Document mosaicing is a process that stitches multiple, overlapping snapshot images of a document together to produce one large, high resolution composite. The document is slid under a stationary, over-the-desk camera by hand until all parts of the document are snapshotted by the camera's field of view. As the document slid under the camera, all motion of the document is coarsely tracked by the vision system. The document is periodically snapshotted such that the successive snapshots are overlap by about 50%. The system then finds the overlapped pairs and stitches them together repeatedly until all pairs are stitched together as one piece of document. The document mosaicing can be divided into four main processes. Tracking Feature detecting Correspondences establishing Images mosaicing. == Tracking (simple correlation process) == In this process, the motion of the document slid under the camera is coarsely tracked by the system. Tracking is performed by a process called simple correlation process. In the first frame of snapshots, a small patch is extracted from the center of the image as a correlation template. The correlation process is performed in the four times size of the patch area of the next frame. The motion of the paper is indicated by the peak in the correlation function. The peak in the correlation function indicates the motion of the paper. The template is resampled from this frame and the tracking continues until the template reaches the edge of the document. After the template reaches the edge of the document, another snapshot is taken and the tracking process performs repeatedly until the whole document is imaged. The snapshots are stored in an ordered list to facilitate pairing the overlapped images in later processes. == Feature detecting for efficient matching == Feature detection is the process of finding the transformation that aligns one image with another. There are two main approaches for feature detection. Feature-based approach : Motion parameters are estimated from point correspondences. This approach is suitable for the case that there is plenty supply of stable and detectable features. Featureless approach : When the motion between the two images is small, the motion parameters are estimated using optical flow. On the other hand, when the motion between the two images is large, the motion parameters are estimated using generalised cross-correlation. However, this approach requires a computationally expensive resources. Each image is segmented into a hierarchy of columns, lines, and words to match the organised sets of features across images. Skew angle estimation and columns, lines and words finding are the examples of feature detection operations. === Skew angle estimation === Firstly, the angle that the rows of text make with the image raster lines (skew angle) is estimated. It is assumed to lie in the range of ±20°. A small patch of text in the image is selected randomly and then rotated in the range of ±20° until the variance of the pixel intensities of the patch summed along the raster lines is maximised. To ensure that the found skew angle is accurate, the document mosaic system performs calculation at many image patches and derive the final estimation by finding the average of the individual angles weighted by the variance of the pixel intensities of each patch. === Columns, lines and words finding === In this operation, the de-skewed document is intuitively segmented into a hierarchy of columns, lines and words. The sensitivity to illumination and page coloration of the de-skewed document can be removed by applying a Sobel operator to the de-skewed image and thresholding the output to obtain the binary gradient, de-skewed image. The operation can be roughly separated into 3 steps: column segmentation, line segmentation and word segmentation. Columns are easily segmented from the binary gradient, de-skewed images by summing pixels vertically. Baselines of each row are segmented in the same way as the column segmentation process but horizontally. Finally, individual words are segmented by applying the vertical process at each segmented row. These segmentations are important because the document mosaic is created by matching the lower right corners of words in overlapping images pair. Moreover, the segmentation operation can organize the list of images in the context of a hierarchy of rows and column reliably. The segmentation operation involves a considerable amount of summing in the binary gradient, de-skewed images, which done by construct a matrix of partial sums whose elements are given by p i y = ∑ u = 1 i ∑ v = 1 j b u v {\displaystyle p_{iy}=\sum _{u=1}^{i}\sum _{v=1}^{j}b_{uv}} The matrix of partial sums is calculated in one pass through the binary gradient, de-skewed image. ∑ u = u 1 u 2 ∑ v = v 1 v 2 b u v = p u 2 v 2 + p u 1 v 1 − p u 1 v 2 − p u 2 v 1 {\displaystyle \sum _{u=u_{1}}^{u_{2}}\sum _{v=v_{1}}^{v_{2}}b_{uv}=p_{u_{2}v_{2}}+p_{u_{1}v_{1}}-p_{u_{1}v_{2}}-p_{u_{2}v_{1}}} == Correspondences