AI Data Center Zoning

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

  • Screenless video

    Screenless video

    Screenless video is any system for transmitting visual information from a video source without the use of a screen. Screenless computing systems can be divided into three groups: Visual Image, Retinal Direct, and Synaptic Interface. == Visual image == Visual Image screenless display includes any image that the eye can perceive. The most common example of Visual Image screenless display is a hologram. In these cases, light is reflected off some intermediate object (hologram, LCD panel, or cockpit window) before it reaches the retina. In the case of LCD panels the light is refracted from the back of the panel, but is nonetheless a reflected source. Google has proposed a similar system to replace the screens of tablet computers and smartphones. == Retinal display == Virtual retinal display systems are a class of screenless displays in which images are projected directly onto the retina. They are distinguished from visual image systems because light is not reflected from some intermediate object onto the retina, it is instead projected directly onto the retina. Retinal Direct systems, once marketed, hold out the promise of extreme privacy when computing work is done in public places because most snooping relies on viewing the same light as the person who is legitimately viewing the screen, and retinal direct systems send light only into the pupils of their intended viewer. == Synaptic interface == Synaptic Interface screenless video does not use light at all. Visual information completely bypasses the eye and is transmitted directly to the brain. While such systems have only been implemented in humans in rudimentary form - for example, displaying single Braille characters to blind people – success has been achieved in sampling usable video signals from the biological eyes of a living horseshoe crab through their optic nerves, and in sending video signals from electronic cameras into the creatures' brains using the same method.

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  • Data security

    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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  • Hyper-encryption

    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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  • Social media and psychology

