AI Data Delivery F5

AI Data Delivery F5 — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Semi-automation

  Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.

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  Data security

  Data security or data protection is the process of securing digital information to protect it from online threats. Data security or protection means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. Data security protects computer hardware, software, storage devices, and the data of user devices. Data security also protects the data of organizations, companies and administrative controls. Data security guarantees the protection of individual data, such as identity documents and bank data, and protects against unauthorized access, theft and loss of individual data. Data security also protects data breaches that occurs in companies and industries. Good security measures in industries reduce the probability of data breaches, and employees can rely on the company with their data and private information to be kept secured while companies can continue to maintain a stable reputation. The CIA Triad (Confidentiality, Integrity, and Availability) is what is used to practice what an information security is required to follow. Confidentiality, protects information from being accessed by unauthorized persons. Integrity, makes sure data is trustworthy; and Availability, meaning that data can be accessed by approved users when it is needed; are three goals for data security. Non-repudiation in data security definition, is a device/service that shows where the data originated from and the proof of integrity. == Technologies == === Disk encryption === Disk encryption refers to encryption technology that encrypts data on a hard disk drive. It takes data from a storage device and coverts it into an unreadable format. Disk encryption typically takes form in either software (see disk encryption software) or hardware (see disk encryption hardware) which can be used together. Disk encryption is often referred to as on-the-fly encryption (OTFE) or transparent encryption. Full disk encryption encrypts each individual sector of a disk volume. Files and user data are encrypted to hinder unauthorized users from accessing without a decryption key. A diversifier permits a plaintext of a specific disk sector to be encrypted into different ciphertexts, which does not require additional storage, such as an initialization vector (IV) or message authentication code (MAC). === Software versus hardware-based mechanisms for protecting data === Software-based security solutions encrypt the data to protect it from theft. However, a malicious program or a hacker could corrupt the data to make it unrecoverable, making the system unusable. Hardware-based security solutions prevent read and write access to data, which provides very strong protection against tampering and unauthorized access. Hardware-based security or assisted computer security offers an alternative to software-only computer security. Security tokens such as those using PKCS#11 or a mobile phone may be more secure due to the physical access required in order to be compromised. Access is enabled only when the token is connected and the correct PIN is entered (see two-factor authentication). However, dongles can be used by anyone who can gain physical access to it. Newer technologies in hardware-based security solve this problem by offering full proof of security for data. Working off hardware-based security: A hardware device allows a user to log in, log out and set different levels through manual actions. Many devices use biometric technology to prevent malicious users from logging in, logging out, and changing privilege levels. The current state of a user of the device is read by controllers in peripheral devices such as hard disks. Illegal access by a malicious user or a malicious program is interrupted based on the current state of a user by hard disk and DVD controllers making illegal access to data impossible. Hardware-based access control is more secure than the protection provided by the operating systems as operating systems are vulnerable to malicious attacks by viruses and hackers. The data on hard disks can be corrupted after malicious access is obtained. With hardware-based protection, the software cannot manipulate the user privilege levels. A hacker or a malicious program cannot gain access to secure data protected by hardware or perform unauthorized privileged operations. This assumption is broken only if the hardware itself is malicious or contains a backdoor. The hardware protects the operating system image and file system privileges from being tampered with. Therefore, a completely secure system can be created using a combination of hardware-based security and secure system administration policies. === Backups === Backup is the process of reproducing copies of essential data and storing in a separate, secured place. It is used to ensure data that is lost can be recovered from another source. Backups contains a minimum of one copy of the data that requires preservation. It is considered essential to keep a backup of any data in most industries and the process is recommended for any files of importance to a user. There are 3 types of backups; full backups, incremental backups, and differential backups. Full backups secure all data from a production system, such as a server, database, or other connected data source. It is impossible to lose all data in a full backup if a breach or corruption were to occur. Full backups require a significantly large amount of time to back up and may be time-consuming taking hours to days to complete. Incremental backups only secures changed data since last backup. While all backups are done in full backups, incremental backups only save data that is recently or frequently changed. Incremental backups require lower storage costs making it a prominent solution for growing datasets. === Data Privacy === Data privacy (or information privacy) is the right for individual's data to be secured to obstruct the use of unauthorized access. It gives individuals control over their data and how it can be shared to third parties. The U.S Privacy Protection Law (see Privacy laws of the United States) requires organizations to inform individuals of how their data is collected and when a data breach occurs. By implementing an encryption, it ensures that private data is unreadable to cybercriminals. === Data masking === Data masking of structured data is the process of obscuring (masking) specific data within a database table or cell to ensure that data security is maintained and sensitive information is not exposed to unauthorized personnel. This may include masking the data from users (for example so banking customer representatives can only see the last four digits of a customer's national identity number), developers (who need real production data to test new software releases but should not be able to see sensitive financial data), outsourcing vendors, etc. Data masking is a form of encryption, as it obscures data by modifying particular letters and numbers to keep data concealed and protected from potential hackers. The individual that has access to the code that decrypts the replaced characters are the only ones that can uncover the data. === Data erasure === Data erasure (or data deletion, data destruction) is a method of software-based overwriting that permanently clears all electronic data residing on a hard drive or other digital media to ensure that no sensitive data is lost when an asset is retired or reused. Article 17: Right to be Forgotten states that users have the right to permanently remove all of their private information from their old devices/services to give people more control over their data. Users are able to switch between devices efficiently. == Threats == === Malware === Malware (or malicious software) is designed to destroy, corrupt or gain unauthorized access to a computer for the purpose of stealing, or destroying data. Hackers who use malware typically utilize many types of malware, which includes computer virus, computer worms, ransomware, spyware and Trojan horse to create a vast system of disruption and cause easy data theft. One of the victims of the vast system of disruption includes healthcare workers, who are targeted by compromised systems by infections and then having their data attacked. === Phishing === Phishing is a type of scam that allows hackers to hoax people using psychological and social engineering (using human emotions such as their trust and fear) tactics into giving personal data through emails and messages, and install computer viruses if the individual were to click on a malicious link unknowingly. Attackers are able to create websites that are very similar to original websites, which makes it difficult to detect a fake website, causing individuals to fall for giving in information. Phishing attackers use human emotion to exploit them, such as making them feel fear, urgency, sympathy with the message

