Human-in-the-loop (HITL) is used in multiple contexts. It can be defined as a model requiring human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL, along with the related human-on-the-loop, are also used in relation to lethal autonomous weapons. Further, HITL is used in the context of machine learning.It is also used in conversational AI to manage complex interactions that require human empathy. == Machine learning == In machine learning, HITL is used in the sense of humans aiding the computer in making the correct decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. == Simulation == In simulation, HITL models may conform to human factors requirements as in the case of a mockup. In this type of simulation, a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation. HITL is often referred to as an interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator. === Benefits === Human-in-the-loop allows the user to change the outcome of an event or process. The immersion effectively contributes to a positive transfer of acquired skills into the real world. This can be demonstrated by trainees utilizing flight simulators in preparation to become pilots. HITL also allows for the acquisition of knowledge regarding how a new process may affect a particular event. Utilizing HITL allows participants to interact with realistic models and attempt to perform as they would in an actual scenario. HITL simulations bring to the surface issues that would not otherwise be apparent until after a new process has been deployed. A real-world example of HITL simulation as an evaluation tool is its usage by the Federal Aviation Administration (FAA) to allow air traffic controllers to test new automation procedures by directing the activities of simulated air traffic while monitoring the effect of the newly implemented procedures. As with most processes, there is always the possibility of human error, which can only be reproduced using HITL simulation. Although much can be done to automate systems, humans typically still need to take the information provided by a system to determine the next course of action based on their judgment and experience. Intelligent systems can only go so far in certain circumstances to automate a process; only humans in the simulation can accurately judge the final design. Tabletop simulation may be useful in the very early stages of project development for the purpose of collecting data to set broad parameters, but the important decisions require human-in-the-loop simulation. HITL reflects scenarios where human input remains essential despite advances in automation. === Within the virtual simulation taxonomy === Virtual simulations inject HITL in a central role by exercising motor control skills (e.g. flying an airplane), decision making skills (e.g. committing fire control resources to action), or communication skills (e.g. as members of a C4I team). === Examples === Flight simulators Driving simulators Marine simulators Video games Supply chain management simulators Digital puppetry === Misconceptions === Although human-in-the-loop simulation can include a computer simulation in the form of a synthetic environment, computer simulation is not necessarily a form of human-in-the-loop simulation, and is often considered as human-out-of-the loop simulation. In this particular case, a computer model’s behavior is modified according to a set of initial parameters. The results of the model differ from the results stemming from a true human-in-the-loop simulation because the results can easily be replicated time and time again, by simply providing identical parameters. == Weapons == === Taxonomy === Three classifications of the degree of human control of autonomous weapon systems were laid out by Bonnie Docherty in a 2012 Human Rights Watch report. human-in-the-loop: a human must instigate the action of the weapon (in other words not fully autonomous) human-on-the-loop: a human may abort an action human-out-of-the-loop: no human action is involved === Positive human action === In discussions of autonomous weapons and nuclear command and control, the phrase positive human action has been used alongside "human-in-the-loop" to emphasize that a human operator must affirmatively authorize the use of force. Descriptions of the United States Navy's Aegis Combat System have used the phrase in characterizing a requirement for affirmative human action to initiate live firing. A survey of autonomous weapons systems described the Aegis "Auto SM" mode as one in which "the system fully develops the engagement process however engagement requires positive human action". The phrase entered United States federal law in the National Defense Authorization Act for Fiscal Year 2025, which stipulates that artificial intelligence systems not compromise "the principle of requiring positive human actions in execution of decisions by the President with respect to the employment of nuclear weapons".