    Social media and psychology

    Social media began in the form of generalized online communities. These online communities formed on websites like Geocities.com in 1994, Theglobe.com in 1995, and Tripod.com in 1995. Many of these early communities focused on social interaction by bringing people together through the use of chat rooms. The chat rooms encouraged users to share personal information, ideas, or even personal web pages. Later the social networking community Classmates took a different approach by simply having people link to each other by using their personal email addresses. By the late 1990s, social networking websites began to develop more advanced features to help users find and manage friends. These newer generation of social networking websites began to flourish with the emergence of SixDegrees.com in 1997, Makeoutclub in 2000, Hub Culture in 2002, and Friendster in 2002. However, the first profitable mass social networking website was the South Korean service, Cyworld. Cyworld initially launched as a blog-based website in 1999 and social networking features were added to the website in 2001. Other social networking websites emerged like Myspace in 2002, LinkedIn in 2003, and Bebo in 2005. In 2009, the social networking website Facebook (launched in 2004) became the largest social networking website in the world. Both Instagram and Kik were launched in October 2010. Active users of Facebook increased from just a million in 2004 to over 750 million by the year 2011. Making internet-based social networking both a cultural and financial phenomenon. In September 2011, Snapchat was launched and reported over 300 million users in 2021. == Psychology of social networking == A social network is a social structure made up of individuals or organizations who communicate and interact with each other. Social networking sites – such as Facebook, Twitter, Instagram, Pinterest and LinkedIn – are defined as technology-enabled tools that assist users with creating and maintaining their relationships. A study found that middle schoolers reported using social media to see what their friends are doing, to post pictures, and to connect with friends. Human behavior related to social networking is influenced by major individual differences, meaning that people differ quite systematically in the quantity and quality of their social relationships. Two of the main personality traits that are responsible for this variability are the traits of extraversion and introversion. Extraversion refers to the tendency to be socially dominant, exert leadership, and influence on others. In contrast, introversion reflects a tendency towards shyness, social phobia, or even avoid social situations altogether, which could potentially reduce the number of social contacts a person may have. These individual differences may result in different social networking outcomes. Other psychology factors related to social media and Media psychology are depression, anxiety, attachment, self-identity, well-being, and the need to belong. === Neuroscience === The three domains that neural systems rely on to be strengthened to support social media use are social cognition, self-referential cognition, and social rewarding. When someone posts something on social media, they think of how their audience will react, while the audience thinks of the motivations behind posting the information. Both parties are analyzing the other's thoughts and feelings, which coherently rely on multiple network systems of the brain including the dorsomedial prefrontal cortex, bilateral temporoparietal junction, anterior temporal lobes, inferior frontal gyri, and posterior cingulate cortex. All of these systems work to help us process social behaviors and thoughts drawn out on social media. Social media requires a great deal of self-referential thought. People use social media as a platform to express their opinions and show off their past and present selves. In other words, as Bailey Parnell said in her Ted Talk, we're showing off our "highlight reel" (4). When one receives feedback from others, the individual obtains more reflected self-appraisal which leads to comparisons of their social behaviors or "highlights" to other users. Self-referential thought involves activity in the medial prefrontal cortex and the posterior cingulate cortex. The brain uses these systems when thinking of oneself. A 2021 umbrella review found that most associations between adolescent social media use and mental health were characterized as weak or inconsistent, though certain studies identified 'substantial' negative impacts, particularly linked to passive consumption and problematic use. Social media also provides a constant supply of rewards that keeps users coming back for more. Whenever users receive a like or a new follower, it activates the brain's social reward system which includes the ventromedial prefrontal cortex, ventral striatum, and ventral tegmental area. This system has been found to activate in response to positive feedback from peers, suggesting that users experience online acceptance in a similar manner to other material rewards or positive experiences, further acting as a potential reward. While these areas of the brain become strengthened, other parts of the brain start to weaken. Technology is encouraging multi-tasking, especially because of how easy it is to switch from one task to another by opening another tab or using two devices at once. The brain's hippocampus is mainly associated with long-term memory. In a study done by Russell Poldark, a professor at UCLA, they found that "for the task learned without distraction, the hippocampus was involved. However, for the task learned with the distraction of the beeps, the hippocampus was not involved; but the striatum was, which is the brain system that underlies our ability to learn new skills." The study concludes that multitasking can cause reliance on the striatum more than the hippocampus, which can change the way we learn. The striatum is known to be connected to mainly the brain's reward system. The brain will strengthen the neurons to the striatum while it weakens the neurons to the hippocampus to make the brain more efficient. Because our brain starts to rely on the striatum more than the hippocampus, it becomes harder for us to process new information. Nicholas Carr, author of The Shallows: How The Internet Is Changing Our Brains, agrees: "What psychologists and brain scientists tell us about interruptions is that they have a fairly profound effect on the way we think. It becomes much harder to sustain attention, to think about one thing for a long period of time, and to think deeply when new stimuli are pouring at you all day long. I argue that the price we pay for being constantly inundated with information is a loss of our ability to be contemplative and to engage in the kind of deep thinking that requires you to concentrate on one thing." === Well-Being === How does well-being relate to social media? In an article titled Social Impact of Psychological Research on Well-Being Shared in Social Media, Pulido et al. found a 15.7% social impact in their results. These new results were compared to a previous study conducted by Pulido et al., which had a high of 4.98% compared to 27.5% in the new study. These results show the ESISM, which is evidence of social impact present. In a two-year span, the difference between social impact rose 22.52% according to these studies. When taking into consideration that an increasingly large number of teens report either being active on, or having used, some form of social media, ranging from apps such as Facebook to TikTok, researching the effects of social media on the well-being of teens and young adults has become more of a topic of focus in recent years. === Depression === Especially in today's society, social media has gained a new perspective on younger generations. It is what younger generations are born into and are growing up to use, particularly what is running today's society. Social Media has its downfalls regarding depression and mental health. Many users often compare their lives regarding what they see on these platforms. In an article Does Social Media Cause Depression? by the Child Mind Institute, Miller states that "several studies, teenage and young adult users who spend the most time on Instagram, Facebook and other platforms for have shown to have substantially (from 13 to 66 percent) higher rates of reported depression than those who spent the least time", what the study shows how Facebook and Instagram, platforms showcasing daily lives and or lifestyles, or less fulfilling or less satisfied or more flaunting base or superficial. Instead of social community, there has become a perception of individuals striving for a life that is not real, whether that is editing photos or making life seem perfect when it is not. This causes a sense of depression by the weight of a comparing game. In "How Social Media Affects Y