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  Personal network

  A personal network is a set of human contacts known to an individual, with whom that individual would expect to interact at intervals to support a given set of activities. In other words, a personal network is a group of caring, dedicated people who are committed to maintain a relationship with a person in order to support a given set of activities. Having a strong personal network requires being connected to a network of resources for mutual development and growth. Personal networks can be understood by: who knows you what you know about them what they know about you what are you learning together how you work at that Personal networks are intended to be mutually beneficial, extending the concept of teamwork beyond the immediate peer group. The term is usually encountered in the workplace, though it could apply equally to other pursuits outside work. Personal networking is the practice of developing and maintaining a personal network, which is usually undertaken over an extended period. The concept is related to business networking and is often encouraged by large organizations, in the hope of improving productivity, and so a number of tools exist to support the maintenance of networks. Many of these tools are IT-based, and use Web 2.0 technologies. == History of networking and business success == In the second half of the twentieth century, U.S. advocates for workplace equity popularized the term and concept of networking as part of a larger social capital lexicon—which also includes terms such as glass ceiling, role model, mentoring, and gatekeeper—serving to identify and address the problems barring non-dominant groups from professional success. Mainstream business literature subsequently adopted the terms and concepts, promoting them as pathways to success for all career climbers. In 1970 these terms were not in the general American vocabulary; by the mid-1990s they had become part of everyday speech. Before the mid-twentieth century, what we call networking today was framed in the language of family and friendship. These close personal relationships provided a range of opportunities to preferred subsets of people, such as access to job opportunities, information, credit, and partnerships. Family networks and nepotism have proven particularly strong throughout history. However, other common bonds—from ethnicity and religion to school ties and club memberships—can connect subsets of people as well. Of course people whom insiders consider undesirable have been barred from such networks, with important consequences. Those who tap into influential networks can be nurtured toward success. Those who are shut out from networks can lose hope of success. Numerous business heroes of the past—such as Benjamin Franklin, Andrew Carnegie, Henry Ford, and John D. Rockefeller—exploited networks to great effect. The business networks that seemed natural and transparent to these white men were a closed book to women and minorities for much of American history. Drawing on work from the social sciences, these outsider groups had to identify and then harness the mechanisms behind networking's power. A prominent early example of this process was the formation of corporate caucuses by black men at Xerox starting in 1969. Groups of black salesmen met regularly to share information about Xerox's culture and strategies for navigating it most effectively. Through confrontation and collaboration with a relatively accommodating upper management, the caucuses helped open opportunities for high-performing black employees. The popular and business press began using the terms "network" and "networking" in the mid-1970s in the context of businesswomen consciously pursuing this strategy. Authors encouraged female workers to recognize and exploit the informal workplace systems that provided advancement. They urged women to identify mentors, use social contacts, and build peer and authority networks. The push for networking drew on ideas and relationships from the era's feminist movement, and dictionaries of the time explicitly linked business networking to women's efforts to succeed in the workplace. Since the closing decades of the twentieth century, networking has become a pervasive term and concept in American society. People now invoke networking in relation to everything from business to child rearing to science. While ambitious careerists seek networks as an indispensable talisman, companies purposefully encourage networking among their employees to boost performance and gain competitive advantage. At the same time, Americans are forgetting the workplace activism that first illuminated the power of networking. Unfortunately, this loss of historical context can fuel a backlash against outsider groups who still seek to synthesize networks so they can access the same opportunities enjoyed by insiders. == Characteristics of networks == Broadly speaking, all networks have the following characteristics: Purpose – A network can be established for learning, mission, business, idea, and family or personal reasons. Structure – A network