Eigenmoments

EigenMoments is a set of orthogonal, noise robust, invariant to rotation, scaling and translation and distribution sensitive moments. Their application can be found in signal processing and computer vision as descriptors of the signal or image. The descriptors can later be used for classification purposes. It is obtained by performing orthogonalization, via eigen analysis on geometric moments. == Framework summary == EigenMoments are computed by performing eigen analysis on the moment space of an image by maximizing signal-to-noise ratio in the feature space in form of Rayleigh quotient. This approach has several benefits in Image processing applications: Dependency of moments in the moment space on the distribution of the images being transformed, ensures decorrelation of the final feature space after eigen analysis on the moment space. The ability of EigenMoments to take into account distribution of the image makes it more versatile and adaptable for different genres. Generated moment kernels are orthogonal and therefore analysis on the moment space becomes easier. Transformation with orthogonal moment kernels into moment space is analogous to projection of the image onto a number of orthogonal axes. Nosiy components can be removed. This makes EigenMoments robust for classification applications. Optimal information compaction can be obtained and therefore a few number of moments are needed to characterize the images. == Problem formulation == Assume that a signal vector s ∈ R n {\displaystyle s\in {\mathcal {R}}^{n}} is taken from a certain distribution having correlation C ∈ R n × n {\displaystyle C\in {\mathcal {R}}^{n\times n}} , i.e. C = E [ s s T ] {\displaystyle C=E[ss^{T}]} where E[.] denotes expected value. Dimension of signal space, n, is often too large to be useful for practical application such as pattern classification, we need to transform the signal space into a space with lower dimensionality. This is performed by a two-step linear transformation: q = W T X T s , {\displaystyle q=W^{T}X^{T}s,} where q = [ q 1 , . . . , q n ] T ∈ R k {\displaystyle q=[q_{1},...,q_{n}]^{T}\in {\mathcal {R}}^{k}} is the transformed signal, X = [ x 1 , . . . , x n ] T ∈ R n × m {\displaystyle X=[x_{1},...,x_{n}]^{T}\in {\mathcal {R}}^{n\times m}} a fixed transformation matrix which transforms the signal into the moment space, and W = [ w 1 , . . . , w n ] T ∈ R m × k {\displaystyle W=[w_{1},...,w_{n}]^{T}\in {\mathcal {R}}^{m\times k}} the transformation matrix which we are going to determine by maximizing the SNR of the feature space resided by q {\displaystyle q} . For the case of Geometric Moments, X would be the monomials. If m = k = n {\displaystyle m=k=n} , a full rank transformation would result, however usually we have m ≤ n {\displaystyle m\leq n} and k ≤ m {\displaystyle k\leq m} . This is specially the case when n {\displaystyle n} is of high dimensions. Finding W {\displaystyle W} that maximizes the SNR of the feature space: S N R t r a n s f o r m = w T X T C X w w T X T N X w , {\displaystyle SNR_{transform}={\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}},} where N is the correlation matrix of the noise signal. The problem can thus be formulated as w 1 , . . . , w k = a r g m a x w w T X T C X w w T X T N X w {\displaystyle {w_{1},...,w_{k}}=argmax_{w}{\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}}} subject to constraints: w i T X T N X w j = δ i j , {\displaystyle w_{i}^{T}X^{T}NXw_{j}=\delta _{ij},} where δ i j {\displaystyle \delta _{ij}} is the Kronecker delta. It can be observed that this maximization is Rayleigh quotient by letting A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} and therefore can be written as: w 1 , . . . , w k = a r g m a x x w T A w w T B w {\displaystyle {w_{1},...,w_{k}}={\underset {x}{\operatorname {arg\,max} }}{\frac {w^{T}Aw}{w^{T}Bw}}} , w i T B w j = δ i j {\displaystyle w_{i}^{T}Bw_{j}=\delta _{ij}} === Rayleigh quotient === Optimization of Rayleigh quotient has the form: max w R ( w ) = max w w T A w w T B w {\displaystyle \max _{w}R(w)=\max _{w}{\frac {w^{T}Aw}{w^{T}Bw}}} and A {\displaystyle A} and B {\displaystyle B} , both are symmetric and B {\displaystyle B} is positive definite and therefore invertible. Scaling w {\displaystyle w} does not change the value of the object function and hence and additional scalar constraint w T B w = 1 {\displaystyle