SEMAT
SEMAT (Software Engineering Method and Theory) is an initiative to reshape software engineering such that software engineering qualifies as a rigorous discipline. The initiative was launched in December 2009 by Ivar Jacobson, Bertrand Meyer, and Richard Soley with a call for action statement and a vision statement. The initiative was envisioned as a multi-year effort for bridging the gap between the developer community and the academic community and for creating a community giving value to the whole software community. The work is now structured in four different but strongly related areas: Practice, Education, Theory, and Community. The Practice area primarily addresses practices. The Education area is concerned with all issues related to training for both the developers and the academics including students. The Theory area is primarily addressing the search for a General Theory in Software Engineering. Finally, the Community area works with setting up legal entities, creating websites and community growth. It was expected that the Practice area, the Education area and the Theory area would at some point in time integrate in a way of value to all of them: the Practice area would be a "customer" of the Theory area, and direct the research to useful results for the developer community. The Theory area would give a solid and practical platform for the Practice area. And, the Education area would communicate the results in proper ways. == Practice area == The first step was here to develop a common ground or a kernel including the essence of software engineering – things we always have, always do, always produce when developing software. The second step was envisioned to add value on top of this kernel in the form of a library of practices to be composed to become specific methods, specific for all kinds of reasons such as the preferences of the team using it, kind of software being built, etc. The first step is as of this writing just about to be concluded. The results are a kernel including universal elements for software development – called the Essence Kernel, and a language – called the Essence Language - to describe these elements (and elements built on top of the kernel (practices, methods, and more). Essence, including both the kernel and language, has been published as an OMG standard in beta status in July 2013 and is expected to become a formally adopted standard in early 2014. The second step has just started, and the Practice area will be divided into a number of separate but interconnected tracks: the practice (library track), the tool track are so far identified and work has started or is about to get started. The practice track is currently working on a Users Guide. == Education area == The area focuses on leveraging the work of SEMAT in software engineering education, both within academia and industry. It promotes global education based on a common ground called Essence. The area's target groups are instructors such as university professors and industrial coaches as well as their students and learning practitioners. The goal of the area is to create educational courses and course materials that are internationally viable, identify pedagogical approaches that are appropriate and effective for specific target groups and disseminate experience and lessons learned. The area includes members from a number of universities and institutes worldwide. Most members have already been involved in leveraging aspects of SEMAT in the context of their software engineering courses. They are gathering their resources and starting a common venture towards defining a new generation of SEMAT-powered software engineering curricula. As of 2018, some studies of utilizing Essence in educational settings exist. One example of the use of Essence in university education was a software engineering course carried out in Norwegian University of Science and Technology. A study was conducted by introducing Essence into a project-based software engineering course, with the aim of understanding what difficulties the students faced in using Essence, and whether they considered it to have been useful. The results indicated that Essence could also be useful for novice software engineers by (1) encouraging them to look up and study new practices and methods in order to create their own, (2) encouraging them to adjust their way-of-working reflectively and in a situation-specific manner, (3) helping them structure their way of working. The findings of another study introducing students to Essence through a digital game supported these findings: the students felt that Essence will be useful to them in future, real-world projects, and that they wish to utilize it in them. == Theory area == An important part of SEMAT is that a general theory of software engineering is planned to emerge with significant benefits. A series of workshops held under the title SEMAT Workshop on a General Theory of Software Engineering (GTSE) are a key component in awareness building around general theories. In addition to community awareness building, SEMAT also aims to contribute with a specific general theory of software engineering. This theory should be solidly based on the SEMAT Essence language and kernel, and should support software engineering practitioners' goal-oriented decision making. As argued elsewhere, such