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  Vulnerability assessment (computing)

  Vulnerability assessment is a process of defining, identifying and classifying the security holes in information technology systems. An attacker can exploit a vulnerability to violate the security of a system. Some known vulnerabilities are Authentication Vulnerability, Authorization Vulnerability and Input Validation Vulnerability. == Purpose == Before deploying a system, it first must go through from a series of vulnerability assessments that will ensure that the build system is secure from all the known security risks. When a new vulnerability is discovered, the system administrator can again perform an assessment, discover which modules are vulnerable, and start the patch process. After the fixes are in place, another assessment can be run to verify that the vulnerabilities were actually resolved. This cycle of assess, patch, and re-assess has become the standard method for many organizations to manage their security issues. The primary purpose of the assessment is to find the vulnerabilities in the system, but the assessment report conveys to stakeholders that the system is secured from these vulnerabilities. If an intruder gained access to a network consisting of vulnerable Web servers, it is safe to assume that he gained access to those systems as well. Because of assessment report, the security administrator will be able to determine how intrusion occurred, identify compromised assets and take appropriate security measures to prevent critical damage to the system. == Assessment types == Depending on the system a vulnerability assessment can have many types and level. === Host assessment === A host assessment looks for system-level vulnerabilities such as insecure file permissions, application level bugs, backdoor and Trojan horse installations. It requires specialized tools for the operating system and software packages being used, in addition to administrative access to each system that should be tested. Host assessment is often very costly in term of time, and thus is only used in the assessment of critical systems. Tools like COPS and Tiger are popular in host assessment. === Network assessment === In a network assessment one assess the network for known vulnerabilities. It locates all systems on a network, determines what network services are in use, and then analyzes those services for potential vulnerabilities. This process does not require any configuration changes on the systems being assessed. Unlike host assessment, network assessment requires little computational cost and effort. == Vulnerability assessment vs penetration testing == Vulnerability assessment and penetration testing are two different testing methods. They are differentiated on the basis of certain specific parameters. == Regulatory requirements == Vulnerability assessments are mandated or strongly recommended by several regulatory frameworks. In the United States healthcare sector, the Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to conduct periodic evaluations of their security posture, and a December 2024 Notice of Proposed Rulemaking would explicitly require vulnerability scanning at least every six months for systems containing electronic protected health information. The Payment Card Industry Data Security Standard (PCI DSS) requires quarterly vulnerability scans for organizations that process credit card transactions, and the NIST Cybersecurity Framework includes vulnerability assessment as a core component of its Identify function.

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  Biometric device

  A biometric device is a security identification and authentication device. Such devices use automated methods of verifying or recognising the identity of a living person based on a physiological or behavioral characteristic. These characteristics include fingerprints, facial images, iris and voice recognition. == History == Biometric devices have been in use for thousands of years. Non-automated biometric devices have been in use since 500 BC, when ancient Babylonians would sign their business transactions by pressing their fingertips into clay tablets. Automation in biometric devices was first seen in the 1960s. The Federal Bureau of Investigation (FBI) in the 1960s, introduced the Indentimat, which started checking for fingerprints to maintain criminal records. The first systems measured the shape of the hand and the length of the fingers. Although discontinued in the 1980s, the system set a precedent for future Biometric Devices. == Subgroups == The characteristic of the human body is used to access information by the users. According to these characteristics, the sub-divided groups are Chemical biometric devices: Analyses the segments of the DNA to grant access to the users. Visual biometric devices: Analyses the visual features of the humans to grant access which includes iris recognition, face recognition, Finger recognition, and Retina Recognition. Behavioral biometric devices: Analyses the Walking Ability and Signatures (velocity of sign, width of sign, pressure of sign) distinct to every human. Olfactory biometric devices: Analyses the odor to distinguish between varied users. Auditory biometric devices: Analyses the voice to determine the identity of a speaker for accessing control. == Uses == === Workplace === Biometrics are being used to establish better and accessible records of the hour's employee's work. With the increase in "Buddy Punching" (a case where employees clocked out coworkers and fraudulently inflated their work hours) employers have looked towards new technology like fingerprint recognition to reduce such fraud. Additionally, employers are also faced with the task of proper collection of data such as entry and exit times. Biometric devices make for largely fool proof and reliable ways of enabling to collect data as employees have to be present to enter biometric details which are unique to them. === Immigration === As the demand for air travel grows and more people travel, modern-day airports have to implement technology in such a way that there are no long queues. Biometrics are being implemented in more and more airports as they enable quick recognition of passengers and hence lead to lower volume of people standing in queues. One such example is of the Dubai International Airport which plans to make immigration counters a relic of the past as they implement IRIS on the move technology (IOM) which should help the seamless departures and arrivals of passengers at the airport. === Handheld and personal devices === Fingerprint sensors can be found on mobile devices. The fingerprint sensor is used to unlock the device and authorize actions, like money and file transfers, for example. It can be used to prevent a device from being used by an unauthorized person. It is also used in attendance in number of colleges and universities. == Present day biometric devices == === Personal signature verification systems === This is one of the most highly recognised and acceptable biometrics in corporate surroundings. This verification has been taken one step further by capturing the signature while taking into account many parameters revolving around this like the pressure applied while signing, the speed of the hand movement and the angle made between the surface and the pen used to make the signature. This system also has the ability to learn from users as signature styles vary for the same user. Hence by taking a sample of data, this system is able to increase its own accuracy. === Iris recognition system === Iris recognition involves the device scanning the pupil of the subject and then cross referencing that to data stored on the database. It is one of the most secure forms of authentication, as while fingerprints can be left behind on surfaces, iris prints are extremely hard to be stolen. Iris recognition is widely applied by organisations dealing with the masses, one being the Aadhaar identification system issued by the Government of India to keep records of its population. The reason for this is that iris recognition makes use of iris prints of humans, which change little over the course of one's lifetime. == Problems with present day biometric devices == === Biometric spoofing === Biometric spoofing is a method of fooling a biometric identification management system, where a counterfeit mold is presented in front of the biometric scanner. This counterfeit mold emulates the unique biometric attributes of an individual so as to confuse the system between the artifact and the real biological target and gain access to sensitive data/materials. One such high-profile case of Biometric spoofing came to the limelight when it was found that German Defence Minister, Ursula von der Leyen's fingerprint had been successfully replicated by Chaos Computer Club. The group used high quality camera lenses and shot images from 6 feet away. They used a professional finger software and mapped the contours of the Ministers thumbprint. Although progress has been made to stop spoofing. Using the principle of pulse oximetry — the liveliness of the test subject is taken into account by measure of blood oxygenation and the heart rate. This reduces attacks like the ones mentioned above, although these methods aren't commercially applicable as costs of implementation are high. This reduces their real world application and hence makes biometrics insecure until these methods are commercially viable. === Accuracy === Accuracy is a major issue with biometric recognition. Passwords are still extremely popular, because a password is static in nature, while biometric data can be subject to change (such as one's voice becoming heavier due to puberty, or an accident to the face, which could lead to improper reading of facial scan data). When testing voice recognition as a substitute to PIN-based systems, Barclays reported that their voice recognition system is 95 percent accurate. This statistic means that many of its customers' voices might still not be recognised even when correct. This uncertainty revolving around the system could lead to slower adoption of biometric devices, continuing the reliance of traditional password-based methods. == Benefits of biometric devices over traditional methods of authentication == Biometric data cannot be lent and hacking of Biometric data is complicated hence it makes it safer to use than traditional methods of authentication like passwords which can be lent and shared. Passwords do not have the ability to judge the user but rely only on the data provided by the user, which can easily be stolen while Biometrics work on the uniqueness of each individual. Passwords can be forgotten and recovering them can take time, whereas Biometric devices rely on biometric data which tends to be unique to a person, hence there is no risk of forgetting the authentication data. A study conducted among Yahoo! users found that at least 1.5 percent of Yahoo users forgot their passwords every month, hence this makes accessing services more lengthy for consumers as the process of recovering passwords is lengthy. These shortcomings make Biometric devices more efficient and reduces effort for the end user. == Future == Researchers are targeting the drawbacks of present-day biometric devices and developing to reduce problems like biometric spoofing and inaccurate intake of data. Technologies which are being developed are- The United States Military Academy are developing an algorithm that allows identification through the ways each individual interacts with their own computers; this algorithm considers unique traits like typing speed, rhythm of writing and common spelling mistakes. This data allows the algorithm to create a unique profile for each user by combining their multiple behavioral and stylometric information. This can be very difficult to replicate collectively. A recent innovation by Kenneth Okereafor and, presented an optimized and secure design of applying biometric liveness detection technique using a trait randomization approach. This novel concept potentially opens up new ways of mitigating biometric spoofing more accurately, and making impostor predictions intractable or very difficult in future biometric devices. A simulation of Kenneth Okereafor's biometric liveness detection algorithm using a 3D multi-biometric framework consisting of 15 liveness parameters from facial print, finger print and iris pattern traits resulted in a system efficiency of the 99.2% over a cardinality of 125 distinct randomization combinat