w^{T}Bw=1} can be imposed on w {\displaystyle w} and no solution would be lost when the objective function is optimized. This constraint optimization problem can be solved using Lagrangian multiplier: max w w T A w {\displaystyle \max _{w}{w^{T}Aw}} subject to w T B w = 1 {\displaystyle {w^{T}Bw}=1} max w L ( w ) = max w ( w T A w − λ w T B w ) {\displaystyle \max _{w}{\mathcal {L}}(w)=\max _{w}(w{T}Aw-\lambda w^{T}Bw)} equating first derivative to zero and we will have: A w = λ B w {\displaystyle Aw=\lambda Bw} which is an instance of Generalized Eigenvalue Problem (GEP). The GEP has the form: A w = λ B w {\displaystyle Aw=\lambda Bw} for any pair ( w , λ ) {\displaystyle (w,\lambda )} that is a solution to above equation, w {\displaystyle w} is called a generalized eigenvector and λ {\displaystyle \lambda } is called a generalized eigenvalue. Finding w {\displaystyle w} and λ {\displaystyle \lambda } that satisfies this equations would produce the result which optimizes Rayleigh quotient. One way of maximizing Rayleigh quotient is through solving the Generalized Eigen Problem. Dimension reduction can be performed by simply choosing the first components w i {\displaystyle w_{i}} , i = 1 , . . . , k {\displaystyle i=1,...,k} , with the highest values for R ( w ) {\displaystyle R(w)} out of the m {\displaystyle m} components, and discard the rest. Interpretation of this transformation is rotating and scaling the moment space, transforming it into a feature space with maximized SNR and therefore, the first k {\displaystyle k} components are the components with highest k {\displaystyle k} SNR values. The other method to look at this solution is to use the concept of simultaneous diagonalization instead of Generalized Eigen Problem. === Simultaneous diagonalization === Let A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} as mentioned earlier. We can write W {\displaystyle W} as two separate transformation matrices: W = W 1 W 2 . {\displaystyle W=W_{1}W_{2}.} W 1 {\displaystyle W_{1}} can be found by first diagonalize B: P T B P = D B {\displaystyle P^{T}BP=D_{B}} . Where D B {\displaystyle D_{B}} is a diagonal matrix sorted in increasing order. Since B {\displaystyle B} is positive definite, thus D B > 0 {\displaystyle D_{B}>0} . We can discard those eigenvalues that large and retain those close to 0, since this means the energy of the noise is close to 0 in this space, at this stage it is also possible to discard those eigenvectors that have large eigenvalues. Let P ^ {\displaystyle {\hat {P}}} be the first k {\displaystyle k} columns of P {\displaystyle P} , now P T ^ B P ^ = D B ^ {\displaystyle {\hat {P^{T}}}B{\hat {P}}={\hat {D_{B}}}} where D B ^ {\displaystyle {\hat {D_{B}}}} is the k × k {\displaystyle k\times k} principal submatrix of D B {\displaystyle D_{B}} . Let W 1 = P ^ D B ^ − 1 / 2 {\displaystyle W_{1}={\hat {P}}{\hat {D_{B}}}^{-1/2}} and hence: W 1 T B W 1 = ( P ^ D B ^ − 1 / 2 ) T B ( P ^ D B ^ − 1 / 2 ) = I {\displaystyle W_{1}^{T}BW_{1}=({\hat {P}}{\hat {D_{B}}}^{-1/2})^{T}B({\hat {P}}{\hat {D_{B}}}^{-1/2})=I} . W 1 {\displaystyle W_{1}} whiten B {\displaystyle B} and reduces the dimensionality from m {\displaystyle m} to k {\displaystyle k} . The transformed space resided by q ′ = W 1 T X T s {\displaystyle q'=W_{1}^{T}X^{T}s} is called the noise space. Then, we diagonalize W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} : W 2 T W 1 T A W 1 W 2 = D A {\displaystyle W_{2}^{T}W_{1}^{T}AW_{1}W_{2}=D_{A}} , where W 2 T W 2 = I {\displaystyle W_{2}^{T}W_{2}=I} . D A {\displaystyle D_{A}} is the matrix with eigenvalues of W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} on its diagonal. We may retain all the eigenvalues and their corresponding eigenvectors since most of the noise are already discarded in previous step. Finally the transformation is given by: W = W 1 W 2 {\displaystyle W=W_{1}W_{2}} where W {\displaystyle W} diagonalizes both the numerator and denominator of the SNR, W T A W = D A {\displaystyle W^{T}AW=D_{A}} , W T B W = I {\displaystyle W^{T}BW=I} and the transformation of signal s {\displaystyle s} is defined as q = W T X T s = W 2 T W 1 T X T s {\displaystyle q=W^{T}X^{T}s=W_{2}^{T}W_{1}^{T}X^{T}s} . === Information loss === To find the information loss when we discard some of the eigenvalues and eigenvectors we can perform following analysis: η = 1 − t r a c e ( W 1 T A W 1 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) = 1 − t r a c e ( D B ^ − 1 / 2 P ^ T A P ^ D B ^ − 1 / 2 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) {\displaystyle {\begin{array}{lll}\eta &=&

