas of memory—for example, locations containing cryptographic keys. Even the operating system does not have full access to curtained memory. The exact implementation details are vendor specific. === Sealed storage === Sealed storage protects private information by binding it to platform configuration information including the software and hardware being used. This means the data can be released only to a particular combination of software and hardware. Sealed storage can be used for DRM enforcing. For example, users who keep a song on their computer that has not been licensed to be listened will not be able to play it. Currently, a user can locate the song, listen to it, and send it to someone else, play it in the software of their choice, or back it up (and in some cases, use circumvention software to decrypt it). Alternatively, the user may use software to modify the operating system's DRM routines to have it leak the song data once, say, a temporary license was acquired. Using sealed storage, the song is securely encrypted using a key bound to the trusted platform module so that only the unmodified and untampered music player on his or her computer can play it. In this DRM architecture, this might also prevent people from listening to the song after buying a new computer, or upgrading parts of their current one, except after explicit permission of the vendor of the song. === Remote attestation === Remote attestation allows changes to the user's computer to be detected by authorized parties. For example, software companies can identify unauthorized changes to software, including users modifying their software to circumvent commercial digital rights restrictions. It works by having the hardware generate a certificate stating what software is currently running. The computer can then present this certificate to a remote party to show that unaltered software is currently executing. Numerous remote attestation schemes have been proposed for various computer architectures, including Intel, RISC-V, and ARM. Remote attestation is usually combined with public-key encryption so that the information sent can only be read by the programs that requested the attestation, and not by an eavesdropper. To take the song example again, the user's music player software could send the song to other machines, but only if they could attest that they were running an authorized copy of the music player software. Combined with the other technologies, this provides a more restricted path for the music: encrypted I/O prevents the user from recording it as it is transmitted to the audio subsystem, memory locking prevents it from being dumped to regular disk files as it is being worked on, sealed storage curtails unauthorized access to it when saved to the hard drive, and remote attestation prevents unauthorized software from accessing the song even when it is used on other computers. To preserve the privacy of attestation responders, Direct Anonymous Attestation has been proposed as a solution, which uses a group signature scheme to prevent revealing the identity of individual signers. Proof of space (PoS) have been proposed to be used for malware detection, by determining whether the L1 cache of a processor is empty (e.g., has enough space to evaluate the PoSpace routine without cache misses) or contains a routine that resisted being evicted. === Trusted third party === == Known applications == The Microsoft products Windows Vista, Windows 7, Windows 8 and Windows RT make use of a Trusted Platform Module to facilitate BitLocker Drive Encryption. Other known applications with runtime encryption and the use of secure enclaves include the Signal messenger and the e-prescription service ("E-Rezept") by the German government. == Possible applications == === Digital rights management === Trusted Computing would allow companies to create a digital rights management (DRM) system which would be very hard to circumvent, though not impossible. An example is downloading a music file. Sealed storage could be used to prevent the user from opening the file with an unauthorized player or computer. Remote attestation could be used to authorize play only by music players that enforce the record company's rules. The music would be played from curtained memory, which would prevent the user from making an unrestricted copy of the file while it is playing, and secure I/O would prevent capturing what is being sent to the sound system. Circumventing such a system would require either manipulation of the computer's hardware, capturing the analogue (and thus degraded) signal using a recording device or a microphone, or breaking the security of the system. New business models for use of software (services) over Internet may be boosted by the technology. By strengthening the DRM system, one could base a business model on renting programs for a specific time periods or "pay as you go" models. For instance, one could download a music file which could only be played a certain number of times before it becomes unusable, or the music file could be used only within a certain time period. === Preventing cheating in online games === Trusted Computing could be used to combat cheating in online games. Some players modify their game copy in order to gain unfair advantages in the game; remote attestation, secure I/O and memory curtaining could be used to determine that all players connected to a server were running an unmodified copy of the software. === Verification of remote computation for grid computing === Trusted Computing could be used to guarantee participants in a grid computing sys

  Read more →
• 

  AlphaChip (controversy)

