- Open Access
Instant messaging with emotion-embedded vectorized handwritings on mobile devices
© The Author(s) 2017
- Received: 1 August 2016
- Accepted: 26 February 2017
- Published: 11 March 2017
Generally, handwriting can reflect writers’ personality, thoughts, and emotions, i.e., handwriting can deliver emotion- and sincereness-embedded messages. However, texting messages and notes such as emails and instant messages replace handwriting letters and notes in communication due to the popularity and availability of mobile devices and personal computers. Furthermore, the commonly used input methods and devices also limit handwriting messaging. For example, limited mobile screen sizes make writing multiple Chinese characters difficult. As a result, this work aims at designing and developing a handwriting messaging system based on our handwriting characteristic exploration and texting-handwriting difference discovery. We first discover Chinese texting issues for mobile devices and emotion-delivered effectiveness of handwriting with pilot studies. Then, our emotion-embedded handwriting messaging framework is implemented to record writing strokes on the entire touch screen, vectorize them as Bézier curves, and send them as instant messages. Vectorizing strokes can preserve personal and emotional handwriting features with compact networking traffic without deteriorating the convenience of instant messaging. Finally, we conduct user studies to verify that our handwriting messaging system is preferred while intending to deliver contents with sincerity and emotion.
- Emotional handwriting
- Effective emotion delivery
- Emotional texting
- Vectorized handwriting
With the advance of technology, computer-based personal messages such as emails and instant messages become popular and replace handwriting letters and notes in communication. Mobile devices make them even more universally popular. Users generally choose a preferred systematic style and font for texting, and this makes messages lack of personality, emotion, and sincerity. Because different users may have different handwriting, and even the same person may have different handwriting under different emotions, it is easier to deliver emotion, feeling, and sincerity. Therefore, this work aims at adding handwriting characteristics into instant messaging for more personality and emotion.
Before further describing and discussing the focus and technical details of this article, we define the terms used in this article for clarity. Traditionally, handwriting is used to describe the process of writing with a pen or pencil in the hand, and this work uses it to denote the act of composing digital messages with an electronic device while maintaining writer’s personal writing characteristics including stroke shapes and orders. Handwriting messages and notes are the products of handwriting. Similarly, we use texting to describe the process of composing digital messages encoding with the format for personal computers and mobile devices, typically consisting of alphabetic and numeric characters. Currently, there are three popular input methods including typing, speech recognition [1–4], and stroke recognition [5–9]. We give the details of each input method in Section 3. Texting messages and notes are the products of texting. This work uses messaging/instant messaging to describe the act of sending and receiving electronic messages between two or more users with stationary and mobile devices. Therefore, we use handwriting texting messaging to denote the act of messaging under the contents composed by handwriting/texting respectively.
Handwriting can more easily deliver emotion and personality.
Handwriting can directly express their emotion with drawings with less constraints in remembering the vocabularies and symbols existing in other input methods.
Handwriting can also achieve availability, portability, convenience, and instantaneity which are important requirement for instant messaging.
After implementation, we conduct a set of user studies to verify that our system can let users prefer handwriting over other texting input methods in some scenarios due to easiness in expressing personality and emotions and arbitrary symbol drawing with few constraints. Moreover, our proposed input can free users from frustration of writing recognition to get better experiences and make instant messaging more intuitive and friendly.
This paper makes the following contributions: First, we identify an interesting and important input method for instant messaging on mobile devices: an intuitive and emotion-embedded handwriting input framework. Second, the core of our method is to record and send user’s handwriting messages with vectorized strokes using Bézier curves for preservation of personal and emotional handwriting features with compact networking traffic without deteriorating the convenience of instant messaging. Furthermore, we also simulate a scroll paper through a moving writing window and use the entire touch screen to ease the handwriting process and overcome fat finger issues. Finally, we embed the input framework onto a popular instant messaging system to show its easiness of implementation. As demonstrated in the results, our system is preferred while intending to deliver contents with sincerity and emotion. The rest of the paper is organized as follows: Section 2 describes the method used in this study. Section 3 reviews those previous research done related to this work. Section 4 describes our pre-studies to understand issues existing in current texting. Section 5 gives implementation details of our designed handwriting texting mechanic using the concepts collected in Section 4. Section 6 discusses evaluations of our designed handwriting texting mechanic. Section 7 concludes with a discussion of limitations and future works.
