AI Essay No Plagiarism

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  Three-factor learning

  In neuroscience and machine learning, three-factor learning is the combination of Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning. This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implemented through neuromodulators. == Description == Three-factor learning introduces the concept of eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learning can be done. Biological basis for Three-factor learning rules have been supported by experimental evidence. This approach addresses the instability of classical Hebbian learning by minimizing autocorrelation and maximizing cross-correlation between inputs.

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  Data philanthropy

  Data philanthropy refers to the practice of private companies donating corporate data. This data is usually donated to nonprofits or donation-run organizations that have difficulty keeping up with expensive data collection technology. The concept was introduced through the United Nations Global Pulse initiative in 2011 to explore corporate data assets for humanitarian, academic, and societal causes. For example, anonymized mobile data could be used to track disease outbreaks, or data on consumer actions may be shared with researchers to study public health and economic trends. == Definition == A large portion of data collected from the internet consists of user-generated content, such as blogs, social media posts, and information submitted through lead generation and data forms. Additionally, corporations gather and analyze consumer data to gain insight into customer behavior, identify potential markets, and inform investment decisions. United Nations Global Pulse director Robert Kirkpatrick has referred to this type of data as "massive passive data" or "data exhaust." == Challenges == While data philanthropy can enhance development policies, making users' private data available to various organizations raises concerns regarding privacy, ownership, and the equitable use of data. Different techniques, such as differential privacy and alphanumeric strings of information, can allow access to personal data while ensuring user anonymity. However, even if these algorithms work, re-identification may still be possible. Another challenge is convincing corporations to share their data. The data collected by corporations provides them with market competitiveness and insight regarding consumer behavior. Corporations may fear losing their competitive edge if they share the information they have collected with the public. Numerous moral challenges are also encountered. In 2016, Mariarosaria Taddeo, a digital ethics professor at the University of Oxford, proposed an ethical framework to address them. == Sharing strategies == The goal of data philanthropy is to create a global data commons where companies, governments, and individuals can contribute anonymous, aggregated datasets. The United Nations Global Pulse offers four different tactics that companies can use to share their data that preserve consumer anonymity: Share aggregated and derived data sets for analysis under nondisclosure agreements (NDA) Allow researchers to analyze data within the private company's own network under NDAs Real-Time Data Commons: data pooled and aggregated among multiple companies of the same industry to protect competitiveness Public/Private Alerting Network: companies mine data behind their own firewalls and share indicators == Application in various fields == Many corporations take part in data philanthropy, including social networking platforms (e.g., Facebook, Twitter), telecommunications providers (e.g., Verizon, AT&T), and search engines (e.g., Google, Bing). Collecting and sharing anonymized, aggregated user-generated data is made available through data-sharing systems to support research, policy development, and social impact initiatives. By participating in such efforts, these organizations contribute to causes regarded as beneficial to society, allowing institutions to give back meaningfully. With the onset of technological advancements, the sharing of data on a global scale and an in-depth analysis of these data structures could mitigate the effects of global issues such as natural disasters and epidemics. Robert Kirkpatrick, the Director of the United Nations Global Pulse, has argued that this aggregated information is beneficial for the common good and can lead to developments in research and data production in a range of varied fields. === Digital disease detection === Health researchers use digital disease detection by collecting data from various sources—such as social media platforms (e.g., Twitter, Facebook), mobile devices (e.g., cell phones, smartphones), online search queries, mobile apps, and sensor data from wearables and environmental sensors—to monitor and predict the spread of infectious diseases. This approach allows them to track and anticipate outbreaks of epidemics (e.g., COVID-19, Ebola), pandemics, vector-borne diseases (e.g., malaria, dengue fever), and respiratory illnesses (e.g., influenza, SARS), improving response and intervention strategies for the spread of diseases. In 2008, Centers for Disease Control and Prevention collaborated with Google and launched Google Flu Trends, a website that tracked flu-related searches and user locations to track the spread of the flu. Users could visit Google Flu Trends to compare the amount of flu-related search activity versus the reported numbers of flu outbreaks on a graphical map. One drawback of this method of tracking was that Google searches are sometimes performed due to curiosity rather than when an individual is suffering from the flu. According to Ashley Fowlkes, an epidemiologist in the CDC Influenza division, "The Google Flu Trends system tries to account for that type of media bias by modeling search terms over time to see which ones remain stable." Google Flu Trends is no longer publishing current flu estimates on the public website; however, visitors to the site can still view and download previous estimates. Current data can be shared with verified researchers. A study from the Harvard School of Public Health (HSPH), published in the October 12, 2012 issue of Science, discussed how phone data helped curb the spread of malaria in Kenya. The researchers mapped phone calls and texts made by 14,816,521 Kenyan mobile phone subscribers. When individuals left their primary living location, the destination and length of journey were calculated. This data was then compared to a 2009 malaria prevalence map to estimate the disease's commonality in each location. Combining all this information, the researchers could estimate the probability of an individual carrying malaria and map the movement of the disease. This research can be used to track the spread of similar diseases. === Humanitarian aid === Calling patterns of mobile phone users can determine the socioeconomic standings of the populace, which can be used to deduce "its access to housing, education, healthcare, and basic services such as water and electricity." Researchers from Columbia University and Karolinska Institute used daily SIM card location data from both before and after the 2010 Haiti earthquake to estimate the movement of people both in response to the earthquake and during the related 2010 Haiti cholera outbreak. Their research suggests that mobile phone data can provide rapid and accurate estimates of population movements during disasters and outbreaks of infectious disease. Big data can also provide information on looming disasters and can assist relief organizations in rapid-response and locating displaced individuals. By analyzing specific patterns within this 'big data', governments and NGOs can enhance responses to disruptive events such as natural disasters, disease outbreaks, and global economic crises. Leveraging real-time information enables a deeper understanding of individual well-being, allowing for more effective interventions. Corporations utilize digital services, such as human sensor systems, to detect and solve impending problems within communities. This is a strategy used by the private sector to anonymously share customer information for public benefit, while preserving user privacy. === Impoverished areas === Poverty still remains a worldwide issue, with over 2.5 billion people currently impoverished. Statistics indicate the widespread use of mobile phones, even within impoverished communities. Additional data can be collected through Internet access, social media, utility payments and governmental statistics. Data-driven activities can lead to the accumulation of 'big data', which in turn can assist international non-governmental organizations in documenting and evaluating the needs of underprivileged populations. Through data philanthropy, NGOs can distribute information while cooperating with governments and private companies. === Corporate === Data philanthropy incorporates aspects of social philanthropy by allowing corporations to create profound impacts through the act of giving back by dispersing proprietary datasets. The public sector collects and preserves information, considered an essential asset. Companies track and analyze users' online activities to gain insight into their needs related to new products and services. These companies view the welfare of the population as key to business expansion and progression by using their data to highlight global citizens' issues. Experts in the private sector emphasize the importance of integrating diverse data sources—such as retail, mobile, and social media data—to develop essential solutions for global challenges. In Data Philanthropy:

