Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model), is adapted such that it can generate images of novel, user-provided concepts. These concepts are typically unseen during training, and may represent specific objects (such as the user's pet) or more abstract categories (new artistic style or object relations). Text-to-Image personalization methods typically bind the novel (personal) concept to new words in the vocabulary of the model. These words can then be used in future prompts to invoke the concept for subject-driven generation, inpainting, style transfer and even to correct biases in the model. To do so, models either optimize word-embeddings, fine-tune the generative model itself, or employ a mixture of both approaches. == Technology == Text-to-Image personalization was first proposed during August 2022 by two concurrent works, Textual Inversion and DreamBooth. In both cases, a user provides a few images (typically 3–5) of a concept, like their own dog, together with a coarse descriptor of the concept class (like the word "dog"). The model then learns to represent the subject through a reconstruction based objective, where prompts referring to the subject are expected to reconstruct images from the training set. In Textual Inversion, the personalized concepts are introduced into the text-to-image model by adding new words to the vocabulary of the model. Typical text-to-image models represent words (and sometimes parts-of-words) as tokens, or indices in a predefined dictionary. During generation, an input prompt is converted into such tokens, each of which is converted into a ‘word-embedding’: a continuous vector representation which is learned for each token as part of the model's training. Textual Inversion proposes to optimize a new word-embedding vector for representing the novel concept. This new embedding vector can then be assigned to a user-chosen string, and invoked whenever the user's prompt contains this string. In DreamBooth, rather than optimizing a new word vector, the full generative model itself is fine-tuned. The user first selects an existing token, typically one which rarely appears in prompts. The subject itself is then represented by a string containing this token, followed by a coarse descriptor of the subject's class. A prompt describing the subject will then take the form: "A photo of
Document mosaicing
Document mosaicing is a process that stitches multiple, overlapping snapshot images of a document together to produce one large, high resolution composite. The document is slid under a stationary, over-the-desk camera by hand until all parts of the document are snapshotted by the camera's field of view. As the document slid under the camera, all motion of the document is coarsely tracked by the vision system. The document is periodically snapshotted such that the successive snapshots are overlap by about 50%. The system then finds the overlapped pairs and stitches them together repeatedly until all pairs are stitched together as one piece of document. The document mosaicing can be divided into four main processes. Tracking Feature detecting Correspondences establishing Images mosaicing. == Tracking (simple correlation process) == In this process, the motion of the document slid under the camera is coarsely tracked by the system. Tracking is performed by a process called simple correlation process. In the first frame of snapshots, a small patch is extracted from the center of the image as a correlation template. The correlation process is performed in the four times size of the patch area of the next frame. The motion of the paper is indicated by the peak in the correlation function. The peak in the correlation function indicates the motion of the paper. The template is resampled from this frame and the tracking continues until the template reaches the edge of the document. After the template reaches the edge of the document, another snapshot is taken and the tracking process performs repeatedly until the whole document is imaged. The snapshots are stored in an ordered list to facilitate pairing the overlapped images in later processes. == Feature detecting for efficient matching == Feature detection is the process of finding the transformation that aligns one image with another. There are two main approaches for feature detection. Feature-based approach : Motion parameters are estimated from point correspondences. This approach is suitable for the case that there is plenty supply of stable and detectable features. Featureless approach : When the motion between the two images is small, the motion parameters are estimated using optical flow. On the other hand, when the motion between the two images is large, the motion parameters are estimated using generalised cross-correlation. However, this approach requires a computationally expensive resources. Each image is segmented into a hierarchy of columns, lines, and words to match the organised sets of features across images. Skew angle estimation and columns, lines and words finding are the examples of feature detection operations. === Skew angle estimation === Firstly, the angle that the