AI Detector Gemini

AI Detector Gemini — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Artifact (app)

Artifact was a personalized social news aggregator app that uses recommender systems to suggest articles. Launched in January 2023 by Nokto, Inc., a company founded by co-founders of Instagram Kevin Systrom and Mike Krieger, the app is available for iOS and Android. The app's name is a portmanteau of the words "articles", "artificial intelligence", and "fact". The app shut down in January 2024 as a result of low interest. == History == Nokto, Inc. was established on March 3, 2022, as a foreign stock company in California, with its headquarters in San Francisco. The company's main product, Artifact, is the first new product launched by Krieger and Systrom since their 2018 resignation from Instagram after conflicts with parent company Meta, which acquired Instagram in 2012. Artifact launched on January 31, 2023, after the team had been working on it for over a year, offering the option to sign up for a waiting list for its private beta, which grew to about 160,000 people, and then launching in open beta on February 22, 2023. With a team of seven employees in San Francisco, the app was free throughout its lifetime, with the founders explaining at the time that different business models - such as advertising or subscription fees - could be explored in the future. In January 2024, cofounder Kevin Systrom announced that the app would be shutting down after concluding that "the market opportunity isn’t big enough to warrant continued investment in this way." In April 2024, it was announced Artifact had been acquired by Yahoo, who intended to use the service's technology in an upgraded Yahoo! News app. == Features == Frequently described as "TikTok for text" and a competitor to Twitter, Artifact was a news aggregator that used machine learning to make personalized recommendations based on topics, news sources, and authors that the reader is interested in. In addition to reading articles, the app offered the ability to like articles, leave comments, or listen to an audio version of an article read by AI-generated voices, including a simulation of the voices of Snoop Dogg or Gwyneth Paltrow. AI also would rewrite clickbait headlines that users flagged. Artifact later expanded to a social network where users could post links, images and text to their profile, which could be liked or commented on by other users. Similar to other social news websites like Reddit, reader accounts had profiles with reputation scores.
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Why We Post

Why We Post is a research project funded by the European Research Council and launched in 2012 by Daniel Miller with the objective of examining the global impact of new social media. The study is based on ethnographic data collected through the course of 15 months in China, India, Turkey, Italy, United Kingdom, Trinidad, Chile and Brazil. The results of this project were released on 29 February 2016. This included the first three of eleven Open Access books (available via UCL Press), a five-week e-course (MOOC) on FutureLearn in English, also available in Chinese, Portuguese, Hindi, Tamil, Italian, Turkish, and Spanish on UCLeXtend. In addition a website containing key discoveries, stories and over 100 films is available in the same 8 languages.
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Voice inversion

Voice inversion scrambling is an analog method of obscuring the content of a transmission. It is sometimes used in public service radio, automobile racing, cordless telephones and the Family Radio Service. Without a descrambler, the transmission makes the speaker "sound like Donald Duck". Despite the term, the technique operates on the passband of the information and so can be applied to any information being transmitted. == Forms and details == There are various forms of voice inversion which offer differing levels of security. Overall, voice inversion scrambling offers little true security as software and even hobbyist kits are available from kit makers for scrambling and descrambling. The cadence of the speech is not changed. It is often easy to guess what is happening in the conversation by listening for other audio cues like questions, short responses and other language cadences. In the simplest form of voice inversion, the frequency p {\displaystyle p} of each component is replaced with s − p {\displaystyle s-p} , where s {\displaystyle s} is the frequency of a carrier wave. This can be done by amplitude modulating the speech signal with the carrier, then applying a low-pass filter to select the lower sideband. This will make the low tones of the voice sound like high ones and vice versa. This process also occurs naturally if a radio receiver is tuned to a single sideband transmission but set to decode the wrong sideband. There are more advanced forms of voice inversion which are more complex and require more effort to descramble. One method is to use a random code to choose the carrier frequency and then change this code in real time. This is called Rolling Code voice inversion and one can often hear the "ticks" in the transmission which signal the changing of the inversion point. Another method is split band voice inversion. This is where the band is split and then each band is inverted separately. A rolling code can also be added to this method for variable split band inversion (VSB). Common carrier frequencies are: 2.632 kHz, 2.718 kHz, 2.868 kHz, 3.023 kHz, 3.107 kHz, 3.196 kHz, 3.333 kHz, 3.339 kHz, 3.496 kHz, 3.729 kHz and 4.096 kHz. Voice inversion offers no security at all and software is available to restore the original voice, which is why it is no longer used to protect conversations today. However, voice inversion is still found in low-end Chinese walkie talkies.
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CANaerospace