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  Forking lemma

  The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

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  Data analysis

  Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of unstructured data. All of the above are varieties of data analysis. == Data analysis process == Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Statistician John Tukey, defined data analysis in 1961, as:"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." There are several phases, and they are iterative, in that feedback from later phases may result in additional work in earlier phases. === Data requirements === The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). === Data collection === Data may be collected from a variety of sources. A list of data sources are available for study & research. The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization. Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. === Data processing === Data integration is a precursor to data analysis: Data, when initially obtained, must be processed or organized for analysis. For instance, this may involve placing data into rows and columns in a table format (known as structured data) for further analysis, often through the use of spreadsheet (e.g. Excel) or statistical software. === Data cleaning === Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that the data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable. Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. Quantitative data methods for outlier detection can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Text data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words are contextually (i.e., semantically and idiomatically) correct. === Exploratory data analysis === Once the datasets are cleaned, they can then begin to be analyzed using exploratory data analysis. The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned above. Descriptive statistics, such as the average, median, and standard deviation, are often used to broadly characterize the data. Data visualization is also used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights about messages within the data. === Modeling and algorithms === Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. === Communication === Once data is analyzed, it may be presented in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data. == Quantitative messages == Stephen Few described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs. Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the salespersons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount. Frequency distribution:

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  The Most Dangerous Writing App

  The Most Dangerous Writing App is a web application for free writing that combats writer's block by deleting all progress if the user stops typing for five seconds. It is targeted at creative writers who want to write first drafts without worrying about editing or formatting. == Features == The app is designed to "shut down your inner editor and get you into a state of flow", referring to the psychological concept of being in a flow state. Users start a writing session by choosing a time or word limit, and can only save or download their work if they complete the set limit without interruption. An optional "hardcore mode" blurs out everything the user has written so far, making it impossible to edit before finishing the writing session. == History == The Most Dangerous Writing App was created by software engineer Manuel Ebert and was released as free, open source software on February 29, 2016. It was reviewed by Wired, Forbes, Vogue, Huffington Post, The Verge, The Next Web, and others. It has been used in free writing contests and is recommended by NaNoWriMo. In April 2019, The Most Dangerous Writing App was acquired by Squibler, but the original version remains freely accessible.

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  Simply Local

  Simply Local is a decentralized community social networking and neighborhood broadcasting service developed by Simply Local, based in New Delhi. The app is used as a tool by residents to bridge the information gap and know what is happening in the locality. Simply Local creates private geo-fenced networks for people living in an area and provides social and community related services within that network. The user doesn’t post to a single person but broadcasts to a chosen community. One of its primary purposes is also to connect citizens to their elected representatives. Each community is independent of the other and information shared remains telescoped to that particular community. The app has been designed to maintain privacy and security of users and provides decentralized social networking in the sense that it forms an owner-independent, micro community, which is not connected with the world outside. Simply Local is available on Android Play and iOS App Store. It is available in two languages - English and Hindi. Simply Local’s founder and CEO is Nikhil Bapna. == History == 2020 May: Included as a Top 5 Useful App by Zee News. 2020: Used to connect candidates with local residents during the Delhi assembly elections. 2019: Renamed from Gadfly to its current name. 2018: Used for Karnataka State Elections to get detailed information on candidates. 2017: Launched under the name Gadfly as a tool to connect citizens with their elected representatives.