rows of text make with the image raster lines (skew angle) is estimated. It is assumed to lie in the range of ±20°. A small patch of text in the image is selected randomly and then rotated in the range of ±20° until the variance of the pixel intensities of the patch summed along the raster lines is maximised. To ensure that the found skew angle is accurate, the document mosaic system performs calculation at many image patches and derive the final estimation by finding the average of the individual angles weighted by the variance of the pixel intensities of each patch. === Columns, lines and words finding === In this operation, the de-skewed document is intuitively segmented into a hierarchy of columns, lines and words. The sensitivity to illumination and page coloration of the de-skewed document can be removed by applying a Sobel operator to the de-skewed image and thresholding the output to obtain the binary gradient, de-skewed image. The operation can be roughly separated into 3 steps: column segmentation, line segmentation and word segmentation. Columns are easily segmented from the binary gradient, de-skewed images by summing pixels vertically. Baselines of each row are segmented in the same way as the column segmentation process but horizontally. Finally, individual words are segmented by applying the vertical process at each segmented row. These segmentations are important because the document mosaic is created by matching the lower right corners of words in overlapping images pair. Moreover, the segmentation operation can organize the list of images in the context of a hierarchy of rows and column reliably. The segmentation operation involves a considerable amount of summing in the binary gradient, de-skewed images, which done by construct a matrix of partial sums whose elements are given by p i y = ∑ u = 1 i ∑ v = 1 j b u v {\displaystyle p_{iy}=\sum _{u=1}^{i}\sum _{v=1}^{j}b_{uv}} The matrix of partial sums is calculated in one pass through the binary gradient, de-skewed image. ∑ u = u 1 u 2 ∑ v = v 1 v 2 b u v = p u 2 v 2 + p u 1 v 1 − p u 1 v 2 − p u 2 v 1 {\displaystyle \sum _{u=u_{1}}^{u_{2}}\sum _{v=v_{1}}^{v_{2}}b_{uv}=p_{u_{2}v_{2}}+p_{u_{1}v_{1}}-p_{u_{1}v_{2}}-p_{u_{2}v_{1}}} == Correspondences establishing == The two images are now organized in hierarchy of linked lists in following structure : image=list of columns row=list of words column=list of row word=length (in pixels) At the bottom of the structure, the length of each word is recorded for establishing correspondence between two images to reduce to search only the corresponding structures for the groups of words with the matching lengths. === Seed match finding === A seed match finding is done by comparing each row in image1 with each row in image2. The two rows are then compared to each other by every word. If the length (in pixel) of the two words (one from image1 and one from image2) and their immediate neighbours agree with each other within a predefined tolerance threshold (5 pixels, for example), then they are assumed to match. The row of each image is assumed a match if there are three or more word matches between the two rows. The seed match finding operation is terminated when two pairs of consecutive row match are found. === Match list building === After finishing a seed match finding operation, the next process is to build the match list to generate the correspondences points of the two images. The process is done by searching the matching pairs of rows away from the seed row. == Images mosaicing == Given the list of corresponding points of the two images, finding the transformation of the overlapping portion of the images is the next process. Assuming a pinhole camera model, the transformation between pixels (u,v) of image 1 and pixels (u0, v0) of image 2 is demonstrated by a plane-to-plane projectivity. [ s u ′ s v ′ s ] = [ p 11 p 12 p 13 p 21 p 22 p 23 p 31 p 32 1 ] [ u v 1 ] E q .1 {\displaystyle \left[{\begin{array}{c}su'\\sv'\\s\end{array}}\right]=\left[{\begin{array}{ccc}p_{11}&p_{12}&p_{13}\\p_{21}&p_{22}&p_{23}\\p_{31}&p_{32}&1\end{array}}\right]\left[{\begin{array}{c}u\\v\\1\end{array}}\right]\qquad Eq.1} The parameters of the projectivity is found from four pairs of matching points. RANSAC regression technique is used to reject outlying matches and estimate the projectivity from the remaining good matches. The projectivity is fine-tuned using correlation at the corners of the overlapping portion to obtain four correspondences to sub-pixel accuracy. Therefore, image1 is then transformed into image2's coordinate system using Eq.1. The typical result of the process is shown in Figure 5. === Many images coping === Finally, the whole page composition is built up by mapping all the images into the coordinate system of an "anchor" image, which is normally the one nearest the page center. The transformations to the anchor frame are calculated by concatenating the pair-wise transformations found earlier. The raw document mosaic is shown in Figure 6. However, there might be a problem of non-consecutive images that are overlap. This problem can be solved by performing Hierarchical