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  • Smart object

    Smart object

    A smart object is an object that enhances the interaction with not only people but also with other smart objects. Also known as smart connected products or smart connected things (SCoT), they are products, assets and other things embedded with processors, sensors, software and connectivity that allow data to be exchanged between the product and its environment, manufacturer, operator/user, and other products and systems. Connectivity also enables some capabilities of the product to exist outside the physical device, in what is known as the product cloud. The data collected from these products can be then analysed to inform decision-making, enable operational efficiencies and continuously improve the performance of the product. It can not only refer to interaction with physical world objects but also to interaction with virtual (computing environment) objects. A smart physical object may be created either as an artifact or manufactured product or by embedding electronic tags such as RFID tags or sensors into non-smart physical objects. Smart virtual objects are created as software objects that are intrinsic when creating and operating a virtual or cyber world simulation or game. The concept of a smart object has several origins and uses, see History. There are also several overlapping terms, see also smart device, tangible object or tangible user interface and Thing as in the Internet of things. == History == In the early 1990s, Mark Weiser, from whom the term ubiquitous computing originated, referred to a vision "When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial", Although Weiser did not specifically refer to an object as being smart, his early work did imply that smart physical objects are smart in the sense that they act as digital information sources. Hiroshi Ishii and Brygg Ullmer refer to tangible objects in terms of tangibles bits or tangible user interfaces that enable users to "grasp & manipulate" bits in the center of users' attention by coupling the bits with everyday physical objects and architectural surfaces. The smart object concept was introduced by Marcelo Kallman and Daniel Thalmann as an object that can describe its own possible interactions. The main focus here is to model interactions of smart virtual objects with virtual humans, agents, in virtual worlds. The opposite approach to smart objects is 'plain' objects that do not provide this information. The additional information provided by this concept enables far more general interaction schemes, and can greatly simplify the planner of an artificial intelligence agent. In contrast to smart virtual objects used in virtual worlds, Lev Manovich focuses on physical space filled with electronic and visual information. Here, "smart objects" are described as "objects connected to the Net; objects that can sense their users and display smart behaviour". More recently in the early 2010s, smart objects are being proposed as a key enabler for the vision of the Internet of things. The combination of the Internet and emerging technologies such as near field communications, real-time localization, and embedded sensors enables everyday objects to be transformed into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of things and enable novel computing applications. In 2018, one of the world's first smart houses was built in Klaukkala, Finland in the form of a five-floor apartment block, using the Kone Residential Flow solution created by KONE, allowing even a smartphone to act as a home key. == Characteristics == Although we can view interaction with physical smart object in the physical world as distinct from interaction with virtual smart objects in a virtual simulated world, these can be related. Poslad considers the progression of: how humans use models of smart objects situated in the physical world to enhance human to physical world interaction; versus how smart physical objects situated in the physical world can model human interaction in order to lessen the need for human to physical world interaction; versus how virtual smart objects by modelling both physical world objects and modelling humans as objects and their subsequent interactions can form a predominantly smart virtual object environment. === Smart physical objects === The concept smart for a smart physical object simply means that it is active, digital, networked, can operate to some extent autonomously, is reconfigurable and has local control of the resources it needs such as energy, data storage, etc. Note, a smart object does not necessarily need to be intelligent as in exhibiting a strong essence of artificial intelligence—although it can be designed to also be intelligent. Physical world smart objects can be described in terms of three properties: Awareness: is a smart object's ability to understand (that is, sense, interpret, and react to) events and human activities occurring in the physical world. Representation: refers to a smart object's application and programming model—in particular, programming abstractions. Interaction: denotes the object's ability to converse with the user in terms of input, output, control, and feedback. Based upon these properties, these have been classified into three types: Activity-Aware Smart Objects: Are objects that can record information about work activities and its own use. Policy-Aware Smart Objects: Are objects that are activity-aware Objects can interpret events and activities with respect to predefined organizational policies. Process-Aware Smart Objects: Processes play a fundamental role in industrial work management and operation. A process is a collection of related activities or tasks that are ordered according to their position in time and space. === Smart virtual objects === For the virtual object in a virtual world case, an object is called smart when it has the ability to describe its possible interactions. This focuses on constructing a virtual world using only virtual objects that contain their own interaction information. There are four basic elements to constructing such a smart virtual object framework. Object properties: physical properties and a text description Interaction information: position of handles, buttons, grips, and the like Object behavior: different behaviors based on state variables Agent behaviors: description of the behavior an agent should follow when using the object Some versions of smart objects also include animation information in the object information, but this is not considered to be an efficient approach, since this can make objects inappropriately oversized. === Categorization === The terms smart, connected product or smart product can be confusing as it is used to cover a broad range of different products, ranging from smart home appliances (e.g., smart bathroom scales or smart light bulbs) to smart cars (e.g., Tesla). While these products share certain similarities, they often differ substantially in their capabilities. Raff et al. developed a conceptual framework that distinguishes different smart products based on their capabilities, which features 4 types of smart product archetypes (in ascending order of "smartness"). Digital Connected Responsive Intelligent == Advantages == Smart, connected products have three primary components: Physical – made up of the product's mechanical and electrical parts. Smart – made up of sensors, microprocessors, data storage, controls, software, and an embedded operating system with enhanced user interface. Connectivity – made up of ports, antennae, and protocols enabling wired/wireless connections that serve two purposes, it allows data to be exchanged with the product and enables some functions of the product to exist outside the physical device. Each component expands the capabilities of one another resulting in "a virtuous cycle of value improvement". First, the smart components of a product amplify the value and capabilities of the physical components. Then, connectivity amplifies the value and capabilities of the smart components. These improvements include: Monitoring of the product's conditions, its external environment, and its operations and usage. Control of various product functions to better respond to changes in its environment, as well as to personalize the user experience. Optimization of the product's overall operations based on actual performance data, and reduction of downtimes through predictive maintenance and remote service. Autonomous product operation, including learning from their environment, adapting to users' preferences and self-diagnosing and service. === The Internet of things (IoT) === The Internet of things is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. The phrase "Internet of things" reflects the gro