establishing == The two images are now organized in hierarchy of linked lists in following structure : image=list of columns row=list of words column=list of row word=length (in pixels) At the bottom of the structure, the length of each word is recorded for establishing correspondence between two images to reduce to search only the corresponding structures for the groups of words with the matching lengths. === Seed match finding === A seed match finding is done by comparing each row in image1 with each row in image2. The two rows are then compared to each other by every word. If the length (in pixel) of the two words (one from image1 and one from image2) and their immediate neighbours agree with each other within a predefined tolerance threshold (5 pixels, for example), then they are assumed to match. The row of each image is assumed a match if there are three or more word matches between the two rows. The seed match finding operation is terminated when two pairs of consecutive row match are found. === Match list building === After finishing a seed match finding operation, the next process is to build the match list to generate the correspondences points of the two images. The process is done by searching the matching pairs of rows away from the seed row. == Images mosaicing == Given the list of corresponding points of the two images, finding the transformation of the overlapping portion of the images is the next process. Assuming a pinhole camera model, the transformation between pixels (u,v) of image 1 and pixels (u0, v0) of image 2 is demonstrated by a plane-to-plane projectivity. [ s u ′ s v ′ s ] = [ p 11 p 12 p 13 p 21 p 22 p 23 p 31 p 32 1 ] [ u v 1 ] E q .1 {\displaystyle \left[{\begin{array}{c}su'\\sv'\\s\end{array}}\right]=\left[{\begin{array}{ccc}p_{11}&p_{12}&p_{13}\\p_{21}&p_{22}&p_{23}\\p_{31}&p_{32}&1\end{array}}\right]\left[{\begin{array}{c}u\\v\\1\end{array}}\right]\qquad Eq.1} The parameters of the projectivity is found from four pairs of matching points. RANSAC regression technique is used to reject outlying matches and estimate the projectivity from the remaining good matches. The projectivity is fine-tuned using correlation at the corners of the overlapping portion to obtain four correspondences to sub-pixel accuracy. Therefore, image1 is then transformed into image2's coordinate system using Eq.1. The typical result of the process is shown in Figure 5. === Many images coping === Finally, the whole page composition is built up by mapping all the images into the coordinate system of an "anchor" image, which is normally the one nearest the page center. The transformations to the anchor frame are calculated by concatenating the pair-wise transformations found earlier. The raw document mosaic is shown in Figure 6. However, there might be a problem of non-consecutive images that are overlap. This problem can be solved by performing Hierarchical sub-mosaics. As shown in Figure 7, image1 and image2 are registered, as are image3 and image4, creating two sub-mosaics. These two sub-mosaics are later stitched together in another mosaicing process. == Applied areas == There are various areas that the technique of document mosaicing can be applied to such as : Text segmentation of images of documents Document Recognition Interaction with paper on the digital desk Video mosaics for virtual environments Image registration techniques == Relevant research papers == Huang, T.S.; Netravali, A.N. (1994). "Motion and structure from feature correspondences: A review". Proceedings of the IEEE. 82 (2): 252–268. doi:10.1109/5.265351. D.G. Lowe. [1] Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Boston, 1985. Irani, M.; Peleg, S. (1991). "Improving resolution by image registration". CVGIP: Graphical Models and Image Processing. 53 (3): 231–239. doi:10.1016/1049-9652(91)90045-L. S2CID 4834546. Shivakumara, P.; Kumar, G. Hemantha; Guru, D. S.; Nagabhushan, P. (2006). "
Tail latency
Tail latency is a term used to describe the high-percentile response times seen in a system. This is usually measured at the 95th, 99th, or 99.9th percentile, not the average latency. In distributed systems, cloud computing, and large-scale web services, even a small number of slow requests can make the user experience and system performance much worse. Tail latency often happens because of things like resource contention, network variability, garbage collection pauses, and hardware heterogeneity. A major problem in system design is managing tail latency, because lowering average latency doesn't always make the worst-case performance better. To lessen its effects, people often use techniques like request hedging, replication, load balancing, and adaptive timeouts. In latency-sensitive applications like search engines, financial systems, and real-time services, where service-level objectives (SLOs) are often based on high-percentile latencies, it is especially important to understand and improve tail latency.