sub-mosaics. As shown in Figure 7, image1 and image2 are registered, as are image3 and image4, creating two sub-mosaics. These two sub-mosaics are later stitched together in another mosaicing process. == Applied areas == There are various areas that the technique of document mosaicing can be applied to such as : Text segmentation of images of documents Document Recognition Interaction with paper on the digital desk Video mosaics for virtual environments Image registration techniques == Relevant research papers == Huang, T.S.; Netravali, A.N. (1994). "Motion and structure from feature correspondences: A review". Proceedings of the IEEE. 82 (2): 252–268. doi:10.1109/5.265351. D.G. Lowe. [1] Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Boston, 1985. Irani, M.; Peleg, S. (1991). "Improving resolution by image registration". CVGIP: Graphical Models and Image Processing. 53 (3): 231–239. doi:10.1016/1049-9652(91)90045-L. S2CID 4834546. Shivakumara, P.; Kumar, G. Hemantha; Guru, D. S.; Nagabhushan, P. (2006). "
Social media and suicide
Since the rise of social media, there have been numerous cases of individuals being influenced towards committing suicide or self-harm through their use of social media, and even of individuals arranging to broadcast suicide attempts, some successful, on social media. Researchers have studied social media and suicide to determine what, if any, risks social media poses in terms of suicide, and to identify methods of mitigating such risks, if they exist. The search for a correlation has not yet uncovered a clear answer. == Background == Suicide is one of the leading causes of death worldwide, and as of 2020, the second leading cause of death in the United States for those aged 15–34. According to the Center for Disease Control and Prevention, suicide was the third leading cause of death among adolescents in the US, from 1999 to 2006. In 2020, people in the US had a suicide rate of 13.5 per 100,000. Suicide was a leading cause of death in the United States accounting for 48,183 deaths in 2021. Suicide rates increased by 30 per cent from 2000 to 2018 and declined in 2019 and 2020. Suicide remains a significant public health issue worldwide, despite prevention efforts and treatments. Suicide has been identified not only as an individual phenomenon but also as being influenced by social and environmental factors. There is growing evidence that online activity has influenced suicide-related behavior. The use of social media throughout the 21st century has grown exponentially. For this reason, there are a variety of sources that are accessible to the public in various forms, especially social media sites such as Facebook, Instagram, Twitter, YouTube, Snapchat, TikTok and many more. Although these platforms were intended to allow people to connect virtually, these platforms can lead to cyber-bullying, insecurity, and emotional distress, and sometimes may influence a person to attempt suicide. Bullying, whether on social media or elsewhere, physical or not, significantly increases victims' risk of suicidal behavior. Since social media was introduced some people have taken their lives as a result of cyberbullying. Furthermore, suicide rates among teenagers have increased from 2010 to 2022 as social media has become something that people interact with more throughout their day-to-day lives. Media algorithms tend to popularize videos and posts to inform the country of the rising trouble, which may create a popular appeal to the young and immature minds of teenagers. This is why, social media could provide higher risks with the promotion of different kinds of pro-suicidal sites, message boards, chat rooms, and forums. Moreover, the Internet not only reports suicide incidents but documents suicide methods (for example, suicide pacts, an agreement between two or more people to kill themselves at a particular time and often by the same lethal means). Therefore, the role the Internet plays, particularly social media, in suicide-related behavior is a topic of growing interest. == Cyberbullying == There is substantial evidence that the Internet and social media can influence suicide-related behavior. Such evidence includes an increase in exposure to graphic content. A research study conducted by Sameer Hinduja and Justin Patchin found a correlation between cyberbullying and suicide. According to their findings, cyber-bullying increases suicidal thoughts by 14.5 percent and suicide attempts by 8.7 percent. Particularly alarming is the fact that children and young people under 25 who are victims of cyberbullying are more than twice as likely to self-harm and engage in suicidal behavior. Overall, teen suicide rates have increased within the past decade.This presents a significant public health concern, with over 40,000 suicides in the United States and nearly one million worldwide annually. Adolescents involved in cyberbullying often downplay its seriousness by calling it a joke or blaming the victim. These moral disengagement strategies can normalize harmful behavior and reduce feelings of guilt. This normalization may increase emotional distress and contribute to