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  • HashClash

    HashClash

    HashClash was a volunteer computing project running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform to find collisions in the MD5 hash algorithm. It was based at Department of Mathematics and Computer Science at the Eindhoven University of Technology, and Marc Stevens initiated the project as part of his master's degree thesis. The project ended after Stevens defended his M.Sc. thesis in June 2007. However, SHA1 was added later, and the code repository was ported to git in 2017. The project was used to create a rogue certificate authority certificate in 2009.

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  • Correlation immunity

    Correlation immunity

    In mathematics, the correlation immunity of a Boolean function is a measure of the degree to which its outputs are uncorrelated with some subset of its inputs. Specifically, a Boolean function is said to be correlation-immune of order m if every subset of m or fewer variables in x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} is statistically independent of the value of f ( x 1 , x 2 , … , x n ) {\displaystyle f(x_{1},x_{2},\ldots ,x_{n})} . == Definition == A function f : F 2 n → F 2 {\displaystyle f:\mathbb {F} _{2}^{n}\rightarrow \mathbb {F} _{2}} is k {\displaystyle k} -th order correlation immune if for any independent n {\displaystyle n} binary random variables X 0 … X n − 1 {\displaystyle X_{0}\ldots X_{n-1}} , the random variable Z = f ( X 0 , … , X n − 1 ) {\displaystyle Z=f(X_{0},\ldots ,X_{n-1})} is independent from any random vector ( X i 1 … X i k ) {\displaystyle (X_{i_{1}}\ldots X_{i_{k}})} with 0 ≤ i 1 < … < i k < n {\displaystyle 0\leq i_{1}<\ldots Read more →

  • Control break

    Control break

    In computer programming, a control break is a change in the value of one of the keys on which a file is sorted, which requires some extra processing. For example, with an input file sorted by post code, the number of items found in each postal district might need to be printed on a report, and a heading shown for the next district. Quite often there is a hierarchy of nested control breaks in a program, such as streets within districts within areas, with the need for a grand total at the end. Structured programming techniques have been developed to ensure correct processing of control breaks in languages such as COBOL and to ensure that conditions such as empty input files and sequence errors are handled properly. With fourth-generation languages such as SQL, the programming language should handle most of the details of control breaks automatically.

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  • Enterprise cognitive system