This work aims at exploring handwriting characteristics and discovering differences between texting and handwriting in order to design a handwriting-based instant messaging system. In order to better understand the progress of different input methods, this article first reviews directly related works including the personality connection with handwriting, stroke recognition, handwriting delivery, speech recognition, and stroke vectorization. In order to achieve this goal, we take the following steps: First, we conduct a pilot study to understand the effectiveness in emotional delivery with handwriting and discover texting issues in commonly used Chinese input methods for mobile devices. Second, according to the criteria concluded in the pilot study, we design and implement emotion-embedded handwriting messaging by recording writing strokes on the entire touch screen, vectorizing them as Bézier curves, and sending them as instant messages. Third, we verify our designed system by conducting user studies to show that users prefer our handwriting messaging system while they intend to deliver information with sincerity and emotion. All participants of both initial pilot studies and final user studies are selected randomly in front of Gongguan Station of Metro Transport in Taipei. They have varied sexes, ages, and experience in mobile and computer usage, but all participants have experience in using instant messaging on mobile devices and computers. We interview the participants and ask them to answer our designed questionnaires. All questions are in 5-point Likert scales where 1 to 5 ranks the degree of satisfaction and agreement to the criteria as very disagree, disagree, neutral, agree, and very agree. After collecting all valid questionnaires, we use the mean score to indicate the degree of satisfaction and agreement of all questionnaires.
Texting input is the research topics for decades because of the need to control the computers, to write documents, and to write emails and instant messages, and there are a large number of results on this field. However, since this work aims at developing a handwriting messaging framework, the following only discusses those works directly related to this research.
Handwriting vs. personality: Generally, we believe that handwriting generally reflects writer’s emotion and personality. Therefore, there are research [10–13] aiming at discovering these relationships. Castelnuovo et al.  analyze the connection of “imitation” and “natural writing” with intelligence, originality, anxiety, compulsiveness, sexuality, and temperament and conclude that handwriting highly reflects writer’s personality and “natural writing” does more profoundly than “imitation.” Lemke et al.  use handwriting to predict personality and intelligence. Williams et al.  reveal that there is obvious connection between personal handwriting and extraversion and behavioral model. Peeples et al.  also analyze the affection of handwriting on different genders and personality traits. All research reveal that handwriting has obvious connection with several personality traits, i.e., handwriting generally reveals personal styles and emotions.
Stroke recognition: Stroke recognition can be viewed as the problem of transforming text in the 2D spatial cursive forms into symbolic representation. There are two categories: online and offline based on the way of data collection. Online indicates that the writing order is available while offline only has the finished writing results. Order information are collected using an electronic pad or touch screen by recording the ordered two-dimensional coordinates of successive points pressing on the device. Plamondon et al.  provide a comprehensive survey on stroke recognition in both categories, and Tappert et al.  review those online recognition state-of-the-art algorithms. However, all research reviewed in these two papers mainly focus on the recognition of alphabetic languages, but Chinese Mandarin is a pictographic language by connecting pictographic characters to form sentences instead of spelling the vocabularies. A Chinese character is generally composed of mostly straight lines or “poly-line” strokes. Many characters can be decomposed into relatively independent substructures, called radicals, and different characters may share some common radicals. Generally, recognition can utilize this property to simplify their recognition. Liu et al.  survey the advances in online Chinese character recognition. Although there are more feature extraction algorithms [14, 15] invented for image content understanding, developing proper features for Chinese characters is still problematic because of different personal writing habits. Furthermore, the concept can be extended furthermore to recognize the traffic sign while driving . With the advance of mobile devices, social messaging becomes very important . As a result, instant messaging replaces letter writing for personal communication, and typing is the most common input manner for message creation. Since mobile devices have a small screen for typing, they generally cause the following typing difficulties: fast input and fat finger. Therefore, with the entrance of scanners, graphics tablets, and touch screens, Plamondon et al.  propose to take handwriting as input and use stroke recognition to replace inconvenient typing. Prochasson et al.  collect handwriting data including skeletons, rebuses, and phonetic writing to construct a recognition system for texting input. Although stroke recognition can make texting more intuitive, the robustness of recognition is still problematic due to different writing habits among different users. Therefore, we intend to directly deliver the writing or drawing in a vectorized format without recognition. Furthermore, computer characters and words cannot deliver the emotion and feeling of the user while he/she creates the message. Therefore, this work aims at adding emotional aspects which are missing from texting into instant messaging through handwriting.
