Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services. Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others. Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users with advertising. Scientists and machine learning researchers extract insights and design product features. Users may not understand how platforms use their data. Users tend to click through Terms of Use agreements without reading them, leading to ethical questions about whether platforms adequately protect users' privacy. During the 2016 United States presidential election, Facebook allowed Cambridge Analytica, a political consulting firm linked to the Trump campaign, to analyze the data of an estimated 87 million Facebook users to profile voters, creating controversy when this was revealed. == Background == As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Instagram, Photobucket, or Picasa), news aggregation (Google Reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegrees.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media. == Uses == Social media mining is used across several industries including business development, social science research, health services, and educational purposes. Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. These forces are then measured via statistical analysis of the nodes and connections between these nodes. Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. This provides important social information about users' emotions on specific topics. These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. Analysts also value measures of homophily: the tendency of two similar individuals to become friends. Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. These analyses can also help create recommendations for individuals in a tailored capacity. By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with. == Perception == Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google. Companies such as these, considered "Big Tech" are companies that build algorithms that take advantage of user input to understand their preferences, and keep them on the platform as much as possible. These inputs, that can be as simple as time spent on a given screen, provide the data being mined, and lead to companies profiting heavily from using that data to capitalize on extremely accurate predictions about user behavior. The growth of platforms accelerated rapidly once these strategies were put in place; Most of the largest platforms now average over 1 billion active users per month as of 2021. It has been claimed by a multitude of anti-algorithm personalities, like Tristan Harris or Chamath Palihapitiya, that certain companies (specifically Facebook) valued growth above all else, and ignored potential negative impacts from these growth engineering tactics. At the same time, users have now created their own data arbitrages with the help of their own data, through content monetization and becoming influencers. Users typically have access to a varied set of analytics specific to people that interact with them on social media, and can use these as building blocks for their own targeting and growth strategies through ads and posts that cater to their audiences. Influencers also commonly promote products and services for established brands, creating one of the largest digital industries: Influencer marketing. Instagram, Facebook, Twitter, YouTube, Google, and others have long given access to platform analytics, and allowed third parties to access that information as well, at times unbeknownst to even the user whose data is being viewed/bought. == Research == === Research areas === Social media event detection – Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. Public health monitoring and surveillance - Using large-scale analysis of social media to study large cohorts of patients and the general public, e.g. to obtain early warning signals of drug-drug interactions and adverse drug reactions, or understand human reproduction and sexual interest. Community structure (Community Detection/Evolution/Evaluation) – Identifying communities on social networks, how t
PressWise
PressWise was digital imposition software to quickly and easily impose most any variety of flat and folding layouts. It was acquired by the Aldus Prepress Group affectionately known in the print and publishing industry as the Aldus WiseGuys in August 1991 from Emulation Technologies Inc. of Cleveland, Ohio. It was further developed by the Aldus Press Group and launched as the first of many Aldus prepress products in 1993. It was subsequently owned by Adobe Systems, then Luminous Corporation (Seattle), then Imation, and finally ScenicSoft. PressWise was discontinued by ScenicSoft in 1999 ultimately. == History == In February 2009, the PressWise copyright was acquired by Aethos Technologies and a new print automation product was launched by its creator, Eric Wold of Santa Rosa, California. This new product has no relationship to the old imposition software of the same name. It's notable that Larry Letteney, former President of Creo Americas was a board member and shareholder of Aethos Technologies during its early phase. Datatech SmartSoft acquired exclusive distribution rights to the software in September 2009. In September 2010 Datatech SmartSoft completed the acquisition of the PressWise brand and product.