Automotive security

Automotive security refers to the branch of computer security focused on the cyber risks related to the automotive context. The increasingly high number of ECUs in vehicles and, alongside, the implementation of multiple different means of communication from and towards the vehicle in a remote and wireless manner led to the necessity of a branch of cybersecurity dedicated to the threats associated with vehicles. Not to be confused with automotive safety. == Causes == The implementation of multiple ECUs (Electronic Control Units) inside vehicles began in the early '70s thanks to the development of integrated circuits and microprocessors that made it economically feasible to produce the ECUs on a large scale. Since then the number of ECUs has increased to up to 100 per vehicle. These units nowadays control almost everything in the vehicle, from simple tasks such as activating the wipers to more safety-related ones like brake-by-wire or ABS (Anti-lock Braking System). Autonomous driving is also strongly reliant on the implementation of new, complex ECUs such as the ADAS, alongside sensors (lidars and radars) and their control units. Inside the vehicle, the ECUs are connected with each other through cabled or wireless communication networks, such as CAN bus (controller area network), MOST bus (Media Oriented System Transport), FlexRay (Automotive Network Communications Protocol) or RF (radio frequency) as in many implementations of TPMSs (tire-pressure monitoring systems). Many of these ECUs require data received through these networks that arrive from various sensors to operate and use such data to modify the behavior of the vehicle (e.g., the cruise control modifies the vehicle's speed depending on signals arriving from a button usually located on the steering wheel). Since the development of cheap wireless communication technologies such as Bluetooth, LTE, Wi-Fi, RFID and similar, automotive producers and OEMs have designed ECUs that implement such technologies with the goal of improving the experience of the driver and passengers. Safety-related systems such as the OnStar from General Motors, telematic units, communication between smartphones and the vehicle's speakers through Bluetooth, Android Auto and Apple CarPlay. == Threat model == Threat models of the automotive world are based on both real-world and theoretically possible attacks. Most real-world attacks aim at the safety of the people in and around the car, by modifying the cyber-physical capabilities of the vehicle (e.g., steering, braking, accelerating without requiring actions from the driver), while theoretical attacks have been supposed to focus also on privacy-related goals, such as obtaining GPS data on the vehicle, or capturing microphone signals and similar. Regarding the attack surfaces of the vehicle, they are usually divided in long-range, short-range, and local attack surfaces: LTE and DSRC can be considered long-range ones, while Bluetooth and Wi-Fi are usually considered short-range although still wireless. Finally, USB, OBD-II and all the attack surfaces that require physical access to the car are defined as local. An attacker that is able to implement the attack through a long-range surface is considered stronger and more dangerous than the one that requires physical access to the vehicle. In 2015 the possibility of attacks on vehicles already on the market has been proven possible by Miller and Valasek, that managed to disrupt the driving of a Jeep Cherokee while remotely connecting to it through remote wireless communication. === Controller area network attacks === The most common network used in vehicles and the one that is mainly used for safety-related communication is CAN, due to its real-time properties, simplicity, and cheapness. For this reason the majority of real-world attacks have been implemented against ECUs connected through this type of network. The majority of attacks demonstrated either against actual vehicles or in testbeds fall in one or more of the following categories: ==== Sniffing ==== Sniffing in the computer security field generally refers to the possibility of intercepting and logging packets or more generally data