Social media marketing is the use of social media platforms and websites to promote a product or service. Although the terms e-marketing and digital marketing are still dominant in academia, social media marketing is becoming more popular for practitioners and researchers. Social media platforms such as Facebook, LinkedIn, Instagram, and Twitter, among others, have built-in data analytics tools that companies can use to track the progress, success, and engagement of social media marketing campaigns. Companies address a range of stakeholders through social media marketing, including current and potential customers, current and potential employees, journalists, bloggers, and the general public. On a strategic level, social media marketing includes the management of a marketing campaign, governance, setting the scope (e.g. more active or passive use) and the establishment of a firm's desired social media "culture" and "tone". Firms that use social media marketing can allow customers and Internet users to post user-generated content (e.g., online comments, product reviews, etc.), also known as "earned media", rather than use marketer-prepared advertising copy. == Purposes and tactics == Social media may be employed in marketing as a communications tool that makes companies accessible to those who are interested in their product and visible to those who are not familiar with their products. It is used by companies to create buzz, learn from customers, and target them. Of the top 10 factors that correlate with a strong Google organic search, seven are social media-dependent. This means that if brands with little to no social media presence tend to show up less on Google searches. While platforms such as Twitter, Facebook and—in the past—Google+ have a larger number of monthly users, the visual media-sharing-based mobile platforms garner a higher interaction rate in comparison, and have registered the fastest growth, and have changed the ways in which consumers engage with brand content. Instagram has an interaction rate of 1.46% with an average of 130 million users monthly as opposed to Twitter, which has a .03% interaction rate with an average of 210 million monthly users. Unlike traditional media that are often cost-prohibitive to many companies, a social media strategy does not require significant financial investment. To this end, companies make use of platforms such as Facebook, Twitter, YouTube, TikTok and Instagram to reach audiences much wider than through traditional print, television, or radio advertisements alone at a fraction of the cost, as most social networking sites can be used at little or no cost (however, some websites charge companies for premium services). This has changed the ways that companies approach and interact with customers, as a substantial percentage of consumer interactions are now being carried out over online platforms with much higher visibility. Customers can post reviews of products and services, rate customer service, and ask questions or voice concerns directly to companies through social media platforms. According to Measuring Success, over 80% of consumers use the web to research products and services. Thus social media marketing is also used by businesses in order to build relationships of trust with consumers. To this aim, companies may hire personnel to specifically handle these social media interactions, who usually report under the title of online community managers. Handling these interactions in a satisfactory manner can result in an increase of consumer trust. To both this aim and to fix the public's perception of a company, three steps are taken in order to address consumer concerns: Identifying the extent of the social chatter Engaging the influencers to help Developing a proportional response == Strategies == === Passive approach === Social media can be a useful source of market information and a way to hear customers' perspectives. Blogs, content communities, and forums are platforms where individuals share their reviews and recommendations of brands, products, and services. Businesses are able to tap into and analyze customer voices and feedback generated in social media for marketing purposes. In this sense, social media is a relatively inexpensive source of market intelligence which can be used by marketers and managers to track and respond to consumer-identified problems and detect market opportunities. === Active approach === Social media can be used as a public relations tool, a direct marketing tool, and a communication channel to target very specific audiences, with social media influencers and social media personalities as effective customer engagement tools. This tactic is widely known as influencer marketing, which gives brands the opportunity to reach their target audience via a group of selected influencers advertising their product or service. Brands were projected to spend up to $15 billion on influencer marketing by 2022, per Business Insider Intelligence estimates, based on Mediakix data. The use of customer influencers, such as popular bloggers, can be an efficient and cost-effective method to launch new products or services. == Engagement == Engagement with the social web means that customers and