Conditional disclosure of secrets
Conditional disclosure of secrets (CDS) is a primitive, studied in information-theoretic cryptography, that allows distributed, non-communicating parties to coordinate the release of information to a third party. CDS was initially introduced for use in the context of private information retrieval, and has been related to communication complexity and non-local quantum computation. == Definition of conditional disclosure of secrets == The conditional disclosure of secrets setting involves three players; Alice, Bob and the referee. Alice receives an input x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} and a secret z ∈ { 0 , 1 } {\displaystyle z\in \{0,1\}} , and Bob receives a string y ∈ { 0 , 1 } n {\displaystyle y\in \{0,1\}^{n}} . A choice of Boolean function f : { 0 , 1 } 2 n → { 0 , 1 } {\displaystyle f:\{0,1\}^{2n}\rightarrow \{0,1\}} is fixed in advance and known to all players. Alice and Bob cannot communicate with one another, but share a string of random bits which we label r {\displaystyle r} . Alice and Bob compute messages m A = m A ( x , z , r ) {\displaystyle m_{A}=m_{A}(x,z,r)} and m B = m B ( y , r ) {\displaystyle m_{B}=m_{B}(y,r)} , which they send to the referee. The referee knows ( x , y ) {\displaystyle (x,y)} . A CDS protocol consists of the encoding maps applied by Alice and Bob. A protocol is said to be ϵ {\displaystyle \epsilon } -correct if, for all ( x , y ) ∈ f − 1 ( 1 ) {\displaystyle (x,y)\in f^{-1}(1)} , the referee can determine z {\displaystyle z} with probability 1 − ϵ {\displaystyle 1-\epsilon } . A protocol is said to be δ {\displaystyle \delta } -secure if, for all ( x , y ) ∈ f − 1 ( 0 ) {\displaystyle (x,y)\in f^{-1}(0)} the distribution of the messages is δ {\displaystyle \delta } close to a simulator distribution (in total variation distance), where the simulator distribution is independent of z {\displaystyle z} . The communication complexity of a CDS protocol P is the total number of bits of message sent by Alice and Bob. The CDS communication cost of a function, C D S ϵ , δ ( f ) {\displaystyle CDS_{\epsilon ,\delta }(f)} is the minimal communication cost of an ϵ {\displaystyle \epsilon } -correct, δ {\displaystyle \delta } secure protocol that implements f {\displaystyle f} . The randomness complexity and randomness cost of implementing a function in the CDS model are defined similarly, but consider the number of bits of shared random bits held by Alice and Bob. == Basic properties of the primitive == === Amplification === Supposing we have an ϵ {\displaystyle \epsilon } -correct and δ {\displaystyle \delta } -secure CDS protocol, it is known that we can find a new protocol which reduces the correctness and privacy errors at the expense of an increased communication and randomness cost. More specifically, the following theorem has been proven Theorem (Amplification). A CDS protocol for f which supports a single-bit secret with privacy and correctness error of 1/3 can be transformed into a new CDS protocol with privacy and correctness error of 2 − Ω ( k ) {\displaystyle 2^{-\Omega (k)}} and communication/randomness complexity which are larger than those of the original protocol by a multiplicative factor of O(k). In fact, somewhat more than the above theorem is true in that the size of the secret can also be made to be of length k {\displaystyle k} , while simultaneously reducing the correctness and privacy errors as above. The proof involves first encoding the secret z {\displaystyle z} into a secret sharing scheme, and then running the original CDS protocol on each share of the resulting scheme. === Closure === If a CDS protocol for a function f {\displaystyle f} is known, then certain simple modifications of f {\displaystyle f} have CDS protocols with similar efficiency. The simplest case is to consider a CDS protocol for function f {\displaystyle f} and ask for a similarly efficient protocol for the negation of f {\displaystyle f} , labelled ¬ f {\displaystyle \neg f} . This is addressed by the following theorem Theorem (CDS is closed under complement). Suppose that f has a CDS protocol with randomness cost of ρ {\displaystyle \rho } bits, communication complexity of t {\displaystyle t} bits, and privacy and correctness errors δ = ϵ = 2 − k {\displaystyle \delta =\epsilon =2^{-k}} . Then ¬ f {\displaystyle \neg f} has a CDS scheme with similar privacy and correctness errors, and randomness and communication complexity of O ( k 3 ρ 2 t + k 3 ρ 3 ) {\displaystyle O(k^{3}\rho ^{2}t+k^{3}\rho ^{3})} . The cost of a CDS protocol is also closed under formula's, in the following sense. Consider two functions f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . Then, the communication and randomness costs of f 1 ∧ f 2 {\displaystyle f_{1}\wedge f_{2}} as well as f 1 ∨ f 2 {\displaystyle f_{1}\vee f_{2}} are not much larger than the sum of the costs for f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . See Applebaum et al. for a precise statement. == Upper and lower bounds