from a network. In the case of CAN, since it is a bus network, every node listens to all communication on the network. It is useful for the attacker to read data to learn the behavior of the other nodes of the network before implementing the actual attack. Usually, the final goal of the attacker is not to simply sniff the data on CAN, since the packets passing on this type of network are not usually valuable just to read. ==== Denial of service ==== Denial of service (DoS) in information security is usually described as an attack that has the objective of making a machine or a network unavailable. DoS attacks against ECUs connected to CAN buses can be done both against the network, by abusing the arbitration protocol used by CAN to always win the arbitration, and targeting the single ECU, by abusing the error handling protocol of CAN. In this second case the attacker flags the messages of the victim as faulty to convince the victim of being broken and therefore shut itself off the network. ==== Spoofing ==== Spoofing attacks comprise all cases in which an attacker, by falsifying data, sends messages pretending to be another node of the network. In automotive security usually spoofing attacks are divided into masquerade and replay attacks. Replay attacks are defined as all those where the attacker pretends to be the victim and sends sniffed data that the victim sent in a previous iteration of authentication. Masquerade attacks are, on the contrary, spoofing attacks where the data payload has been created by the attacker. == Real life automotive threat example == Security researchers Charlie Miller and Chris Valasek have successfully demonstrated remote access to a wide variety of vehicle controls using a Jeep Cherokee as the target. They were able to control the radio, environmental controls, windshield wipers, and certain engine and brake functions. The method used to hack the system was implementation of pre-programmed chip into the controller area network (CAN) bus. By inserting this chip into the CAN bus, he was able to send arbitrary message to CAN bus. One other thing that Miller has pointed out is the danger of the CAN bus, as it broadcasts the signal which the message can be caught by the hackers throughout the network. The control of the vehicle was all done remotely, manipulating the system without any physical interaction. Miller states that he could control any of some 1.4 million vehicles in the United States regardless of the location or distance, the only thing needed is for someone to turn on the vehicle to gain access. The work by Miller and Valasek replicated earlier work completed and published by academics in 2010 and 2011 on a different vehicle. The earlier work demonstrated the ability to compromise a vehicle remotely, over multiple wireless channels (including cellular), and the ability to remotely control critical components on the vehicle post-compromise, including the telematics unit and the car's brakes. While the earlier academic work was publicly visible, both in peer-reviewed scholarly publications and in the press, the Miller and Valesek work received even greater public visibility. == Security measures == The increasing complexity of devices and networks in the automotive context requires the application of security measures to limit the capabilities of a potential attacker. Since the early 2000 many different countermeasures have been proposed and, in some cases, applied. Following, a list of the most common security measures: Sub-networks: to limit the attacker capabilities even if he/she manages to access the vehicle from remote through a remotely connected ECU, the networks of the vehicle are divided in multiple sub-networks, and the most critical ECUs are not placed in the same sub-networks of the ECUs that can be accessed from remote. Gateways: the sub-networks are divided by secure gateways or firewalls that block messages from crossing from a sub-network to the other if they were not intended to. Intrusion Detection Systems (IDS): on each critical sub-network, one of the nodes (ECUs) connected to it has the goal of reading all data passing on the sub-network and detect messages that, given some rules, are considered malicious (made by an attacker). The arbitrary messages can be caught by the passenger by using IDS which will notify the owner regarding with unexpected message. Authentication protocols: in order to implement authentication on networks where it is not already implemented (such as CAN), it is possible to design an authentication protocol that works on the higher layers of the ISO OSI model, by using part of the data payload of a message to authenticate the message itself. Hardware Security Modules: since many ECUs are not powerful enough to keep real-time delays whi