support is predicated on the predictive capabilities of the theory. Thus, the SEMAT Essence should be augmented to allow the prediction of critical software engineering phenomena. The GTSE workshop series assists in the development of the SEMAT general software engineering theory by engaging a larger community in the search for, development of, and evaluation of promising theories, which may be used as a base for the SEMAT theory. == Organizational structure == === Main organization === SEMAT is chaired by Sumeet S. Malhotra of Tata Consultancy Services. The CEO of the organization is Ste Nadin of Fujitsu. The Executive Management Committee of SEMAT are Ivar Jacobson, Ste Nadin, Sumeet S. Malhotra, Paul E. McMahon, Michael Goedicke and Cecile Peraire. === Japan Chapter === Japan Chapter was established in April 2013, and it has more than 250 members as of November 2013. Member activities include carrying out seminars about SEMAT, considering utilization of SEMAT Essence for integrating different requirements engineering techniques and body of knowledges (BoKs), and translating articles into Japanese. === Korea Chapter === The chapter was inaugurated with about 50 members in October 2013. Member activities include: 2e Consulting started rewriting their IT service engagement methods using the Essence kernel, and uEngine Solutions started developing a tool to orchestrate Essence-kernel based practices into a project method. Korean government supported KAIST to conduct research in Essence. === Latin American Chapter === Semat Latin American Chapter was created in August 2011 in Medellin (Colombia) by Ivar Jacobson during the Latin American Software Engineering Symposium. This Chapter has 9 Executive Committee members from Colombia, Venezuela, Peru, Brazil, Argentina, Chile, and Mexico, chaired by Dr. Carlos Zapata from Colombia. More than 80 people signed the initial declaration of the Chapter and nowadays the Chapter members are in charge of disseminating the Semat ideas in all Latin America. Chapter members have participated in various Latin American conferences, including the Latin American Conference on Informatics (CLEI), the Ibero American Software Engineering and Knowledge Engineering Journeys (JIISIC), the Colombian Computing Conference (CCC), and the Chilean Computing Meeting (ECC). The Chapter contributed in the submission sent in response to the OMG call for proposals and currently studies didactic strategies for teaching the Semat kernel by games, theoretical studies about some kernel elements, and practical representations of several software development and quality methods by using the Semat kernel. Some of the members also translated the Essence book and some other Semat materials and papers into Spanish. === Russia Chapter === Russian Chapter has about 20 members. A few universities have incorporated SEMAT in their training courses , including Moscow State University, Moscow Institute of Physics and Technology, Higher School of Economics, Moscow State University of Economics, Statistics, and Informatics. The chapter and some commercial companies are carrying out seminars about SEMAT. INCOSE Russian Chapter is working on an extension of SEMAT to systems engineering. EC-leasing is working on an extension of the Kernel for Software Life Cycle. Russian Chapter attended in two conferences: Actual Problems of System and Software Engineering and SECR with SEMAT section and articles. Translation of the Essence book into Russian is in progress. == Practical Applications of SEMAT == Ideas developed by the SEMAT community have been applied by both industry and ac
Filter (social media)
Filters are digital image effects often used on social media. They initially simulated the effects of camera filters, and they have since developed with facial recognition technology and computer-generated augmented reality. Social media filters—especially beauty filters—are often used to alter the appearance of selfies taken on smartphones or other similar devices. While filters are commonly associated with beauty enhancement and feature alterations, there is a wide range of filters that have different functions. From adjusting photo tones to using face animations and interactive elements, users have access to a range of tools. These filters allow users to enhance photos and allow room for creative expression and fun interactions with digital content. == History == Beauty filters originate from Purikura ("print club"), a type of Japanese photographic arcade game machine conceived in 1994 by Sasaki Miho, a female employee at Atlus, and released in 1995 by Atlus and Sega primarily for female visitors at Japanese arcades. They allowed the manipulation of digital selfie photos with kawaii beauty filters similar to later Snapchat filters. Purikura filters included beautifying the image, cat whiskers, bunny ears, writing text, scribbling graffiti, selecting backdrops, borders, insertable decorations, icons, hair extensions, twinkling diamond tiaras, tenderized light effects, and predesigned decorative margins. To capitalize on the Purikura phenomenon in Japan during the late 1990s, Japanese mobile phones began including a front-facing camera, starting with the Kyocera Visual Phone VP‑210 in 1999. The Sanyo SCP-5300 released in 2002 was the first camera phone with filter effects, such as illumination, white‑balance