is a group of interlinked entities that form a cluster. Most social structures tend to be characterized by dense clusters of strong connections. Style – The place, space, pace and style of interaction of the networks give an understanding of the style of the networks. Namkee Park, Seungyoon Lee and Jang Hyun Kim examined the relations between personal network characteristics and Facebook use. According to their study, personal networks are investigated through several structural characteristics, which can be categorized into three major dimensions according to the level of analysis: Dyadic tie attributes which include the characteristics of ego-alter ties such as duration, multiplexity, and proximity. Ego-alter tie attributes represent various dimensions of relationships between the focal person and their close contacts. First, tie duration refers to the length of time since the tie was originally initiated, which indicates the duration of relationships. Second, multiplexity includes a focal individual's degree of involvement in various types of interactions with network members. The third dimension is the physical proximity between ego and alter. Theories of proximity suggest that physical proximity between people affects their interaction and subsequently, their formation of network ties. The characteristics of alter-alter ties including personal network density. When moving to ties at the alter-alter level, ego-network density, which refers to the extent to which one's alters are connected with each other, is an important dimension of personal networks. Dense personal network structure indicates close interpersonal contacts among alters, and consequently, is considered to promote the sharing of resources. On the other hand, loose connections, or structural holes in ego-networks, have been found to facilitate the flow of information and to provide advantages in searching and obtaining resources (e.g., getting a job). The composition of alter attributes centered on the heterogeneity of alters in one's personal network. The heterogeneity of alters in one's personal network is associated with access to diverse resources and information It is expected, thus, that the heterogeneity attributes may enhance the focal actor's social activities. Each of these characteristics represents unique aspects of individuals' network relationships. == Types of personal networks == Personal networks can be used for two main reasons: social and professional. In 2012, LinkedIn along with TNS conducted a survey of 6,000 social network users to understand the difference between personal social networks and personal professional networks. The "Mindset Divide" of users of these networks was compared as follows: Emotions: Personal social networks: Nostalgia, fun, distraction. Personal professional networks: Achievement, success, aspiration. Use: Personal social networks: Users are in a casual mindset often just passing time. They use social networks to socialize, stay in touch, be entertained and kill time. Personal professional networks: In this purposeful mindset, users invest time to improve themselves and their future. These networks are used to maintain professional identity, make useful contacts, search for opportunities and stay in touch. Content: Personal professional networks: These provide information about career, brand updates and current affairs. Professional development: Personal development networks: These provide access to those who can provide information, knowledge, advice, support, expertise, guidance, and concrete resources to learn and work effectively—thus those who support the continuing professional development. == Personal network management == Personal network management (PNM) is a crucial aspect of personal information management and can be understood as the practice of managing the links and connections for social and profession

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  Hyper-encryption

  Hyper-encryption is a form of encryption invented by Michael O. Rabin which uses a high-bandwidth source of public random bits, together with a secret key that is shared by only the sender and recipient(s) of the message. It uses the assumptions of Ueli Maurer's bounded-storage model as the basis of its secrecy. Although everyone can see the data, decryption by adversaries without the secret key is still not feasible, because of the space limitations of storing enough data to mount an attack against the system. Unlike almost all other cryptosystems except the one-time pad, hyper-encryption can be proved to be information-theoretically secure, provided the storage bound cannot be surpassed. Moreover, if the necessary public information cannot be stored at the time of transmission, the plaintext can be shown to be impossible to recover, regardless of the computational capacity available to an adversary in the future, even if they have access to the secret key at that future time. A highly energy-efficient implementation of a hyper-encryption chip was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be ~205 times more efficient in terms of Energy-Performance-Product.