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  T-pose

  In computer animation, a T-pose is a default posing for a humanoid 3D model's skeleton before it is animated. It is called so because of its shape: the straight legs and arms of a humanoid model combine to form a capital letter T. When the arms are angled downwards, the pose is sometimes referred to as an A-pose instead. Likewise, if the arms are angled upward, it is called a Y-pose. Generic terms encompassing all these (especially for non-humanoid models) include bind pose, blind pose, and reference pose. == Usage == The T-pose is primarily used as the default armature pose for skeletal animation in 3D software, which is then manipulated to create animation. The purpose of the T-pose relates to the important elements of the body being axis-aligned, thereby making it easier to rig the model for animation, physics, and other controls. Depending on the exact geometry of the model, other poses such as the A-pose may be more suitable for vertex deformation around areas such as the shoulders. Outside of being default poses in animation software, T-poses are typically used as placeholders for animation not yet completed, particularly in 3D animated video games. In some motion capture software, a T-pose must be assumed by the actor in the motion capture suit before motion capturing can begin. There are other poses used, but the T-pose is the most common one. == As an Internet meme == Starting in 2016 and resurfacing in 2017, the T-pose has become a widespread Internet meme due to its bizarre and somewhat comedic appearance, especially in video game glitches where a character's animation is unexpectedly supplanted by a T-pose. In a prerelease video of the game NBA Elite 11, the demo was filled with glitches, notably one unintentionally showing a T-pose in place of the proper animation for the model of player Andrew Bynum. The glitch later gained fame as the "Jesus Bynum glitch". Publisher EA eventually cancelled the game as they found it unsatisfactory. A similar occurrence happened with Cyberpunk 2077. In the 2023 Formula One season, driver George Russell performed a T-pose in the opening credits of the series' TV broadcasts. This quickly became a meme within the motorsports community. Russell repeated the pose after claiming pole position at the 2024 Canadian Grand Prix and winning the 2024 Austrian Grand Prix.

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  Czekanowski distance

  The Czekanowski distance (sometimes shortened as CZD) is a per-pixel quality metric that estimates quality or similarity by measuring differences between pixels. Because it compares vectors with strictly non-negative elements, it is often used to compare colored images, as color values cannot be negative. This different approach has a better correlation with subjective quality assessment than PSNR. == Definition == Androutsos et al. give the Czekanowski coefficient as follows: d z ( i , j ) = 1 − 2 ∑ k = 1 p min ( x i k , x j k ) ∑ k = 1 p ( x i k + x j k ) {\displaystyle d_{z}(i,j)=1-{\frac {2\sum _{k=1}^{p}{\text{min}}(x_{ik},\ x_{jk})}{\sum _{k=1}^{p}(x_{ik}+x_{jk})}}} Where a pixel x i {\displaystyle x_{i}} is being compared to a pixel x j {\displaystyle x_{j}} on the k-th band of color – usually one for each of red, green and blue. For a pixel matrix of size M × N {\displaystyle M\times N} , the Czekanowski coefficient can be used in an arithmetic mean spanning all pixels to calculate the Czekanowski distance as follows: 1 M N ∑ i = 0 M − 1 ∑ j = 0 N − 1 ( 1 − 2 ∑ k = 1 3 min ( A k ( i , j ) , B k ( i , j ) ) ∑ k = 1 3 ( A k ( i , j ) + B k ( i , j ) ) ) {\displaystyle {\frac {1}{MN}}\sum _{i=0}^{M-1}\sum _{j=0}^{N-1}{\begin{pmatrix}1-{\frac {2\sum _{k=1}^{3}{\text{min}}(A_{k}(i,j),\ B_{k}(i,j))}{\sum _{k=1}^{3}(A_{k}(i,j)+B_{k}(i,j))}}\end{pmatrix}}} Where A k ( i , j ) {\displaystyle A_{k}(i,j)} is the (i, j)-th pixel of the k-th band of a color image and, similarly, B k ( i , j ) {\displaystyle B_{k}(i,j)} is the pixel that it is being compared to. == Uses == In the context of image forensics – for example, detecting if an image has been manipulated –, Rocha et al. report the Czekanowski distance is a popular choice for Color Filter Array (CFA) identification.

