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Topic Review
Biography
Topic Review
Æternity
æternity is a public, open-source, blockchain-based distributed computing and digital-asset platform that builds upon decentralized cryptographic P2P technology. Real-world data can interface with smart contracts through "oracles". Scalability and trustless Turing-complete state channels set æternity apart from Blockchain 2.0 projects. æternity provides a virtual machine which can execute scripts implementing a so-called "smart contracts" using an decentralized global network of public nodes maintaining the blockchain. State channels enable smart contracts to remain private and scalable as long as there is no disagreement between the counterparties. Inherently integrated in æternity is a value token called an "æon" or "AE token", which can be transferred between participants and is used to compensate participant nodes for computations performed. AE tokens are used to pay for space and computation time on the virtual machine and prevent spam on the network while allocating storage and computation time proportionally to the incentive offered by the request.
532
18 Nov 2022
Topic Review
Shellshock (Software Bug)
Shellshock, also known as Bashdoor, is a family of security bugs in the Unix Bash shell, the first of which was disclosed on 24 September 2014. Shellshock could enable an attacker to cause Bash to execute arbitrary commands and gain unauthorized access to many Internet-facing services, such as web servers, that use Bash to process requests. On 12 September 2014, Stéphane Chazelas informed Bash's maintainer Chet Ramey of his discovery of the original bug, which he called "Bashdoor". Working with security experts, Mr. Chazelas developed a patch (fix) for the issue, which by then had been assigned the vulnerability identifier CVE-2014-6271. The existence of the bug was announced to the public on 2014-09-24, when Bash updates with the fix were ready for distribution. The bug Chazelas discovered caused Bash to unintentionally execute commands when the commands are concatenated to the end of function definitions stored in the values of environment variables. Within days of its publication, a variety of related vulnerabilities were discovered (CVE-2014-6277, CVE-2014-6278, CVE-2014-7169, CVE-2014-7186 and CVE-2014-7187). Ramey addressed these with a series of further patches. Attackers exploited Shellshock within hours of the initial disclosure by creating botnets of compromised computers to perform distributed denial-of-service attacks and vulnerability scanning. Security companies recorded millions of attacks and probes related to the bug in the days following the disclosure. Because of the potential to compromise millions of unpatched systems, Shellshock was compared to the Heartbleed bug in its severity.
1.1K
18 Nov 2022
Topic Review
Electrochemical RAM
Electrochemical Random-Access Memory (ECRAM) is a type of non-volatile memory (NVM) with multiple levels per cell (MLC) designed for deep learning analog acceleration. An ECRAM cell is a three-terminal device composed of a conductive channel, an insulating electrolyte, an ionic reservoir, and metal contacts. The resistance of the channel is modulated by ionic exchange at the interface between the channel and the electrolyte upon application of an electric field. The charge-transfer process allows both for state retention in the absence of applied power, and for programming of multiple distinct levels, both differentiating ECRAM operation from that of a field-effect transistor (FET). The write operation is deterministic and can result in symmetrical potentiation and depression, making ECRAM arrays attractive for acting as artificial synaptic weights in physical implementations of artificial neural networks (ANN). The technological challenges include open circuit potential (OCP) and semiconductor foundry compatibility associated with energy materials. Universities, government laboratories, and corporate research teams have contributed to the development of ECRAM for analog computing. Notably, Sandia National Laboratories designed a lithium-based cell inspired by solid-state battery materials, Stanford University built an organic proton-based cell, and International Business Machines (IBM) demonstrated in-memory selector-free parallel programming for a logistic regression task in an array of metal-oxide ECRAM designed for insertion in the back end of line (BEOL). In 2022, researchers at Massachusetts Institute of Technology built an inorganic, CMOS-compatible protonic technology that achieved near-ideal modulation characteristics using nanosecond fast pulses
560
18 Nov 2022
Topic Review
Matthews Correlation Coefficient
The Matthews correlation coefficient (MCC) or phi coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975. The MCC is defined identically to Pearson's phi coefficient, introduced by Karl Pearson, also known as the Yule phi coefficient from its introduction by Udny Yule in 1912. Despite these antecedents which predate Matthews's use by several decades, the term MCC is widely used in the field of bioinformatics and machine learning. The coefficient takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient between the observed and predicted binary classifications; it returns a value between −1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and −1 indicates total disagreement between prediction and observation. However, if MCC equals neither −1, 0, or +1, it is not a reliable indicator of how similar a predictor is to random guessing because MCC is dependent on the dataset. MCC is closely related to the chi-square statistic for a 2×2 contingency table where n is the total number of observations. While there is no perfect way of describing the confusion matrix of true and false positives and negatives by a single number, the Matthews correlation coefficient is generally regarded as being one of the best such measures. Other measures, such as the proportion of correct predictions (also termed accuracy), are not useful when the two classes are of very different sizes. For example, assigning every object to the larger set achieves a high proportion of correct predictions, but is not generally a useful classification. The MCC can be calculated directly from the confusion matrix using the formula: In this equation, TP is the number of true positives, TN the number of true negatives, FP the number of false positives and FN the number of false negatives. If any of the four sums in the denominator is zero, the denominator can be arbitrarily set to one; this results in a Matthews correlation coefficient of zero, which can be shown to be the correct limiting value. The MCC can be calculated with the formula: using the positive predictive value, the true positive rate, the true negative rate, the negative predictive value, the false discovery rate, the false negative rate, the false positive rate, and the false omission rate. The original formula as given by Matthews was: This is equal to the formula given above. As a correlation coefficient, the Matthews correlation coefficient is the geometric mean of the regression coefficients of the problem and its dual. The component regression coefficients of the Matthews correlation coefficient are Markedness (Δp) and Youden's J statistic (Informedness or Δp'). Markedness and Informedness correspond to different directions of information flow and generalize Youden's J statistic, the [math]\displaystyle{ \delta }[/math]p statistics and (as their geometric mean) the Matthews Correlation Coefficient to more than two classes. Some scientists claim the Matthews correlation coefficient to be the most informative single score to establish the quality of a binary classifier prediction in a confusion matrix context.