stakeholders are active participants rather than passive spectators. An example of these are consumer advocacy groups and groups that criticize companies (e.g., lobby groups or advocacy organizations). The use of Social media in a business or political context allows people to express and share opinions about a company's products, services or business practices, or a government's actions. On social media, each participant becomes part of the marketing department (or a challenge to the marketing effort) as other customers read their comments or reviews. The effectiveness of social media marketing campaigns is dependent on the promotion of online engagement. With the advent of social media marketing, it has become increasingly important to gain customer interest in products and services, which can eventually be translated into buying behavior, or voting and donating behavior in a political context. New online marketing concepts of engagement and loyalty have emerged which aim to build customer participation and brand reputation. Engagement in social media for the purpose of a social media strategy is divided into two parts. The first is proactive, regular posting of new online content, which can be seen through digital photos, digital videos, text, and conversations. It is also represented through sharing of content and information from others via weblinks. The second part is reactive conversations, with social media users responding to those who reach out to others' social media profiles through comments or messages. == Campaigns == === Local businesses === Small businesses use social networking sites as a promotional technique. Businesses can follow individuals' social media usage in their local area and advertise specials and deals, which can be exclusive and in the form of "get a free drink with a copy of this tweet". This type of message encourages other locals to follow the business on their official websites in order to obtain the promotional deal. The business's brand visibility is enhanced in the process. Social networking sites are also used by small businesses to develop their own market research on new products and services. By encouraging their customers to give feedback on new product ideas, businesses can gain insights on whether or not a product may be accepted by their target market enough to merit full production. In addition, customers will feel the company has engaged them in the process of co-creation—the process in which the business uses customer feedback to create or modify a product or service to fill a need of the target market. Such feedback can be presented in various forms, such as surveys, contests, and polls. Social networking sites such as LinkedIn, also provide opportunities for small businesses to find candidates to fill staff positions. Review sites such as Yelp help small businesses build their reputation beyond brand visibility. Positive customer peer reviews help influence new prospects to purchase goods and services more than company advertising. == Benefits == Social Media Marketing allows companies to promote themselves to large, diverse audiences that could not be reached through traditional marketing such as phone and email-based advertising. Marketing on most social media platforms also comes at little to no cost, making it accessible to virtually any size business. Social Media Marketing accommodates personalized and direct marketing that targets specific demographics and markets. Companies can engage with customers directly, allowing them to obtain feedback and resolve issues almost immediately. Another advantage of social media marketing is that it's an ideal environment for a company to conduct market research. It can be used
Large language model
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable. As of 2026, the most capable LLMs are based on transformer architectures, which, according to the 2017 paper "Attention Is All You Need", can be more efficient and parallelizable than earlier statistical and recurrent neural network models. Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety. == History == Before the emergence of transformer-based models in 2017, some language models were considered large relative to the computational and data constraints of their time. In the early 1990s, IBM's statistical models pioneered word alignment techniques for machine translation, laying the groundwork for corpus-based language modeling. In 2001, a smoothed n-gram model, such as those employing Kneser–Ney smoothing, trained on 300 million words, achieved state-of-the-art perplexity on benchmark tests. During the 2000s, with the rise of widespread internet access, researchers began compiling massive text datasets from the web ("web as corpus") to train statistical language models. Moving beyond n-gram models, researchers started in 2000 to use neural networks as language models. Following the breakthrough of deep neural networks in image classification around 2012, similar architectures were adapted for language tasks. This shift was marked by the development of word embeddings (e.g., Word2Vec by Mikolov in 2013) and sequence-to-sequence (seq2seq) models using LSTM. In 2016, Google transitioned its translation service to neural machine translation (NMT), replacing statistical phrase-based models with deep recurrent neural networks. These early NMT systems used LSTM-based encoder-decoder architectures, as they preceded the invention of transformers. At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their landmark paper "Attention Is All You Need". This paper's goal was to improve upon 2014 seq2seq technology, and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. The following year in 2018, BERT was introduced and quickly became "ubiquitous". Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model. Academic and research usage of BERT began to decline in 2023, following rapid improvements in the abilities of decoder-only models (such as GPT) to solve tasks via prompting. Although decoder-only GPT-1 was introduced in 2018, it was GPT-2 in 2019 that caught widespread attention because OpenAI claimed to have initially deemed it too powerful to release publicly, out of fear of malicious use. GPT-3 in 2020 went a step further and as of 2025 is available only via API with no offering of downloading the model to execute locally. But it was the consumer-facing chatbot ChatGPT in late 2022 that received extensive media coverage and public attention by 2023. The 2023 GPT-4 was praised for its increased accuracy and as a "holy grail" for its multimodal capabilities. OpenAI did not reveal the high-level architecture and the number of parameters of GPT-4. The release of ChatGPT led to an uptick in LLM usage across several research subfields of computer science, including robotics, software engineering, and societal impact work. In 2024, OpenAI released the reasoning model OpenAI o1, which generates long chains of thought before returning a final answer. Many LLMs with parameter counts comparable to those of OpenAI's GPT series have been developed. Since 2022, weights-available models have been gaining popularity, especially at first with BLOOM and LLaMA, though both have restrictions on usage and deployment. Mistral AI's open-weight models Mistral 7B and Mixtral 8x7B have a more permissive Apache License. In January 2025, DeepSeek released DeepSeek R1, a 671-billion-parameter open-weight model that performs comparably to OpenAI o1 but at a much lower price per token for users. Since 2023, many LLMs have been trained to be multimodal, having the ability to also process or generate other types of data, such as images, audio, or 3D meshes. Open-weight LLMs have become more influential since 2023. Per Vake et al. (2025), community-driven contributions to open-weight models improve their efficiency and performance via collaborative platforms such as Hugging Face. == Dataset preprocessing == === Tokenization === As machine learning algorithms process numbers rather than text, the text must be converted to numbers. In the first step, a vocabulary is decided upon, then integer indices are arbitrarily but uniquely assigned to each vocabulary entry, and finally, an embedding is associated with the integer index. Algorithms include byte-pair encoding (BPE) and WordPiece. There are also special tokens serving as control characters, such as [MASK] for masked-out token (as used in BERT), and [UNK] ("unknown") for characters not appearing in the vocabulary. Also, some special symbols are used to denote special text formatting. For example, "Ġ" denotes a preceding whitespace in RoBERTa and GPT and "##" denotes continuation of a preceding word in BERT. For example, the BPE tokenizer used by the legacy version of GPT-3 would split tokenizer: texts -> series of numerical "tokens" as Tokenization also compresses the datasets. Because LLMs generally require input to be an array that is not jagged, the shorter texts must be "padded" until they match the length of the longest one. ==== Byte-pair encoding ==== As an example, consider a tokenizer based on byte-pair encoding. In the first step, all unique characters (including blanks and punctuation marks) are treated as an initial set of n-grams (i.e. initial set of uni-grams). Successively the most frequent pair of adjacent characters is merged into a bi-gram and all instances of the pair are replaced by it. All occurrences of adjacent pairs of (previously merged) n-grams that most frequently occur together are then again merged into even lengthier n-gram, until a vocabulary of prescribed size is obtained. After a tokenizer is trained, any text can be tokenized by it, as long as it does not contain characters not appearing in the initial-set of uni-grams. === Dataset cleaning === In the context of training LLMs, datasets are typically cleaned by removing low-quality, duplicated, or toxic data. Cleaned datasets can increase training efficiency and lead to improved downstream performance. A trained LLM can be used to clean datasets for training a further LLM. With the increasing proportion of LLM-generated content on the web, data cleaning in the future may include filtering out such content. LLM-generated content can pose a problem if the content is similar to human text (making filtering difficult) but of lower quality (degrading performance of models trained on it). === Synthetic data === Training of largest language models might need more linguistic data