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  Attention (machine learning)

  In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network (RNN) language translation system, but a more recent design, namely the transformer, removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention in humans, the attention mechanism was developed to address the weaknesses of using information from the hidden layers of recurrent neural networks. Recurrent neural networks favor information contained in words at the end of a sentence and thus deemed more recent, thereby tending to attenuate the significance and associated predictive weight assigned to information earlier in the sentence. Attention allows a token equal access to any part of a sentence directly, rather than only through the previous state. == History == Additional surveys of the attention mechanism in deep learning are provided by Niu et al. and Soydaner. The major breakthrough came with self-attention, where each element in the input sequence attends to all others, enabling the model to capture global dependencies. This idea was central to the Transformer architecture, which replaced recurrence with attention mechanisms. As a result, Transformers became the foundation for models like BERT, T5 and generative pre-trained transformers (GPT). == Overview == The modern era of machine attention was revitalized by grafting an attention mechanism (Fig 1. orange) to an Encoder-Decoder. Figure 2 shows the internal step-by-step operation of the attention block (A) in Fig 1. === Interpreting attention weights === In translating between languages, alignment is the process of matching words from the source sentence to words of the translated sentence. Networks that perform verbatim translation without regard to word order would show the highest scores along the (dominant) diagonal of the matrix. The off-diagonal dominance shows that the attention mechanism is more nuanced. Consider an example of translating I love you to French. On the first pass through the decoder, 94% of the attention weight is on the first English word I, so the network offers the word je. On the second pass of the decoder, 88% of the attention weight is on the third English word you, so it offers t'. On the last pass, 95% of the attention weight is on the second English word love, so it offers aime. In the I love you example, the second word love is aligned with the third word aime. Stacking soft row vectors together for je, t', and aime yields an alignment matrix: Sometimes, alignment can be multiple-to-multiple. For example, the English phrase look it up corresponds to cherchez-le. Thus, "soft" attention weights work better than "hard" attention weights (setting one attention weight to 1, and the others to 0), as we would like the model to make a context vector consisting of a weighted sum of the hidden vectors, rather than "the best one", as there may not be a best hidden vector. == Variants == Many variants of attention implement soft weights, such as fast weight programmers, or fast weight controllers (1992). A "slow" neural network outputs the "fast" weights of another neural network through outer products. The slow network learns by gradient descent. It was later renamed as "linearized self-attention". Bahdanau-style attention, also referred to as additive attention, Luong-style attention, which is known as multiplicative attention, Early attention mechanisms similar to modern self-attention were proposed using recurrent neural networks. However, the highly parallelizable self-attention was introduced in 2017 and successfully used in the Transformer model, positional attention and factorized positional attention. For convolutional neural networks, attention mechanisms can be distinguished by the dimension on which they operate, namely: spatial attention, channel attention, or combinations. These variants recombine the encoder-side inputs to redistribute those effects to each target output. Often, a correlation-style matrix of dot products provides the re-weighting coefficients. In the figures below, W is the matrix of context attention weights, similar to the formula in Overview section above. == Optimizations == === Flash attention === The size of the attention matrix is proportional to the square of the number of input tokens. Therefore, when the input is long, calculating the attention matrix requires a lot of GPU memory. Flash attention is an implementation that reduces the memory needs and increases efficiency without sacrificing accuracy. It achieves this by partitioning the attention computation into smaller blocks that fit into the GPU's faster on-chip memory, reducing the need to store large intermediate matrices and thus lowering memory usage while increasing computational efficiency. === FlexAttention === FlexAttention is an attention kernel developed by Meta that allows users to modify attention scores prior to softmax and dynamically chooses the optimal attention algorithm. == Applications == Attention is widely used in natural language processing, computer vision, and speech recognition. In NLP, it improves context understanding in tasks like question answering and summarization. In vision, visual attention helps models focus on relevant image regions, enhancing object detection and image captioning. === Attention maps as explanations for vision transformers === From the original paper on vision transformers (ViT), visualizing attention scores as a heat map (called saliency maps or attention maps) has become an important and routine way to inspect the decision making process of ViT models. One can compute the attention maps with respect to any attention head at any layer, while the deeper layers tend to show more semantically meaningful visualization. Attention rollout is a recursive algorithm to combine attention scores across all layers, by computing the dot product of successive attention maps. Because vision transformers are typically trained in a self-supervised manner, attention maps are generally not class-sensitive. When a classification head is attached to the ViT backbone, class-discriminative attention maps (CDAM) combines attention maps and gradients with respect to the class [CLS] token. Some class-sensitive interpretability methods originally developed for convolutional neural networks can be also applied to ViT, such as GradCAM, which back-propagates the gradients to the outputs of the final attention layer. Using attention as basis of explanation for the transformers in language and vision is not without debate. While some pioneering papers analyzed and framed attention scores as explanations, higher attention scores do not always correlate with greater impact on model performances. == Mathematical representation == === Standard scaled dot-product attention === For matrices: Q ∈ R m × d k , K ∈ R n × d k {\displaystyle Q\in \mathbb {R} ^{m\times d_{k}},K\in \mathbb {R} ^{n\times d_{k}}} and V ∈ R n × d v {\displaystyle V\in \mathbb {R} ^{n\times d_{v}}} , the scaled dot-product, or QKV attention, is defined as: Attention ( Q , K , V ) = softmax ( Q K T d k ) V ∈ R m × d v {\displaystyle {\text{Attention}}(Q,K,V)={\text{softmax}}\left({\frac {QK^{T}}{\sqrt {d_{k}}}}\right)V\in \mathbb {R} ^{m\times d_{v}}} where T {\displaystyle {}^{T}} denotes transpose and the softmax function is applied independently to every row of its argument. The matrix Q {\displaystyle Q} contains m {\displaystyle m} queries, while matrices K , V {\displaystyle K,V} jointly contain an unordered set of n {\displaystyle n} key-value pairs. Value vectors in matrix V {\displaystyle V} are weighted using the weights resulting from the softmax operation, so that the rows of the m {\displaystyle m} -by- d v {\displaystyle d_{v}} output matrix are confined to the convex hull of the points in R d v {\displaystyle \mathbb {R} ^{d_{v}}} given by the rows of V {\displaystyle V} . To understand the permutation invariance and permutation equivariance properties of QKV attention, let A ∈ R m × m {\displaystyle A\in \mathbb {R} ^{m\times m}} and B ∈ R n × n {\displaystyle B\in \mathbb {R} ^{n\times n}} be permutation matrices; and D ∈ R m × n {\displaystyle D\in \mathbb {R} ^{m\times n}} an arbitrary matrix. The softmax function is permutation equivariant in the sense that: softmax ( A D B ) = A softmax ( D ) B {\displays

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  Security of the Java software platform