  The AlphaChip controversy refers to a series of public, scholarly, and legal disputes surrounding a 2021 Nature paper by Google-affiliated researchers. The paper describes an approach to macro placement, a stage of chip floorplanning, based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions by optimizing performance-based reward signals. The primary technical question is whether the new techniques are better than existing (non-AI) techniques. Both internal Google studies and external attempts to replicate the algorithm have failed to show the claimed benefits. No head-to-head comparison is available because the data used in the paper is proprietary, and Google has not released any results from running its algorithm on public benchmarks. This has resulted in considerable skepticism over the paper's claims. In addition, the inability of others (both inside and outside of Google) to replicate the claimed results have sparked concerns about the paper’s methodology, reproducibility, and scientific integrity. The lead researchers of the Nature paper were affiliated with Google Brain, which became part of Google DeepMind, and later spun off into the company Ricursive. == Motivation for research: Macro placement in chip layout == Chip design for modern integrated circuits is a complex, expert-driven process that relies on electronic design automation. It determines the performance of the final chip, and takes weeks or months to complete. Advances that produce better designs, or complete the process faster, are commercially and academically significant. Macro placement is a step during chip design that determines the locations of large circuit components (macros) within a chip. It is followed by detailed placement, which places the far more numerous but much smaller standard cells. Alternatively, mixed-size placement simultaneously places both large macros and millions of small cells, requiring algorithms to handle objects that differ by several orders of magnitude in area and mobility. The number of macros per circuit typically ranges from several to thousands. Wiring must be performed after placement, and the details of this wiring strongly influence the power, performance, and area (PPA) of the completed chip. The full wiring calculation is very resource intensive, so placement tools typically use a proxy cost, a simplified objective function used to guide the placement algorithm during training and evaluation. The faithfulness of the chosen proxy cost to the final objective cost is a critical aspect of placer performance. === State of the art as of 2021 === Chips have been designed since the 1960s, so there were many existing methods as of 2021. Available options included manual design, academic tools, and commercial offerings. Academic methods include combinatorial optimization techniques such as simulated annealing, analytical placement, hierarchical heuristics, and as of 2019 reinforcement learning and broader machine learning techniques.. Existing (non-AI) academic tools for solving the same problem include APlace, NTUplace3, ePlace, RePlace, and DREAMPlace. Commercial EDA vendors also offered automated software tools for floorplanning and mixed-size placement. For instance, as of 2019 Cadence’s Innovus implementation software offered a Concurrent Macro Placer (CMP) feature to automatically place large blocks and standard cells. == The 2021 Nature paper and its claims == In 2021, Nature published a paper under the title “A graph‑placement methodology for fast chip design” co‑authored by 21 Google-affiliated researchers. The paper reported that an RL agent could generate macro placements for integrated circuits "in under six hours" and achieve improvements over human-designed layouts in power, timing performance, and area (PPA), standard chip-quality metrics referring respectively to energy consumption, chip operating speed, and silicon footprint (evaluated after wire routing). It introduced a sequential macro placement algorithm in which macros are placed one at a time instead of optimizing their locations concurrently. At each step, the algorithm selects a location for a single macro on a discretized chip canvas, conditioning its decision on the placements of previously placed macros. This sequential formulation converts macro placement into a long-horizon decision process in which early placement choices constrain later ones. After macro placement, force-directed placement is applied to place standard cells connected to the macros. Deep reinforcement learning is used to train a policy network to place macros by maximizing a reward that reflects final placement quality (for example, wirelength and congestion). Policy learning occurs during self‑play for one or multiple circuit designs. Further placement optimizations refine the overall layout by balancing wirelength, density, and overlap constraints, while treating the macro locations produced by the RL policy as fixed obstacles. The approach relies on pre-training, in which the RL model is first trained on a corpus of prior designs (twenty in the Nature paper) to learn general placement patterns before being fine-tuned on a specific chip. Circuit examples used in the study were parts of proprietary Google TPU designs, called blocks (or floorplan partitions). The paper reported results on five blocks and described the approach as generalizable across chip designs. == Controversy == Soon after the paper's publication, controversy arose over whether the claims were true, whether they were sufficiently proven, and whether academic standards were followed. These controversies arose both within Google and among external academic experts. === Internal dispute at Google and legal proceedings === In 2022, Satrajit Chatterjee, a Google engineer involved in reviewing the AlphaChip work, raised concerns internally and drafted an alternative analysis, (Stronger Baselines) arguing that established methods outperformed the RL approach under fair comparison. In March 2022, Google declined to publish this analysis and terminated Chatterjee's employment. Chatterjee filed a wrongful dismissal lawsuit, alleging that representations related to the AlphaChip research involved fraud and scientific misconduct. According to court documents, Chatterjee's study was conducted "in the context of a large potential Google Cloud deal". He noted that it "would have been unethical to imply that we had revolutionary technology when our tests showed otherwise" and claimed Google was deliberately withholding material information. Furthermore, the committee that reviewed his paper and disapproved its publication was allegedly chaired by subordinates of Jeff Dean, a senior co-author of the Nature paper. Google’s subsequent motion to dismiss was denied, holding that Chatterjee had plausibly alleged retaliation for refusing to engage in conduct he believed would violate state or federal law. === External controversy === The external questions can be summarized in four main points: (a) Are the claims supported by the evidence provided? (b) Did the paper provide enough information to allow the results to be independently reproduced and verified? If so, are the results an improvement over existing academic and commercial tools? (c) Were the comparisons in the paper done fairly and with full disclosure? (d) Were academic standards followed? Each of these is discussed below. ==== Are the claims supported by the evidence provided? ==== The Nature paper described the reduction in design-process time as going from "days or weeks" to "hours", but did not provide per-design time breakdowns or specify the number of engineers, their level of expertise, or the baseline tools and workflow against which this comparison was made. It was also unclear whether the "days or weeks" baseline included time spent on other tasks such as functional design changes. The paper also evaluated the method on fewer benchmarks (five) than is common in the field, and showed mixed results across different evaluation goals While the approach was described as improving circuit area, this claim seems unsupported, as the RL optimization did not alter the overall circuit area, as it adjusted only the locations of fixed-shape non-overlapping circuit components within a fixed rectangular layout boundary. ==== Comparison with existing methods, and replicating the algorithm ==== Because macro placement is largely geometric and its fundamental algorithms are not tied to a specific process node, competing approaches can be evaluated on public benchmarks (tests) across technologies, rather than primarily on proprietary internal designs. This is standard procedure when comparing academic placers, see . In contrast, Google has only reported results only on internal proprietary designs, and as of 2026 has not offered comparisons with prior methods on common benchmarks. Researchers at the University of Califor