    Enterprise cognitive system

    Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome. While general-purpose cognitive systems can be used for different outputs, prescriptive, suggestive, instructive, or simply entertaining, an enterprise cognitive system is focused on action, not insight, to help in assessing what to do in a complex situation. == Key characteristics == ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning from data requires an agile information federation approach to ingest incremental data updates as they occur, and an unsupervised learning approach to ensure that new best practice is leveraged across the organization in a timely manner. Interactive: They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people. In the Enterprise, interactions are controlled via existing workflows and UIs. Therefore, embedding best practices directly into these existing interfaces, in the context of a specific step, is critical to ensure maximum end-user adoption. Iterative and stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. In the Enterprise, business context is often structured by a business process, and therefore sufficiently data-rich to make relevant recommendations without significant iterations from the end-user. A stateful memory of overall interactions across communication channels is critical for understanding of context, as a static profile will not capture intent and outcome potential the way behavior does. Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user's profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). In the Enterprise, Context is fragmented and must be aggregated across data types, sources, and locations. In most business environments, such data is captured in existing enterprise information systems, and the effort is linked to quickly source and unify such information. It is rare to have to directly process sensor, audio or visual data in real-time as direct input into the enterprise cognitive system. Instead, these data types are captured by Enterprise Applications and pre-processed into a binary or text format prior to consumption by the System. == Business applications powered by an ECS == Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring

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  • Information Networking Institute

    Information Networking Institute

    Information Networking Institute (INI) is an academic department within the College of Engineering at Carnegie Mellon University. The institute was established in 1989 as the nation's first research and education center devoted to information networking. The INI also partners with research and outreach entities to extend educational and training programs to a broad audience of people using information networking as part of their daily lives. The INI is the educational partner of Carnegie Mellon CyLab, a university-wide, multidisciplinary research center involving more than 50 faculty and 100 graduate students. == Center of Academic Excellence Designations == Through the work of the INI and CyLab, Carnegie Mellon University has been designated by the National Security Agency and the Department of Homeland Security as a National Center of Academic Excellence in Information Assurance/Cyber Defense Education (CAE-IA/CD) and a National Center of Academic Excellence in Information Assurance/Cyber Defense Research (CAE-R). It has also been designated by the NSA and the U.S. Cyber Command as a National Center of Academic Excellence in Cyber Operations (CAE-Cyber Ops). Through these designations, the INI and CyLab participate in the: Federal CyberCorps Scholarship for Service (SFS) Program - Students pursuing graduate degrees in information security (MSIS or MSISPM) are eligible for scholarships under the SFS program. Information Assurance Scholarship Program (IASP) - Students pursuing graduate degrees in information security and seeking careers with the Department of Defense may be eligible for scholarships under the IASP. Capacity Building Program for Faculty from Historically Black and Hispanic Serving Institutions - The INI and CyLab developed a month-long, in-residence summer program to help build information assurance education and research capacity at colleges and universities designated as Minority Serving Institutions – specifically, Historically Black Colleges and Universities (HBCUs) and Hispanic Serving Institutions (HSIs). This program is supported through a grant from the National Science Foundation. == Faculty and researchers == Faculty involved in teaching and advising in the INI programs are conducting research in all aspects of information networking and information security. Affiliated research centers are: Carnegie Mellon CyLab SEI's CERT Division == Alumni == The INI has graduated over 1,400 alumni who currently occupy positions in a variety of sectors across industry, government and academia.

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  • Back-Up Interceptor Control

    Back-Up Interceptor Control

    Backup Interceptor Control (BUIC, ) was the Electronic Systems Division 416M System to backup the SAGE 416L System in the United States and Canada. BUIC deployed Cold War command, control, and coordination systems to SAGE radar stations to create dispersed NORAD Control Centers. == Background == Prior to the SAGE Direction Centers becoming operational, the USAF deployed data link systems at NORAD Control Centers with ground computers for controlling crewed interceptors. After SAGE IBM AN/FSQ-7 Combat Direction Centrals became operational and the Super Combat Centers with improved (digital) computers were cancelled, a backup to SAGE was planned in the event the above-ground SAGE Air Defense Direction Center failed. == General Electric AN/GPA-37 Course Directing Group == BUIC began with deployment of General Electric AN/GPA-37 Course Directing Groups to several Long Range Radar stations. Units designated included the "U.S. Air Force 858th Air Defense Group (BUIC) [which became] a permanent operating facility" at Naval Air Station Fallon in Nevada. == BUIC II == BUIC II was used to command and control sites using the Burroughs AN/GSA-51 Radar Course Directing Group. North Truro AFS became the first ADC installation configured for BUIC II. == BUIC III == The AN/GYK-19 (initially AN/GSA-51A) was an upgraded version of the BUIC II system designated AN/GSA-51A and required a larger building than the AN/GSA-51. The first BUIC III site was Fort Fisher AFS, and Air Defense Command's was first installed at Fort Fisher Air Force Station, North Carolina. Although more advanced systems were contemplated, the final design of the BUIC III system was an upgraded version of the BUIC II with around twice the performance. == Closure and upgrade == In 1972, the USAF decided to shut down most of the BUIC sites; most of the sites mothballed by 1974, except for the BUIC III site at Tyndall Air Force Base. In Canada the BUIC site at Senneterre was shut down, but St Margarets remained open. The remaining sites were closed between 1983-1984 when SAGE was replaced by the Joint Surveillance System. The AN/FYQ-47 Common Digitizer for the Joint Surveillance System, and the Radar Video Data Processor (RVDP) was a combined system for the Air Force and Federal Aviation Administration (FAA), it replaced the SAGE Burroughs AN/FST-2 Coordinate Data Transmitting Sets.