Bisection (software engineering)

Bisection is a method used in software development to identify change sets that result in a specific behavior change. It is mostly employed for finding the patch that introduced a bug. Another application area is finding the patch that indirectly fixed a bug. == Overview == The process of locating the changeset that introduced a specific regression was described as "source change isolation" in 1997 by Brian Ness and Viet Ngo of Cray Research. Regression testing was performed on Cray's compilers in editions comprising one or more changesets. Editions with known regressions could not be validated until developers addressed the problem. Source change isolation narrowed the cause to a single changeset that could then be excluded from editions, unblocking them with respect to this problem, while the author of the change worked on a fix. Ness and Ngo outlined linear search and binary search methods of performing this isolation. Code bisection has the goal of minimizing the effort to find a specific change set. It employs a divide and conquer algorithm that depends on having access to the code history which is usually preserved by revision control in a code repository. == Bisection method == === Code bisection algorithm === Code history has the structure of a directed acyclic graph which can be topologically sorted. This makes it possible to use a divide and conquer search algorithm which: splits up the search space of candidate revisions tests for the behavior in question reduces the search space depending on the test result re-iterates the steps above until a range with at most one bisectable patch candidate remains === Algorithmic complexity === Bisection is in LSPACE having an algorithmic complexity of O ( log ⁡ N ) {\displaystyle O(\log N)} with N {\displaystyle N} denoting the number of revisions in the search space, and is similar to a binary search. === Desirable repository properties === For code bisection it is desirable that each revision in the search space can be built and tested independently. === Monotonicity === For the bisection algorithm to identify a single changeset which caused the behavior being tested to change, the behavior must change monotonically across the search space. For a Boolean function such as a pass/fail test, this means that it only changes once across all changesets between the start and end of the search space. If there are multiple changesets across the search space where the behavior being tested changes between false and true, then the bisection algorithm will find one of them, but it will not necessarily be the root cause of the change in behavior between the start and the end of the search space. The root cause could be a different changeset, or a combination of two or more changesets across the search space. To help deal with this problem, automated tools allow specific changesets to be ignored during a bisection search. == Automation support == Although the bisection method can be completed manually, one of its main advantages is that it can be easily automated. It can thus fit into existing test automation processes: failures in exhaustive automated regression tests can trigger automated bisection to localize faults. Ness and Ngo focused on its potential in Cray's continuous delivery-style environment in which the automatically isolated bad changeset could be automatically excluded from builds. The revision control systems Fossil, Git and Mercurial have built-in functionality for code bisection. The user can start a bisection session with a specified range of revisions from which the revision control system proposes a revision to test, the user tells the system whether the revision tested as "good" or "bad", and the process repeats until the specific "bad" revision has been identified. Other revision control systems, such as Bazaar or Subversion, support bisection through plugins or external scripts. Phoronix Test Suite can do bisection automatically to find performance regressions.

