Label noise refers to errors or inaccuracies in the class labels of data instances. This is a widespread issue in machine learning datasets, arising from human annotator mistakes, unclear labeling instructions, automated labeling methods, or adversarial attacks in supervised learning. Label noise can be roughly divided into random noise, where labels are flipped independently of input features, and systematic noise, where mislabeling is dependent on certain patterns or biases in the data. Label noise can be damaging to model performance, especially for complex models that may overfit to noisy labels rather than generalizable patterns. Many approaches have been proposed to deal with the effects of label noise, including robust loss functions, noise-tolerant algorithms, data cleaning methods, and semi-supervised learning approaches. To reduce the impact of wrong labels during training, techniques like label smoothing, sample reweighting and using trusted validation sets are used. The role of noise-robust training paradigms and curriculum learning strategies to improve resilience against mislabeled data is also explored in recent research.
Probiv
Probiv (Russian: пробив, literally "to pierce" or "to punch through") is an illicit data market operating primarily in Russia, where personal information from restricted government and corporate databases is bought and sold through networks of corrupt officials and insiders. The probiv market operates as a parallel information economy built on corrupt officials from various sectors including traffic police, banks, telecommunications companies, and security services who sell access to restricted databases. For fees ranging from as little as $10 to several hundred dollars, buyers can obtain passport numbers, addresses, travel histories, vehicle registrations, and telecommunications records. The market operates through various channels, including specialized Telegram bots and darknet forums. == Notable uses == Probiv services have been utilized by diverse actors for various purposes. Investigative journalists have used the market to conduct high-profile investigations, including tracing the FSB unit allegedly behind the poisoning of Alexei Navalny. Russian police and security services themselves have routinely used the black market to track activists and opposition figures. Since Russia's invasion of Ukraine, Ukrainian intelligence services have exploited the market to identify Russian military officials. == Government response == In late 2024, Russian authorities introduced legislation imposing penalties of up to ten years in prison for accessing or distributing leaked data. Several operators of probiv services, including the teams behind Usersbox and Solaris, have been arrested. However, the crackdown appears to have had unintended consequences. Many operators have relocated their businesses abroad, where they operate with fewer constraints. Some services that previously cooperated with Russian authorities have severed those ties and moved staff out of the country.
Circular thresholding
Circular thresholding is an algorithm for automatic image threshold selection in image processing. Most threshold selection algorithms assume that the values (e.g. intensities) lie on a linear scale. However, some quantities such as hue and orientation are a circular quantity, and therefore require circular thresholding algorithms. The example shows that the standard linear version of Otsu's method when applied to the hue channel of an image of blood cells fails to correctly segment the large white blood cells (leukocytes). In contrast the white blood cells are correctly segmented by the circular version of Otsu's method. == Methods == There are a relatively small number of circular image threshold selection algorithms. The following examples are all based on Otsu's method for linear histograms: (Tseng, Li and Tung 1995) smooth the circular histogram, and apply Otsu's method. The histogram is cyclically rotated so that the selected threshold is shifted to zero. Otsu's method and histogram rotation are applied iteratively until several heuristics involving class size, threshold location, and class variance are satisfied. (Wu et al. 2006) smooth the circular histogram until it contains only two peaks. The histogram is cyclically rotated so that the midpoint between the peaks is shifted to zero. Otsu's method and histogram rotation are applied iteratively until convergence of the threshold. (Lai and Rosin 2014) applied Otsu's method to the circular histogram. For the two class circular thresholding task they showed that, for a histogram with an even number of bins, the optimal solution for Otsu's criterion of within-class variance is obtained when the histogram is split into two halves. Therefore the optimal solution can be efficiently obtained in linear rather than quadratic time. == References and further reading == D.-C. Tseng, Y.-F. Li, and C.-T. Tung, Circular histogram thresholding for color image segmentation in Proc. Int. Conf. Document Anal. Recognit., 1995, pp. 673–676. J. Wu, P. Zeng, Y. Zhou, and C. Olivier, A novel color image segmentation method and its application to white blood cell image analysis in Proc. Int. Conf. Signal Process., vol. 2. 2006, pp. 16–20. Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), 992–1001 (2014). doi:10.1109/TIP.2013.2297014
System requirements specification
A System Requirements Specification (SysRS) (abbreviated SysRS to be distinct from a software requirements specification (SRS)) is a structured collection of information that embodies the requirements of a system. A business analyst (BA), sometimes titled system analyst, is responsible for analyzing the business needs of their clients and stakeholders to help identify business problems and propose solutions. Within the systems development life cycle domain, the BA typically performs a liaison function between the business side of an enterprise and the information technology department or external service providers.