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  • KLJN Secure Key Exchange

    KLJN Secure Key Exchange

    Random-resistor-random-temperature Kirchhoff-law-Johnson-noise key exchange, also known as RRRT-KLJN or simply KLJN, is an approach for distributing cryptographic keys between two parties that claims to offer unconditional security. This claim, which has been contested, is significant, as the only other key exchange approach claiming to offer unconditional security is Quantum key distribution. The KLJN secure key exchange scheme was proposed in 2005 by Laszlo Kish and Granqvist. It has the advantage over quantum key distribution in that it can be performed over a metallic wire with just four resistors, two noise generators, and four voltage measuring devices---equipment that is low-priced and can be readily manufactured. It has the disadvantage that several attacks against KLJN have been identified which must be defended against. "Given that the amount of effort and funding that goes into Quantum Cryptography is substantial (some even mock it as a distraction from the ultimate prize which is quantum computing), it seems to me that the fact that classic thermodynamic resources allow for similar inherent security should give one pause," wrote Henning Dekant, the founder of the Quantum Computing Meetup, in April 2013. The Cybersecurity Curricula 2017, a joint project of the Association for Computing Machinery, the IEEE Computer Society, the Association for Information Systems, and the International Federation for Information Processing Technical Committee on Information Security Education (IFIP WG 11.8) recommends teaching the KLJN Scheme as part of teaching "Advanced concepts" in its knowledge unit on cryptography. == See Also/Further Reading ==

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  • Apache ORC

    Apache ORC

    Apache ORC (Optimized Row Columnar) is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop. In February 2013, the Optimized Row Columnar (ORC) file format was announced by Hortonworks in collaboration with Facebook. A calendar month later, the Apache Parquet format was announced, developed by Cloudera and Twitter. Apache ORC format is widely supported including Amazon Web Services' Glue,Google Cloud Platform's BigQuery, and Pandas (software). == History ==