3.8K
18 Nov 2022
Topic Review
MDFAT
DriveSpace (initially known as DoubleSpace) is a disk compression utility supplied with MS-DOS starting from version 6.0 in 1993 and ending in 2000 with the release of Windows Me. The purpose of DriveSpace is to increase the amount of data the user could store on disks by transparently compressing and decompressing data on-the-fly. It is primarily intended for use with hard drives, but use for floppy disks is also supported. This feature was removed in Windows XP and later.
264
18 Nov 2022
Topic Review
Lithium Technologies
Lithium Technologies is a San Francisco-based provider of software that allows businesses to connect with their customers on social media and digital channels. Lithium was founded in 2001 as a spin-out from GX Media, which created technologies for professional rankings and tournaments and now hosts a number of popular gaming sites. The company's founders include Dennis Fong, Lyle Fong, Michel Thouati, Kirk Yokomizo, John Joh, Nader Alizadeh, Michael Yang, and Matt Ayres. The Lithium platform comprises a set of products for digital marketing and social customer support, including Lithium Communities, which act as the hub for a brand's digital conversations; Lithium Response, a social customer care tool; and Lithium Reach a social media management tool. Klout data powers the Lithium platform with its proprietary algorithms and volume of data with over 750M scored profiles." Lithium's SaaS platform combines online customer community applications such as forums, blogs, innovation management, product reviews, and tribal knowledge bases with the broader social web and traditional CRM business processes, resulting in a wide range of online customer interaction methods. Stemming from its gaming roots, the platform incorporates elaborate rating systems for contributors, with ranks, badges, and "kudos counts". Lithium hosts an annual user conference, LiNC (Lithium Networking Conference) designed to give digital strategists a deeper understanding of trends in social customer engagement. Since its inception in 2008, the event has grown 1,100% to 900+ attendees in 2016. In 2017, the company held a series of localized LiNC events in cities like London, San Francisco, and Sydney.
725
18 Nov 2022
Topic Review
Planck Length
In physics, the Planck length, denoted ℓP, is a unit of length in the system of Planck units that was originally proposed by physicist Max Planck, equal to 1.616255(18)×10−35 m.[note 1] The Planck length can be defined from three fundamental physical constants: the speed of light, the Planck constant, and the gravitational constant. It is also the reduced Compton wavelength of a particle with Planck mass. Regardless of whether it represents some fundamental limit to the universe, it is a useful unit in theoretical physics.
23.2K
18 Nov 2022
Topic Review
Shift (Command)
This article presents a list of commands used by DOS operating systems, especially as used on x86-based IBM PC compatibles (PCs). Other DOS operating systems are not part of the scope of this list. In DOS, many standard system commands were provided for common tasks such as listing files on a disk or moving files. Some commands were built into the command interpreter, others existed as external commands on disk. Over the several generations of DOS, commands were added for the additional functions of the operating system. In the current Microsoft Windows operating system, a text-mode command prompt window, cmd.exe, can still be used.
446
18 Nov 2022
Topic Review
ID2SBVR: Semantics of Business Vocabulary and Rules
Semantics of Business Vocabulary and Rules (SBVR) is a standard that is applied in describing business knowledge in the form of controlled natural language. Business process designers develop SBVR from formal documents and later translate it into business process models. In many immature companies, these documents are often unavailable and could hinder resource efficiency efforts. ID2SBVR mines fact type candidates using word patterns or extracting triplets (actor, action, and object) from sentences.
484
18 Nov 2022
Topic Review
From Word Embeddings to Pre-Trained Language Models
With the advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. Prior embedding models as well as breakthroughs in the field of PLMs are provided in this entry.
1.3K
18 Nov 2022
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