on communication cost == Given a function f {\displaystyle f} we would like to understand the communication and randomness costs to implement f {\displaystyle f} in the CDS setting. Towards understanding this, protocols for implementing CDS have been developed (which give an upper bound on the cost) and lower bound strategies have been developed. For most functions, there is a large gap between the known upper and lower bound, so understanding the cost of CDS remains largely an open problem. This section presents some of what is known so far about the cost of CDS. === Secret sharing based upper bound === A subject with a close relationship to CDS is secret sharing. Secret sharing constructions provide an upper bound on the cost of CDS protocols. A secret sharing scheme encodes a secret, s {\displaystyle s} into a set of shares S 1 , . . . , S n {\displaystyle S_{1},...,S_{n}} . Associated to any secret sharing scheme is an access structure, which consists of a set of authorized sets A = A 1 , . . . , A k {\displaystyle {\mathcal {A}}={A_{1},...,A_{k}}} with A i ⊆ { S 1 , . . . , S n } {\displaystyle A_{i}\subseteq \{S_{1},...,S_{n}\}} . The authorized sets are those subsets of the A i {\displaystyle A_{i}} from which it is possible to recover the secret recorded into the scheme. A succinct way to describe an access structure is in terms of a function f A : { 0 , 1 } n → { 0 , 1 } {\displaystyle f_{\mathcal {A}}:\{0,1\}^{n}\rightarrow \{0,1\}} . Each subset of the shares K [ x ] ⊂ { S 1 , . . . , S n } {\displaystyle K[x]\subset \{S_{1},...,S_{n}\}} is labelled by a string x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} such that x i = 1 {\displaystyle x_{i}=1} if and only if S i ∈ K {\displaystyle S_{i}\in K} . Then we define f A {\displaystyle f_{\mathcal {A}}} to be such that f A ( x ) = 1 {\displaystyle f_{\mathcal {A}}(x)=1} if and only if K [ x ] ∈ A {\displaystyle K[x]\in {\mathcal {A}}} . In words, the function f A {\displaystyle f_{\mathcal {A}}} is 1 when given an authorized subset as input, and 0 otherwise. A basic result in the theory of secret sharing is that an access structure A {\displaystyle {\mathcal {A}}} can be realized in a secret sharing scheme if and only if f A {\displaystyle f_{\mathcal {A}}} is monotone. The size of a secret sharing scheme is defined as the total number of bits in the shares S i {\displaystyle S_{i}} . For monotone functions, there is an upper bound on the communication cost in CDS for any monotone function f {\displaystyle f} in terms of the size of any secret sharing scheme with access structure given by f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S h a r i n g S i z e ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SharingSize(f)} For some concrete classes of secret sharing schemes, this relationship can be extended to general (non-monotone) Boolean functions. This leads to an upper bound on CDS cost in terms of the size of any span program that computes f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S P k ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SP_{k}(f)} The class of problems with efficient (polynomial size) span program is the complexity class M o d k L {\displaystyle Mod_{k}L} , so problems in this class have efficient CDS protocols. === Sub-exponential upper bounds for all functions === Using a matching vector family based construction, it has been proven that ∀ f , C D S ϵ = 0 , δ = 0 ( f ) ≤ 2 O ( n log n ) {\displaystyle \forall f,\,\,\,\,\,\,CDS_{\epsilon =0,\delta =0}(f)\leq 2^{O({\sqrt {n\log n}})}} . The technique for this proof is similar to one used to prove upper bounds on private information retrieval. This upper bound on CDS also leads to sub-exponential upper bounds on the size of a large class of secret sharing schemes. === Lower bounds from communication complexity === In a CDS protocol, the referee is given the inputs ( x , y ) {\displaystyle (x,y)} . This means it is not clear if the messages sent by Alice a
CARE Principles for Indigenous Data Governance
The CARE Principles for Indigenous Data Governance are a set of principles intended to guide open data projects in engaging Indigenous Peoples rights and interests. CARE was created in 2019 by the International Indigenous Data Sovereignty Interest Group, a group that is a part of the Research Data Alliance. It outlines collective rights related to open data in the context of the United Nations Declaration on the Rights of Indigenous Peoples and Indigenous data sovereignty. CARE is an acronym which stands for Collective Benefit, Authority to Control, Responsibility, Ethics. The CARE Principles are 'people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination', and intended as a complement to the data-oriented perspective of other standards such as FAIR data (findable, accessible, interoperable, reusable). The CARE principles have been embedded into the Beta version of Standardised Data on Initiatives (STARDIT). CARE principles were the basis of a submission to the UN's Global Digital Compact.