Morphobank
MorphoBank is a web application for collaborative evolutionary research, specifically phylogenetic systematics or cladistics, on the phenotype. Historically, scientists conducting research on phylogenetic systematics have worked individually or in small groups employing traditional single-user software applications such as MacClade, Mesquite and Nexus Data Editor. As the hypotheses under study have grown more complex, large research teams have assembled to tackle the problem of discovering the Tree of Life for the estimated 4-100 million living species(Wilson 2003, pp. 77–80) and the many thousands more extinct species known from fossils. Because the phenotype is fundamentally visual, and as phenotype-based phylogenetic studies have continued to increase in size, it becomes important that observations be backed up by labeled images. Traditional desktop software applications currently in wide use do not provide robust support for team-based research or for image manipulation and storage. MorphoBank is a particularly important tool for the growing scientific field of phenomics. The development of MorphoBank, which began in 2001, has been funded by the National Science Foundation's Directorates for Geosciences, Biological Sciences and Computer and Information Science and Engineering. The significance of the scientific work on MorphoBank has been featured in the New York Times(here and here), among other publications. == Advantages == Teams of scientists studying phylogenetics to build the Tree of Life assemble large spreadsheets of observations about species (referred to as "matrices"). These teams require simultaneous access by each team member to a single and secure copy of the team's data during a scientific research project. This single copy of the data also changes with great frequency during the data collection phase. Images that can be very helpful for documenting homology statements must be displayed, labeled and shared as homology statements develop. This cannot be accomplished elegantly with a desktop software package alone because in a desktop environment each collaborator is working on his own private copy of project data. Changes made by one participant cannot automatically propagate to others, preventing collaborators from seeing each other's data edits until they are manually (and due to the effort involved, often only periodically) merged into a single "true" dataset. In all but the smallest and most disciplined of teams, file version control and the reconciliation of changes made on multiple copies of the data emerge quickly as significant drags on productivity. MorphoBank is an attempt to address these issues by leveraging the ubiquity of the web and modern web-based application techniques, including Ajax, web service layers, and rich web applications to provide a full-featured, net-accessible collaborative workspace for phylogenetic research. In particular, MorphoBank makes it easy to: Share all kinds of data with geographically separated team members, including taxonomy, character and specimen data, media (including images, video and audio), phylogenetic matrices (including data in the widely used NEXUS and TNT format) and other data such as documents and genetic sequences. Label high-resolution images using a web-based image annotation application. Collaboratively edit project data such as phylogenetic matrices using a built-in web-based matrix editor. The editor allows the linking of labeled images to individual cells of a matrix. Manage access to project data. Access ranges from full-access for team members to anonymous read-only access for potential reviewers. Publish completed project data on the web in support of a published paper with a persistent URL. Search The Encyclopedia of Life for taxon exemplar images. Store high resolution CT data Create ontologies for updating and populating matrix cells. These tasks are difficult or impossible in most existing software applications. == History == In 2001 the National Science Foundation (NSF) sponsored a workshop, at the American Museum of Natural History in New York to develop the outlines of a web-based system for a collaborative, media-rich research tool for morphological phylogenetics. An application prototype presented at the workshop was later refined with feedback from the workshop and became MorphoBank version 1.0. A grant from the US National Oceanic and Atmospheric Administration funded further revisions resulting in version 2.0, released in 2005. Current support from the NSF is funding current feature enhancements to MorphoBank. MorphoBank was hosted by Stony Brook University until late October 2021 and received back up support from the American Museum of Natural History. The current version is 3.0. Rationale for the software was described in the journal Cladistics. MorphoBank has also received support from NESCENT and the San Diego Supercomputer Center. Since 2018, MorphoBank has been supported in part by Phoenix Bioinformatics, a non-profit company founded to sustain databases for the basic sciences. A permanent move of MorphoBank from Stony Brook University to Phoenix Bioinformatics was complete in late October 2021. The San Diego Supercomputer Center has previously provided technical and hosting resources to the MorphoBank project. == Usage == MorphoBank hosts the products of peer-reviewed scientific research on phenotypes. An increasing volume of systematics data is "born digital" and MorphoBank is well suited to handle this type of material. On August 24, 2007, 62 active research projects were hosted by MorphoBank, as well as 6 completed (and published) projects. By 2017 over 2000 scientists and their students were registered content builders (users are not required to register and are even more numerous) and has more than 500 publicly available projects with approximately 80,000 images that are the products of scientific research. Over 1,500 active research projects are hosted by MorphoBank. The software has been used to assemble phylogenetic research on such groups as mammals, from bats to whales, bivalve molluscs, arachnids, fossil plants and living and extinct amniotes. It has also been used more broadly in evolutionary and paleontological research to host curated images associated with published research on lacewing insects geckos, raptor birds, dinosaurs, frogs and nematodes. MorphoBank is increasingly used in conjunction with the Paleobiology Database. Example published projects: Project 1097: Blank CE, 2013 Origin and early evolution of photosynthetic eukaryotes in freshwater environments – reinterpreting proterozoic paleobiology and biogeochemical processes in light of trait evolution Project 2520: Carvalho, T. P., R. E. Reis, and J. P. Friel, 2017 A new species of Hoplomyzon (Siluriformes: Aspredinidae) from Maracaibo Basin, Venezuela: osteological description using high-resolution Project 2651: Baron, M. G., Norman, D. B., Barrett, P. M., 2017 A new hypothesis of dinosaur relationships and early dinosaur evolution MorphoBank has been particularly important to the Assembling the Tree of Life initiative sponsored by the National Science Foundation. MorphoBank is well-suited to such projects because of its tools for merging taxonomic, character and matrix-based data, as well as its collaborative features. Highlights of this research include a collaborative matrix on mammal evolution published in Science that included over 4,000 phenomic characters scored for over 80 species, a matrix on extant baleen whales featuring nearly 600 images, and more.