than naturally available, or that the naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. == Training == An LLM is a type of foundation model (large X model) trained on language. LLMs can be trained in different ways. In particular, GPT models are first pretrained to predict the next word on a large amount of data, before being fine-tuned. === Cost === Substantial infrastructure is necessary for training the largest models. The tendency towards larger models is visible in the list of large language models. For example, the training of GPT-2 (i.e. a 1.5-billion-parameter model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameter model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. The qualifier "large" in "large language model" is inherently vague, as there is no definitive threshold for the number of parameters required to qualify as "large". === Fine-tuning === Before being fine-tuned, most LLMs are next-token predictors. The fine-tuning shapes the LLM's behavior via techniques like reinforcement learning from human feedback (RLHF) or constitutional AI. Instruction fine-tuning is a form of supervised learning used to teach LLMs to follow user instructions. In 2022, OpenAI demonstrated InstructGPT, a version of GPT-3 similarly fine-tuned to follow instructions. Reinforcement learning from human feedback (RLHF) involves training a reward model to predict which text humans prefer. Then, the LLM can be fine-tuned through reinforcement learning to better satisfy this reward model. Since humans typically prefer truthful, helpful and harmless answers, RLHF favors such answers. == Architecture == LLMs are generally based on the tra
Web performance
Web performance refers to the speed in which web pages are downloaded and displayed on the user's web browser. Web performance optimization (WPO), or website optimization is the field of knowledge about increasing web performance. Faster website download speeds have been shown to increase visitor retention and loyalty and user satisfaction, especially for users with slow internet connections and those on mobile devices. Web performance also leads to less data travelling across the web, which in turn lowers a website's power consumption and environmental impact. Some aspects which can affect the speed of page load include browser/server cache, image optimization, and encryption (for example SSL), which can affect the time it takes for pages to render. The performance of the web page can be improved through techniques such as multi-layered cache, light weight design of presentation layer components and asynchronous communication with server side components. == History == In the first decade or so of the web's existence, web performance improvement was focused mainly on optimizing website code and pushing hardware limitations. According to the 2002 book Web Performance Tuning by Patrick Killelea, some of the early techniques used were to use simple servlets or CGI, increase server memory, and look for packet loss and retransmission. Although these principles now comprise much of the optimized foundation of internet applications, they differ from current optimization theory in that there was much less of an attempt to improve the browser display speed. Steve Souders coined the term "web performance optimization" in 2004. At that time Souders made several predictions regarding the impact that WPO as an "emerging industry" would bring to the web, such as websites being fast by default, consolidation, web standards for performance, environmental impacts of optimization, and speed as a differentiator. One major point that Souders made in 2007 is that at least 80% of the time that it takes to download and view a website is controlled by the front-end structure. This lag time can be decreased through awareness of typical browser behavior, as well as of how HTTP works. == Optimization techniques == Web performance optimization improves user experience (UX) when visiting a website and therefore is highly desired by web designers and web developers. They employ several techniques that streamline web optimization tasks to decrease web page load times. This process is known as front end optimization (FEO) or content optimization. FEO concentrates on reducing file sizes and "minimizing the number of requests needed for a given page to load." In addition to the techniques listed below, the use of a content delivery network—a group of proxy servers spread across various locations around the globe—is an efficient delivery system that chooses a server for a specific user based on network proximity. Typically the server with the quickest response time is selected. The following techniques are commonly used web optimization tasks and are widely used by web developers: Web browsers open separate Transmission Control Protocol (TCP) connections for each Hypertext Transfer Protocol (HTTP) request submitted when downloading a web page. These requests total the number of page elements required for download. However, a browser is limited to opening only a certain number of simultaneous connections to a single host. To prevent bottlenecks, the number of individual page elements are reduced using resource consolidation whereby smaller files (such as images) are bundled together into one file. This reduces HTTP requests and the number of "round trips" required to load a web page. Web pages are constructed from code files such JavaScript and Hypertext Markup Language (HTML). As web pages grow in complexity, so do their code files and subsequently their load times. File compression can reduce code files by about 40 percent, thereby improving site responsiveness. Web Caching Optimization reduces server load, bandwidth usage and latency. CDNs use dedicated web caching software to store copies of documents passing through their system. Many website platforms, such as SiteGround, IONOS, Wix, and Hostinger, rely on global CDNs and caching technologies to deliver faster page loads across different geographical regions. Subsequent requests from the cache may be fulfilled should certain conditions apply. Web caches are located on either the client side (forward position) or web-server side (reverse position) of a CDN. Web browsers are also able to store content for re-use through the HTTP cache or web cache. Requests web browsers make are typically routed to the HTTP cache to validate if a cached response may be used to fulfill a request. If such a match is made, the response is fulfilled from the cache. This can be helpful for reducing network latency and costs associated with data-transfer. The HTTP cache is configured using request and response headers. Code minification distinguishes discrepancies between codes written by web developers and how network elements interpret code. Minification removes comments and extra spaces as well as crunch variable names in order to minimize code, decreasing files sizes by as much as 60%. In addition to caching and compression, lossy compression techniques (similar to those used with audio files) remove non-essential header information and lower original image quality on many high resolution images. These changes, such as pixel complexity or color gradations, are transparent to the end-user and do not noticeably affect perception of the image. Another technique is the replacement of raster graphics with resolution-independent vector graphics. Vector substitution is best suited for simple geometric images. Lazy loading of images and video reduces initial page load time, initial page weight, and system resource usage, all of which have positive impacts on website performance. It is used to defer initialization of an object right until the point at which it is needed. The browser loads the images in a page or post when they are needed such as when the user scrolls down the page and not all images at once, which is the default behavior, and naturally, takes more time. == HTTP/1.x and HTTP/2 == Since web browsers use multiple TCP connections for parallel user requests, congestion and browser monopolization of network resources may occur. Because HTTP/1 requests come with associated overhead, web performance is impacted by limited bandwidth and increased usage. Compared to HTTP/1, HTTP/2 is binary instead of textual is fully multiplexed instead of ordered and blocked can therefore use one connection for parallelism uses header compression to reduce overhead allows servers to "push" responses proactively into client caches Instead of a website's hosting server, CDNs are used in tandem with HTTP/2 in order to better serve the end-user with web resources such as images, JavaScript files and Cascading Style Sheet (CSS) files since a CDN's location is usually in closer proximity to the end-user. == Metrics == In recent years, several metrics have been introduced that help developers measure various aspects of the performance of their websites. In 2019, Google introduced metrics such as Time to First Byte (TTFB), First Contentful Paint (FCP), First Paint (FP), First Input Delay (FID), Cumulative Layout Shift (CLS) and Largest Contentful Paint (LCP) allow for website owner to gain insights into issues that might hurt the performance of their websites making it seem sluggish or slow to the user. Other metrics including Request Count (number of requests required to load a page), DOMContentLoaded (time when HTML document is completely loaded and parsed excluding CSS style sheets, images, etc.), Above The Fold Time (content that is visible without scrolling), Round Trip Time, number of Render Blocking Resources (such as scripts, stylesheets), Onload Time, Connection Time, Total Page Size help provide an accurate picture of latencies and slowdowns occurring at the networking level which might slow down a site. Modules to measure metrics such as TTFB, FCP, LCP, FP etc are provided with major frontend JavaScript libraries such as React, NuxtJS and Vue. Google publishes a library, the core-web-vitals library that allows for easy measurement of these metrics in frontend applications. In addition to this, Google also provides the Lighthouse, a Chrome dev-tools component and PageSpeed Insight a site that allows developers to measure and compare the performance of their website with Google's recommended minimums and maximums. In addition to this, tools such as the Network Monitor by Mozilla Firefox help provide insight into network-level slowdowns that might occur during transmission of data.