CANaerospace is a higher layer protocol based on Controller Area Network (CAN) which has been developed by Stock Flight Systems in 1998 for aeronautical applications. == Background == CANaerospace supports airborne systems employing the Line-replaceable unit (LRU) concept to share data across CAN and ensures interoperability between CAN LRUs by defining CAN physical layer characteristics, network layers, communication mechanisms, data types and aeronautical axis systems. CANaerospace is an open source project, was initiated to standardize the interface between CAN LRUs on the system level. CANaerospace is continuously being developed further and has also been published by NASA as the Advanced General Aviation Transport Experiments Databus Standard in 2001. It found widespread use in aeronautical research worldwide. A major research aircraft that employs several CANaerospace networks for real-time computer interconnection is the Stratospheric Observatory for Infrared Astronomy (SOFIA), a Boeing 747SP with a 2.5m astronomic telescope. CANaerospace is also frequently used in flight simulation and connects entire aircraft cockpits (i.e. in Eurofighter Typhoon simulators) to the simulation host computers. In Italy CANaerospace is used as UAV data bus technology. Furthermore, CANaerospace serves as communication network in several general aviation avionics systems. The CANaerospace interface definition closes the gap between the ISO/OSI layer 1 and 2 CAN protocol (which is implemented in the CAN controller itself) and the specific requirements of distributed systems in aircraft. It may be used as a primary or ancillary avionics network and was designed to meet the following requirements: Democratic network: CANaerospace does not require any master/slave relationships between LRUs or a "bus controller", thereby avoiding a potential single source of failure. Every node in the network has the same rights for participation in the bus traffic. Self-identifying message format: Each CANaerospace message contains information about the type of the data and the transmitting node. This allows the data to be unambiguously recognized at each receiving node. Continuous Message Numbering: Each CANaerospace message contains a continuously incremented number which allows coherent processing of messages in the receiving stations. Message Status Code: Each CANaerospace message contains information about the integrity of the data is conveying. This allows receiving stations to evaluate the quality of the received data and to react accordingly. Emergency Event Signaling: CANaerospace defines a mechanism that allows each node to transmit information about exception or error situations. This information can be used by other stations to determine the network health. Node Service Interface: As an enhancement to CAN, CANaerospace provides a means for individual stations on the network to communicate with each other using connection-oriented and connectionless services. Predefined CAN Identifier Assignment: CANaerospace offers a predefined identifier assignment list for normal operation data. In addition to the predefined list, user-defined identifier assignment lists may be used. Ease of Implementation: The amount of code to implement CANaerospace is very little by design in order to minimize the effort for testing and certification of flight safety critical systems. Openness to Extensions: All CANaerospace definitions are extendable to provide flexibility for future enhancements and to allow adaptions to the requirements of specific applications. Free Availability: No cost whatsoever apply for the use of CANaerospace. The specification can be downloaded from the Internet == Physical interface == To ensure interoperability and reliable communication, CANaerospace specifies the electrical characteristics, bus transceiver requirements and data rates with the corresponding tolerances based on ISO 11898. The bit timing calculation (baud rate accuracy, sample point definition) and robustness to electromagnetic interference are given special emphasis. Also addressed are CAN connector, wiring considerations and design guidelines to maximize electromagnetic compatibility. == Communication layers == The Bosch CAN specification itself allows messages being transmitted both periodically and aperiodically but does not cover issues like data representation, node addressing or connection-oriented protocols. CAN is entirely based on Anyone-to-Many (ATM) communication which means that CAN messages are always received by all stations in the network. The advantage of the CAN concept is inherent data consistency between all stations, the drawback is that it does not allow node addressing which is the basis for Peer-to-Peer (PTP) communication. Using CAN networks in aeronautical applications, however, demands a standard targeted to the specific requirements of airborne systems which implies that communication between individual stations in the network must be possible to enable the required degree of system monitoring. Consequently, CANaerospace defines additional ISO/OSI layer 3, 4 and 6 functions to support node addressing and unified ATM/PTP communication mechanisms. PTP communication allows to set up client/server interactions between individual stations in the network either temporarily or permanently. More than one of these interactions may be in effect at any given time and each node may be client for one operation and server for another at the same time. This CANaerospace mechanism is called "Node Service Concept" and allows i.e. to distribute system functions over several stations in the network or to control dynamic system reconfiguration in case of failure. The Node Service concept supports both connection-oriented and connectionless interactions like with TCP/IP and UDP/IP for Ethernet. Enabling both ATM and PTP communication for CAN requires the introduction of independent network layers to isolate the different types of communication. This is realized for CANaerospace by forming CAN identifier groups as shown in Figure 1. The resulting structure creates Logical Communication Channels (LCCs) and assigns a specific communication type (ATM, PTP) to each of the LCCs. User-defined LCCs provide the necessary freedom for designers and allow the implementation of CANaerospace according to the needs of specific applications. Figure 1: Logical Communication Channels for CANaerospace As a side effect, the CAN identifier groups in Figure 1 affect the priority of the message transmission in case of bus arbitration. The communication channels are therefore arranged according to their relative importance: Emergency Event Data Channel (EED): This communication channel is used for messages which require immediate action (i.e. system degradation or reconfiguration) and have to be transmitted with very high priority. Emergency Event Data uses ATM communication exclusively. High/Low Priority Node Service Data Channel (NSH/NSL): These communication channels are used for client/server interactions using PTP communication. The corresponding services may be of the connection-oriented as well as the connectionless type. NSH/NSL may also be used to support test and maintenance functions. Normal Operation Data Channel (NOD): This communication channel is used for the transmission of the data which is generated during normal system operation and described in the CANaerospace identifier assignment list. These messages may be transmitted periodically or aperiodically as well as synchronously or asynchronously. All messages which cannot be assigned to other communication channels shall use this channel. High/Low Priority User-Defined Data Channel (UDH/UDL): This channel is dedicated to communication which cannot, due to their specific characteristics, be assigned other channels without violating the CANaerospace specification. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. To ensure interoperability it is highly recommended that the use of these channels is minimized. Debug Service Data Channel (DSD): This channel is dedicated to messages which are used temporarily for development and test purposes only and are not transmitted during normal operation. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. == Data representation == The majority of the real-time control systems used in aeronautics employ "big endian" processor architectures. This data representation was therefore specified for CANaerospace as well. With big endian data representation, the most significant bit of any datum is arranged leftmost and transmitted first on CANaerospace as shown in Figure 2. Figure 2: "Big Endian" Data Representation for CANaerospace CANaerospace uses a self-identifying message
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Concurrency control