control, sepia, black and white, and negative colors. Purikura-like beauty filters later appeared in smartphone apps such as Instagram and Snapchat in the 2010s. In 2010, Apple introduced the iPhone 4—the first iPhone model with a front-facing camera. It gave rise to a dramatic increase in selfies, which could be touched up with more flattering lighting effects with applications such as Instagram. The American photographer Cole Rise was involved in the creation of the original filters for Instagram around 2010, designing several of them himself, including Sierra, Mayfair, Sutro, Amaro, and Willow. However, the technology for virtual lens filters was invented and patented by Patrick Levy-Rosenthal in 2007. The patent received 100 citations, including Facebook, Nvidia, Microsoft, Samsung, and Snap. In September, 2011, the Instagram 2.0 update for the application introduced "live filters," which allowed the user to preview the effect of the filter while shooting with the application's camera. #NoFilter, a hashtag label to describe an image that had not been filtered, became popular around 2013. An update in 2014 allowed users to adjust the intensity of the filters as well as fine-tune other aspects of the image, features that had been available for years on applications such as VSCO and Litely. In 2014, Snapchat started releasing sponsored filters to monetize the participatory use of the application. In September 2015, Snapchat acquired Looksery and released a feature called "lenses," animated filters using facial recognition technology. Some of the early lenses available on Snapchat at the time were Heart Eyes, Terminator, Puke Rainbows, Old, Scary, Rage Face, Heart Avalanche. The Coachella filter released April 2016 was a popular early augmented reality filter. In April 2017, Facebook released the Camera Effects Platform, which is the first augmented reality platform that allows developers to create their own filters and effects on Facebook's Camera. In December 2017, Snapchat also launched their Lens Studio augmented reality developer tool that allows users and advertisers to do the same on the Snapchat application. In April 2022,TikTok joined the two, and launched their own augmented reality developer platform called Effect house. In February 2023, Effect House gave opened up the access to generative AI tools that allowed creators to change facial features in real time. In November 2023, TikTok released a feature where users no longer needed Effect House to create their own filters, as they are now able to create their own effects on the TikTok application. In August 2024, Meta announced that it would be removing third-party filter effects from its family of apps by January 14, 2025. The AR development software Meta Spark AR will also be retired at the same time; it was at one point the "world's largest mobile AR platform". Brand and creator effects represent the vast majority of filters available on Meta platforms, with over 2 million third-party filters available as of 2021. == Beauty filter == A beauty filter is a filter applied to still photographs, or to video in real time, to enhance the physical attractiveness of the subject. Typical effects of such filters include smoothing skin texture and modifying the proportions of facial features, for example enlarging the eyes or narrowing the nose. Filters may be included as a built-in feature of social media apps such as Instagram or Snapchat, or implemented through standalone applications such as Facetune. In 2020, the "Perfect Skin" filter for Snapchat and Instagram which was created by Brazilian augmented reality developer Brenno Faustino gained more than 36 million impressions in the first 24 hours of its release. In 2021, TikTok users pointed out how the default front-facing camera on the platform automatically applied the retouch and other feature-altering filters. Users noted that these filters slimmed down faces, smoothed skin, whitened teeth, and altered facial features such as nose and eye size, without the option to disable this feature through settings. In March 2023, the "Bold Glamour" filter was released on TikTok and instantly went viral with over 18 million videos created within its first week. This filter subtly enhances the user's facial features seamlessly, giving the illusion of fuller eyebrows, taller cheekbones, enhanced eye make up, a smaller nose, plumper lips, and clearer skin, giving off a natural yet distinct effect. As of May 2024, the filter has been used in over 220 million videos and has become a pivotal moment for beauty filters on digital platforms. Critics have raised concerns that the widespread use of such filters on social media may lead to negative body image, particularly among girls. Though Meta's intention of removing third-party filters will likely see all beauty filters removed, academics feel that the damage of beautifying filters is already done. === Background === The manipulation of photos to enhance attractiveness has long been possible using software such as Adobe Photoshop and, before that, analogue techniques such as airbrushing. However, such tools required considerable technical and artistic skill, and so their use was mostly limited to professional contexts, such as magazines or advertisements. By contrast, filters work in an automated fashion through the use of