Robert Abel and Associates
Robert Abel and Associates (RA&A) was an American pioneering animation production company specializing in television commercials made with computer graphics. Founded by Robert Abel and Con Pederson in 1971, RA&A was especially known for their art direction and won many Clio Awards. Abel and his team created some of the most advanced and impressive computer-animated works of their time, including full ray-traced renders and fluid character animation at a time when such things were largely unknown. A variety of high-profile television advertisements, graphics sequences for motion pictures (including The Andromeda Strain and Tron), and work on laserdisc video games such as Cube Quest, put Abel and his team on the map in the early 1980s. The company was also originally commissioned to create the visual effects for Star Trek: The Motion Picture, but were subsequently taken off the project for mishandling funds. The company was also notable on its work for The Jacksons' 1981 music video "Can You Feel It." RA&A was on the southwest corner of Highland Avenue and Romaine in the heart of Hollywood, California. RA&A closed in 1987 following an ill-fated merger with now-defunct Omnibus Computer Graphics, Inc., a company which had been based in Toronto. Many people who worked at RA&A went on to other ground-breaking projects, including the founding of Wavefront Technologies, Rhythm & Hues and other studios. Many RA&A people went on to win Academy Awards.
G7 Rapid Response Mechanism
The G7 Rapid Response Mechanism (RRM) is an initiative introduced in the "Charlevoix Commitment on Defending Democracy from Foreign Threats", issued by the leaders of the Group of Seven (G7) countries—United States, Canada, Japan, United Kingdom, France, Germany and Italy—on June 9, 2018, during their summit in Charlevoix, Quebec. The RRM's mandate is to strengthen the coordination of G7 member countries, as well as "to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response" The G7 is an informal international intergovernmental economic organization that meets annually, whose members represent the seven wealthiest advanced economies in the world, as measured by the International Monetary Fund (IMF). == Constituents == The following countries and organisations are members and observers (associate members) of the G7 Rapid Response Mechanism: Australia Canada France Germany Italy Japan Netherlands New Zealand Poland Sweden United Kingdom United States European Union North Atlantic Treaty Organization == Mandate == The RRM was mandated to "strengthen coordination to prevent, thwart and respond to malign and evolving threats to G7 democracies." It "will share information and threat analysis related to various threats to democracy, and is an established mechanism to identify opportunities for coordinated response." According to the Institute for Research on Public Policy's Policy Options magazine, the "RRM initiative seeks to strengthen the leading democracies' coordination to identify and respond to diverse and evolving threats…including through sharing information and analysis, and identifying opportunities for a coordinated response." == Administration == The RRM initiative is led by Canada through Global Affairs Canada's Centre for International Digital Policy. Tara Denham, Director of the Centre for International Digital Policy at Global Affairs Canada, directed the team responsible for setting up the RRM Coordination Unit. Global Affairs Canada—the Department of Foreign Affairs, Trade and Development—is the federal Canadian ministry responsible for diplomatic and consular relations, international trade, and international development and humanitarian assistance. The Centre for International Digital Policy includes the Digital Inclusion Lab and the RRM. Denham is also the RRM's Canadian Focal Point. At a briefing on "the security and intelligence threats to elections" of the House of Commons Standing Committee on Access to Information, Privacy and Ethics, the chair Bob Zimmer (CPC), said that the 2019 general election "may be different" from past elections in Canada. as the "tools that were used to strengthen civic engagement are being used to undermine, disrupt and destabilize democracy." "Democracies around the world have entered a new era—an era of heightened threat and heightened vigilance—and 2019 will see a number of countries brace for volleys of attempted disruption: India, Australia, Ukraine, Switzerland, Belgium, the EU and, of course, Canada. Evidence has confirmed that the most recent Canadian general election, in 2015, was unencumbered by interference, although there were some relatively primitive attempts to disrupt, misinform and divide. These efforts were few in number and uncoordinated, and had no visible impact on the voter, either online or in line." Zimmer described the initiative's three pillars. "enhancing citizen preparedness" through the "digital citizen initiative" "improving organizational readiness" with national security and intelligence agencies supporting Elections Canada "ensure a comprehensive understanding of and response to any threats to Canada's democratic process." by establishing the Security and Intelligence Threats to Elections Task Force (SITE) which works as a team with the Communications Security