In information technology and computer science, especially in the fields of computer programming, operating systems, multiprocessors, and databases, concurrency control ensures that correct results for concurrent operations are generated, while getting those results as quickly as possible. Computer systems, both software and hardware, consist of modules, or components. Each component is designed to operate correctly, i.e., to obey or to meet certain consistency rules. When components that operate concurrently interact by messaging or by sharing accessed data (in memory or storage), a certain component's consistency may be violated by another component. The general area of concurrency control provides rules, methods, design methodologies, and theories to maintain the consistency of components operating concurrently while interacting, and thus the consistency and correctness of the whole system. Introducing concurrency control into a system means applying operation constraints which typically result in some performance reduction. Operation consistency and correctness should be achieved with as good as possible efficiency, without reducing performance below reasonable levels. Concurrency control can require significant additional complexity and overhead in a concurrent algorithm compared to the simpler sequential algorithm. For example, a failure in concurrency control can result in data corruption from torn read or write operations. == Concurrency control in databases == Comments: This section is applicable to all transactional systems, i.e., to all systems that use database transactions (atomic transactions; e.g., transactional objects in Systems management and in networks of smartphones which typically implement private, dedicated database systems), not only general-purpose database management systems (DBMSs). DBMSs need to deal also with concurrency control issues not typical just to database transactions but rather to operating systems in general. These issues (e.g., see Concurrency control in operating systems below) are out of the scope of this section. Concurrency control in Database management systems (DBMS; e.g., Bernstein et al. 1987, Weikum and Vossen 2001), other transactional objects, and related distributed applications (e.g., Grid computing and Cloud computing) ensures that database transactions are performed concurrently without violating the data integrity of the respective databases. Thus concurrency control is an essential element for correctness in any system where two database transactions or more, executed with time overlap, can access the same data, e.g., virtually in any general-purpose database system. Consequently, a vast body of related research has been accumulated since database systems emerged in the early 1970s. A well established concurrency control theory for database systems is outlined in the references mentioned above: serializability theory, which allows to effectively design and analyze concurrency control methods and mechanisms. An alternative theory for concurrency control of atomic transactions over abstract data types is presented in (Lynch et al. 1993), and not utilized below. This theory is more refined, complex, with a wider scope, and has been less utilized in the Database literature than the classical theory above. Each theory has its pros and cons, emphasis and insight. To some extent they are complementary, and their merging may be useful. To ensure correctness, a DBMS usually guarantees that only serializable transaction schedules are generated, unless serializability is intentionally relaxed to increase performance, but only in cases where application correctness is not harmed. For maintaining correctness in cases of failed (aborted) transactions (which can always happen for many reasons) schedules also need to have the recoverability (from abort) property. A DBMS also guarantees that no effect of committed transactions is lost, and no effect of aborted (rolled back) transactions remains in the related database. Overall transaction characterization is usually summarized by the ACID rules below. As databases have become distributed, or needed to cooperate in distributed environments (e.g., Federated databases in the early 1990, and Cloud computing currently), the effective distribution of concurrency control mechanisms has received special attention. === Database transaction and the ACID rules === The concept of a database transaction (or atomic transaction) has evolved in order to enable both a well understood database system behavior in a faulty environment where crashes can happen any time, and recovery from a crash to a well understood database state. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands). Every database transaction obeys the following rules (by support in the database system; i.e., a database system is designed to guarantee them for the transactions it runs): Atomicity - Either the effects of all or none of its operations remain ("all or nothing" semantics) when a transaction is completed (committed or aborted respectively). In other words, to the outside world a committed transaction appears (by its effects on the database) to be indivisible (atomic), and an aborted transaction does not affect the database at all. Either all the operations are done or none of them are. Consistency - Every transaction must leave the database in a consistent (correct) state, i.e., maintain the predetermined integrity rules of the database (constraints upon and among the database's objects). A transaction must transform a database from one consistent state to another consistent state (however, it is the responsibility of the transaction's programmer to make sure that the transaction itself is correct, i.e., performs correctly what it intends to perform (from the application's point of view) while the predefined integrity rules are enforced by the DBMS). Thus since a database can be normally changed only by transactions, all the database's states are consistent. Isolation - Transactions cannot interfere with each other (as an end result of their executions). Moreover, usually (depending on concurrency control method) the effects of an incomplete transaction are not even visible to another transaction. Providing isolation is the main goal of concurrency control. Durability - Effects of successful (committed) transactions must persist through crashes (typically by recording the transaction's effects and its commit event in a non-volatile memory). The concept of atomic transaction has been extended during the years to what has become Business transactions which actually implement types of Workflow and are not atomic. However also such enhanced transactions typically utilize atomic transactions as components. === Why is concurrency control needed? === If transactions are executed serially, i.e., sequentially with no overlap in time, no transaction concurrency exists. However, if concurrent transactions with interleaving operations are allowed in an uncontrolled manner, some unexpected, undesirable results may occur, such as: The lost update problem: A second transaction writes a second value of a data-item (datum) on top of a first value written by a first concurrent transaction, and the first value is lost to other transactions running concurrently which need, by their precedence, to read the first value. The transactions that have read the wrong value end with incorrect results. The dirty read problem: Transactions read a value written by a transaction that has been later aborted. This value disappears from the database upon abort, and should not have been read by any transaction ("dirty read"). The reading transactions end with incorrect results. The incorrect summary problem: While one transaction takes a summary over the values of all the instances of a repeated data-item, a second transaction updates some instances of that data-item. The resulting summary does not reflect a correct result for any (usually needed for correctness) precedence order between the two transactions (if one is executed before the other), but rather some random result, depending on the timing of the updates, and whether certain update results have been included in the summary or not. Most high-performance transactional systems need to run transactions concurrently to meet their performance requirements. Thus, without concurrency control such systems can neither provide correct results nor maintain their databases consistently. === Concurrency control mechanisms === ==== Categories ==== The main categories of concurrency control mechanis
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Cover-coding