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  Render layers

  When creating computer-generated imagery, final scenes appearing in movies and television productions are usually produced by rendering more than one "layer" or "pass," which are multiple images designed to be put together through digital compositing to form a completed frame. Rendering in passes is based on a traditions in motion control photography which predate CGI. As an example, for a visual effects shot, a camera could be programmed to move past a physical model of a spaceship in one pass to film the fully lit beauty pass of the ship, and then to repeat exactly the same camera move passing the ship again to photograph additional elements such as the illuminated windows in the ship or its thrusters. Once all of the passes were filmed, they could then be optically printed together to form a completed shot. The terms render layers and render passes are sometimes used interchangeably. However, rendering in layers refers specifically to separating different objects into separate images, such as a layer each for foreground characters, sets, distant landscape, and sky. On the other hand, rendering in passes refers to separating out different aspects of the scene, such as shadows, highlights, or reflections, into separate images.

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  Convergent encryption

  Convergent encryption, also known as content hash keying, is a cryptosystem that produces identical ciphertext from identical plaintext files. This has applications in cloud computing to remove duplicate files from storage without the provider having access to the encryption keys. The combination of deduplication and convergent encryption was described in a backup system patent filed by Stac Electronics in 1995. This combination has been used by Farsite, Permabit, Freenet, MojoNation, GNUnet, flud, and the Tahoe Least-Authority File Store. The system gained additional visibility in 2011 when cloud storage provider Bitcasa announced they were using convergent encryption to enable de-duplication of data in their cloud storage service. == Overview == The system computes a cryptographic hash of the plaintext in question. The system then encrypts the plaintext by using the hash as a key. Finally, the hash itself is stored, encrypted with a key chosen by the user. == Known Attacks == Convergent encryption is open to a "confirmation of a file attack" in which an attacker can effectively confirm whether a target possesses a certain file by encrypting an unencrypted, or plain-text, version and then simply comparing the output with files possessed by the target. This attack poses a problem for a user storing information that is non-unique, i.e. also either publicly available or already held by the adversary - for example: banned books or files that cause copyright infringement. An argument could be made that a confirmation of a file attack is rendered less effective by adding a unique piece of data such as a few random characters to the plain text before encryption; this causes the uploaded file to be unique and therefore results in a unique encrypted file. However, some implementations of convergent encryption where the plain-text is broken down into blocks based on file content, and each block then independently convergently encrypted may inadvertently defeat attempts at making the file unique by adding bytes at the beginning or end. Even more alarming than the confirmation attack is the "learn the remaining information attack" described by Drew Perttula in 2008. This type of attack applies to the encryption of files that are only slight variations of a public document. For example, if the defender encrypts a bank form including a ten digit bank account number, an attacker that is aware of generic bank form format may extract defender's bank account number by producing bank forms for all possible bank account numbers, encrypt them and then by comparing those encryptions with defender's encrypted file deduce the bank account number. Note that this attack can be extended to attack a large number of targets at once (all spelling variations of a target bank customer in the example above, or even all potential bank customers), and the presence of this problem extends to any type of form document: tax returns, financial documents, healthcare forms, employment forms, etc. Also note that there is no known method for decreasing the severity of this attack -- adding a few random bytes to files as they are stored does not help, since those bytes can likewise be attacked with the "learn the remaining information" approach. The only effective approach to mitigating this attack is to encrypt the contents of files with a non-convergent secret before storing (negating any benefit from convergent encryption), or to simply not use convergent encryption in the first place.