complex algorithms, requiring little or no input from the user. This ease of use, in combination with the increase in processing power of smartphones, and the rise of social media and selfie culture, have led to photographic manipulation occurring on a much wider scale than ever before. One of the earliest examples of a content-aware digital photographic filter is red-eye reduction. === Effects === Typical changes applied by beauty filters include: Smoothing skin texture; minimizing fine lines and blemishes Erasing under-eye bags Erasing naso-labial lines ("laugh lines") Application of virtual makeup, such as lipstick or eyeshadow Slimming the face; erasing double chins Enlarging the eyes Whitening teeth Narrowing the nose Increasing fullness of the lips Beauty filters most frequently target the face, though in some cases they may affect other body parts. For example, the app "Retouch Me" was reported to have a feature which allows users to superimpose visible abdominal muscles (a "six pack") onto photos featuring the subject's bare stomach. === Reception and psychological effects === Some commentators have expressed concern that beauty filters may create unrealistic beauty standards, particularly among girls, and contribute to rates of body dysmorphic disorder. A correlation has been established between negative body image and the use of beautifying filters, though the direction of causation is unknown. The inability to discern whether a particular image has been filtered is thought to exacerbate their negative psychological effects. Policymakers have advocated for social networks to disclose the use of filters; TikTok, Instagram, and Snapchat all label filtered photos and videos with the name of the filter applied. It has also been noted that beauty filters on social media tend to highlight Eurocentric features, like lighter eyes, a smaller nose, and flushed ch
Facebook is an American social networking service owned by the American technology conglomerate Meta Platforms. It was founded in 2004 by Mark Zuckerberg, along with his Harvard College roommates and fellow students Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes. The name Facebook derives from the face book directories often given to American university students. The service was initially limited to Harvard students before gradually expanding to other universities in North America. Since 2006, Facebook has permitted registration by individuals aged 13 and older, with the exception of South Korea, Spain, and Quebec, where the minimum age is 14. As of December 2023, Facebook reported approximately 3.07 billion monthly active users worldwide. As of July 2025, it was ranked as the third-most-visited website in the world, with 23 percent of its traffic originating from the United States. It was the most downloaded mobile application of the 2010s. Facebook can be accessed from devices with Internet connectivity, such as personal computers, tablets and smartphones. After registering, users can create a profile revealing personal information about themselves. They can post text, photos and multimedia which are shared either publicly or exclusively with other users who have agreed to be their friend, depending on privacy settings. Users can also communicate directly with each other with Messenger, edit messages (within 15 minutes after sending), join common-interest groups, and receive notifications on the activities of their Facebook friends and the pages they follow. Facebook has often been criticized over issues such as user privacy (as with the Facebook–Cambridge Analytica data scandal), political manipulation (as with the 2016 U.S. elections) and mass surveillance. The company has also been subject to criticism over its psychological effects such as addiction and low self-esteem, and over content such as fake news, conspiracy theories, copyright infringement, and hate speech. Commentators have accused Facebook of willingly facilitating the spread of such content, as well as overemphasizing its number of users to appeal to advertisers. == History == The history of Facebook traces its growth from a college networking site to a global social networking service. While attending Phillips Exeter in the early 2000s, Zuckerberg met Kris Tillery. Tillery, a one-time project collaborator with Zuckerberg, would create a school-based social networking project called Photo Address Book. Photo Address Book was a digital face book, created through a linked database composed of student information derived from the official records of the Exeter Student Council. The database contained linkages such as name, dorm-specific landline numbers, and student headshots. Mark Zuckerberg built a website called "Facemash" in 2003 while attending Harvard University. The site was comparable to Hot or Not and used photos from online face books, asking users to choose the 'hotter' person". Zuckerberg was reported and faced expulsion, but the charges were dropped. A "face book" is a student directory featuring photos and personal information. In January 2004, Zuckerberg coded a new site known as "TheFacebook", stating, "It is clear that the technology needed to create a centralized Website is readily available ... the benefits are many." Zuckerberg met with Harvard student Eduardo Saverin, and each agreed to invest $1,000. On February 4, 2004, Zuckerberg