risks like depression and suicidal thoughts. Recent data from the Centers for Disease Control and Prevention reveals that 14.9 per cent of teenagers have experienced online bullying, while 13.6 per cent of teenagers have seriously attempted suicide. Both of these incidents are in increasing numbers in the United States. Furthermore, in numerous recent incidents, cyber-bullying led the victim to commit suicide; this phenomenon is now known as cyberbullicide. Many parents and children are unaware of the dangers and potential legal consequences of cyberbullying. As a response, anti-bullying regulations implemented by schools aim to prevent any form of bullying, including through technology, and protect students from online harassment. While some states have enacted laws against cyberbullying, there are currently no federal regulations addressing this issue. == Social media's influence on suicide == The media may portray suicidal behavior or language which can potentially influence people to act on these suicidal ideation. This may include news reports of actual suicides that have occurred or television shows and films that reenact suicides. Some organizations have proposed guidelines about how the media should report suicide. There is evidence that compliance with the guidelines varies. Some research showed that it is unclear whether the guidelines have successfully reduced the number of suicides. On the contrary, other research studies stated that the guidelines have worked in some cases. == Impact of pro-suicidal sites, message boards, chat rooms and forums == Social media platforms have transformed traditional methods of communication by allowing instantaneous and interactive sharing of information created and controlled by individuals, groups, organizations, and governments. As of the third quarter of 2022, Facebook had 266 million monthly active users, between Canada and the US. An immense quantity of information on the topic of suicide is available on the Internet and via social media. The information available on social media on the topic of suicide can influence suicidal behavior, both negatively and positively. The social cognitive theory plays a vital role in suicide attempts influenced through social media. This theory is demonstrated when one is influenced by what they see through various processes that form into modeled behaviors. This can be shown when people post their suicide attempts online or promote suicidal behavior in general. Contributors to these social media platforms may also exert peer pressure and encourage others to take their own lives, idolize those who have killed themselves, and facilitate suicide pacts. These pro-suicidal sites reported the following. For example, on a Japanese message board in 2008, it was shared that people can kill themselves using hydrogen sulfide gas. Shortly afterwards, 220 people attempted suicide in this way, and 208 were successful. Biddle et al. conducted a systematic Web search of 12 suicide-associated terms (e.g., suicide, suicide methods, how to kill yourself, and best suicide methods) to analyze the search results, and found that pro-suicide sites and chat rooms that discussed general issues associated with suicide most often occurred within the first few hits of a search. In another study, 373 suicide-related websites were found using Internet search engines and examined. Among them, 31% were suicide-neutral, 29% were anti-suicide, and 11% were pro-suicide. Together, these studies have shown that obtaining pro-suicide information on the Internet, including detailed information on suicide methods, is very easy. While social media has been prevalent in young adult suicide, some young adults find comfort and solace through these platforms. Young adults are making connections with people in like situations that are helping them feel less lonely. Although the public opinion is that message boards are harmful, the following studies show how they point to suicide prevention and have positive influences. A study using content analysis analyzed all of the postings on the AOL Suicide Bulletin Board over 11 months and concluded that most contributions contained positive, empathetic, and supportive postings. Then, a multi-method study was able to demonstrate that the users of such forums experience a great deal of social support and only a small amount of social strain. Lastly, in the survey participants were asked to assess the extent of their suicidal thoughts on a 7-level scale (0, absolutely no suicidal thoughts, to 7, very strong suicidal thoughts) for the time directly before their first forum visit and at the time of the survey. The study found a significant reduction after using the forum. The study however cannot conclude the forum is the only reason for the decrease. Together, these studies show how forums can reduce the number of
Data storage
Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. Biological molecules such as RNA and DNA are considered by some as data storage. Recording may be accomplished with virtually any form of energy. Electronic data storage requires electrical power to store and retrieve data. Data stored in a digital, machine-readable medium is called digital data. Computer data storage is one of the core functions of a general-purpose computer. Electronic documents can be stored in much less space than paper documents. Barcodes and magnetic ink character recognition (MICR) are two ways of recording machine-readable data on paper. == Recording media == A recording medium is physical material that holds information. Newly created information is distributed and can be stored in four storage media–print, film, magnetic, and optical–and seen or heard in four information flows–telephone, radio, TV, and the Internet as well as being observed directly. Digital information is stored on electronic media in many different recording formats. With electronic media, the data and the recording media are sometimes referred to as "software" despite the more common use of the word to describe computer software. With (traditional art) static media, art materials such as crayons may be considered both equipment and medium as the wax, charcoal or chalk material from the equipment becomes part of the surface of the medium. Some recording media may be temporary, either by design or by nature. Volatile organic compounds may be used to purposely make data expire over time or to reduce environmental impact. Data such as smoke signals or skywriting are temporary by nature. Depending on the volatility, a gas (e.g., atmosphere, smoke) or a liquid surface such as a lake would be considered a temporary recording medium, if it could be considered a recording medium at all. == Global capacity, digitization, and trends == A 2003 UC Berkeley report estimated that about five exabytes of new information were produced in 2002 and that 92% of this data was stored on magnetic media (primarily hard disk drives). This was about twice the data produced in 1999. The amount of data transmitted over telecommunications systems in 2002 was nearly 18 exabytes—three and a half times more than was recorded on non-volatile storage. Telephone calls constituted 98% of the telecommunicated information in 2002. The researchers' highest estimate for the growth rate of newly stored information (uncompressed) was more than 30% per year. In a more limited study, the International Data Corporation estimated that the total amount of digital data in 2007 was 281 exabytes and that the total amount of digital data produced exceeded the global storage capacity for the first time. A 2011 article in Science estimated that the year 2002 was the beginning of the digital age for information storage: an age in which more information is stored on digital storage devices than on analog storage devices. In 1986, approximately 1% of the world's capacity to store information was in digital format; this grew to 3% by 1993, to 25% by 2000, and to 94% by 2007. These figures correspond to less than three compressed exabytes in 1986, and 295 compressed exabytes in 2007. The quantity of digital storage doubled roughly every three to four years. It is estimated that around 120 zettabytes of data will be generated in 2023, an increase of 60x from 2010, and that it will increase to 181 zettabytes generated in 2025. == Mass storage ==
IEBus
IEBus (Inter Equipment Bus) is a communication bus specification "between equipments within a vehicle or a chassis" of Renesas Electronics. It defines OSI model layer 1 and layer 2 specification. IEBus is mainly used for car audio and car navigations, which established de facto standard in Japan, though SAE J1850 is major in United States. IEBus is also used in some vending machines, which major customer is Fuji Electric. Each button on the vending machine has an IEBus ID, i.e. has a controller. Detailed specification is disclosed to licensees only, but protocol analyzers are provided from some test equipment vendors. Its modulation method is PWM (Pulse-Width Modulation) with 6.00 MHz base clock originally, but most of automotive customers use 6.291 MHz, and physical layer is a pair of differential signalling harness. Its physical layer adopts half-duplex, asynchronous, and multi-master communication with carrier-sense multiple access with collision detection (CSMA/CD) for medium access control. It allows for up to fifty units on one bus over a maximum length of 150 meters. Two differential signalling lines are used with Bus+ / Bus− naming, sometimes labeled as Data(+) / Data(−). It is sometimes described as "IE-BUS", "IE-Bus," or "IE Bus," but these are incorrect. In formal, it is "IEBus." IEBus® and Inter Equipment Bus® are registered trademark symbols of Renesas Electronics Corporation, formerly NEC Electronics Corporation, (JPO: Reg. No.2552418 and 2552419, respectively). == History == In the middle of '80s, semiconductor unit of NEC Corporation, currently Renesas Electronics, started the study for increasing demands for automotive audio systems. IEBus is introduced as a solution for the distributed control system. In the late 1980s, several similar specifications, including the Domestic Digital Bus (D2B), the Japanese Home Bus (HBS), and the European Home System (EHS) are proposed by different companies