Unspent transaction output
In cryptocurrencies, an unspent transaction output (UTXO, often capitalized as UTxO) is a distinctive element in a subset of digital currency models. A UTXO represents a certain amount of cryptocurrency that has been authorized by a sender and is available to be spent by a recipient. The utilization of UTXOs in transaction processes is a key feature of many cryptocurrencies, but it primarily characterizes those implementing the UTXO model. UTXOs employ public key cryptography to ascertain and transfer ownership. More specifically, the recipient's public key is formatted into the UTXO, thereby limiting the capability to spend the UTXO to the account that can demonstrate ownership of the corresponding private key. A valid digital signature associated with the public key must be included for the UTXO to be spent. In the UTXO model, each unit of currency is treated as a discrete object. The history of a UTXO is documented only within the blocks where it is transferred. To ascertain the total balance of an account, one must scan each block to find the latest UTXOs linked to that account. While all nodes within a blockchain network must consent to the block history, the blocks relevant to an account's balance are unique to that account. UTXOs constitute a chain of ownership depicted as a series of digital signatures dating back to the coin's inception, regardless of whether the coin was minted via mining, staking, or another procedure determined by the cryptocurrency protocol. The UTXO model was invented for Bitcoin. Cardano uses an extended version of the UTXO model known as EUTXO. == Origins == The conceptual framework of the UTXO model can be traced back to Hal Finney's Reusable Proofs of Work proposal, which itself was based on Adam Back's 1997 Hashcash proposal. Bitcoin, released in 2009, was the first widespread implementation of the UTXO model in practice. == UTXO model vs. account Model == Cryptocurrencies that utilize the UTXO model function differently compared to those using the account model. In the UTXO model, individual units of cryptocurrency, termed as unspent transaction outputs (UTXOs), are transferred between users, analogous to the exchange of physical cash. This model impacts how transactions and ownership are recorded and verified within the blockchain network. The account model preserves a record of each account and its corresponding balance for every block added to the network. This setup enables quicker balance verification without the need to scan historical blocks, but it increases the raw size of each block (though data compression techniques can be utilized to alleviate this). However, both models necessitate the inspection of past blocks to fully authenticate the origin of coins. In the UTXO model, each object is immutable - units of coins cannot be 'edited' in the same way an account balance is modified when a transaction occurs. Rather, the balance is computed from the transaction history dating back to when the coins were first minted. This simplicity enhances security, as a UTXO either exists in its anticipated form or it does not. In contrast, the account model requires meticulous verification of the account's status during transactions, which can lead to oversights if not conducted correctly. In valid blockchain transactions, only unspent outputs (UTXOs) are permissible for funding subsequent transactions. This requirement is critical to prevent double-spending and fraud. Accordingly, inputs in a transaction are removed from the UTXO set, while outputs create new UTXOs that are added to the set. The holders of private keys, such as those with cryptocurrency wallets, can utilize these UTXOs for future transactions.
IMazing
iMazing is mobile device management software that allows users to transfer files and data between iOS devices (iPhone, iPad and iPod Touch) and macOS or Windows computers, in addition to many other features beyond the scope of what Apple's own tools enable. == History == Developed by DigiDNA, iMazing was initially released in 2008 as DiskAid, enabling users to transfer data and files from the iPhone or iPod Touch to Mac or Windows computers. DiskAid was renamed iMazing in 2014. Version 2.0 was released on September 13, 2016. In August 2021, version 2.14 of iMazing added a spyware detection feature. The feature is based on Amnesty International’s Mobile Verification Toolkit to detect Pegasus Spyware following the publication of Pegasus Project. == Description == With iMazing, an iPhone or iPad can be used similarly to an external hard drive. It performs tasks that iTunes doesn’t offer, including incremental backups of iOS devices, browsing and exporting text and voicemail messages, managing apps, encryption, and migrating data from an old phone to a new one. The menu bar app iMazing Mini enables automatic, wireless and encrypted backups of iPhones. The iMazing HEIC Converter is a free desktop app for Mac and PC that lets users convert photos from HEIC format to JPG or PNG.
Computer Law & Security Review
The Computer Law & Security Review is an international peer-reviewed journal published by Elsevier. It has been published six times a year since 1985 and is indexed in Scopus and SSCI. It is accessible to a wide range of professional legal and IT practitioners, businesses, academics, researchers, libraries and organisations in both the public and private sectors. The journal regularly covers: CLSR Briefing with special emphasis on UK/US developments European Union update National news from 10 European jurisdictions Pacific rim news column Refereed practitioner and academic papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection and freedom of information and many other topics. The Journal's Correspondent Panel includes more than 40 specialists in IT law and security. Each issue contains articles, case law analysis and current news on information and communications technology. Special Features High quality peer reviewed papers from internationally renowned practitioner and academic experts Latest developments reported in situ by more than 20 leading law firms from around the world Highly experienced and respected editor and correspondents panel Online access to all 23 volumes of CLSR with embedded web links to primary sources Contact details of all authors A pool of expertise that can collectively identify the key topics that need to be examined.