Social media optimization
Social media optimization (SMO) is the use of online platforms to generate income or publicity to increase the awareness of a brand, event, product or service. Types of social media involved include RSS feeds, blogging sites, social bookmarking sites, social news websites, video sharing websites such as YouTube and social networking sites such as Facebook, Instagram, TikTok and X (Twitter). SMO is similar to search engine optimization (SEO) in that the goal is to drive web traffic, and draw attention to a company or creator. SMO's focal point is on gaining organic links to social media content. In contrast, SEO's core is about reaching the top of the search engine hierarchy. In general, social media optimization refers to optimizing a website and its content to encourage more users to use and share links to the website across social media and networking sites. SMO is used to strategically create online content ranging from well-written text to eye-catching digital photos or video clips that encourages and entices people to engage with a website. Users share this content, via its weblink, with social media contacts and friends. Common examples of social media engagement are "liking and commenting on posts, retweeting, embedding, sharing, and promoting content". Social media optimization is also an effective way of implementing online reputation management (ORM), meaning that if someone posts bad reviews of a business, an SMO strategy can ensure that the negative feedback is not the first link to come up in a list of search engine results. In the 2010s, with social media sites overtaking TV as a source for news for young people, news organizations have become increasingly reliant on social media platforms for generating web traffic. Publishers such as The Economist employ large social media teams to optimize their online posts and maximize traffic, while other major publishers now use advanced artificial intelligence (AI) technology to generate higher volumes of web traffic. == Relationship with search engine optimization == Social media optimization is an increasingly important factor in search engine optimization, which is the process of designing a website in a way so that it has as high a ranking as possible on search engines. Search engines are increasingly utilizing the recommendations of users of social networks such as Reddit, Facebook, Tumblr, Twitter, YouTube, LinkedIn, Pinterest and Instagram to rank pages in the search engine result pages. The implication is that when a webpage is shared or "liked" by a user on a social network, it counts as a "vote" for that webpage's quality. Thus, search engines can use such votes accordingly to properly ranked websites in search engine results pages. Furthermore, since it is more difficult to tip the scales or influence the search engines in this way, search engines are putting more stock into social search. This, coupled with increasingly personalized search based on interests and location, has significantly increased the importance of a social media presence in search engine optimization. Due to personalized search results, location-based social media presences on websites such as Yelp, Google Places, Foursquare, and Yahoo! Local have become increasingly important. While social media optimization is related to search engine marketing, it differs in several ways. Primarily, SMO focuses on driving web traffic from sources other than search engines, though improved search engine ranking is also a benefit of successful social media optimization. Further, SMO is helpful to target particular geographic regions in order to target and reach potential customers. This helps in lead generation (finding new customers) and contributes to high conversion rates (i.e., converting previously uninterested individuals into people who are interested in a brand or organization). == Relationship with viral marketing == Social media optimization is in many ways connected to the technique of viral marketing or "viral seeding" where word of mouth is created through the use of networking in social bookmarking, video and photo sharing websites. An effective SMO campaign can harness the power of viral marketing; for example, 80% of activity on Pinterest is generated through "repinning." Furthermore, by following social trends and utilizing alternative social networks, websites can retain existing followers while also attracting new ones. This allows businesses to build an online following and presence, all linking back to the company's website for increased traffic. For example, with an effective social bookmarking campaign, not only can website traffic be increased, but a site's rankings can also be increased. In a similar way, the engagement with blogs creates a similar result by sharing content through the use of RSS in the blogosphere. Social media optimization is considered an integral part of an online reputation management (ORM) or search engine reputation management (SERM) strategy for organizations or individuals who care about their online presence. SMO is one of six key influencers that affect Social Commerce Construct (SCC). Online activities such as consumers' evaluations and advices on products and services constitute part of what creates a Social Commerce Construct (SCC). Social media optimization is not limited to marketing and brand building. Increasingly, smart businesses are integrating social media participation as part of their knowledge management strategy (i.e., product/service development, recruiting, employee engagement and turnover, brand building, customer satisfaction and relations, business development and more). Additionally, social media optimization can be implemented to foster a community of the associated site, allowing for a healthy business-to-consumer (B2C) relationship. == Origins and implementation == According to technologist Danny Sullivan, the term "social media optimization" was first used and described by