Artificial intelligence industry in Canada

The artificial intelligence industry in Canada is a rapidly expanding sector. Although Canada held a pioneering role in the early development of artificial intelligence, transforming research excellence into broad commercial adoption has proven challenging. Despite globally recognized scientific achievements and a deep pool of skilled experts, by June 2024, Canada recorded the lowest rate of AI integration among OECD countries, with only 12% of firms implementing AI in their products or services. However, AI adoption has shown significant momentum—doubling from mid-2024 to mid-2025, rising from 6.1% to 12.2%. As of September 2025, Statistics Canada indicated that while about one-third of Canadian businesses had no plans to adopt artificial intelligence in the next year, 14.5% reported intentions to begin using AI for producing goods or delivering services. The primary reasons for not moving forward with AI were lack of relevance, insufficient knowledge, and privacy concerns. According to Public Works Canada (PwC), the pace of AI adoption in Canada is roughly three-quarters of the United States rate, highlighting a notable gap between the two countries in business integration of this technology. British-Canadian computer scientist Geoffrey Hinton stated in 2025 that Canadian companies are adopting artificial intelligence at a slower pace, which may result in the loss of the country's early advantages in the field. At the "All In AI" conference held in Montreal in September 2025, the Minister of Artificial Intelligence and Digital Innovation Evan Solomon, described "Building digital sovereignty" as the most pressing democratic issue of the time. He introduced a 26-person task force focused on updating Canada's AI strategy. In their 2024 report " "Learning Together for Responsible Artificial Intelligence" report, the Innovation, Science, and Economic Development Canada stressed that public awareness, trust, and AI literacy are essential for the responsible adoption and governance of AI in Canada. Montreal workshops in 2021 expanded the OECD's 2019 definition of AI as "the set of computer techniques that enable a machine (e.g., a computer or telephone) to perform tasks that typically require intelligence, such as reasoning or learning. It is also referred to as the automation of intelligent tasks. Scientific developments in AI, such as deep-learning techniques, have made it possible to design access to huge amounts of data and ever-increasing computing power. These new techniques have been rapidly deployed on a large scale in all areas of social life, in transport, education, culture and health." == Federal investments and policy == The 2025 federal budget allocates over $1 billion over the next five years to bolster Canada's artificial intelligence and quantum computing ecosystem. == Industry landscape or research hubs == AlexNet, an influential deep convolutional neural network developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, marked a pivotal turning point in modern artificial intelligence. In 2012, it achieved a dramatic reduction in error rates for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the practical power of deep learning and GPU acceleration. The success of AlexNet helped cement Canada’s reputation for AI leadership and inspired rapid adoption of deep learning across the technology sector, with ongoing impact in both academic and commercial domains. In healthcare, AlexNet has been adapted for medical imaging to assist with analyzing radiographs, mammograms, and other scans, including identifying abnormalities and supporting clinical diagnosis. In 2015, the Ottawa-based start-up Advanced Symbolics Inc. (ASI) began developing Polly, an artificial intelligence system designed to analyze and anticipate how target audiences behave—enabling more effective communication strategies and advertising campaigns. Polly was named after its first assignment analyzing the politics of Brexit. The AI gained widespread attention in 2016 for accurately forecasting both the Brexit referendum and the 2016 U.S. presidential election won by Donald Trump. The company states that Polly is used by organizations in diverse sectors—including healthcare, politics, entertainment, and mental health research—to support decision-making based on predictive analytics. Chartwatch, an AI tool developed in Canada, has been shown to reduce unexpected hospital deaths by 26% according to a 2024 study. The system analyzes patient data to detect subtle signs of deterioration, supporting healthcare teams in providing timely interventions. === Notable figures in AI in Canada === Geoffrey Hinton's decades-long work eventually formed the foundation of artificial intelligence, which earned him the Nobel Prize for physics in 2024. Yoshua Bengio, who won the Turing Award in 2018 for his pioneering work in deep learning, founded what would become Mila in 1993. Mila, is currently a collaboration between four Montreal-based academic partners.