  The Java software platform provides a number of features designed for improving the security of Java applications. This includes enforcing runtime constraints through the use of the Java Virtual Machine (JVM), a security manager that sandboxes untrusted code from the rest of the operating system, and a suite of security APIs that Java developers can utilise. Despite this, criticism has been directed at the programming language, and Oracle, due to an increase in malicious programs that revealed security vulnerabilities in the JVM, which were subsequently not properly addressed by Oracle in a timely manner. == Security features == === The JVM === The binary form of programs running on the Java platform is not native machine code but an intermediate bytecode. The JVM performs verification on this bytecode before running it to prevent the program from performing unsafe operations such as branching to incorrect locations, which may contain data rather than instructions. It also allows the JVM to enforce runtime constraints such as array bounds checking. This means that Java programs are significantly less likely to suffer from memory safety flaws such as buffer overflow than programs written in languages such as C which do not provide such memory safety guarantees. The platform does not allow programs to perform certain potentially unsafe operations such as pointer arithmetic or unchecked type casts. It manages memory allocation and initialization and provides automatic garbage collection which in many cases (but not all) relieves the developer from manual memory management. This contributes to type safety and memory safety. === Security manager === The platform provides a security manager which allows users to run untrusted bytecode in a "sandboxed" environment designed to protect them from malicious or poorly written software by preventing the untrusted code from accessing certain platform features and APIs. For example, untrusted code might be prevented from reading or writing files on the local filesystem, running arbitrary commands with the current user's privileges, accessing communication networks, accessing the internal private state of objects using reflection, or causing the JVM to exit. The security manager also allows Java programs to be cryptographically signed; users can choose to allow code with a valid digital signature from a trusted entity to run with full privileges in circumstances where it would otherwise be untrusted. Users can also set fine-grained access control policies for programs from different sources. For example, a user may decide that only system classes should be fully trusted, that code from certain trusted entities may be allowed to read certain specific files, and that all other code should be fully sandboxed. === Security APIs === The Java Class Library provides a number of APIs related to security, such as standard cryptographic algorithms, authentication, and secure communication protocols. === The sun.misc.Unsafe class === sun.misc.Unsafe is an internal utility class in the Java programming language which is a collection of low-level unsafe operations. While it is not a part of the official Java Class Library, it is called internally by the Java libraries. It resides in an unofficial Java module named jdk.unsupported. Beginning in Java 11, it has been partially migrated to jdk.internal.misc.Unsafe (which resides in module java.base). Its primary feature is to allow direct memory management (similar to C memory management) and memory address manipulation, manipulating objects and fields, thread manipulation, and concurrency primitives. Its declaration is: public final class Unsafe;, and it is a singleton class with a private constructor. It contains the following methods, many of which are declared native (invoking Java Native Interface): static Unsafe getUnsafe(): retrieves the Unsafe instance. It uses sun.reflect.Reflection to do so. int getInt(Object o, long offset): fetches a value (a field or array element) in the object at the given offset. (There are corresponding getBoolean(), getByte(), getShort(), getChar(), getLong(), getFloat(), and getDouble() methods as well.) void putInt(Object o, long offset, int x): stores a value into an object at the given offset. (There are corresponding putBoolean(), putByte(), putShort(), putChar(), putLong(), putFloat(), and putDouble() methods as well.) Object getObject(Object o, long offset): fetches a reference value from an object at the given offset. void putObject(Object o, long offset, Object x): stores a reference value into an object at the given offset. int getInt(long address): fetches a value at the given address. (There are corresponding getBoolean(), getByte(), getShort(), getChar(), getLong(), getFloat(), and getDouble() methods as well.) void putInt(long address, int x): stores a value into the given address. (There are corresponding putBoolean(), putByte(), putShort(), putChar(), putLong(), putFloat(), and putDouble() methods as well.) long getAddress(long address): fetches a native pointer from a given address. void putAddress(long address, long x): stores a native pointer into a given address. long allocateMemory(long bytes): allocates a block of native memory of the given size (similar to malloc()). long reallocateMemory(long address, long bytes): resizes a block of native memory to the given size (similar to realloc()). void setMemory(Object o, long offset, long bytes, byte value), void setMemory(long address, long bytes, byte value): sets all bytes in a block of memory to a fixed value (similar to memset()). void copyMemory(Object srcBase, long srcOffset, Object destBase, long destOffset, long bytes), void copyMemory(long srcAddress, long destAddress, long bytes): sets all bytes in a given block of memory to a copy of another block (similar to memcpy()). void freeMemory(long address): deallocates a block of native memory obtained from allocateMemory() or reallocateMemory(), similar to free()). long staticFieldOffset(Field f): obtains the location of a given field in the storage allocation of its class. long objectFieldOffset(Field f): obtains the location of a given static field in conjunction with staticFieldBase(). Object staticFieldBase(Field f): obtains the location of a given static field in conjunction with staticFieldOffset(). void ensureClassInitialized(Class c): ensures the given class has been initialized. int arrayBaseOffset(Class arrayClass): obtains the offset of the first element in the storage allocation of a given array class. int arrayIndexScale(Class arrayClass): obtains the scale factor for addressing elements in the storage allocation of a given array class. static int addressSize(): obtains the size (in bytes) of a native pointer. int pageSize(): obtains the size (in bytes) of a native memory page. Class defineClass(String name, byte[] b, int off, int len, ClassLoader loader, ProtectionDomain protectionDomain): signals to the JVM to define a class without security checks. Class defineAnonymousClass(Class hostClass, byte[] data, Object[] cpPatches): signals to the JVM to define a class but do not make it known to the class loader or system directory. Object allocateInstance(Class cls) throws InstantiationException: allocates an instance of a class without running its constructor. void monitorEnter(Object o): locks an object. void monitorExit(Object o): unlocks an object. boolean tryMonitorEnter(Object o): tries to lock an object, returning whether the lock succeeded. void throwException(Throwable ee): throws an exception without telling the verifier. final boolean compareAndSwapInt(Object o, long offset, int expected, int x): updates a variable to x if it is holding expected, returning whether the operation succeeded. (There are corresponding compareAndSwapLong() and compareAndSwapObject() methods as well.) int getIntVolatile(Object o, long offset): volatile version of getInt(). (There are corresponding getBooleanVolatile(), getByteVolatile(), getShortVolatile(), getCharVolatile(), getLongVolatile(), getFloatVolatile(), getDoubleVolatile(), and getObjectVolatile() methods as well.) void putIntVolatile(Object o, long offset, int x): volatile version of putInt(). (There are corresponding putBooleanVolatile(), putByteVolatile(), putShortVolatile(), putCharVolatile(), putLongVolatile(), putFloatVolatile(), putDoubleVolatile(), and putObjectVolatile() methods as well.) void putOrderedInt(Object o, long offset, int x): version of putIntVolatile() not guaranteeing immediate visibility of storage to other threads. (There are corresponding putOrderedLong() and putOrderedObject() methods as well.) void unpark(Object thread): unblocks a thread. void park(boolean isAbsolute, long time): blocks the current thread. int getLoadAverage(double[] loadavg, int nelems): gets the load average in the system run queue assigned to available processors averaged over various periods of time. void invokeCleaner(ByteBuffe