marketer Rohit Bhargava on his marketing blog in August 2006. In the same post, Bhargava established the five important rules of social media optimization. Bhargava believed that by following his rules, anyone could influence the levels of traffic and engagement on their site, increase popularity, and ensure that it ranks highly in search engine results. An additional 11 SMO rules have since been added to the list by other marketing contributors. The 16 rules of SMO, according to one source, are as follows: Increase your linkability Make tagging and bookmarking easy Reward inbound links Help your content to "travel" via sharing Encourage the mashup, where users are allowed to remix content Be a user resource, even if it doesn't help you (e.g., provide resources and information for users) Reward helpful and valuable users Participate (join the online conversation) Know how to target your audience Create new, quality content ("web scraping" of existing online content is ignored by good search engines) Be "real" in the tone and style of the posts Don't forget your roots; be humble Don't be afraid to experiment, innovate, try new things and "stay fresh" Develop an SMO strategy Choose your SMO tactics wisely Make SMO a key part of your marketing process and develop company best practices Bhargava's initial five rules were more specifically designed to SMO, while the list is now much broader and addresses everything that can be done across different social media platforms. According to author and CEO of TopRank Online Marketing, Lee Odden, a Social Media Strategy is also necessary to ensure optimization. This is a similar concept to Bhargava's list of rules for SMO. The Social Media Strategy may consider: Objectives e.g. creating brand awareness and using social media for external communications. Listening e.g. monitoring conversations relating to customers and business objectives. Audience e.g. finding out who the customers are, what they do, who they are influenced by, and what they frequently talk about. It is important to work out what customers want in exchange for their online engagement and attention. Participation and content e.g. establishing a presence and community online and engaging with users by sharing useful and interesting information. Measurement e.g. keeping a record of likes and comments on posts, and the number of sales to monitor growth and determine which tactics are most useful in optimizing social media. According to Lon Safko and David K. Brake in The Social Media Bible, it is also important to act like a publisher by maintaining an effective organizational strategy, to have an original concept and unique "edge" that differentiates one's approach from competitors, and to experiment with new ideas if things do not work the first time. If a business is blog-based, an effective method of SMO is using widgets that allow users to share content to their personal social media platforms. This will ultimately reach a wider target audience and drive mor
EXAPT
EXAPT (a portmanteau of "Extended Subset of APT") is a production-oriented programming language that allows users to generate NC programs with control information for machining tools and facilitates decision-making for production-related issues that may arise during various machining processes. EXAPT was first developed to address industrial requirements. Through the years, the company created additional software for the manufacturing industry. Today, EXAPT offers a suite of SAAS products and services for the manufacturing industry. The trade name, EXAPT, is most commonly associated with the CAD/CAM-System, production data, and tool management software of the German company EXAPT Systemtechnik GmbH based in Aachen, DE. == General == EXAPT is a modularly built programming system for all NC machining operations as Drilling Turning Milling Turn-Milling Nibbling Flame-, laser-, plasma- and water jet cutting Wire eroding Operations with industrial robots Due to the modular structure, the main product groups, EXAPTcam and EXAPTpdo, are gradually expandable and permit individual software for the manufacturing industry used individually and also in a compound with an existing IT environment. == Functionality == EXAPTcam meets the requirements for NC planning, especially for the cutting operations such as turning, drilling, and milling up to 5-axis simultaneous machining. Thereby new process technologies, tool, and machine concepts are constantly involved. In the NC programming data from different sources such as 3D CAD models, drawings or tables can flow in. The possibilities of NC programming reaches from language-oriented to feature-oriented NC programming. The integrated EXAPT knowledge database and intelligent and scalable automatisms support the user. The EXAPT NC planning also covers the generation of production information as clamping and tool plans, presetting data or time calculations. The realistic simulation possibilities of NC planning and NC control data provide with production reliability. EXAPTpdo (EXAPT ProductionsDataOrganization) provides a neutrally applicable technology platform for the information compound of the NC planning - to the shop floor. This applies to all NC production data that are necessary for the set-up of NC machines, for the provision, presetting, and stocking of manufacturing resources and provided by EXAPTpdo in a central database. Besides classical functions of the tool management system (TMS) as the management of cutting tools, measuring, testing and clamping devices the technology data management and tool lifecycle management (TLM) is also included. System-supported "where-used lists" helps to handle the manufacturing resource cycle by secured requirement determination and requirement fulfillment. Unnecessary transports and unplanned dispositive adjustments are dropped, stocks are reduced, set-up times reduced and the throughput is increased. EXAPTpdo synchronizes involved systems within the value chain. Stock systems, MES systems or ERP systems (e.g. from the purchasing or production areas) do not work in isolation from each other but they interact with each other. EXAPTpdo provides the base to Smart Factory, for more flexibility in production and faster communication. == History == With the foundation of the EXAPT-Verein in 1967 as spin-off of the universities Aachen, Berlin and Stuttgart the further development "EXAPT (EXtended Subset of APT)" of the programming language "APT (Automatically Programmed Tool)" was focused and so the first milestone for the EXAPT history was set. In the same year the system EXAPT 1 for drilling and simple milling tasks became available. 