—the Pan-Canadian Artificial Intelligence Strategy includes Alberta's Amii, Toronto's Vector Institute, and Mila. Fakhreddine Karray's work on operational AI has had tangible impact across several Canadian-relevant sectors, notably intelligent transportation systems, virtual healthcare, and driver safety. === AI in the oil and gas industry === According to a 2020 Ernst & Young report the oil and gas industry in Canada is using AI in automating routine, repetitive, and dangerous tasks with technologies like robotic process automation and machine learning; optimizing production and processing; enhancing transportation logistics; improving equipment operation and monitoring; and enabling preventative maintenance. AI is also deployed for data analysis to improve prediction and decision-making, and is expected to automate up to 50% of job competencies in upstream oil and gas by 2040. Oilsands giant Suncor Energy operates a large fleet of autonomous trucks and has started using AI in its dispatch system at the Mildred Lake mine. As of 2024, AI manages routine tasks such as allocating trucks to dump stations and sending them to refuelling locations. === Indigenous and Inuit Innovation in AI === Indigenous organizations have been working on the creation of new technologies for language revitalization in partnership with National Research Council of Canada since the mid-2010s. In 2025, Inuit researchers and technology partners launched an AI-powered initiative to support the revitalization and preservation of Inuktitut, demonstrating how artificial intelligence can be adapted for Indigenous language and cultural priorities. A 2025 CBC article notes that, while AI can help revitalize Inuktitut, Inuit leaders emphasize concerns about data sovereignty, information ownership, and the need for Indigenous leadership to ensure transparency, privacy, and accountability in AI development. == Regulation == Canada's Artificial Intelligence and Data Act (AIDA) was proposed in November 2022, as part of the Digital Charter Implementation Act (Bill C-27). As well voluntary codes, such as the September 2023 Code of Conduct for Generative AI, and landmark investments in advanced computing infrastructure and the Canadian Artificial Intelligence Safety Institute (CAISI) reflect Canada's commitment to both safety and global competitiveness. == AI infrastructure == Canada has undertaken efforts to expand its AI computing infrastructure at both provincial and federal levels. The federal government's Canadian Sovereign AI Compute Strategy, allocated up to C$2 billion in Budget 2024, aims to enhance computing capacity to support domestic AI industry growth and AI adoption across the economy, with up to C$700 million designated to mobilize private sector investment in new or expanded data centres. Alberta has introduced an AI Data Centres Strategy to position itself as a leading North American destination for data centre investment, targeting C$100 billion worth of AI data centres under development by 2030. One major project under Alberta's strategy is the Wonder Valley AI Data Centre Park near Grande Prairie, which was exempted from provincial environmental impact assessment in April 2026 but still requires permits demonstrating safe construction and operation. According to Statista, as of April 2026, Canada has 287 data centres.

Applied Information Science in Economics

The Applied Information Science in Economics (Russian: Прикладная информатика в Экономике) or Applied Computer Science in Economics is a professional qualification generally awarded in Russian Federation. The degree inherited from the U.S.S.R. education system also known as Specialist degree. The degree is awarded after five years of full-time study and includes several internships, course-works, thesis writing and defense. The degree has similarities with German Magister Artium or Diplom degree. However, due to the Bologna Process number of such degrees are declining. Degree focuses on applying mathematical methods in economics involving maximum information technology. It is very close to applied mathematics, but includes also major part of computer science. == List of specialty codes in the education system == 080801 - Applied computer science in economics 351400 - Applied computer science == Fields of activity == Organization and management; Project design; Experimental research; Marketing; Consulting; Operational and Maintenance. == Major == Information Science and Programming. High Level Methods of Information Science and Programming. Information Technologies in Economics. Computer Systems, Networks and Telecommunications Services. Operational Environments, Systems and Shells. Architecture and Design of Information Systems for Companies. Data Bases. Information security. Information Management. Imitative Simulation.