Cover-coding is a technique for obscuring the data that is transmitted over an insecure link, to reduce the risks of snooping. An example of cover-coding would be for the sender to perform a bitwise XOR (exclusive OR) of the original data with a password or random number which is known to both sender and receiver. The resulting cover-coded data is then transmitted from sender to the receiver, who uncovers the original data by performing a further bitwise XOR (exclusive OR) operation on the received data using the same password or random number. ISO 18000-6C (EPC Class 1 Generation 2) RFID tags protect some operations with a cover code. The reader requests a random number from the tag, and the tag responds with a new random number. The reader then encrypts future communications with this number, using bitwise XOR, to the data it sends. Cover coding is secure if the tag signal can't be intercepted and the random number is not re-used. Compared to the loud transmissions from the reader, tag backscatter is much weaker and difficult -- but not impossible -- to intercept.
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Clustered file system

A clustered file system (CFS) is a file system which is shared by being simultaneously mounted on multiple servers. There are several approaches to clustering, most of which do not employ a clustered file system (only direct attached storage for each node). Clustered file systems can provide features like location-independent addressing and redundancy which improve reliability or reduce the complexity of the other parts of the cluster. Parallel file systems are a type of clustered file system that spread data across multiple storage nodes, usually for redundancy or performance. == Shared-disk file system == A shared-disk file system uses a storage area network (SAN) to allow multiple computers to gain direct disk access at the block level. Access control and translation from file-level operations that applications use to block-level operations used by the SAN must take place on the client node. The most common type of clustered file system, the shared-disk file system – by adding mechanisms for concurrency control – provides a consistent and serializable view of the file system, avoiding corruption and unintended data loss even when multiple clients try to access the same files at the same time. Shared-disk file-systems commonly employ some sort of fencing mechanism to prevent data corruption in case of node failures, because an unfenced device can cause data corruption if it loses communication with its sister nodes and tries to access the same information other nodes are accessing. The underlying storage area network may use any of a number of block-level protocols, including SCSI, iSCSI, HyperSCSI, ATA over Ethernet (AoE), Fibre Channel, network block device, and InfiniBand. There are different architectural approaches to a shared-disk filesystem. Some distribute file information across all the servers in a cluster (fully distributed). === Examples === == Distributed file systems == Distributed file systems do not share block level access to the same storage but use a network protocol. These are commonly known as network file systems, even though they are not the only file systems that use the network to send data. Distributed file systems can restrict access to the file system depending on access lists or capabilities on both the servers and the clients, depending on how the protocol is designed. The difference between a distributed file system and a distributed data store is that a distributed file system allows files to be accessed using the same interfaces and semantics as local files – for example, mounting/unmounting, listing directories, read/write at byte boundaries, system's native permission model. Distributed data stores, by contrast, require using a different API or library and have different semantics (most often those of a database). === Design goals === Distributed file systems may aim for "transparency" in a number of aspects. That is, they aim to be "invisible" to client programs, which "see" a system which is similar to a local file system. Behind the scenes, the distributed file system handles locating files, transporting data, and potentially providing other features listed below. Access transparency: clients are unaware that files are distributed and can access them in the same way as local files are accessed. Location transparency: a consistent namespace exists encompassing local as well as remote files. The name of a file does not give its location. Concurrency transparency: all clients have the same view of the state of the file system. This means that if one process is modifying a file, any other processes on the same system or remote systems that are accessing the files will see the modifications in a coherent manner. Failure transparency: the client and client programs should operate correctly after a server failure. Heterogeneity: file service should be provided across different hardware and operating system platforms. Scalability: the file system should work well in small environments (1 machine, a dozen machines) and also scale gracefully to bigger ones (hundreds through tens of thousands of systems). Replication transparency: Clients should not have to be aware of the file replication performed across multiple servers to support scalability. Migration transparency: files should be able to move between different servers without the client's knowledge. === History === The Incompatible Timesharing System used virtual devices for transparent inter-machine file system access in the 1960s. More file servers were developed in the 1970s. In 1976, Digital Equipment Corporation created the File Access Listener (FAL), an implementation of the Data Access Protocol as part of DECnet Phase II which became the first widely used network file system. In 1984, Sun Microsystems created the file system called "Network File System" (NFS) which became the first widely used Internet Protocol based network file system. Other notable network file systems are Andrew File System (AFS), Apple Filing Protocol (AFP), NetWare Core Protocol (NCP), and Server Message Block (SMB) which is also known as Common Internet File System (CIFS). In 1986, IBM announced client and server support for Distributed Data Management Architecture (DDM) for the System/36, System/38, and IBM mainframe computers running CICS. This was followed by the support for IBM Personal Computer, AS/400, IBM mainframe computers under the MVS and VSE operating systems, and FlexOS. DDM also became the foundation for Distributed Relational Database Architecture, also known as DRDA. There are many peer-to-peer network protocols for open-source distributed file systems for cloud or closed-source clustered file systems, e. g.: 9P, AFS, Coda, CIFS/SMB, DCE/DFS, WekaFS, Lustre, PanFS, Google File System, Mnet, Chord Project. === Examples === == Network-attached storage == Network-attached storage (NAS) provides both storage and a file system, like a shared disk file system on top of a storage area network (SAN). NAS typically uses file-based protocols (as opposed to block-based protocols a SAN would use) such as NFS (popular on UNIX systems), SMB/CIFS (Server Message Block/Common Internet File System) (used with MS Windows systems), AFP (used with Apple Macintosh computers), or NCP (used with OES and Novell NetWare). == Design considerations == === Avoiding single point of failure === The failure of disk hardware or a given storage node in a cluster can create a single point of failure that can result in data loss or unavailability. Fault tolerance and high availability can be provided through data replication of one sort or another, so that data remains intact and available despite the failure of any single piece of equipment. For examples, see the lists of distributed fault-tolerant file systems and distributed parallel fault-tolerant file systems. === Performance === A common performance measurement of a clustered file system is the amount of time needed to satisfy service requests. In conventional systems, this time consists of a disk-access time and a small amount of CPU-processing time. But in a clustered file system, a remote access has additional overhead due to the distributed structure. This includes the time to deliver the request to a server, the time to deliver the response to the client, and for each direction, a CPU overhead of running the communication protocol software. === Concurrency === Concurrency control becomes an issue when more than one person or client is accessing the same file or block and want to update it. Hence updates to the file from one client should not interfere with access and updates from other clients. This problem is more complex with file systems due to concurrent overlapping writes, where different writers write to overlapping regions of the file concurrently. This problem is usually handled by concurrency control or locking which may either be built into the file system or provided by an add-on protocol. == History == IBM mainframes in the 1970s could share physical disks and file systems if each machine had its own channel connection to the drives' control units. In the 1980s, Digital Equipment Corporation's TOPS-20 and OpenVMS clusters (VAX/ALPHA/IA64) included shared disk file systems.
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Information leakage