Arattai
Arattai Messenger (or simply Arattai) is an encrypted messaging service for instant messaging, voice calls, and video calls, developed by Zoho Corporation. The name Arattai means "chat" or "conversation" in Tamil. The app was soft-launched in January 2021. The app saw a sharp surge in downloads in September 2025, partially fueled by endorsements from Indian government officials. However, the app dropped from the top rankings in October 2025. == History == Arattai was initially tested internally among Zoho employees before being released publicly in early 2021. The launch coincided with a surge in interest for privacy-focused and messaging services, triggered by concerns over WhatsApp's updated terms of service. In September 2025, Arattai experienced a major surge in adoption, with daily sign-ups reportedly increasing 100-fold, from around 3,000 to more than 350,000 in three days. The surge in downloads was attributed to Zoho products being promoted by Indian government officials as part of their Make in India push for homegrown alternatives to foreign‐owned apps, amid deteriorating India–US relations. The growth temporarily strained Zoho's infrastructure, prompting rapid scaling of servers and capacity expansion. During the same period, the app reached the top position in Apple's App Store charts for the "Social Networking" category in India. The app dropped from the top ranking in late October 2025. == Reception == At launch, Arattai was positioned as a potential domestic rival to WhatsApp in India, but analysts noted that it faced challenges with encryption, ecosystem, and network effect. Critics pointed to occasional sync delays.
Networked Help Desk
Networked Help Desk is an open standard initiative to provide a common API for sharing customer support tickets between separate instances of issue tracking, bug tracking, customer relationship management (CRM) and project management systems to improve customer service and reduce vendor lock-in. The initiative was created by Zendesk in June 2011 in collaboration with eight other founding member organizations including Atlassian, New Relic, OTRS, Pivotal Tracker, ServiceNow and SugarCRM. The first integration, between Zendesk and Atlassian's issue tracking product, Jira, was announced at the 2011 Atlassian Summit. By August 2011, 34 member companies had joined the initiative. A year after launching, over 50 organizations had joined. Within Zendesk instances this feature is branded as ticket sharing. == Basis == Support tools are generally built around a common paradigm that begins with a customer making a request or an incident report, these create a ticket. Each ticket has a progress status and is updated with annotations and attachments. These annotations and attachments may be visible to the customer (public), or only visible to analysts (private). Customers are notified of progress made on their ticket until it is complete. If the people necessary to complete a ticket are using separate support tools, additional overhead is introduced in maintaining the relevant information in the ticket in each tool while notifying the customer of progress made by each group in completing their ticket. For example, if a customer support issue is caused by a software bug and reported to a help desk using one system, and then the fix is documented by the developers in another, and analyzed in a customer relationship management tool, keeping the records in each system up-to-date and notifying the customer manually using a swivel chair approach is unnecessarily time-consuming and error-prone. If information is not transferred correctly, a customer may have to re-explain their problem each time their ticket is transferred. For systems with the Networked Help Desk API implemented, it is possible for several different applications related to a customer's support experience to synchronize data in one uniquely identified shared ticket. While many applications in these domains have implemented APIs that allow data to be imported, exported and modified, Network Help Desk provide a common standard for customer support information to automatically synchronize between several systems. Once implemented, two systems can quickly share tickets with just a configuration change as they both understand the same interface. Communication between two instances on a specific ticket occurs in three steps, an invitation agreement, sharing of ticket data and continued synchronization of tickets. The standard allows for "full delegation" (analysts in both systems each make public and private comments and synchronize status) as well as "partial delegation" where the instance receiving the ticket can only make private comments and status changes are not synchronized. Tickets may be shared with multiple instances. == Implementation list ==