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  Cryptographic Service Provider

  A cryptographic service provider (CSP) is a package that "provides a concrete implementation of certain cryptographic services." A CSP offers operations and protocols to support a variety of use cases. The cryptographic application programming interface (API) provided by the CSP provides common solutions for different platforms, for example hardware and cloud services. == Microsoft Windows == In Microsoft Windows, a Cryptographic Service Provider is a software library that implements the Microsoft CryptoAPI (CAPI). CSPs implement encoding and decoding functions, which computer application programs may use, for example, to implement strong user authentication or for secure email. CSPs are independent modules that can be used by different applications. A user program calls CryptoAPI functions and these are redirected to CSPs functions. Since CSPs are responsible for implementing cryptographic algorithms and standards, applications do not need to be concerned about security details. Furthermore, each application can define which CSP it is going to use on its calls to CryptoAPI. In fact, all cryptographic activity is implemented in CSPs. CryptoAPI only works as a bridge between the application and the CSP. CSPs are implemented basically as a special type of DLL with special restrictions on loading and use. Every CSP must be digitally signed by Microsoft and the signature is verified when Windows loads the CSP. In addition, after being loaded, Windows periodically re-scans the CSP to detect tampering, either by malicious software such as computer viruses or by the user him/herself trying to circumvent restrictions (for example on cryptographic key length) that might be built into the CSP's code. To obtain a signature, non-Microsoft CSP developers must supply paperwork to Microsoft promising to obey various legal restrictions and giving valid contact information. As of circa 2000, Microsoft did not charge any fees to supply these signatures. For development and testing purposes, a CSP developer can configure Windows to recognize the developer's own signatures instead of Microsoft's, but this is a somewhat complex and obscure operation unsuitable for nontechnical end users. The CAPI/CSP architecture had its origins in the era of restrictive US government controls on the export of cryptography. Microsoft's default or "base" CSP then included with Windows was limited to 512-bit RSA public-key cryptography and 40-bit symmetric cryptography, the maximum key lengths permitted in exportable mass market software at the time. CSPs implementing stronger cryptography were available only to U.S. residents, unless the CSPs themselves had received U.S. government export approval. The system of requiring CSPs to be signed only on presentation of completed paperwork was intended to prevent the easy spread of unauthorized CSPs implemented by anonymous or foreign developers. As such, it was presented as a concession made by Microsoft to the government, in order to get export approval for the CAPI itself. After the Bernstein v. United States court decision establishing computer source code as protected free speech and the transfer of cryptographic regulatory authority from the U.S. State Department to the more pro-export Commerce Department, the restrictions on key lengths were dropped, and the CSPs shipped with Windows now include full-strength cryptography. The main use of third-party CSPs is to interface with external cryptography hardware such as hardware security modules (HSM) or smart cards. === Smart Card CSP === These cryptographic functions can be realized by a smart card, thus the Smart Card CSP is the Microsoft way of a PKCS#11. Microsoft Windows is identifying the correct Smart Card CSP, which have to be used, analyzing the answer to reset (ATR) of the smart card, which is registered in the Windows Registry. Installing a new CSP, all ATRs of the supported smart cards are enlisted in the registry. === Use of CSP in MS Office password protection === Cryptographic service providers can be used for encryption of Word, Excel, and PowerPoint documents starting from Microsoft Office XP. A standard encryption algorithm with a 40-bit key is used by default, but enabling a CSP enhances key length and thus makes decryption process more continuous. This only applies to passwords that are required to open document because this password type is the only one that encrypts a password-protected document.