or organizations. These were once discussed as IEC 61030, but it was withdrawn in 2006. IEBus is also a similar specification (refer to "Transfer signal format" section), but not listed in these criteria. As the result, IEBus becomes a de facto standard of car audio in Japan. Regarding the Domestic Digital Bus (D2B), it is re-defined as D2B Optical by Mercedes-Benz independently. As for Japanese Home Bus System (HBS), it is defined in 1988 as Home Bus System Standard Specification, ET-2101 by JEITA and REEA (Radio Engineering & Electronics Assiation) in Japan. It is being used by several Japanese air conditioner manufacturers (for example, M-Net from Mitsubishi and the P1/P2 or F1/F2 bus from Daikin). Fujitsu provided HBPC (Home Bus Protocol Controller) chip as MB86046B. But it is unclear whether Fujitsu (currently, Cypress) still manufactures this HBPC LSI as of 2018. Mitsumi Electric provides the MM1007 and MM1192 driver ICs for HBS. The HBS specification is also discussed in the Echonet Consortium. In 2014, a utility model patent for protocol converter from HBS to RS-485 is granted in China as "CN204006496U." Regarding the replacement of IEBus, a paper by Hyundai Autonet, currently Hyundai Mobis, describes as follows. "In communication methods for digital input capable amplifiers, Inter Equipment Bus (IEBus) was used in early times, but for now, Controller Area Network (CAN) is mainly used." == Protocol overview == A master talks to a slave. Each unit has a master and a slave address register. Only one device can talk on the bus at any given time. There is a pecking order for the types of communications which will take precedence over another. Each communication from master to slave must be replied to by the slave going back to the master with acknowledge bits each of those show ACK or NAK. If the master does not receive the ACK within a predefined time allowance for a mode, it drops the communication and returns to its standby (listen) mode. Detailed specification of OSI model layer 2 is disclosed to licensees only, but protocol analyzers are provided from some test equipment vendors. In 2012, one of Chinese manufacturer's patent is granted as "CN202841169U". An open-source software emulator called "IEBus Studio" exists on a repository of SourceForge, but the last update was on 2008-02-24. Another open-source analyzer software called "IEBusAnalyzer" is available on GitHub repository. Some hobbyist made some tools also. === Physical layer (OSI model layer 1) specification overview === From μPD6708 data sheet. and μPD78098B Subseries user's manual, hardware. Communication system Half-duplex asynchronous communication Multi-master system All the units connected to the IEBus can transfer data to the other units. Broadcast communication function (communication between one unit and multiple units) Normally, communication is individually carried out from one unit to another. By using the broadcast communication function, however, communication can be executed from one unit to plural units as follows: Group broadcast communication: Broadcast communication to group units Simultaneous broadcast communication: Broadcast communication to all units Effective transmission rate The effective transmission rate can be selected from the following three communication modes: Mixture of the plural of modes in the same bus line is not allowed. Correct communication between different base clock is not possible. Access control CSMA/CD (Carrier Sense Multiple Access with Collision Detection) The priority of occupying IEBus is as follows: «1» Broadcast communication takes precedence over individual communication. «2» The lower the master address, the higher the priority. Communication scale Number of units: 50 MAX. Cable length: 150 m MAX. (when a twisted pair cable is used) Load capacity: MAX. 8000 pF; between Bus+ and Bus−, (6.000000 MHz base clock) MAX. 7100 pF; between Bus+ and Bus−, (6.291456 MHz base clock) Terminating resistor: 120 Ω Logic level Logic 1: Low level. Voltage difference between Bus+ and Bus− is under 20mV Logic 0: High Level. Voltage difference between Bus+ and Bus− is over 120mV In-phase input voltage high: Bus+ ≤ (VDD-1.0) V, Bus− ≥ 1.0 V === Transfer signal format === From μPD6708 data sheet. and μPD78098B Subseries user's manual, hardware. This frame format is much similar to that of Domestic Digital Bus (D2B). All fields are MSB first. ==== Functions of Control bits ==== === Bit format === Each IEBus bit consists of four periods. Preparation period: The first or subsequent low-level (logic "1") period Synchronization period: Next high-level (logic "0") period Data period: Period indicating value of bit; ether low-level (logic "1") or high-level (logic "0") Stop period: The last low-level (logic "1") period Synchronization is done by each bit. Time lengths of the synchronization period and data period are almost the same. The time of the entire bits' and each bit's specification, related to the time of each period allocated to it, differ depending both on the type of the transmit bit and on whether the unit is the master or a slave unit. == Automotive manufacturers