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  Intel Management Engine

  The Intel Management Engine (ME), also known as the Intel Manageability Engine, is an autonomous subsystem that has been incorporated in virtually all of Intel's processor chipsets since 2008. It is located in the Platform Controller Hub of modern Intel motherboards. The Intel Management Engine always runs as long as the motherboard is receiving power, even when the computer is turned off. This issue can be mitigated with the deployment of a hardware device which is able to disconnect all connections to mains power as well as all internal forms of energy storage. The Electronic Frontier Foundation and some security researchers have voiced concern that the Management Engine is a backdoor. Intel's main competitor, AMD, has incorporated the equivalent AMD Secure Technology (formally called Platform Security Processor) in virtually all of its post-2013 CPUs. == Difference from Intel AMT == The Management Engine is often confused with Intel AMT (Intel Active Management Technology). AMT runs on the ME, but is only available on processors with vPro. AMT gives device owners remote administration of their computer, such as powering it on or off, and reinstalling the operating system. However, the ME itself has been built into all Intel chipsets since 2008, not only those with AMT. While AMT can be unprovisioned by the owner, there is no official, documented way to disable the ME. == Design == The subsystem primarily consists of proprietary firmware running on a separate microprocessor that performs tasks during boot-up, while the computer is running, and while it is asleep. As long as the chipset or SoC is supplied with power (via battery or power supply), it continues to run even when the system is turned off. Intel claims the ME is required to provide full performance. Its exact workings are largely undocumented and its code is obfuscated using confidential Huffman tables stored directly in hardware, so the firmware does not contain the information necessary to decode its contents. === Hardware === Starting with ME 11 (introduced in Skylake CPUs), it is based on the Intel Quark x86-based 32-bit CPU and runs the MINIX 3 operating system. The ME firmware is stored in a partition of the SPI BIOS Flash, using the Embedded Flash File System (EFFS). Previous versions were based on an ARC core, with the Management Engine running the ThreadX RTOS. Versions 1.x to 5.x of the ME used the ARCTangent-A4 (32-bit only instructions) whereas versions 6.x to 8.x used the newer ARCompact (mixed 32- and 16-bit instruction set architecture). Starting with ME 7.1, the ARC processor could also execute signed Java applets. The ME has its own MAC and IP address for the out-of-band management interface, with direct access to the Ethernet controller; one portion of the Ethernet traffic is diverted to the ME even before reaching the host's operating system, for what support exists in various Ethernet controllers, exported and made configurable via Management Component Transport Protocol (MCTP). The ME also communicates with the host via PCI interface. Under Linux, communication between the host and the ME is done via /dev/mei or /dev/mei0. Until the release of Nehalem processors, the ME was usually embedded into the motherboard's northbridge, following the Memory Controller Hub (MCH) layout. With the newer Intel architectures (Intel 5 Series onwards), the ME is integrated into the Platform Controller Hub (PCH). === Firmware === By Intel's current terminology as of 2017, ME is one of several firmware sets for the Converged Security and Manageability Engine (CSME). Prior to AMT version 11, CSME was called Intel Management Engine BIOS Extension (Intel MEBx). Management Engine (ME) – mainstream chipsets Server Platform Services (SPS) – server chipsets and SoCs Trusted Execution Engine (TXE) – tablet/embedded/low power It was also found that the ME firmware version 11 runs MINIX 3. Management of the ME modules for provisioning inside the UEFI is done via a tool called Intel Flash Image Tool (FITC). ==== Modules ==== Active Management Technology (AMT) Intel Boot Guard (IBG) and Secure Boot Quiet System Technology (QST), formerly known as Advanced Fan Speed Control (AFSC), which provides support for acoustically optimized fan speed control, and monitoring of temperature, voltage, current and fan speed sensors that are provided in the chipset, CPU and other devices present on the motherboard. Communication with the QST firmware subsystem is documented and available through the official software development kit (SDK). Protected Audio Video Path, enforces HDCP Intel Anti-Theft Technology (AT), discontinued in 2015 Serial over LAN (SOL) Intel Platform Trust Technology (PTT), a firmware-based Trusted Platform Module (TPM) Near Field Communication, a middleware for NFC readers and vendors to access NFC cards and provide secure element access, found in later MEI versions. == The intricacies of working with Intel ME == It should also be noted that the ME region requires special cleaning and subsequent initialisation, for example, after replacing the platform hub on the motherboard. Usually, this requires an SPI programmer. There are known successful cases of this operation being performed. == Security vulnerabilities == Several weaknesses have been found in the ME. On May 1, 2017, Intel confirmed a Remote Elevation of Privilege bug (SA-00075) in its Management Technology. Every Intel platform with provisioned Intel Standard Manageability, Active Management Technology, or Small Business Technology, from Nehalem in 2008 to Kaby Lake in 2017 has a remotely exploitable security hole in the ME. Several ways to disable the ME without authorization that could allow ME's functions to be sabotaged have been found. Additional major security flaws in the ME affecting a very large number of computers incorporating ME, Trusted Execution Engine (TXE), and Server Platform Services (SPS) firmware, from Skylake in 2015 to Coffee Lake in 2017, were confirmed by Intel on November 20, 2017 (SA-00086). Unlike SA-00075, this bug is even present if AMT is absent, not provisioned or if the ME was "disabled" by any of the known unofficial methods. In July 2018, another set of vulnerabilities was disclosed (SA-00112). In September 2018, yet another vulnerability was published (SA-00125). === Ring −3 rootkit === A ring −3 rootkit was demonstrated by Invisible Things Lab for the Q35 chipset; it does not work for the later Q45 chipset as Intel implemented additional protections. The exploit worked by remapping the normally protected memory region (top 16 MB of RAM) reserved for the ME. The ME rootkit could be installed regardless of whether the AMT is present or enabled on the system, as the chipset always contains the ARC ME coprocessor. (The "−3" designation was chosen because the ME coprocessor works even when the system is in the S3 state. Thus, it was considered a layer below the System Management Mode rootkits.) For the vulnerable Q35 chipset, a keystroke logger ME-based rootkit was demonstrated by Patrick Stewin. === Zero-touch provisioning === Another security evaluation by Vassilios Ververis showed serious weaknesses in the GM45 chipset implementation. In particular, it criticized AMT for transmitting unencrypted passwords in the SMB provisioning mode when the IDE redirection and Serial over LAN features are used. It also found that the "zero touch" provisioning mode (ZTC) is still enabled even when the AMT appears to be disabled in BIOS. For about 60 euros, Ververis purchased from GoDaddy a certificate that is accepted by the ME firmware and allows remote "zero touch" provisioning of (possibly unsuspecting) machines, which broadcast their HELLO packets to would-be configuration servers. === SA-00075 (a.k.a. Silent Bob is Silent) === In May 2017, Intel confirmed that many computers with AMT have had an unpatched critical privilege escalation vulnerability (CVE-2017-5689). The vulnerability was nicknamed "Silent Bob is Silent" by the researchers who had reported it to Intel. It affects numerous laptops, desktops and servers sold by Dell, Fujitsu, Hewlett-Packard (later Hewlett Packard Enterprise and HP Inc.), Intel, Lenovo, and possibly others. Those researchers claimed that the bug affects systems made in 2010 or later. Other reports claimed the bug also affects systems made as long ago as 2008. The vulnerability was described as giving remote attackers: "full control of affected machines, including the ability to read and modify everything. It can be used to install persistent malware (possibly in firmware), and read and modify any data." === PLATINUM === In June 2017, the PLATINUM cybercrime group became notable for exploiting the serial over LAN (SOL) capabilities of AMT to perform data exfiltration of stolen documents. SOL is disabled by default and must be enabled to exploit this vulnerability. === SA-00086 === Some months after the previous bugs, and subsequent warnings from the EFF, securi