1969 The industrial application of EXAPT 2 for the programming of NC machines with 2-axis linear and path control begins. In the following year, the development of the EXAPT modular system starts. 1972 BASIC-EXAPT is provided for the universal, homogeneous programming of all NC tasks. The support is made by the EXAPT applications consultancy. 1973 EXAPT 1.1 is provided for the programming of straight-cut and continuous-path controlled drilling and milling machines and machining centers. At the Hanover Fair (IHA 73) the interactive access to a mainframe via a time-sharing terminal for the part program entry and correction is presented and starts the replacement of the punch card. 1974 The possibilities for the use of process computers for the NC data transfer are leveled out. EXAPT offers the possibility of the result simulation when using plotters with display of tool paths and tools in assignment to the workpiece. In April 1975, the EXAPT NC Systemtechnik GmbH was founded with the aim, of enabling entry into the NC technique for small and medium-sized companies by a complete product and service program. In the following year, the system portfolio is extended with further system modules and service programs and the provision of postprocessors. 1978 The development activities on the EXAPT module system started in 1970 are completed. Using modern software techniques, the different system parts BASIC-EXAPT, EXAPT 1, EXAPT 1.1, and EXAPT 2 are composed of a total system. System support and applications consultancy become a new working focus. From the beginning to the middle of the 1980s Beside new portable software modules for CAD/CAM applications (e. g. CAPEX, NESTEX, CADEX, CADCPL), the first version of the EXAPT DNC system and extensions of the EXAPT NC programming system for the machining of sculptured surfaces are presented. 1988 EXAPT expands the software product range by systems for tool data management (BMO) and production data management (FDO). EXAPT trains more than 1,300 course participants including company-specific courses. 1992 The first version of the completely new product generation EXAPTplus is presented and the agency in Dresden is opened. 1993 The company name "EXAPT NC Systemtechnik GmbH" is changed to "EXAPT Systemtechnik GmbH." EXAPTplus is presented on PC under Windows NT at the EMO '93. The decentralization of the use of EXAPT systems expands the range of applications. In the following year, EXAPT-DNC is executable under Windows on a customary PC. Special hardware is not needed and so it can be used in compound with the database-supported EXAPT production data management system (FDO). 1995 EXAPTplus is also ready for complex application cases such as machining of tubes at extrusion tools. EXAPT-CADI provides the transfer of 2D CAD data to EXAPTplus. With the new office Gießen the marketing is strengthened. In the following year the EXAPT NC editor is developed for the direct processing of NC control data with tool path display and visualization of the tools. In the course of the market entry of more comfortable 3D CAD systems for the solid modelling of components a detailed evaluation of current systems is made in 1997. It is decided to use SolidWorks as a reference system for the solid-oriented NC planning with EXAPT. 1998 The first solution for the transfer of geometry data between SolidWorks and EXAPTplus is generated. The EXAPT organization systems are (beside SQL) also executable under Oracle now. The use of client server solutions supports the data flow in the production. 1999 AFR functions are provided in connection with EXAPTsolid to support a workpiece modelling for NC. The millennium capability is ensured for all EXAPT systems. AFR is a ground-breaking for the integration of third-party products. 2002 EXAPT-BMG is developed for the generation and visualization of tools with additional functions for the assembly from components. The acquisition of tools with their geometric and technological presentation offers extensive support of the NC planning with EXAPT systems. 2003 EXAPTpdo is available to optimize the process chains in production planning and production execution optimally regarding the increasing requirements of changing production conditions. 2004 Diverse system extensions are made in EXAPTplus, EXAPTsolid, EXAPT NC editor, EXAPTpdo for the complete machining on turning/milling centres with result reliability because of more extensive simulation based on realNC (Tecnomatix), for the use of new complex tool systems and the compound use between ERP systems as SAP and intelligent CNC systems. In the following year, EXAPTpdo is extended for the cross-order set-up optimization and provision of manufacturing re-sources especially for single and small series production with connection to purchase and physical portfolio management. 2006 The EXAPT systems are available for extended use as an information platform for production, the time management, and similar requirements. EXAPTsolid is extended for the feature-oriented milling operation and machine simulation. The NC programming of complex machine tools, e.g. three-turret-turning/milling centers is supported by EXAPT systems, as well as the use of multi-functional tools. 2007 A module for 3-5-axis simultaneous milling machining is presented.
