The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque
Coalition for App Fairness
The Coalition for App Fairness (CAF) is a coalition comprised by companies, who aim to reach a fairer deal for the inclusion of their apps into the Apple App Store or the Google Play Store. The organization's executive director is Meghan DiMuzio and its headquarters are located in Washington, D.C. == Background == In July 2015, Spotify launched an email campaign to urge its App Store subscribers to cancel their subscriptions and start new ones through its website, bypassing the 30% transaction fee for in-app purchases required for iOS applications by technology company Apple Inc. A later update to the Spotify app on iOS was rejected by Apple, prompting Spotify's general counsel Horacio Gutierrez to write a letter to Apple's then-general counsel Bruce Sewell, stating: "This latest episode raises serious concerns under both U.S. and EU competition law. It continues a troubling pattern of behavior by Apple to exclude and diminish the competitiveness of Spotify on iOS and as a rival to Apple Music, particularly when seen against the backdrop of Apple's previous anticompetitive conduct aimed at Spotify … we cannot stand by as Apple uses the App Store approval process as a weapon to harm competitors." In August 2020, Epic Games updated their Fortnite Battle Royale game app on both Apple's App Store and Google's Google Play to include its own storefront that offered a 20% discount on V-Bucks, the in-game currency, if players bought through there rather than through the app stores' storefront, both which take a 30% revenue cut of the sale. Both Apple and Google removed the Fortnite app within hours, as this alternate storefront violated their terms of use that required all in-app purchases to be made through their storefronts. Epic immediately filed lawsuits against both companies challenging their storefront policies on antitrust principles, arguing that their non-negotiable 30% revenue cut is too high and the restrictions against alternate storefronts anticompetitive. Apple countersued Epic over its behavior, leading to a highly publicized 2021 bench trial. Ultimately, Epic largely lost its lawsuit against Apple, though the court did order Apple to allow developers to point users to alternative payment methods. Conversely, Epic won its antitrust lawsuit against Google in late 2023. == Foundation == On 24 September 2020, Epic Games joined forces with thirteen other prominent companies—including the music streaming platform Spotify, Tinder owner Match Group, the encrypted mail service Proton Mail, and the crypto currency website Blockchain.com—to establish the Coalition for App Fairness. It also includes Basecamp. The coalition criticizes the fact that for now the app stores of both Apple and Google charge their clients a 30% fee on any purchases made over their stores. Apple and Google defended themselves by arguing that the 30% transaction fee is a standard in the industry while the Coalition for App Fairness states that there is no other transaction fee which is even close to the 30%. In October 2020, it was reported that the coalition grew from 13 to 40 members since its foundation and received more than 400 applications for membership. In October 2025, X (formerly Twitter) joined CAF. This was seen as a larger pushback in the industry against Apple and Google, and a step towards hopefully passing the Bipartisan Open App Markets Act. == Aims == The group has broadened their demands for the app stores and now also aim for a better treatment for the apps available in the App Store. They claim that Apple favors its own services before other services available on the market and unjustifiably excludes other apps from their App Store. The group has also been viewing other transaction fees like the 5% fee which is charged by credit card companies, and states that Apple charges up to 600% more and would like the 30% fee, which was only included in 2011 by Apple, adapted to a comparable percentage that charge other providers of payment solutions. Its demands are mainly directed at Apple's strict control over its App Store, but to a lesser extent are also directed towards Google. Google allows apps to be downloaded over an independent web link or also another App Store, such as the Epic Game App Store. The organization emphasizes that no app developer should come into the position in which they are discriminated and are not granted the same rights as to the developers of the owner of the app store. == Reactions == In October 2020, Microsoft presented a new framework concerning the access to its Windows 10 operating system by app stores other than the one offered by Microsoft. The new framework is based on the demands of the Coalition for App Fairness. Microsoft emphasized though, that these principles would not apply to the Xbox. In December 2020, Apple announced that they would be lowering the revenue cut Apple takes for app developers making $1M or less from 30% to 15% if app developers fill out an application for the lowered revenue cut. In March 2021, Google followed suit by also lowering the revenue cut from the Play Store from 30% to 15% for the first million in revenue earned by a developer each year. == Notable members == Members listed are notable companies listed as members the groups website: Blockchain.com Deezer Epic Games European Digital SME Alliance Fanfix Life360 Masimo Nium Proton Mail Spotify TapTap Threema Vipps
Synaptic transistor