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  • Stegomalware

    Stegomalware

    Stegomalware is a form of malicious software that leverages steganography techniques to conceal its code, configuration data, or command-and-control (C&C) communications within seemingly benign digital media such as images, audio files, videos, documents, or network traffic. It typically embeds encrypted or obfuscated payloads into digital media and only extracts and executes them at runtime, which makes traditional signature-based and sandbox-based detection significantly more difficult. Stegomalware has been observed in attacks ranging from advanced persistent threats (APTs) to financially motivated cybercrime, and is now the subject of dedicated academic surveys, research projects, and international law-enforcement initiatives. The key distinction between stegomalware and traditional obfuscated malware lies in the encoding location. After obfuscation, malicious code remains present within the executable and can theoretically be discovered through static analysis. In contrast, stegomalware hides the payload entirely within a cover medium (image, audio, etc.), remaining invisible until the malware dynamically extracts and executes it at runtime. == History == The term stegomalware was formally introduced by researchers Águila, Laskov, and others in the context of mobile malware and presented at the Inscrypt (Information Security and Cryptology) conference in 2014. This marked the first academic formalization of the concept, though earlier work had already identified that botnets and mobile malware could use steganography and covert channels for command-and-control communication over probabilistically unobservable channels. Since its introduction, stegomalware has evolved from a theoretical concern to a documented threat. In 2011, the APT operation known as "Operation Shady RAT" became one of the first documented cases of stegomalware in the wild, using digital images to hide Internet Protocol addresses and command-and-control server addresses. The same year, the Duqu malware (targeting industrial manufacturers) embedded victim data into JPEG image files before exfiltration, making the data transfer virtually undetectable to network-level security tools. From 2014 onwards, stegomalware became more prevalent in organized cybercrime and advanced persistent threat campaigns. Notable examples include Zeus/Zbot, which masked configuration data in images; Gatak/Stegoloader, which hid shellcode in PNG files; TeslaCrypt, which embedded C&C commands in JPEGs; and Cerber, which concealed ransomware payloads within images. By the 2010s, stegomalware had become established as a preferred evasion technique for espionage, financial theft, and ransomware distribution campaigns. Recent surveys (2020–2025) document that stegomalware has increasingly been exploited by adversaries targeting banks, enterprises, government agencies, educational institutions, and internet users via malvertising campaigns. The technique is now considered a sophisticated method of attack worthy of dedicated international law-enforcement attention. == Technical Characteristics and Definitions == Stegomalware operates through a three-component architecture: Stegotext (R): An innocent-looking digital asset (image, audio file, etc.) into which the malicious payload is embedded. Secret key (sk): A key used by the embedding and extraction algorithms, typically hardcoded into the malware. Payload (p): The actual malicious code, configuration data, or C&C commands hidden within the stegotext. The malware extracts the payload at runtime using the secret key and either executes it directly or uses it to download additional stages of the attack. Stegomalware can be classified into several types based on deployment method: Type 0 (Autonomous): Both the stegotext and extraction algorithm are embedded within the malware application itself. The malicious payload is extracted and executed locally without external communication. Type I (Update): The stegotext and secret key are downloaded from a remote server at runtime; only the extraction algorithm is included in the malware. This variant is more flexible, allowing attackers to push updated payloads. Type II (External Algorithm): Neither the stegotext nor the extraction algorithm are distributed with the malware; both are fetched from an attacker-controlled infrastructure, providing maximum flexibility and evasion. == Steganography techniques == === Spatial domain methods === Stegomalware predominantly uses steganographic methods designed for images, as images are the most common cover medium in the wild. The most basic spatial domain technique is Least Significant Bit (LSB) substitution, which replaces the least significant bits of pixel color values with payload bits. While simple and easy to implement, LSB is also relatively easy to detect through statistical analysis. More sophisticated spatial domain techniques include: HUGO (High Undetectable steGO) (2010): Minimizes detectable distortion by distributing the payload across multiple pixels, achieving embedding capacity with reduced statistical footprint. WOW (Wavelet Obtained Weights) (2012): Embeds data preferentially in textured regions of images where modifications are less perceptually noticeable. UNIWARD (Universal Wavelet Relative Distortion) (2014): Uses a universal distortion function applicable to multiple image formats, balancing payload capacity with undetectability. HILL (2014): Applies high-pass and low-pass filters to identify robust embedding regions. MiPOD (Minimizing the Power of Optimal Detector) (2016): Designed to minimize the power of theoretical optimal steganalysis detectors. === Transform domain methods === Transform domain techniques convert images into the frequency domain (e.g., using DCT or DWT) before embedding, allowing for more robust hiding in JPEG and other compressed formats: Embedding in DCT coefficients (used in JPEG compression) Embedding in DWT coefficients (used in lossless formats) Spread spectrum techniques, which distribute the payload across many frequency components Transform domain methods are generally more resistant to noise, compression, and image transformations than spatial methods. === Generative adversarial network (GAN) methods === Recent advances in machine learning have introduced GAN-based steganography, where a generative model produces stego images that minimize detectable artifacts: SGAN (Steganographic GAN) (2017): First GAN applied to steganography, using a generator, discriminator, and steganalysis network. ASDL-GAN (2017): Performs automatic steganographic distortion learning at the pixel level. SteganoGAN (2019): Improves upon earlier GAN models, achieving higher embedding capacity and robustness. HiGAN (Hiding Images GAN) (2020): Enables hiding one image within another while maintaining visual plausibility. GAN-based approaches are more resilient to standard steganalysis attacks but remain an emerging threat requiring further research. == Notable malware campaigns == Stegomalware has been documented in numerous high-profile cyber attacks and campaigns. Notable examples include: Operation Shady RAT (2011): Used digital images to hide command-and-control server addresses in targeted espionage. Duqu (2011): Embedded victim data into JPEG files to exfiltrate industrial control system information. Zeus/Zbot (2014): Masked banking configuration data inside JPEG files exploited via malvertising. Gatak/Stegoloader (2015): Hid shellcode in PNG files for software licensing attacks and bot command execution. TeslaCrypt (2015): Embedded C&C commands and ransomware keys in JPEG images. Cerber (2016): Concealed executable ransomware code in JPEG files distributed via phishing. DNSChanger (2016): Embedded malicious code in PNG files for DNS hijacking campaigns. Sundown Exploit Kit (2017): Distributed exploit code in PNG files via malvertising. AdGholas (2017): Used JPEG steganography to distribute ransomware via malvertising. Synccrypt (2017): Hidden ransomware components in JPEG-steganographic encrypted archives. ZeroT/PlugX (2017): Hid Remote Access Trojan payloads in BMP files for espionage. Loki Bot (2018): Concealed malware installers in JPEG and video files. Waterbug (APT28) (2019): Injected malicious DLLs into WAV audio files. Shlayer (macOS adware) (2019): Hid malicious URLs in JPEG files via malvertising. === Attack vectors === The most common attack vectors for stegomalware include: Phishing emails with malicious attachments or links Malvertising campaigns using malicious banner advertisements Exploit kits through compromised or malicious websites Legitimate application vulnerabilities (e.g., watering-hole attacks) Fake software distribution (cracked software, keygen tools) === Exploitation stages === Stegomalware typically serves one or more roles in attack lifecycles: Payload delivery: Stego images contain full executable code or shellcode. C&C communication: Hidden data contains server addresses or command instructio