HTTP Strict Transport Security
HTTP Strict Transport Security (HSTS) is a policy mechanism that helps to protect websites against man-in-the-middle attacks such as protocol downgrade attacks and cookie hijacking. It allows web servers to declare that web browsers (or other complying user agents) should automatically interact with it using only HTTPS connections, which provide Transport Layer Security (TLS/SSL), unlike the insecure HTTP used alone. HSTS is an IETF standards track protocol and is specified in RFC 6797. The HSTS Policy is communicated by the server to the user agent via an HTTP response header field named Strict-Transport-Security. HSTS Policy specifies a period of time during which the user agent should only access the server in a secure fashion. Websites using HSTS often do not accept clear text HTTP, either by rejecting connections over HTTP or systematically redirecting users to HTTPS (though this is not required by the specification). The consequence of this is that a user-agent not capable of doing TLS will not be able to connect to the site. The protection normally only applies after a user has visited the site at least once, relying on the principle of "trust on first use". The way this protection works is that when a user entering or selecting an HTTP (not HTTPS) URL to the site, the client, such as a Web browser, will automatically upgrade to HTTPS without making an HTTP request, thereby preventing any HTTP man-in-the-middle attack from occurring. To counteract this problem, an HSTS preload list maintained by Google Chrome and used by other major web browsers is maintained. If a domain is on this list, the browser skips the initial request and encrypts all communication immediately. Additional domains can be registered at no cost. == Specification history == The HSTS specification was published as RFC 6797 on 19 November 2012 after being approved on 2 October 2012 by the IESG for publication as a Proposed Standard RFC. The authors originally submitted it as an Internet Draft on 17 June 2010. With the conversion to an Internet Draft, the specification name was altered from "Strict Transport Security" (STS) to "HTTP Strict Transport Security", because the specification applies only to HTTP. The HTTP response header field defined in the HSTS specification however remains named "Strict-Transport-Security". The last so-called "community version" of the then-named "STS" specification was published on 18 December 2009, with revisions based on community feedback. The original draft specification by Jeff Hodges from PayPal, Collin Jackson, and Adam Barth was published on 18 September 2009. The HSTS specification is based on original work by Jackson and Barth as described in their paper "ForceHTTPS: Protecting High-Security Web Sites from Network Attacks". Additionally, HSTS is the realization of one facet of an overall vision for improving web security, put forward by Jeff Hodges and Andy Steingruebl in their 2010 paper The Need for Coherent Web Security Policy Framework(s). == HSTS mechanism overview == A server implements an HSTS policy by supplying a header over an HTTPS connection (HSTS headers over HTTP are ignored). For example, a server could send a header such that future requests to the domain for the next year (max-age is specified in seconds; 31,536,000 is equal to one non-leap year) use only HTTPS: Strict-Transport-Security: max-age=31536000. When a web application issues HSTS Policy to user agents, conformant user agents behave as follows: Automatically turn any insecure links referencing the web application into secure links (e.g. http://example.com/some/page/ will be modified to https://example.com/some/page/ before accessing the server). If the security of the connection cannot be ensured (e.g. the server's TLS certificate is not trusted), the user agent must terminate the connection and should not allow the user to access the web application. This helps protect web application users against some passive (eavesdropping) and active network attacks. A man-in-the-middle attacker has a greatly reduced ability to intercept requests and responses between a user and a web application server while the user's browser has HSTS Policy in effect for that web application. == Applicability == The most important security vulnerability that HSTS can fix is SSL-stripping man-in-the-middle attacks, first publicly introduced by Moxie Marlinspike in his 2009 BlackHat Federal talk "New Tricks For Defeating SSL In Practice". The