A synaptic transistor is an electrical device that can learn in ways similar to a neural synapse. It optimizes its own properties for the functions it has carried out in the past. The device mimics the behavior of the property of neurons called spike-timing-dependent plasticity, or STDP. == Structure == Its structure is similar to that of a field effect transistor, where an ionic liquid takes the place of the gate insulating layer between the gate electrode and the conducting channel. That channel is composed of samarium nickelate (SmNiO3, or SNO) rather than the field effect transistor's doped silicon. == Function == A synaptic transistor has a traditional immediate response whose amount of current that passes between the source and drain contacts varies with voltage applied to the gate electrode. It also produces a much slower learned response such that the conductivity of the SNO layer varies in response to the transistor's STDP history, essentially by shuttling oxygen ions between the SNO and the ionic liquid. The analog of strengthening a synapse is to increase the SNO's conductivity, which essentially increases gain. Similarly, weakening a synapse is analogous to decreasing the SNO's conductivity, lowering the gain. The input and output of the synaptic transistor are continuous analog values, rather than digital on-off signals. While the physical structure of the device has the potential to learn from history, it contains no way to bias the transistor to control the memory effect. An external supervisory circuit converts the time delay between input and output into a voltage applied to the ionic liquid that either drives ions into the SNO or removes them. A network of such devices can learn particular responses to "sensory inputs", with those responses being learned through experience rather than explicitly programmed.
Gremlin (query language)
Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation. Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors. Gremlin's automata and functional language foundation enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, and hybrid depth- and breadth-first evaluation with Turing completeness. As an explanatory analogy, Apache TinkerPop and Gremlin are to graph databases what the JDBC and SQL are to relational databases. Likewise, the Gremlin traversal machine is to graph computing as what the Java virtual machine is to general purpose computing. == History == 2009-10-30 the project is born, and immediately named "TinkerPop" 2009-12-25 v0.1 is the first release 2011-05-21 v1.0 is released 2012-05-24 v2.0 is released 2015-01-16 TinkerPop becomes an Apache Incubator project 2015-07-09 v3.0.0-incubating is released 2016-05-23 Apache TinkerPop becomes a top-level project 2016-07-18 v3.1.3 and v3.2.1 are first releases as Apache TinkerPop 2017-12-17 v3.3.1 is released 2018-05-08 v3.3.3 is released 2019-08-05 v3.4.3 is released 2020-02-20 v3.4.6 is released 2021-05-01 v3.5.0 is released 2022-04-04 v3.6.0 is released 2023-07-31 v3.7.0 is released 2025-11-12 v3.8.0 is released == Vendor integration == Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors. The table below outlines those graph vendors that support Gremlin. == Traversal examples == The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below. === Simple traversals === For each vertex in the graph, emit its label, then group and count each distinct label. What year was the oldest movie made? What is Die Hard's average rating? === Projection traversals === For each category, emit a map of its name and the number of movies it represents. For each movie with at least 11 ratings, emit a map of its name and average rating. Sort the maps in decreasing order by their average rating. Emit the first 10 maps (i.e. top 10). === Declarative pattern matching traversals === Gremlin supports declarative graph pattern matching similar to SPARQL. For instance, the following query below uses Gremlin's match()-step. What 80's action movies do 30-something programmers like? Group count the movies by their name and sort the group count map in decreasing order by value. Clip the map to the top 10 and emit the map entries. === OLAP traversal === Which movies are most central in the implicit 5-stars graph? == Gremlin graph traversal machine == Gremlin is a virtual machine composed of an instruction set as well as an execution engine. An analogy is drawn between Gremlin and Java. === Gremlin steps (instruction set) === The following traversal is a Gremlin traversal in the Gremlin-Java8 dialect. The Gremlin language (i.e. the fluent-style of expressing a graph traversal) can be represented in any host language that supports function composition and function nesting. Due to this simple requirement, there exists various Gremlin dialects including Gremlin-Groovy, Gremlin-Scala, Gremlin-Clojure, etc. The above Gremlin-Java8 traversal is ultimately compiled down to a step sequence called a traversal. A string representation of the traversal above provided below. The steps are the primitives of the Gremlin graph traversal machine. They are the parameterized instructions that the machine ultimately executes. The Gremlin instruction set is approximately 30 steps. These steps are sufficient to provide general purpose computing and what is typically required to express the common motifs of any graph traversal query. Given that Gremlin is a language, an instruction set, and a virtual machine, it is possible to design another traversal language that compiles to the Gremlin traversal machine (analogous to how Scala compiles to the JVM). For instance, the popular SPARQL graph pattern match language can be compiled to execute on the Gremlin machine. The following SPARQL query would compile to In Gremlin-Java8, the SPARQL query above would be represented as below and compile to the identical Gremlin step sequence (i.e. traversal). === Gremlin Machine (virtual machine) === The Gremlin graph traversal machine can execute on a single machine or across a multi-machine compute cluster. Execution agnosticism allows Gremlin to run over both graph databases (OLTP) and graph processors (OLAP).