Handwriting delivery: Norihisa et al.  introduce an on-door communication system by designing a communication interface to let users read/write information on the same virtual whiteboard. However, it aims at providing a multi-person simultaneous working area, and thus, it directly records the writing and drawing as a bitmap which is not efficient for memory usage and network transmission. Furthermore, it is also intended to leave short messages, and thus, it has a limited writing area. Although this approach can indeed increase the joy and practicability through instant messaging, it is not suitable for high-frequency communication usage. Therefore, our system overcomes these limitations by encoding the writing with our designed vectorized format to write an infinite long sheet in a similar manner of texting.
Speech recognition: Speech is one of the most natural and user-friendly mechanisms for information access and spoken language processing technologies. Therefore, speech recognition is the research focus for decades. Kong et al.  and Wang et al.  focus on extracting spectro-temporal modulation information for enhancing recognition accuracy. There are research [5, 6] focusing on Mandarin Chinese texting input. Furthermore, Jin et al.  propose a syllable-lattice-based speaker-independent large-vocabulary continuous speech recognition system. McLoughlin et al.  present a subjective intelligibility testing method for Mandarin Chinese input. The improvement in recognition accuracy also pushes the possibility of using speech recognition to become a real-world input method. After decades of diligent research efforts, speech recognition reaches a point where many useful and commercially beneficial applications have recently become feasible. However, these algorithms generally require high computation cost. With advance in hardware, they become feasible and have been adopted as an input method for all mobile devices in current days. For example, Janet et al.  propose iSay-SMS to input texts with speech recognition, and IOS provides Siri to act as a voice secretary. Lee et al.  provide an overview on the field and its application possibility. Furthermore, some instant messaging softwares such as iMessage  and WhatsApp  also allow users to directly record and deliver personal voice and speech. However, sending speeches requires larger Internet bandwidth valuated in mobile service, i.e., sending speeches is more expensive and costly. Additionally, both speech recognition and speech messaging require to talk to devices which may not be suitable in some situations and locations such as private contents and being in public libraries. Our system can overcome these limitations by vectorizing handwriting for messaging.
Stroke vectorization: There are research [24–26] focusing on vectorizing the black region of a scanned image for infinite resolution. However, scanned input images lose online construction information and require a complex algorithm to detect the connection and cross regions. Since our algorithm directly record the handwriting process, we can directly vectorizes the handwriting motion gesture of the finger for network traffic efficiency. The process is easier and efficient for easy embedment into all mobile devices.
Texting messaging is publicly accepted because of its availability, formality, and easy message composition. However, it still has certain limitations such as typing special symbol and delivering emotion and sincereness. Therefore, we conduct a pilot study to understand its effectiveness and usage limitations. Our study consists of two phases: The first focuses on understanding handwriting characteristics and differences between handwriting and texting through a set of questionnaires. The second devotes to discovering the issues of texting methods for mobile devices through a set of interviews and another set of questionnaires. Finally, we summarize the surveyed results as design criteria for our handwriting-based instant messaging system.
4.1 Difference between handwriting and texting
With the advance of computer technologies, texting replaces handwriting. We would like to understand the differences between handwriting and texting on the aspect of characteristics and usages through questionnaires. We conduct the study by randomly selecting 168 subjects in front of Gongguan Station of Metro Transport in Taipei. Their ages range from 15 to 65 years old with a mean of 35.6, and the participants comprise 50 females and 118 males. All are volunteers and have experience in using instant messaging on mobile devices and computers.