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  Imieliński–Lipski algebra

  In database theory, Imieliński–Lipski algebra is an extension of relational algebra onto tables with different types of null values. It is used to operate on relations with incomplete information. Imieliński–Lipski algebras are defined to satisfy precise conditions for semantically meaningful extension of the usual relational operators, such as projection, selection, union, and join, from operators on relations to operators on relations with various kinds of "null values". These conditions require that the system be safe in the sense that no incorrect conclusion is derivable by using a specified subset F of the relational operators; and that it be complete in the sense that all valid conclusions expressible by relational expressions using operators in F are in fact derivable in this system. For example, it is well known that the three-valued logic approach to deal with null values, supported treatment of nulls values by SQL is not complete, see Ullman book. To show this, let T be: Take SQL query Q SQL query Q will return empty set (no results) under 3-valued semantics currently adopted by all variants of SQL. This is the case because in SQL, NULL is never equal to any constant – in this case, neither to “Spring” nor “Fall” nor “Winter” (if there is Winter semester in this school). NULL='Spring' will evaluate to MAYBE and so will NULL='Fall'. The disjunction MAYBE OR MAYBE evaluates to MAYBE (not TRUE). Thus Igor will not be part of the answer (and of course neither will Rohit). But Igor should be returned as the answer. Indeed, regardless what semester Igor took the Networks class (no matter what was the unknown value of NULL), the selection condition will be true. This “Igor” will be missed by SQL and the SQL answer would be incomplete according to completeness requirements specified in Tomasz Imieliński, Witold Lipski, 'Incomplete Information in Relational Databases'. It is also argued there that 3-valued logic (TRUE, FALSE, MAYBE) can never provide guarantee of complete answer for tables with incomplete information. Three algebras which satisfy conditions of safety and completeness are defined as Imielinski–Lipski algebras: the Codd-Tables algebra, the V-tables algebra and the Conditional tables (C-tables) algebra. == Codd-tables algebra == Codd-tables algebra is based on the usual Codd's single NULL values. The table T above is an example of Codd-table. Codd-table algebra supports projection and positive selections only. It is also demonstrated in [IL84 that it is not possible to correctly extend more relational operators over Codd-Tables. For example, such basic operation as join is not extendable over Codd-tables. It is not possible to define selections with Boolean conditions involving negation and preserve completeness. For example, queries like the above query Q cannot be supported. In order to be able to extend more relational operators, more expressive form of null value representation is needed in tables which are called V-table. == V-tables algebra == V-tables algebra is based on many different ("marked") null values or variables allowed to appear in a table. V-tables allow to show that a value may be unknown but the same for different tuples. For example, in the table below Gaurav and Igor order the same (but unknown) beer in two unknown bars (which may, or may not be different – but remain unknown). Gaurav and Jane frequent the same unknown bar (Y1). Thus, instead one NULL value, we use indexed variables, or Skolem constants . V-tables algebra is shown to correctly support projection, positive selection (with no negation occurring in the selection condition), union, and renaming of attributes, which allows for processing arbitrary conjunctive queries. A very desirable property enjoyed by the V-table algebra is that all relational operators on tables are performed in exactly the same way as in the case of the usual relations. === Conditional tables (c-tables) algebra === Example of conditional table (c-table) is shown below. It has additional column “con” which is a Boolean condition involving variables, null values – same as in V-tables. over the following table c-table Conditional tables algebra, mainly of theoretical interest, supports projection, selection, union, join, and renaming. Under closed-world assumption, it can also handle the operator of difference, thus it can support all relational operators. == History == Imieliński–Lipski algebras were introduced by Tomasz Imieliński and Witold Lipski Jr. in Incomplete Information in Relational Databases.

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  PANGU (software)

  The PANGU (Planet and Asteroid Natural scene Generation Utility) is a computer graphics utility of which the development was funded by ESA and performed by University of Dundee. It generates scenes of planets, moons, asteroids, spacecraft and rovers. The main purpose of the tool is to test and validate navigation techniques based on the processing of images coming from on-board sensors, such as a camera or imaging LIDAR on a planetary lander.

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  Halloween Problem

  In computing, the Halloween Problem refers to a phenomenon in databases in which an update operation causes a change in the physical location of a row, potentially allowing the row to be visited again later in the same update operation. This could even cause an infinite loop in some cases where updates continually place the updated record ahead of the scan performing the update operation. The potential for this database error was first discovered by Don Chamberlin, Pat Selinger, and Morton Astrahan in the mid-1970s, on Halloween day, while working on query optimization. They wrote a SQL query supposed to give a ten percent raise to every employee who earned less than $25,000. This query would run successfully, with no errors, but when finished all the employees in the database earned at least $25,000, because it kept giving them a raise until they reached that level. The expectation was that the query would iterate over each of the employee records with a salary less than $25,000 precisely once. In fact, because even updated records were visible to the query execution engine and so continued to match the query's criteria, salary records were matching multiple times and each time being given a 10% raise until they were all greater than $25,000. Contrary to what some believe, the name is not descriptive of the nature of the problem but rather was given due to the day it was discovered on. As recounted by Don Chamberlin: Pat and Morton discovered this problem on Halloween... I remember they came into my office and said, "Chamberlin, look at this. We have to make sure that when the optimizer is making a plan for processing an update, it doesn't use an index that is based on the field that is being updated. How are we going to do that?" It happened to be on a Friday, and we said, "Listen, we are not going to be able to solve this problem this afternoon. Let's just give it a name. We'll call it the Halloween Problem and we'll work on it next week." And it turns out it has been called that ever since.

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  Supervisor Mode Access Prevention

  Supervisor Mode Access Prevention (SMAP) is a feature of some CPU implementations such as the Intel Broadwell microarchitecture that allows supervisor mode programs to optionally set user-space memory mappings so that access to those mappings from supervisor mode will cause a trap. This makes it harder for malicious programs to "trick" the kernel into using instructions or data from a user-space program. == History == Supervisor Mode Access Prevention is designed to complement Supervisor Mode Execution Prevention (SMEP), which was introduced earlier. SMEP can be used to prevent supervisor mode from unintentionally executing user-space code. SMAP extends this protection to reads and writes. == Benefits == Without Supervisor Mode Access Prevention, supervisor code usually has full read and write access to user-space memory mappings (or has the ability to obtain full access). This has led to the development of several security exploits, including privilege escalation exploits, which operate by causing the kernel to access user-space memory when it did not intend to. Operating systems can block these exploits by using SMAP to force unintended user-space memory accesses to trigger page faults. Additionally, SMAP can expose flawed kernel code which does not follow the intended procedures for accessing user-space memory. However, the use of SMAP in an operating system may lead to a larger kernel size and slower user-space memory accesses from supervisor code, because SMAP must be temporarily disabled any time supervisor code intends to access user-space memory. == Technical details == Processors indicate support for Supervisor Mode Access Prevention through the Extended Features CPUID leaf. SMAP is enabled when memory paging is active and the SMAP bit in the CR4 control register is set. SMAP can be temporarily disabled for explicit memory accesses by setting the EFLAGS.AC (Alignment Check) flag. The stac (Set AC Flag) and clac (Clear AC Flag) instructions can be used to easily set or clear the flag. When the SMAP bit in CR4 is set, explicit memory reads and writes to user-mode pages performed by code running with a privilege level less than 3 will always result in a page fault if the EFLAGS.AC flag is not set. Implicit reads and writes (such as those made to descriptor tables) to user-mode pages will always trigger a page fault if SMAP is enabled, regardless of the value of EFLAGS.AC. == Operating system support == Linux kernel support for Supervisor Mode Access Prevention was implemented by H. Peter Anvin. It was merged into the mainline Linux 3.7 kernel (released December 2012) and it is enabled by default for processors which support the feature. FreeBSD has supported Supervisor Mode Execution Prevention since 2012 and Supervisor Mode Access Prevention since 2018. OpenBSD has supported Supervisor Mode Access Prevention and the related Supervisor Mode Execution Prevention since 2012, with OpenBSD 5.3 being the first release with support for the feature enabled. NetBSD support for Supervisor Mode Execution Prevention (SMEP) was implemented by Maxime Villard in December 2015. Support for Supervisor Mode Access Prevention (SMAP) was also implemented by Maxime Villard, in August 2017. NetBSD 8.0 was the first release with both features supported and enabled. Haiku support for Supervisor Mode Execution Prevention (SMEP) was implemented by Jérôme Duval in January 2018. macOS has support for SMAP at least since macOS 10.13 released 2017.