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  Airborne Networking

  An Airborne Network (AN) is the infrastructure owned by the United States Air Force that provides communication transport services through at least one node that is on a platform capable of flight. == Background == === Definition === The intent of the US Air Force's Airborne Network is to expand the Global Information Grid (GIG) to connect the three major domains of warfare: Air, Space, and Terrestrial. The Transformational Satellite Communications System network currently provides connectivity for all communication through space assets. The Combat Information Transport System and Theater Deployable Communications provide terrestrial connectivity for theatre based operations. The Airborne Network is engineered to utilize all airborne assets to connect with space and surface networks building a seamless communications platform across all domains. === Capabilities === The capabilities identified by this type of system are vastly beyond that of our current military. This system will enable the Air Force to provide a transportable network, flexible enough to communicate with any air, space, or ground asset in the area. The network will provide a beyond line-of-sight (LoS) communications infrastructure that can be packed up and moved in and out of the designated battlespace, enabling the military to have a reliable and secure communications network that extends globally. The network is designed to be flexible enough to provide the right communication and network packages for a specific region, mission, or technology. Operationally, The AN is designed to be self-forming, self-organizing, and self-generating, with nodes joining and leaving the network as they enter and exit a specific region. The network consists of dedicated tactical links, wideband air-to-air links, and ad hoc networks constructed by the Joint Tactical Radio System (JTRS) networking services. JTRS is a software-defined radio that will work with many existing military and civilian radios. It includes integrated encryption and Wideband Networking Software to create mobile ad hoc networks. It also provides system performance analysis and fault diagnostics automatically, reducing the demand for human intervention and network maintenance. === Intended Use === The AN was designed as the cornerstone for the new military doctrine known as Network Centric Warfare. This doctrine was developed to use information superiority to equip warfighters with more precise information enabling commanders and shooters to make smarter decisions faster. The AN contributes to Network Centric Warfare by enabling commanders to provide real-time information to warfighters in the air and on the ground. Warfighters can then utilize more information and make more educated decisions about how to act in a particular situation. Once the act has been carried out commanders will have immediate information about the result and can make judgments on how to continue. All-in-all the AN was designed to reduce the time necessary to identify a target, make clear and educated decisions to pull or not to pull the trigger, and assess battle == Topologies == There are four main network topologies that will be deployed and vary based on the placement of backbone and subnet class networks. === Space, Air, Ground Tether === Establishing a direct connection to another aircraft or ground node, via a point-to-point link for nodes within LOS or via a Satellite Communications (SATCOM) link for nodes that are beyond line-of-sight is known as tethering. SATCOM links provide connectivity to a network ground entry point. Strike aircraft that accompany C2 aircraft such as an AWACS are tethered via point-to-point links. Finally, C2 or intelligence, surveillance, and reconnaissnce (ISR) aircraft may connect via a LOS link directly to a network ground entry point. Each of these tethered alternatives works exactly like a hub or switch that has an entry point to a larger network and allows their connected users access to that network. === Flat Ad Hoc === A flat ad hoc topology refers to establishing nonpersistent network connections as needed among AN nodes that are present at a given time. With this network the nodes dynamically “discover” other nodes to which they can interconnect and form the network. The specific interconnections between the nodes are not planned in advance, but are made as opportunities arise. The nodes join and leave the network at will, continually changing connections to neighbor nodes based upon their location and mobility characteristics. === Tiered Ad Hoc === Ad hoc networks can be flat in the sense that all nodes are peers of each other in a single network, as discussed above, or they can dynamically organize themselves into hierarchical tiers such that higher tiers are used to move data between more localized subnets. This network topology can be compared to any conventional deployed network that utilizes routers, switches, and hubs to temporarily connect users. === Persistent Backbone === A network topology characterized by a persistent backbone is established using relatively persistent wideband connections among high-value platforms flying relatively stable orbits. It provides the connectivity between the tactical subnets which are considered edge networks relative to the backbone. This provides concentration points for connectivity to the space backbone as well as to terrestrial networks. This type of network topology is comparable to a conventional permanent network with established data trunks, routers, switches, and hubs to connect users. == Architecture == === Network Management === The platform management system enables operators to manage all on-board network elements. It interfaces and interoperates with the Airborne Network management system to enable operators to manage remote network elements in the airborne network. The network management system monitors the health of the network by passively testing the network for faults and latency. The system will also actively troubleshoot faults with probes to identify and isolate faulty connections, and enables operators to apply network parameters and security changes to all systems based on the status of the network. === Routing/Switching === Routing and switching enables data to be dynamically transmitted over the network to other nodes. Routing protocols must be able to identify nodes transmitted within their own platform and data to be sent to other platforms regardless of the current topology. The routing protocol must also provide seamless roaming by ensuring that no routed packets are lost when a node changes its point of attachment to the network. Maintaining scalability is important in routing as the network is constantly changing. The network must be able to function with numerous levels of platforms, varying numbers of fast moving platforms, and varying amounts of traffic per platform. Routers and switches will use metrics to determine the best paths to take when routing data. The routing protocol utilized for the AN will be an Adaptive Quality of Service routing protocol. === Gateways/Proxies === Gateways and proxies enable the connection numerous technology types regardless of age to communicate across the IP-based network. Gateways and proxies are essential in the operation of this network because so many different technologies are used to communicate in each domain. These systems will facilitate the transition of the legacy on-board infrastructure, transmission systems, tactical data link systems, and user applications to the objective airborne network systems. Therefore, they are only temporary until all platforms use a standardized IP radio for transmission. === Performance Enhancing Proxies === Performance Enhancing Proxies improve the performance of user applications running across the Airborne Network by countering wireless network impairments, such as limited bandwidth, long delays, high loss rates, and disruptions in network connections. Proxy systems are implemented between the user application and the network and can be used to improve performance at the application and transport functional layers of the OSI model. Some techniques that can be employed include: Compression: Data compression or header compression can be used to minimize the number of bits sent over the network. Data bundling: Smaller data packets can be combined (bundled) into a single large packet for transmission over the network. Caching: A local cache can be used to save and provide data objects that are requested multiple times, reducing transmissions over the network (and improving response times). Store and forward: Message queuing can be used to ensure message delivery to users who become disconnected from the network or are unable to connect to the network for a period of time. Once the platform connects, the stored messages are sent. Pipelining: Rather than opening several separate network connections pipelining can be used to share a single networ