  Read more →
• 

  Azuqua

  Azuqua is an American cloud-based integration and automation company headquartered in Seattle, Washington. As such, they integrate SaaS applications and create automations that are designed to eliminate manual work. Azuqua's platform has the ability to set up workflows between multiple applications so disparate teams can stay in the loop. Azuqua's customers include companies such as Charles Schwab, General Electric, General Motors, HubSpot, and Airbnb. == History == Nikhil Hasija and Craig Unger founded Azuqua in 2011. In 2013, the team participated in Techstars Microsoft's Windows Azure Accelerator, a Seattle-based incubator that helps entrepreneurs gain traction through deep mentor engagement and rapid iteration cycles. Azuqua announced in 2014 that they have received their Series A funding from Ignition Partners which amounted to $5 million. 2017 included a 65% growth in new customers, a doubling of new SaaS connectors, and a 50% growth in overall employee headcount. Azuqua also received their Series B funding which totaled to $10.8 million. This funding was led by Insight Ventures Partners, with DFJ and Ignition Partners also joining the round In March 2018, Azuqua hired Todd Owens as CEO. Owens was previously CEO of Appuri, a customer data platform. Hasija has transitioned to the role of Chief Product Officer. Azuqua also hired on Dan Kogan who has taken on the role of Chief Marketing Officer. Kogan previously worked at Tableau, a BI and analytics company, as a Senior Director of Product Marketing. Okta acquired Azuqua in 2019. == Product Description/Features == Logic Library: Logic functions that can be used for data processing, branching logic, and business rules Drag and Drop Visual Designer: No-code visual designer Use of API's for each cloud service a business is using to allow the various apps to communicate and share data API Publishing: Integrations and automations can be made available as secure endpoints, webhooks, or open services Connector Builder: Build a connector to an application Connector Library: Pre-built connectors to SaaS applications Error Handling: Automations that execute when an error is detected