using IEBus == Each manufacturer has its own name, but it is not an alias of IEBus. Those are specifications of wire harness which comprise control cables based on IEBus, OSI model layer 3 and above communication protocol, audio cables, interconnection couplers, and so on. === Pioneer === Pioneer Corporation employed IEBus for its original branded car audio in early '90s. In its earlier stage, it was used just for control bus between the head unit in dashboard and the CD changer usually placed in trunk room. Nowadays, the specification includes connection between head units, navigation systems, rear speaker systems, and so on. IP-Bus: Wire harness specification. === Toyota === Pioneer Corporation pushed Toyota Motor Corporation to adopt IEBus as the genuine parts. In 1994, Toyota decided to employ IEBus for its genuine specification, but it is slightly different from that of Pioneer. It is named as AVC-LAN. AVC-LAN: Wire harness specification, based on mode 2. === Honda/Acura === Pioneer Corporation also pushed Honda Motor. Honda also decided to adopt IEBus as its genuine parts specification just after Toyota do so. GA-NET II: Wire harness specification. Honda Music Link: Honda genuine gadget to connect Apple Inc. products. A hobbyist made touch screen controller on Acura TSX for a Car PC installed in the trunk. === Sirius XM Satellite Radio === Sirius XM Satellite Radio is a satellite broadcasting radio operator in US. Its digital media receiver equipment utilizes IEBus. == Evaluation boards == === SAKURA board === GR-SAKUKRA board and GR-SAKURA-FULL board are Renesas official promotion boards of RX63N chip, which enables IEBus mode 0 and 1, but not mode 2, i.e. not available for Toyota AVC-LAN. They are an Arduino pin compatible low-price ones, suitable for hobbyists. Their color of printed circuit board is SAKURA in Japanese, which means cherry blossom. To e
Character computing
Character computing is a trans-disciplinary field of research at the intersection of computer science and psychology. It is any computing that incorporates the human character within its context. Character is defined as all features or characteristics defining an individual and guiding their behavior in a specific situation. It consists of stable trait markers (e.g., personality, background, history, socio-economic embeddings, culture,...) and variable state markers (emotions, health, cognitive state, ...). Character computing aims at providing a holistic psychologically driven model of human behavior. It models and predicts behavior based on the relationships between a situation and character. Three main research modules fall under the umbrella of character computing: character sensing and profiling, character-aware adaptive systems, and artificial characters. == Overview == Character computing can be viewed as an extension of the well-established field of affective computing. Based on the foundations of the different psychology branches, it advocates defining behavior as a compound attribute that is not driven by either personality, emotions, situation or cognition alone. It rather defines behavior as a function of everything that makes up an individual i.e., their character and the situation they are in. Affective computing aims at allowing machines to understand and translate the non-verbal cues of individuals into affect. Accordingly, character computing aims at understanding the character attributes of an individual and the situation to translate it to predicted behavior, and vice versa. ''In practical terms, depending on the application context, character computing is a branch of research that deals with the design of systems and interfaces that can observe, sense, predict, adapt to, affect, understand, or simulate the following: character based on behavior and situation, behavior based on character and situation, or situation based on character and behavior.'' The Character-Behavior-Situation (CBS) triad is at the core of character computing and defines each of the three edges based on the other two. Character computing relies on simultaneous development from a computational and psychological perspective and is intended to be used by researchers in both fields. Its main concept is aligning the computational model of character computing with empirical results from in-lab and in-the-wild psychology experiments. The model is to be continuously built and validated through the emergence of new data. Similar to affective and personality computing, the model is to be used as a base for different applications towards improving user experience. == History == Character computing as such was first coined in its first workshop in 2017. Since then it has had 3 international workshops and numerous publications. Despite its young age, it has already drawn some interest in the research community, leading to the publication of the first book under the same title in early 2020 published by Springer Nature. Research that can be categorized under the field dates much older than 2017. The notion of combining several factors towards the explanation of behavior or traits and states has long been investigated