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  Social computing

  Social computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or fostering existing social conventions and social contexts through the use of software and technology. Blogs, email, instant messaging, social network services, wikis, social bookmarking and other instances of what is often called social software illustrate ideas from social computing. The rise in social computing is attributed to the prevalence of personal devices and increased overall computing power. This enables a growing number of users to participate in sharing content and interact with another. == Definitions == Humans—and human behavior—are profoundly social. Humans tend to orient to one another and develop abilities to interact with each other and other species. This ranges from expression and gesture through spoken, written, and body language. Humans are influenced by the behavior of those around them and can rely on social context and cues to make decisions. An example of a behavior relying on social contexts is applauding at the end of the play. This is based on the context that the show ended, and other audience members are applauding. Social information provides a basis for inferences, planning, and coordinating activity. == Examples == Common tools include blogs, email, instant messaging, social networking sites, wikis, and social bookmarking platforms. These technologies enable users to generate content, share knowledge, and interact in real time. == Applications == The rise of social computing has highlighted opportunities for businesses. Businesses are interacting on social computing platforms and investing in facilities to support and research social computing.Business models can leverage the massive customer bases that accumulate through social computing channels. Some organizations have started their own blogs and networks (McAfee, 2006, Joe, 2005). Organizations from diverse industry sectors such as Google, Cisco, and Fox, have sought to acquire or invest in successful social computing enterprises. A business blog can serve as a source of information and promotion for the company. This allows the company to share content about the company and their initiatives. Businesses have also interacted with social computing to market themselves and interact with customers. A notable example is Wendy's with their X (formerly Twitter) account. The account was primarily used to promote business promotions and interact with users in a playful or meaningful way. E-commerce web sites have allowed users to leave reviews and feedback on purchases which has improved online shopping experience for sellers and consumers.As another example of social computing’s business applications, many e-commerce Web sites have adopted online product/vendor feedback/reputation systems. Such systems provide an asynchronous platform for the consumer community to share experiences collectively and influence their purchasing behavior. They also provide a vehicle for eliciting feedback information valuable to the vendors and e-commerce site operators.Consumers can use the feedback systems to make a more educated choice on a purchase by comparing reviews between products or vendors. Sellers can track consumer behaviors and trends regarding a product and adjust their supply according to the demand. == Challenges and criticism == Social computing raises several concerns related to privacy, data security, and algorithmic bias. The widespread collection and analysis of user-generated data can lead to ethical dilemmas, especially when users are unaware of how their information is used. Critics also highlight issues of digital labor, surveillance, and the spread of misinformation, which can influence public opinion and social dynamics. === Term appearance === The term appeared in the mid 1990s after technology advancements and development of the web. In 1994, the concept of social computing was first proposed by Schuler. He thought, "Social computing is a computing application, with software as the medium or focus of social relationships." === Premise === The premise of social computing is that it is possible to design digital systems that support useful functionality by making socially produced information available to their users. This information may be provided directly, as when systems show the number of users who have rated a review as helpful or not. Or the information may be provided after being filtered and aggregated, as is done when systems recommend a product based on what else people with similar purchase history have purchased. Alternatively, the information may be provided indirectly, as is the case with Google's page rank algorithms which orders search results based on the number of pages that (recursively) point to them. In all of these cases, information that is produced by a group of people is used to provide or enhance the functioning of a system. Social computing is concerned with systems of this sort and the mechanisms and principles that underlie them. Social computing can be defined as follows: "Social Computing" refers to systems that support the gathering, representation, processing, use, and dissemination of information that is distributed across social collectivities such as teams, communities, organizations, and markets. Moreover, the information is not "anonymous" but is significantly precise because it is linked to people, who are in turn linked to other people. More recent definitions, however, have foregone the restrictions regarding anonymity of information, acknowledging the continued spread and increasing pervasiveness of social computing. As an example, Hemmatazad, N. (2014) defined social computing as "the use of computational devices to facilitate or augment the social interactions of their users, or to evaluate those interactions in an effort to obtain new information." Social computing has to do with supporting "computations" that are carried out by groups of people, an idea that has been popularized in James Surowiecki's book, The Wisdom of Crowds. Examples of social computing in this sense include collaborative filtering, online auctions, reputation systems, computational social choice, tagging, and verification games. The social information processing page focuses on this sense of social computing. == History == === Technology infrastructure === Users were able to interact more with websites after the development of Web 2.0. This was an advancement from Web 1.0. Comode G. and Krishnamurthy B. (2008) note that "content creators were few in Web 1.0 with the vast majority of users simply acting as consumers of content." Web 2.0 provided functionalities that allowed for low-cost web-hosting services and introduced features with browser windows that used basic information structure and expanded it to as many devices as possible using HTTP, or Hypertext Transfer Protocol. Sometimes referred to as "Enterprise 2.0", a term derived from Web 2.0, social software for enterprise generally refers to the use of social computing in corporate intranets and in other medium- and large-scale business environments. It consisted of a class of tools that allowed for networking and social changes to businesses at the time. It was a layering of the business tools on Web 2.0 and brought forth several applications and collaborative software with specific uses. FinanceElectronic negotiation, which first came up in 1969 and was adapted over time to suit financial markets networking needs, represents an important and desirable coordination mechanism for electronic markets. Negotiation between agents (software agents as well as humans) allows cooperative and competitive sharing of information to determine a proper price. Recent research and practice has also shown that electronic negotiation is beneficial for the coordination of complex interactions among organizations. Electronic negotiation has recently emerged as a very dynamic, interdisciplinary research area covering aspects from disciplines such as Economics, Information Systems, Computer Science, Communication Theory, Sociology and Psychology.Social computing has become more widely known because of its relationship to a number of recent trends. These include the growing popularity of social software and Web 3.0, increased academic interest in social network analysis, the rise of open source as a viable method of production, and a growing conviction that all of this can have a profound impact on daily life. A February 13, 2006 paper by market research company Forrester Research suggested that: === Developments === PLATO was one of the earliest examples of social computing in a live production environment with initially hundreds and soon thousands of users. The PLATO computer system was developed by the University of Illinois at Urbana Champaign in 1960s. In the 70s, the system supported social software applications for multi-us