AARON
AARON is the collective name for a series of computer programs written by artist Harold Cohen that create original artistic images autonomously, which set it apart from previous programs. Proceeding from Cohen's initial question "What are the minimum conditions under which a set of marks functions as an image?", AARON was in development between 1972 and the 2010s. As the software is not open source, its development effectively ended with Cohen's death in 2016. The name "AARON" does not seem to be an acronym; rather, it was a name chosen to start with the letter "A" so that the names of successive programs could follow it alphabetically. However, Cohen did not create any other major programs. Initial versions of AARON created abstract drawings that grew more complex through the 1970s. More representational imagery was added in the 1980s; first rocks, then plants, then people. In the 1990s more representational figures set in interior scenes were added, along with color. AARON returned to more abstract imagery, this time in color, in the early 2000s. Cohen used machines that allowed AARON to produce physical artwork. The first machines drew in black and white using a succession of custom-built "turtle" and flatbed plotter devices. Cohen would sometimes color these images by hand in fabric dye (Procion), or scale them up to make larger paintings and murals. In the 1990s Cohen built a series of digital painting machines to output AARON's images in ink and fabric dye. His later work used a large-scale inkjet printer on canvas. Development of AARON began in the C programming language then switched to Lisp in the early 1990s. Cohen credits Lisp with helping him solve the challenges he faced in adding color capabilities to AARON. An article about Cohen appeared in Computer Answers that describes AARON and shows two line drawings that were exhibited at the Tate gallery. The article goes on to describe the workings of AARON, then running on a DEC VAX 750 minicomputer. Raymond Kurzweil's company has produced a downloadable screensaver of AARON for Microsoft Windows PCs. This version of AARON can also produce printable images. AARON's source code is not publicly available, but Cohen has described AARON's operations in various essays and it is discussed in abstract in Pamela McCorduck's book. AARON cannot learn new styles or imagery on its own; each new capability must be hand-coded by Cohen. It is capable of producing a practically infinite supply of distinct images in its own style. Examples of these images have been exhibited in galleries worldwide. AARON's artwork has been used as an artistic equivalent of the Turing test. It does seem however that AARON's output follows a noticeable formula (figures standing next to a potted plant, framed within a colored square is a common theme). Cohen is very careful not to claim that AARON is creative. But he does ask "If what AARON is making is not art, what is it exactly, and in what ways, other than its origin, does it differ from the 'real thing?' If it is not thinking, what exactly is it doing?" — The further exploits of AARON, Painter. The Whitney Museum featured AARON in 2024, showcasing the evolution of AARON as the earliest artificial intelligence (AI) program for artmaking.
Gas (app)
Gas (sometimes stylized in all caps), formerly known as Melt as well as Crush, was an American anonymous social media app. Launched in August 2022, the app is oriented towards high schoolers. The app was developed by Nikita Bier, Isaiah Turner, and former Facebook engineer Dave Schatz. Gas was largely based upon the prior tbh app developed by co-founder Nikita Bier, along with Erik Hazzard, Kyle Zaragoza, and Nicolas Ducdodon in September 2017. tbh was acquired by Facebook inc. (now Meta Platforms) on October 16, 2017, and nearly a year later in July 2018 was dissolved, owing to low usage. Gas follows a similar purpose to tbh in being a social media app oriented towards high schoolers. In the app, users participate in anonymous polls regarding pre-written complimentary statements to their peers, such as "I'd say yes if (blank) asked me out on a date," "I think (blank) is the coolest kid in school," or "would make an ugly face and still look pretty." Winners of said polls receive a "flame." The name of the app is derived from this, with "gassing someone up" being Gen Z slang for complimenting someone. Users can pay a $6.99 subscription that enables "God Mode," which shows hints regarding who voted for them in a poll. Gas overtook TikTok and BeReal as the most downloaded app on the Apple App Store in October 2022 (the app is currently not available for Android). The app has over 5.1 million downloads as of early November 2022, over a million active users and 300 thousand daily downloads as of October 2022. Currently, the app is available in Canada and the majority of the United States. On January 17, 2023, Gas was acquired by Discord, however it would remain a standalone app and its developers became Discord staff members. On October 18, 2023, Discord announced that service for Gas would be permanently ending effective November 7, 2023, due to a steep decline in users. Effective November 7, the app became completely unusable. == Controversy regarding human-trafficking == Beginning in October 2022, rumors spread largely throughout TikTok and Snapchat alleged that the app was linked to human trafficking (in particular sex trafficking). According to Bier, the rumor originated with a single user review from China on October 5, and then was disseminated through TikTok accounts with "few to no US teen followers." Although largely dismissed as a hoax by experts, who cite how the app doesn't log user locations and general anonymity, the hoax became pervasive to the extent that various police departments, school systems, and local news outlets began issuing warnings regarding the app. For instance, on October 31, 2022, the police department of Piedmont, Oklahoma issued a warning to parents, encouraging them to check their children's phones, while on November 3, the Oklahoma Oktaha Public School system stated in a Facebook post that "Children are being kidnapped in other towns and this new app is thought to be the source of predators finding their location." (both statements have since been retracted by Police Chief Scott Singer and Superintendent Jerry Needham respectively). Additionally, local medial outlets such as KOCO in Oklahoma City ran stories making similar statements. The rumor had a negative impact on the app, with downloads plateauing for a two-week period in late October and with 3% of users in a single day reportedly uninstalling the app. Revenue and ratings have also reportedly dropped and the company's social media accounts have been bombarded with comments labeling them as sex-traffickers. Additionally, the four-person development team has reportedly been bombarded with various death threats as a result.