SSL (and TLS) stripping attack works by transparently converting a secure HTTPS connection into a plain HTTP connection. The user can see that the connection is insecure, but crucially there is no way of knowing whether the connection should be secure. At the time of Marlinspike's talk, many websites did not use TLS/SSL, therefore there was no way of knowing (without prior knowledge) whether the use of plain HTTP was due to an attack, or simply because the website had not implemented TLS/SSL. Additionally, no warnings are presented to the user during the downgrade process, making the attack fairly subtle to all but the most vigilant. Marlinspike's sslstrip tool, presented at Black Hat DC 2009, fully automates the attack. HSTS addresses this problem by informing the browser that connections to the site should always use TLS/SSL. The HSTS header can be stripped by the attacker if this is the user's first visit. Google Chrome, Mozilla Firefox, Internet Explorer, and Microsoft Edge attempt to limit this problem by including a "pre-loaded" list of HSTS sites. Unfortunately this solution cannot scale to include all websites on the internet. See limitations, below. HSTS can also help to prevent having one's cookie-based website login credentials stolen by widely available tools such as Firesheep. Because HSTS is time limited, it is sensitive to attacks involving shifting the victim's computer time e.g. using false NTP packets. == Limitations == The initial request remains unprotected from active attacks if it uses an insecure protocol such as plain HTTP or if the URI for the initial request was obtained over an insecure channel. The same applies to the first request after the activity period specified in the advertised HSTS Policy max-age (sites should set a period of several days or months depending on user activity and behavior). === Solutions with preload list === Google Chrome, Mozilla Firefox, and Internet Explorer/Microsoft Edge address this limitation by implementing a "HSTS preloaded list", which is a list that contains known sites supporting HSTS. This list is distributed with the browser so that it uses HTTPS for the initial request to the listed sites as well. As previously mentioned, these pre-loaded lists cannot scale to cover the entire Web. A potential solution might be achieved by using DNS records to declare HSTS Policy, and accessing them securely via DNSSEC, optionally with certificate fingerprints to ensure validity (which requires running a validating resolver to avoid last mile issues). Junade Ali has noted that HSTS is ineffective against the use of false domains; by using DNS-based attacks, it is possible for a man-in-the-middle interceptor to serve traffic from an artificial domain which is not on the HSTS Preload list, this can be made possible by DNS Spoofing Attacks, or simply a domain name that misleadingly resembles the real domain name such as www.example.org instead of www.example.com. Even with an HSTS preloaded list, HSTS cannot prevent advanced attacks against TLS itself, such as the BEAST or CRIME attacks introduced by Juliano Rizzo and Thai Duong. Attacks against TLS itself are orthogonal to HSTS policy enforcement. Neither can it protect against attacks on the server - if someone compromises it, it will happily serve any content over TLS. === Privacy issues === HSTS can be used to near-indelibly tag visiting browsers with recoverable identifying data (supercookies) which can persist in and out of browser "incognito" privacy modes. By creating a web page that makes multiple HTTP requests to selected domains, for example, if twenty browser requests to twenty different domains are used, theoretically over one million visitors can be distinguished (220) due to the resulting requests arriving via HTTP vs. HTTPS; the latter being the previously recorded binary "bits" established earlier via HSTS headers. == Browser support == Chromium and Google Chrome since version 4.0.211.0 Firefox since version 4; with Firefox 17, Mozilla integrates a list of websites supporting HSTS. Opera since version 12 Safari since OS X Mavericks (version 10.9, late 2013) Internet Explorer 11 on Windows 8.1 and Windows 7 with KB3058515 installed (Released as a Windows Update in June 2015) Microsoft Edge and Internet Explorer 11 on Windows 10 BlackBerry 10 Browser and WebView since BlackBerry OS 10.3.3. == Deployment best practices == Depending on the actual deployment there are certain threats (e.g. cookie injection attacks) t