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  WS-SecurityPolicy

  WS-Security Policy is a web services specification, created by IBM and 12 co-authors, that has become an OASIS standard as of version 1.2. It extends the fundamental security protocols specified by the WS-Security, WS-Trust and WS-Secure Conversation by offering mechanisms to represent the capabilities and requirements of web services as policies. Security policy assertions are based on the WS-Policy framework. Policy assertions can be used to require more generic security attributes like transport layer security , message level security or timestamps, and specific attributes like token types. Most policy assertion can be found in following categories: Protection assertions identify the elements of a message that are required to be signed, encrypted or existent. Token assertions specify allowed token formats (SAML, X509, Username etc.). Security binding assertions control basic security safeguards like transport and message level security, cryptographic algorithm suite and required timestamps. Supporting token assertions add functions like user sign-on using a username token. Policies can be used to drive development tools to generate code with certain capabilities, or may be used at runtime to negotiate the security aspects of web service communication. Policies may be attached to WSDL elements such as service, port, operation and message, as defined in WS Policy Attachment. == Sample Policies == Namespaces used by the following XML-snippets: ... Include a timestamp: Use either transport layer security (https) or message level security (XML Dsig/XML Enc): ... ... To define a SAML assertion as security token: ...#SAMLV2.0 Issued token assertion of providers with reference to the STS and required token format: http://sampleorg.com/sts http://docs.oasis-open.org/wss/oasis-wss-saml-token-profile-1.0#SAMLAssertionID ... ... Specify that message header and body need to be signed, and attachments are left unsigned: ? ... specify that message open source license need to be signed, and hydra security are left unsigned: ? ... == Other WS policy languages == The term Web Services Security Policy Language is used for two different XML-based languages: As described above, based on the WS-Policy framework, as defined in, published as version 1.3 in Feb. 2009 WSPL, based on XACML profile for Web-services, but that was not finalized.

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  Prism Video Converter

  Prism is a multi-format video converter developed by NCH Software for Windows and Mac OS. It offers converting tools for instant media conversions. Prism Video Converter can handle large and high-quality resolution media files. It provides built-in compressor and adjuster settings, allowing users to customize and optimize their videos according to their needs. The software also includes features such as previewing videos and adding effects. Prism offers a free version for non-commercial use as well as a premium version. == Features == Prism Video File Converter supports a wide range of file formats. It enables users to convert videos into formats like AVI, ASF, WMV, MP4, 3GP, etc. It offers the ability to convert DVDs into various formats. It provides tools for adjusting colour and filter options. Prism Video File Converter provides several customizable options for tweaking the output files during the conversion process. Users can adjust compression/encoder rates, set the resolution and frame rate, and specify the desired output file size. The software also offers various effects like video rotation, captions, watermarks, and text overlay. It also includes a built-in preview feature, that enables users to view their videos before and after the conversion process. It supports batch conversion and running conversion in background. == Controversy == Previously, Prism and certain other NCH Software products were bundled with optional browser plugins, including the Google Chrome toolbar and the Conduit toolbar. This resulted in user complaints and raised concerns from antivirus software companies like Norton and McAfee, which flagged them as potential malware. NCH Software has since removed all toolbars, browsers, and third-party app offerings in all Prism versions.

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  Ray tracing (graphics)