4.1.1 Emotion delivery
4.1.2 Texting and handwriting usage circumstance preference
4.1.3 Emotion preference
4.2 Usage of different input methods
As shown in the previous study, texting is preferred for more circumstances because of its convenience. Therefore, it is important to understand the strength and limitations of different input methods. We start our analysis by randomly interviewing 20 heavy mobile messaging users about the problems and preferences of all texting methods they have ever used. Their ages range from 18 to 40 years old with a mean of 25.6, and the participants comprise 5 females and 15 males. They are students and workers from National Taiwan University of Science and Technology. All are volunteers. According to our interview, there are three main input methods including keyboard typing, stroke recognition, and speech recognition. The interview also reveals the important aspects which affect the preference of users to choose the input method while texting messaging. The following sections list the issues we discover for corresponding input methods. Then, we use these found aspects to form questionnaires and ask participants to answer them with 164 valid ones. Their ages range from 15 to 65 years old with a mean of 34.6, and the participants comprise 49 females and 115 males. They are randomly selected in front of Gongguan Station of Metro Transport in Taipei. All are volunteers and have experience in using instant messaging on mobile devices and computers. Furthermore, participants only fill in those questions related to the input methods which they have experienced before.
4.2.1 Keyboard typing
It is similar to the familiarity problem: for those who are familiar with the candidate selection system, it is not confusing and inconvenient. We can conclude that the practicability of keyboard typing highly depends on its familiarity. In other words, users can type text fast and smoothly with a keyboard once they get used to it. On the contrary, users do not choose it if they have trouble in learning it.
4.2.2 Stroke recognition
4.2.3 Speech recognition
4.2.4 Other input types
Handwriting generally can deliver and encode more personal feeling, sincerity, emotion, and characteristics than texting. Therefore, this work aims at messaging with handwriting instead of texting.
Texting is the preferred message construction method for instant messaging on mobile devices and computers because of its convenience, portability, and availability. Therefore, any input method must be able to provide comparable functionalities along with other characteristics for the replacement possibility.
Texting is sometimes not plausible because it requires a strenuous learning curve for new users especially for those elders and children. Therefore, our system must be more intuitive and easy to use without a strenuous learning curve.
Due to the limitation of encoding space, texting cannot deliver some contents especially those containing complex symbols and expressions. Therefore, our system must allow users to deliver more complicated contents.
Stroke recognition still has robustness issues and does not aim at composing easiness. Furthermore, speech recognition is intuitive, but it also has robustness issues and is not suitable in many occasions. Therefore, our system should provide more intuitive writing input and allows the user to use in as many occasions as possible.
Texting has difficulties in delivering emotion and personal feelings inside messages. On the contrary, handwriting can easily embed emotion with fewer constraints when entering characters, numbers, and symbols. During our investigation, some participants complain about the radiculitis of composing Chinese characters by spelling phonetically and selecting from the candidate set even when they can write the characters. However, since the spirit of communication is to understand each other, it is not so necessary to use the formal and default computer character set, and it is possible to compose message directly using handwriting for easiness and intuitiveness. Consequently, we aim at developing a handwriting-simulated communication system to relieve the texting issues and meet the demands of handwriting. We develop our system for smart mobile devices with a handwriting-simulated input interface to record those strokes written on the touch screen directly, vectorize the stroke for compactness, and deliver them instantly to render on others’ screens. Generally, users would like to review their previously composed messages for effective communication and examination, and thus, we design a paper-scroll-simulated interface for browsing the sent and received handwriting messages. This section lists out the potential issues of our system and our corresponding solutions along with our implementation.
5.1 User interface
It is difficult to write some complex characters in a small limited area for stroke recognition; similarly, our stroke recording area should be as large as possible.
Since our system aims at rebuilding the letter-writing experience, our system should have a manner to accommodate all words in a scroll of letter. Furthermore, our system must also take largely and sparsely written characters into consideration because users must write them with their fat fingers on small devices.
Our system must provide intuitive revisions and modification functions to correct the input errors.
Handwriting is the main input for our system, but users may still want to use texting for messaging. Therefore, our system also provides intuitive ways to incorporate texting with our handwriting. Furthermore, we also provide an intuitive way to switch to other input methods.