Computers & Graphics

Computers & Graphics is a peer-reviewed scientific journal that covers computer graphics and related subjects such as data visualization, human-computer interaction, virtual reality, and augmented reality. It was established in 1975 and originally published by Pergamon Press. It is now published by Elsevier, which acquired Pergamon Press in 1991. From 2018 to 2022 Graphics and Visual Computing was an open access sister journal sharing the same editorial team and double-blind peer-review policies. It has since merged into GMOD, the International Journal of Graphical Models. == History == The journal was established in 1975 by founding editor-in-chief Robert Schiffman (University of Colorado, Boulder), as Computers & Graphics-UK. Schiffman, who co-organized the first SIGGRAPH conference in 1974, had the conference proceedings published as the first issue of the journal. He was succeeded in 1978 by Larry Feeser (Rensselaer Polytechnic Institute). In 1983 José Luis Encarnação (Technische Hochschule Darmstadt) took over. Joaquim Jorge (University of Lisbon) has been Editor-in-Chief since 2007. == Replicability == The journal is working with the Graphics Replicability Stamp Initiative to promote replicable results in publication. == Abstracting and indexing == The journal is abstracted and indexed in: Current Contents/Engineering, Computing & Technology EBSCO databases Ei Compendex Inspec ProQuest databases Science Citation Index Expanded Scopus Chinese Computer Federation/Recommended List of International Conferences and Journals on CAD & Graphics and Multimedia. According to the Journal Citation Reports, the journal has a 2022 impact factor of 2.5.

OpenSMILE

openSMILE is source-available software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under a source-available license. For commercial use of the tool, the company audEERING offers custom license options. == Application Areas == openSMILE is used for academic research as well as for commercial applications in order to automatically analyze speech and music signals in real-time. In contrast to automatic speech recognition which extracts the spoken content out of a speech signal, openSMILE is capable of recognizing the characteristics of a given speech or music segment. Examples for such characteristics encoded in human speech are a speaker's emotion, age, gender, and personality, as well as speaker states like depression, intoxication, or vocal pathological disorders. The software further includes music classification technology for automatic music mood detection and recognition of chorus segments, key, chords, tempo, meter, dance-style, and genre. The openSMILE toolkit serves as benchmark in manifold research competitions such as Interspeech ComParE, AVEC, MediaEval, and EmotiW. == History == The openSMILE project was started in 2008 by Florian Eyben, Martin Wöllmer, and Björn Schuller at the Technical University of Munich within the European Union research project SEMAINE. The goal of the SEMAINE project was to develop a virtual agent with emotional and social intelligence. In this system, openSMILE was applied for real-time analysis of speech and emotion. The final SEMAINE software release is based on openSMILE version 1.0.1. In 2009, the emotion recognition toolkit (openEAR) was published based on openSMILE. "EAR" stands for "Emotion and Affect Recognition". In 2010, openSMILE version 1.0.1 was published and was introduced and awarded at the ACM Multimedia Open-Source Software Challenge. Between 2011 and 2013, the technology of openSMILE was extended and improved by Florian Eyben and Felix Weninger in the context of their doctoral thesis at the Technical University of Munich. The software was also applied for the project ASC-Inclusion, which was funded by the European Union. For this project, the software was extended by Erik Marchi in order to teach emotional expression to autistic children, based on automatic emotion recognition and visualization. In 2013, the company audEERING acquired the rights to the code-base from the Technical University of Munich and version 2.0 was published under a source-available research license. Until 2016, openSMILE was downloaded more than 50,000 times worldwide and has established itself as a standard toolkit for emotion recognition. == Awards == openSMILE was awarded in 2010 in the context of the ACM Multimedia Open Source Competition. The software tool is applied in numerous scientific publications on automatic emotion recognition. openSMILE and its extension openEAR have been cited in more than 1000 scientific publications until today.