launched "TheFacebook". Membership was initially restricted to students of Harvard College. Dustin Moskovitz, Andrew McCollum, and Chris Hughes joined Zuckerberg to help manage the growth of the site. It became available successively to most universities in the US and Canada. In 2004, Napster co-founder Sean Parker became company president and the company moved to Palo Alto, California. PayPal co-founder Peter Thiel gave Facebook its first investment. In 2005, the company dropped "the" from its name after purchasing the domain name Facebook.com. In 2006, Facebook opened to everyone at least or only 13 years old with a valid email address. Facebook introduced key features like the News Feed, which became central to user engagement. By late 2007, Facebook had 100,000 pages on which companies promoted themselves. Facebook had surpassed MySpace in global traffic and became the world's most popular social media platform. Microsoft announced that it had purchased a 1.6% share of Facebook for $240 million ($373 million in 2025 dollars), giving Facebook an implied value of around $15 billion ($23.3 billion in 2025 dollars). Facebook focused on generating revenue through targeted advertising based on user data, a model that drove its rapid financial growth. In 2012, Facebook went public with one of the largest IPOs in tech history. Acquisitions played a significant role in Facebook's dominance. In 2012, it purchased Instagram, followed by WhatsApp and Oculus VR in 2014, extending its influence beyond social networking into messaging and virtual reality. The Facebook–Cambridge Analytica data scandal in 2018 revealed misuse of user data to influence elections, sparking global outcry and leading to regulatory fines and hearings. Facebook's role in global events, including its use in organizing movements like the Arab Spring and its impact on events like the Rohingya genocide in Myanmar, highlighted its dual nature as a tool for both empowerment and harm. In 2021, Facebook rebranded as Meta, reflecting its shift toward building the "metaverse" and focusing on virtual reality and augmented reality technologies. == Features == Facebook does not officially publish a maximum character limit for posts; however, user posts can be lengthy, with unofficial sources suggesting a high character limit. Posts may also include images and videos. According to Facebook's official business documentation, videos can be up to 240 minutes long and 10 GB in file size, with supported resolutions up to 1080p. Users can "friend" users, both sides must agree to being friends. Posts can be changed to be seen by everyone (public), friends, people in a certain group (group) or by selected friends (private). Users can join groups. Groups are composed of persons with shared interests. For example, they might go to the same sporting club, live in the same suburb, have the same breed of pet or share a hobby. Posts posted in a group can be seen only by those in a group, unless set to public. Users are able to buy, sell, and swap things on Facebook Marketplace or in a Buy, Swap and Sell group. Facebook users may advertise events, which can be offline, on a website other than Facebook, or on Facebook. == Website == === Technical aspects === The site's primary color is blue as Zuckerberg is red–green colorblind, a realization that occurred after a test taken around 2007. Facebook was initially built using PHP, a popular scripting language designed for web development. PHP was used to create dynamic content and manage data on the server side of the Facebook application. Zuckerberg and co-founders chose PHP for its simplicity and ease of use, which allowed them to quickly develop and deploy the initial version of Facebook. As Facebook grew in user base and functionality, the company encountered scalability and performance challenges with PHP. In response, Facebook engineers developed tools and technologies to optimize PHP performance. One of the most significant was the creation of the HipHop Virtual Machine (HHVM). This significantly improved the performance and efficiency of PHP code execution on Facebook's servers. The site upgraded from HTTP to the more secure HTTPS in January 2011. ==== 2012 architecture ==== Facebook is developed as one monolithic application. According to an interview in 2012 with Facebook build engineer Chuck Rossi, Facebook compiles into a 1.5 GB binary blob which is then distributed to the servers using a custom BitTorrent-based release system. Rossi stated that it takes about 15 minutes to build and 15 minutes to release to the servers. The build and release process has zero downtime. Changes to Facebook are rolled out daily. Facebook used a combination platform based on HBase to store data across distributed machines. Using a tailing architecture, events are stored in log files, and the logs are tailed. The system rolls these events up and writes them to storage. The user interface then pulls the data out and displays it to users. Facebook handles requests as AJAX behavior. These requests are written to a log file using Scribe (developed by Facebook). Data is read from these log files using Ptail, an internally built tool to aggregate data from multiple Scribe stores. It tails the log files and pulls data out. Ptail data are separated into three streams and sent to clusters in different data centers (Plugin impression, News feed impressions, Actions (plugin + news feed)). Puma is used to manage periods of high data