Establishment (CSE), the Canadian Security Intelligence Service (CSIS), the Royal Canadian Mounted Police (RCMP), as well as Global Affairs Canada Zimmer said that as part of the third pillar, "We have activated the G7 rapid response mechanism, announced at the G7 leaders' summit in Charlevoix, to strengthen coordination among our G7 allies and to ensure that there is international collaboration and coordination in responding to foreign threats to democracy." == Background == === Charlevoix summit === The G7 met from June 8 to 9, 2018 during their summit at the Manoir Richelieu in Charlevoix, in La Malbaie, Quebec. The Charlevoix Summit was the 44th G7 summit. The group issued eight "Commitments" at the summit. They included: Commitment on Defending Democracy from Foreign Threats Commitment on Equality and Economic Growth Commitment to End Sexual and Gender-Based Violence, Abuse and Harassment in Digital Contexts Declaration on Quality Education for Girls, Adolescent Girls and Women in Developing Countries Commitment on Innovative Financing for Development. Prime Minister Justin Trudeau announced five themes for Canada's G7 presidency which began in January 2018. === Defending Democracy from Foreign Threats === "We commit to take concerted action in responding to foreign actors who seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security as outlined in the Charlevoix Commitment on Defending Democracy from Foreign Threats. We recognize that such threats, particularly those originating from state actors, are not just threats to G7 nations, but to international peace and security and the rules-based international order. We call on others to join us in addressing these growing threats by increasing the resilience and security of our institutions, economies and societies, and by taking concerted action to identify and hold to account those who would do us harm." They committed to "cooperate in defending democracies from foreign threats and establish a response mechanism for that purpose". "Democracy and the rules-based international order are increasingly being challenged by authoritarianism and the defiance of international norms. In particular, foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working with other governments that share our democratic values." The Charlevoix Commitment states that "foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working together with other governments that share our democratic values." The Charlevoix Summit resolved to "establish a G7 Rapid Response Mechanism to strengthen our coordination to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response." == Monitored elections == === 2019 European Parliament election === RRM Canada's comprehensive report on the 2019 European Parliament election analyzed open data "related to foreign interference during and leading up to the 2019 European Union Parliamentary Elections, May 23–26, 2019". RRM Canada did not find "significant evidence of state-based foreign interference, or any large-scale, organized and coordinated efforts by non-state actors". They did find that "national or international non-state actors" used tactics based on those used by the Russian sponsored Internet Research Agency (IRA) in previous elections, "such as the 2016 U.S. Elections". For example, blogs, webpages, and social media accounts on Twitter, Facebook and Reddit "were used to spread divisive and false information to damage and negatively impact social cohesion and trust in democratic processes and institutions" in coordinated networks of Facebook groups. === 2019 Alberta general election === RRM Canada's analyz report on the 2019 Alberta general election was intended to "identify any emerging tactics in foreign interference and draw lessons learned for the Canadian general elections scheduled to take place in October 2019." No foreign activity was detected, although the data revealed ""suspicious account creation pattern that is indicative of troll or bot activity". They found "automated inauthentic behaviour and trolling activities" but concluded that they were "very likely domestic". The data showed "suspicious account creation pattern that is indicative of troll or bot activity", and "spikes in account creation" which suggested the "presence of accounts developed for a specific purpose." The accounts were very likely domestic and were "mainly comprised of supporters of the United Conservative Party (UCP)." A seco
Data remanence
Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously written data to be recovered. Data remanence may make inadvertent disclosure of sensitive information possible should the storage media be released into an uncontrolled environment (e.g., thrown in refuse containers or lost). Various techniques have been developed to counter data remanence. These techniques are classified as clearing, purging/sanitizing, or destruction. Specific methods include overwriting, degaussing, encryption, and media destruction. Effective application of countermeasures can be complicated by several factors, including media that are inaccessible, media that cannot effectively be erased, advanced storage systems that maintain histories of data throughout the data's life cycle, and persistence of data in memory that is typically considered volatile. Several