Information leakage happens whenever a system that is designed to be closed to an eavesdropper reveals some information to unauthorized parties nonetheless. In other words: Information leakage occurs when secret information correlates with, or can be correlated with, observable information. For example, when designing an encrypted instant messaging network, a network engineer without the capacity to crack encryption codes could see when messages are transmitted, even if he could not read them. == Risk vectors == A modern example of information leakage is the leakage of secret information via data compression, by using variations in data compression ratio to reveal correlations between known (or deliberately injected) plaintext and secret data combined in a single compressed stream. Another example is the key leakage that can occur when using some public-key systems when cryptographic nonce values used in signing operations are insufficiently random. Bad randomness cannot protect proper functioning of a cryptographic system, even in a benign circumstance, it can easily produce crackable keys that cause key leakage. Information leakage can sometimes be deliberate: for example, an algorithmic converter may be shipped that intentionally leaks small amounts of information, in order to provide its creator with the ability to intercept the users' messages, while still allowing the user to maintain an illusion that the system is secure. This sort of deliberate leakage is sometimes known as a subliminal channel. Generally, only very advanced systems employ defenses against information leakage. Following are the commonly implemented countermeasures : Use steganography to hide the fact that a message is transmitted at all. Use chaffing to make it unclear to whom messages are transmitted (but this does not hide from others the fact that messages are transmitted). For busy re-transmitting proxies, such as a Mixmaster node: randomly delay and shuffle the order of outbound packets - this will assist in disguising a given message's path, especially if there are multiple, popular forwarding nodes, such as are employed with Mixmaster mail forwarding. When a data value is no longer going to be used, erase it from the memory.
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Digital transaction management

Digital transaction management (DTM) is a category of cloud services designed to digitally manage document-based transactions. DTM removes the friction inherent in transactions that involve people, documents, and data to create faster, easier, more convenient, and secure processes. DTM goes beyond content and document management to include e-signatures, authentication and non-repudiation; enabling co-browsing between the customer and the business; document transfer and certification; secure archiving that goes beyond records management; and a variety of meta-processes around managing electronic transactions and the documents associated with them. DTM standards are proposed and managed by the xDTM Standard Association Aragon Research has estimated that "by YE 2016, 70% of large enterprises will have a DTM initiative underway or fully implemented."
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Brooklyn Bridge (software)