IDN Times
IDN Times is a digital multi-platform media outlet that provides news and entertainment for Millennials and Gen Z in Indonesia. IDN Times is one of IDN’s business units under the Digital Media pillar, founded by Winston Utomo and William Utomo on June 8, 2014. Currently, senior journalist Uni Zulfiani Lubis serves as the Editor-in-Chief of IDN Times. == History == IDN Times was initially known as Indonesian Times, a blog featuring articles written by Winston Utomo while he was working at Google Singapore. As interest and readership grew, Indonesian Times evolved into IDN Times, a digital multi-platform media company focused on delivering relevant content for Indonesia’s younger generations. == Bureau == IDN Times has a representative bureau that has spread over 12 provinces in Indonesia: == Events == === Indonesia Millennial and Gen Z Summit === The Indonesia Millennial and Gen-Z Summit (IMGS) is an annual event organized by IDN. This event aims to empower Indonesia’s younger generations through discussions and interdisciplinary collaborations. IMGS features inspirational figures, professionals, and leaders from various fields who share insights and drive positive change. The event hosts dozens of discussion sessions in collaboration with eight prominent communities. Topics covered include politics, economics, technology, and pop culture. === Indonesia Writers Festival === The Indonesia Writers Festival is an independent writing festival organized by IDN Times. The event seeks to empower Indonesians through writing by inviting experts and literacy activists from various backgrounds. == Duniaku.com == Duniaku.com is a multi-platform digital media part of IDN Times which presents content about geek culture ranging from video games, anime, comics, films, technology and gadgets. Duniaku.com was officially launched on September 6, 2019 by the Minister of Communication and Informatics Rudiantara together with CEO of IDN Media Winston Utomo and IDN Times and Editor-in-Chief of Duniaku.com Uni Lubis. == Awards == 2019 IDN won WAN-IFRA Asia Digital Media Awards 2019 as the Best Digital Project to Engage Younger and/or Millennial Audiences for IDN Times’ #MillennialsMemilih program 2020 IDN Times (IDN Times Community) won WAN-IFRA Asia Digital Media Awards 2019 in The Best in Audience Engagement category. 2021 IDN Times journalists won awards at the Subroto Award, Ministry of Energy and Mineral Resources (ESDM) on 28 September 2021. 2024 IDN Times won WAN-IFRA event at both the Asia and Global levels in Best Use of AI in Revenue Strategy. === #Interconnected22 by Pulitzer Center === One of the IDN Times journalists, Dhana Kencana, was the speaker at the #Interconnected22 conference held from June 9 to June 10, 2022, in Washington DC, United States of America. Dhana Kencana is also a grant recipient Pulitzer Center through the Rainforest Journalism Fund (RJF) program, a funding program for journalists that makes a number of coverage of the rainforest.
Cache language model
A cache language model is a type of statistical language model. These occur in the natural language processing subfield of computer science and assign probabilities to given sequences of words by means of a probability distribution. Statistical language models are key components of speech recognition systems and of many machine translation systems: they tell such systems which possible output word sequences are probable and which are improbable. The particular characteristic of a cache language model is that it contains a cache component and assigns relatively high probabilities to words or word sequences that occur elsewhere in a given text. The primary, but by no means sole, use of cache language models is in speech recognition systems. To understand why it is a good idea for a statistical language model to contain a cache component one might consider someone who is dictating a letter about elephants to a speech recognition system. Standard (non-cache) N-gram language models will assign a very low probability to the word "elephant" because it is a very rare word in English. If the speech recognition system does not contain a cache component, the person dictating the letter may be annoyed: each time the word "elephant" is spoken another sequence of words with a higher probability according to the N-gram language model may be recognized (e.g., "tell a plan"). These erroneous sequences will have to be deleted manually and replaced in the text by "elephant" each time "elephant" is spoken. If the system has a cache language model, "elephant" will still probably be misrecognized the first time it is spoken and will have to be entered into the text manually; however, from this point on the system is aware that "elephant" is likely to occur again – the estimated probability of occurrence of "elephant" has been increased, making it more likely that if it is spoken it will be recognized correctly. Once "elephant" has occurred several times, the system is likely to recognize it correctly every time it is spoken until the letter has been completely dictated. This increase in the probability assigned to the occurrence of "elephant" is an example of a consequence of machine learning and more specifically