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  Clipping (computer graphics)

  Clipping, in the context of computer graphics, is a method to selectively enable or disable rendering operations within a defined region of interest. Mathematically, clipping can be described using the terminology of constructive geometry. A rendering algorithm only draws pixels in the intersection between the clip region and the scene model. Lines and surfaces outside the view volume (aka. frustum) are removed. Clip regions are commonly specified to improve render performance. Pixels that will be drawn are said to be within the clip region. Pixels that will not be drawn are outside the clip region. More informally, pixels that will not be drawn are said to be "clipped." == In 2D graphics == In two-dimensional graphics, a clip region may be defined so that pixels are only drawn within the boundaries of a window or frame. Clip regions can also be used to selectively control pixel rendering for aesthetic or artistic purposes. In many implementations, the final clip region is the composite (or intersection) of one or more application-defined shapes, as well as any system hardware constraints In one example application, consider an image editing program. A user application may render the image into a viewport. As the user zooms and scrolls to view a smaller portion of the image, the application can set a clip boundary so that pixels outside the viewport are not rendered. In addition, GUI widgets, overlays, and other windows or frames may obscure some pixels from the original image. In this sense, the clip region is the composite of the application-defined "user clip" and the "device clip" enforced by the system's software and hardware implementation. Application software can take advantage of this clip information to save computation time, energy, and memory, avoiding work related to pixels that aren't visible. == In 3D graphics == In three-dimensional graphics, the terminology of clipping can be used to describe many related features. Typically, "clipping" refers to operations in the plane that work with rectangular shapes, and "culling" refers to more general methods to selectively process scene model elements. This terminology is not rigid, and exact usage varies among many sources. Scene model elements include geometric primitives: points or vertices; line segments or edges; polygons or faces; and more abstract model objects such as curves, splines, surfaces, and even text. In complicated scene models, individual elements may be selectively disabled (clipped) for reasons including visibility within the viewport (frustum culling); orientation (backface culling), obscuration by other scene or model elements (occlusion culling, depth- or "z" clipping). Sophisticated algorithms exist to efficiently detect and perform such clipping. Many optimized clipping methods rely on specific hardware acceleration logic provided by a graphics processing unit (GPU). The concept of clipping can be extended to higher dimensionality using methods of abstract algebraic geometry. === Near clipping === Beyond projection of vertices & 2D clipping, near clipping is required to correctly rasterise 3D primitives; this is because vertices may have been projected behind the eye. Near clipping ensures that all the vertices used have valid 2D coordinates. Together with far-clipping it also helps prevent overflow of depth-buffer values. Some early texture mapping hardware (using forward texture mapping) in video games suffered from complications associated with near clipping and UV coordinates. === Occlusion clipping (Z- or depth clipping) === In 3D computer graphics, "Z" often refers to the depth axis in the system of coordinates centered at the viewport origin: "Z" is used interchangeably with "depth", and conceptually corresponds to the distance "into the virtual screen." In this coordinate system, "X" and "Y" therefore refer to a conventional cartesian coordinate system laid out on the user's screen or viewport. This viewport is defined by the geometry of the viewing frustum, and parameterizes the field of view. Z-clipping, or depth clipping, refers to techniques that selectively render certain scene objects based on their depth relative to the screen. Most graphics toolkits allow the programmer to specify a "near" and "far" clip depth, and only portions of objects between those two planes are displayed. A creative application programmer can use this method to render visualizations of the interior of a 3D object in the scene. For example, a medical imaging application could use this technique to render the organs inside a human body. A video game programmer can use clipping information to accelerate game logic. For example, a tall wall or building that occludes other game entities can save GPU time that would otherwise be spent transforming and texturing items in the rear areas of the scene; and a tightly integrated software program can use this same information to save CPU time by optimizing out game logic for objects that aren't seen by the player. == Algorithms == Line clipping algorithms: Cohen–Sutherland Liang–Barsky Fast-clipping Cyrus–Beck Nicholl–Lee–Nicholl Skala O(lg N) algorithm Polygon clipping algorithms: Greiner–Hormann Sutherland–Hodgman Weiler–Atherton Vatti Rendering methodologies Painter's algorithm