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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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  Feature (machine learning)

  In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression. == Feature types == In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorical features are discrete values that can be grouped into categories. Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features. == Classification == A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold. Algorithms for classification from a feature vector include nearest neighbor classification, neural networks, and statistical techniques such as Bayesian approaches. == Examples == In character recognition, features may include histograms counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches, logarithmic Mel-scale spectral vectors and Mel-frequency cepstral coefficients, which represent the frequency characteristics of audio signals. In spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text. In computer vision, there are a large number of possible features, such as edges and objects. == Feature vectors == In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms. Feature vectors are equivalent to the vectors of explanatory variables used in statistical procedures such as linear regression. Feature vectors are often combined with weights using a dot product in order to construct a linear predictor function that is used to determine a score for making a prediction. The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. Higher-level features can be obtained from already available features and added to the feature vector; for example, for the study of diseases the feature 'Age' is useful and is defined as Age = 'Year of death' minus 'Year of birth' . This process is referred to as feature construction. Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions {=, ≠}, the arithmetic operators {+,−,×, /}, the array operators {max(S), min(S), average(S)} as well as other more sophisticated operators, for example count(S, C) that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems. Applications include studies of disease and emotion recognition from speech. == Selection and extraction == The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of machine learning and pattern recognition consists of selecting a subset of features, or constructing a new and reduced set of features to facilitate learning, and to improve generalization and interpretability. Extracting or selecting features is a combination of art and science; developing systems to do so is known as feature engineering. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the domain expert. Automating this process is feature learning, where a machine not only uses features for learning, but learns the features itself.

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  Media evaluation

  Media evaluation is a discipline of the external and logical social sciences and centres on the analysis of media content, rating the exposure using a number of pre-designated criteria commonly including tonal value and presence of key messages. It is said to be one of the fastest-growing areas of mass communications research. The International Association for Measurement and Evaluation of Communication (AMEC) is the industry-appointed trade body for companies and individuals involved in research, measurement, and evaluation in editorial media coverage and related communications issues. To be a full member of AMEC, companies must be able to: a) offer comprehensive media evaluation, research, and interpretation services, b) have been in business for at least two years, and c) have a media evaluation turnover of more than £150,000 when applying. In addition, all companies abide by a strict code of ethics and must implement tight quality control procedures. These requirements guarantee that all media evaluation services provided are of the highest caliber. The Commission on Public Relations Measurement & Evaluation is a different organization that was established in 1998 under the direction of the Institute for Public Relations. The Commission's main functions are to set standards and procedures for research and measurement in public relations and to publish authoritative white papers on best practices.