of pattern recognition. There exist variants of the cache language model in which not only single words but also multi-word sequences that have occurred previously are assigned higher probabilities (e.g., if "San Francisco" occurred near the beginning of the text subsequent instances of it would be assigned a higher probability). The cache language model was first proposed in a paper published in 1990, after which the IBM speech-recognition group experimented with the concept. The group found that implementation of a form of cache language model yielded a 24% drop in word-error rates once the first few hundred words of a document had been dictated. A detailed survey of language modeling techniques concluded that the cache language model was one of the few new language modeling techniques that yielded improvements over the standard N-gram approach: "Our caching results show that caching is by far the most useful technique for perplexity reduction at small and medium training data sizes". The development of the cache language model has generated considerable interest among those concerned with computational linguistics in general and statistical natural language processing in particular: recently, there has been interest in applying the cache language model in the field of statistical machine translation. The success of the cache language model in improving word prediction rests on the human tendency to use words in a "bursty" fashion: when one is discussing a certain topic in a certain context, the frequency with which one uses certain words will be quite different from their frequencies when one is discussing other topics in other contexts. The traditional N-gram language models, which rely entirely on information from a very small number (four, three, or two) of words preceding the word to which a probability is to be assigned, do not adequately model this "burstiness". Recently, the cache language model concept – originally conceived for the N-gram statistical language model paradigm – has been adapted for use in the neural paradigm. For instance, recent work on continuous cache language models in the recurrent neural network (RNN) setting has applied the cache concept to much larger contexts than before, yielding significant reductions in perplexity. Another recent line of research involves incorporating a cache component in a feed-forward neural language model (FN-LM) to achieve rapid domain adaptation.
Electronic kit
An electronic kit is a package of electrical components used to build an electronic device. Generally, kits are composed of electronic components, a circuit diagram (schematic), assembly instructions, and often a printed circuit board (PCB) or another type of prototyping board. There are two types of kits. Some build a single device or system. Other types used for education demonstrate a range of circuits. These will include a solderless construction board of some type, such as: Components mounted in plastic blocks with side contacts, that are held together in a base, e.g. Denshi blocks Springs on a card board, the springs trap wire leads, or component leads, such as Philips EE electronic experiment kits. These are a cheap and flexible option Professional type prototyping boards, (breadboards) into which component leads are inserted, following documentation of the "kit". The first type of kit for constructing a single device normally uses a PCB on which components are soldered. They normally come with extended documentation describing which component goes where into the PCB. For advanced hobby projects, sometimes the kit may only consist of a printed circuit board and assembly instructions, and the purchaser may have to source all the parts independently; or, the vendor may provide hard-to-get or pre-programmed parts while expecting the purchaser to obtain the rest of the components. People primarily purchase electronic kits to have fun and learn how things work. They were once popular as a means to reduce the cost of buying goods, but there is usually no cost saving in buying a kit today. Some electronic kits were assembled to make complete complex devices such as color television sets, oscilloscopes, high-end audio amplifiers, amateur radio equipment, electric organs, and even computers such as the Heathkit H-8, and the LNW-80. Many of the early microprocessor computers were sold as either electronic kits or assembled and tested. Heathkit sold millions of electronic kits during its 45-year history. Home assembly of common consumer electronics items no longer provides a cost advantage over commercially manufactured and distributed devices. People still build kits for custom devices and special-purpose electronics for professional and educational use and as a hobby. Also emerging is a trend to simplify the complexity by providing preprogrammed or modular kits often provided by many suppliers online. The fun and thrill of making your own electronics have shifted, in many cases, from easy-to-comprehend applications and analog devices to more sophisticated digital devices. == Examples == The Altair 8800 (the first home computer) was also sold as a kit, as were the MK14, Sinclair ZX80, Sinclair ZX81 and Acorn Atom computers. Many S-100 bus system cards were sold only as kits. Building a Robot kit, most often with a micro controller inside, is now in fashion.