Sample exclusion dimension
In computational learning theory, sample exclusion dimensions arise in the study of exact concept learning with queries. In algorithmic learning theory, a concept over a domain X is a Boolean function over X. Here we only consider finite domains. A partial approximation S of a concept c is a Boolean function over Y ⊆ X {\displaystyle Y\subseteq X} such that c is an extension to S. Let C be a class of concepts and c be a concept (not necessarily in C). Then a specifying set for c w.r.t. C, denoted by S is a partial approximation S of c such that C contains at most one extension to S. If we have observed a specifying set for some concept w.r.t. C, then we have enough information to verify a concept in C with at most one more mind change. The exclusion dimension, denoted by XD(C), of a concept class is the maximum of the size of the minimum specifying set of c' with respect to C, where c' is a concept not in C.
Cloud Security Alliance
Cloud Security Alliance (CSA) is a not-for-profit organization with the mission to "promote the use of best practices for providing security assurance within cloud computing, artificial intelligence and to provide education on the uses of cloud computing to help secure all other forms of computing." The CSA has over 80,000 individual members worldwide. The CSA gained significant reputability in 2011 when the American Presidential Administration selected the CSA Summit as the venue for announcing the federal government’s cloud computing strategy. == History == The CSA was formed in December 2008 as a coalition by individuals who saw the need to provide objective enterprise user guidance on the adoption and use of cloud computing. Its initial work product, Security Guidance for Critical Areas of Focus in Cloud Computing, was put together in a Wiki-style by dozens of volunteers. In 2014, the Chairman of the Board of the CSA was Dave Cullinane, VP of Global Security and Privacy for Catalina Marketing, St. Petersburg, Florida, and former CISO for eBay. Cullinane has said, "If you have an application exposed to the Internet that will allow people to make money, it will be probed." == Profile == In 2009, the Cloud Security Alliance incorporated in Nevada as a Corporation and achieved US Federal 501(c)6 non-profit status. It is registered as a Foreign Non-Profit Corporation in Washington. == Policy maker support == The CSA works to support a number of global policy makers in their focus on cloud security initiatives including the National Institute of Standards and Technology (NIST), European Commission, Singapore Government, and other data protection authorities. In March 2012, the CSA was selected to partner with three of Europe’s largest research centers (CERN, EMBL and ESA) to launch Helix Nebula – The Science Cloud. == Size == The Cloud Security Alliance employs roughly sixty full-time and contract staff worldwide. It has several thousand active volunteers participating in research, working groups and chapters at any time. == Membership == According to CSA, they are a member-driven organization, chartered with promoting the use of best practices for providing security assurance within Cloud Computing, and providing education on the uses of Cloud Computing to help secure all other forms of computing. === Individuals === Individuals who are interested in cloud computing and have experience to assist in making it more secure receive a complimentary individual membership based on a minimum level of participation. === Chapters === The Cloud Security Alliance has a network of chapters worldwide. Chapters are separate legal entities from the Cloud Security Alliance, but operate within guidelines set down by the Cloud Security Alliance In the United States, Chapters may elect to benefit from the non-profit tax shield that the Cloud Security Alliance has. Chapters are encouraged to hold local meetings and participate in areas of research. Chapter activities are coordinated by the Cloud Security Alliance worldwide. === International scope === There are separate legal entities in Europe and Asia Pacific, called Cloud Security Alliance (Europe), a Scottish company in the United Kingdom, and Cloud Security Alliance Asia Pacific Ltd, in Singapore. Each legal entity is responsible for overseeing all Cloud Security Alliance-related activities in their respective regions. These legal entities operate under an agreement with Cloud Security Alliance that give it oversight power and have separate Boards of Directors. Both are companies Limited By Guarantee. The Managing Directors of each are members of the Executive Team of Cloud Security Alliance. == Areas of research == The Cloud Security Alliance has 25+ active working groups. Key areas of research include cloud standards, certification, education and training, guidance and tools, global reach, and driving innovation. Security Guidance for Critical Areas of Focus in Cloud Computing. Foundational best practices for securing cloud computing. Top Threats to Cloud Computing. Helps organizations make educated risk management decisions regarding their cloud adoption strategies. GRC (Governance, Risk and Compliance) Stack. A toolkit for key stakeholders to instrument