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  POODLE

  POODLE (which stands for "Padding Oracle On Downgraded Legacy Encryption") is a security vulnerability which takes advantage of the fallback to SSL 3.0. If attackers successfully exploit this vulnerability, on average, they only need to make 256 SSL 3.0 requests to reveal one byte of encrypted messages. Bodo Möller, Thai Duong and Krzysztof Kotowicz from the Google Security Team discovered this vulnerability; they disclosed the vulnerability publicly on October 14, 2014 (despite the paper being dated "September 2014"). On December 8, 2014, a variation of the POODLE vulnerability that affected TLS was announced. The CVE-ID associated with the original POODLE attack is CVE-2014-3566. F5 Networks filed for CVE-2014-8730 as well, see POODLE attack against TLS section below. == Prevention == To mitigate the POODLE attack, one approach is to completely disable SSL 3.0 on the client side and the server side. However, some old clients and servers do not support TLS 1.0 and above. Thus, the authors of the paper on POODLE attacks also encourage browser and server implementation of TLS_FALLBACK_SCSV, which will make downgrade attacks impossible. Another mitigation is to implement "anti-POODLE record splitting". It splits the records into several parts and ensures none of them can be attacked. However the problem of the splitting is that, though valid according to the specification, it may also cause compatibility issues due to problems in server-side implementations. A full list of browser versions and levels of vulnerability to different attacks (including POODLE) can be found in the article Transport Layer Security. Opera 25 implemented this mitigation in addition to TLS_FALLBACK_SCSV. Google's Chrome browser and their servers had already supported TLS_FALLBACK_SCSV. Google stated in October 2014 it was planning to remove SSL 3.0 support from their products completely within a few months. Fallback to SSL 3.0 has been disabled in Chrome 39, released in November 2014. SSL 3.0 has been disabled by default in Chrome 40, released in January 2015. Mozilla disabled SSL 3.0 in Firefox 34 and ESR 31.3, which were released in December 2014, and added support of TLS_FALLBACK_SCSV in Firefox 35. Microsoft published a security advisory to explain how to disable SSL 3.0 in Internet Explorer and Windows OS, and on October 29, 2014, Microsoft released a fix which disables SSL 3.0 in Internet Explorer on Windows Vista / Server 2003 and above and announced a plan to disable SSL 3.0 by default in their products and services within a few months. Microsoft disabled fallback to SSL 3.0 in Internet Explorer 11 for Protect Mode sites on February 10, 2015, and for other sites on April 14, 2015. Apple's Safari (on OS X 10.8, iOS 8.1 and later) mitigated against POODLE by removing support for all CBC protocols in SSL 3.0, however, this left RC4 which is also completely broken by the RC4 attacks in SSL 3.0. POODLE was completely mitigated in OS X 10.11 (El Capitan 2015) and iOS 9 (2015). To prevent the POODLE attack, some web services dropped support of SSL 3.0. Examples include CloudFlare and Wikimedia. Network Security Services version 3.17.1 (released on October 3, 2014) and 3.16.2.3 (released on October 27, 2014) introduced support for TLS_FALLBACK_SCSV, and NSS will disable SSL 3.0 by default in April 2015. OpenSSL versions 1.0.1j, 1.0.0o and 0.9.8zc, released on October 15, 2014, introduced support for TLS_FALLBACK_SCSV. LibreSSL version 2.1.1, released on October 16, 2014, disabled SSL 3.0 by default. == POODLE attack against TLS == A new variant of the original POODLE attack was announced on December 8, 2014. This attack exploits implementation flaws of CBC encryption mode in the TLS 1.0 - 1.2 protocols. Even though TLS specifications require servers to check the padding, some implementations fail to validate it properly, which makes some servers vulnerable to POODLE even if they disable SSL 3.0. SSL Pulse showed "about 10% of the servers are vulnerable to the POODLE attack against TLS" before this vulnerability was announced. The CVE-ID for F5 Networks' implementation bug is CVE-2014-8730. The entry in NIST's NVD states that this CVE-ID is to be used only for F5 Networks' implementation of TLS, and that other vendors whose products have the same failure to validate the padding mistake in their implementations like A10 Networks and Cisco Systems need to issue their own CVE-IDs for their implementation errors because this is not a flaw in the protocol but in the implementation. The POODLE attack against TLS was found to be easier to initiate than the initial POODLE attack against SSL. There is no need to downgrade clients to SSL 3.0, meaning fewer steps are needed to execute a successful attack.

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  Cryptochannel

  In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  Plane Finder

  Plane Finder is a United Kingdom-based real-time flight tracking service launched in 2009, that is able to show flight data globally. The data available includes flight numbers, how fast an aircraft is moving, its elevation and destination of travel. Several variants of the service are available as mobile apps including free, premium 3D and augmented reality versions. The flight tracking map and database can be accessed by web browsers. Plane Finder allows registered users to share their ADS-B and MLAT data via the Plane Finder ADS-B Client, available for macOS, Windows and Linux. Plane Finder supports VFR charts from NATS, and was the first major flight tracking app to introduce a replay feature, allowing users to replay flights dating back to 2011. == Flight tracking == Plane Finder collects data from its own global network of receivers, using the following sources. === Automatic dependent surveillance-broadcast (ADS-B) === A network of automatic dependent surveillance-broadcast (ADS-B) receivers gathers aircraft data such as callsign, position and speed. Plane Finder serves to supplement this data with additional information, including aircraft registration/tail number, departure airport, destination, artwork, and photographs. Plane Finder users can apply for an ADS-B receiver in exchange for their flight data. === Multilateration (MLAT) === To deliver aircraft position data where ADS-B is unavailable, Plane Finder uses multilateration (MLAT). Using three or more receivers running Plane Finder client software, monitoring the aircraft simultaneously, the aircraft’s position is calculated using receiver location and accurate timestamps. While European airspace is widely covered, only some parts of North American airspace are covered. === Federal Aviation Administration (FAA) feed === ADS-B is prevalent across Europe and Australia, but not in North America. Where MLAT or ADS-B data is unavailable, a feed from the Federal Aviation Administration provides flight information. The FAA feed covers United States and Canadian airspace, including bordering areas of the Atlantic and Pacific Oceans. === FLARM feed === Plane Finder collects data from a centralised FLARM feed, for monitoring small aircraft and gliders. == Flight data source == The Plane Finder website and database is widely used as an information source to support articles in the media. The Independent used Plane Finder flight tracking to demonstrate to readers the flight path of flight MT2706, which turned back as a result of last minute Egyptian government flight restrictions on 6 November 2015. The Independent also used Plane Finder information to demonstrate a timeline of the speed/altitude of flight 7K 9268, a Russian plane which crashed on 31 October 2015. The BBC cited Plane Finder in regard to the point at which at British Airways flight turned back to Heathrow Airport to make an emergency landing after smoke was seen coming from its engines. Plane Finder data has also been used to create original imagery for the media, such as the Washington Post, which used Plane Finder as a source to show flight patterns immediately after the Brussels bombings in March 2016.