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  Corporate surveillance

  Corporate surveillance describes the practice of businesses monitoring and extracting information from their users, clients, or staff. This information may consist of online browsing history, email correspondence, phone calls, location data, and other private details. Acts of corporate surveillance frequently look to boost results, detect potential security problems, or adjust advertising strategies. These practices have been criticized for violating ethical standards and invading personal privacy. Critics and privacy activists have called for businesses to incorporate rules and transparency surrounding their monitoring methods to ensure they are not misusing their position of authority or breaching regulatory standards. Monitoring can feel intrusive and give the impression that the business does not promote ethical behavior among its personnel. Staff satisfaction, productivity, and staff turnover may all suffer as a result of the invasion of privacy. == Monitoring methods == Employers may be authorized to gather information through keystroke logging and mouse tracking, which involves recording the keys individuals interact with and cursor position on computers. In cases where employment contracts permit it, they may also monitor webcam activity on company-provided computers. Employers may be able to view the emails sent from business accounts and may be able to see the websites visited when using a corporate internet connection. The screenshot capability is another tool that enables companies to see what remote workers are doing. This feature, which can be found in tracking software, takes screenshots throughout the day at predetermined or arbitrary intervals. Additionally, people who don't work in offices are observed. For instance, it has been claimed that Amazon has incorporated tracking technology to monitor warehouse staff and delivery drivers. == Use of collected information == Information collected by corporations can be used for a variety of uses including marketing research, targeting advertising, fraud detection and prevention, ensuring policy adherence, preventing lawsuits, and safeguarding records and company assets. == Privacy concerns == Concerns over corporate privacy have become more important due to companies collection and manipulation of personal data. Since these practices have been recognized there has been a rising concern about both the security and the possible mishandling of the data accumulated. Social Media data collection and monitoring has been one of the most concerned areas regarding corporate surveillance. Recently, many employers on CareerBuilder have checked their potential candidates' social media activities before the hiring process. This approach can be excusable since it is important to be aware of a future employee or applicant's online presence, and how it might affect the company's reputation in the future. This is crucial since employers are often made legally responsible for their worker's digital actions. These data can also be used to enact political gains. The Facebook-Cambridge Analytica data scandal in 2018 revealed that its British branch to have surreptitiously sold American psychological data to the Trump campaign. This information was supposed to be private, but Facebook's inability to protect user information had reportedly not been a top priority of the company at the time. == Laws and regulations == The National Labor and Relations Act (NLRA) safeguards workplace democracy by giving workers in the private sector the basic freedom to demand better working conditions and choice of representation without fear of retaliation. General Data Protection Regulation (GDPR) outlines the broad responsibilities of data controllers and the "processors" that handle personal data on their behalf. They must adopt the necessary security measures in accordance with the risk involved in the data processing operations they carry out.[1] Electronics Communication Privacy Act (ECPA), as amended, provides protection for electronic, oral, and wire communications while they are being created, while they are being sent, and while they are being stored on computers. Email, phone calls, and electronically stored data are covered by the Act. == Sale of customer data == If it is business intelligence, data collected on individuals and groups can be sold to other corporations, so that they can use it for the aforementioned purpose. It can be used for direct marketing purposes, such as targeted advertisements on Google and Yahoo. These ads are tailored to the individual user of the search engine by analyzing their search history and emails (if they use free webmail services). For example, the world's most popular web search engine stores identifying information for each web search. Google stores an IP address and the search phrase used in a database for up to 2 years. Google also scans the content of emails of users of its Gmail webmail service, in order to create targeted advertising based on what people are talking about in their personal email correspondences. Google is, by far, the largest web advertising agency. Their revenue model is based on receiving payments from advertisers for each page-visit resulting from a visitor clicking on a Google AdWords ad, hosted either on a Google service or a third-party website. Millions of sites place Google's advertising banners and links on their websites, in order to share this profit from visitors who click on the ads. Each page containing Google advertisements adds, reads, and modifies cookies on each visitor's computer. These cookies track the user across all of these sites, and gather information about their web surfing habits, keeping track of which sites they visit, and what they do when they are on these sites. This information, along with the information from their email accounts, and search engine histories, is stored by Google to use for building a profile of the user to deliver better-targeted advertising. == Surveillance of workers == In 1993, David Steingard and Dale Fitzgibbons argued that modern management, far from empowering workers, had features of neo-Taylorism, where teamwork perpetuated surveillance and control. They argued that employees had become their own "thought police" and the team gaze was the equivalent of Bentham's panopticon guard tower. A critical evaluation of the Hawthorne Plant experiments has in turn given rise to the notion of a Hawthorne effect, where workers increase their productivity in response to their awareness of being observed or because they are gratified for being chosen to participate in a project. According to the American Management Association and the ePolicy Institute, who undertook a quantitative survey in 2007 about electronic monitoring and surveillance with approximately 300 US companies, "more than one fourth of employers have fired workers for misusing email and nearly one third have fired employees for misusing the Internet." Furthermore, about 30 percent of the companies had also fired employees for usage of "inappropriate or offensive language" and "viewing, downloading, or uploading inappropriate/offensive content." More than 40 percent of the companies monitor email traffic of their workers, and 66 percent of corporations monitor Internet connections. In addition, most companies use software to block websites such as sites with games, social networking, entertainment, shopping, and sports. The American Management Association and the ePolicy Institute also stress that companies track content that is being written about them, for example by monitoring blogs and social media, and scanning all files that are stored in a filesystem. == Government use of corporate surveillance data == The United States government often gains access to corporate databases, either by producing a warrant for it, or by asking. The Department of Homeland Security has openly stated that it uses data collected from consumer credit and direct marketing agencies—such as Google—for augmenting the profiles of individuals that it is monitoring. The US government has gathered information from grocery store discount card programs, which track customers' shopping patterns and store them in databases, in order to look for terrorists by analyzing shoppers' buying patterns. == Corporate surveillance of citizens == According to Dennis Broeders, "Big Brother is joined by big business". He argues that corporations are in any event interested in data on their potential customers and that placing some forms of surveillance in the hands of companies, results in companies owning video surveillance data for stores and public places. The commercial availability of surveillance systems has led to their rapid spread. Therefore it is almost impossible for citizens to maintain their anonymity. When businesses can monitor their customers, such customers run the risk of facing prejudice when applying for housing, loans, jobs, and other economic opportun