and assess clouds against industry established best practices, standards and critical compliance requirements. Cloud Controls Matrix (CCM). Security controls framework for cloud provider and cloud consumers. CloudTrust Protocol. The mechanism by which cloud service consumers ask for and receive information about the elements of transparency as applied to cloud service providers. Consensus Assessments Initiative Research. Tools and processes to perform consistent measurements of cloud providers. Software Defined Perimeter. A proposed security framework that can be deployed to protect application infrastructure from network-based attacks. It will incorporate standards from organizations such as OASIS and NIST and security concepts from organizations like the U.S. DoD into an integrated framework. == Working groups and initiatives == Mobile Working Group Big Data Working Group Security as a Service Working Group Trusted Cloud Initiative CloudAudit CloudCERT CloudSIRT Cloud Metrics Security, Trust and Assurance Registry (STAR) Cloud Data Governance Turbot (business) Blockchain/Distributed Ledger
Generalized blockmodeling
In generalized blockmodeling, the blockmodeling is done by "the translation of an equivalence type into a set of permitted block types", which differs from the conventional blockmodeling, which is using the indirect approach. It's a special instance of the direct blockmodeling approach. Generalized blockmodeling was introduced in 1994 by Patrick Doreian, Vladimir Batagelj and Anuška Ferligoj. == Definition == Generalized blockmodeling approach is a direct one, "where the optimal partition(s) is (are) identified based on minimal values of a compatible criterion function defined by the difference between empirical blocks and corresponding ideal blocks". At the same time, the much broader set of block types is introduced (while in conventional blockmodeling only certain types are used). The conventional blockmodeling is inductive due to nonspecification of neither the clusters or the location of block types, while in generalized blockmodeling the blockmodel is specified with more detail than just the permition of certain block types (e.g., prespecification). Further, it's possible to define departures from the permitted (ideal) blocktype, using criterion function. Using local optimization procedure, firstly the initial clustering (with specified number of clusters is done, based on random creation. How the clusters are neighboring to each other, is based on two transformations: 1) a vertex is moved from one to another cluster or 2) a pair of vertices is interchanged between two different clusters. This process of transformation steps is repeated many times, until only the best fitting partitions (with the minimized value of the criterion function) are kept as blockmodels for the future exploration of the network. Different types of generalized blockmodeling are: generalized binary blockmodeling, generalized valued blockmodeling and generalized homogeneity blockmodeling. == Benefits == According to Patrick Doreian, the benefits of generalized blockmodeling, are as follows: usage of explicit criterion function, compatible with a given type of equivalence, results to in-built measure of fit, which is integral to the establishment of the blockmodels (in conventional blockmodeling, there is no compelling and coherent measures of fit); partitions, based on generalized blockmodeling, regularly outperform and never perform less well than the partitions, based on conventional approach; with generalized blockmodeling it's possible to specify new types of blockmodels; this potentially unlimited set of new block types also results in permittion of inclusion of substantively driven blockmodels; in generalized blockmodeling, the specification of the block types and the location of some of them in the blockmodel is possible; researcher can speficy which (pair of) vertices must be (not) clustered together; this approach also allows the imposition of penalties, resulting into identification of empirical null blocks without inconsistencies with a corresponding ideal null block. == Problems == According to Doreian, the problems of generalized blockmodeling, are as follows: unknown sensitivity to particular data features, examination of boundary problems, computationally burdensome, which results in a constraint regarding practical network size (generalized blockmodeling is thus primarily used to analyse smaller networks (below 100 units)), identifying structure from incomplete network information, most of generalized blockmodeling is based on binary networks, but there is also development in the field of valued networks, criterion function is minimized for a specified blockmodel, with results in issues of evaluating statistically, based on the structural data alone, problems regarding three dimensional network data, problems regarding the evolution of fundamental network structure. == Book == The book with the same title, Generalized blockmodeling, written by Patrick Doreian, Vladimir Batagelj and Anuška Ferligoj, was in 2007 awarded the Harrison White Outstanding Book Award by the Mathematical Sociology Section of American Sociological Association.