  In 3D computer graphics, ray tracing is a technique for modeling light transport for use in a wide variety of rendering algorithms for generating digital images. On a spectrum of computational cost and visual fidelity, ray tracing-based rendering techniques, such as ray casting, recursive ray tracing, distribution ray tracing, photon mapping and path tracing, are generally slower and higher fidelity than scanline rendering methods. Thus, ray tracing was first deployed in applications where taking a relatively long time to render could be tolerated, such as still CGI images, and film and television visual effects (VFX), but was less suited to real-time applications such as video games, where speed is critical in rendering each frame. Since 2018, however, hardware acceleration for real-time ray tracing has become standard on new commercial graphics cards, and graphics APIs have followed suit, allowing developers to use hybrid ray tracing and rasterization-based rendering in games and other real-time applications with a lesser hit to frame render times. Ray tracing is capable of simulating a variety of optical effects, such as reflection, refraction, soft shadows, scattering, depth of field, motion blur, caustics, ambient occlusion and dispersion phenomena (such as chromatic aberration). It can also be used to trace the path of sound waves in a similar fashion to light waves, making it a viable option for more immersive sound design in video games by rendering realistic reverberation and echoes. In fact, any physical wave or particle phenomenon with approximately linear motion can be simulated with ray tracing. Ray tracing–based rendering techniques that sample light over a domain typically generate multiple rays and often rely on denoising to reduce the resulting noise. == History == The idea of ray tracing comes from as early as the 16th century, when it was described by Albrecht Dürer, who is credited for its invention. Dürer described multiple techniques for projecting 3-D scenes onto an image plane. Some of these project chosen geometry onto the image plane, as is done with rasterization today. Others determine what geometry is visible along a given ray, as is done with ray tracing. Using a computer for ray tracing to generate shaded pictures was first accomplished by Arthur Appel in 1968. Appel used ray tracing for primary visibility (determining the closest surface to the camera at each image point) by tracing a ray through each point to be shaded into the scene to identify the visible surface. The closest surface intersected by the ray was the visible one. This non-recursive ray tracing-based rendering algorithm is today called "ray casting". His algorithm then traced secondary rays to the light source from each point being shaded to determine whether the point was in shadow or not. Later, in 1971, Goldstein and Nagel of MAGI (Mathematical Applications Group, Inc.) published "3-D Visual Simulation", wherein ray tracing was used to make shaded pictures of solids. At the ray-surface intersection point found, they computed the surface normal and, knowing the position of the light source, computed the brightness of the pixel on the screen. Their publication describes a short (30-second) film "made using the University of Maryland's display hardware outfitted with a 16mm camera. The film showed the helicopter and a simple ground-level gun emplacement. The helicopter was programmed to undergo a series of maneuvers including turns, take-offs, and landings, etc., until it eventually is shot down and crashed." A CDC 6600 computer was used. MAGI produced an animation video called MAGI/SynthaVision Sampler in 1974. Another early instance of ray casting came in 1976, when Scott Roth created a flip book animation in Bob Sproull's computer graphics course at Caltech. The scanned pages are shown as a video in the accompanying image. Roth's computer program noted an edge point at a pixel location if the ray intersected a bounded plane different from that of its neighbors. Of course, a ray could intersect multiple planes in space, but only the surface point closest to the camera was noted as visible. The platform was a DEC PDP-10, a Tektronix storage-tube display, and a printer which would create an image of the display on rolling thermal paper. Roth extended the framework, introduced the term ray casting in the context of computer graphics and solid modeling, and in 1982 published his work while at GM Research Labs. Turner Whitted was the first to show recursive ray tracing for mirror reflection and for refraction through translucent objects, with an angle determined by the solid's index of refraction, and to use ray tracing for anti-aliasing. Whitted also showed ray traced shadows. He produced a recursive ray traced film called The Compleat Angler in 1979 while an engineer at Bell Labs. Whitted's deeply recursive ray tracing algorithm reframed rendering from being primarily a matter of surface visibility determination to being a matter of light transport. His paper inspired a series of subsequent work by others that included distribution ray tracing and finally unbiased path tracing, which provides the rendering equation framework that has allowed computer-generated imagery to be faithful to reality. For decades, global illumination in major films using computer-generated imagery was approximated with additional lights. Ray tracing-based rendering eventually changed that by enabling physically based light transport. Early feature films rendered entirely using path tracing include Monster House (2006), Cloudy with a Chance of Meatballs (2009), and Monsters University (2013). == Algorithm overview == Optical ray tracing describes a method for producing visual images constructed in 3D computer graphics environments, with more photorealism than either ray casting or scanline rendering techniques. It works by tracing a path from an imaginary eye through each pixel in a virtual screen, and calculating the color of the object visible through it. Scenes in ray tracing are described mathematically by a programmer or by a visual artist (normally using intermediary tools). Scenes may also incorporate data from images and models captured by means such as digital photography. Typically, each ray must be tested for intersection with some subset of all the objects in the scene. Once the nearest object has been identified, the algorithm will estimate the incoming light at the point of intersection, examine the material properties of the object, and combine this information to calculate the final color of the pixel. Certain illumination algorithms and reflective or translucent materials may require more rays to be re-cast into the scene. It may at first seem counterintuitive or "backward" to send rays away from the camera, rather than into it (as actual light does in reality), but doing so is many orders of magnitude more efficient. Since the overwhelming majority of light rays from a given light source do not make it directly into the viewer's eye, a "forward" simulation could potentially waste a tremendous amount of computation on light paths that are never recorded. Therefore, the shortcut taken in ray tracing is to presuppose that a given ray intersects the view frame. After either a maximum number of reflections or a ray traveling a certain distance without intersection, the ray ceases to travel and the pixel's value is updated. === Calculate rays for rectangular viewport === On input we have (in calculation we use vector normalization and cross product): E ∈ R 3 {\displaystyle E\in \mathbb {R^{3}} } eye position T ∈ R 3 {\displaystyle T\in \mathbb {R^{3}} } target position θ ∈ [ 0 , π ] {\displaystyle \theta \in [0,\pi ]} field of view - for humans, we can assume ≈ π / 2 rad = 90 ∘ {\displaystyle \approx \pi /2{\text{ rad}}=90^{\circ }} m , k ∈ N {\displaystyle m,k\in \mathbb {N} } numbers of square pixels on viewport vertical and horizontal direction i , j ∈ N , 1 ≤ i ≤ k ∧ 1 ≤ j ≤ m {\displaystyle i,j\in \mathbb {N} ,1\leq i\leq k\land 1\leq j\leq m} numbers of actual pixel v → ∈ R 3 {\displaystyle {\vec {v}}\in \mathbb {R^{3}} } vertical vector which indicates where is up and down, usually v → = [ 0 , 1 , 0 ] {\displaystyle {\vec {v}}=[0,1,0]} - roll component which determine viewport rotation around point C (where the axis of rotation is the ET section) The idea is to find the position of each viewport pixel center P i j {\displaystyle P_{ij}} which allows us to find the line going from eye E {\displaystyle E} through that pixel and finally get the ray described by point E {\displaystyle E} and vector R → i j = P i j − E {\displaystyle {\vec {R}}_{ij}=P_{ij}-E} (or its normalization r → i j {\displaystyle {\vec {r}}_{ij}} ). First we need to find the coordinates of the bottom left viewport pixel P 1 m {\displaystyle P_{1m}} and find the next pixel by making a shift along directions parallel to viewport (vectors b → n {\displaystyle {\vec {b}}_{n

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  Swap chain

  In computer graphics, a swap chain (also swapchain) is a series of virtual framebuffers used by the graphics card and graphics API for frame rate stabilization, stutter reduction, and several other purposes. Because of these benefits, many graphics APIs require the use of a swap chain. The swap chain usually exists in graphics memory, but it can exist in system memory as well. A swap chain with two buffers is a kind of double buffer. == Function == In every swap chain there are at least two buffers. The first framebuffer, the screenbuffer, is the buffer that is rendered to the output of the video card. The remaining buffers are known as backbuffers. Each time a new frame is displayed, the first backbuffer in the swap chain takes the place of the screenbuffer, this is called presentation or swapping. A variety of other actions may be taken on the previous screenbuffer and other backbuffers (if they exist). The screenbuffer may be simply overwritten or returned to the back of the swap chain for further processing. The action taken is decided by the client application and is API dependent. == Direct3D == Microsoft Direct3D implements a SwapChain class. Each host device has at least one swap chain assigned to it, and others may be created by the client application. The API provides three methods of swapping: copy, discard, and flip. When the SwapChain is set to flip, the screenbuffer is copied onto the last backbuffer, then all the existing backbuffers are copied forward in the chain. When copy is set, each backbuffer is copied forward, but the screenbuffer is not wrapped to the last buffer, leaving it unchanged. Flip does not work when there is only one backbuffer, as the screenbuffer is copied over the only backbuffer before it can be presented. In discard mode, the driver selects the best method. == Comparison with triple buffering == Outside the context of Direct3D, triple buffering refers to the technique of allowing an application to draw to whichever back buffer was least recently updated. This allows the application to always proceed with rendering, regardless of the pace at which frames are being drawn by the application or the pace at which frames are being sent to the display. Triple buffering may result in a frame being discarded without being displayed if two or more newer frames are completely rendered in the time it takes for one frame to be sent to the display. By contrast, Direct3D swap chains are a strict first-in, first-out queue, so every frame that is drawn by the application will be displayed even if newer frames are available. Direct3D does not implement a most-recent buffer swapping strategy, and Microsoft's documentation calls a Direct3D swap chain of three buffers "triple buffering". Triple buffering as described above is superior for interactive purposes such as gaming, but Direct3D swap chains of more than three buffers can be better for tasks such as presenting frames of a video where the time taken to decode each frame may be highly variable.

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