The Brooklyn Bridge from White Crane Systems was a data transfer enabler. Although it came with some hardware, it was the software which was the basis of the product. It also could transform the data's format. == Overview == The New York Times described its category as being among "communications packages used to transfer files." In an era of 300 baud, Brooklyn Bridge operated at "115,200 baud" so that a transfer which "at 300 baud took 4 minutes and 36 seconds" only needed 5 seconds. Unlike some communications packages, this one retains the original version-date, so as not to alarm people when they seem to have what looks like an update, when it's not. == Description == Once the software is installed, users comfortable with typing the word "COPY" can do so as readily as they sneakernet. An earlier review described it as "less cumbersome than conventional communications software" The use of neither specialized hardware nor specialized software is ideal in an era when this can be done using online or other "outside" services.
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Viral marketing

Viral marketing is a business strategy that uses existing social networks to promote a product or service on social media platforms. Its name refers to how consumers spread information about a product with other people, much in the same way that a virus spreads from one person to another. It can be delivered by word of mouth, or enhanced by the network effects of the Internet and mobile networks. The concept is often misused or misunderstood, as people apply it to any successful enough story without taking into account the word "viral". Viral advertising is personal and, while coming from an identified sponsor, it does not mean businesses pay for its distribution. Most of the well-known viral ads circulating online are ads paid by a sponsor company, launched either on their own platform (company web page or social media profile) or on social media websites such as YouTube. Consumers receive the page link from a social media network or copy the entire ad from a website and pass it along through e-mail or posting it on a blog, web page or social media profile. Viral marketing may take the form of video clips, advergames, ebooks, brandable software, images, text messages, email messages, or web pages. The most commonly utilized transmission vehicles for viral messages include pass-along based, incentive based, trendy based, and undercover based. However, the creative nature of viral marketing enables an "endless amount of potential forms and vehicles the messages can utilize for transmission", including mobile devices. The ultimate goal of marketers interested in creating successful viral marketing programs is to create viral messages that appeal to individuals with high social networking potential (SNP) and that have a high probability of being presented and spread by these individuals and their competitors in their communications with others in a short period. The term "viral marketing" has also been used pejoratively to refer to stealth marketing campaigns—marketing strategies that advertise a product to people without them knowing they are being marketed to. == History == The emergence of "viral marketing", as an approach to advertisement, has been tied to the popularization of the notion that ideas spread like viruses. The field that developed around this notion, memetics, peaked in popularity in the 1990s. As this then began to influence marketing gurus, it took on a life of its own in that new context. The brief career of Australian pop singer Marcus Montana is largely remembered as an early example of viral marketing. In early 1989, thousands of posters declaring "Marcus is Coming" were placed around Sydney, generating discussion and interest within the media and the community about the meaning of the mysterious advertisements. The campaign successfully made Montana's musical debut a talking point, but his subsequent music career was a failure. The term is found in PC User magazine in 1989 with a somewhat differing meaning. It was later used by Jeffrey Rayport in the 1996 Fast Company article "The Virus of Marketing", and Tim Draper and Steve Jurvetson of the venture capital firm Draper Fisher Jurvetson in 1997 to describe Hotmail's practice of appending advertising to outgoing mail from their users. Doug Rushkoff, a media critic, wrote about viral marketing on the Internet in 1996. Bob Gerstley wrote about algorithms designed to identify people with high "social networking potential." Gerstley employed SNP algorithms in quantitative marketing research. In 2004, the concept of the alpha user was coined to indicate that it had now become possible to identify the focal members of any viral campaign, the "hubs" who were most influential. Alpha users could be targeted for advertising purposes most accurately in mobile phone networks, due to their personal nature. In early 2013, the first ever Viral Summit was held in Las Vegas. == Factors == Marketer Jonah Berger defines six key factors that drive virality, organized in an acronym called STEPPS: Social currency – the better something makes people look, the more likely they will be to share it Triggers – things that are "top of mind" are more likely to be "tip of the tongue" Emotion – when people care, they share Public – the easier something is to see, the more likely people are to imitate it Practical value – people share useful information to help others Stories – like a Trojan Horse, stories carry messages and ideas along for the ride. Another important factor that drives virality is the propagativity of the content, referring to the ease with which consumers can redistribute it. == Psychology == To form deeper connections with viewers and increase the chances of virality, many marketers use psychological principles. They argue that this approach is scientific and can foster an environment where the odds of gaining traction are much higher. People find psychological safety and can develop a sense of trust when more people interact with online content. For this reason, marketers work to develop media that resonates with viewers on a deeper, emotional level as this approach frequently results in higher engagement. This level of interaction serves as a sign of approval, reducing the personal risk that is subconsciously linked to associating oneself with a company or brand’s content. Professor Jonah Berger at the University of Pennsylvania's Wharton School of Business affirms that marketing campaigns that trigger psychological responses linked to strong emotions tend to perform better. In particular, Berger found that positive emotions like happiness, joy, and excitement have more successful share rates than their negative counterparts. This outcome results from the human instinct to respond more positively to content with activating emotions, increasing the desire to share content, which contributes to its virality. Viral marketing utilizes the primitive feeling of frisson to increase their view and share counts. This feeling of excitement is considered powerful because of its ability to cause a physical response. From increased heart rates to full body chills, Professor Brent Coker at the University of Melbourne describes that this approach to marketing triggers a primitive response that immerses the viewer in the content on a deeper level. Researchers Juliana Fernandes from the University of Florida and Sigal Segev from the Florida International University also found that people are more inclined to share emotional campaigns over those that are heavily informational. They claim that consumers do not often care to learn about a product’s actual features and benefits. Instead, people prefer to be immersed in experience-based content that creates an emotional impact. Companies and brands can benefit from treating their content in this manner and go viral more frequently than those who do not. Social proof is another psychological phenomenon that impacts viral content. Experts in this field argue that it is a natural instinct to want to behave similarly to others because it results in positive validation. This phenomenon explains the human need to conform, so marketers focus on creating engaging content that encourages interactions and causes a snowball effect. This subconsciously influences people to like, comment, and share if they already see others doing the same. Social proof goes further by providing people with a form of social currency. When individuals interact with and share content, they become associated with the topics at hand. People naturally tend to perceive one another, and this pattern carries over to the digital world. As a result, many people tend to be vigilant about the viral marketing they engage with, since they want to be perceived positively. Companies and brands have the opportunity to develop social currency themselves by aligning with their target audiences and creating marketing campaigns that fit their interests or match their values. == Methods and metrics == According to marketing professors Andreas Kaplan and Michael Haenlein, to make viral marketing work, three basic criteria must be met, i.e., giving the right message to the right messengers in the right environment: Messenger: Three specific types of messengers are required to ensure the transformation of an ordinary message into a viral one: market mavens, social hubs, and salespeople. Market mavens are individuals who are continuously 'on the pulse' of things (information specialists); they are usually among the first to get exposed to the message and who transmit it to their immediate social network. Social hubs are people with an exceptionally large number of social connections; they often know hundreds of different people and have the ability to serve as connectors or bridges between different subcultures. Salespeople might be needed who receive the message from the market maven, amplify it by making it more relevant and persuasive, and then transmit it to the social hub for further distr
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Undeniable signature