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  ServerNet

  ServerNet is a switched fabric communications link primarily used in proprietary computers made by Tandem Computers, Compaq, and HP. Its features include good scalability, clean fault containment, error detection and failover. The ServerNet architecture specification defines a connection between nodes, either processor or high performance I/O nodes such as storage devices. == History == Tandem Computers developed the original ServerNet architecture and protocols for use in its own proprietary computer systems starting in 1992, and released the first ServerNet systems in 1995. Early attempts to license the technology and interface chips to other companies failed, due in part to a disconnect between the culture of selling complete hardware / software / middleware computer systems and that needed for selling and supporting chips and licensing technology. A follow-on development effort ported the Virtual Interface Architecture to ServerNet with PCI interface boards connecting personal computers. Infiniband directly inherited many ServerNet features. As of 2017, systems still ship based on the ServerNet architecture.

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  SIGINT Activity Designator

  A SIGINT Activity Designator (or SIGAD) identifies a signals intelligence (SIGINT) line of collection activity associated with a signals collection station, such as a base or a ship. For example, the SIGAD for Menwith Hill in the UK is USD1000. SIGADs are used by the signals intelligence agencies of Australia, Canada, New Zealand, the United Kingdom, and the United States (the Five Eyes). There are several thousand SIGADs including the substation SIGADs denoted with a trailing alpha character. Several dozen of these are significant. The leaked Boundless Informant reporting screenshot showed that it summarized 504 active SIGADs during a 30-day period in March 2013. == General format == A SIGAD consists of five to eight case insensitive alphanumeric characters. It takes the general form of an alphanumeric designator normally composed of a two- or three-letter prefix followed by one to three numbers. Often a dash is used to separate the alphabetic and numeric characters in the primary part of the designator, but less frequently a space is used as a separator or the alphabetic and numeric characters are concatenated together. An additional alphabetic character can be added to denote a sub-designator for a subset of the primary unit, such as a detachment. Lastly, a numeric character can be added after the aforementioned alphabetic to provide for a sub-sub-designator. In the examples below an X represents an alphabetic character and an N represents a numeric character that are part of the primary designator. Likewise, an x represents an alphabetic character and an n represents a numeric character that are part of a sub-designator. Here are valid generalized examples of SIGADs: The first two characters show which country operates the particular SIGINT facility, which can be US for the United States, UK for the United Kingdom, CA for Canada, AU for Australia and NZ for New Zealand. A third letter shows what sort of staff runs the station. SIGADs beginning with US without a third letter are used for intercept facilities run by the NSA. == PRISM SIGAD == One prominent SIGAD as of April 2013 is US-984XN, with an unclassified codename of PRISM. It is "the number one source of raw intelligence used for NSA analytic reports" according to National Security Agency sources in a document leaked by Edward Snowden. The President's Daily Brief, an all-source intelligence product, cited SIGAD US-984XN as a source in 1,477 items in 2012. The U.S. government operates the PRISM electronic surveillance collection program through NSA's Special Source Operations, an alliance with trusted telecommunications providers. == SIGADs for spy ships == The declassified SIGAD for the USS Liberty (AGTR-5) was USN-855. The USS Liberty incident occurred on 8 June 1967, during the Six-Day War, when Israeli Air Force jet fighter aircraft and Israeli Navy motor torpedo boats attacked the USS Liberty in international waters. The USS Pueblo (AGER-2) was a technical research ship, which was boarded and captured by North Korean forces on 23 January 1968, in what is known as the Pueblo incident. The declassified SIGAD for the NSA Direct Support Unit (DSU) from the Naval Security Group (NSG) on the USS Pueblo patrol involved in the incident was USN-467Y. The USS Pueblo, which officially remains a commissioned vessel of the United States Navy, is the only ship of the U.S. Navy currently being held captive. == Vietnam War SIGADs == The following are the Vietnam War-era declassified SIGADs from inside South Vietnam during the period of 1969 to 1975: Some locations have multiple SIGADs due to different types of collection activities and/or collection at different times during the period. The SIGADs beginning with USA were operated by the United States Air Force's United States Air Force Security Service (USAFSS). The SIGADs beginning with USM were operated by the United States Army's Army Security Agency (ASA). Lastly, the SIGADs beginning with USN were operated by the United States Navy's Naval Security Group (NAVSECGRU). All three of these units have been merged into other units or inactivated. The above list consists of the higher-echelon SIGADs. It does not include the numerous miscellaneous and temporary detachments, or direction finding stations belonging to major units or sites unless that detachment or site was the only one stationed in South Vietnam. Many of the "dets" were short-lived, often formed to support ongoing MACV operations or forward deployments of combat operational or maneuver units. These detachments usually were designated by a letter suffix attached to the higher-echelon SIGAD such as "USM-633J," which was a detachment of the 372d Radio Research Company, USM-633, supporting the United States Army's 25th Infantry Division. === Supporting Southeast Asia SIGADs === The following declassified SIGADs were highly relevant to the Vietnam Campaign, but were located in areas outside of South Vietnam in Southeast Asia. Again, detachments are not listed separately. In the case of the USS Maddox, naval Direct Support Units (DSUs) used the SIGAD USN-467 as a generic designator for their missions. Each specific patrol received a letter suffix for its duration. The subsequent mission would receive the next letter in an alphabetic sequence. Thus, SIGAD USN-467N specifically designates the USS Maddox patrol involved with the Gulf of Tonkin incident. == Joint Base SIGADs == In November 2005, the US Congress performed a fifth round of Base Realignment and Closure. This 2005 law also created twelve joint bases by merging adjacent installations belonging to different services in an effort to reduce costs and improve efficiencies. Joint bases with a primarily SIGINT mission have SIGADs that begin with USJ. A joint base would have a primary SIGAD in the general form of USJ-NNN, where NNN are numeric characters. An actual example is not given, since these units are currently active.