5.2 Maximize effective handwriting region
Generally, the allowable writing area of stroke recognition for both the IOS and Android systems is too small to write complex characters. This situation is even worse since users generally use their fingers instead of pens for writing. Enough writing spaces should improve the handwriting experiences on mobile devices, and thus, our first goal is to enlarge the limited writing region. Our solution to this issue is to use all available screen space for writing. Furthermore, we also want to let users examine those previously sent and received messages while writing new messages. Therefore, we make the writing region as a transparent cover on the top of the chatting screen as shown in Fig. 8 b to allow seeing the chatting screen and writing on the full screen simultaneously. However, our system still keeps editing buttons for operation easiness and convenience along with a paper-scroll-simulated previewing window to let writers examine their writing characters and drawing symbols. Therefore, our actual effective writing region is the full screen area except for these buttons and the previewing window.
5.3 Simulate letter writing on mobile devices
5.4 Handwriting revision
Modification is generally necessary for human writing. Therefore, we also provide similar editing tools as those provided by most texting systems. First, we provide a “undo” function to unwind back to the latest saved point. In order to provide smooth writing experiences to users, our system records the focusing frame position of each written character along with all strokes written in the frame position to ensure that the “undo” function can bring back to the same previous writing position. For example, after writing a character and sliding to write another, he/she suddenly finds the incorrectness of the previous character and wants to remove the character with the “undo” function. Our system first slides the focusing frame back and removes all the stroke information recorded along with the same frame position to let him/her continue their writing seamlessly. Second, we also provide a “redo” function to recover the steps saved in the “undo” stack. Third, we also provide an “eraser” function to let users use one-finger dragging gesture to remove the undesired strokes because users sometimes want to erase those strokes and characters written long time ago, and the “undo” function no longer works. The “eraser” function is also redoable and undoable. For instance, if users find that they mistakenly remove desired strokes, they can also use the “undo” function to restore those removed strokes. As a result, users can edit the writing message everywhere in our system.
5.5 Hybrid input with text and vectorized handwriting
Since our target devices are those mobile devices, the size of recorded and encoded stroke data should be small enough for efficient transportation. In addition, our system should be able to render the recorded strokes properly and automatically without having aliasing artifacts onto the screen with a different screen size of different mobile devices. Therefore, we record the strokes as vectorized Bézier curves.
Because our handwriting system is an auxiliary tool for instant messaging through network communication, our system should also provide a client-to-client communication framework.
5.6.1 Stroke information
Our system must be able to resize the bitmaps for different resolutions.
The resolution of bitmaps must be high enough for the highest possible devices in order to reconstruct the results without aliasing artifacts.
Both require a really high resolution of bitmaps which are not efficient for instant messaging. As a result, although there are research focusing on enhancing bitmap resolution [27, 28] and developing efficient encoding algorithms for stroke-based handwriting and drawing bitmaps, our system chooses not to use them as our encoding mechanism. Since strokes and drawings can be decomposed into a set of strokes, we can store these strokes as a set of vectorized Bézier curves. Therefore, we regard each stroke as a vector graphics by encoding a stroke as a set of the control points rather than a map of pixel values. After recording strokes as a set of control points, we can directly render the vectorized Bézier curves on other screens based on the screen size using the Bézier curve rendering algorithm while only using relatively smaller memory spaces. We go one step further to reduce the size of the handwriting information as follows. Instead of representing a control point in the floating point format as most of vector graphics based applications, we adopt unsigned short precision since floating point is not necessary to display a nice stroke on the screen of mobile devices in our case. By using the unsigned short precision, we can reduce the size of a control point to be just 4 bytes, and each axis stores integer data from 0 to 65,535 which is large enough to write a message. Later, our system compresses the stroke information with GZip and transforms the zipped data to the Base64 string format for efficient Internet transportation.
Although our system should be a simple a client-to-client communication framework, we cannot guarantee that all users be online while one of them attempts to handwrite a message to send. It seems not appropriate to use a peer-to-peer protocol. Therefore, we choose a client-server model. When a user wants to deliver messages to others, the message is sent to our server first for temporary storage, and while other users are online to check their messages, our server then forwards the stored messages to them. While all communication parties are online, our system passes the delivered messages to the others as soon as possible by using MQTT (formerly MQ Telemetry Transport) as our communication interface along with node.js and mongodb to build the server and database, respectively.