  Read more →
• 

  White-box cryptography

  In cryptography, the white-box model refers to an extreme attack scenario, in which an adversary has full unrestricted access to a cryptographic implementation, most commonly of a block cipher such as the Advanced Encryption Standard (AES). A variety of security goals may be posed (see the section below), the most fundamental being "unbreakability", requiring that any (bounded) attacker should not be able to extract the secret key hardcoded in the implementation, while at the same time the implementation must be fully functional. In contrast, the black-box model only provides an oracle access to the analyzed cryptographic primitive (in the form of encryption and/or decryption queries). There is also a model in-between, the so-called gray-box model, which corresponds to additional information leakage from the implementation, more commonly referred to as side-channel leakage. White-box cryptography is a practice and study of techniques for designing and attacking white-box implementations. It has many applications, including digital rights management (DRM), pay television, protection of cryptographic keys in the presence of malware, mobile payments and cryptocurrency wallets. Examples of DRM systems employing white-box implementations include CSS and Widevine. White-box cryptography is closely related to the more general notions of obfuscation, in particular, to Black-box obfuscation, proven to be impossible, and to Indistinguishability obfuscation, constructed recently under well-founded assumptions but so far being infeasible to implement in practice. As of January 2023, there are no publicly known unbroken white-box designs of standard symmetric encryption schemes. On the other hand, there exist many unbroken white-box implementations of dedicated block ciphers designed specifically to achieve incompressibility (see § Security goals). == Security goals == Depending on the application, different security goals may be required from a white-box implementation. Specifically, for symmetric-key algorithms the following are distinguished: Unbreakability is the most fundamental goal requiring that a bounded attacker should not be able to recover the secret key embedded in the white-box implementation. Without this requirement, all other security goals are unreachable since a successful attacker can simply use a reference implementation of the encryption scheme together with the extracted key. One-wayness requires that a white-box implementation of an encryption scheme can not be used by a bounded attacker to decrypt ciphertexts. This requirement essentially turns a symmetric encryption scheme into a public-key encryption scheme, where the white-box implementation plays the role of the public key associated to the embedded secret key. This idea was proposed already in the famous work of Diffie and Hellman in 1976 as a potential public-key encryption candidate. Code lifting security is an informal requirement on the context, in which the white-box program is being executed. It demands that an attacker can not extract a functional copy of the program. This goal is particularly relevant in the DRM setting. Code obfuscation techniques are often used to achieve this goal. A commonly used technique is to compose the white-box implementation with so-called external encodings. These are lightweight secret encodings that modify the function computed by the white-box part of an application. It is required that their effect is canceled in other parts of the application in an obscure way, using code obfuscation techniques. Alternatively, the canceling counterparts can be applied on a remote server. Incompressibility requires that an attacker can not significantly compress a given white-box implementation. This can be seen as a way to achieve code lifting security (see above), since exfiltrating a large program from a constrained device (for example, an embedded or a mobile device) can be time-consuming and may be easy to detect by a firewall. Examples of incompressible designs include SPACE cipher, SPNbox, WhiteKey and WhiteBlock. These ciphers use large lookup tables that can be pseudorandomly generated from a secret master key. Although this makes the recovery of the master key hard, the lookup tables themselves play the role of an equivalent secret key. Thus, unbreakability is achieved only partially. Traceability (Traitor tracing) requires that each distributed white-box implementation contains a digital watermark allowing identification of the guilty user in case the white-box program is being leaked and distributed publicly. == History == The white-box model with initial attempts of white-box DES and AES implementations were first proposed by Chow, Eisen, Johnson and van Oorshot in 2003. The designs were based on representing the cipher as a network of lookup tables and obfuscating the tables by composing them with small (4- or 8-bit) random encodings. Such protection satisfied a property that each single obfuscated table individually does not contain any information about the secret key. Therefore, a potential attacker has to combine several tables in their analysis. The first two schemes were broken in 2004 by Billet, Gilbert, and Ech-Chatbi using structural cryptanalysis. The attack was subsequently called "the BGE attack". The numerous consequent design attempts (2005-2022) were quickly broken by practical dedicated attacks. In 2016, Bos, Hubain, Michiels and Teuwen showed that an adaptation of standard side-channel power analysis attacks can be used to efficiently and fully automatically break most existing white-box designs. This result created a new research direction about generic attacks (correlation-based, algebraic, fault injection) and protections against them. == Competitions == Four editions of the WhibOx contest were held in 2017, 2019, 2021 and 2024 respectively. These competitions invited white-box designers both from academia and industry to submit their implementation in the form of (possibly obfuscated) C code. At the same time, everyone could attempt to attack these programs and recover the embedded secret key. Each of these competitions lasted for about 4-5 months. WhibOx 2017 / CHES 2017 Capture the Flag Challenge targeted the standard AES block cipher. Among 94 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for 28 days. WhibOx 2019 / CHES 2019 Capture the Flag Challenge again targeted the AES block cipher. Among 27 submitted implementations, 3 programs stayed unbroken throughout the competition, but were broken after 51 days since the publication. WhibOx 2021 / CHES 2021 Capture the Flag Challenge changed the target to ECDSA, a digital signature scheme based on elliptic curves. Among 97 submitted implementations, all were broken within at most 2 days. WhibOx 2024 / CHES 2024 Capture the Flag Challenge again targeted ECDSA. Among 47 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for almost 5 days.

  Read more →