standards exist for the secure removal of data and the elimination of data remanence. == Causes == Many operating systems, file managers, and other software provide a facility where a file is not immediately deleted when the user requests that action. Instead, the file is moved to a holding area (i.e. the "trash"), making it easy for the user to undo a mistake. Similarly, many software products automatically create backup copies of files that are being edited, to allow the user to restore the original version, or to recover from a possible crash (autosave feature). Even when an explicit deleted file retention facility is not provided or when the user does not use it, operating systems do not actually remove the contents of a file when it is deleted unless they are aware that explicit erasure commands are required, like on a solid-state drive. (In such cases, the operating system will issue the Serial ATA TRIM command or the SCSI UNMAP command to let the drive know to no longer maintain the deleted data.) Instead, they simply remove the file's entry from the file system directory because this requires less work and is therefore faster, and the contents of the file—the actual data—remain on the storage medium. The data will remain there until the operating system reuses the space for new data. In some systems, enough filesystem metadata are also left behind to enable easy undeletion by commonly available utility software. Even when undelete has become impossible, the data, until it has been overwritten, can be read by software that reads disk sectors directly. Computer forensics often employs such software. Likewise, reformatting, repartitioning, or reimaging a system is unlikely to write to every area of the disk, though all will cause the disk to appear empty or, in the case of reimaging, empty except for the files present in the image, to most software. Finally, even when the storage media is overwritten, physical properties of the media may permit recovery of the previous contents. In most cases however, this recovery is not possible by just reading from the storage device in the usual way, but requires using laboratory techniques such as disassembling the device and directly accessing/reading from its components. § Complications below gives further explanations for causes of data remanence. == Countermeasures == There are three levels commonly recognized for eliminating remnant data: === Clearing === Clearing is the removal of sensitive data from storage devices in such a way that there is assurance that the data may not be reconstructed using normal system functions or software file/data recovery utilities. The data may still be recoverable, but not without special laboratory techniques. Clearing is typically an administrative protection against accidental disclosure within an organization. For example, before a hard drive is re-used within an organization, its contents may be cleared to prevent their accidental disclosure to the next user. === Purging === Purging or sanitizing is the physical rewrite of sensitive data from a system or storage device done with the specific intent of rendering the data unrecoverable at a later time. Purging, proportional to the sensitivity of the data, is generally done before releasing media beyond control, such as before discarding old media, or moving media to a computer with different security requirements. === Destruction === The storage media is made unusable for conventional equipment. Effectiveness of destroying the media varies by medium and method. Depending on recording density of the media, and/or the destruction technique, this may leave data recoverable by laboratory methods. Conversely, destruction using appropriate techniques is the most secure method of preventing retrieval. == Specific methods == === Overwriting === A common method used to counter data remanence is to overwrite the storage media with new data. This is often called wiping or shredding a disk or file, by analogy to common methods of destroying print media, although the mechanism bears no similarity to these. Because such a method can often be implemented in software alone, and may be able to selectively target only part of the media, it is a popular, low-cost option for some applications. Overwriting is generally an acceptable method of clearing, as long as the media is writable and not damaged. The simplest overwrite technique writes the same data everywhere—often just a pattern of all zeros. At a minimum, this will prevent the data from being retrieved simply by reading from the media again using standard system functions. The UEFI in modern machines may offer an ATA class disk erase function as well. The ATA-6 standard governs secure erases specifications. Bitlocker is whole disk encryption and illegible without the key. Writing a fresh GPT allows a new file system to be established. Blocks will set empty but LBA read is illegible. New data will be unaffected and work fine. In an attempt to counter more advanced data recovery techniques, specific overwrite patterns and multiple passes have often been prescribed. These may be generic patterns intended to eradicate any trace signatures; an example is the seven-pass pattern 0xF6, 0x00, 0xFF,
Modulation error ratio
The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.