After designing our system, we design several user studies to verify whether it achieves our design goals and satisfies user requirements: a friendly and intuitive mechanism can let users prefer handwriting in message creation and our system is simple and intuitive and has a short learning curve and usage comparison with other mechanisms. Totally, there are 42 participants attending our user studies, and all with normal or corrected-to-normal vision. Their ages range from 15 to 55 years old and 15 are females and 27 are males. Furthermore, there are 13 participants aging from 15 to 24, 16 participants aging from 25 to 34, 9 participants aging from 35 to 44, and 4 participants aging greater than 45. All are volunteers and do not have any experience with our system. The study is conducted under a iPhone 4s with 3.5-in. (diagonal) widescreen multi-touch display of a 960 × 640 resolution. Before the studies, we first briefly introduce the characteristics, functions, and usage of our system and let users use the system for 15 min. During the try-out period, participants can freely discuss with the conductors and other participants about our system. Then, participants are asked to run several designed tests. After testing, participants are asked to fill out our designed questionnaires containing the following parts. First, whether does he/she change the preference from handwriting to texting in some circumstances? Second, why does he/she change the usage preference in the specific circumstances? Third, what are their opinions about the design criteria of our system? Finally, what are their satisfaction rating of our system when comparing to other input methods?
6.1 User preferences
6.2 Preference analysis
Handwriting contains more emotions and sincerity than texting, and it also provides more freedom to write and draw contents. In addition to understand that our system is preferred for certain circumstances, we also would like to investigate the reasons why users change their preference from texting to handwriting while using our system. Based on the main opinions expressed by 42 participants in the interviewing section, we list our reasons as “handwriting can embed emotion and sincerity,” “handwriting can input all possible contents,” and “handwriting allows drawing arbitrarily.” Figure 14 shows the statistics. “handwriting can embed emotion and sincerity” accounts for 81%. This also reflects the same trend revealed in the previous section: 89% participants prefer handwriting in the sincerity circumstance. Furthermore, “handwriting can input all possible contents” accounts for 85.7%, and it is also a significant factor. Finally, “handwriting allows drawing arbitrarily” accounts for 64.3%, and it is also an important factor, but it is less important than the other two. This reflects that not all users want to draw figures in their messages. However, drawing is indeed more funny than just typing plain text to make more than half subjects approve on this reason.
6.4 Comparison with other methods
Our user studies show that no matter users are familiar with texting or not, users may change from texting to handwriting because of its intuitiveness, no need of recognition, the requirement of no extra learning, and its capability of expressing emotions and sincerity. Moreover, handwriting allows to write/draw contents that are hard to express via other input methods.
This work explores the difference between handwriting and texting in emotion and sincerity delivery and focuses on solving texting input issues on mobile phones. After conducting pre-studies, we identify issues in texting for instant messaging and design a handwriting-based mechanism for quick, simple, and intuitive message construction. Finally, we conduct usability tests to learn that our mechanism is preferred under certain circumstances because of its easiness to create messages and abilities to deliver emotion and sincerity. Furthermore, our handwriting interface can also help intuitively, easily, and quickly create texting messages when incorporating with stroke recognition. However, our system is not without limitations. Since we treat input as pictorial data, it is hard for character-based processing such as copying and replacing. Although communication generally does not need these functions, they are helpful for speed enhancement. Therefore, we would like to incorporate our handwriting with stroke recognition for possible character-based manipulation. Additionally, our input mechanism still cannot totally replace pen writing because of tactics. We would like to incorporate a stylus for enhancing pen writing tactics when inputting. Finally, our writing user interface is for small screen sizes such as iPhones and not optimized for plate computers. Since plates have a larger screen size, we would like to design a more suitable and intuitive interface for smoothly and intuitively texting.
1 Because Chinese characters are much more complicated than English characters, we use Chinese characters for evaluation to show that our interface is convenient for handwriting even in complicated Chinese characters.
We thank National Science Council of Taiwan for funding support.
This work was supported by the National Science Council of Taiwan under Grants MOST 104-2221-E-011-029-MY3, MOST103-2221-E-011-114-MY2, MOST104-2218-E-011-006, MOST105-2221-E-011-120-MY2, MOST105-2218-E-011-014-MY3, and MOST105-2218-E-011-005.
J-WK has come up the idea and participated in conducting the pilot studies, implementing the system, and conducting the final verification user studies. N-SS has participated at drafting the manuscript. C-YY and Y-CL have conceived of the study, participated in its design and coordination, and helped to finalize the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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