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  Actifsource

  Actifsource is a domain-specific modeling workbench. It is realized as plug-in for the software development environment Eclipse. Actifsource supports the creation of multiple domain models which can be linked together. It comes with a UML-like graphical editor to create domain-specific languages and a general graphical editor to edit structures in the created languages. It supports code generation using user-defined generic code templates which are directly linked to the domain models. Code generation is integrated into Eclipse's incremental build process. == Interoperability == Actifsource can use models from other modelling tools by importing and exporting the ecore format which is defined by the Eclipse Modeling Framework. == Licensing policy == There are two versions of actifsource available: The free community edition which can be used freely for non-commercial projects and the enterprise edition which contains additional features. The enterprise edition comes with customer support and maintenance for a limited period of time. This package allows the customers to upgrade to new versions and maintenance releases during their support period.

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  Social media surgery

  A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".

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  Forward anonymity

  Forward anonymity is a property of a cryptographic system which prevents an attacker who has recorded past encrypted communications from discovering its contents and participants in the future. This property is analogous to forward secrecy. An example of a system which uses forward anonymity is a public key cryptography system, where the public key is well-known and used to encrypt a message, and an unknown private key is used to decrypt it. In this system, one of the keys is always said to be compromised, but messages and their participants are still unknown by anyone without the corresponding private key. In contrast, an example of a system which satisfies the perfect forward secrecy property is one in which a compromise of one key by an attacker (and consequent decryption of messages encrypted with that key) does not undermine the security of previously used keys. Forward secrecy does not refer to protecting the content of the message, but rather to the protection of keys used to decrypt messages. == History == Originally introduced by Whitfield Diffie, Paul van Oorschot, and Michael James Wiener to describe a property of STS (station-to-station protocol) involving a long term secret, either a private key or a shared password. == Public Key Cryptography == Public Key Cryptography is a common form of a forward anonymous system. It is used to pass encrypted messages, preventing any information about the message from being discovered if the message is intercepted by an attacker. It uses two keys, a public key and a private key. The public key is published, and is used by anyone to encrypt a plaintext message. The Private key is not well known, and is used to decrypt cyphertext. Public key cryptography is known as an asymmetric decryption algorithm because of different keys being used to perform opposing functions. Public key cryptography is popular because, while it is computationally easy to create a pair of keys, it is extremely difficult to determine the private key knowing only the public key. Therefore, the public key being well known does not allow messages which are intercepted to be decrypted. This is a forward anonymous system because one compromised key (the public key) does not compromise the anonymity of the system. == Web of Trust == A variation of the public key cryptography system is a Web of trust, where each user has both a public and private key. Messages sent are encrypted using the intended recipient's public key, and only this recipient's private key will decrypt the message. They are also signed with the senders private key. This creates added security where it becomes more difficult for an attacker to pretend to be a user, as the lack of a private key signature indicates a non-trusted user. == Limitations == A forward anonymous system does not necessarily mean a wholly secure system. A successful cryptanalysis of a message or sequence of messages can still decode the information without the use of a private key or long term secret. == News == Forward anonymity, along with other privacy-protecting measures, received a burst of media attention after the leak of classified information by Edward Snowden, beginning in June, 2013, which indicated that the NSA and FBI, through specially crafted backdoors in software and computer systems, were conducting mass surveillance over large parts of the population of both the United States (see Mass surveillance in the United States), Europe, Asia, and other parts of the world. They justified this practice as an aid to catch predatory pedophiles. Opponents to this practice argue that leaving in a back door to law enforcement increases the risk of attackers being able to decrypt information, as well as questioning its legality under the US Constitution, specifically being a form of illegal Search and Seizure.

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