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  • BitFunnel

    BitFunnel

    BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost. == History == Progress on the implementation of BitFunnel was made public in early 2016, with the expectation that there would be a usable implementation later that year. In September 2016, the source code was made available via GitHub. A paper discussing the BitFunnel algorithm and implementation was released as through the Special Interest Group on Information Retrieval of the Association for Computing Machinery in 2017 and won the Best Paper Award. == Components == BitFunnel consists of three major components: BitFunnel – the text search/retrieval system itself WorkBench – a tool for preparing text for use in BitFunnel NativeJIT – a software component that takes expressions that use C data structures and transforms them into highly optimized assembly code == Algorithm == === Initial problem and solution overview === The BitFunnel paper describes the "matching problem", which occurs when an algorithm must identify documents through the usage of keywords. The goal of the problem is to identify a set of matches given a corpus to search and a query of keyword terms to match against. This problem is commonly solved through inverted indexes, where each searchable item is maintained with a map of keywords. In contrast, BitFunnel represents each searchable item through a signature. A signature is a sequence of bits which describe a Bloom filter of the searchable terms in a given searchable item. The bloom filter is constructed through hashing through several bit positions. === Theoretical implementation of bit-string signatures === The signature of a document (D) can be described as the logical-or of its term signatures: S D → = ⋃ t ∈ D S t → {\displaystyle {\overrightarrow {S_{D}}}=\bigcup _{t\in D}{\overrightarrow {S_{t}}}} Similarly, a query for a document (Q) can be defined as a union: S Q → = ⋃ t ∈ Q S t → {\displaystyle {\overrightarrow {S_{Q}}}=\bigcup _{t\in Q}{\overrightarrow {S_{t}}}} Additionally, a document D is a member of the set M' when the following condition is satisfied: S Q → ∩ S D → = S Q → {\displaystyle {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}} This knowledge is then combined to produce a formula where M' is identified by documents which match the query signature: M ′ = { D ∈ C ∣ S Q → ∩ S D → = S Q → } {\displaystyle M'=\left\{D\in C\mid {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}\right\}} These steps and their proofs are discussed in the 2017 paper. === Pseudocode for bit-string signatures === This algorithm is described in the 2017 paper. M ′ = ∅ foreach D ∈ C do if S D → ∩ S Q → = S Q → then M ′ = M ′ ∪ { D } endif endfor {\displaystyle {\begin{array}{l}M'=\emptyset \\{\texttt {foreach}}\ D\in C\ {\texttt {do}}\\\qquad {\texttt {if}}\ {\overrightarrow {S_{D}}}\cap {\overrightarrow {S_{Q}}}={\overrightarrow {S_{Q}}}\ {\texttt {then}}\\\qquad \qquad M'=M'\cup \{D\}\\\qquad {\texttt {endif}}\\{\texttt {endfor}}\end{array}}}

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