in both Psychology and Computer Science, for example. == Character == The word character originates from the Greek word meaning “stamping tool”, referring to distinctive features and traits. Over the years it has been given many different connotations, like the moral character in philosophy, the temperament in psychology, a person in literature or an avatar in various virtual worlds, including video games. According to character computing character is a unification of all the previous definitions, by referring back to the original meaning of the word. Character is defined as the holistic concept representing all interacting trait and state markers that distinguish an individual. Traits are characteristics that mainly remain stable over time. Traits include personality, affect, socio-demographics, and general health. States are characteristics that vary in short periods of time. They include emotions, well-being, health, cognitive state. Each characteristic has many representation methods and psychological models. The different models can be combined or one model can be preset for each characteristic. This depends on the use-case and the design choices. == Areas == Research into character computing can be divided into three areas, which complement each other but can each be investigated separately. The first area is sensing and predicting character states and traits or ensuing behavior. The second area is adapting applications to certain character states or traits and the behavior they predict. It also deals with trying to change or monitor such behavior. The final area deals with creating artificial agents e.g., chatbots or virtual reality avatars that exhibit certain characteristics. The three areas are investigated separately and build on existing findings in the literature. The results of each of the three areas can also be used as a stepping stone for the next area. Each of the three areas has already been investigated on its own in different research fields with focus on different subsets of character. For example, affective computing and personality computing both cover different areas with a focus on some character components without the others to account for human behavior. == The Character-Behavior-Situation triad == Character computing is based on a holistic psychologically driven model of human behavior. Human behavior is modeled and predicted based on the relationships between a situation and a human's character. To further define character in a more formal or holistic manner, we represent it in light of the Character–Behavior–Situation triad. This highlights that character not only determines who we are but how we are, i.e., how we behave. The triad investigated in Personality Psychology is extended through character computing to the Character–Behavior–Situation triad. Any member of the CBS triad is a function of the two other members, e.g., given the situation and personality, the behavior can be predicted. Each of the components in the triad can be further decomposed into smaller units and features that may best represent the human's behavior or character in a particular situation. Character is thus behind a person's behavior in any given situation. While this is a causality relation, the correlation between the three components is often more easily used to predict the components that are most difficult to measure from those measured more easily. There are infinitely many components to include in the representation of any of C, B, and S. The challenge is always to choose the smallest subset needed for prediction of a person's behavior in a particular situation.
Data thinking
Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, with a focus on user experience and data analytics, including data collection and interpretation The framework aims to apply data literacy and inform decision-making through data-driven insights. == Major components == According to "Computational thinking in the era of data science": Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules. Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary. The framework highlights the importance of preserving domain-specific meaning during data analysis. Data thinking incorporates statistical and logical analysis to identify patterns and irregularities. Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data. The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations. == Major phases == === Strategic context and risk analysis === Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. === Ideation and exploration === In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. === Prototyping === A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. === Implementation and monitoring === Solutions are tested and monitored for performance and continual improvement. == Implementing Data Thinking == The following resources explain more about data thinking and its applications: "Data Thinking: Framework for data-based solutions" by StackFuel "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group "Data Science Thinking" by SpringerLink These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.