An undeniable signature is a digital signature scheme which allows the signer to be selective to whom they allow to verify signatures. The scheme adds explicit signature repudiation, preventing a signer later refusing to verify a signature by omission; a situation that would devalue the signature in the eyes of the verifier. It was invented by David Chaum and Hans van Antwerpen in 1989. == Overview == In this scheme, a signer possessing a private key can publish a signature of a message. However, the signature reveals nothing to a recipient/verifier of the message and signature without taking part in either of two interactive protocols: Confirmation protocol, which confirms that a candidate is a valid signature of the message issued by the signer, identified by the public key. Disavowal protocol, which confirms that a candidate is not a valid signature of the message issued by the signer. The motivation for the scheme is to allow the signer to choose to whom signatures are verified. However, that the signer might claim the signature is invalid at any later point, by refusing to take part in verification, would devalue signatures to verifiers. The disavowal protocol distinguishes these cases removing the signer's plausible deniability. It is important that the confirmation and disavowal exchanges are not transferable. They achieve this by having the property of zero-knowledge; both parties can create transcripts of both confirmation and disavowal that are indistinguishable, to a third-party, of correct exchanges. The designated verifier signature scheme improves upon deniable signatures by allowing, for each signature, the interactive portion of the scheme to be offloaded onto another party, a designated verifier, reducing the burden on the signer. == Zero-knowledge protocol == The following protocol was suggested by David Chaum. A group, G, is chosen in which the discrete logarithm problem is intractable, and all operation in the scheme take place in this group. Commonly, this will be the finite cyclic group of order p contained in Z/nZ, with p being a large prime number; this group is equipped with the group operation of integer multiplication modulo n. An arbitrary primitive element (or generator), g, of G is chosen; computed powers of g then combine obeying fixed axioms. Alice generates a key pair, randomly chooses a private key, x, and then derives and publishes the public key, y = gx. === Message signing === Alice signs the message, m, by computing and publishing the signature, z = mx. === Confirmation (i.e., avowal) protocol === Bob wishes to verify the signature, z, of m by Alice under the key, y. Bob picks two random numbers: a and b, and uses them to blind the message, sending to Alice: c = magb. Alice picks a random number, q, uses it to blind, c, and then signing this using her private key, x, sending to Bob: s1 = cgq ands2 = s1x. Note that s1x = (cgq)x = (magb)xgqx = (mx)a(gx)b+q = zayb+q. Bob reveals a and b. Alice verifies that a and b are the correct blind values, then, if so, reveals q. Revealing these blinds makes the exchange zero knowledge. Bob verifies s1 = cgq, proving q has not been chosen dishonestly, and s2 = zayb+q, proving z is valid signature issued by Alice's key. Note that zayb+q = (mx)a(gx)b+q. Alice can cheat at step 2 by attempting to randomly guess s2. === Disavowal protocol === Alice wishes to convince Bob that z is not a valid signature of m under the key, gx; i.e., z ≠ mx. Alice and Bob have agreed an integer, k, which sets the computational burden on Alice and the likelihood that she should succeed by chance. Bob picks random values, s ∈ {0, 1, ..., k} and a, and sends: v1 = msga and v2 = zsya, where exponentiating by a is used to blind the sent values. Note that v2 = zsya = (mx)s(gx)a = v1x. Alice, using her private key, computes v1x and then the quotient, v1xv2−1 = (msga)x(zsgxa)−1 = msxz−s = (mxz−1)s. Thus, v1xv2−1 = 1, unless z ≠ mx. Alice then tests v1xv2−1 for equality against the values: (mxz−1)i for i ∈ {0, 1, …, k}; which are calculated by repeated multiplication of mxz−1 (rather than exponentiating for each i). If the test succeeds, Alice conjectures the relevant i to be s; otherwise, she conjectures random value. Where z = mx, (mxz−1)i = v1xv2−1 = 1 for all i, s is unrecoverable. Alice commits to i: she picks a random r and sends hash(r, i) to Bob. Bob reveals a. Alice confirms that a is the correct blind (i.e., v1 and v2 can be generated using it), then, if so, reveals r. Revealing these blinds makes the exchange zero knowledge. Bob checks hash(r, i) = hash(r, s), proving Alice knows s, hence z ≠ mx. If Alice attempts to cheat at step 3 by guessing s at random, the probability of succeeding is 1/(k + 1). So, if k = 1023 and the protocol is conducted ten times, her chances are 1 to 2100.
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CloudPassage