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  MIME Object Security Services

  MIME Object Security Services (MOSS) is a protocol that uses the multipart/signed and multipart/encrypted framework to apply digital signature and encryption services to MIME objects. == Details == The services are offered through the use of end-to-end cryptography between an originator and a recipient at the application layer. Asymmetric (public key) cryptography is used in support of the digital signature service and encryption key management. Symmetric (secret key) cryptography is used in support of the encryption service. The procedures are intended to be compatible with a wide range of public key management approaches, including both ad hoc and certificate-based schemes. Mechanisms are provided to support many public key management approaches. == Spreading == MOSS was never widely deployed and is now abandoned, largely due to the popularity of PGP.

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  Polygraphic substitution

  Polygraphic substitution is a substitution cipher in which a uniform substitution is performed on blocks of letters. When the length of the block is specifically known, more precise terms are used: for instance, a cipher in which pairs of letters are substituted is bigraphic. As a concept, polygraphic substitution contrasts with monoalphabetic (or simple) substitutions in which individual letters are uniformly substituted, or polyalphabetic substitutions in which individual letters are substituted in different ways depending on their position in the text. In theory, there is some overlap in these definitions; one could conceivably consider a Vigenère cipher with an eight-letter key to be an octographic substitution. In practice, this is not a useful observation since it is far more fruitful to consider it to be a polyalphabetic substitution cipher. == Specific ciphers == In 1563, Giambattista della Porta devised the first bigraphic substitution. However, it was nothing more than a matrix of symbols. In practice, it would have been all but impossible to memorize, and carrying around the table would lead to risks of falling into enemy hands. In 1854, Charles Wheatstone came up with the Playfair cipher, a keyword-based system that could be performed on paper in the field. This was followed up over the next fifty years with the closely related four-square and two-square ciphers, which are slightly more cumbersome but offer slightly better security. In 1929, Lester S. Hill developed the Hill cipher, which uses matrix algebra to encrypt blocks of any desired length. However, encryption is very difficult to perform by hand for any sufficiently large block size, although it has been implemented by machine or computer. This is therefore on the frontier between classical and modern cryptography. == Cryptanalysis of general polygraphic substitutions == Polygraphic systems do provide a significant improvement in security over monoalphabetic substitutions. Given an individual letter 'E' in a message, it could be encrypted using any of 52 instructions depending on its location and neighbors, which can be used to great advantage to mask the frequency of individual letters. However, the security boost is limited; while it generally requires a larger sample of text to crack, it can still be done by hand. One can identify a polygraphically-encrypted text by performing a frequency chart of polygrams and not merely of individual letters. These can be compared to the frequency of plaintext English. The distribution of digrams is even more stark than individual letters. For example, the six most common letters in English (23%) represent approximately half of English plaintext, but it takes only the most frequent 8% of the 676 digrams to achieve the same potency. In addition, even in a plaintext many thousands of characters long, one would expect that nearly half of the digrams would not occur, or only barely. In addition, looking over the text one would expect to see a fairly regular scattering of repeated text in multiples of the block length and relatively few that are not multiples. Cracking a code identified as polygraphic is similar to cracking a general monoalphabetic substitution except with a larger 'alphabet'. One identifies the most frequent polygrams, experiments with replacing them with common plaintext polygrams, and attempts to build up common words, phrases, and finally meaning. Naturally, if the investigation led the cryptanalyst to suspect that a code was of a specific type, like a Playfair or order-2 Hill cipher, then they could use a more specific attack.

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