CloudPassage is a company that provides an automation platform, delivered via software as a service, that improves security for private, public, and hybrid cloud computing environments. CloudPassage is headquartered in San Francisco. == History == CloudPassage was founded by Carson Sweet, Talli Somekh, and Vitaliy Geraymovych in 2010. The company used cloud computing and big data analytics to implement security monitoring and control in a platform called Halo. CloudPassage spent a year in stealth developing the Halo technology, coming out of stealth mode to a closed beta in January 2011. In June 2012, the company launched the commercial product that included configuration security monitoring, network microsegmentation, and two-factor authentication for privileged access management. By 2013, CloudPassage expanded Halo to support large enterprises with advanced security and compliance requirements with a product called Halo Enterprise. The first round of venture funding for the company raised $6.5 million. In April 2012, CloudPassage raised $14 million. The financing round was led by Tenaya Capital. In February 2014, CloudPassage announced that it had raised $25.5 million in funding led by Shasta Ventures. In total, the company has invested over $30 million in its technology and raised approximately $88 million in capital. == Product == The CloudPassage platform provides cloud workload security and compliance for systems hosted in public or private cloud infrastructure environments, including hybrid cloud and multi-cloud workload hosting models. The flagship product the company offers is called Halo. Halo secures virtual servers in public, private, and hybrid cloud infrastructures and provides file integrity monitoring (FIM) while also administering firewall automation, vulnerability monitoring, network access control, security event alerting, and assessment. The Halo platform also provides security applications such as privileged access management, software vulnerability scanning, multifactor authentication, and log-based IDS. In December 2013, CloudPassage set up six servers with Microsoft Windows and Linux operating systems and combinations of popular programs and invited hackers to attempt to hack into the servers. The top prize was $5,000 and the winning hacker was a novice that completed the task in four hours. CloudPassage programmed the servers to use basic default security settings to show how vulnerable cloud computing programs can be to security threats. == Awards and recognition == In May 2011, Gigaom named CloudPassage in its list of the Top 50 Cloud Innovators. That same month, eWeek recognized CloudPassage as one of 16 Hot Startup Companies Flying Under the Radar. SC Magazine named CloudPassage an Industry Innovator in the Virtualization and Cloud Security category in 2012. Also in 2012, The Wall Street Journal named CloudPassage a runner-up in the Information Security category of its Technology Innovation Awards. The CloudPassage large-scale security program, Halo, won Best Security Solution in 2014 at the SIIA Codie awards.
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Social media therapy

Social media therapy is a form of expressive therapy. It uses the act of creating and sharing user-generated content as a way of connecting with and understanding people. Social media therapy combines different expressive therapy aspects of talk therapy, art therapy, writing therapy, and drama therapy and applies them to the web domain. Within social media therapy, synchronous or asynchronous dialogue occurs through exchanges of audio, text or visual information. The digital content is published online to serve as a form of therapy. == Background == Time spent online via email, websites, instant messaging and social media has increased: since 1999, more than 2,554 million people have become internet users. This alters the way people communicate with each other, and alters the connotation of certain words. The concepts of "identity", "friend", "like" and "connected" have adapted alongside technology. People are influenced by data sharing, social marketing, and technological tools. There are multiple therapeutic services offered through the internet. E-therapy, online counseling, cyber therapy, and social media therapy are similar in that each utilizes the internet in order to provide therapy for patients. == Controversy == There are pros and cons when it comes to the subject of online therapy. Criticism of providing therapy through online methods comes from concerns over the lack of physical contact. There are important features of therapy created through face-to-face therapy such as transference and countertransference that can not be created through online therapy. Patricia R. Recupero and Samara E. Rainey stated in their article "Informed Consent to E-Therapy" of American Journal of Psychotherapy that the lack of face-to-face interaction increased the risk of misdiagnosis and misunderstanding between the E-therapist and patient, thereby increasing the risk of uncertainty for the clinician. There are also concerns over the internet creating a distraction from the therapy itself. Confidentiality and privacy concerns have been raised as well. However, several systematic reviews have found that online psychotherapy can produce clinical outcomes comparable to face-to-face treatment, suggesting that physical distance does not inherently reduce therapeutic effectiveness.
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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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